Informal Insurance in the Presence of Poverty Traps: Evidence from Southern Ethiopia Paulo Santos and Christopher B. Barrett Cornell University September.

Slides:



Advertisements
Similar presentations
Creating a Window of Opportunity for Policy Change By Nancy Yinger, The Population Reference Bureau AMDD Conference Kuala Lumpur, 2003.
Advertisements

Chapter 12: Testing hypotheses about single means (z and t) Example: Suppose you have the hypothesis that UW undergrads have higher than the average IQ.
Moving Up or Moving Out? Explaining the Livelihood Trends in Pastoralist Areas Andy Catley.
Stephen McCray and David Courard-Hauri, Environmental Science and Policy Program, Drake University Introduction References 1.Doran, P. T. & Zimmerman,
Social Network Capital, Economic Mobility and Poverty Traps Sommarat Chantarat and Chris Barrett Cornell University Seminar at Watson Institute, Brown.
Multiple Linear Regression Model
Heterogeneous Wealth Dynamics: On the roles of risk and ability Paulo Santos and Christopher B. Barrett.
Evaluating Hypotheses Chapter 9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics.
Index-based Livestock Insurance: Logic and Design Christopher B. Barrett Cornell University March 16, 2009 Nairobi, Kenya.
1 Risk and Asset Management in the Presence of Poverty Traps: Implications for Growth and Social Protection Christopher B. BarrettMichael R. Carter Cornell.
Christopher B. Barrett Vet Med 615 Guest Lecture February 28, 2006.
Evaluating Hypotheses Chapter 9 Homework: 1-9. Descriptive vs. Inferential Statistics n Descriptive l quantitative descriptions of characteristics ~
Poverty Traps, Safety Nets and Sustainability Chris Barrett Robin Hill Seminar Cornell University April 28, 2005.
Social Network Capital, Economic Mobility and Poverty Traps Sommarat Chantarat and Chris Barrett Cornell University May 3, 2007 Seminar Ohio State University.
Welfare Dynamics in Rural Kenya and Madagascar Christopher B. Barrett, Paswel Marenya, John McPeak, Bart Minten, Festus Murithi, Willis Oluoch- Kosura,
Heterogeneous Wealth Dynamics: On the roles of risk and ability Paulo Santos and Christopher B. Barrett Cornell University Michigan State University guest.
Economics of Poverty Traps and Persistent Poverty: An Asset-based Approach Michael R. Carter University of Wisconsin Christopher B. Barrett Cornell University.
DISTRIBUTION OF INCOME, CONSUMPTION, SAVING AEG MEETING WASHINGTON DC, 8-10 SEPTEMBER 2014 Presented by Jennifer Ribarsky (OECD)
An Asset-Based Approach to Poverty Dynamics and Safety Nets: Research and Policy Questions Christopher B. Barrett Cornell University Michael R. Carter.
Christopher B. Barrett and Michael R. Carter Seminar at University of California at Riverside May 24, 2012 T HE E CONOMICS OF P OVERTY T RAPS AND P ERSISTENT.
Christopher B. Barrett Vet Med 6273 Guest Lecture January 31, 2012.
Main Question What is the impact of index-based livestock insurance (IBLI) on herd stocking and movement choices of East African pastoralists (livestock.
Why IBLI? On Poverty Traps, Catastrophic Risk and Index Insurance Christopher B. Barrett Index Based Livestock Insurance Mini Workshop International Livestock.
DESCRIBING KNOWLEDGE ASSETS AND INTELLECTUAL CAPITAL MEASUREMENT TECHNIQUES These are the topics for today: What are knowledge assets? Why are they so.
June 19, 2008Stat Lecture 12 - Testing 21 Introduction to Inference More on Hypothesis Tests Statistics Lecture 12.
© 2008 McGraw-Hill Higher Education The Statistical Imagination Chapter 9. Hypothesis Testing I: The Six Steps of Statistical Inference.
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
Land Rental Markets in the Process of Structural Transformation: Productivity and Equity Impacts in China Songqing Jin and Klaus Deininger World Bank.
Christopher B. Barrett Cornell University May 22, 2013 remarks at panel discussion on Risk & Vulnerability Monash Centre for Development Economics Resilience.
Eng.Mosab I. Tabash Applied Statistics. Eng.Mosab I. Tabash Session 1 : Lesson 1 IntroductiontoStatisticsIntroductiontoStatistics.
Welfare Dynamics in Rural Kenya and Madagascar: Preliminary Quantitative Findings Chris Barrett Cornell University March 15, 2004 BASIS CRSP Project Annual.
Inter-Generational Transfer of Household Poverty in KwaZulu Natal: Evidence from KIDS (1993 – 2004) Antonie Pool University of the Free State TIPS Conference,
ATTRIBUTEDESCRIPTION Focal Knowledge, Skills, Abilities The primary knowledge / skills / abilities (KSAs) targeted by this design pattern. RationaleHow/why.
