metadata: authors: 0: author: Saleth, R. Maria
1: author: Dinar, Ariel
count: Sri Lanka
docna: 0: docna: Quantifying institutional impacts and
            development synergies in water resource programs : a
            methodology with application to the Kala Oya basin, Sri Lanka
docty: Policy Research Working Paper
subtopic: Climate Change and Agriculture,Crops and Crop Management Systems,Livestock and Animal Husbandry,Agricultural Economics,Nutrition,Hydrology,Food Security
teratopic: Macroeconomics and Economic Growth,Industry,Poverty Reduction,Private Sector Development,Environment
abstracts: cdata!: The success of development programs,
            including water resource projects, depends on two key
            factors: the role of underlying institutions and the impact
            synergies from other closely related programs. Existing
            methodologies have limitations in accounting for these
            critical factors. This paper fills this gap by developing a
            methodology, which quantifies both the roles that
            institutions play in impact generation and the extent of
            impact synergies that flows from closely related programs
            within a unified framework.  The methodology is applied to
            the Kala Oya Basin in Sri Lanka in order to evaluate the
            impacts of three water-related programs and the roles of 11
            institutions in the context of food security.  The results
            provide considerable insights on the relative role of
            institutions and the flow of development synergies both
            within and across different impact pathways. The methodology
            can also be used to locate slack in impact chains and
            identify policy options to enhance the impact flows.
geo_reg_key: 80475,80451,80470
repnme: repnme: Quantifying institutional impacts and
            development synergies in water resource programs : a
            methodology with application to the Kala Oya basin, Sri Lanka
display_title: Quantifying institutional impacts
            and development synergies in water resource programs : a
            methodology with application to the Kala Oya basin, Sri Lanka
listing_relative_url: /research/2008/01/8973682/quantifying-institutional-impacts-development-synergies-water-resource-programs-methodology-application-kala-oya-basin-sri-lanka
topicv3: Food Security m1326439 199,Water m1326522 137,Crop Production m1326627 174,Land Tenure m1326631 888,Livestock m1504490 4024
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Industry,Poverty Reduction,Private Sector Development,Environment
no_of_pages: 71
author_keys: 0: author_key: NA
1: author_key: NA
model_extractions: WPS4498 Policy ReseaRch WoRking PaPeR 4498 Quantifying Institutional Impacts and Development Synergies in Water Resource Programs: A Methodology with Application to the Kala Oya Basin, Sri Lanka _R. Maria Saleth Ariel Dinar_ The World Bank Development Research Group Sustainable Rural and Urban Development Team January 2008
Policy ReseaRch WoRking PaPeR 4498

# **Abstract**

The success of development programs, including water resource projects, depends on two key factors: the role of underlying institutions and the impact synergies from other closely related programs. Existing methodologies have limitations in accounting for these critical factors. This paper fills this gap by developing a methodology, which quantifies both the roles that institutions play in impact generation and the extent of impact synergies that flows from closely related programs within a unified framework. The methodology is applied to the Kala Oya Basin in Sri Lanka in order to evaluate the impacts of three water-related programs and the roles of 11 institutions in the context of food security. The results provide considerable insights on the relative role of institutions and the flow of development synergies both within and across different impact pathways. The methodology can also be used to locate slack in impact chains and identify policy options to enhance the impact flows.

This paper—a product of the Sustainable Rural and Urban Development Team, Development Research Group—is part of a larger effort in the department to mainstream research in water resource economics. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at r.saleth@cgiar.org and adinar@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **QUANTIFYING INSTITUTIONAL IMPACTS AND DEVELOPMENT SYNERGIES IN WATER RESOURCE PROGRAMS: A Methodology with Application to the Kala Oya Basin, Sri Lanka***

## **R. Maria Saleth and Ariel Dinar** **

**_Keywords:_** _Development Programs, Econometric Analysis, Food Security, Impact Pathways, Impact Synergy, Institutional Analysis, Institution-Impact Matrix, Kala Oya Basin, Millennium Development Goals, Perception Data, Sri Lanka, Stakeholder Evaluation._

> ∗This paper is a product of the study “ _The Institutional Matrix of the Millennium Development Goals: An Empirical Study of Food Security Goals in Kala Oya Basin, Sri Lanka_ ” funded by the World Bank Research Committee and the International Water Management Institute. Earlier versions of this paper were presented at the International Seminar on _Water Management “Technology, Economics and the Environment”_ , Fundacion Ramon Areces in Madrid, Spain, January 19-20, 2007 and in a seminar at the International Food Policy Research Institute, Washington DC, November 29, 2007.  We thank the participants in the two seminars for their comments.  We would like to thank Susanne Neubert, Bandi Kamaiah, Sarath Abayawardana, Seenithamby Manoharan, Ranjtih Ariyaratne, and Shyamalie de Silva for their support in data collection and analysis, and Carlos Espina and Mahfuz Rahman for their help with the MAPLE and MATHEMATICA software application used for derivative analysis.  We also thank the 67 respondents for their valuable time and insightful information without which this study would not have been possible.  This version of the paper benefitted greatly from comments provided by Daniel Bromley, K. William Easter, Elinor Ostrom, and Vernon Ruttan.

∗∗The authors are respectively Principal Researcher, International Water Management Institute, Colombo and Lead Economist, World Bank, Washington, DC.
ii

# **CONTENTS**

|**SUMMARY...............................................................................................................................................................III**|
|---|
|**1. MOTIVATION AND CONTEXT..........................................................................................................................1**|
|**2. IMPACT SYNERGIES AND INSTITUTIONAL ROLES: AN ILLUSTRATION ..........................................3**|
|**3. THE METHODOLOGICAL FRAMEWORK.....................................................................................................5**|
|3.1. CONCEPTUALSETTING.......................................................................................................................................6|
|3.2. BUILDINGBLOCKS OF THEMETHODOLOGY........................................................................................................6|
|3.3. INSTITUTION-IMPACTMATRIX............................................................................................................................9|
|**4. THE EMPIRICAL CONTEXT: THE KALA OYA BASIN, SRI LANKA......................................................12**|
|**5. EMPIRICAL SPECIFICATION OF THE MODEL .........................................................................................13**|
|**6. DATA SOURCES..................................................................................................................................................18**|
|**7. RESULTS AND INTERPRETATIONS..............................................................................................................20**|
|**8. EVIDENCE FOR DEVELOPMENT SYNERGIES AND INSTITUTIONAL EFFECTS .............................28**|
|**9. CONCLUSIONS AND POLICY IMPLICATIONS...........................................................................................34**|
|**ENDNOTES...............................................................................................................................................................38**|
|**REFERENCES ..........................................................................................................................................................41**|
|**ANNEX A: TECHNICAL NOTES ..........................................................................................................................57**|
|**ANNEX B: SURVEY INSTRUMENT.....................................................................................................................58**|
|**ANNEX C: DERIVING THE REDUCED FORM EQUATION...........................................................................65**|
iii

# **SUMMARY**

With increasing investments in development programs in general and water-related programs in particular, there are obvious concerns regarding their actual impacts on development objectives. Two persistent aspects hamper their proper evaluation both in economic literature and in development policy: (a) the specific roles that institutions play in the process of impact generation and transmission, and (b) the impact synergies that a development program derives from past, ongoing, and planned programs. Exclusion of these aspects is very important, particularly in the evaluation of _meta_ development goals such as food security, where the realization of the final objective is linked with the progress of several intermediate but related targets of a hierarchy of programs spanning across sectors and time.

This paper develops and applies a methodology that explicitly captures the effects of both institutional roles and development synergies within a unified framework and quantitative context. The development of the framework is illustrated by (a) referring to a set water and agriculture-related development programs (system rehabilitation, bulk water delivery, and crop diversification), (b) tracing their impact pathways and interaction points, (c) locating relevant institutions in these points and pathways, and (d) linking them all with the final goal of food security—one of the key targets of the Millennium Development Goals. This framework is, then, translated into a system model with 21 sequentially linked equations, comprised of a set of development, institutional, and impact variables. For the practical application of the framework, the Kala Oya Basin in Sri Lanka is taken as the empirical context and the perception-based information collected from a sample of 67 experts is used as the data source.

The estimation of the model coefficients provides considerable insights on the nature of both the roles that different institutions play at various points of the impact pathways as well as the synergies that development programs derive from each other. The sensitivity analysis performed with the reduced form equation suggests that, in terms of the marginal effects on food security, market institution has the highest effect, followed by others such as price regulation and trade policy. Unlike these institutions with a positive effect, there are others with a negative contribution such as land tenure and rural development policy.

From the perspective of practical policy, this paper has two main contributions. First, it demonstrates the importance of accounting for the institutional impacts and development synergies when planning and implementing any new development program in a given region. Second, it provides a diagnostic tool both for locating the weak spots and slack links in various impact pathways of a development program by identifying the institutions and impact chains that are to be strengthened to improve the impact flows of development programs.
1

# **1. MOTIVATION AND CONTEXT**

The motivation for this paper comes from two major gaps persisting in the theory and practice related to the critical subjects of development impact and institutional analysis. First, there is a lack of proper treatment of the synergies inherent among development programs, projects or policies with common or closely related goals.<sup>1</sup> These synergies occur not just among ongoing programs but even flow from those completed in the past and planned for the future. The evaluation of the impacts of development programs cannot be complete without accounting for these synergies. These synergies are particularly important for composite or _meta_ development goals (e.g., Millennium Development Goals,<sup>2</sup> combating climatic change, and governance reforms). This is because the realization of these goals is critically linked with the realization of several intermediate but related goals of a hierarchy of development programs. Second, institutions, defined as a system of legal, policy, and organizational components (Bromley, 1989; Ostrom, 1990), play a central role both in the facilitation and transmission of development impact. Although the general roles of individual institutions are being evaluated in various contexts and details (Saleth and Dinar, 2004 and 2008), there is an insufficient attention on the individual and joint roles several institutions in the specific context of impact generation and transmission. These two gaps are obviously serious in view of the bias and gaps they could cause in the planning, implementation, and assessment of development programs.

