model translates exogenous economic and technological settings into a path of economic outcome variables. For notational convenience, let β denote the economic and technological parameters of the CGE. Relevant outcome variables are denoted by the vector z. Thus, it holds: z ¼ z(β). Because we are using a quasidynamic CGE, all exogenous and endogenous variables evolve over time, where Zt and βt denote the variable values in time period t. Accordingly, we denote by the matrix ZT ¼ [zt] the development of the relevant outcome variables, where the vector zt corresponds to the sequence of values of the outcome variable z over the time periods t ¼ t1 ,.., tT. Analogously, the matrix βT is the matrix of the development of exogenous CGE parameters over the period t ¼ t1 , ... , tT. A standard CGE application simulates the impact of exogenous policy shocks on the CGE equilibrium path (i.e., on the development path of Z). In particular, policy parameters (γ) are incorporated into the CGE model. Technically, this incorporation is accomplished via a policy implementation function, which transforms policy parameters into CGE parameters: β ¼ β(γ). Using the PIFs, a sequence of policy shocks (γt) is transformed into a sequence of exogenous parameter shocks βt ¼ PIF(γt), which are translated into a development path of outcome changes dZT when solving the sequential CGE. A CGPE model extends the standard CGE model by incorporating a policy-decision module (PDM). The PDM determines endogenously the policy choices γt that occur over time. In particular, according to our theoretical CGPE approach the PDM corresponds to a two-stage decision-making model, where at a first stage relevant political actors select a direction, ΔγM, in which the status-quo policy is shifted and at a second stage political actors vote on a distance (λ) the status quo policy is shifted towards the agreed direction. Let i¼1, ..., nI denote the index of relevant political actors including a subset of legislators and a subset of stakeholders, while g¼1, ..., ng denotes the index of legislators. Each political agent has spatial policy preferences U(γ). Policy preferences are derived from political support maximization.

According to the mean voter decision rule legislative bargaining implies:

ΔγM¼ Σg φg Yg, while the distance results as median, λmed of the individually preferred distances of legislators, λg.

To include lobbying influence prefered policy positions of legislators result as:

$$\mathbf{Y} \mathbf{g} = \Sigma\_{\mathbf{j}} \mathbf{M}\_{\mathbf{g}\mathbf{j}} \mathbf{Y}\_{\mathbf{j}}.$$

Accordingly, lobbying implies an extended mean voter rule:

$$
\Delta \mathbf{y}^{\mathsf{M}} = \Sigma \mathbf{j} \, \mathbf{C}\_{\mathbf{j}} \mathbf{Y}\_{\mathbf{j}}, \mathbf{C}\_{\mathbf{j}} = \Sigma\_{\mathsf{g}} \, \mathbf{q}\_{\mathbf{g}} \, \mathbf{M}\_{\mathbf{g}\mathbf{j}}.
$$

The final policy choice is gradually implemented for a sufficiently small dλ:

$$\begin{aligned} \label{eq:EMT1} \chi\_t &= \chi\_0 + t \, d\lambda \, \Delta \boldsymbol{\eta}^M, & \quad \text{if} \quad \lambda^{mcd} &> (t-1) \, d\lambda + \chi\_0, \\\chi\_t &= \chi\_0 + (t-2) \, d\lambda \, \Delta \boldsymbol{\eta}^M, & \quad \text{if} \quad \lambda^{mcd} &< (t-1) \, d\lambda + \chi\_0 \end{aligned}$$

However, we do not assume that politicians have perfect information regarding the political technology (i.e., the transformation of policies into policy impacts); instead, agents are unaware of the true PIF and CGE model.

Hence, we assume that policy choices depend on political beliefs A~t . Assuming that beliefs are perfectly exogenous implies that initial beliefs perfectly determine all future policy choices. However, as explained above, politicians engage in policy learning processes, i.e. politicans update their beliefs based on observed policy outcomes and policy beliefs communicated by other actors. Belief-up-dating via communicational and reinforcement learning is modeled in the belief updating module (PBD), which is also incorporated in the eCGPE approach. In particular, communication learning is modeled applying the Friedkin model, while reinforcement learning is applied to the individual preferred distance λi and is based on agents' political support, Si(z). All CGPE modules are programmed in GAMS and integrated into a sequentially linked eCGPE model, as described in Chapter "Modeling and Evaluation of Political Processes: A New Quantitative Approach" above.

#### 2.2 Empirical Calibration of the eCGPE and Data

Empirical calibration of a eCGPE model includes the calibration of all four modules (i.e., the CGE model, the policy impact function [PIF], the belief formation model [PBF] and the political decisionmaking model [PDM]). Because the empirical calibration of the CGE model is a well-known standard procedure, we only describe the empirical calibration of the other three models, including the required empirical data. A detailed description of the empirical estimation procedures for the Malawi case has already been described in Chapters "Modeling and Evaluation of Political Processes: A New Quantitative Approach" and "A Network Based Approach to Evaluate Participatory Policy Processes: An Application to CAADP in Malawi."

As described in detail in Chapter "A Network Based Approach to Evaluate Participatory Policy Processes: An Application to CAADP in Malawi," the main parameters of the PBF and PDM can be specified based on data collected via a policy network survey. This type of survey is a standard approach in political sociology and empirical policy network studies (Henning 2009; Knoke et al. 1996; Pappi and Henning 1999; Pappi et al. 1995). In the first step of the policy network study, the set of relevant governmental and nongovernmental organizations (N), the set of relevant policy concerns (z) and the set of relevant policy instruments (γ), are identified via expert interviews and document analyses.

In the second step of the policy network study, personal interviews will be conducted with all identified relevant organizations. Within the personal interview, policy network relations with other organizations, including political communication, are collected. Based on the stated network relations of all interviewed organizations, the corresponding global networks can be derived directly (Laumann and Knoke 1987; Henning 2009; Knoke et al. 1996; Pappi and Henning 1999; Pappi et al. 1995). Alternatively, an advanced econometric approach can be applied to estimate global network structures based on the stated network relations of the involved agents (Assmann et al. Chapter "The Formation of Elite Communication Networks in Malawi: A Bayesian Econometric by Approach", Snijders 2002). This approach facilitates the identification of the underlying network-generating process and allows an adequate imputation of missing data. Moreover, this approach allows for the identification of determinants of the structure of policy networks and the identification of possible strategies for designing network structures that imply more efficient policy processes. Further, the stated policy preferences of organizations are collected. In the first stage, organizations state their relative interests (X) and their preferred positions ðZbÞ, with respect to identified policy concerns. In the second stage, organizations state their relative interests (Θg ) and preferred positions with respect to identified policies ð Þ bγ .

Based on the stated policy positions of all relevant organizations, the underlying macro policies (γP ) can be identified as latent variables by conducting a principal component analysis (see below for details). The stated relative interest in policy concerns is used to specify the Cobb–Douglas parameters of the individual support functions (Xi ¼ [Xik]).

#### 2.3 Estimation of the Policy Impact Function

The core of a standard CGE application corresponds to a simulation of exogenous shocks. With respect to content, shocks are changes in economic or political framework conditions. Technically, the impact of exogenous shocks is modeled via shifts of exogenous CGE parameters. However, when specific policy shocks are simulated, policies must be incorporated into the CGE model (i.e., shocks must be transformed into changes in CGE parameters). Technically, this transformation is implemented via PIFs. Some policies (e.g., direct and indirect taxes or tariffs) are already directly implemented in the standard CGE model. However, other policies, such as structural adjustment policies, must be translated into CGE parameters. In particular, reducing poverty and promoting overall economic growth is a key factor for achieving the first MDG goal (Diao et al. 2007; Fan and Rosegrant 2008). A range of policy instruments exist that governments can use to promote the required overall economic growth (e.g., technical progress [t.p.] and improving market access by lowering transaction costs). One key factor for sustainable economic growth is t.p. Thus, following Benin et al. (2012), we focus our policy impact analyses on the promotion of t.p. Please note that the PIF approach can be easily extended to include policy impacts on market access and direct transfers to enterprises or households. However, because this chapter aims to demonstrate how a CGPE approach can be applied empirically and to describe the generated results, we restrict the PIF to t.p. When focusing on policy impacts on the promotion of t.p., two questions arise. First, the sectors in which t.p. has the largest impact on the achievement of the envisaged political goals (e.g., poverty reduction or economic growth) remain unknown. Second, the optimal distribution of scarce financial resources across different policy programs for inducing the largest increase in t.p. (e.g., extension services or interest rate subsidies, etc.) must be determined.

With respect to the first question, Fan and Rosegrant (2008) emphasize that many African countries spend too little on promoting agricultural growth compared to non-agricultural growth. Further, with respect to the second question, budget allocations to different agricultural policy programs significantly affect the effectiveness of total budget expenditures. For example, within the Comprehensive Agricultural Development Plan, four different pillars are specified as policy subdomains. Moreover, beyond agricultural growth, overall welfare development is also determined by economic growth in non-agricultural sectors and by the provision of public goods, such as health, education and other social services. Therefore, at the country level, an overall budget allocation must include the allocation of total financial resources for the promotion of economic growth in the agricultural and non-agricultural sectors and the allocation of financial resources for the provision of public goods. For example, Badiane et al. (2011) state that budget allocation to programs that promote future economic growth and the provision of public goods has a significant impact on present and future welfare allocations.

Thus, to identify optimal government budget allocations for promoting economic growth within our CGPE framework, we suggest the following PIF approach. Total government expenditure (Btot) results as the sum of total spending across policy programs: Btot ¼ P p γp. Total government spending determines t.p. realized in the economy, and the effective impact on technical progress (tps) that is realized in a specific economic sector s depends on the allocation of governmental spending across policy programs. To capture the importance of different policy programs p to the impact on technological progress that is realized in a specific sector s, the following two-stage policy impact functions (PIFs (γ)) are defined for each sector (s):

$$PHF^t(\boldsymbol{\gamma}) = a\_s^o E\_s \left[ \boldsymbol{B}\_s^{eff} \right]^{a\_s} \tag{1}$$

$$\mathcal{B}\_s^{eff} = \alpha\_s \left[ \sum\_p \mu\_{ps} \left[ \chi\_p \right]^{-\rho\_s} \right]^{-1/\rho\_s} \tag{2}$$

$$\frac{\kappa h\_{ps}}{\kappa h\_{qs}} = \left[\frac{\mu\_{ps}}{\mu\_{qs}}\right]^{(1+\rho\_x)}\tag{3}$$

In the lower stage Eq. (2), budget allocation is transformed into effective budget allocation according to a CES function specification. In the upper stage Eq. (1), an effective budget is translated into t.p. according to a Cobb– Douglas function (i.e., the marginal impact of additional effective budget spending is diminishing and approximates zero for a sufficiently large effective budget). α0 s is a normalization parameter that implies that Es is the maximal rate of t.p. that can be achieved with empirically relevant total budget expenditures for policy programs γ.

The suggested PIF basically follows the work of Fan and Zhang (2004). However, in contrast to the original approach, the PIF approach is more general and implies a nonlinear relationship between governmental spending and induced technical progress in economic sectors. Moreover, this approach explicitly considers the composition of budget spending for different policy programs. Finally, please note that optimal budget allocation to different agricultural and non-agricultural policy programs varies across different economic sectors (i.e., the same budget allocation translates into different effective budgets that induce different t.p. in different sectors).

In general, an empirical estimation of the PIF function demands a large database of budget expenditures for different policy programs and empirical observations of induced technical progress achieved in different economic sectors. Such a large database is not available for most countries. Accordingly, Diao et al. (2012) estimated an aggregated function that relates the total budget expenditures for agricultural and non-agricultural policy programs to the average t.p. realized in the total agricultural sector.