Climate Change, Climate Variability And Poverty Traps: The Role (and Limits) of Index Insurance for East African Pastoralists Christopher B. Barrett Cornell.
Objectives: To model the spatio-temporal herd allocation choices of pastoralists (livestock herders) in the arid and semi-arid lands (ASAL) of east Africa.
The perceived role of Networking or Herding behaviour on the migration intentions and the Entrepreneurial Activity of African immigrants to South Africa.
Adapting Index-based Livestock Insurance (IBLI) for Ethiopia: Logic and Design Christopher B. Barrett Cornell University Workshop at International Livestock.
Smallholder Market Participation: Concepts and Evidence from Eastern and Southern Africa Christopher B. Barrett, Cornell University FAO workshop on Staple.
Rural Poverty Dynamics: Development Policy Implications Christopher B. Barrett August th triennial IAAE conference Durban, South Africa.
Sample Size Considerations for Answering Quantitative Research Questions Lunch & Learn May 15, 2013 M Boyle.
Introduction to the Practice of Statistics Fifth Edition Chapter 6: Introduction to Inference Copyright © 2005 by W. H. Freeman and Company David S. Moore.
Non-experimental methods Markus Goldstein The World Bank DECRG & AFTPM.
On Climate Variability And Resource- Dependent Wealth Dynamics: The Case of Ethiopian Pastoralists Paulo Santos University of Sydney Christopher B. Barrett.
Issues concerning the interpretation of statistical significance tests.
Lecture 17 Dustin Lueker.  A way of statistically testing a hypothesis by comparing the data to values predicted by the hypothesis ◦ Data that fall far.
Welfare Effect of Foreign Migration and Remittances in Kosovo Sachiko Miyata, World Bank Irina Shaorshadze, Cambridge University.
Research Methodology and Methods of Social Inquiry Nov 8, 2011 Assessing Measurement Reliability & Validity.
N318b Winter 2002 Nursing Statistics Specific statistical tests Chi-square (  2 ) Lecture 7.
Applied Quantitative Analysis and Practices LECTURE#25 By Dr. Osman Sadiq Paracha.
Presented by Macroeconomic Policy Division June 2015 Addis Ababa, Ethiopia Economic Forecasts.
A Gendered Approach to Credit Demand: Evidence from Marsabit District, Kenya Anne Gesare, Megan Sheahan, Andrew Mude, Rupsha Banerjee ADRAS IBLI Academic.
By R. Gambacorta and A. Neri Bank of Italy - Statistical Analysis Directorate Wealth and its returns: economic inequality in Italy, The Bank.
The vulnerability of indebted households during the crisis: evidence from the euro area The vulnerability of indebted households during the crisis: evidence.
The Performance of Index Based Livestock Insurance (IBLI): Ex Ante Assessment in the Presence of a Poverty Trap Chris Barrett ISS Seminar, Cornell University.
Marshall University School of Medicine Department of Biochemistry and Microbiology BMS 617 Lecture 6 –Multiple hypothesis testing Marshall University Genomics.
Dynamic Effects of Index Based Livestock Insurance on Household Intertemporal Behavior and Welfare Munenobu Ikegami, Christopher B. Barrett, and Sommarat.
1 Heterogeneous Wealth Dynamics: on the roles of risk and ability. Paulo Santos and Christopher Barrett Cornell University Conference on Pastoralism and.
Hypothesis Testing. Statistical Inference – dealing with parameter and model uncertainty  Confidence Intervals (credible intervals)  Hypothesis Tests.
Tests of Significance We use test to determine whether a “prediction” is “true” or “false”. More precisely, a test of significance gets at the question.
Is the demand of the index-based livestock insurance and informal insurance network substitute or complement? Kazushi Takahashi (with Chris Barrett and.
Module 7 Vulnerability to Poverty
Human Capabilities and Poverty Dynamics in the Face of Agro-Ecological Shocks Christopher B. Barrett Cornell University USAID/NBER event on “Resilience.
Royal Economics Society Conference April 19th, 2011
Human Capabilities and Poverty Dynamics in the Face of Agro-Ecological Shocks Christopher B. Barrett Cornell University World Bank seminar “Resilience.
Poverty Traps, Safety Nets and Sustainability
Christopher B. Barrett Cornell University Michael R. Carter
Michael R. Carter University of Wisconsin Christopher B. Barrett
Christopher B. Barrett, Cornell University
Welfare Dynamics in Rural Kenya and Madagascar
Heterogeneous wealth dynamics: The role of risk and ability
Presentation transcript:

