The Distortionary Effects of Classifications on Aid ... · Lindsay Dolan Columbia University...

Introduction Theory Design Results

The Distortionary Effects of Classifications onAid Allocation Decisions

Lindsay Dolan

Columbia University

[email protected]

November 18, 2017

Development Classifications and Aid Allocation Lindsay Dolan (Columbia University)


Motivation



Dissertation Project

Classifications coordinate the perceptions and behaviors ofinfluential actors in the global economy.

I Evidence? Cross-national statistical analysis and interviewsshow that MICs receive less aid but are rated as moredemocratic and creditworthy than LICs

I Why? Classifications (1) simplify cognitive decision-makingand (2) allow certain actors to strategically justify theirdecisions

I So what?I Helps us understand power of IOs (Barnett and Finnemore 1999, 2004)

I Illustrates why beliefs and ideas affect market behavior (Blyth

2002; MacKenzie 2006; Shiller 2017)

I Fits into broader literature on labels, metrics, indicators ingovernance (Buthe 2012; Cooley and Snyder 2015; Gray 2013; Brooks, Cunha and Mosley

2015; Kelley and Simmons 2015)



Explaining Aid Allocation

Why would donors use classifications to allocate aid?

I Puzzle: Classifications do not convey any private or expertinformation

1. CognitiveI Classifications simplify or distill information (Kahneman 2011)

I Conflicting information about developing countries (Jerven 2013)

2. StrategicI Using classifications created by credible third party improves

appearance of impartialityI “When we can show that most of our aid is benefiting Least

Developed Countries, this helps our numbers.”— U.S. Treasury official



Allocation Activity

Timor-Leste $Zambia $South Africa $Tonga $El Salvador $



Allocation Activity: Classifications Treatment

Timor-Leste $Zambia $South Africa $Tonga $El Salvador $



Identifying the Classification EffectClassifications

Treatment Control



Distinguishing the Mechanism

I Classifications treatment captures cognitive effect becausetreatment and control groups face identical incentives

I Introduce second treatment (factorial design) to vary thestrategic incentives participants face

I Control: Randomly selected participants receive prize

I Treatment: Randomly selected participants receive prize onlyif a “judge” approves of their allocations — “The judge willbe an intern (i.e an entry-level staffer) in the office of apolitician (i.e. a congressperson/MP) in a major donorcountry. The judge is NOT an expert on development.”



Factorial DesignClassifications

Treatment Control

Treatment

Jud

ges

Control



Identification Strategy

I How to operationalize a participant’s allocation?

I Ideal : Amount given to country if classified as lower-middleincome versus amount given to country if classified asupper-middle income

I Obstacles: Country-specific characteristics, no deceptionI Solution: Exploit variation in data

I GNI figures revised over timeI Identify instances when various estimates put above/below

thresholdI Circulate both versions (to different participants)I Compare allocations made by participants seeing “just-below”

version to those seeing “just-above” version



Example

I Paraguay’s 2013 GNI per capita, reported in WDI inNovember 2014: $4,040

I Paraguay’s 2013 GNI per capita, reported in WDI inSeptember 2015: $4,190

I 2013 threshold separating LMICs from UMICs: $4,125



Example



“Just-Below” RandomizationClassifications

Treatment Control

Treatment •◦•◦•

•◦•◦

◦•◦••

◦•◦•

◦◦•••

◦•◦◦

◦•◦◦◦

•◦••

Jud

ges

••◦◦◦

•◦◦•

◦•◦••

◦◦•◦

◦•••◦

•◦••

◦•◦◦•

•◦••

Control •◦◦•◦

◦◦••

•◦•◦•

◦◦•◦

•••◦◦

•◦◦◦

•◦•◦•

◦•◦◦

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••◦◦

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Identifying Equation

Yij =α + γCi + λJBj + σJi+

δ1(Ci ∗ JBj) + δ2(Ji ∗ JBj) + δ3(Ci ∗ Ji )+

δ4(Ci ∗Mi ∗ JBj) + µj + ρj + εij

δ1 = Total effect of classificationsδ4 = Strategic effect of classificationsδ1 − δ4 = Cognitive effect of classificationsµr = Round F.E.ρr = Country identity F.E.Standard errors clustered at participant level



