1.
Understanding links between
ecosystem services/ governance
and human well-being:
reflections on conceptualization
and operationalisation
Frank Vollmer
School of GeoSciences,
University of Edinburgh
frank.vollmer@ed.ac.uk

2.
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

3.
Conceptual Frameworks
Source: Fisher, J. et al (2013: 1108-09)
3

4.
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

5.
• 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

6.
“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

7.
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

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

9.
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

10.
Measuring poverty
10
Gaddis, I. and Klasen, S. (2012), Mapping MPI and Monetary Poverty: The Case of Uganda, at Dynamic Comparison between the
Multidimensional 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?

11.
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

12.
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

13.
13
Poverty Index
Domain 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.
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.
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.
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.
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 differences
Source: 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)

18.
Understanding links between
ecosystem services/ governance
and human well-being:
reflections on conceptualization
and operationalisation
Frank Vollmer
School of GeoSciences,
University of Edinburgh
frank.vollmer@ed.ac.uk