Ironically, the issue of the lack of or insufficient treatment of impact synergies and the institutional roles in development impact is not entirely new as are its consequences to impact assessment, institutional analysis, and development planning. But, the problem persist essentially due to the absence of an empirically applicable methodological framework that can bring together the multiple impact pathways of two or more development programs within a common analytical framework and single evaluation context. These pathways are very important as they capture the various routes through which the impacts of a program are transmitted on to the final goal and these routes can be characterized by a chain of sequentially and functionally related development, institutional, and impact
2 variables.<sup>3</sup> Existing impact assessment approaches are of no or little help in this context due to their inherent analytical limitations.<sup>4</sup> Since they do not elaborate the impact process to capture the entire set of impact pathways, they miss the opportunity to locate and evaluate the impact role of institutions in the specific contexts of different pathways. With their _ex-post_ orientation and reliance on objective data, the existing approaches also become unsuitable particularly in the context of multiple and time-lagged projects with continuing, lagged, and uncertain flow of impacts. Here, _ex-ante_ approach and subjective information are unavoidable.

This paper aims to develop and empirically illustrate a methodology that can directly capture both the development synergies and the institutional roles within a unified framework and quantitative context. The methodology is based on an analytical framework that traces the major impact pathways between the development programs and the development goal and characterizes these pathways in terms of the sequential and functional linkages among the development, institutional, and impact variables involved. Since these linkages can be translated into a system of structurally linked equations, each capturing different impact pathways, the framework can be mathematically translated for empirical application and quantitative evaluation. This paper demonstrates the application of this methodology in the empirical context of the Kala Oya Basin in Sri Lanka by taking (a) the food security related to the first MDG as the development goal, (b) a set of three water-related programs—namely, system rehabilitation, bulk water delivery, and crop diversification—as the candidate development programs,<sup>5</sup> and (c) the ex-ante information from a sample of 67 stakeholders—consisting largely of government officials and national experts—as the data source.

From here, the paper is structured as follows. Section 2 provides a graphical illustration of the welfare implications of both the development synergies and institutional role and indicates the policy value of their _ex-ante_ evaluation. Section 3 sets the analytical framework with the conceptual foundation and building blocks of the proposed methodology. It also presents an institution-impact matrix developed in a generic context that can serve as a platform to operationalize the conceptual model into an empirically applicable form. Section 4 describes the study
3 area and highlights its major development challenges. Section 5 applies the institution-impact matrix to the development and institutional context of the study region. It provides both a graphical presentation of the impact pathways in terms of a flow diagram and a mathematical representation of these pathways as a set of functional relationships evident among development, institutional, and impact variables. Section 6 describes the data source and also provides empirical precedence and theoretical justification for using stakeholder-based _ex-ante_ information. Section 7 presents and analyzes the results of the econometric models of institution-impact interaction. It provides statistical evidence for the relative role and significance of the development, institutional, and impact variables in difference equations representing various impact layers and also demonstrates the structural linkages among these equations or layers of impact pathways. Section 8 provides numerical evidence for the size and flow of development synergies and institutional impacts based on a sensitivity analysis of the reduced form equation in terms of the local and system-wide impacts of a marginal change in different variables. The final section concludes with the analytical and empirical insights of the paper, its limitations, and the scope for its future extension and refinement.

# **2. IMPACT SYNERGIES AND INSTITUTIONAL ROLES: AN ILLUSTRATION**

When selecting development programs, policy makers usually make an _ex-ante_ assessment of their effects both on overall welfare and on its distribution across groups in the society. But, such assessments often ignore the issue of how this welfare and its distribution would change when the roles of institutional impacts and synergies from related programs are taken into account. The practical importance and policy value of considering these changes in such an _ex-ante_ assessment is graphically demonstrated using Figure 1, which is an adaptation of a framework suggested by Just et al. (2004).

Figure 1 depicts a simple economy with two individuals (or groups), i.e., _I_ (rich) and _J_ (poor), who, with a given bundle of resources, can produce/consume two goods, i.e., food ( _F_ ) and recreation ( _R_ ). Given current technologies and institutions, the production possibility frontier for the economy is OP. Assume that the economy is in a status quo at ( _i_ , _j_ )0 with a corresponding welfare levels for the
4 two-person society. Based on the Edgeworth Box analysis, J’s welfare is: JF(0)+JR(0) and I’s welfare is [P-JF(0)]+[O-JR(0)]. Now, suppose the government wants to take the economy towards the frontier OP and improve, thereby, both the total welfare and its distribution. For this, it considers two programs, which are expected, _a priori_ , to achieve such economic and social objectives, i.e., a ‘dashed’ (dashed line) program (D) and a ‘solid’ (solid line) program (S). As can be seen from Figure 1, the ‘dashed’ program moves the economy from ( _i_ , _j_ ) 0 to ( _i_ , _j_ ) _D_ and the ‘solid’ program moves the economy to ( _i_ , _j_ ) _S_ . Both policies are Pareto optimal in the sense that they satisfy the condition of utility maximization for both individuals/groups. But, the ‘dashed’ program is less efficient as it falls short of the production possibilities frontier (OP) and ends with an inner frontier, O’P’<OP. However, from a political economy perspective, the ‘dashed’ program can be the second best option and it may very well be the final choice of the policy maker. This is because I (rich) is less likely to oppose this program with a lower loss in allocation as compared to the ‘solid’ program.

In the discussion so far, the focus is only on the welfare and distribution implications of two alternative programs. In this case, an _ex-ante_ assessment of the development path and its economic implications is usually conducted with actual and expected information before the policy choice is made. But, such assessments do not account for the economic externalities from institutional facilitations and impact synergies from related programs. Using Figure 1, we can demonstrate how the additional welfare gains are missed when impact synergies and institutional roles are ignored. Let us assume that the economy is, again, in status quo at ( _i_ , _j_ ) 0 . Suppose that a development program, say, an irrigation project, is implemented, leading to a development path represented by the dashed line. Now, the economy attains a new equilibrium at ( _i_ , _j_ ) _D_ on the production frontier O’P’. Clearly, the new equilibrium, though increasing the welfare with more food and recreation, falls short of the optimum. Suppose, there is another development program, say, crop intensification that is also implemented either along with or subsequent to the irrigation project. Since crop intensification enhances the welfare impacts of the irrigation-based program, the latter can get
5 considerable development synergies from the former program. When these impact synergies are taken into account, the economy will reach a different production frontier and development path with a new equilibrium, say, at ( _i_ , _j_ ) _S_ . This new equilibrium, which accounts for the impact synergies, generates higher and still equitable allocation of food and recreation.

In a similar vein, we can also demonstrate the welfare gains from incorporating the role of institutions. Suppose that the irrigation-based development program is implemented in conjunction with the introduction of a water allocation institution (e.g., rotational water supply or volumetric water allocation). In this case, the production possibility frontier will shift outward and the development path will also change from the dashed line. To minimize notations and complications, let us consider that the new production frontier is OP and the development path is the solid line. With this, the equilibrium will move from ( _i_ , _j_ ) _D S_ to ( _i_ , _j_ ) . The difference between the two equilibriums shows the welfare gain of considering the role of institutions in development process. We can note here that in the context of impact synergies between development programs, although the economy is actually at a higher welfare level, project-based impact assessments are not able to fully account for them. But, the problem is still more serious in the context of institutional effects because the roles of institutions are not incorporated with proper detail in development planning itself, let alone the actual assessment of their impact.

# **3. THE METHODOLOGICAL FRAMEWORK**

The reality of impact synergies associated with multiple programs and the intricate roles that institutions play in enhancing and channeling these synergies obviously requires a major change in the way development impacts are assessed. In particular, what is needed is an analytical framework that is capable of capturing both the individual and collective impacts of development programs as well as the intrinsic roles of institutions within the impact generation and transmission process. We also need an evaluation methodology that can bring together the impacts of multiple programs and the roles of institutions within a common analytical framework. Clearly, this is a major challenge because the required methodology
6 has to be generic enough to transcend disciplinary boundaries and empirical limitations. But, as we will argue below, it is possible to construct such a generic methodology by selectively combining useful elements from the methodologies, which are used both in the impact assessment and institutional analysis literatures.

# **3.1. Conceptual Setting**

The conceptual setting for the generic methodology is presented in Figure 2. This setting suggests indeed the basic conception of a model of institution-impact interactions in the context of a given region. Specifically, there are three sets of interactions, i.e., among development programs (capturing impact synergy), among institutions (capturing institutional linkages), and among the programs, institutions, and food security (capturing together the development and institutional impacts on food security). The required methodology has to be broad enough to simultaneously handle all these interactions. As will be argued below, such a methodology can be developed by combining suitably adapted analytical and methodological elements from the impact assessment and institutional economics literatures. Specifically, some of the existing impact assessment methods can be used to handle the impacts of and interactions among development programs whereas some of the institutional analysis methods can be used to capture the mediating roles and development impacts of institutions.

# **3.2. Building Blocks of the Methodology**

Despite their analytical and empirical limitations, some of the impact assessment methods do have useful elements for building a generic methodology for evaluating multiple and time-lagged programs. One of them is the Method for Impact Assessment of Programmes and Projects (MAPP).<sup>6</sup> Although MAPP allows an integrated evaluation of multiple programs with stakeholder-based information, it has few major but avoidable analytical and empirical limitations. First, it is not capturing all impact pathways and channels operating between the programs and their final goals. Second, the important roles of institutional factors, especially their interactions with other development and impact factors are not incorporated. In fact, this problem emerges directly from the first limitation because the institutional roles cannot be evaluated without considering the impact pathways in the first
7 place. Finally but more importantly, the point-based evaluation used in this method allows only a qualitative but not a quantitative analysis. This is because the points awarded by stakeholders are not on metric scale with an absolute point to serve as benchmark. While the first two problems can be rectified with suitable extensions and adjustments of MAPP, the last one can be solved with few intermediate steps for making the point system relative and comparable (see Neubert, 2006).

