Understanding links between

ecosystem services/ governance

and human well-being:

reflections on conceptualization

and operationalisationFrank Vollmer

School of GeoSciences,

University of Edinburgh

frank.vollmer@ed.ac.uk

Fisher et al (2013): Conceptual frameworks for ecosystem services and

poverty alleviation research reviewed:

o Environmental Entitlements Framework (Leach et al., 1999)

o Framework for Ecosystem Services Provision (Rounsevell et al.,

2010).

o Millennium Ecosystem Assessment (MEA, 2005b).

o Political Ecology (Blaikie and Brookfield, 1987).

o Resilience (Folke, 2006 and Holling, 1973).

o Sustainable Livelihoods (Chambers and Conway, 1992 and

Scoones, 1998).

o The Social Assessment of Protected Areas (linked to Sustainable

Livelihoods) (Schreckenberg et al., 2010).

o The Economics of Ecosystems and Biodiversity (TEEB, 2010a).

o Vulnerability (Adger, 2006 and Fussel, 2007).

Conceptual Frameworks

Source: Fisher, J. A., G. Patenaude, P. Meir, A. J. Nightingale, M. D. A. Rounsevell, M. Williams and I. H. Woodhouse (2013) 'Strengthening conceptual foundations: Analysing frameworks for ecosystem services and poverty alleviation research.' Global Environmental Change, 23(5), 1098-1111 2

Conceptual Frameworks

Source: Fisher, J. et al (2013: 1108-09)

3

Source: Fisher, J. A., Patenaude, G., Giri, K., Lewis, K., Meir, P., Pinho, P., Rounsevell, M., Williams, M. (2014) Understanding the relationships between ecosystem services and poverty alleviation: a conceptual framework. Ecosystem Services, 7: 34-45 4

• Sense of complexity

• Comprehensive frameworks such as [Fisher et al

(2014)] make things harder to overlook, [and] they

dictate what is on the agenda. This leads to a central

limitation: if frameworks are used mechanistically or

uncritically, they can hinder a deeper, questioning

analysis, that remains open, for instance, to factors

that do not feature in the framework [“other means

than ES”] (Fisher et al., 2014: 35).

Conceptual Frameworks: Pros and Cons of

Operationalization

Source: Fisher, J. A., G. Patenaude, P. Meir, A. J. Nightingale, M. D. A. Rounsevell, M. Williams and I. H. Woodhouse (2013) 'Strengthening conceptual foundations: Analysing frameworks for ecosystem services and poverty alleviation research.' Global Environmental Change, 23(5), 1098-1111 5

“Forest resources [and their derived ES] may contribute to

local livelihoods through:

(1) a needs-driven forest reliance, whereby local poor

people depend on low-value forest resources to some

extent for their livelihoods, perhaps in response to

shocks (“safety nets”), or

(2) because they are unable to make the transition out of

this resource dependent mode (“poverty traps”); and

(3) an opportunity driven forest reliance, whereby local

people use higher-value forest resources as a source

of cash products in order to get richer (“pathways out

of poverty)”(Clements et al (2014: 125-126)

Links between ES and livelihood: what do we

know?

Source: Clements et al (2014), “Impacts of Protected Areas on Local Livelihoods in Cambodia”, World Development Vol. 64, pp. S125–S134, 2014 6

Impact evaluation methods utilised to investigate the effect of protected

areas (PAs) on poverty and livelihoods in Cambodia (comparing

households inside PAs with bordering villages and controls) found that:

There was no evidence that PAs exacerbated local poverty or

reduce agricultural harvests in comparison with controls

(Households bordering the PAs were significantly better off, not

because of the PA but due to greater access to markets and

services).

Non-timber forest product collectors inside PAs were significantly

better off than controls and had greater rice harvests, because they

had more secure access to land and forest resources.

The PAs in Cambodia therefore have some positive impacts on

households that use forest and land resources for their livelihoods

(Clements et al (2014: 125)

Links between EG and livelihood: what do we

know?

Source: Clements et al (2014), “Impacts of Protected Areas on Local Livelihoods in Cambodia”, World Development Vol. 64, pp. S125–S134, 2014 7

Development as

1. welfare,

2. utility or

3. freedom maximisation (and equalisation)

All concepts still in use, dependent on

• Academic discipline,

• Underlying assumptions, e.g. regarding the welfare models

(growth-mediated vs. support-led strategies),

• Practical concerns (used in structural equation models,

linear correlation analysis, qualitative evaluations, impact

assessments, etc).

