Do Voices Echo Quantitative Data? A Q-Squared Study of Well-being Dynamics in Nicaragua

By Ximena Del Carpio ~The World Bank

Outline

A bit about Q-squared

Motivations for using Q-squared

Motivations for looking at Nicaragua derived from LSMS data

Objectives and research questions

Sampling and methods

Mixing methods and illustrating complementarities

What we learned

Q-squared: definition and more…

What is it? A combination of data obtained through two distinct methods “qualitative and quantitative”.

Substitutes? NO, each method offers tradeoffs (depth and breadth) have strengths and weaknesses, build on each other

Which is better? All data is affected by perceptions and beliefs of researcher and informants. None lead to absolute and objective truth

Why use mixed-methods? Triangulation. Reduce skepticism of findings. Improve instruments and sharpens understanding of findings by decreasing misinterpretation.

When to use it? Complex social environments, particularly when human behavior and attitudes are studied

General motivations for Q-squared The evolution of poverty requires innovative approaches that

evidence its dynamism and allow incorporating experiences in the design of programs and policies

The multi-dimensionality of poverty calls for an enhanced understanding of poverty and its determinants to lessen limitations and contextualize information (Bourguignon 2003, Ravallion 2003)

The context really seems to matter and mixing methods allows for policy recommendations that are more operationally relevant (Rao and Woolcock 2004)

Motivations for looking at Nicaragua derived from LSMS data

Positive macroeconomic patterns, wide reforms nationwide (ex. utilities), high social and private investment, key trade agreements but no change in poverty between 1998 and 2005, this presents a puzzle…

Statistically insignificant change in inequality, change in the composition of poverty (reduction of extreme poverty) leading to a fall in the poverty gap, what happened?

Widening gap between growth rates of income and consumption, particularly in the upper and lowest deciles of the distribution.

Regional disparity (increasing), high concentration of poverty in the Atlantic and other rural areas of the country, is poverty a regional phenomenon?

Objective Answer existing puzzles going beyond numbers by using a

mixed-methods approach

Does the quantitative definition of poverty, to include its determinants, resemble the definition given by the people?

Opportunities and obstacles: important factors

complementing survey data to explain mobility and identify inequalities that hinder progress?

Can qualitative information contribute to identifying and

targeting the poor?

Research questions

Sampling and methods Sampling: stratified by poverty rates and

mobility patterns, randomly selected within region, with Atlantic region overrepresented (using LSMS panel, 1998, 2001, 2005)

Research questions rooted on the puzzles

presented in analytical work using 2,400 household panel

18 communities, all five regions nationwide, selected (150 out of 2,400 households were re-visited) and approximately 2,000 additional households and 100 leaders participated in exercises

Nine qualitative instruments complementing quantitative surveys

Mixing methods and illustrating complementarities

Poverty is measured along similar dimensions by economists and people…

Housing

Land

cattle/animals

Productive assets

Income opportunities

Self-income opport.

Food

Water

Electricity

Education (access)

Education (participation)

Health (access)

Health (affordability)

Productive/income programs

Food programs

Community cohesion

Empowerment

Dimensions

Mat

eria

lP

hysi

cal

Hum

anS

ocia

l

LSMS data tells us that: sector of employment matter

access to services

education levels

age of household head and family composition

The Influence of these correlates on wellbeing (+ or -) are important however “why” they are so is less clear…

The effect of the social dimension matters to people

Examples of how Qual can complement Quant.. Example 1: Education, perception of quality by young adults, low

value (long-term) of education vis-à-vis local jobs, road quality winter months, security, discrimination and low self-esteem, drug use, single motherhood, abuse and neglect by parents, illness of parent/sibling, fees outside of the law (cleaning, PTA)

Example 2: Land, the quality of cultivable land rather than the size or ownership is what matters

Example 3: Empowerment, voiceless, hopeless and dependent on luck and kindness of others and discrimination. Bad leadership at the community level

2001 2005 2001 2005 2001 2005 2001 2005Managua 8.1 31.7 0 5.6 0 0 73.2 40.2Rest Urban 3.6 6.2 14.6 20 1.5 0 58.4 54.5Poor  4.8 5.1 14.2 14.2 9.4 10.5 43.4 40.9Urban 1.8 2.1 13 18.6 0 0 73.6 50.3Rural 5.8 6.2 14.6 12.7 12.7 14.2 32.9 37.5Non-Poor 2.5 15.8 10.1 14.3 12.5 3 35.7 36Urban 4.8 24 14 22.3 2.9 0 49.5 37.2Rural 0 4.7 6 3.5 22.5 7.1 21.4 34.4

% Family Problems % Not interested % Distance % Monetary problems

Source: LSMS data, 2001 and 2005, NI-PA Education. Note: Children 7-12 who stayed out of school in 2001 and 2005

There are regional inequities captured with LSMS data and enriched by qualitative information …despite average improvements in well-being in the Atlantic and

Central regions, poverty is still concentrated there…Why? Customary practices Psychological effects from the war and present security concerns Low “technification” of agriculture due to access and tradition

ATL-RU

ATL-UR

CENT-UR

PAC-URMAN-RU

MAN-UR

CENT-RU

PAC-RU

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00

1998

2005

Avg. Durable Asset Values (1998-2005)

Avg. Maximum Education (1998-2005)

Using LSMS and Qual data to accurately target the poor

Qual methods yield lower poverty estimates The composition within the poor is different based on

perceptions of what matters Quality of services and availability of basic infrastructure affect

people’s perceptions Happiness, hope and historical events influence poverty

perceptions (downward bias)

MunicipalityPoor Not-poor Poor Not-poor Poor Not-poor Poor Not-poor

Waspam (RU) 100% 0% 88% 13% 73% 27% 64% 36%

Quilalí (RU) 100% 0% 100% 0% 58% 42% na naJinotega (UR) 25% 75% 63% 38% 43% 57% na naMatagalpa (RU) 63% 38% 88% 13% 65% 35% na naCiudad Dario (RU) 67% 33% 89% 11% 44% 56% na na

Total 71% 29% 85% 15% 56% 44% 64% 36%

Qualitative (SA)Consumption Proxy means Qualitative (WR)

What new insight into poverty have we obtained from mixing LSMS with Qual?

From 1998 to 2005 Nicaragua has progressed at a positive but slow rate. Besides confirming the importance of standard determinants for progress people also identified:

Ability to seize opportunities Good judgment, wise use of resources Income diversification (locally or abroad) Access to social and productive programs Access to formal property titles

So…do voices echo quantitative data? Yes. Nicaraguan voices DO echo quantitative findings

BUT they force us to extend beyond correlates and means and take into account: happiness, history and hope in the calculation of well-being

…but not always in the same order in terms of priorities. LEADERS PEOPLE BOTH

MAJOR THEMES % % %

Water project (drinking and septic) 13.86% 14.07% 13.96%

Construction and repair of street/roads 11.88% 13.03% 12.45%

Productive opportunities 12.87% 11.96% 12.43%

Health center/personnel/goods 10.89% 10.02% 10.47%

House building or improvements(poor or single moms) 6.93% 8.25% 7.58%

School (pre-school, primary and secondary) 7.92% 4.99% 6.49%

Electricity/solar power/street electricity 5.94% 4.12% 5.05%

Vocational school/training 5.94% 4.08% 5.03%

Recreational park/sports for youth 4.95% 4.12% 4.55%

Church 2.97% 1.03% 2.02%

Other 15.84% 24.31% 19.99%