Another method that has useful inputs for the present purpose is the Poverty and Social Impact Assessment (PSIA) method.<sup>7</sup> Despite its focus on single program, PSIA not only focuses on few specific impact pathways but also allows the use of perceptional data and ex-ante analysis (see Coudouel, Dani, and Paternostro, 2006:12). This method with few adjustments can provide some key elements for building a more generic and robust methodology. First, the number of impact pathways has to be increased to consider major, if not all possible, impact pathways. This is essential to enrich the analytical framework so that it can capture both the development synergies and institutional impacts, which are captured and channeled through various impact pathways. Second, even though the PSIA recognizes the exogenous influence of institutional factors, it is necessary to explicitly incorporate them as part of the evaluation framework itself and also analytically capture their interactions with other development and impact variables characterizing different impact pathways. Third, the use of _ex-ante_ approach and stakeholder information, though important to deal with the impact expectation and uncertainty, still may lead to a serious bias as long as the evaluation is performed by the direct beneficiaries (Coudouel, Dani, and Paternostro, 2006:11). This is an important empirical point, which strongly suggests the need to select neutral stakeholders or a more balanced group of respondents.<sup>8</sup> Besides the selective adaptation and use of relevant analytical and empirical elements from the impact assessment literature, we also need similar elements from the institutional economics literature, mainly for capturing the institutional dimension of the required methodology. In this respect, the theoretical framework developed by Saleth and Dinar (2004) is particularly useful for explicitly accounting for the specific roles of institutions within the process of impact generation and transmission.<sup>9</sup> The central components of this framework are: the _institutional_
8 _ecology_ principle, the _institutional decomposition and analysis_ (IDA) approach [similar to that of E. Ostrom (1990)], the _ex-ante_ approach, and the _adaptive instrumental evaluation_ (Tool, 1977; Kahneman and Tversky, 1984; Bromley, 1985). While these concepts are elaborated in detail by Saleth and Dinar (2004) and described briefly in Annex A, let us note here how they are used to set the analytical framework for evaluating the institution-impact interaction in the present context. The institutional ecology principle enables one to view regional or river basin level institutions as a nested and interlinked system embedded within a given physical, social, and political economy setting. The IDA framework allows an analytical unbundling of both the impact transmission process and the regional or basin institutions (i.e., those related to water, land, agriculture and environment). The unbundling can identify the key impact pathways and trace the relevant institutional configurations operating beneath the pathways associated with different development programs. As we will show later, the ‘adaptive instrumental evaluation’ is used to provide theoretical support and practical justification for the reliance on perception-based _ex-ante_ qualitative information collected from a sample of stakeholders.

As we adopt the framework of Saleth and Dinar (2004), we also need to introduce some important adjustments. The needed adjustments are: First, institutional evaluation is to be specialized within a regional context (e.g., river basin or other compact regions), where it is easier to (a) identify relevant development programs, which can be completed, ongoing, or planned, (b) trace the major and theoretically possible impact pathways of these programs, (c) map all the relevant institutions operating at various points of these impact pathways, and (d) evaluate the development impacts and institutional roles in various paths with contextual information.

Second, the evaluation is to be extended to cover not just water institutions but also the land, agricultural, rural, and economic institutions within an integrated framework.

And, third, the evaluation has also to be performed within the framework of a multi-dimensional institution-impact matrix, which captures the major impact
9 pathways of different development programs, the roles of the underlying development, institutional, and impact variables, and the ultimate implications for the development goals. The derivation of this institutional-impact matrix, including its analytical implications, is illustrated in the following section.

# **3.3. Institution-Impact Matrix**

The institution-impact matrix translates the conceptual model shown in Figure 2 into an operational form. This matrix captures the functional relationships and synergy among the impact pathways of development programs, the underlying institutional configurations, and the development goals. To illustrate how this institution-impact matrix can be derived for the context of multiple development programs, let us take food security as the development goal and the following three as the candidate development programs, i.e., water development project (or dam construction), introducing a new crop variety, and watershed development for land/soil improvement. These three programs are related to each other not only in terms of their development synergies but also in terms of their direct or indirect impacts on the development goal, i.e., food security, and also on its three subcomponents, i.e., income (employment), food prices (output), and sustainability (resource use efficiency). The next step is to identify the major impact pathways of these three programs and characterize the possible institutional configurations that shape these pathways. Given these impact pathways and their institutional configurations, the next step is to link them with the income, price, and resource components (or the intermediary targets) of the food security goal. As we put them together in a matrix form, as shown in Figure 3, we obtain an institutionimpact matrix, which gives a generic operational form for the conceptual model depicted in Figure 2.

Let us note few points that will enhance our understanding of the institutionimpact matrix. First, it is only to simplify its exposition that the matrix includes only the main impact pathways of the development programs. Since the impacts in each of these pathways are transmitted through several routes, there will be more rows than five, each with different institutional configurations.<sup>10</sup> Second, although each of the impact pathways obviously involve the physical, agronomic, and
10 economic variables, they are not shown in the matrix partly to avoid expositional complications and partly to highlight what sort of institutional elements can be involved in these impact pathways. Third, the institutional configurations specified for different impact pathways are not exhaustive but only illustrative. It only shows how different institutional configurations are involved in the generation and transmission of impacts through various pathways. Fourth, although the rows in Figure 3 show only the generic institutional aspects, it is possible to identify one or more specific variables to represent these aspects. With such variables as well as the variables underlying various impact pathways, it is also possible to characterize the interaction between institutional and impact variables.<sup>11</sup> And, finally, even though the institutional configurations are common across impact pathways, the relative impacts of individual institutions in these configurations can be different, depending not only on the non-institutional variables with which they interact but also on the sub-components to which they are related.

The points noted above suggest that each row of the matrix implicitly has additional rows representing the various possible impact routes underlying different impact pathways. Since we have three intermediary target goals in this example, each of these rows also involves three separate but related causal relationships. That is, in these relationships, the impact and institutional variables will form the independent variables and the variable(s) representing the three goals will be the dependent variable. In this sense, all the rows corresponding to each of the three development programs can, in principle, be translated into an empirically testable set of relationships. These relationships or equations capture the interactions among the development programs, existing institutions, the interim impacts, and the ultimate impacts on the final development goal. Obviously, the dimension of the matrix or the number of these equations depends on the number of development programs, the impact pathways and their underlying impact routes, and the sub-goals being considered. But, the key point to note here is that in view of the intrinsic relationships among the development, institutional, and impact variables both within and across equations, there is a structurally linkage among equations. This fact allows us to embed all the equations into a single but long chain equation.
11 To demonstrate how the set of implicit equations within the institution-impact matrix is transformed into a single equation, let us define the following sets of four equations: _<u>D</u> d_ = _d_ ( _<u>D</u> d_ − _p_ , _<u>N</u> d_ ) ........................................................................................... [A] _<u>N</u> n_ = _n_ ( _<u>N</u> n_ − _p_ , _<u>D</u> n_ ) ........................................................................................... [B] _<u>M</u> m_ = _m_ ( _<u>N</u> m_ , _<u>D</u> m_ , _<u>M</u> m_ − _p_ ) ................................................................................... [C] _G_ = _g_ ( _<u>M</u> g_ ) .................................................................................................. [D]

[A] represents a set of equations capturing the effects of the vectors of independent development variables ( _<u>D</u> d_<sup>−</sup> _p_ ) and institutional variables ( _<u>N</u> d_ ) on the vector of dependent development variables ( _<u>D</u> d_ ). Similarly, [B] represents a set of equations that capture the effects of the vectors of development variables ( _<u>D</u> n_ ) and institutional variables ( _<u>N</u> n_<sup>−</sup> _p_ ) on the vector of dependent institutional variables ( _<u>N</u> n_ ). [C] represents a set of equations capturing the effects of the vectors development variables ( _<u>D</u> m_ ), institutional variables ( _<u>N</u> m_ ), and independent impact variables ( _<u>M</u> m_<sup>−</sup> _p_ ) on the vector of dependent impact variables ( _<u>M</u> m_ ). But, [D] is a single equation capturing the effects of the vector of impact variables ( _<u>M</u> g_ ) on the final Goal ( _G_ ). Having specified the system, let us note four points. First, the vector _<u>X</u>_ − _x p_ ∀ _X_ = _D_ , _M_ , _N_ ; _x_ = _d_ , _m_ , _n_ is actually a subset of the vector _<u>X</u> x_ , which covers only the independent variables within the larger set. Second, the development programs, institutional variables, and their multifarious impacts on development goals are all represented by suitably defined variables appearing on both side of the equations. Third, the total number of equations underlying the system defined by [A]-[D] will be: 1 + _d_ + _m_ + _n_ . Finally, but more importantly, the equations within the system have a clear sequential linkage among them. Given these points, it is easy to show the system defined by [A]-[D] can be reduced into a single equation: _G_ = _g_ ( _m_ ( _n_ ( _<u>N</u> n_ − _p_ , _<u>D</u> n_ ), _d_ ( _<u>D</u> d_ − _p_ , _n_ ( _<u>N</u> n_ − _p_ , _<u>D</u> n_ )), _<u>M</u> m_ − _p_ )) ..........................................................[E] This equation, a mathematical replica of the institution-impact matrix, is an important analytical and empirical tool. When an equation of this form is empirically specified and statistically estimated, it can provide very valuable
12 theoretical and policy insights both on the internal dynamics of institution-impact interaction and on the extent and flow of development synergies and institutional impacts. The empirical derivation and application of the framework set by [A] to [E] will be illustrated in the case of the Kala Oya Basin in Sri Lanka.

# **4. THE EMPIRICAL CONTEXT: THE KALA OYA BASIN, SRI LANKA**

The development and application of the institution-impact assessment methodology is demonstrated here by considering the institutional and development setting of the Kala Oya Basin in Sri Lanka (Figure 4). The Kala Oya Basin, which is one of the 108 basins in Sri Lanka, covers an area of 2,873 square kilometers and supports a population of about 0.41 million. Of the total land area of 287,303 hectares (ha), far less than a third is cultivable due to land slope and quality issues and other soilrelated problems and water-related constraints. Paddy cultivation and home gardens with coconuts and fruit trees account for 40 percent of the cultivated area (de Silva et al., 2006). The average farm size is only about one ha in areas under minor irrigation and dryland farming and less than half a ha in areas under major irrigation schemes. In addition, only 27 percent of the population own a homestead and 11 percent of the population own neither a homestead nor land (see Bandara, undated). On the demographic side, increasing population density, out-migration, and aging are major issues.

Water scarcity is a serious problem due to low level and seasonal pattern of rainfall as well as groundwater quality problems. The Basin is generally dry for the most of the year with the rainfall ranging from less than 50 mm to about 300 mm. While the high rainfall level occurs only during October and November, the low level occurs in February, March, June, July, and August. With an annual local inflow of about 343 million cubic meters (mcm), the Basin also receives an annual diversion of about 480 mcm from the Mahaweli system. But, given the total demand of 1695.28 mcm, there is still a major supply short fall, creating a serious water scarcity problem for the basin (see Bandara, undated; de Silva et al, 2006). The issue is further complicated by serious groundwater quality problems caused by hardness, fluoride content, and iron concentrations. Of the basin groundwater
13 resources, 74 percent is affected by various forms of fluoride problems and 40 percent is affected by unsafe iron concentrations (Bandara, undated).