In this context, we suggest a different approach for estimating detailed and sector-specific PIFs. In particular, we apply a Bayesian estimation procedure that uses interview data from political experts to estimate the parameters of the PIFs. According to our theory, governmental and nongovernmental organizations derive their preferred policy positions ð Þ bγ from the maximization of their political support S(z), where support is generated via policy outcomes z. These outcomes are induced by policy choices, giving the underlying political technology T(z, γ). As described above, in the CGPE approach, the political technology corresponds to the CGE model and the PIFs, where the CGE model translates the exogenously given t.p. of different economic sectors into the growth rates of different policy concerns z. Let wz denote the vector of the annual growth rates of relevant policy concerns that are induced by a vector of technical progress changes (Δtp). Then we can approximate the vector of the annual growth rates of policy concerns implied by exogenously given change in technical progress (Δtp) as follows:

$$
\Delta w\_z = \sum\_s \xi\_{zs}^{CGE} \Delta tp + w\_z^0 + \xi^0 \chi \tag{4}
$$

w0 z is the vector of the growth rates that results in the base run, assuming technical progress would not change, while ξCGE zs denote the CGE elasticities and ξ0 denotes the vector of direct policy impacts on outcomes. Both ξCGE zs , ξ0 andw0 z can be derived via CGE simulations.

Given this approximation, the support maximization problem of a political agent i results as:

$$\begin{array}{l} \mathbf{Max}S\_i(1+w\_z) \\ \mathbf{s.t.}: \\ w\_z = \sum\_s \xi\_{zs}^{CGE}(\Delta tp) + w\_z^0 + \xi^0 \chi \\ tp = PIF(\chi) \end{array} \tag{5}$$

The solution of the maximization problem results in the optimal policy positions bγi ð Þ and the induced preferred policy outcomes (i.e., the growth rates of policy concerns ðwbziÞ) of a political agent i. Accordingly, based on the observed optimal policy positions and the preferred policy outcomes of a set of political agents, the PIF parameters could be estimated econometrically. However, given the large number of parameters, one would need a large set of relevant political agents. Because the set of relevant political agents is rather small (e.g., 36 governmental and nongovernmental organizations in Malawi) a direct estimation of the PIF parameter is impossible because the econometric model is underdetermined (i.e., the number of parameters is larger than the number of observations). To address the specification of underdetermined models, Golan, Judge and Miller suggested the Generalized Maximum Entropy (GME) and Generalized Cross Entropy (GCE) techniques. In a very interesting paper, Heckelei et al. (2008) discussed an alternative Bayesian estimation approach to the GME and GCE techniques. To understand the Bayesian approach for estimating the parameters of an underdetermined model, let χ denote the vector of the parameters of our PIF functions. Hence, the first order conditions of the political support maximization problem of all relevant political agents correspond to an underdetermined equation system, which we denote FOC (χ). Further, let V(χ) denote any prior distribution among the set of PIF parameters χ. Then a solution to the original equation system FOC(χ) can be obtained from:

$$\text{Max}\_{\chi}V(\chi) \\ \text{s.t.} \\ FOC(\chi) = 0 \tag{6}$$

As long as the prior distribution has a unique maximum within the feasible set of parameters (FOC(χ) ¼ 0), the original parameter estimation problem has a unique solution. Moreover, HMJ demonstrates that the parameter vector χ that maximizes V(χ) within the subset of feasible parameter solutions is the mode of the posterior distribution and corresponds to the Highest Posterior Density (HPD) estimate of χ (Heckelei et al. 2008). Furthermore, we can also add noise to the first order conditions (i.e., FOC(χ) + ε, where ε is a vector of error terms). Then assuming that the error terms were independently drawn from N(0,1), the HPD estimator of χ results as:

$$\operatorname{Max}\_{\chi,\epsilon} V(\chi) \prod\_{i,p} p\_{\epsilon}(\varepsilon\_{ip}) \\ \text{s.t.} \\ FOC(\chi) + \varepsilon = 0 \tag{7}$$

where pe(.) is the standard normal univariate density. Finally, one can also add further restrictions on the parameters χ, which correspond to further prior information regarding the parameters χ. This prior information might correspond to theoretical constraints of the parameters χ or to further empirical information (e.g., expert information regarding minimal or maximal values for specific parameters). Let RES(χ) ¼ 0 denote any further parameter restrictions. Then the HPD estimator of χ is obtained from:

$$\begin{aligned} \textit{Max}\_{\chi,\varepsilon} & V(\chi) \prod\_{i,p} p\_{\varepsilon}(\varepsilon\_{ip}) \\ \upharpoonright\_{\chi,t.} & \\ FOC(\chi) + \varepsilon = 0 \\ \textit{RES}(\chi) &= 0 \end{aligned} \tag{8}$$

Overall, an HPD estimation follows from Eq. (8) using interview data from the policy network survey if a prior probability density function of the model parameters χ, V(χ), has been specified and if additional relevant parameter restrictions RES(χ) have been specified.

In particular, we assumed that individual parameters are independently normally distributed [e.g., the corresponding prior density function results as: vec(χ) ~ N(χ0 , ∑)]. We derived the prior means χ0 based on existing estimations in the literature (Benin et al. 2012), while the covariance matrix was set equal to the diagonal matrix with the elements [vec(χ0 ) 2 ]. The specification of the variance of the prior parameters corresponds to the assumption that the coefficient of variance is 1 for all parameters with a non-zero prior mean. If the prior mean was equal to zero, we set the diagonal element to 0.01.

Given these assumptions regarding the prior density function, the HPD estimator of χ results as:

$$\begin{aligned} \text{Minx}[\text{vec}(\chi) - \text{vec}(\chi^0)]' & \sum \text{--}^{-1} \left[ \text{vec}(\chi) - \text{vec}(\chi^0) \right] + W\_\varepsilon \sum\_{i,p} \varepsilon\_{ip} \\ \text{s.t.}\\ \text{FOC}(\chi) + \varepsilon &= 0 \\ \text{RES}(\chi) &= 0 \end{aligned} \tag{9}$$

Wε is the relative weight of the interview data in relation to the expert prior information, which we set exogenously. A high relative weight implies that the estimated PIF parameters are more driven by the interview data from the political agents, while a low weight implies that the final parameter estimations are more driven by the prior information obtained from existing studies. The Bayesian estimation procedure was also implemented in GAMS.

# 2.4 Estimation of Individual Policy Beliefs and Political Knowledge

We understand the policy beliefs of individual political agents as simple mental models for how CAADP policies translate into changes in policy concerns. To capture policy beliefs within the CGPE framework, we estimate for each stakeholder organization the set of PIF parameters and the CGE multiplier that imply that individual political support maximization exactly replicates the policy positions (bγ and Zb), that an organization stated in the interview of the policy network survey. Basically, we apply the same Bayesian estimation approach described above using only the data and the first order conditions of the political support maximization of one individual stakeholder. Accordingly, we obtain for each individual political agent an estimation χ∗ i of the parameters χ. Hence, the estimated parameters χ∗ i incorporate the individual policy beliefs of a stakeholder organization. Further, we aggregated estimated individual political technology parameters to common policy beliefs by applying factor and cluster analyses. In particular, we first derived the matrix of first order differentials Ai ¼ ½aij ¼ ∂Zi ∂γj h i ¼ P s ξCGE zs ∂PIFs ∂γj h i as a linear

approximation of the estimated individual political technology. Based on the individual matrix elements (aij), we first conducted a factor analysis. Based on the factor scores derived for individual stakeholder organizations, we conducted a cluster analysis to identify organizations that hold similar policy beliefs.

Beyond policy beliefs, we are interested in the level of political knowledge of relevant stakeholder organizations (i.e., the degree to which stakeholders' policy beliefs correspond to the true political technology). In the CGPE framework, we measure political knowledge as the level of political support an individual organization i realized based on its stated policy position (γi) compared to the maximal political support this organization would achieve given the true political technology. If we denote the optimal policy position of an organization as the policy position that maximizes its political support given the true political technology by bγ opt i , it follows for individual knowledge-gaps:

$$Know-gap\_i = 1 - \frac{S\_i(\hat{\mathcal{Y}}\_i)}{S\_i\left(\hat{\mathcal{Y}}\_i^{opt}\right)}\tag{10}$$

Obviously, individual political knowledge gaps depend on the congruence of stakeholders' policy beliefs and the true political technology. Because it is difficult to identify the true political technology empirically, we will calculate knowledge gaps by simulating different political technologies.

#### 3 Results

#### 3.1 Political Incentives

Empirically, we derive the political incentives of relevant political agents from their relative interest in different policy concerns, which we collected via personal interviews within the policy network survey. As shown in Fig. 2, the main political interest is the welfare of small-scale farmers (Z1), followed by poverty reduction (Z2) and interest in general public services (Z3). In contrast, interest in the welfare of agribusiness (Z4), urban consumer welfare (Z5) and interest in the welfare of agricultural export sectors (Z6) are comparatively low. Interest in environmental

Fig. 2 Interest in policy concerns according to organizational category. Source: Authors

protection (Z7) occupies a middle ground between the high interest concerns Z1– Z3 and the low interest concerns Z4–Z6. Of course, interest group associations that are specialized in the representation of the particular interests of a specific socioeconomic group (e.g., agribusiness and farmer organizations) have extremely high interest in the welfare of their clientele (e.g., farmer associations in Z1 and agribusiness organizations in Z4) (see Fig. 2). In comparison to socioeconomic interest groups, government and international donors and civic society organizations have a relatively higher interest in poverty reduction (Z2) and environmental sustainability (Z7).

Further, we derive a social welfare function based on the collected stakeholder interests. In particular, we set the relative weights of the welfare of particular economic interests (i.e., Z4 and Z6) to zero, while we calculate the relative welfare weights of the remaining policy concerns Z1–Z3, Z5 and Z7 as the average interests of stakeholder organizations. Overall, the following welfare weights result: Xw1 ¼ 0.299 , Xw2 ¼ 0.259 , Xw3 ¼ 0.189 , Xw4 ¼ 0 , Xw5 ¼ 0.08 , Xw6 ¼ 0 , Xw7 ¼ 0.173.

#### 3.2 Policy Beliefs and Political Knowledge

As described above, based on stated policy positions and the achievement of policy goals, we estimated the individual parameters of the PIF and the CGE multipliers that imply that the stated policy positions of relevant governmental and nongovernmental organizations can be replicated from the corresponding political support maximization. Based on the estimated parameters, we calculated the matrix dZ dγ h i as a linear approximation of the political technology, which we interpret as the policy beliefs of an individual organization. We conducted a factor analysis based on the 7 9 ¼ 63 matrix entries for the 36 interviewed organizations and derived the factor scores for the organizations. Based on the computed eigenvalues, we preferred a 7-factor solution.

Moreover, we conducted a cluster analyses of the calculated factor scores of all 36 political organizations, where we preferred a 4-cluster solution. The cluster membership of different organizations is presented in Table 5 in the Appendix, where the identified clusters correspond to similar policy beliefs. To illustrate the estimated policy beliefs, we present a two-dimensional policy belief factor space in Fig. 3. Moreover, we also mapped the factor scores calculated for the original prior parameters of the PIF and the CGE multipliers and for the factor scores derived for the empirically identified political technology (labelled new-prior in Fig. 3). As explained above, the latter parameter was estimated based on the stated policy positions and the targeted policy concern achievements of the interviewed political organizations by applying the Bayesian estimation approach described above.

Figure 3 demonstrates that we can identify a governmental belief cluster (cluster1, colored in green) that includes the most powerful political actors: MoFAS,

Fig. 3 Factor space of policy beliefs. Source: Authors

MOF and the president, as well as the governmental party MCP, the MOIWD and the governmental agencies LU and ADD (Organization labels are explained in Chapter "A Network Based Approach to Evaluate Participatory Policy Processes: An Application to CAADP in Malawi"). In addition to the governmental belief cluster, we identified a specific agricultural belief cluster (cluster 3, colored in dark gray in Fig. 3) and a civil society belief cluster (cluster 4, colored in blue in Fig. 3); most donor organizations and Bunda College (BC), as the principal national research organization, form a separate donor belief cluster (cluster 2, colored in orange in Fig. 3).

