SlideShare a Scribd company logo
1 of 21
ETHIOPIAN DEVELOPMENT
                                        RESEARCH INSTITUTE




 Aspirations and well-being outcomes in
Ethiopia Evidence from a randomized field
               experiment
                   Tanguy Bernard, Stefan Dercon, Kate Orkin ,
                   Fanaye Tadesse, and Alemayehu Seyoum
                   Taffesse
                   IFPRI ESSP-II and University of Oxford

                   Ethiopian Economic Association Conference
                   July 19, 2011
                   Addis Ababa


                                                                 1
"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)




                                                          2
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.
• 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 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)
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         – Assets – house, furniture, consumer
from half a hectare
                              goods, vehicles
                            – Social status – whether people in the
0 ETB
                              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
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
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 – 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;
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
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
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
Estimation strategy
                                                      16
    ys2,v ,i      T    ns ,v ,i    y1,v ,i    s  v   i
                                T
                                               s
                                                      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.
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
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
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
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
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 have 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

More Related Content

What's hot

BBBY_AR2007_proxy_v3
BBBY_AR2007_proxy_v3BBBY_AR2007_proxy_v3
BBBY_AR2007_proxy_v3finance44
 
VaR of Operational Risk
VaR of Operational RiskVaR of Operational Risk
VaR of Operational RiskRahmat Mulyana
 
reliance steel & aluminum 2006_AnnualReport
reliance steel & aluminum  2006_AnnualReportreliance steel & aluminum  2006_AnnualReport
reliance steel & aluminum 2006_AnnualReportfinance32
 
Spatially resolved characterization of capping using nondestructive ultrasoni...
Spatially resolved characterization of capping using nondestructive ultrasoni...Spatially resolved characterization of capping using nondestructive ultrasoni...
Spatially resolved characterization of capping using nondestructive ultrasoni...Boehringer Ingelheim Pharmaceuticals, Inc.
 
Bank of baroda_q4_fy10
Bank of baroda_q4_fy10Bank of baroda_q4_fy10
Bank of baroda_q4_fy10mittalmanisha
 

What's hot (10)

Amd Q207 Financials
Amd Q207 FinancialsAmd Q207 Financials
Amd Q207 Financials
 
Final analysis
Final analysisFinal analysis
Final analysis
 
BBBY_AR2007_proxy_v3
BBBY_AR2007_proxy_v3BBBY_AR2007_proxy_v3
BBBY_AR2007_proxy_v3
 
VaR of Operational Risk
VaR of Operational RiskVaR of Operational Risk
VaR of Operational Risk
 
reliance steel & aluminum 2006_AnnualReport
reliance steel & aluminum  2006_AnnualReportreliance steel & aluminum  2006_AnnualReport
reliance steel & aluminum 2006_AnnualReport
 
credit-suisse Quarterly Report Q2/2006
 credit-suisse Quarterly Report Q2/2006 credit-suisse Quarterly Report Q2/2006
credit-suisse Quarterly Report Q2/2006
 
Bfi_barcelona08
Bfi_barcelona08Bfi_barcelona08
Bfi_barcelona08
 
Amd Q308 Financials
Amd Q308 FinancialsAmd Q308 Financials
Amd Q308 Financials
 
Spatially resolved characterization of capping using nondestructive ultrasoni...
Spatially resolved characterization of capping using nondestructive ultrasoni...Spatially resolved characterization of capping using nondestructive ultrasoni...
Spatially resolved characterization of capping using nondestructive ultrasoni...
 
Bank of baroda_q4_fy10
Bank of baroda_q4_fy10Bank of baroda_q4_fy10
Bank of baroda_q4_fy10
 

Similar to Aspirations and well being outcomes in ethiopia evidence from a randomized field experiment -alemayehu s.t.ppt

Asian core presentation 2012_an
Asian core presentation 2012_anAsian core presentation 2012_an
Asian core presentation 2012_anangidon
 
Tamara Presentation
Tamara PresentationTamara Presentation
Tamara Presentationbalhawk23
 
Paper1 presentation v.4
Paper1 presentation v.4Paper1 presentation v.4
Paper1 presentation v.4balhawk23
 
Armanios august aom
Armanios august aomArmanios august aom
Armanios august aomChuck Eesley
 
06.21.2012 - Vivian Hoffmann
06.21.2012 - Vivian Hoffmann06.21.2012 - Vivian Hoffmann
06.21.2012 - Vivian HoffmannAMDSeminarSeries
 