The incidence of poverty remains substantial in the basin. For example, in the Anuradhapura district, which accounts for half of the basin area, the percentage of people below the official poverty line (Rs. 1423 or US$14/capita/month) was estimated to be 20 percent during 2000-01 (de Silva et al., 2006). In addition, 44 percent of the families in the basin rely regularly on _Samurdhi,_ the safety net program of the government for the poor. Closely related to the poverty issue, food insecurity is also a serious problem, as many villages in the basin area fall under the most vulnerable categories of food insecurity (DCS and WFP, 2005). A more detailed review of the Basin’s poverty level and the strategic reasons for its selection for our case study can be found in Saleth et al. (2007).

# **5. EMPIRICAL SPECIFICATION OF THE MODEL**

For the empirical translation of the institution-impact matrix in Figure 3, we need to specify the development goal, select the candidate development programs, and identify the relevant set of institutions. Considering the poverty levels and food insecurity conditions in the study area, we obviously selected food security as the development goal for analysis. This goal, which is directly related to the hunger reduction target of the first MDG, is also one of the priority goals of policy set by the government in Sri Lanka. As to the candidate development programs, we selected three water and food-related programs relevant for the study region. They are: system rehabilitation (already completed) and bulk water delivery (being piloted), and crop diversification (potentially relevant).<sup>12</sup> This choice allows us to evaluate not only the interaction between the water-related programs but also their implications for other programs in a related sector such as agriculture. Given the choice of the development goal and programs, it is now possible to trace and delineate some of the major pathways through which these programs could affect food security. Given these impact pathways, it is also possible to identify the relevant set of institutions (i.e., water, land, agriculture, and food-related legal, policy, and organizational aspects) that are likely to affect the generation and transmission of impacts at different points of the impact transmission process.
14 Figure 5 depicts these impact pathways and their underlying institutional configurations.

Before interpreting Figure 5, it is important to recognize that it shows only one of the many possible ways of conceptualizing the impact pathways of the development programs. But, depending on the details required, it can be made richer (and also be made more complicated) by adding more development programs, impact pathways, and the institutional and impact details. Even though Figure 5 covers only a few of the many possible impact pathways, it is still able to capture the most important and also policy-wise more relevant among these pathways. Notably, these pathways will be different depending on the kind of development programs, regional contexts, and final development goal being considered for evaluation. More importantly, the flow diagram of impact pathways always assumes a particular environment as characterized by the physical, economic, and institutional context of the region being evaluated. Any change in this environment is likely to affect the relationship among the variables characterizing the impact pathways being considered. These changes can be captured either through observed or _ex-ante_ information.<sup>13</sup> With the points noted above, we can see in Figure 5 how the development programs interact with each other and with the physical and economic aspects in order to generate their development impacts and synergies. In the impact transmission process, we can also identify which institutions influence the process at what points. While it is common to read Figure 5 from left to right in line with the direction of pathways and impact flows, for analytical convenience, it is useful to move recursively, i.e., starting first with the development goal, then, tracing back to its immediate and intermediate determinants till we reach finally the development program. In doing so, we can identify all possible impact pathways and channels evident in Figure 5. In view of their functional and sequential linkages, these pathways and channels can be characterized as formal relations using appropriate chains of development, institutional, and impact variables defined in Table 1.<sup>14</sup> That is, using these variables, Figure 5 can be equally represented in a mathematical from as a system of linked equations. But, before attempting this,
15 it is instructive to understand the nature, format, and logic of the variables listed in Table 1.

As can be seen from Table 1, the model includes one development goal variable, three variables for the development programs, 17 impact variables, and 11 institutional variables.<sup>15</sup> Obviously, the variables differ considerably in terms of their unit of measurement, amenability for observation, and availability of data. To avoid the problems due to their diverse features, we conceive all the variables essentially in a notional and qualitative sense to be evaluated on an interval of 1- 10, with 1 being the lowest and 10 being the highest.<sup>16</sup> In this format, the variables capture only the overall perception of the evaluators (i.e., sample stakeholders) as to their status, change, effectiveness, or impact. For example, the food security variable represents only an overall perception of its notional status considering implicitly, the adequacy and quality of food consumption across income/social groups. It is considered to be affected by three proximate variables, i.e., income, food prices, and food availability. Similarly, the variables representing the development programs are considered to capture their overall impact potential. Since the impact potential is intimately linked program planning and implementation, the program-related variables also implicitly capture the effectiveness of development administration.

Institutional variables capture the status, effectiveness, or impact of institutions with respect to different impact pathways. For example, the variable LANTENUR captures the conduciveness of land tenure (farm size and ownership) to crop pattern changes, land productivity improvements, etc. CUSINSTN captures the effects of local customs and conventions on economic and social aspects such as crop choice, wage fixation, water sharing, and community management. Similarly, WATINSTN represents the effectiveness of the organizational aspects related to water allocation and distribution within the system level whereas WAGELAWS represents the effectiveness of legal provisions related to minimum wage and working conditions. Likewise, the other institutional variables capture the effectives of other legal, policy, and organization-related institutional aspects in different contexts. The impact variables, which are essentially physical and economic in nature, capture the actual or expected changes due to the impacts of
16 programs and institutions in different stages of the impact generation and transmission process. Among the income variables, a distinction is made between AGLINCOM and FAMINCOM with the former covering only the income from agriculture and the latter covering both agricultural and livestock incomes). But, LABINCOM, the income of laborers, covers wage and livestock incomes. These three income variables are needed to capture the differential income potentials between those with and without land and livestock. Given the set of variables listed in Table 1, the institution-impact framework in Figure 5 can be formally represented in a mathematical form with a set of following 21 equations that comprise the system model of institution-impact interaction.

|BULKWATD|=**_f1_** (SYSREHAB,<br>LANTENUR<br>)....................................................[1]|
|---|---|
|CROPDIVR|=**_f2_**(BULKWATD,FAMINSTN<br>)...................................................[2]|
|CROPATEN|=**_f3_**(CROPDIVR,LANTENUR,<br>CUSINSTN<br>) ...................................[3]|
|WATINSTN|=**_f4_**(BULKWATD,LANTENUR,<br>CUSINSTN<br>) ..................................[4]|
|WATPRODY|=**_f5_**(CROPATEN, WATINSTN,FAMINSTN<br>)...................................[5]|
|LANHELTH|=**_f6_**(CROPATEN, WATPRODY,LANTENUR<br>) ..................................[6]|
|LANPRODY|=**_f7_**(CROPATEN, LANHELTH,FAMINSTN<br>) ...................................[7]|
|FEDSUPLY|=**_f8_**(CROPATEN,CUSINSTN<br>)....................................................[8]|
|LIVSTOCK|=**_f9_**(FEDSUPLY,TRDPOLCY<br>).....................................................[9]|
|NFAMENTS|=**_f10_**(CROPATEN,RDVPOLCY<br>) ................................................[10]|
|LABPRODY|=**_f11_**(LANPRODY, CROPATEN) ................................................[11]|
|WAGERATE|=**_f12_**(LABPRODY, NFAMENTS,WAGELAWS<br>) ..............................[12]|
|RURALEMP|=**_f13_**(LANPRODY, WAGERATE, NFAMENTS, LIVSTOCK)...............[13]|
|CULTCOST|=**_f14_**(CROPATEN, WAGERATE,FAMINSTN<br>, SUBPOLCY<br>) ..............[14]|
|AGLINCOM|=**_f15_**(LANPRODY, CULTCOST,MKTINSTN<br>)................................[15]|
|FAMINCOM|=**_f16_**(AGLINCOM, NFAMENTS, LIVSTOCK) ................................[16]|
|LABINCOM|=**_f17_**(RURALEMP, NFAMENTS, LIVSTOCK,SAMPOLCY<br>)...............[17]|
|FOODPROD|=**_f18_**(CROPATEN, LANPRODY, WATPRODY)...............................[18]|
|FOODAVAL|=**_f19_**(FOODPROD,TRDPOLCY<br>,MKTINSTN<br>) ...............................[19]|
|FOODPRIC|=**_f20_**(FOODPROD,PRICREGL<br>,MKTINSTN<br>)................................[20]|
|FOODSECT|=**_f21_ **(FOODAVAL, FOODPRIC, FAMINCOM, LABINCOM) ..............[21]|
17 It can be verified that each of these equations correspond to one of the 21 impact pathways evident in Figure 5. The configuration of variables chosen for each equation is based on two considerations: (a) the functional relationship expected between them and the independent variable as per economic reasoning and (b) the need for avoiding linkages among independent variables to minimize the scope for the econometric problem of multicollinearity. The equations are arranged sequentially, starting with the initiation and implementation of the development programs, then, with their impacts in the order of their occurrences, and finally, ending with the impact on the ultimate development goal, i.e., food security. Figure 6 depicts the pattern of sequential linkages evident among the model equations. The order in which the equations are sequenced captures the relative position of different layers within the upstream-downstream continuum of impact transmission. Given the functional linkages among variables and sequential linkages among equations, the impact and institutional variables can be hierarchically arranged by tracing their role and positions both within and across the impact pathways. As argued in section 3.3 and will be show in section 8, this sequential feature is used to derive the single-equation reduced form for system model and also to trace size and flow of the system-wide impacts of marginal changes in different variables.

Another important aspect of the system model is that of the 32 variables, the 11 underlined variables are independent or exogenous, which includes one of the development programs (SYSREHAB) and all the institutional variables except water institution (WATINSTN). But, the remaining 21 variables are dependent or endogenous covering 17 impact variables, two development variables representing respectively the two programs of CROPDIVR and BULKWATD, and one institutional variable representing WATINSTN. From an econometric perspective, let us also note that given the way all the 21 equations are specified in terms of the configuration of endogenous and exogenous variables, they satisfy both the rank and order conditions necessary for their econometric identification and unbiased estimation (Kennedy, 1987).<sup>17</sup>
18

# **6. DATA SOURCES**

The system model is econometrically consistent and intuitively appealing, but it has a major empirical challenge. This is because consistent and comparable data on both the development, institutional, and impact variables are very difficult to obtain. It is certainly possible to acquire observed data on some of the impact variables (e.g., productivity, employment, income, and wage rates) through, for example, published records or primary surveys. But, such data can represent only the past impact of an already implemented development program and can not capture the synergy from the expected impacts of ongoing and planned program. Still more serious are the difficulties in getting the data on institutional variables, especially on their diverse but specific roles in the generation and transmission of development impacts.