However, not all donor organization appear to hold similar policy beliefs (i.e., USAID is grouped into the farm cluster 3, while the Norwegian donor organization NORAD is grouped in the civil society cluster, e.g. cluster 4, colored red in Fig. 3). Moreover, for comparison, we also mapped the factor scores of the original prior parameters (labelled prior-old in Fig. 3) and the estimated parameters using the stated policy positions of all interviewed organizations (labelled prior-new). Because Fig. 3 only presents the factor scores of the first two factors, while the clustering was conducted using all 7 factors, we will describe the differences between the identified policy beliefs clusters and the prior political technology in more detail in the discussion that follows.

Basically, the impact of CAADP polices on policy outcomes can be separated in three different aspects. First, the relative impact of a specific policy program on the induced technical progress in a specific sector is captured by the CES parameters (μsp). The second aspect corresponds to the efficiency of effective budget expenditure in the generation of t.p. This aspect is captured by the CD parameters αs 1 , which represent the budget elasticity in the production of t.p. (i.e., the percent increase in t.p. change that is induced by a 1% increase in effective budget allocated to promote t.p. in a specific sector s). The third aspect corresponds to the impact of increased t.p. in a specific sector on the change in the achievement of different policy goals. This aspect is captured by the CGE elasticities, where the value of ξCGE ZS denotes the change in the annual growth rate of a policy concern z that is induced by a change in the rate of t.p. in the sector s. Hence, the larger a sector in terms of the employment share or the share in GDP, the larger will be c.p. the effect of the CGE elasticities on income growth or poverty reduction, respectively. However, beyond the size of an economic sector, the corresponding CGE elasticities are also determined by interlinkages with other sectors and households. The average estimated parameters are reported for all four belief clusters in Table 5 in the Appendix. Moreover, we also report the original prior parameters and the parameters estimated using the complete policy position data for all of the interviewed stakeholder organizations (labeled new-prior in Table 5). Comparing the estimated parameters of the policy belief clusters to the original prior parameters, we can draw the following conclusions:

generating t.p. in the agricultural sectors, especially the livestock sector, when compared to the prior technology. Moreover, compared to the prior political technology, investment in general infrastructure (γ4) is much more effective in generating t.p. in the trading and public service sectors, according to the policy beliefs of all stakeholders. Analogously, for generating t.p. in the industrial sectors, stakeholder beliefs commonly indicate a comparatively high efficiency of pillar IV policies (e.g., the promotion of extension services (γ7) and R&D activities (γ8) (see Table 5 in the Appendix).

Fig. 4 Preferred CAADP policy positions of Malawi stakeholders in two worlds. Source: Authors

5. The estimated political technology parameters correspond to a compromise between these two worlds, as shown in Fig. 5. Accordingly, assuming this compromise corresponds to the true political technology, the implementation of more research-based policies can hardly be achieved via increased stakeholder participation, because none of the stakeholder organizations hold policy beliefs that correspond with the scientific world of economic modelling. Interestingly, even international donor organizations fail to hold policy beliefs that closely correspond with the wisdom derived from economic modelling. Given the common assumption in the literature on participatory policy analysis (for example, see Greuts and Joldersma 2001, p. 302) that scientific policy analysts and policymakers exist in two separate communities in reality, we consider this finding to be a remarkable result from our analyses that confirms this common assumption.

A determination of which of the three identified worlds best fits reality is of interest (i.e., is the true political technology better represented by the prior parameters derived from scientific models, by the parameters derived from the policy beliefs of stakeholder organizations as political practitioners or by a compromise between these two worlds, as suggested by arguments of the participatory policy analysis?). The latter possibility corresponds to the PIF parameters estimated using the prior parameter distributions and the complete set of stated policy positions of all involved stakeholder organizations. This question is difficult to answer without further empirical data on specific policy strategies and their impact on realized t.p. and implied poverty reduction and income growth. The answer to this question,

Fig. 5 Preferred CAADP policy positions of Malawi stakeholders in three worlds. Source: Authors

however, is crucial for evaluating policy processes, particularly the impact of stakeholder participation structures on political performance. Therefore, we will assess political knowledge and incentive gaps by assuming different political technologies corresponding to (a) the prior parameters derived from scientific models, (b) the parameters that result from the Bayesian estimation using prior information and expert data from the policy network survey, as well as the political technologies corresponding to the estimated policy beliefs of the four identified stakeholder belief clusters (labelled a–d for belief clusters 1–4 in the text that follows).

#### 3.3 Assessing Political Knowledge

In the CGPE framework, we measure political knowledge as the loss of political support that an individual organization i realizes by comparing the political support achieved under its stated policy position (γi) to the maximal political support this organization would achieve if it knew the true political technology. Let bγ opt i denote the optimal policy position of an organization (i.e., the policy position that maximizes its political support given the true political technology). Then we can calculate the individual political knowledge gaps of each stakeholder organization as defined in Eq. (10) above. In Table 1, we present the average political knowledge gaps calculated for different stakeholder categories for the PIF-Scenarios (a–f) assuming different political technologies. As shown in Table 1, assuming that the true political technology corresponds to the prior-PIF derived from economic modelling, the average political knowledge gaps of stakeholder organizations are high ranging from 30 to 71% with an average gap amounting 60%. However, knowledge gaps are significantly smaller for the PIF-scenarios assuming that stakeholder beliefs match true political technology (scenarios a–d in Table 1).

Table 1 Political knowledge gaps of stakeholder organizations, assuming different political technologies

Source: Author's own calculations, based on the CGPE model for Malawi

Accordingly, average political knowledge gaps significantly decrease from 60 to 37% when assuming that the estimated PIF parameter corresponds to the true political knowledge. However, as shown in Table 1, political knowledge gaps vary also significantly across stakeholder categories. On average, the lowest political knowledge gaps are found for national research organizations (Res), followed by national farmer (Farm) and agribusiness organizations (AGIND). Relatively high political knowledge gaps can be found for the central governmental organizations (Gov), particularly MoFAS and MOF, e.g. for the estimated PIF scenario (e) an average knowledge gap of 46.3% results for governmental organizations. Only for the PIF-scenario (a) assuming the true political technology corresponds directly to the policy beliefs of the governmental organizations a relatively low gap of 18.8% is found.

Hence, the political participation of nongovernmental organizations increases the political knowledge used in the political process. In addition, also the political influence of international donor organizations would significantly increase the use of political knowledge. Interestingly, these central results holds true independently of the assumed PIF-scenario with the exception of scenario a.

#### 4 Political Diagnosis

#### 4.1 Assessing Political Performance

The first indicator of the overall political performance of the political system in Malawi corresponds to the difference between the actual budget allocations decided under the CAADP reform in 2010 and the optimal budget allocations derived from social welfare maximization. To this end, we calculated the optimal budget allocations across CAADP programs and non-agricultural policy programs from social welfare maximization assuming different political technologies. Moreover, we calculated the share of budget expenditures for economic policies in total state budget including additionally expenditures for the provision of public good services. As shown in Table 2, observed budget allocations under the status-quo differ significantly from optimal budget allocations derived for all political technology scenarios. In particular, assuming that the prior PIF corresponds to the true political technology implies a rather low efficiency of economic policy programs in generating t.p.. Accordingly, social welfare maximization implies that the state budget is primarily used to provide public services, such as education, health or social security with an optimal budget share of only 2.5% for economic policy programs. Although the optimal budget share of total spending for economic policy programs increases significantly, assuming true political technology corresponds to stakeholder beliefs (scenarios cluster1–4 in Table 2), optimal expenditure shares for economic policy remain low when compared to the status quo. Only following the governmental belief (scenario cluster1) implies a significant higher total state

Table 2 Budget shares under status-quo policy and optimal policy under different political technology scenarios in %

Source: Authors

budget share of 45% for economic policy (see Table 2). Interestingly, CAADP budget shares derived for the different PIF-scenarios (cluster1–cluster4) vary also significantly ranging from only 6% under the cluster4 scenario to 30% (64.7% 0.459) following governmental beliefs (cluster1 in Table 2), while under the status-quo policy Malawi spends 13% of total state budget for CAADP policies (0.432 30%, see Table 2). Further, the allocation of budget expenditures across different CAADP pillars differs significantly among political technology scenarios; in particular, spending on subsidy programs under pillar I is drastically reduced under the optimal budget allocation compared to status-quo allocations.

Specifically, under the status-quo policy, a share of over 27% of total expenditures for economic policy programs is allocated to input subsidies under pillar I (γ1 and γ2). The corresponding optimal budget shares range from nearly 0% for the Prior-PIF scenario to 10.5% for the PIF-scenario corresponding to the beliefs of the civic society cluster (i.e., cluster 4). Vice versa, budget resources allocated to pillar II, particularly resources allocated to improving the general infrastructure (γ4), will be much higher according to optimal budget allocations, where the optimal budget shares of pillar II programs range from 10% for the civic society cluster beliefs to over 37% for the donor belief cluster (cluster2). Interestingly, the optimal budget share for pillar II is remarkably higher based on prior and estimated political technology parameters; when compared to the status-quo with a comparatively low budget share of only 6.4%.

To assess the impact of misallocated public budget resources across policy programs, we compare the t.p. induced in different sectors of the Malawi economy under optimal budget allocations to the t.p. induced based on present allocations, as implemented under CAADP by the Malawi government in 2010. In Table 3, the average t.p. rates calculated for different sectors are presented. As demonstrated in Table 3, compared to the status quo scenario, an optimal allocation of public resources across CAADP programs and non-agricultural policy programs implies a significant increase in induced t.p. for most scenarios. Specifically, based on the policy beliefs of stakeholders, the potential t.p. rates that can be maximally induced given optimal budget allocations across policy programs are high for the

Table 3 Simulated technical progress gaps implied by the CAADP reform in Malawi

Source: Authors

agricultural and agribusiness sectors, as well as the industry sectors, with t.p. rates ranging between 3.5 and 16.4 (see Table 3). In contrast, following stakeholder beliefs, the potential t.p. rates are comparatively lower for the trading sector and the public service sector, with values ranging between 1.3 and 3.2. In contrast, based on prior PIF-parameters, the optimal t.p. rates are much lower, even lower than under the status quo policy. This follows directly form the fact that for the Prior PIF investments in t.p. are rather inefficient. Accordingly, optimal budget allocations to agricultural and non-agricultural policy programs would be rather low (i.e., only 2.5% of the total state budget), while a major share of the state budget will be more efficiently used to provide public services.

#### 4.1.1 Knowledge or Incentive Gaps?

When using the CGPE framework as a relevant theoretical background, the empirically observed CAADP policies differ from optimal policies (i.e., social welfaremaximizing policies) for two reasons. First, relevant political agents have biased incentives [i.e., S(z) differs from the social welfare function SW(z)]. Second, political agents have biased policy beliefs (i.e., agents' simple mental models approximating the political technology differ from the true political technology). Within the CGPE approach, we can not only estimate the individual policy beliefs and the political incentives of involved stakeholder organizations that determine their stated policy positions, but we can also simulate agents' preferred policy positions derived by assuming different policy beliefs or political incentives. Hence, we can simulate final policy choices by assuming that the policy beliefs of all involved stakeholders correspond perfectly to the true political technology. Comparing the social welfare derived for this scenario to the maximal social welfare derived for optimal policy choices allows us to measure the political incentive gap (i.e., the impact of biased political incentives on political performance). Vice versa, comparing social welfare derived under the assumption that all relevant stakeholder organizations maximize social welfare while maintaining their individual policy beliefs to the corresponding maximal social welfare provides a measure of the knowledge gap (i.e., the political performance gap induced by the lack of political knowledge).