Bank loans and borrower value during the recent financial crisis
Bank loans and borrower value during the recent financial crisisBank loans and borrower value during the recent financial crisis
Bank loans and borrower value during the recent financial crisisChristophe J. Godlewski
 
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...IFSD14
 
atmos enerrgy lehman090208
atmos enerrgy lehman090208atmos enerrgy lehman090208
atmos enerrgy lehman090208finance35
 
Borgatti dagstuhl 2008 presentation 2c
Borgatti   dagstuhl 2008 presentation 2cBorgatti   dagstuhl 2008 presentation 2c
Borgatti dagstuhl 2008 presentation 2cSteve Borgatti
 
Security analysis and portfolio management
Security analysis and portfolio managementSecurity analysis and portfolio management
Security analysis and portfolio managementHimanshu Jain
 
Igl Board Of Realtors April 2011
Igl Board Of Realtors April 2011Igl Board Of Realtors April 2011
Igl Board Of Realtors April 2011kevert
 
SLM Q308EarningsStatisticsFinal
SLM  Q308EarningsStatisticsFinalSLM  Q308EarningsStatisticsFinal
SLM Q308EarningsStatisticsFinalfinance42
 
Graduate Thesis Presentation
Graduate Thesis PresentationGraduate Thesis Presentation
Graduate Thesis Presentationdiedar
 
Lec 02 The State Of The Profession
Lec 02 The State Of The ProfessionLec 02 The State Of The Profession
Lec 02 The State Of The ProfessionDrAlana
 
Decision analysis problems online 1
Decision analysis problems online 1Decision analysis problems online 1
Decision analysis problems online 1Soumendra Roy
 
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3togelius
 

Similar to Aspirations and well being outcomes in ethiopia evidence from a randomized field experiment -alemayehu s.t.ppt (20)

Asian core presentation 2012_an
Asian core presentation 2012_anAsian core presentation 2012_an
Asian core presentation 2012_an
 
Tamara Presentation
Tamara PresentationTamara Presentation
Tamara Presentation
 
Paper1 presentation v.4
Paper1 presentation v.4Paper1 presentation v.4
Paper1 presentation v.4
 
Armanios august aom
Armanios august aomArmanios august aom
Armanios august aom
 
06.21.2012 - Vivian Hoffmann
06.21.2012 - Vivian Hoffmann06.21.2012 - Vivian Hoffmann
06.21.2012 - Vivian Hoffmann
 
Bank loans and borrower value during the recent financial crisis
Bank loans and borrower value during the recent financial crisisBank loans and borrower value during the recent financial crisis
Bank loans and borrower value during the recent financial crisis
 
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...
Economics, Policy and Value Chains: Barriers to Technology Adoption in the CA...
 
atmos enerrgy lehman090208
atmos enerrgy lehman090208atmos enerrgy lehman090208
atmos enerrgy lehman090208
 
WEAI 2021 Presentation
WEAI 2021 PresentationWEAI 2021 Presentation
WEAI 2021 Presentation
 
Aid and taxation
Aid and taxationAid and taxation
Aid and taxation
 
Borgatti dagstuhl 2008 presentation 2c
Borgatti   dagstuhl 2008 presentation 2cBorgatti   dagstuhl 2008 presentation 2c
Borgatti dagstuhl 2008 presentation 2c
 
Security analysis and portfolio management
Security analysis and portfolio managementSecurity analysis and portfolio management
Security analysis and portfolio management
 
Igl Board Of Realtors April 2011
Igl Board Of Realtors April 2011Igl Board Of Realtors April 2011
Igl Board Of Realtors April 2011
 
SLM Q308EarningsStatisticsFinal
SLM  Q308EarningsStatisticsFinalSLM  Q308EarningsStatisticsFinal
SLM Q308EarningsStatisticsFinal
 
Graduate Thesis Presentation
Graduate Thesis PresentationGraduate Thesis Presentation
Graduate Thesis Presentation
 
Gendered sensitive access to climate information services for farmers in Senegal
Gendered sensitive access to climate information services for farmers in SenegalGendered sensitive access to climate information services for farmers in Senegal
Gendered sensitive access to climate information services for farmers in Senegal
 
Lec 02 The State Of The Profession
Lec 02 The State Of The ProfessionLec 02 The State Of The Profession
Lec 02 The State Of The Profession
 