The absence or lack of data on most variables does not, however, mean a complete absence of information. Highly relevant information is constantly processed, coded, and stored in the minds of people involved in the development process either as planners and implementers or as beneficiaries. Such real but latent information, embodied in individuals, can be tapped through a carefully designed and conducted stakeholder surveys. This form of survey data, though based on stakeholders’ perception, have many desirable properties often missed by the so called objective or observed data. For example, unlike observed data characterizing a past and static situation, the perception-based data can capture and synthesize objective, subjective, and aspiration-related information. Similarly, they can also capture the _ex-ante_ and dynamic elements. It is also theoretically legitimate in view of the subjective nature of institutions (Commons, 1934; V. Ostrom, 1980; Douglas, 1986; E. Ostrom, 1990) and the roles that the ‘subjective model’ of the ‘agents of institutional change’ play in institutional change and performance (North, 1990). As a result, there is a long tradition of using such data for institutional analysis (e.g., Knack and Keefer, 1986; Gray and Kaufmann, 1998; Barret and Graddy, 2000; Kaufmann, Kraay, and Mastruzzi, 2006). As noted already, qualitative data are also used in the few impact assessment methods discussed earlier (Neubert, 2000; Coudouel, Dani, and Paternostro, 2006).
19 Perceptions can be used as an evaluation mechanism not only to synthesize objective, _de facto_ , _ex-ante_ , and subjective information but also to bring variables in different domains into a common evaluation context. In view of these properties, perception-based information is similar in format and quality to those derived from alternative non-market data generation techniques such as ‘Delphi’, ‘Contingent Valuation’, and ‘Stated Preference’ (see Saleth and Dinar, 2004). More importantly, perception can also be used to operationalize the ‘adaptive instrumental evaluation’ approach, where the outcomes are evaluated in positive and relative terms with respect to reference points that are not static but change with learning and expectations (Tool, 1977; Kahneman and Tversky, 1984; Bromley, 1985) Thus, the comparability of perception-based information from different stakeholders can be ensured when their reference points are, more or less, similar, if not common. This reference point can be related to the minimum and maximum values or the best and worst performance, which are either expected or observed in practice. Given this fact and the possibility for convergence in understanding and evaluation standards through interaction and learning, it is reasonable to use stakeholderbased information for empirical evaluation of the model of institution-impact interactions.

While the rationale and justification for the use of perception-based data are clear, to collect such data with good quality and consistency, it is important to follow few steps. First is the clear specification of the spatial context of evaluation. Since we consider multiple institutions that transcend sectoral boundaries and vary across provinces, it is essential to select the study area to be entirely within a single jurisdictional boundary. It is for this reason that our evaluation is confined to the North Central Province, which accounts for 80 percent of the Kala Oya Basin. Second is the selection of suitable sample. The sample of stakeholders selected has to be sufficient enough to capture diversity and balanced enough to minimize bias. On this count, the sample selected here covers 67 individuals, who are directly involved in, or familiar with, the development planning, implementation, and evaluation in the Kala Oya Basin.<sup>18</sup> They include high-ranking officials from government departments working at different spatial levels (32), researchers, academics, and NGO members (32), and farmers and community leaders (3).<sup>19</sup>
20 The final but more important aspect is the design and administration of the survey instrument used for eliciting the perception-based information. To collect the information on all the 32 variables included in the model, a special survey instrument was developed and administered to sample stakeholders in May 2006. The survey instrument is included as Annex B. It shows how different variables are defined and how their values were derived from the answers to one or more questions. These questions were presented in such a way as to elicit information on a given variable from different angles and perspectives. In almost all cases, the values of the variables were obtained as the average of the values for related questions.<sup>20</sup> Table 2 presents the descriptive statistics for the 32 variables.

# **7. RESULTS AND INTERPRETATIONS**

Before presenting the econometric results, it is instructive to note first the way the model and the equations are specified. In order to select the appropriate functional forms for the model equations, the specification test suggested by Hausman (1978) was performed. This test was used to compare two models both with constant terms but with different functional forms, i.e., linear and log-linear. Since this test suggested that the linear specification yields a more efficient and consistent estimates, we adopted this specification for all the equations in the model. We also performed a test for multicollinearity.<sup>21</sup> Although multicollinearity was not a serious problem with the model variables, we also tried to ensure that this is also true at the level of individual equations. The correlation matrix for variables at the equation levels suggest that in many equations, a few variables are highly correlated mainly with the constant term. To eliminate this potential for multicollinearity, we estimated all equations without the constant term.

With the same specification, i.e., the linear form with no constant term, we have also estimated two versions of the model of institution-impact interaction. The first is a single equation model, where food security is postulated as a simple linear function of all the remaining 31 development, institutional, and impact variables. This simple model, in fact, captures the conventional approach, which assumes away the specifics and dynamics of institution-impact interactions. The second version is the system model, which specifically captures the mechanics of impact
21 generation and transmission in terms of 21 sequentially linked equations. By comparing the two models and their results, we can show the additional insights from a more realistic modeling and evaluation of the process of institution-impact interaction and also the specific points where different institutions have their influence on and interaction with other impact variables. As to the estimation procedure, the single equation model was estimated using the Ordinary Least Square (OLS) method while the system model was estimated using the Three-Stage Least Squares (3-SLS) approach.

The OLS results of the single equation model, which captures the conventional approach to institution-impact interaction, are presented in Table 3. Since the single equation model postulates the development, institutional, and impact variables to directly influence food security, it is not able to characterize the actual paths and mechanics of the interactions and impacts. Consequently, as can be seen from Table 3, the OLS results show that none of the institutional variables is statistically significant. This is also true with the variables representing the three development programs. Even among the 17 impact variables, only five are significant at 10 percent or better. These significant impact variables are: LABPRODY, WAGERATE, AGLINCOM, FAMINCOM, and LABINCOM. Notably, all of them, except AGLINCOM, have the expected positive effect. The negative effect of AGLINCOM, especially given the positive effect of FAMINCOM, is clearly inconsistent with expectation, as it suggests a negative association between agricultural income and food security. This inconsistency taken with the insignificance of institutional and development variables clearly suggests the potential for serious anomalies when a single equation model is used to describe the reality of a complex set of sequential and simultaneous interactions among the model variables. This problem gets still more serious because institutional roles are treated superficially or exogenously, missing the reality of their intricate and endogenous roles within development process.

In contrast, the system model results presented in Table 4 demonstrate the additional policy insights that can be derived with a more realistic treatment of institutions, especially considering their mediating roles both in the generation and transmission of development impacts. Since development impacts and institutions
22 influence each other, the mediating roles can be seen better when the specific points at which these influences are fed into the process of impact transmission. From this perspective, the key aspect to note from Table 4 is the way both the institutional influences and the development impacts are transmitted across the equations. The operational mechanisms for such transmissions are the sequential linkages among the equations (see Figure 6). Given this fact, our interpretation of the results will proceed along the equations to show how the dependent variables in the initial and intermediate equations capture and transmit development synergies and institutional impacts onto the ultimate goal of food security. In the interpretation, we also show how the relative size and statistical significance of the coefficients of different institutional and impact variables can be used to indicate the possible weak spots and missing links both within and across the impact transmission pathways.

Before proceeding, we can note that the results in Table 4 have the necessary econometric credentials, particularly in terms of their efficiency, consistency, and stability properties as ensured both by the specification test and multicollinearity correction. Despite a low R<sup>2</sup> for individual equations, the System R<sup>2</sup> is relatively high and the Chi-Square statistic is statistically significant, suggesting a strong overall explanatory power of the model as a whole. These econometric properties only suggest that the model has fitted well the data, but what is more important for the interpretation of the results are the econometric implications of the nature of the data source being used. Since the data is based on stakeholders’ perception, the estimated coefficients of the model provide a statistical representation of the prevailing consensus on the nature and direction of the effects of different variables. For that same reason, the coefficients also summarize the effects of both the perceived objective reality and the revealed subjective expectation of sample stakeholders. It is with these points in mind that we interpret the equation-specific results along with their system level implications.

Equations [1] and [2] capture the potential impact synergies among the three development programs. These synergies are both positive and statistically significant. In Equation [1], SYSREHAB, the physical intervention that can improve the performance of water infrastructure, has a significant positive effect on the
23 BULKWATD, the institutional intervention that can improve water distribution. As can be seen in Equation 2, BULKWATD, in turn, has a significant positive effect on the agricultural intervention of CROPDIVR, suggesting that bulk water allocation enhances the prospects for crop diversification.<sup>22</sup> The role of institutional variable in these equations, unlike the LANTENUR in Equation [1], FAMINSTN in Equation [2] is significant and also has a positive effect. This suggests that land tenure is not at all a constraint for bulk water distribution whereas farm institutions related to input supply and extension have a facilitative role in crop diversification.

The results of equations [1] and [2] suggest that the infrastructural and institutional factors are conducive for crop diversification in the region. But, it is important to see whether such a diversification prospect is actually translated in terms of changes in existing crop pattern. Obviously, the extent of crop pattern changes depends not only on the effectiveness of the crop diversification program but also on the role of institutional factors such as land tenure and customary practices in crop choice. The results for Equation [3] show that all the three variables—representing both the CROPDIVR program and the CUSINSTN and LANTENUR institutions—have significant positive effects on CROPATEN. Since CROPATEN captures the extent the food crops dominate the existing crop pattern, the positive effects of both CUSINSTN and LANTENUR are understandable in view of the fact that both customs and farm size favor food crops. But, the positive effect of CROPDIVR means that the diversification program even when it is actually implemented in the region will not be able to alter existing food crop dominated crop pattern. In effect, this suggests the powerful role of customs and other economic and institutional constraints.

Equation [4] captures the effects that bulk water delivery, land tenure, and customary institutions have on the overall functioning and performance of water institution. As expected, the program of bulk water delivery has a positive and highly significant effect on water institutions, suggesting its potential role in strengthening user organizations and promoting orderly water distribution. But, both the customary practices and land tenure system have a negative but nonsignificant effect, which means that these two institutions do not pose any problem to the functioning of water institutions. Equation [5] evaluates the relative role of
24 factors affecting water productivity. The results for this equation shows that of the three variables postulated to affect WATPRODY, only CROPATEN is significant with a positive effect. Despite their positive effects, both WATINSTN and FAMINSTN remain insignificant. This means that water productivity is perceived to depend not on the water and farm-related institutions but only on the food-crop dominated crop pattern. This result seems to contradict the expectation that water productivity, especially in value terms, will be higher with non-food and commercial crops. But, given the ineffectiveness of crop diversification program, the weakness of water and farm institutions, and the low physical productivity and poor market institutions for non-food crops, this result need not be surprising.