Because we are unaware of the true political technology, we calculated the total political performance gaps and the incentive and knowledge gaps that result for the status-quo policy (i.e., the CAADP reform in 2010 in Malawi) for all six political technology scenarios defined above. Figure 6 presents the calculated performance gaps for different political technology scenarios. As demonstrated in Fig. 6, the political performance of the Malawi governmental system crucially depends on the assumption of the true political technology. In the scientific modeling world, political performance is rated low, with a total political performance gap of 72% (i.e., compared to the optimal budget allocation, the status-quo CAADP policies imply a social welfare that is 72% lower than the maximum achievable social welfare). Moreover, low political performance results from low political knowledge, where the corresponding knowledge gap also amounts to 72%. The social welfare losses induced by biased incentives amount to only 3.7% of the maximum social welfare. In contrast, in the world of stakeholder beliefs, political performance would be significantly higher, with total political performance gaps ranging from 7.5 to 19.4%. Moreover, in contrast to the scientific world of economic modeling, in the world of stakeholders' beliefs, political performance gaps result from both incentive and knowledge gaps.

For example, assuming that governmental policy beliefs (cluster 1) match real economic processes in Malawi implies that political performance primarily results

Fig. 6 Total, knowledge and incentive political performance gaps for different political technology scenarios. Source: Authors

due to biased incentives, while political knowledge gaps are negligible and correspond to only 0.9% of the maximum social welfare (see Fig. 6).

However, we trust that the Bayesian estimation using both prior information from economic modelling and practical expertise from stakeholder organizations best fits the true political technology. Hence, based on the estimated PIF, we identify significant political performance gaps that correspond to social welfare losses of nearly 35% compared to an optimal policy decision. The political knowledge gaps are much more important, with corresponding social welfare losses of nearly 34% in comparison to incentive gaps that correspond to a social welfare loss of only 7.4% (see Fig. 6).

Moreover, an analysis of the differences in realized growth rates with respect to the achievement of different policy concerns that are induced by political performance gaps is of interest. In Fig. 7, the differences between the growth rates of different policy concerns achieved under the optimal policy and the status-quo policy are presented, assuming that the estimated PIF parameters correspond to the true political technology.

As shown in Fig. 7, performance gaps imply significant differences in realized achievement levels for different policy concerns. In particular, Fig. 7 demonstrates that the status-quo policy implies that the achieved growth rates in agribusiness welfare (Z4), reduction in poverty (Z2) and farm income (Z1) are too high when compared to the optimal achievement levels induced by the optimal policy. In contrast, the increase of total budget expenditures (Z3) for public services and the achieved growth rate in urban consumer welfare are too low when compared to the optimal policy. Please note that these CGPE simulations nicely demonstrate the fact

Fig. 7 Total, knowledge and incentive gaps in the achievement of policy goals: Differences in the achievement of policy goals as percentages of the optimal policy. Source: Authors

that a rapid reduction of poverty does not necessarily correspond to a maximization of social welfare. This follows intuitively from the fact that a rapid poverty reduction might be realized at the expense of a significantly lower income growth or at the expense of a low growth of the public service sector.

# 4.2 From Diagnosis to Political Therapy: Simulating Political Performance Gaps with Increased Stakeholder Participation

To assess the impact of increased stakeholder participation on political performance, we first analyze the changes in final policy decisions and induced political performance that occur when we exogenously assume that specific stakeholder groups have higher political power. In a second step, we analyze the impact of different formal and informal institutional reforms on the political power of different stakeholder groups. Overall, the first simulation experiment reveals changes in participation structures that could improve political performance and the second simulation identifies potential institutional reform strategies for realizing these changes. At the methodological level, the political decisionmaking module of the CGPE approach integrates a modified legislative bargaining model of a Baron/ Ferejohn type with a lobbying model that corresponds to an extended Grossman/ Helpman model. Accordingly, as described in detail in Chapter "Modeling and Evaluation of Political Processes: A New Quantitative Approach," we simulate the impact of different constitutional rules via corresponding changes in the legislative decisionmaking power of relevant political agents (i.e., governmental departments and legislative parties in the parliament). We simulate changes in informal lobbying and communication structures via corresponding changes in political network multipliers (for technical details, see Chapter "A Network Based Approach to Evaluate Participatory Policy Processes: An Application to CAADP in Malawi").

#### 4.2.1 Simulation Scenarios

	- A. Increased political power of international donor organizations (Don).
	- B. Increased political power of farmer organizations (Farm).
	- C. Increased political power of civic society organizations (Civic).
	- D. Increased political power of national research organizations (Res).
	- E. Increased political power of agribusiness organizations (Agind).

The political power of stakeholder groups in the base run and in the participation scenarios A–E is reported in Table 4.

Table 4 Simulated political power of stakeholder groups in the CAADP policy domain of Malawi

Source: Authors

To analyze the influence of different formal constitutional rules and informal policy network structures on policy decisions and outcomes, we run the CGPE approach under the scenarios described below.

#### 4.2.2 Benchmark Scenarios

#### 4.2.3 Simulating Constitutional Reforms

4. PF: For the PF-scenario we assume that the finance ministry has agenda-setting power vis-a-vis the president, the MoA and all other involved ministries.

#### 4.2.4 Simulating the Informal Political Influence of Nongovernmental Organizations

For all scenarios except the status-quo and the optimal benchmark scenarios, we assume that political agents engage in reinforcement and communication learning (i.e., based on marginal political support, stakeholders update their policy beliefs as described in Chapter "Modeling and Evaluation of Political Processes: A New Quantitative Approach"). In particular, as a result of the legislative bargaining process, the direction in which the status quo policy is shifted is determined as the mean voter position, where the constitutional decision-making power, which is measured using the generalized Banzhaf index, corresponds to the weight of individual agents. Before the formal political decision is made, agents engage in communication learning, where agents update their preferred direction according to the communicated positions of other agents with whom they communicate. At the stationary point of political communication, each agent's preferred policy direction results as the weighted average of the agents' initial policy positions, where the weight of agent j's initial position in agent i's final position is determined by the communication network (see the theoretical section above for further details). Accordingly, simulating the impact of formal institutions, we assume different formal decision-making power of governmental organizations according to the calculated Banzhaf indices. To simulate the impact of political influence of nongovernmental organizations, we assume different interest in political support and different own control values for the weak, strong and autarkic state scenarios, as described above.

#### 4.3 Whither Participation?

As demonstrated in Fig. 8 below, it is nearly impossible to identify a vision of participation that could significantly increase political performance. In particular, irrespective of the assumed political technology, neither increased participation of any stakeholder group nor increased participation of international donor organizations implies a significant increase of total political performance when compared to the base run scenario. This result appears surprising at first glance; however, given the fact that the preferred policy positions of stakeholder organizations are primarily determined by policy beliefs, while heterogeneous interests among stakeholders have a comparatively low impact on their preferred policy positions, it follows that any change in participation structures has little impact on policy performance.

The latter conclusion follows directly, because changed participation structures only shift the relative political power of stakeholders and the relative weight of individual stakeholder positions in determining the final policy decision. Thus, because stakeholder positions are empirically similar when compared to the scientific world of economic modelers that is encapsulated in the prior parameters (see Figs. 4 and 5 above), it follows that basically any

Fig. 8 Total political performance gaps in % under different participation scenarios. Source: Authors

participation structure that corresponds to a linear combination of preferred stakeholder positions merely maps into the world of stakeholder beliefs separated from the scientific world of economic modelers. Hence, assuming that the true political technology corresponds to stakeholder beliefs implies a relatively high political performance for any linear combination of policy positions in the world of stakeholder beliefs. Vice versa, assuming that the true political technology corresponds to the prior parameters derived from economic modeling implies that any linear combinations of policy positions in the subspace of the world stakeholder beliefs is rather distant from the subspace defined by the scientific world of economic modeling.

Hence, political performance is low for the base run, but it can barely be improved by any vision of increased stakeholder participation. Basically, this situation drives the results presented in Fig. 9. Hence, if we assume that the estimated PIF parameters correspond to the best representation of the true political technology, one fundamental implication of our simulation analyses is that the interaction between political practice and theoretical modeling implies a particularly successful strategy for improving political performance in Malawi and likely in many other African countries.

In contrast, increased participation of any stakeholder group, such as national farmer associations, civil society organizations, research institutions or international donor organizations, exerts little if any impact on political performance. Basically, this fundamental conclusion does not change if the political performance gaps of constitutional reforms are analyzed. As shown in Fig. 10, the total political performance changes only slightly when assuming different constitutional reforms.

Fig. 9 Total, knowledge and incentive gaps in Malawi (in % of maximum social welfare). Source: Authors

Fig. 10 Total, knowledge and incentive gaps in Malawi (in % of the maximum social welfare). Source: Authors

#### 5 Conclusion and Outlook on Future Research

The common observation that governments persistently fail to implement effective policy and investment strategies that translate into the achievement of development goals in most African countries raises questions about government performance. In particular, low government performance occurs in two forms. First, low government performance occurs as a political incentive gap (i.e., elected politicians lack sufficient incentives to serve public interests and instead serve particular interests or pursue their own interests). Second, low government performance occurs as a political knowledge gap (i.e., the government lacks adequate knowledge and the capacity to identify and implement efficient policies).

In this paper, we develop and apply the CGPE model as a new quantitative approach to analyzing the performance of policy processes with respect to the production of efficient policy choices. In contrast to existing political economy models focusing on biased government incentives the CGPE approach incorporates explicitly the lack of adequate political knowledge as another important source of low government performance. Within the CGPE approach participation of stakeholder organizations is modeled in two ways. First, as classical lobbying influence and second as informational influence within a model of political belief formation. According to our model, the main determinants of the accumulation of political knowledge and the speed of policy learning correspond to policy network structures that reflect the communication and interaction patterns between governmental and nongovernmental organizations.

An empirical application of the CGPE model analyzing the policy processes that underlie the ongoing CAADP reforms in Malawi delivered the following results:

Finally, the following two limitations of our presented CGPE approach must be considered:

formulated by focusing on specific subsectors. For example, investing in the infrastructure of specific regions or investing in the railroad system versus the road system might be more or less effective for different subsectors. These differences occur because subsectors might be regionally concentrated or dependent on specific infrastructure systems. Thus, including a third stage in our PIF function that allows for sector-specific subprograms within a specific policy program would imply that preferred policy positions across stakeholder organizations become more heterogeneous; hence, c.p., the induced incentive gaps would also be higher. Again, we leave a more detailed three-stage modeling of PIF functions for future research.

#### Appendix

Table 5 Overview of CAADP policy programs

(continued)

(continued)

#### Table 5 (continued)

Source: Authors

#### References

Christian Henning is professor and chair of agricultural economics, University of Kiel (Germany). He studied economics, agricultural economics, sociology, political science and mathematics. He earned a PhD in economics, in agricultural economics and in political science. His main areas of specialization are applied political economy and applied economic modelling of agricultural policies. To date he has published five books and various articles in refereed journals including PLoS ONE, American Journal of Agricultural Economics, European Review of Agricultural Economics, Journal of Public Policy, Journal of Mathematical Sociology and Journal of Theoretical Politics.

Johannes Hedtrich is a computer scientist and a phd student in agricultural economics.He studied computer science and agricultural economics at the University of Kiel (Germany). His main areas of specialization are computational economics, Bayesian econometrics and social network analysis.

Ligane Massamba Se`ne is a Research Economist at the African Union. He holds a PhD in Agricultural Economics from the University of Kiel and a MSc Degree—Engineer in Statistics and Economics from ENSAE. He also studied Mathematics, Physics and Computer Science. His research interests include macroeconomic, macrosectoral and economic modeling of development and agricultural policies—Poverty, Inclusive growth and optimizing public expenditures—Taxation, social policies and household welfare—Modeling and Econometrics.

Eva Krampe is an agricultural sector expert at the European Investment Bank. She studied agricultural economics and earned a PhD in agricultural economics at the University of Kiel. Her main areas of specialization are Africa's Agricultural Policy, Applied Political Economy and Economic Analysis of Agricultural Investment Projects.

Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

# Part III Concluding Remarks

# Strategic Analysis and Knowledge Support Systems (SAKSS): Translating Evidence into Action

Michael E. Johnson

# 1 Introduction

Many sub-Saharan African countries today have committed to the continent-wide goals of the Comprehensive African Agriculture Development Programme (CAADP) of the Africa Union and New Partnership for Africa's Development (NEPAD). The goals draw attention towards a shared commitment of allocating at least 10% of their national budgets to agriculture in order to achieve a 6% annual sector growth rate and meet the Millennium Development Goal (MDG) of halving poverty by 2015. As a result, policymakers have been called on to allocate more resources and design strategies to accelerate agricultural growth in order to meet these goals. The challenge now is ensuring that selected policies and investment strategies are effective in producing their intended goals of sustaining broad-based growth and poverty reduction.

The desire for more effective policy and investment strategies that translate into the achievement of shared CAADP goals has revived questions about capacities for policy analysis and participatory processes of designing and implementing development strategies in Africa. Many past studies in the region have documented the failure of past central planning or top down approaches of implementation (Brinkerhoff 1996; Crosby 1996; Killick 1976; Montjoy and O'Toole 1979; Wildavsky 1973). Such inadequacies led to calls for more decentralized, evidence-based, and participatory processes, to be complemented by strong monitoring and evaluation (M&E) systems that allow for adaptation to changing conditions over time given the nonlinear and dynamic nature of designing and implementing development strategies.

CARE USA, Washington, DC, USA e-mail<EMAIL_ADDRESS>
© The Author(s) 2018 C. Henning et al. (eds.), Development Policies and Policy Processes in Africa, Advances in African Economic, Social and Political Development, DOI 10.1007/978-3-319-60714-6\_12

M.E. Johnson (\*)

Recognizing the complexities of formulating and implementing effective policies and investment strategies requires a lot of evidence to accurately assess the choices available to a government and the tradeoffs inherent in any choice they make.1 To supply this evidence, the country must have a solid foundation of analytical capacity throughout its planning agencies and academic institutions. In addition, governments need policymakers who have the motivation and ability to demand and use the information (Omamo 2004). The intersection of these two sides and the knowledge generated describes the state of a national knowledge system, defined here as the existing stock of knowledge and established links between people and organizations on both the supply and demand side in influencing the type of knowledge products generated and utilized in policy dialogue and decisionmaking.

How effective a national knowledge system is in generating and promoting the use of evidence depends on many factors: the perceived credibility and relevance of the evidence generated; the type of relationships and linkages that develop among all individual actors and organizations involved; capacities to comprehend and utilize the evidence, the local policy process and political climate; and local beliefs and norms (Young 2005; Cash et al. 2003). Often, it is the linkage among individuals and organizations that help to bridge the supply and demand for evidence which is especially weak. It requires establishing effective mechanisms by which both sides can be more closely tied as part of ongoing dialogue and decisionmaking processes. These mechanisms effectively link suppliers and users of knowledge through the creation and use of knowledge products.

For many African countries, the state of their national knowledge systems remains very weak and poses a serious challenge for strengthening the effectiveness of future strategy design and implementation efforts. Data collection and analysis continues to suffer from a shortage of attention and resources. Knowledge sharing is often minimal, with planning ministries that operate in isolation and uncoordinated ministries, research institutes, and statistical bureaus. Government agencies, NGOs, and development partners carry out parallel and overlapping processes of information gathering. Often development partners have more input into the strategy process than legislative bodies or the national civil society does. The M&E frameworks of many strategies rarely deal with issues of causality and attribution between investments, policy changes, and outcomes.

The large capacity gaps also exacerbated an inherently weak link between the supply and demand of evidence. For example, local universities rarely undertake research directly relevant to local decisionmaking needs while national institutions and agencies seldom have sufficient capacities and experience to provide relevant information needed to guide strategy formulation and implementation.

1 By evidence we mean data statistics and analysis of past trends, economic analysis of future policy alternatives, impact assessment of past investments, research findings from a number of disciplines (public policy, socioeconomic and political sciences, and the biophysical sciences), and lessons from practice and experience.

The Strategic Analysis and Knowledge Support System (SAKSS) concept was developed in direct response to these serious capacity gaps in many African countries. Its principle goals are to: (a) bring quality and strategic analysis to bear on identifying key investments, institutional mechanisms, and policy options, as well as the implementation of selected options for agricultural policy and investment strategies (this includes harmonizing and generating standardized information for development planning and M&E, and to be available as global public goods); (b) build and strengthen national and regional capacities for policy analysis, M&E, while helping to bridge the research and policy divide; and, (c) broker the dialogue and links between institutions and individuals who supply and use data and information related to agricultural strategies by establishing network for information exchange and knowledge management.

The SAKSS was developed around two key concepts—'strategic analysis' and 'knowledge support systems' which have since defined its overall purpose and utility for supporting CAADP implementation. The 'strategic analysis' concept describes generating information that is not only scientifically credible but has important relevance to the range of questions and issues being faced by policymakers in formulating and implementing their country's agricultural development strategies. The 'knowledge support system' concept defines a network of individuals and institutions that are linked in ways intended to help bring strategic analysis and research evidence to bear during the design and implementation of the agricultural development strategy. We now review these in more detail.

#### 2 The Strategic Analysis Concept and Approach

Strategic analysis describes a logical series of analyses which help identify policy and investment options for achieving growth and poverty outcomes, beginning with a broader, economywide perspective and ending with a more targeted sector and community level perspective. This type of integrated analysis is intended to guide a credible action plan of development priorities in agriculture which contribute the most to the achievement of desirable targets for growth and poverty reduction. Such prioritization implies finding answers to a range of strategic questions such as: What is the role of agriculture in promoting overall economic growth and poverty reduction in the different stages of development given a country's natural resource endowments? How should public resources be mobilized and allocated among different sectors, sub-sectors, and regions? What have been the lessons and effect of agricultural policies and investments on outcomes and impact? Answers to these questions can help arm policymakers with useful evidence on the kinds of tradeoffs and outcomes associated with their policy and investment choices.

The analysis is considered 'strategic' so long as it contributes to the narrowing down of investment options that will help lead to the achievement of these high-end development targets. It means weighing in the costs and benefits for undertaking one strategy over another. For example, should government focus on promoting a rapid expansion in food staples production? It will definitely benefit consumers, but producers could be devastated if prices drop too fast following a bumper harvest. Or, governments may wish to introduce a policy that is designed to target the poor and yet undercuts private sector participation and long term sustainability, such as in the case of output procurement and the provision of modern inputs.

The sequence and types of analysis are not fixed, however. Different local contexts may require a different set of analyses. Because SAKSS has been primarily developed for African countries with a large agricultural sector, the analysis is focused on identifying options for agriculture as a source of economic growth and poverty reduction. Various economic tools and methodologies exist, but which tools and approaches are used will not only depend on the question being asked but on many other important considerations as well, such as: the availability of data and expertise, time to undertake the analysis, cost, access to analytical tools and economic models, and underlying assumptions and limitations. Johnson and Flaherty (2011) provide a review of some of these tools, as well as a guidance on which tool is most appropriate under what conditions with respect to a range of factors such as data availability, cost, and time to complete the analysis.

To illustrate, we offer a number of very broad but logically sequenced series of 'strategic analysis' type questions to consider when assessing the policy and investment alternatives for achieving goals of agricultural growth and poverty reduction. These include: How can agriculture contribute the most to overall development objectives? How should resources be mobilized and allocated more efficiently? How can individual policies and interventions be better targeted? How can lessons be monitored and evaluated during and after implementation? We review each of these below.

# 2.1 How Can Agriculture Contribute the Most to Overall Development Objectives?

From the outset, it is useful to first establish the country's current situation and whether its trajectory will lead to the achievement of the CAADP goals. It should do so within the context of the country's overall economy in order to highlight a broad set of strategic options and tradeoffs—e.g. whether simply promoting faster growth is more important than considering poverty and food security, or even environmental degradation. This context is needed because policies at the macro level, such as trade and market liberalization, can have a profound impact on growth, and even more so on agriculture, the rural economy, and poverty (Dorward et al. 2004). At the same time, policies that directly affect rural areas and agriculture can have an impact on the overall economy and in turn have feedback effects on the rural sector. By examining many of these policy options within the context of the broader economy, key relationships and welfare implications can be assessed in ways that lessen any potential adverse impacts on the poor.

The economywide perspective permits other higher-level strategic questions to be posed for shaping an agricultural strategy within the context of overall national development goals, and in so doing, provides the greatest strategic leverage to priority setting (Byerlee 2000). The potential role of agriculture, including individual subsectors in agriculture, can then be explored with respect to how they contribute to economywide growth and national development priorities, such as reducing poverty. Within this normative mode of analysis, questions regarding the long-term distributional consequences of alternative investment and policy choices for meeting these targets can also be explored. Specific to rural sector strategies, sectorwide investment options should be examined more closely, especially with regard to how they affect the incentives for rural agricultural production and commercialization.

Various economic analysis tools can be used. For example, the Computable General Equilibrium (CGE) model is particularly well suited for analyzing a country's progress towards achieving its national development goals through agriculture. CGE models help analyze the effects of policy shifts and alternative sector growth scenarios on overall economic growth and poverty reduction. They have the advantage of capturing both direct and indirect effects of policy changes on poverty and income distribution given a country's overall economic structure. The effects are channeled through changes in employment, wages and relative prices while considering forward and backward linkages in the economy. From this, policymakers can weigh the costs and benefits associated with focusing attention on stimulating growth in different sectors and subsectors.

Existing examples that apply this level of analysis involve the work that has been undertaken by IFPRI researchers in a number of countries in Africa (e.g. Ethiopia, Ghana, Mozambique, Rwanda, Zambia and Malawi). From these studies, for example, it became quickly evident that most countries could not meet the MDG poverty target of halving poverty by 2015, with the exception of Ghana, Mozambique and Uganda. Model results further showed that the additional growth would need to be driven mostly by food staple sectors as these have a larger impact on poverty reduction than similar growth in export-oriented crops (see example for Zambia in Fig. 1). This impact occurs because yield improvements in food crops not only benefit households directly, by increasing incomes from agricultural production, but also by allowing farmers to diversify into higher-value crops. Food crops also typically have stronger growth-linkages to non-agriculture, which stimulates broader economywide growth and poverty reduction.

# 2.2 How Should Resources Be Mobilized and Allocated More Efficiently?

An essential component of a development strategy is its plan for prioritizing investments and mobilizing resources. A strategy grounded in country-specific

Fig. 1 Example of 'strategic analysis' results of investment options for Zambia. Source: CAADP Modeling results from Thurlow et al. (2008b)

context must be based on a thorough assessment of the public investment situation and potential to contribute to the development goals. Public investments can be thematic (e.g. roads, marketing institutions), sectorwide (e.g. research and extension, irrigation), and subsector specific (e.g. commodity-based research).

All these investments affect rural poverty through many channels. For example, public investment in agricultural research, rural education and health, and infrastructure increases farmers' income directly by increasing agricultural productivity and lowering transaction costs of both inputs and outputs, which in turn reduces rural poverty. Indirect impacts come from higher agricultural wages and improved nonfarm employment opportunities induced by growth in agricultural productivity and increases in market opportunities. Growth in agricultural output from rural investment often yields lower food prices, again helping the poor indirectly because they are often net buyers of food crops. Redistribution of land caused by higher agricultural growth also has important impacts on rural poverty. In addition to their productivity impact, public investments in rural education, health, and infrastructure directly promote rural wages, nonfarm employment, and migration, thereby reducing rural poverty. For example, improved infrastructure access will help farmers set up small nonfarm businesses in rural areas such as food processing and marketing enterprises, electronic repairs shops, transportation and trade, and restaurant services. A key underlying assumption is that public and private investments are complements (Anderson et al. 2006), so that an increase in public goods and accumulation of capital stock raises the productivity of all factors in agricultural production, which in turn leads to higher farm wages and incomes and poverty reduction.