Informal Insurance in the Presence of Poverty Traps: Evidence from Southern Ethiopia Paulo Santos and Christopher B. Barrett Cornell University September 14, 2006 seminar Michigan State University

Core Question Models of consumption smoothing and informal insurance typically rely on the assumption of stationary income processes. Our question: what happens when that assumption does not hold?

Outline 1: What do we know 2: Asset shocks and insurance 3: Data 4: Who gives to whom 5: Who knows whom 6: Conclusions

1: What do we know Lybbert et al (2004 EJ)  Evidence of multiple equilibria  Asset risk is largely idiosyncratic  But asset transfers are quite small

What do we know Santos and Barrett (2006)  Asset shocks associated with adverse rainfall events are the source of non-linear asset dynamics (multiple equilibria)  Boran pastoralists perceive this.  Ability matters !

2: Asset shocks and insurance Poverty trap models emphasize assets and thresholds. So we focus on asset dynamics, risk and transfers around thresholds. Basic intertemporal decision model: Max {ct, ijt} E{  t=0…T  t U(c t (k t ))|  } subject to:k t = g( k t-1 +  t + ji t - ij t ) c T (k T ) = k T k 0 given,  ~[-k t,0], t ={0, } Transfers ( ) and asset shocks (  ) affect asset (k) dynamics, underlying income generation and consumption (c).

Asset shocks and insurance Growth dynamics are key to understanding the nature of the resulting informal insurance arrangements. k c t = g c l (k t-1 +  t + ji t - ij t ) if i  c, k t-1 <  = g c h (k t-1 +  t + ji t - ij t ) if i  c, k t-1   for clubs c=1,…,C The most general specification allows for: 1) different clubs w/o thresholds (C>1,  =0), 2) unique club w/ threshold (C=1,  >0), 3) canonical convergence model (C=1,  =0, g(.) concave) that implicitly underpins the standard consumption smoothing and informal insurance literatures

Asset shocks and insurance Convergence: every match is in insurance pool (standard literature) Precautionary savings: only capacity to reciprocate (but not actual losses) matters (McPeak JDE 2006) Poverty traps due to multiple equilibria: 1) exclude the poorer and those with lower ability (i.e, those at lower level equilibria) because it is harder to punish them if they don’t reciprocate. 2) privilege those at the threshold (because maximizes gains from transfer). Losses YesNo Herd size YesPoverty trapsPrecautionary savings NoConvergence?