It can be seen in Equation [6] that of the three variables expected to affect LANHELTH, both CROPATEN and WATPRODY have significant positive effects whereas LANTENUR has a negative but insignificant effect. This result is understandable partly because the biomass of cereals, especially paddy straw, is commonly used for mulching and partly because higher water productivity is likely to lead to efficient water use favorable for soil conservation. Equation [7] provides statistical evidence for the relative role of physical, agronomic, and institutional factors in determining land productivity. The result shows that only the two physical and agronomic variables, i.e., land and soil health and crop pattern, are significant with the expected positive effect. Notably, FAMINSTN, the variable capturing the overall effectiveness and performance of farm input and extension institutions, has a negative but non-significant effect. This clearly means that these institutions, though have the capacity to perform routine roles as well as to support crop diversification (see Equation [2]), are not tuned well to make a difference either in water productivity (see Equation [5]) or in land productivity (see Equation [7]). From a policy perspective, the results also suggest that the farm institutions related to input supply and extension systems need to be reoriented and strengthened, particularly to enhance their performance in their productivity enhancing roles.

The results for Equation [8] show that of the two variables expected to affect the feed supply potential, only CROPATEN has a positive and statistically significant effect on FEDSUPLY. CUSINSTN, the other institutional variable included in this equation has a positive but insignificant effect. This result suggests that the cereal-
25 dominated, especially paddy-dominated, crop pattern obviously contributes to feed and fodder supply in terms of crop residues whereas the potential roles of customary institutions in preserving areas and maintaining rules for open grazing and biomass collection need to be strengthened. This is another instance of institutional gaps, the correction of which requires policies and programs to revive and strengthen local level customary institutions for managing common pool resources. The importance of enhancing the supply of feed supply from a better utilization of the biomass both from crop residues and from common grazing lands is underlined further by the results for Equation [9]. Of the two variables included in this equation, only FEDSUPLY is significant with the expected positive effect on the prospects for livestock development (LIVSTOCK). But, TRDPOLCY, the policyrelated institutional variable capturing the effects of the policy of importing milk and dairy products, though widely considered to be a major deterrent for the livestock development in the country, is not at all significant.

The results for Equation [10] reveal the linkage between farm and non-farm activities observed in the study region. The statistically significant positive effect of CROPATEN suggests that existing crop pattern dominated by food crops has a significant positive effect on the prospects for rural non-farm enterprises. This is mainly due to the fact that most non-farm activities observed in the study region are linked to the processing and marketing of food crops, especially paddy. Such dependence is obviously not conducive for the expansion and diversification of the rural non-farm sector. Nevertheless, the significant positive effect of RDVPOLCY indicates that active rural development policies have the potential to substantially contribute to the growth and diversity of rural non-farm options. Equation [11] suggests that labor productivity depends not on land productivity but mainly on crop pattern. Yet the direct relation between crop pattern and labor productivity requires a more in-depth interpretation.<sup>23</sup> The lack of association between the two productivity variables is easy to explain. That is, with similar crop patterns and productivity levels, land productivity may not be able to explain the variations in labor productivity.

In Equation [12], among the three variables postulated to affect WAGERATE, only NFAMENTS and WAGELAWS are significant with the expected positive effects.
26 Notably, LABPRODY is not significant suggesting that prevailing wage rates are not closely related to labor productivity. In contrast, non-farm activities have a strong effect on wage rates due to their influence on rural labor demand and thus rural wage levels. Similarly, the institution of formal state regulations and informal local conventions governing wage levels and working conditions also have a positive effect wage rates. In equation [13], all variables with the exception of WAGERATE have a statistically significant effect on RURALEMP. As expected, LANPRODY and NFAMENTS have a positive effect whereas LIVSTOCK has a negative effect. Particularly, land productivity, which had an insignificant effect on labor productivity in Equation [11], has here a significant positive effect on rural employment. The positive employment effect of NFAMENTS is consistent with the results in Equation [12]. The negative coefficient for LIVSTOCK suggests that livestock expansion, though good for rural income, has a negative effect on rural employment.<sup>24</sup> Though non-significant, the negative coefficient of WAGERATE suggests that higher wage rates can constrain rural employment.

The results for Equations [14] show that cultivation cost has a strong positive linkage only with the wage rate. This is consistent with the general concern in the study region about the cost implications of higher wage rates. Notably, all the other three variables, i.e., CROPATEN, FAMINSTN, and SUBPOLCY, have negative but non-significant effect on cultivation cost. Although not significant, the negative effect of FAMINSTN and SUBPOLCY does suggest their potential roles in reducing cultivation costs. Indeed, this is another case where improving the performance of institutions (i.e., farm institutions, especially in terms of their roles in delivering farm inputs at reasonable cost and subsidy policy, especially in terms of their design and targeting) can enhance the development impact. Turning to Equation [15], AGLINCOM, which represents the income only from farm operations, is influenced positively by both land productivity and cultivation cost. The positive effect of CULTCOST, unlike that of LANPRODY, is unexpected, particularly given the prevailing concern in the study region, in particular, and the country, in general, regarding the economic viability of farming in the face of rising cultivation costs. But, it is possible to have rising levels of crop income coincided with increasing
27 cultivation costs, as long as land productivity and crop price levels have a dominant and neutralizing effect.

Equations [16] and [17] evaluate the relative size, sign, and significance of the relative effects of the factors influencing respectively the income levels of farmers and landless farm workers. Equation [16] evaluates the relative effects of AGLINCOM, NFAMENTS, and LIVSTOCK on farmers’ income (FAMINCOM). The results show that although all three variables are positive, only NFAMENTS and LIVSTOCK are significant. This is not to be interpreted as agricultural income is unimportant for farmers. What this means is the fact that with a more or less stable income from farming, the other two sources of income are more important in terms of their incremental impact on farmers’ income. As can be seen from Equation [17], the incremental impact of these two income sources of farm workers is still more pronounced, suggesting livestock and non-farm options are much more important as income sources for landless workers. Importantly, RURALEMP has a significant negative effect. This may be partly due to the inverse association between wage rates and rural employment, as seen in equation [13], and partly due to the fact that more farm employment reduces the days available for non-farm and livestock activities causing, a fall in total labor income. Notably, SAMPOLCY, the variable representing payments under _Samurdhi_ , the government’s poverty alleviation program meant for the poor and landless, has a positive but insignificant effect. From an overall perspective, the results of equations [16] and [17] suggest that despite their low level of development in the study region, non-farm and livestock sectors are very important income sources for both small farmers and rural workers.

The next set of three equations captures the relative role and significance of the variables affecting respectively the three key determinants of food security, i.e., food production, food availability, and food prices. In Equation [18], the statistically significant positive effect of CROPATEN and LANPRODY indicates that a crop pattern dominated by food crops and a higher level of land productivity contribute directly to food production. The results for Equation [19] show that food production, trade policy, and market institution all have a positive effect on food availability. However, of these three variables, the effects of only the first two are
28 statistically significant suggesting their relative importance in determining food availability. The results for equation [20] are contrary to expectation because food production, price regulation, and market institutions, which are supposed to discipline food prices, are all significant with a positive effect. However, this result is not entirely inconsistent as it only illustrates how food prices can continue to rise despite increasing food production either due to hoarding or greater gap between food demand and supply. The results also suggest that the procurement, distribution and price related regulations as well as the market mechanisms in the study area are not really effective in moderating food prices. Here is another case where an increase in institutional performance can vastly improve the development impact.

Equation [21] is the ultimate equation in the system, as it brings together various direct and indirect effects of the development, impact, and institutional variables flowing through all the previous equations and also link them with the final development goal of food security. The results for this equation show that all the four variables have the expected signs, though only FOODAVAL and FOODPRIC are statistically significant. The positive sign for FOODAVAL, FAMINCOM, and LABINCOM suggests clearly that better food availability and higher income will directly strengthen food security. The negative sign for FOODPRIC, on the other hand, means that lower (higher) prices will enhance (reduce) food security. But, as we consider the relative size of the coefficients, the price effect is much more important than the supply and income effects. Similarly, among the income variables, farm income is more important than labor income. This result is important as it shows that food security depends more on food prices and farm income than on food availability and labor income. Since food security is stronger among people with access to land than among those without that access, the results also underlines the food security significance of the access to land and other assets.

# **8. EVIDENCE FOR DEVELOPMENT SYNERGIES AND INSTITUTIONAL EFFECTS**

So far, our attention has focused mainly on the relative role and significance of the development, institutional, and impact variables in each of the individual equations
29 within the system model. But, in view of the structural linkages among the model equations (see Figure 6), these local or layer-specific effects of the variables also have impacts not only on the subsequent layers of the system but also on the system as a whole. More importantly, as these impacts flow through the system, they can be magnified, neutralized, or even distorted by the role of other variables interacting in subsequent equations. With the system model and its coefficients, it is possible to develop greater insights into the dynamics of the impact transmission process. The dynamics of impact transmission can be evaluated both analytically and numerically using a reduced form single equation for the system model. This equation can be formed by using the structural linkages among the equations. The derivation of the reduced form equation is explained in Annex C. As can be seen, the reduced form equation shows food security, the ultimate dependent variable of the system model, as a function of all the previous equations with their characterizing variables and embedded linkages.

Since the reduced form is actually an equation of equations, when it is differentiated with respect to a given variable, the effect will not only be on the equation where this variable appears as an argument but also be on all subsequent equations where the dependent variable of this equation appears as an argument. As a result, the calculation of the total effect due to a change in a given variable involves either the multiplication of intermediate coefficients (when the relevant equations are embedded) or the addition of relevant coefficients (when the equations are separate). The former involves a single channel but the latter involves multiple channels. Thus, by differentiating the reduced form equation with respect to different variables and substituting the estimated coefficients from Table 4, we can numerically calculate how the marginal effects of different development and institutional variables are being captured at each equations and how these captured effects are consolidated at subsequent equations to get them finally transmitted into the ultimate equation. It is this exercise that is used here to trace the flow and quantify the impacts of development synergies and institutional effects on all the endogenous or dependent variables of different equations within the system. The results of this exercise with respect to three development programs
30 are presented in Table 5 and the same with respect to 10 institutional variables are presented in Table 6.