Investments in the rural sector not only contribute to growth, more employment opportunities, and higher wages in rural areas, but also help the development of the national economy by providing labor, human and physical capital, cheaper food, and markets for urban industrial and service development. This type of growth in the national economy can then help reduce poverty in both rural and urban sectors. Understanding these different effects provides useful policy insights to improve the effectiveness of government poverty reduction strategies. In particular, it provides information on how public investment can be used to strengthen links between poverty reduction channels to increase efficiency in targeting public resources on poverty reduction. More efficient targeting has become increasingly important in an era of macroeconomic reforms in which governments are under pressure to reduce budgets. For examples of tools and approaches to measure the impact of investments, see Appendix A.5 and Benin et al. (2008b).

The question of how resources should be mobilized and allocated across the different economic sectors and geographic regions is essentially answering a range of high-end questions that inform the design and evaluation of a development strategy, such as: (a) What have been the trends of government expenditures by sector, and what have been the reasons for their changes? (b) How has public investment been financed, and how has the burden of financing investment policy been distributed in society? (c) What have been the economic rates of return to various types of government expenditures, including their impact on growth and poverty reduction? (d) What level of effort in public spending is required to achieve targeted goals for agriculture and overall economic growth?

Analyzing these series of questions not only helps identify the kinds of public sector investments which offer the highest economic rate of return (for an example, see Fan et al. 2004 for Uganda), but they also help assess the extent to which past investments have impacted on overall development goals (a topic covered next). It requires sufficient subnational data on the level and distribution of public sector expenditures and investments over time. A public expenditure review is especially useful as a first step in compiling the required data. It will also help determine the extent to which actual resource allocations are consistent with a country's strategy and goals. Combining this information with other data, such as household survey data on consumption, production, and welfare measures, will allow for more sophisticated analyses. For example, using econometric tools, more detailed analysis can be carried out, drawing on the cross-sectional variation of the data, to measure and attribute differences in outcome variables such as growth and poverty to the accumulated stock of past investments and other socioeconomic variables. Where time series data are also available on the same cross-sectional data, the dynamics and lagged effects of public investments can also be analyzed. When combined with independent estimates of the unit costs of different investments, cost/benefit ratios can be calculated.

Results from the econometric analysis can be further translated into unitless elasticity estimates. An elasticity measures the sensitivity or marginal effect of a 1% change in one variable on the percent change in another variable. For example, an expenditure-to-growth elasticity would measure the effect of a 1% change in the stock of investments (or expenditures) on the change in growth outcomes, whether at the sector or economywide level. This becomes useful for estimating future growth requirements in public investments for generating desired sector and economic growth targets. Using estimates of both an expenditure-to-growth elasticity and a growth-to-poverty elasticity, one can then estimate the level of resources required to achieve desired growth and poverty goals (see Fan et al. 2008). A number of country case studies that have estimated resource requirements for meeting the CAADP goal of 6% include Benin et al. (2008a) and Thurlow et al. (2008a, b).

# 2.3 How Can Individual Policies and Interventions Be Better Targeted?

Many of the challenges and opportunities that national development strategies must negotiate are geographic in nature and affect different communities on the ground. For example, economic opportunities can vary widely by location depending on other important factors such as the natural resource base (e.g. agriculture potential), population density, and access to markets and rural services (e.g., education, health, agriculture extension). Development options would be quite different for more remote and food insecure areas versus those areas located in close proximity to large market centers. Thus any interventions should be specifically targeted towards the unique characteristics of the area and depending on the severity of the problem—such as quantifying the extent and distribution of poverty and malnutrition across geographic areas and population groups is an important first step (Babu and Pinstrup-Andersen 1994).

With the increasing availability of spatially disaggregated data and tools to understand those data, it has become increasingly possible to map indicators of biophysical and socioeconomic indicators showing local comparative advantage for different agricultural and rural development options (see example of Uganda in Fig. 2 below). Agroclimatic factors, access to markets, and population density are some of the more important dimensions for assessing agricultural development potential (Pender et al. 2001). By viewing how these conditions correlate and overlap each other spatially with local welfare measures, assumptions can be made about how different development investments will impact the poor and how changing agricultural land uses may have environmental costs. Taken together, these conditions provide an enhanced picture of the costs and benefits of different investments, allowing better targeting towards the goals of sustainable growth, poverty reduction, and environmental sustainability (see Wood and Chamberlin 2003; Wood et al. 1999).

Taking on a spatial perspective helps to seek answers associated with targeting interventions. For example, a range of questions it may help answer include: (a) What are the distribution and extent of income, poverty and malnutrition across

Fig. 2 Mapping out development domains in Uganda. Source: Johnson and Flaherty (2011)

different locations in the country? (b) What kinds of opportunities and challenges affect rural economic livelihoods in different parts of the country? (c) For agriculture, what are the key development domains based on agriculture potential, market access and population density? (d) Which development domains offer the greatest potential for high investment impact among the key subsectors and economic activities identified as key sources of growth in the economywide analysis above? (e) What kinds of interventions (e.g. infrastructure, R&D and extension, institutional) are needed to spur productivity and income growth among select domains? (f) Among the poorest of the domains, in terms of limited resource assets and livelihood options, what are the alternatives for poverty reduction and food security?

Exploring answers to these questions can be answered with tools such as Geographic information Systems (GIS) and remote sensing. Having access to spatially oriented data, including agroclimatic conditions, land-use, production, urban and markets centers, infrastructure, household consumption, and welfare, is particularly critical but often lacking in many African countries. However, with increasing sophistication of computer technologies and satellite imagery, filling in some of the gaps is increasingly possible. For example, You et al. (2007) recently used a cross-entropy approach to make plausible allocations of crop production by small square grids (or pixels) based on available statistics of larger subnational units and satellite imagery.

A number of examples illustrate the usefulness and application of spatial analysis for targeting investments in agriculture. At the country level, useful examples include the work undertaken by Pender et al. (2001) and Bolwig et al. (2002) for mapping out development domains in Uganda. The more recent work by Chamberlin et al. (2006) builds on this concept. At the regional level, the studies by Omamo et al. (2006) and Johnson et al. (2008) are especially noteworthy. Spatial analysis tools were complemented by various economic analyses to assess future agricultural growth options and research priorities in the Eastern and Western regions of sub-Saharan Africa.

# 2.4 How Can Lessons Be Monitored and Evaluated During and After Implementation?

A critical part of any agricultural strategy is to be able to determine at some point whether the strategy is on track to achieving its goals, or whether at the end of its life, people's lives have been positively or negatively impacted on. It helps to justify resource investments and ensure accountability. It also provides the option to adjust the strategy as new evidence becomes available on what has (or has not) been working. From the perspective of the goals of SAKSS, therefore, helping to provide credible and relevant analysis and information related to M&E is a fundamental prerequisite to promoting evidence-based policymaking. And although the importance of M&E systems is well-documented (see for example, Mackay 2007 and Dalal-Clayton and Bass 2002), it is often the most difficult part to set up and maintain. This is because agricultural strategies operate within a broader and complex environment that is inherently dynamic with respect to constantly changing domestic and global economic conditions, social and political trends, climatic shocks, and participatory and political processes associated with designing and implementing policies and investment strategies.

Ultimately, therefore, an M&E system seeks to answer questions that determine whether development objectives are being fulfilled and if there is impact in order to help revise and improve futures strategies, such as: (a) Is the allocation and level of inputs (e.g. spending, investments, policy interventions) of the agricultural strategy (or project) on target? (b) How can the impact of these investments be traced to improvements in outputs (e.g. productivity, viability of production systems, food processors, agro-industries, markets, and trade)? (c) Have these improvements affected outcomes (e.g. incomes and the poverty status of target populations)? (d) What factors have shaped (positively and negatively) the level of impact achieved to date? What needs to be altered? (e) What was the distribution of these intermediate impacts, e.g. on smallholders, on equity, on gender, on other spillover impacts? (f) Are there key ingredients of success or failure based on past experience and lessons learned? What role for public versus private sector?

A desirable M&E system is one that can encompass not only the monitoring of progress among key inputs, outputs and outcome indicators, but evaluates the magnitude and distribution of impact. For the former, simple descriptive narratives of trends among the indicators help to answer the adequacy question: "have expectations in terms of investment flows and achievement of the growth and poverty reduction targets been met so far?" However, it does not answer the key hypotheses on: "how effectively have different types of policies and investments impacted on the goals so far?" and "what factors have shaped the level of impact that has been achieved?" These last two questions are more concerned with the evaluation or impact assessment part of M&E. Addressing all three provides key information that can help guide decisions on what to adjust, as well as the scale and mix of investment priorities needed, in order to keep a country's agricultural strategy on track. In this way, it is a critical component of the strategic analysis agenda of a SAKSS, by coming full circle to helping inform and strengthen future design and implementation of agricultural strategies.

In order to find answers to questions a good M&E system seeks to provide, there are three challenges facing many African countries: the availability and quality of data from national statistical systems from which to gather baseline information and set future targets; having a clear M&E framework in place that describes the interrelationships (or causality) between inputs, outputs and outcomes; and integral to the development of the M&E framework itself, having in place sufficiently robust methods and tools for evaluating impact over time.

#### 2.4.1 Ensuring Availability and Reliability of Underlying Data Systems

The poor availability of reliable data in most African countries makes it particularly challenging for setting up a national M&E system, just as it is for the other types of strategic analyses discussed in this chapter. Especially problematic is the frequency of data collection and quality on agricultural production and marketing. Most African countries have only undertaken a handful of these surveys since the 1970s. In Ghana, for example, the last comprehensive agricultural census was carried out in the early 1970s followed by a smaller sampled survey in 1986. Other socioeconomic surveys, such as the living standards measurement surveys (LSMS), population and housing census, and demographic and health surveys (DHS) have occurred more frequently, but these vary across countries.

Even if there is data available, its quality is often poor due to overall weak technical and managerial capacities of national statistical systems. Explanations for this have included a generally low regard for statistical information by policymakers; poor links between statistical systems and policy processes; inadequate government spending and technical assistance for statistics over long periods of time; and outdated statistical systems and legislation (Kiregyera 2008; Wingfield-Digby 2007). Another weak data area is having timely and sufficient information on the flows of public sector expenditures and investments in agriculture—especially from public expenditure tracking surveys or PETS (Dorotinsky and Floyd 2004). Such information can serve as key input for setting baselines and targets for investment spending and for undertaking impact studies later on, in addition to helping improve accountability and public sector management more generally. Typically, much of this information is found within finance ministries, with details of agricultural expenditures available from the sector ministry.

There are increasing efforts to improve data systems for agriculture in general for many African countries (see Kiregyera 2008). One recent effort being led by the World Bank is the Living Standards Measurement Study and Integrated Surveys on Agriculture (LSMS-ISA) to improve household level panel data on agriculture in sub-Saharan Africa. According to the World Bank's website on this initiative, six African countries will initially benefit from this. In time, and once scaled out to other countries, this should help improve and complement existing data sets for purposes of monitoring performance and impact of ARD strategies in Africa. Another is the introduction of the AU/NEPAD budgetary tracking system for agricultural expenditures to monitor the national compliance of the Maputo declaration. This has begun to make information on total agriculture spending available (AU/NEPAD 2005). However, because it doesn't cover disaggregate flows by subsector and type of investment it is less useful for impact evaluation (see Benin et al. 2010).

#### 2.4.2 Developing an M&E Framework

Simply having sufficient access to data does not guarantee a good M&E system. It also requires having a credible M&E framework in place, as well as the tools to monitor progress and assess impact. As a start, either a logical or theoretical framework can be useful in this regard. Both seek to layout a simple structure which describes the causal relationship between inputs (e.g. investment flows), outputs (e.g. productivity), and outcomes (e.g. growth and poverty), and from which critical corresponding indicators can be identified for the purpose of monitoring and evaluating impact.