Sample

I Fielded September 2017 at International Conference forSustainable Development (side meeting of UN GeneralAssembly)

I Fielded online and in person

I 232 participants (2088 rounds)



Effects on Allocation to Threshold Country

treat_judge: 0 treat_judge: 1

treat_class: 0treat_class: 1

0 25 50 75 100 0 25 50 75 100

0.00

0.01

0.02

0.03

0.04

0.00

0.01

0.02

0.03

0.04

Allocation to threshold country

Den

sity

Just Above Just Below



Effects on Allocation to Threshold Country

Allocation to threshold country (logged)TreatClass*TreatJudge*JustBelow (δ4) 0.269 0.158Strategic effect (0.174) (0.161)

p=0.12 p=0.32

TreatClass*JustBelow (δ1) −0.068 −0.046Total effect (0.121) (0.115)

p=0.57 p=0.69

TreatClass −0.028 −0.038(0.106) (0.106)

TreatClass*TreatJudge −0.270∗ −0.213(0.152) (0.151)

JustBelow 0.093 0.057(0.070) (0.072)

TreatJudge*JustBelow −0.094 −0.004(0.117) (0.106)

TreatJudge 0.118 0.074(0.092) (0.093)

Observations 2088 2088Num. Participants 232 232Threshold Country F.E.? No YesRound F.E.? No YesNote: ∗p<0.1; ∗∗p<0.05; ∗∗∗p<0.01



Effects on Overall Allocation Behavior

15

20

25

30

1 2 3 4 5

Country Position

Allo

catio

n

treat_class Control Treatment



Effects on Overall Allocation Behavior

treat_judge: Control

treat_judge: Treatment

1 2 3 4 5

10

15

20

25

30

10

15

20

25

30

Country Position

Allo

catio

n

treat_class Control Treatment



Conclusion and Next Steps

I Classifications distort donors’ aid allocation behavior in favorof lower income countries

I Distortion primarily driven by strategic incentives

Additional analysis

I Heterogeneous treatment effects?

I Observational determinants of allocations?

Additional data collection

I Other development conferences in spring 2018?

I Create online sample of development experts?



Extra Slides



Countries Used to Populate Graphics

1 2 3 4 5

Honduras Sri Lanka Georgia B&H MaldivesVietnam Indonesia Mongolia Ecuador AzerbaijanGhana Guatemala Paraguay Macedonia Namibia

Uzbekistan Micronesia Albania Jamaica PeruMoldova Congo Angola Algeria SerbiaBolivia Nigeria Belize Thailand BelarusBhutan Morocco Jordan Tuvalu ColombiaKiribati Philippines Tunisia DR Botswana

PNG Ukraine Guyana Cuba Iraq



Randomization

ClassificationsTreatment Control

Treatment 66 52Ju

dge

s

Control 60 54

N.B. Both treatments balanced on gender, age.



Descriptives: Education

0

30

60

90

High school degree

Bachelor degree

Masters degree

Doctoral degree

NA



Descriptives: Current Occupation

0

20

40

60

80

Grad−

DevR

elated

Grad−

NotD

ev

Undergrad

DevP

artner

Govt

NG

O

Research

PrivateS

ector

Other

PN

TS

NA



Descriptives: Years of Experience in Development

0

30

60

90

120

0 1−4 5−9 10−14 15−20 20+ NA



Descriptives: What Country Are You From?

I Representatives of 48 countriesI Largest delegations from...

I USA (40%)I India (9%)I China (7%)I Nigeria (4%)I Japan (4%)I Germany (3%)I Colombia (3%)



Descriptives: Knowledge About Development

I “What does a GINI coefficient measure?”(Inequality / Democracy / State Fragility / Corruption /Don’t Know)

I 74% answered correctly

I “DFID is an aid agency of which national government?”(France / UK / Germany / Japan / Don’t Know)

I 56% answered correctly


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