While there is a derivative of the reduced form equation behind each cell in both tables, it will be quite complicated and lengthy to present all these derivatives. However, there is a simple and intuitive way to explain how the number in each cell of the both tables is derived in terms of the addition and/or multiplication of relevant coefficients from Table 4. For instance, the first cell in the first column of Table 5 shows the marginal effect of system rehabilitation on bulk water supply. Since bulk water supply is the dependent variable in the first equation of the system, the marginal effect here is just the coefficient of system rehabilitation, i.e., 0.886. Similarly, the second cell of the first column is the marginal effect of system rehabilitation on crop diversification. But, going by the specification of equations [1] and [2], system rehabilitation affects diversification not directly but only indirectly via bulk water supply. Thus, using the chain rule, the relevant marginal effect will be 0.526, which is the product the coefficient of system rehabilitation in the first equation (0.886) and that of bulk water supply in the second equation (i.e., 0.594). Similarly, the third cell of the first column shows the marginal effect of system rehabilitation on crop pattern, which is transmitted indirectly via the two variables, i.e., bulk water supply and crop diversification. Again, using the chain rule, this marginal effect (0.231) is obtained by multiplying the coefficients of system rehabilitation in the first equation (0.884), that of bulk water supply (0.526) from the second equation, and that crop diversification (0.438) from the third equation. Similar procedure of multiplication in the case of indirect effects and addition in the case of multiple effects are used to obtain the values of marginal effects in the remaining cells of Table 5. Needless to add, similar explanation also applies to the values in Table 6. Let us also note that in both tables, we also reported the row and column totals. The row total indicates the total impacts captured by each of the 21 endogenous variables whereas the column total indicates the total impacts generated by the development programs in Table 5 and that by the institutional variables in Table 6.

Table 5 shows the size and flow of the marginal impacts of the three development programs across the equations. These marginal impacts cover both
31 the effects of synergies among the programs and the effects of improved effectiveness and performance of individual programs. Notice, however, that the flow of the synergy among the programs is unidirectional, i.e., from system rehabilitation to bulk water policy and, then, from the latter to crop diversification. This is essentially due to the particular way the interactions among the programs are modeled. That is, the system rehabilitation is considered to facilitate bulk water delivery and the latter, in turn, to influence crop diversification, but crop diversification is modeled to influence neither of the other programs. Considering this fact, the value of synergy derived by BULKWATD from system rehabilitation is 0.886. But, CROPDIVR derives synergies both directly from bulk water delivery policy (i.e., 0.594) and also indirectly from system rehabilitation (i.e., 0.526). The value of direct synergy is simply the value of coefficient for BULKWATD in Equation [2] whereas the value for the indirect synergy is obtained by multiplying the coefficients of SYSREHAB (0.886) in Equation [1] and BULKWATD (0.594) in Equation [2]. Since crop diversification, unlike bulk water delivery policy, has two routes for synergies, its total development synergy is equal to 1.200. As a result, crop diversification receives more synergy than that of bulk water delivery policy. However, the development externalities for both bulk water delivery policy and crop diversification are substantial and from a policy perspective, they can be improved with a better and more effective monitoring and implementation of related development programs.

More importantly, the development synergies flow throughout the system because they are being captured by many, if not all, the intermediate variables and finally transmitted to the ultimate variable of food security. As noted above, besides the synergy effects, these variables also capture the effects of improvement in the performance of the individual programs as well. That is, the effects captured by the intermediary and final variables are actually the combined effects of both the individual and collective performance of the three programs with an effective implementation and coordination. In Table 5, the combined effects as well as the sum of the total effects derived by some of the variables are substantial while those derived by others are very small. For instance, water institution derives the highest combined effects from both system rehabilitation (0.754) and bulk
32 water delivery (0.851). On the other hand, food security, food availability, food price, farmers’ income, and labor productivity capture relatively less of the impacts of all the three programs. In terms of the total synergy derived from all the programs, land productivity comes first with the value of 2.206, followed by water institution (1.605), cultivation cost (1.350), labor income (1.306), and water productivity (1.287). Among the programs, in terms of their total synergy effects, bulk water delivery policy with a value of 7.200 is relatively more important than the other two with a value of around 6.500.

Table 6 shows the size and flow of the impacts of the institutional variables, which are exogenous to the model.<sup>25</sup> The impacts of these variables are captured both directly and indirectly by the 21 endogenous variables or the dependent variables of the 21 equations. The institutional variables obviously differ in terms of their interaction with the development and impact variables depending on their location in the impact pathways. As a result, the impacts of some institutional variables are captured in many equations while the impacts of others are captured only in few equations, especially those capturing the interactions further down the impact pathway. For instance, land tenure, customary institution, and farm institution affect almost all the equations, but market institution, price regulation, trade policy, subsidy policy, and _Samurdhi_ policy affect only few equations. The total impacts generated by the institutional variables affecting many equations are likely to be larger than those affecting only a few equations.

In terms of their relative impacts on food security, some of the institutional variables affecting a few equations (e.g., market institution, price regulation, and trade policy) have a relatively larger impact than those affecting many equations. These results highlight two very important aspects. First is the role of impact dissipation within the impact transmission process due to their long impact chains, weak impact links, and _en route_ impact distortions. Second is the role of relative proximity or shorter impact chains associated with a few institutional variables to the final and proximate goals. It is the impact dissipation that explains why the institutional variables related to land tenure, custom, and farm input supply and extension could not sustain their substantial initial impacts. On the other hand, it is the role of relative proximity that explains why the institutions related to market,
33 price, wage, and trade have a larger impact on food security. From a policy perspective, while it is important to focus on institutions with proximate effects, it is important to address the issue of impact dissipation by locating and strengthening the weak links. From the results in Table 3, it is possible to locate these weak links by looking at what variables have caused such dissipation in which equation.

From an overall perspective, the results in Table 6 suggests that the institutions having a major impact on food security are market institution, trade policy, price regulation, and wage laws. This is mainly due to their direct impact on food supply, food price, and wage income. Notably, despite their positive intermediary impacts, the food security impacts of land tenure, rural development policy, and subsidy policy are all negative, suggesting their ineffectiveness. Although customary institution, farm institution, and land tenure have a larger impact on the system as a whole, their food security impact is either low or negative. Among them, customary institutions are not that easy to change through deliberate policies while land tenure is politically difficult to change. Practical considerations require a greater policy attention on the reorientation of farm input and extension institutions, which are likely to be easier to change. This is particularly so given the substantial contributions that farm institutions can potentially make.

As we compare the total effects of development programs and institutions as captured by different endogenous variables in tables 5 and 6, we find the development impacts are more than the institutional effects in most cases. Notably, in the case of water institution, the total institutional impact is even negative. But, it is interesting to note that in seven cases including food security and some of its proximate variables the total effects derived from institutions are more than that derived form development programs. This clearly suggests the critical role that institutions play in the generation and transmission of development impacts. Finally, besides its role in providing evidence for development synergies and institutional impacts, the numerical analysis impact transmission reported here also has a major policy role. The numerical analysis can be a basis for identifying the weak links within the impact transmission process, including the development, institutional, and impact variables involved in such weak links. The same analysis can also rank
34 the variables in terms of the magnitude of their marginal impacts and intensity of their linkages within the system. Such information is valuable for targeting impact channels and prioritizing variables in development planning and implementation.

# **9. CONCLUSIONS AND POLICY IMPLICATIONS**

This paper has argued that the impact synergies among development programs and the impact enhancing role of institutions, though well known, have neither been properly treated nor fully accounted for in actual development planning, implementation, and evaluation. This problem has far reaching implications, especially for _meta_ -development goals such as MDGs, which require effective institutions and an integrated approach to development planning and implementation for their realization. It is demonstrated graphically how the ignorance of the impact synergies among past, ongoing, and planned programs leads to biased impact assessment and how an insufficient treatment of the impact enhancing role of institutions leads to substantial welfare loss. To help address these serious problems, this paper has presented one approach for developing an evaluation methodology and also illustrated it in the empirical context of the Kala Oya Basin in Sri Lanka, using stakeholder-based information. Although the illustration here has considered the linkages among water-related programs, the methodology is generalizable to deal with more general linkages not only among other development programs but also other projects or policies having a common or closely related development goal.

The analytics of the institution-impact framework shows both the specific point at which different institutions influence the impact generation and transmission process as well as the mechanics of impact synergies among the past, ongoing, and planned programs. The mathematical replica of this framework provides additional insights into the functional relations and sequential linkages among the development, institutional, and impact variables. Evaluation of these analytics, mechanics, and linkages are valuable can be a basis for development design and implementation, especially in the packaging and sequencing programs and in the identification and strengthening of pathways and institutional configurations with major impact potential. The regression results provide
35 considerable insights into the specific roles that institutions play in the generation and transmission of impacts across impact pathways as well as the extent of impact synergies that development programs derive from each other. These development synergies are captured not only by the coefficients of the variables representing the development programs but also by those of the other impact and institutional variables. Since these coefficients are the statistical representation of the consensus prevalent among the selected stakeholders, their size, direction, and significance show the relative importance of the development, impact, and institutional variables included in the model. By unbundling the impact process and deciphering its transmission channels, the system model is also able to capture the flow and direction of development impacts as well as to show which institutions affect what channel. The sensitivity analysis performed with the reduced form equation has also shed light on the flow of development synergies and institutional impacts both at the layer-specific and system-wide contexts. All these are valuable information for policy design, institutional analysis, and impact assessment.