Dependent variable “Well-being”: Equality of what (Sen, 1980)?

8Source: Sen, A. (1980), “Equality of What?”, in McMurrin, S. (ed.): Tanner Lectures on Human Values, Cambridge, Cambridge University Press

Money-metric poverty assessments:

• Identification a) per adult equivalent consumption as the

welfare metric,

• Identification b): an absolute poverty line, usually based on

the Cost of Basic Needs Method

• Aggregation: Use of FGT method (1984) - FGT0 (poverty

headcount (incidence of poverty), FGT1 (poverty gap –

incidence, intensity and depth of poverty) or FGT2

(squared poverty gap - incidence, intensity, depth of

poverty and inequality among the poor).

Measuring poverty

9

Measuring poverty

10

Gaddis, I. and Klasen, S. (2012), Mapping MPI and Monetary Poverty: The Case of Uganda, at Dynamic Comparison between theMultidimensional Poverty Index (MPI) and Monetary Poverty Workshop, November 21-22, 2012, Oxford University. Available at: http://www.ophi.org.uk/wp-content/uploads/Stephan-Klasen-Mapping-MPI-and-Monetary-Poverty-The-Case-of-Uganda.pdf?0a8fd7 (23/03/205)

Poverty line region Line or Rate Household or

People

Mozambique

Poverty line (MZN)

US$1.25/day US$2.5/day MPI

All Mozambique Line

Rate

Rate

Households

People

18.41 (US$0.53)

47.3

54.7

20.05 MZN

53.2

60.6

40.10 MZN

85.6

90.1 69.6%

Gaza and Inhambane,

rural

Line

Rate

Rate

Households

People

18.37

55.2

65.2

20.02

60.3

69.9

40.04

89.4

93.9 60.1%

Each computation of poverty is imperfect and can be critiqued from different angles.

Substance:

• Caloric intake not suitable to assess nutritional quality of diet

• Each person converts means (income) differently to ends (human development,

e.g. a healthy diet)

• Non-deprived in income does not mean access to health care is ensured

Utility:

• “Empirically, MPI poverty much less varied spatially than income poverty” (Gaddis

and Klasen, 2012) – do you want to see spatial differences?! Do you want to see

abrupt/non-linear changes?

Identification

1. The Unit of Analysis

2. Dimensions of poverty

3. Variables/Indicator(s) for dimensions

4. Poverty Cutoffs for each indicator/cross-dimensional

5. Weights within and across dimensions

Aggregation

1. Dashboard approaches

2. Axiomatic measures (Counting approach (e.g. Alkire-

Foster method))

3. Fuzzy set

4. Statistical approaches (e.g. Multivariate Analysis)

Poverty Index

11

Identification

1. basic material needs

for a good life,

2. health,

3. good social relations,

4. security and

5. freedom of choice

and action (MEA,

2005)

Poverty Index

12

Well-being components No of BBNs in which they were included

Total Villages

WSs

National

WS

Provincial

WS

Food Security 9 3 3 3

Good quality farm 6 3 2 1

Cattle 3 3 0 0

Access to drinking water 6 2 2 2

Good quality housing 3 2 1 0

Health care 2 2 0 0

Purchase capacity 3 1 2 0

Education 2 1 0 1

Achieve your dreams 1 1 0 0

Freedom 1 1 0 0

Peace 1 1 0 0

Energy availability 3 0 0 3

Protection against

extreme weather events

2 0 1 1

Wild food 1 0 1 0

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Poverty IndexDomain Dimension Deprived if…

Human capital 1. Sanitation

2. Water

3. Health (under-five

mortality, access

to health care)

4. Formal Education

(illiteracy, highest

qualification

achieved)

• The household´s sanitation facility is not improved (according to the MDG

guidelines), or it is improved but shared with other households

• The household does not have all-year long access to clean drinking water

(according to the MDG guidelines) or clean water is more than 30 minutes walking

from home

• Any child has died in the family; illnesses remain undiagnosed by professional

health specialists

• No household member is able to read and write; no household member achieved

EP1 or attended the Portuguese colonial school system.

Social capital 1. Food security

2. Access to services,

associations and

credit

• Household did experience a food shortage in the past

• The household did not receive advice from an extension agent during the last 12

months, and did not receive a credit in the last 12 months, and is currently not a

member in either an agricultural or forestry association.

