Mixing Qualitative and Quantitative Methods of

Analyzing Poverty Dynamics

Chris BarrettCornell UniversityMarch 11, 2003SAGA Workshop Nairobi, Kenya

Imperative of Poverty Research

Governments committed to (i) poverty reduction and (ii) increased participation of the poor in priority setting exercises.

Need to know:- who are the poor?- whose poverty is chronic, whose transitory?- why are some chronically poor?- what one-off interventions can help put people on an accumulation path out of poverty?

Advances in Poverty Research

Significant recent progress in both qualitative (QUAL) and quantitative (QUANT) methods of poverty analysis:

- rapid rise of participatory poverty assessment (PPA) methods- emergence of widespread, nationally representative household survey data, notably longitudinal panels

Ongoing Challenge of Poverty Research“Poverty” as a complex,

multidimensional concept - Static/dynamic - chronic/transitory- assets vs. income/expenditures - econ/non-economic outcomes - experience/prospect of poverty- outcomes/processes

Powerlessness, vulnerability and resource insufficiencycentral to most conceptualizations … but hard to pin down

So are QUAL and QUANT complements or substitutes?

Considerable conflict among practitioners of each … legacy of 80s/90s disciplinary segregation. But do the methods necessarily conflict too???

So complex a concept requires iteration between (sequential mixing) or integration of (simultaneous mixing) methods for accurate triangulation.

We must learn the lesson of the blind men and the elephant.

Be clear about focus of question:

(1) Data collection methods

(2) Data types (3) Data analysis methods

(4) Audience

Dimensions of QUAL-QUANT Difference

Data collection methods A

nalytic

al C

ove

rag

eG

en

era

l S

pecifi

c

Passive Active Population Involvement in Research

Census

Autobiography

PRA

Random Sample Surveys

Dimensions of QUAL-QUANT Difference

Data types:

“Qualitative” “Quantitative”

Categorical Ordinal Cardinal

Each data collection method can yield both non-numerical and numerical data types

Dimensions of QUAL-QUANT Difference

Data analysis methods:

“Qualitative” “Quantitative”

Inductive Deductive

Related to the specific-general data collection methods distinction, there’s often (not always) a difference in analysis methods.

Dimensions of QUAL-QUANT Difference

Audience:Local community Or global/national

policymakers

Local empowermentor the “big picture”

and “speaking truth to power”

Dimensions of QUAL-QUANT Difference

Myths about QUAL-QUANT differences

(1) One more/less extractive than the other (“ethical superiority”)

(2) One more/less contextual than the other (“historical superiority”)

(3) One inherently numerical/non-numerical(“statistical superiority”)

(4) One more “rigorous” than the other(“scientific superiority”)

Bad practice is bad practice, whatever the method...

Key question: When and how is good practice within one strand still wanting? How can the other fill the blanks?

Mixing Methods

Improve analysis by mixing the two … take the “con” out of econometrics and generalize beyond the “part” of participatory methods

Harness statistical power of quantitative methods for description/aggregation along with narrative power of qualitative methods for nuanced and textured analysis of complex (unmeasurable?) concepts

Triangulation to uncover mechanisms behind poverty

Mixing Methods

“Sequential mixing” or “classical integration”

Practitioners of each method do their best with their own tools on same problem, sometimes taking outputs from one as intermediate inputs to another. Then triangulate to get an integrated result.

Sequential mixingExample: Understanding welfare transitions (BASIS CRSP)

Step 1: Panel survey data collection to construct transition matrices and change measures. Poort

Nonpoort

Step 2: Draw several households Poort+1

from each of 6 cells in matrix and do detailed oral histories. Nonpoort+1

Why? Capture omitted variables, check transitions, 2nd method of inference, problem of identifying thresholds econometrically, value of stories for policy audiences.

_

+

_

+

_

+

“Simultaneous mixing” or “Bayesian integration”Iterative approach to using one method to inform another, then

back to the first, etc., keeping multiple methods interactive throughout the research process to update researchers’ priors continuously.

Feedback loop yields a homeostatic research mechanism:“ethnography” precedes “participatory” which in turn precedes “survey” in dictionary … ought to be the case in the field, too!

Ongoing, creative tension between methods helps ensure originality, robustness and relevance of results

Mixing Methods

Simultaneous mixingExample: improving pastoralists’ risk management (PARIMA)

(1) Participatory risk mapping to identify relevant threats: open-ended, spatially-explicit, pseudo-cardinal(2) Quarterly repeated surveys with open-ended sections and mixed modules:

(i) complex property rights; climate forecasting, resource conflict; land use history; livelihoods strategies, etc.

(ii) complementarity at multiples levels of analysis and different methods (e.g., livestock marketing with data from households, markets and traders)

Effective means to ensure inference consistent (i) across methods (a test of robustness) and (i) with local understandings of the problem(s) (a test of relevance)

The Way Forward: Walking On Two LegsDevelopment scholars and practitioners increasingly

recognize the complementarity between qualitative and quantitative methods.

Best done in multidisciplinary teams with a range of joint and parallel efforts.• Situate participatory poverty appraisals within sampling frame• Introduce more open-ended and subjective questions of causality and interpretation into survey instruments• Explicitly foster self-critique, cross-checking within teams and feedback briefings with different interested parties

Nonetheless, much remains to be done (vocabulary, data cross-referencing, respectful dialogue, etc. …

Thanks very much for your comments and questions!