From a policy perspective, the main message is that when planning a new development program, project, or policy for a given region, it is crucial to take stock of the potential synergies possible from past, ongoing, and planned programs, projects, or policies with common or closely related goals. In our study region, for example, the implementation of system rehabilitation has had a substantial facilitating impact on the performance of bulk water distribution and this positive synergy has the potential to enhance the prospects for crop diversification. The results also indicate that the development synergies among the programs can be enhanced with a fine-tuning of the legal, policy, and organizational components of institutions related to the land, water, agriculture, market, and trade spheres. Although the institutions covered here are not exhaustive, the results do show that among the institutions considered, those operating in the production and marketing spheres are relatively more important in terms of their role in channeling the impacts to the ultimate goal of food security. Specifically, since food prices and farm income are the most dominant factors affecting food security, all their intermediary variables and their underlying institutions (e.g., markets, price regulation, land tenure, and credit and extension) are very important.
36 Besides the production-related farm institutions and distribution-related market institutions, there are also major influences from national level policies and laws such as those related to farm subsidy, rural industrialization, poverty alleviation, and wage rates and working conditions. But, at the same time, customary institutions related to cultivation practices and common grazing lands have significant effects on crop choice and livestock development. Notably, customary tendencies towards paddy cultivation, though a serious constraint for crop diversification, have a positive effect on the supply side of food security. To what extent changes in the performance of these rural institutions could affect the ultimate goal can be evaluated in terms of chain functions capturing how a marginal change in any of the institutions leads to a series changes within the equation systems and culminates finally in the marginal change in food security. Similarly, how impact synergies among development programs contribute to the final goal can also be evaluated in terms of the marginal changes in one or more of the variables characterizing various impact chains. Sensitivity analysis of this nature can provide valuable information for policy makers in prioritizing institutions and sequencing development programs.

From an overall perspective, the econometric property and theoretical consistency of the model results also suggest the proposed methodology of institution-impact interaction to be highly robust and the empirical approach of using perception-based information to be very reasonable. While the methodology and empirical approach are intuitive and the results provide considerable insights into the internal dynamics of and institutional roles in impact transmission, one cannot ignore the limitations of the present attempt and the scope for further refinements. Some of the limitations are obviously related to the specification and structuring of equations. The unexpected signs for and insignificance of crucial variables in some equations are also problems that can be avoided with a more refined specification of equations. Although only the perception-based qualitative data is used here to provide an empirical demonstration of evaluation approach, it is possible to explore ways for using observed and quantitative data from secondary sources and household surveys for as many variables as possible. In this case, a mix of quantitative and qualitative data can be used to estimate the model. More
37 importantly, although the methodology can work well with a reasonable number of programs and impact pathways, it can become complicated when they are increased beyond a certain level. Despite these limitations, the paper is still able to develop and empirically illustrate an evaluation methodology that can deal with the issues of impact synergies and institutional roles in the practical contexts development planning, implementation, and impact assessment.
38

# **ENDNOTES**

- 1 Note that the development synergies can be both positive and negative, depending on the nature of the development programs considered together. These synergies relate only to the enhanced or reduced welfare effects of one program due to the externalities from the other programs.  Thus, these synergies capture the difference between the sums of their individual impacts when implemented in isolation and their joint impacts when implemented and evaluated together.

- 2 The MDGs, which came from the Millennium Declaration adopted in 2000 by all 189 UN member states, set targets for countries to reduce poverty, hunger, disease, illiteracy, and gender bias and improve environmental sustainability and global governance by 2015. These targets are now accepted by the international development and donor agencies as a framework for monitoring development progress

( _http://www.un.org/millenniumgoals/index.html_ ).

- 3 For instance, in the case of the food security impact of an irrigation project, one pathway can be characterized by the chain of variables, i.e., irrigation—productivity—food output— food availability—food prices—food security. Besides this output pathway, there is also an income pathway, i.e., irrigation—cropping intensity—employment—wages—income—food security.  More such paths can also be visualized and constructed.  Note that these paths enable us to incorporate also the role of relevant institutional variables (e.g., production, extension, input, and market institutions, price regulations, and trade and rural development policies). This is illustrated in Figure 5 (where the pathways are traced and depicted) and in the system model (where they are formally characterized as equations).

- 4 For instance, a review of the available approaches, as presented and evaluated by Baker (2000), Bourguignon and de Silva (2003), and Center for Global Development (2006), suggests that their main application is with respect to an individual project, policy, or program, their evaluation confines to isolated impacts, and their focus is on the ultimate policy goal or few of their intermediaries.

- 5 Of them, system rehabilitation was already implemented, but bulk water distribution is being implemented only on a pilot scale in the canal areas of the basin.  Crop diversification, is only being planned, though the Government of Sri Lanka has a national policy to promote agricultural diversification.

- 6 MAPP is a stakeholder-centred method involving an open approach and a seven-step procedure where the stakeholders are asked to award points on various aspects and criteria related to one or more development programs and their impacts on development goals.  See Neubert (2000 and 2006) for a detailed description of this method.

- 7 PSIA is a method for evaluating the distributional consequences of policy reforms on different groups in terms of the direct and indirect as well as the immediate and future impacts as transmitted through channels such as: prices and wages, employment, access to goods and services, assets, transfers and taxes, and authority. To account for the temporal differences and varying nature in the flow of reform impacts, the method combines secondary data and objective and perceptional data from a sample of intended beneficiaries/losers. See the Department of International Development and World Bank (2005) and Coudouel, Dani, and Paternostro (2006) for the description and application of this method.

- 8 Although the PSIA is originally intended to be participatory, with an excessive focus on objective data and scientific quality, it has tended to become more technocratic (Coudouel, Dani, and Paternostro, 2006:12), making it unable to capture the valuable information
39 held by stakeholders—both beneficiaries as well as those involved in development research, planning, and implementation.

- 9 A general application of this framework for a global ranking of institutional health and reform prospects within the water sector is illustrated in Dinar and Saleth (2005).

- 10 For instance, in the case of water development program, we have included only five main paths, though, in reality, each of these paths will affect the development goals through several routes. For instance, the irrigation path will have different but related routes such as production route (i.e., irrigation-cropping intensity-productivity-food supply), income route (irrigation-productivity-employment-income), price routes (irrigation-productionfood prices), resource routes (irrigation-waterlogging-salinity-land degradations), etc. Similar routes and the associated chain of variables can also be found for the other four impact paths.

- 11 In the impact routes characterized by different chains of variables (see note 3), it is possible to include relevant institutional variables.  For instance, production, input, and extension-related institutional variables can be added with the impact variables characterizing the production route.  Similarly, institutional variables related to market, trade, and price regulation can be added with the impact variables underlying the price route.  This will help us to formally and functionally capture the direct and interactive effects of the impact and institutional variables on the intermediary and final goals.  We will see this more clearly in Section 5.

- 12 These programs were actually selected from a list of 16 programs based on the priority points of the sample stakeholders. The list included both the completed, ongoing, and potentially relevant programs in the particular context of the study region.

- 13 Note that some components of this environment (e.g., development policy, production relations, and institutional and macro policy aspects) are internalized within the flow chart. Although the effects of other such factors (e.g., climatic change and national and international political environment) are not considered in Figure 5, it is possible to evaluate them by comparing the institution-impact relations under different scenarios representing the status of these exogenous factors.

- 14 The impact variables are actually the economic, technical, and physical variables that act as the ‘impact transmission variables’. They are not to be confused with those in the impact assessment literature, where ‘impact variables’ relate only to the ultimate endgoals (see Neubert, 2000).  In the context of our framework, it is still appropriate to treat them as impact variables because (a) they do capture the intermediary impacts (or, outcomes) and (b) such impacts are specifically evaluated using equations representing different impact layers.

- 15 Notice that the 17 impact variables also include the four variables, i.e., farm income, wage income, food availability, and food price, which are actually the intermediate goals linked immediately with the final goal of food security.

- 16 Note that in the case of quantitative variables (e.g., productivity, income, employment, and food consumption, these scores can be easily converted into quantitative equivalents by using the range of minimum and maximum values observed in the study area. But, in the context of cross-sectional regression and when using with qualitative variables (e.g., the performance and effects of most institutional variables) where performance scores are indispensable, the results will not be qualitative different whether one uses the scores or their quantitative equivalent for the quantitative variables.

- 17 The order condition requires that in the case of each equation, the number of excluded exogenous variables is greater than the number of included endogenous variables less one.
40 In simple terms, this condition ensures that there are enough exogenous variables excluded so that they can serve as instrumental variables for estimating the endogenous variable appearing as the dependent variable in each equation. The rank condition, though quite technical, requires, in simple terms, that all the equations are distinct in the sense that none of them can be formed with the linear combinations any other two equations in the system (see Kennedy, 1987: 138 & 142).

- 18 Notably, these stakeholders are not the direct beneficiaries of the development programs. This is partly to avoid the potential bias and partly to address the macro-micro dichotomy evident in empirical impact evaluation literature, i.e., micro evaluations report considerable impact whereas macro evaluations find little or no impact, or _vice versa_ (Neubert, 2000; Coudouel, Dani, and Paternostro, 2006).

- 19 In view of the technical nature of the analytical framework and questions, the original plan was to have a sample of only government officials and researchers.  However, we also tried to test whether the questionnaire can be administered to farmers and community leaders.  This is how the three farmers and community leaders were added to the sample.  As the experience shows that they are able to understand and answer the questions well, the present exercise can very well be repeated with a sample exclusively of farmers and local leaders.

- 20 Note that instead of the simple average, it is also possible to use weighted average based on a principal component analysis.  But, simple averages are used essentially to have a more balanced and unbiased values for the variables.

- 21 To test for multicollinearity, a correlation analysis was performed for the 32 variables. The correlation matrix showed that only in four cases (SYSREHAB vs. BULKWATD and WATINSTN, RURALEMP vs. FEEDSUPLY, and AGLINCOM vs. FAMINCOM) did the correlation coefficient was over the threshold of r>0.5 (see Hair et al. 1995).  Since none of them were used together as independent variables in any equation, the multicollinearity can be taken not as a serious problem.

- 22 The result is somewhat surprising because the bulk water provision, as being piloted in the study region, is only to farmer groups and not to individual farmers as needed for promoting independent crop decisions.

- 23 Note that the strong positive effect of CROPATEN on LABPRODY cannot be interpreted simply as food crops are more conducive for labor productivity.  It needs to be explained in the light of the inverse relation between WAGERATE and RURALEMP seen in Equation [13], which means that high wage limits labor use and with a given farm productivity, lesser labor use causes labor productivity to be higher than otherwise.  It is this effect occurring in the face of a labor scarcity and paddy domination that is behind the positive association seen between CROPATEN and LABPRODY.

- 24 The inverse relation between RURALEMP and LIVSTOCK suggests the tradeoff in labor time allocation between wage employment and livestock rearing, which is particularly so among the groups which need both sources of income.

- 25 It is important to note that even though bulk water delivery policy and water institution

- are part of institutions, they remain only endogenous because the former is specified as a function of system rehabilitation and the later is specified as a function of bulk water delivery policy.