Decision analysis problems online 1
Decision analysis problems online 1Decision analysis problems online 1
Decision analysis problems online 1
 
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
WCCI 2008 Tutorial on Computational Intelligence and Games, part 2 of 3
 
IT Industry Statistics 2007 Final Report
IT Industry Statistics 2007 Final ReportIT Industry Statistics 2007 Final Report
IT Industry Statistics 2007 Final Report
 

More from essp2

Constrained Multiplier Analysis.pdf
Constrained Multiplier Analysis.pdfConstrained Multiplier Analysis.pdf
Constrained Multiplier Analysis.pdfessp2
 
Unconstrained Multiplier Analysis.pptx
Unconstrained Multiplier Analysis.pptxUnconstrained Multiplier Analysis.pptx
Unconstrained Multiplier Analysis.pptxessp2
 
1.Introduction to SAMs.pptx
1.Introduction to SAMs.pptx1.Introduction to SAMs.pptx
1.Introduction to SAMs.pptxessp2
 
ESS Data from a Users Perspective
ESS Data from a Users Perspective ESS Data from a Users Perspective
ESS Data from a Users Perspective essp2
 
Sustainable Food Systems
Sustainable Food Systems Sustainable Food Systems
Sustainable Food Systems essp2
 
Impact of the PSNP (2006-2021)
Impact of the PSNP (2006-2021)Impact of the PSNP (2006-2021)
Impact of the PSNP (2006-2021)essp2
 
Some Welfare Consequences of COVID-19 in Ethiopia
Some Welfare Consequences of COVID-19 in EthiopiaSome Welfare Consequences of COVID-19 in Ethiopia
Some Welfare Consequences of COVID-19 in Ethiopiaessp2
 
Improving evidence for better policy making in Ethiopia’s livestock sector
Improving evidence for better policy making in Ethiopia’s livestock sector Improving evidence for better policy making in Ethiopia’s livestock sector
Improving evidence for better policy making in Ethiopia’s livestock sector essp2
 
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysis
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim AnalysisThe COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysis
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysisessp2
 
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...essp2
 
Key Reforms in Agricultural Sector
Key Reforms in Agricultural SectorKey Reforms in Agricultural Sector
Key Reforms in Agricultural Sectoressp2
 
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...essp2
 
AFFORDABILITY OF Nutritious foods IN ETHIOPIA
AFFORDABILITY OF Nutritious foods IN ETHIOPIAAFFORDABILITY OF Nutritious foods IN ETHIOPIA
AFFORDABILITY OF Nutritious foods IN ETHIOPIAessp2
 
The EAT Lancet Publication: Implications for Nutrition Health and Planet
The EAT Lancet Publication: Implications for Nutrition Health and PlanetThe EAT Lancet Publication: Implications for Nutrition Health and Planet
The EAT Lancet Publication: Implications for Nutrition Health and Planetessp2
 
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies essp2
 
Policies and Programs on food and Nutrition in Ethiopia
Policies and Programs on food and Nutrition in EthiopiaPolicies and Programs on food and Nutrition in Ethiopia
Policies and Programs on food and Nutrition in Ethiopiaessp2
 
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...Integrated Use of Social and Behaviour Change Interventions Improved Compleme...
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...essp2
 
Bottlenecks for healthy diets in Ethiopia
Bottlenecks for healthy diets in EthiopiaBottlenecks for healthy diets in Ethiopia
Bottlenecks for healthy diets in Ethiopiaessp2
 
Diets and stunting in Ethiopia
Diets and stunting in Ethiopia Diets and stunting in Ethiopia
Diets and stunting in Ethiopia essp2
 
Irrigation-Nutrition Linkages
Irrigation-Nutrition LinkagesIrrigation-Nutrition Linkages
Irrigation-Nutrition Linkagesessp2
 

More from essp2 (20)

Constrained Multiplier Analysis.pdf
Constrained Multiplier Analysis.pdfConstrained Multiplier Analysis.pdf
Constrained Multiplier Analysis.pdf
 
Unconstrained Multiplier Analysis.pptx
Unconstrained Multiplier Analysis.pptxUnconstrained Multiplier Analysis.pptx
Unconstrained Multiplier Analysis.pptx
 
1.Introduction to SAMs.pptx
1.Introduction to SAMs.pptx1.Introduction to SAMs.pptx
1.Introduction to SAMs.pptx
 