The logical framework (or logframe) approach helps describe a simple flow chart of how inputs will achieve intermediate and final outcomes. Performance indicators are selected as part of a results-oriented log frame matrix, measuring performance in terms of input delivery, implementing activities, producing outputs, and achieving targeted outcomes (World Bank 2004; Kusek and Rist 2004; Crawford and Bryce 2003). The measurement of outputs can also include specifying the extent of coverage among target groups, including non-target groups if spillovers are expected and measurable. Performance indicators are then selected at each stage along a simple causality chain, with assumptions about associated risks and other confounding factors which can influence performance and outcomes. Adaptations to improve the basic logframe have occurred over time by seeking to introduce more participatory approaches and/or theory and analytical rigor along the entire length of the causal chain (White 2006; World Bank 2004). The most popular have included the impact chain analysis, outcome mapping, and impact pathway approaches.

Based on the logframe, the impact chain analysis approach essentially maps out the links in the causal chain based on underlying economic theory and evidence. It allows for a more in-depth theoretical understanding of the cause-and-effect relationships or impact pathways between variables and the confounding effects of other potentially influencing factors. And so long as sufficient data exists, empirical evaluations of impact using econometric techniques can also be modeled as discussed further below. Its main disadvantage is that it can easily become unnecessarily complex, data intensive, and costly.

An alternative is the outcome mapping approach, which is more participatory and qualitative in nature, focusing on changes in development processes and outcomes (Earl et al. 2001; Smutylo 2005). Essentially, it identifies expected impacts and outcome indicators based on a map of interrelated factors from intervention to outcomes within a group session. Given the complexity of considering many other noneconomic factors which can influence outcomes, the approach adopts visualization techniques to instill participants to think through the different factors, their direct and indirect effects, as well as causal linkages. This is especially useful for tracing out qualitative changes in development processes which are not easily measurable (e.g. human behavior). A disadvantage of this approach is that it is limited by the complexity of causal relationships that arise as more factors and stakeholders are identified in the process.

More recently, the impact pathway approach was introduced to try and deal with some of the shortcomings of both outcome mapping and impact chain analysis approaches (see Spinger-Heinze et al. 2003 and Douthwaite et al. 2003). The approach recognizes the presence of a number of impact chains and sequences in explaining the overall change process. The analysis of multiple impact pathways can be quite useful for impact evaluation as they avoid the pitfall of assuming a simple linear relationship between an investment and outcome. The analysis of development domains discussed earlier in this chapter, for example, constitutes the existence of multiple but distinct impact pathways. This is because how investments ultimately lead to outcomes depends on the type of combinations among multiple factors—such as agricultural potential, market access and population pressure (Spinger-Heinze et al. 2003; Pender et al. 2001). While it is an improvement, it shares the same drawbacks of outcome mapping and impact chain analysis as it can easily become unnecessarily complex. Additionally, it can quickly become very data intensive and costly when too many other factors and distinct pathways or outcome mappings are introduced.

No matter which approach is adopted, the real challenge from a practical perspective is maintaining sound theory and rigor while at the same time limiting the degree of complexity in drawing out the causality chain and in selecting a minimum set of indicators for which reliable data exists. Depending on the strategy goals and underlying programs, a balance needs to be struck between the need to attribute impact to program interventions and having in place a cost efficient M&E system, one that has the least likelihood for measurement errors and is simple enough to interpret the information that comes out of it.

Despite various approaches to M&E systems, their basic structure and utility are principally intended to serve as a performance management tool: to help adapt policies and investments during the course of strategy implementation in order to stay on track towards achieving targeted output and outcome goals. An additional advantage of such systems is that they can be developed in a participatory fashion involving broader stakeholder groups. The main drawbacks are that they are less reliable for undertaking a credible impact assessment as they tend to be too static, rigid, and rely only on theoretical assumptions when linking between inputs, outputs and outcomes.

#### 2.4.3 Undertaking Impact Assessment

In order to effectively assess the impact of a strategy (or its underlying projects) after it has been in place for some time requires additional tools, both quantitative and qualitative. A number of quantitative tools exist for analyzing impact at the sector or economywide levels and at the project level. The evaluation of sector or economywide impacts of public sector interventions is particularly useful when the goal is to assess effects on aggregate welfare outcomes (e.g. poverty) and their distribution. Where there is sufficient information on past investments and other key factors which influence outcomes, econometric and statistical methods can help test for the contribution of past changes in investments (e.g. agricultural research, infrastructure, health, and education) on changes in outcome variables. This is very much the same approaches described in the previous section on 'How should resources be mobilized and allocated across the different economic sectors and geographic regions?' emphasizing how ex-post impact evaluation is particularly relevant for informing the design of future strategies.

Given the poor availability and quality of data in many African countries, econometric techniques may not be feasible. Under these conditions, simulation and programming models are useful alternatives. For agricultural R&D, for example, economic surplus models can be used to estimate economic rates of return to investment so long as sufficient information on key technology and behavioral parameters exist (see Alston et al. 1995). Additionally, programming techniques can also be used to estimate changes in agricultural performance (as measured by total factor productivity) that is due to technical change and thus past investments in R&D (see Farrington et al. 1997). For broader categories of investments, it is also possible to apply economywide simulation models as in the earlier section on 'What are the economywide options for reaching high-end agricultural development goals?' if such models already exist. This can be particularly useful for capturing the impact of broader sectorwide investments on overall economic growth.

The application of economic simulation models to the evaluation of impact after the fact (or ex-post) essentially involves simulating how much an actual change in investments or policy may have affected outcomes. A number of indicators that serve as input into the models can be monitored periodically to assess their potential impact on outcomes. As data on outcome variables becomes available, model results can be compared with actual values. If the model predicts an outcome that is below or above the actual observed outcome, it is possible a number of confounding factors could explain the difference, if data and model specification errors are assumed minimal. Results can also be compared against a baseline scenario wherein the intervention is removed to describe a situation 'with and without'—as a type of counterfactual analysis (see example of Bell et al. 1982).

An emerging area is the use of spatial analysis tools for impact assessment. This has become possible as GIS tools, satellite imagery, and computer hardware and software, have advanced over the years. As a result, for example, an increasing number of countries are able to produce high resolution poverty maps. Knowledge of this kind which show disparities in poverty across geographic space, including associated livelihoods and assets, is not only relevant for targeting future investments (as discussed in the section 'How can individual policies and interventions be better targeted?') but can also serve as a monitoring and evaluation tool. Statistical techniques can be applied where there is sufficient data to be able to associate a number of key geographic and socioeconomic factors to changes in welfare over time (e.g. see Minot and Baulch 2005 and Pender et al. 2001).

Impact assessment at the sector or economywide level has its limitations. The effects between investments and outcomes are typically too aggregate to be linked back to any particular intervention (Maredia 2009). The common problem of insufficient data makes econometric and spatial analysis methods difficult and often impractical. While economic simulation models are more feasible under these conditions, their accuracy depends on how well the underlying data, model specification, and behavioral assumptions represent the real world. In many instances, behavioral parameter estimates in a model are drawn from different periods in time and/or different locations.

Impact assessment at the project level, on the other hand, is far more feasible if necessary steps are undertaken from project design to implementation. At this level of analysis, experimental or randomized approaches are more desirable because they can directly test for attribution and causality. This is because randomization allows for measuring impact against some counterfactual or control variable ("before and after," or "with and without," the intervention), while guarding against problems of selection bias in the process. In other words, it can answer questions of how individuals who participated in a program would have fared in the absence of the program, or, how those who were not exposed to the program would have fared in the presence of the program (Duflo et al. 2008; Maredia 2009; White 2006). To ensure sufficient information is collected for randomization, however, early preparations and resources for monitoring and evaluation must be in place when the project is in its design phase—to later provide the "before and after" and/or the "with and without" intervention comparisons.

The experimental or randomization approach is not without its critics. Some question its usefulness at the level of informing policy and strategy design altogether (Ravillion 2009). An obvious limitation is that typically the desire for undertaking an evaluation occurs when a project has already ended which makes the condition for randomization impossible. Another is the ethical question of excluding from treatment a control group when it involves welfare improving interventions. Other problems include: the limitations for scaling up to general equilibrium effects that occur at higher levels of impact but unaccounted for at the project level; the existence of uniquely defined local conditions which affect outcomes but are not always present in other locations; and, the observation that simply identifying 'what' worked from a project is not as useful for policy if it also does not answer 'why' it worked (Deaton 2009). Finally, its high costs may not always be justifiable, especially if they do not contribute much of anything to policy considerations. Weighing in the cost for experimental impact evaluations with the information expected from them should always be considered before undertaking such evaluations.

In most cases, non-experimental and practical alternatives are more suitable when projects are already underway. Among them are commonly accepted statistical and econometric techniques that compare outcomes between project participants and non-participants within a target population, such as controlling for observables, regression discontinuity design estimates, difference-in-differences and fixed effects approaches, as well as the use of instrumental variables (Duflo et al. 2008; Maredia 2009). In other cases, simple economic cost-benefit analysis (or rate of return studies) are just as useful, but only so long as there is sufficient underlying information on costs and behavioral assumptions associated with the project (see Gittinger 1984; Alston et al. 1995, 2000; and Masters et al. 1998).

The choice of economic tools ultimately depends on many factors: whether impact is being evaluated at the project or sector or economywide level; the type of questions being asked to ensure policy relevance; data availability and type; pre-existing models; resources and time available for analysis; and the capacity or skills of the evaluator. Ravillion (2008) offers some useful steps for evaluators to consider in selecting the most appropriate methods and approaches.

Finally, not all impact assessments are amenable to a quantitative set of economic analyses, such as projects that provide services or have a strong social dimension to them. Even when it is, other more qualitative social and political dimensions can also help explain impact. For example, collecting vital information about the social and political context, including the underlying policy landscape and processes, under which impact is being evaluated, can be particularly useful at answering questions about "why" and "how" a strategy or project may have failed or succeeded—rather than simply "what" caused it. In this context, the efficiency and effectiveness of program interventions and projects can also be evaluated.

Some examples of qualitative approaches for impact evaluation include rapid appraisal techniques (e.g. through civil society report cards), surveys of targeted beneficiaries to measure perceptions of impact (Maredia 2009; White 2006); use of impact pathway approaches to better understand processes and behavior; and the application of a sustainable livelihoods framework that considers a broader set of social and political explanations, in addition to the economic ones, in assessing a project's impact on welfare outcomes (Adato and Meinzen-Dick 2007). Unfortunately, the subjective nature of gathering information, including the lack of good statistical properties in validating results, often implies that the information generated and lessons learned are not always easily transferrable (Maredia 2009).

Given the range of approaches to developing an M&E framework and ultimately assessing impact, the choice of what to use will always depend on: the agricultural strategy and corresponding range of policies and investments underlying it; the questions being asked; level of complexity in the known causal relationships; data availability, frequency, and type; existing tools; the skills of the analysts involved; the budget and costs allocated for M&E; the time horizon for data collection and analysis; and individual country circumstances with regard to the social and political landscape and policy processes. With this knowledge, decisionmakers, technical analysts, and the key stakeholders involved, must together decide on which approach and what tools provide the most robust and cost effective M&E for the particular agricultural strategy in mind. This is where the 'knowledge support system' concept and approach of SAKSS becomes more relevant.

#### 3 The Knowledge Support System Concept and Approach

The 'knowledge support system' component of SAKSS describes a network of individuals and institutions that service the need for strategic analysis and information during the formulation and implementation of agricultural strategies. The network includes a range of individuals (researchers, policymakers, and development practitioners) and organizations (government agencies, research institutes, universities, development organizations, and private and civil society groups). These individuals and organizations are linked through this network under the shared interest of seeking tangible solutions to the challenges facing the agricultural sector.