3: Data Pastoral Risk Management (PARIMA) project (USAID GL CRSP) 119 households, Data on insurance networks  5 Random matches [X] within sample : Question 1: Do you know [X]? Question 2: Would you give to [X] if s/he asked?  Advantage/(potential) disadvantages: no bias because lack of knowledge of one side of the relation data on links, not transfers: but transfers are small potential, not real, links: but inference based on this information is reliable (Santos and Barrett, 2006)

Data 1) Gifts  Loans 2) Not everyone knows everyone else 3) Doesn’t know  Doesn’t give 4) Know (not  ) Give Know Give YesNo Yes653 No Gift Loan YesNo Yes4253 No10123

4: Who gives to whom l ij * = α i +  1 f(h j )+  L j + Σ t=1…4 β t E tj + δ X ij + λZ i + ε ij Key variables: h j (recipient herd size), L j (recipient herd loss), E j (recipient equilibrium regime) X ij = (possibly asymmetric) differences between i and j Z i = characteristics of the respondent Assumptions on ε ij : ε ij ~ log(0,  2 /3) E (ε ij,ε ih ) ≠ 0 if j ≠ h E (ε ih,ε jh ) = 0 if i ≠ j  Logit model, observations clustered on the respondent

Who gives to whom Alternative assumption: E (ε ih, ε jh ) ≠ 0 if i ≠ j Ways to check/correct for this possibility: - Udry & Conley (2005), Fafchamps and Gubert (JDE forthcoming) use Conley’s estimator to correct for correlated error structures - Quadratic Assignment Procedure (QAP): nonparametric permutation test that gives correct p-values Ultimately, these more complex error structures matter little

Who gives to whom (1)(2)(3)(4) h j = hjhj E E E LjLj L j * E L j * E L j * (h j =0) Bold indicates statistical significance at 5% level or lower. Result: Transfers respond to losses – i.e., they are state-contingent insurance claims – but also depend on ex post herd size. We thus reject the precautionary transfers and insurance under convergence hypotheses in favor of the insurance in the presence of poverty traps.

Who gives to whom Conclusion: Asset transfers are best understood as insurance of permanent income, preventing recipients from falling into persistent poverty and excluding those who are not expected to be able to reciprocate.

Who gives to whom Does “ability club” membership matters? A priori expectation: those with low ability should not receive gifts, if match’s ability is observed by respondents. Approach followed:  Get estimates of efficiency (high, medium, low)  Re-estimate previous model  Bootstrap results to get correct SE

Who gives to whom (1)(2)(3) Low Medium E2*low E2*medium E2*high L j * E2* lowDropped L j * E2* medium2.856 L j * E2* high2.500 Result: As predicted: transfers related to losses and ex post herd size for those facing multiple equilibria.

Who gives to whom Does the threshold play a role in targeting?  No if transfers are given to those with maximal capacity to reciprocate  Yes if transfers are intended to maximize expected gains from transfer The predictions of the two models diverge for those herders who suffered losses but are above the threshold  Helped in the 1 st model  Not helped in the 2 nd model  Problem: no data in the region where the predictions differ (above the threshold)  Solution: use simulation results on expected gains from transfers

Who gives to whom Simulated expected herd growth (and long-term herd size)

Who gives to whom Result: Transfers seem ex post insurance that takes into account recipient’s expected gains but not his/her expected wealth … a non-monotonic relation between recipient’s wealth and transfers. (1)(2)(3)(4)(5) E (wealth) E (gains) E (wealth) * Loss E (gains) * Loss

Who gives to whom Conclusions: 1) Transfers are influenced: By the existence of thresholds By the existence of ability clubs 2) Asset transfers seem to be best understood as insurance of the permanent component of income and driven largely by expected recipient gains

5: Who knows whom: Social exclusion and poverty traps “[t]o be poor is one thing, but to be destitute is quite another, since it means the person so judged is outside the normal network of social relations and is consequently without the possibility of successful membership in ongoing groups, the members of which can help him if he requires it. The Kanuri [in the West African savannah] say that such a person is not to be trusted”. (Iliffe, 1987, The African Poor) Coef. No cattle since E1 since E2 since E3 since E4 since Lost cattle More cattle Less cattle0.040 Use same logit estimation approach, with “know” as dependent variable now.

6: Conclusions Implications for public transfers - is crowding out really a concern for the poorest? No Our results:  The poorest are (rationally) not recipients of informal transfers: no risk of crowding out at very low levels of assets  Possibility of crowding in (by moving people nearer the threshold, where private transfers can be triggered … see Chantarat and Barrett, 2006)  Targeting may be especially difficult: public transfers must consider [needs * dynamics * ability]  Social invisibility of the poorest makes community based targeting a challenge

Thank you for your attention … I welcome your comments and questions.