ESS Data from a Users Perspective
ESS Data from a Users Perspective ESS Data from a Users Perspective
ESS Data from a Users Perspective
 
Sustainable Food Systems
Sustainable Food Systems Sustainable Food Systems
Sustainable Food Systems
 
Impact of the PSNP (2006-2021)
Impact of the PSNP (2006-2021)Impact of the PSNP (2006-2021)
Impact of the PSNP (2006-2021)
 
Some Welfare Consequences of COVID-19 in Ethiopia
Some Welfare Consequences of COVID-19 in EthiopiaSome Welfare Consequences of COVID-19 in Ethiopia
Some Welfare Consequences of COVID-19 in Ethiopia
 
Improving evidence for better policy making in Ethiopia’s livestock sector
Improving evidence for better policy making in Ethiopia’s livestock sector Improving evidence for better policy making in Ethiopia’s livestock sector
Improving evidence for better policy making in Ethiopia’s livestock sector
 
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysis
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim AnalysisThe COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysis
The COVID-19 Pandemic and Food Security in Ethiopia – An Interim Analysis
 
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...
COVID-19 and its impact on Ethiopia’s agri-food system, food security, and nu...
 
Key Reforms in Agricultural Sector
Key Reforms in Agricultural SectorKey Reforms in Agricultural Sector
Key Reforms in Agricultural Sector
 
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...
Parental Aspirations for Children's Education: Is There a "Girl Effect"? Expe...
 
AFFORDABILITY OF Nutritious foods IN ETHIOPIA
AFFORDABILITY OF Nutritious foods IN ETHIOPIAAFFORDABILITY OF Nutritious foods IN ETHIOPIA
AFFORDABILITY OF Nutritious foods IN ETHIOPIA
 
The EAT Lancet Publication: Implications for Nutrition Health and Planet
The EAT Lancet Publication: Implications for Nutrition Health and PlanetThe EAT Lancet Publication: Implications for Nutrition Health and Planet
The EAT Lancet Publication: Implications for Nutrition Health and Planet
 
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies
Sustainable Undernutrition Reduction in Ethiopia (SURE): Evaluation studies
 
Policies and Programs on food and Nutrition in Ethiopia
Policies and Programs on food and Nutrition in EthiopiaPolicies and Programs on food and Nutrition in Ethiopia
Policies and Programs on food and Nutrition in Ethiopia
 
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...Integrated Use of Social and Behaviour Change Interventions Improved Compleme...
Integrated Use of Social and Behaviour Change Interventions Improved Compleme...
 
Bottlenecks for healthy diets in Ethiopia
Bottlenecks for healthy diets in EthiopiaBottlenecks for healthy diets in Ethiopia
Bottlenecks for healthy diets in Ethiopia
 
Diets and stunting in Ethiopia
Diets and stunting in Ethiopia Diets and stunting in Ethiopia
Diets and stunting in Ethiopia
 
Irrigation-Nutrition Linkages
Irrigation-Nutrition LinkagesIrrigation-Nutrition Linkages
Irrigation-Nutrition Linkages
 

Recently uploaded

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...Aggregage
 
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwait
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai KuwaitThe Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwait
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwaitdaisycvs
 
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRLWhitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRLkapoorjyoti4444
 
Cracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxCracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxWorkforce Group
 
Phases of Negotiation .pptx
 Phases of Negotiation .pptx Phases of Negotiation .pptx
Phases of Negotiation .pptxnandhinijagan9867
 
Value Proposition canvas- Customer needs and pains
Value Proposition canvas- Customer needs and painsValue Proposition canvas- Customer needs and pains
Value Proposition canvas- Customer needs and painsP&CO
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Centuryrwgiffor
 
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort Service
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort ServiceMalegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort Service
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort ServiceDamini Dixit
 
PHX May 2024 Corporate Presentation Final
PHX May 2024 Corporate Presentation FinalPHX May 2024 Corporate Presentation Final
PHX May 2024 Corporate Presentation FinalPanhandleOilandGas
 
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876dlhescort
 
Falcon Invoice Discounting: Unlock Your Business Potential
Falcon Invoice Discounting: Unlock Your Business PotentialFalcon Invoice Discounting: Unlock Your Business Potential
Falcon Invoice Discounting: Unlock Your Business PotentialFalcon investment
 
Falcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investorsFalcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investorsFalcon Invoice Discounting
 
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...Falcon Invoice Discounting
 