Three core activities undertaken by the SAKSS network include collaborative strategic analysis, capacity strengthening, and dialogue. Through these activities relevant information from research findings and data analysis is compiled, synthesized, and packaged into evidence that enriches the dialogue on future agricultural priorities in a timely reliable fashion. The collaborative and participatory manner involved is intended to: help validate the relevant questions being asked by policymakers and civil society and the tools of analysis, data sources and assumptions, needed to address such questions; instill confidence in the evidence generated; and ultimately, enrich the capacity to generate and utilize analytical tools and evidence in the process of formulating and implement agricultural strategies.

Applications of this at the country and regional level exist. For example, a number of regional SAKSS nodes (or ReSAKSS) have focused much of their attention in mobilizing networks of individuals and organizations associated with the CAADP agenda at the regional level. Through these networks and a number of workshop forums, the nodes are helping to fill critical knowledge gaps, as well as bringing together a stock of knowledge, expertise, and tools, as countries begin to shape and align their agricultural strategies within the CAADP framework. A website brings together the information being collected to promote peer review across countries and updates on the progress of CAADP implementation (see www. resakss.org). The networks are in turn helping to establish country SAKSS nodes that are intended to strengthen a country's own ability to generate and provide strategic analysis, monitor key indicators, and assess impact of ongoing efforts against the principals and goals of CAADP.

#### 3.1 Linking Evidence with Policymaking

The unknown question within an individual country's own social and political context is whether there is sufficient room to maneuver in order to bring evidence to bear in local policy processes during the formulation and implementation of their agricultural strategy. How research or evidence feeds into the policy process in developing countries is not yet fully understood. Yet, the importance of it so critical given the observation that many developing countries rely very little on scientificbased evidence when making policy decisions (Juma and Clark 1995). While there is a growing body of literature that seeks to explain the research-to-policy gap in Africa, few have come up with a testable hypothesis. Case study narratives are more common. The ongoing work by the International Development Research Centre (IDRC) in Canada and the Overseas Development Institute (ODI) in the UK are particularly noteworthy. Another team of researchers from Harvard University also offer a few African examples (see Cash et al. 2003).

The common accepted viewpoint in the African literature, as elsewhere in the world, is the observation that policymaking in general is a dynamic and complex process, sometimes simply explained as a chaos of purposes and accidents (Clay and Schaffer 1984). This is because the process involves multiple actors (individuals and organizations) which are defined by local political, social (cultural and belief systems), and institutional realities (bureaucratic structures and capacities). And being about people, vested interests among a few powerful elite, corruption, and external influences, can also play a distinctive role, as they still do in many African countries (Juma and Clark 1995). Power relations (people) and ideas (based on both tacit and explicit knowledge) are therefore particularly important. In fact, scientific knowledge often only plays a marginal role in the decisionmaking process (see Sabatier 2007).

Getting a good handle of a country's own policy process, and no matter its shortcomings, is therefore an important first step to understanding how evidencebased information can play any particular role in it. The question is not simply about how to improve the transfer of research into policy and vice versa, but more so about understanding the peculiar conditions under which links between the two can be made more effective.

Although various theoretical explanations of the policymaking process offer useful perspectives and frameworks on how research becomes embedded in the policy process, we do not cover this here but refer the reader to the expanded review in Johnson and Flaherty (2011). Here we focus more is describing how the 'knowledge support system' component of SAKSS is intended to help strengthen the links and capacities for greater evidenced-based dialogue and policymaking, while also ensuring quality in the evidence generated.

Essentially, the 'knowledge support system' component of SAKSS generally describes a network of individuals and organizations that effectively connect those who know with those who need to know. How effectively this helps to bring evidence into the policy process depends on how well the individuals in the network (both the actors who know and those who need to know) and the organizations they represent (e.g. research institutions, nongovernmental organizations, private organizations, and government agencies) are linked to promote dialogue around the knowledge products (i.e. information and results of research) and policy issues.

How the SAKSS network, in particular, can serve as a 'knowledge support system' in the context of an agricultural strategy is best illustrated using the Context Evidence and Links Framework developed by ODI (Crewe and Young 2002; Court and Young 2003). The framework involves four key elements: external influence, political context, evidence and links. It is appropriate for our purpose because it describes the complex interrelationships among a diverse group of actors, given a local political context and external environment, and thus the manner in which evidence can play a role in contributing to policymaking. Nevertheless, it should be underscored that the complexities of the research and policy interface cannot be adequately represented in a single framework as it involves many other dimensions. We only use it here for illustration purposes.

From Fig. 2 below, the processes and outcomes of the planning, implementation, and M&E activities are themselves greatly influenced by the interrelationships among the three spheres in the center, including the external environment surrounding them. Leadership and governance at the top emphasizes the principal role this plays in managing the agricultural strategy process itself, which is inherently influenced directly by the political context and external environment. Usually, the leader and manager of the agricultural strategy sits in the Ministry of Agriculture or other government agency/department charged with this responsibility. The biggest challenge for leadership and governance of the agricultural strategy is to improve the integration across all three activities—planning, implementation and M&E. But also potentially influencing the outcome of this is the evidence that is being generated in the center, the links that influence the national debate, and the emergence of any changing political and socioeconomic realities. In other words, as new evidence becomes available (e.g. lack of progress or impact), or as socioeconomic and political realities change, or as new and emerging issues are brought to the forefront (e.g. via civil society groups, media), the priorities of the agricultural strategy may have to be altered.

Among the inner circles, the political context is the most critical as it describes the environment and process under which policies are made, and thus greatly affects how evidence plays any role in it (if at all). This includes factors such as political culture, extent of civil and political freedoms, vested interests, capacities of government to respond, and attitudes and incentives among officials (Young 2005). Here, the overlap of political context with evidence describes the process of "strategic analysis," drawing ideas and information from both government and the research community, past research, and the experience and knowledge of actors involved. How the two become more closely linked also depends on how well they both overlap with where 'the rubber hits the road'—the beneficiaries and interest groups of agricultural strategies, such as: development practitioners, farmers and trader groups, media, and civil society in general. On the other hand, the intersection between evidence and links can be viewed as one of discourse and dialogue (e.g. through publications, seminars, and media), while the one between the links and political context is more about advocacy (e.g. the world of campaigning and lobbying among local interest groups, media, and the broader electorate or civil society). Finally, the external environment, including the overall socioeconomic environment, as well as the influence of regional and international actors (e.g. donors), can be quite significant in the African context.

Effective linkages between evidence, dialogue, and policymaking are supposed to occur at the intersection of all three spheres, and it is here that evidence is expected to influence a policy change. The assumption is that when such links are established early enough, the evidence generated and discussed at this intersection is likely to be viewed as relevant and salient to the local context (Young 2005; Cash et al. 2003). The big challenge in most African countries is getting all three to intersect, given the poor state of evidence generation (from poor quality data, poor training and incentives, weak peer review systems), poor links (from poor communications, capacities), political context (power play, vested interests, top down bureaucracies, and elitist attitudes among officials), and external environment, especially the exaggerated influence of donors.

This is what the 'knowledge support system' component of a country SAKSS strives to achieve, in setting up an active network of key local actors (individuals and organizations) who intersect from all three spheres. Through extensive consultations and interactions in the network, activities involving strategic analysis, capacity strengthening, and dialogue are laid out. The scope of work under each of these activities draws on the active input of all stakeholders: local research partners and analysts (evidence), key government actors and agencies (political context), and stakeholder groups (links).

#### 3.2 Supporting CAADP Implementation

The SAKSS concept was adopted in 2006 to support CAADP and its principles for promoting progress review at country level, peer review at the regional level, and mutual review at the continental level. This has involved establishing three regional SAKSS nodes (or ReSAKSS) in each of the major regional economic communities (RECs): Common Market for Eastern and Southern Africa (COMESA), Economic Community of West Africa States (ECOWAS), Southern Africa Development Community (SADC). The ReSAKSS nodes are hosted and led by the International Institute of Tropical Agriculture (IITA) in Ibadan, Nigeria for West Africa; by the International Livestock Research Institute (ILRI) in Nairobi, Kenya for Eastern and Central Africa; and by the International Water Management Institute (IWMI), with the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), in Pretoria, South Africa for Southern Africa. IFPRI is helping to coordinate a common agenda across the three nodes, providing technical and analytical support, and helping maintain and strengthen links with a broad network of CAADP partners. An Africa-wide steering committee provides overall oversight to ensure the ReSAKSS agenda remains relevant and useful in supporting CAADP implementation.

Generally, the ReSAKSS nodes focus their activities in three main areas: Strategic analysis, Knowledge management and communications, and Capacity strengthening. The strategic analysis activities help fill critical knowledge gaps in assessing progress toward realizing the CAADP goals of allocating 10% of the national budget to agriculture, achieving a 6% annual agricultural growth rate, and meeting the first MDG of halving poverty and hunger by 2015. As part of this, the ReSAKSS helped develop a monitoring and evaluation (M&E) framework for CAADP (see Benin et al. 2010).

Under the knowledge management and communications component, ReSAKSS and its network of partners collect data on key indicators such as public spending; integrating and building upon existing data, analytical tools, and knowledge; and facilitating timely access of the knowledge by African policymakers and development partners to allow for more evidence-based decisionmaking. To this end, ReSAKSS has launched a website to share the information it generates and compiles, on these key indicators and on ARD in general (see www.resakss.org).

A number of country level analyses were also undertaken by ReSAKSS to inform country CAADP Roundtables involving Rwanda, Malawi, Kenya, Uganda, and Zambia in 2006 and 2007, and a number of West Africa countries in 2008. The results served as critical input into the stakeholder dialogue and preparations leading up to the signing of a CAADP Roundtable compact within each country (as Fig. 1 previously illustrated). Further support was also provided in helping inform each country's investment plans during the post-CAADP compact period. By the end of 2011, according to the online ReSAKSS Newsletter (resakss. wordpress.com), 22 countries had signed their country CAADP compacts, 18 have developed national agricultural investment plans, and 15 countries have held their technical reviews, out of which five (Ethiopia, Niger, Rwanda, Sierra Leone and Togo) received a total of \$223.5 million from the Global Agriculture and Food Security Program (GAFSP) fund. Other countries continue to sign on to the CAADP framework, including Mozambique, which just formally launched the process on December 13th. Djibouti is the most recent country to sign on in 2012.

In 2010, ReSAKSS began a second phase of its support to CAADP implementation. Much of its activities have been have been focused on consolidating ReSAKSS as a leading knowledge platform for agricultural policy planning and implementation in Africa, more fully operationalizing the CAADP monitoring and

Fig. 3 The 'knowledge support system' framework. Source: Johnson and Flaherty (2011)

evaluation (M&E) system it helped develop at the country level, and providing technical assistance for the establishment of country SAKSS nodes. Because CAADP is meant to be implemented at the country level according to a country's own compacts and investment plans, a goal of the M&E system is to not only help strengthen country capacities for monitoring progress and assessing impact through their SAKSS network of partners, but ensure the adoption of standardized and measurable indicators that are consistent across different countries and regions to facilitate cross-country peer review and mutual learning.

Figure 3 provides a schematic diagram which describes the M&E system of CAADP as set up by the ReSAKSS. Developed around a theoretical framework that is described in more detail in Benin et al. (2010), the system uses a number of indicators to monitor progress of CAADP implementation: inclusive of process, policy, investment and outcome indicators being targeted at all three levels—country, regional and continental. Several important processes associated with CAADP implementation include the signing of CAADP compacts, finalizing investment plans and resource commitments. Other important milestones in the process include tracking and assessing the sorts of policy and strategic issues being raised through stocktaking exercises, reviewing the plans to address them, and assessing the roles of different stakeholders and their capacities to provide and utilize the knowledge.

In many cases, for example, capacity gaps may need to be filled through the provision of appropriate skills training and mentoring for undertaking M&E and policy analysis. These milestones, among others, are represented by the eight circular steps in the country CAADP implementation process at the bottom of the

Fig. 4 Supporting the M&E of the CAADP implementation process. Source: Sam Benin, ReSAKSS slide presentation of support to CAADP M&E, 2011

figure. These help lead to increased and more efficient allocation of resources, and in turn, outputs and outcomes (Fig. 4).