Economic well-

being

1. Income (cash +

subsistence)

2. Assets owned

3. Housing (floor,

roof, walls)

• Quintiles

• If do not own more than one of: radio, TV, telephone, bike, bed, motorbike or

refrigerator and do not own a car or truck

• The household has sand or smoothed mud floor; the household has grass or poles

roof; the household has sand, mud, grass or poles walls

14

Poverty Index

Challenges:

1. Explicit value judgments: as cardinal data is mixed with ordinal

and categorical data, value judgments to set poverty lines are

required

2. What constitutes “adequate housing”, “access to health care”,

“food security” is often multidimensional itself and thus hard to

capture by a single indicator or a proxy

3. Ideally, variables do not correlate much – challenges to link ES

or EG to well-being (e.g. sanitation, clean water access)

4. “Change” analysis: Panel data often not available, necessitates

alternatives (space-for-time substitution). Practical challenges

occur - controlling for similar soils and woodland vegetation and

a similar provision of public services within study sites is, in

reality, a much harder task than on paper

15

Use of multiple dependent variables in

regression

Studies increasingly use multiple dependent variables in regression

analysis

• Hossain et al. (2015) used linear regression, among other statistical

techniques, to analyse how ecosystem services are coupled to economic

growth and well-being in the in the Bangladesh coastal zone (Well-being

defined as poverty (% of population below poverty line), Per capita

income, Gross domestic product)).

• Santos et al (2013), in “Ecosystem and Human Wellbeing in Spain”,

used structural equation modelling to explore "the relationships between

biodiversity loss, ecosystem services, human wellbeing, drivers of

change (both direct and indirect) and policy responses” (10 well-being

indicators)

Sources: Hossain, M.S., Dearing, J.A., Rahman, M.M., and Salehin, M. 2015. Recent changes in ecosystem services and human wellbeing in the Bangladesh coastal zone. Regional Environmental Change (Published onlune 21 January 2015)

Santos-Martin, F et al (2013), Unraveling the Relationships between Ecosystems and Human Wellbeing in Spain, PLoS ONE 8(9)

16

Enhancements of our understanding, some observations…

• Preference for cardinal indicators (less room for different interpretation of

results/easier to show trends (linearity)/ use of quintiles rather than a poverty line

– while it adds knowledge to the picture, it does not capture the entire picture)

Access to services or markets = distance (physical accessibility to services),

but says little about their financial affordability, social acceptability, quality of

services

• Covariates: “Poor matching designs might identify an effect when in fact none

exists or mask effects […]. A simple comparison of households inside the PAs with

bordering villages would come to the conclusion that PAs exacerbate local

poverty. The results of the impact evaluation show that this would be a misleading

comparison, because border villages were closer to market centers, other

services, and main roads, all of which had positive impacts on local poverty

status” (Clements et al (2014), S129 – S130)

Finding the right control variables might be challenging if the dependent

variable is a composite index with various types of variables, links to different

dimensions of well-being, and variables that link either to public or private

goods (Keyword: Endogeneity)

“Soft variables" (social dynamics, exclusion, etc) are harder to use

Use of multiple dependent variables in regression

17

The way forward: Use of Poverty Index in regression

1. Micro regression (determinants of poverty of a person or household)

2. Macro regression (determinants of poverty at the district, state, province

or country level, ethnic group, gradient level)

Endogeneity is a great challenge with multidimensional poverty/well-

being: high correlation between a variable constituting the dependent

variable with an independent variable (the same forces that influence the

input also influence the output – ownership of goods (motorcycle) to

explore forest resources). Alternatives:

• Instrumental variable (exogenous variable thought to have no direct

association with the outcome (harder to find with multidimensional

poverty composed of indicators that are not highly correlated)

• Nonindicator measurement variables, e.g. certain demographic

characteristics or additional socioeconomic characteristics of the

household (ethnicity, hh size, etc.) (possibly not very satisfying)

Well-being determinants might change across spatial differencesSource: Alkire et al (2015), “Multidimensional Poverty Measurement and Analysis: Chapter 10 – Some Regression Models for AF Measures”, in Alkire, S. et al. (eds), Multidimensional Poverty Measurement and Analysis, Oxford University Press (forthcoming)

Understanding links between

ecosystem services/ governance

and human well-being:

reflections on conceptualization

and operationalisationFrank Vollmer

School of GeoSciences,

University of Edinburgh

frank.vollmer@ed.ac.uk