How to Get Started in Social Media for Art League City
How to Get Started in Social Media for Art League CityHow to Get Started in Social Media for Art League City
How to Get Started in Social Media for Art League CityEric T. Tung
 
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...amitlee9823
 
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizhar
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al MizharAl Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizhar
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizharallensay1
 
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...lizamodels9
 
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60% in 6 Months
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60%  in 6 MonthsSEO Case Study: How I Increased SEO Traffic & Ranking by 50-60%  in 6 Months
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60% in 6 MonthsIndeedSEO
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...rajveerescorts2022
 

Recently uploaded (20)

The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
The Path to Product Excellence: Avoiding Common Pitfalls and Enhancing Commun...
 
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabiunwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
unwanted pregnancy Kit [+918133066128] Abortion Pills IN Dubai UAE Abudhabi
 
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwait
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai KuwaitThe Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwait
The Abortion pills for sale in Qatar@Doha [+27737758557] []Deira Dubai Kuwait
 
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRLWhitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL
Whitefield CALL GIRL IN 98274*61493 ❤CALL GIRLS IN ESCORT SERVICE❤CALL GIRL
 
Cracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptxCracking the Cultural Competence Code.pptx
Cracking the Cultural Competence Code.pptx
 
Phases of Negotiation .pptx
 Phases of Negotiation .pptx Phases of Negotiation .pptx
Phases of Negotiation .pptx
 
Value Proposition canvas- Customer needs and pains
Value Proposition canvas- Customer needs and painsValue Proposition canvas- Customer needs and pains
Value Proposition canvas- Customer needs and pains
 
Famous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st CenturyFamous Olympic Siblings from the 21st Century
Famous Olympic Siblings from the 21st Century
 
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort Service
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort ServiceMalegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort Service
Malegaon Call Girls Service ☎ ️82500–77686 ☎️ Enjoy 24/7 Escort Service
 
PHX May 2024 Corporate Presentation Final
PHX May 2024 Corporate Presentation FinalPHX May 2024 Corporate Presentation Final
PHX May 2024 Corporate Presentation Final
 
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
Call Girls in Delhi, Escort Service Available 24x7 in Delhi 959961-/-3876
 
Falcon Invoice Discounting: Unlock Your Business Potential
Falcon Invoice Discounting: Unlock Your Business PotentialFalcon Invoice Discounting: Unlock Your Business Potential
Falcon Invoice Discounting: Unlock Your Business Potential
 
Falcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investorsFalcon Invoice Discounting: The best investment platform in india for investors
Falcon Invoice Discounting: The best investment platform in india for investors
 
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...
Unveiling Falcon Invoice Discounting: Leading the Way as India's Premier Bill...
 
How to Get Started in Social Media for Art League City
How to Get Started in Social Media for Art League CityHow to Get Started in Social Media for Art League City
How to Get Started in Social Media for Art League City
 
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...
Call Girls Kengeri Satellite Town Just Call 👗 7737669865 👗 Top Class Call Gir...
 
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizhar
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al MizharAl Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizhar
Al Mizhar Dubai Escorts +971561403006 Escorts Service In Al Mizhar
 
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
Russian Call Girls In Gurgaon ❤️8448577510 ⊹Best Escorts Service In 24/7 Delh...
 
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60% in 6 Months
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60%  in 6 MonthsSEO Case Study: How I Increased SEO Traffic & Ranking by 50-60%  in 6 Months
SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60% in 6 Months
 
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
👉Chandigarh Call Girls 👉9878799926👉Just Call👉Chandigarh Call Girl In Chandiga...
 

Aspirations and well being outcomes in ethiopia evidence from a randomized field experiment -alemayehu s.t.ppt

  • 1. ETHIOPIAN DEVELOPMENT RESEARCH INSTITUTE Aspirations and well-being outcomes in Ethiopia Evidence from a randomized field experiment Tanguy Bernard, Stefan Dercon, Kate Orkin , Fanaye Tadesse, and Alemayehu Seyoum Taffesse IFPRI ESSP-II and University of Oxford Ethiopian Economic Association Conference July 19, 2011 Addis Ababa 1
  • 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) 2
  • 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 – Assets – house, furniture, consumer from half a hectare goods, vehicles – Social status – whether people in the 0 ETB 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
  • 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.
  • 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    ns ,v ,i    y1,v ,i    s  v   i T s 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)** 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
  • 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)** 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
  • 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 have 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