Ramos E. Mabugu,1 Hélène Maisonnave,2 Martin Henseler,3 Margaret Chitiga-Mabugu4 and Albert Makochekanwa5
This article presents lessons on transcendence, from research on the socioeconomic impacts of the Covid-19 pandemic to policy, using experiences from Zimbabwe. The case study parallels literature on knowledge translation that suggests that the challenge of evidence-informed policy is more a problem of evidence production than evidence translation. The positioning, influence, and leverage of the research team was predominantly built on a platform of personal relationship legacies, academic legitimacy, and networks. The data and model co-produced with state actors could influence policy decisions and behaviours because they were designed with and for policymakers to assist with policy decisions. The results had direct implications for Covid-19 response measures, informing policymakers on what the impact on different groups is likely to be and indicating what policy measures could do to address impacts. Knowledge co‑production also proved pivotal in reducing some of the concerns around the limitations of risk‑based modelling in a crisis.
Collaborative modelling, bilateral learning, risk-based modelling concerns, knowledge translation, evidence-informed policy, Covid-19, Zimbabwe.
This article highlights the importance of the evidence production process for evidence-informed policy. In particular, it shows how co-production between economic modellers and state actors of the research product may reduce some of the concerns around the limitations of risk-based modelling in evidence-informed policymaking. The article examines a project organised by a global thinktank, the Partnership for Economic Policy (PEP), in which the collaboration between local/international researchers and local policymakers creates new research and knowledge of global interest to address a timely policy challenge that is implemented locally.
While the promotion of evidence-based development policymaking, invariably through some form of collaboration and partnership with practitioners, has been gaining momentum (Georgalakis et al. 2017; Nelson 2017), in line with, among others, Mulugeta et al. (2019) and Oliver et al. (2021), this article adds to the literature by demonstrating the importance of the process of generating evidence to be itself as significant as the evidence in evidence-informed policy. We illustrate explicitly the working phases and information flow by presenting an internally consistent approach whereby we combine a quantitative economic research tool (economic modelling) with expert knowledge and policymakers’ interest to create a bilateral learning nexus from modellers to policymakers and vice versa. Even where evidence appeared very well received, throughout the article, we present critical self-reflections and highlight instances where issues such as power dynamics and political expediency could have come into play, for example in influencing the composition of research teams and the nature of evidence that policymakers generally showed a higher preference for.
The case study we present is based on experiences from a PEP project on Zimbabwe that helped policymakers design and implement policies to address the immediate and medium-term socioeconomic effects of the Covid-19 pandemic as it unfolded (Mabugu et al. 2021). The Covid-19 crisis challenged researchers and government policymakers at the time of onset because of the widespread nature of its impacts and the uncertainty concerning their magnitude and duration. The uncertainty and knowledge gap inadvertently created a shared need to understand better the Covid-19 impacts as the health crisis evolved and interrogate how to start building back better after the pandemic.
The model we developed provided quantitative data on the potential impact of the pandemic and the associated socioeconomic shocks on different groups. The reason the data and model could influence policy decisions and behaviours was that it had direct implications for Covid-19 response measures. For example, the disaggregated model could be used to assess various scenarios of possible pandemic severity, mild and severe, with and without mitigation policies under each scenario, a feature policymakers found insightful and useful for their day-to-day work. This approach informed policymakers not only on what the impact on different groups is likely to be, but also indicated what policy measures could do to address the impacts. The model is thus explicitly designed to assist with policy decisions.
The rest of the article is organised into five sections. Section 2 sets out the political economy of evidence production use in Zimbabwe in the context of the Covid-19 crisis. Section 3 looks at some of the critical issues that were central in ensuring that research-generated policy perspectives found an audience in relevant policymaking arenas and society at large; namely, how we set up the arrangements (entry point, securing a trusted adviser, engagement with policymakers) and then how low (donor and civic society community) and high (the Cabinet) the information generated reached. Section 4 presents in a structured way the working phases and information flow we used to create a bilateral learning nexus from modellers to policymakers and vice versa and, in the process, diminishing general concerns around risk-based modelling for policymaking. Section 5 concludes, drawing lessons for the broader audience.
Zimbabwe is a small, impoverished, and highly centralised country where national government enjoys a great deal of leeway in the budget. Thus, policy decisions are mostly discretionary choices made by national decision makers and parliament. In line with Nord (2018), country context, particularly in this case the power of centralised organs such as the Cabinet, played a key role. The Cabinet during the pandemic was making inter- and intra‑governmental decisions that would direct most crisis mitigation expenditures, a feature which played a key role in shaping government responses to Covid-19 research and translating that into policy. Our study hugely benefited from this feature of the centralised decision-making process as it made it relatively straightforward for our research findings to reach the government.
In the case of our study, the findings are mostly used by the Cabinet to make decisions on Covid-19-related issues, and once the decision is made, its implementation will affect all citizens throughout the country. The government, through the National Covid-19 Task Force representative, took on board some of the preliminary recommendations from our research directly following engagement with us and shared these at ensuing Cabinet meetings (see process outlined and elaborated in sections 3 and 4). The research findings were significant to policymakers in that they expanded their knowledge base of Covid-19 impact and responses in a timely and coherent macro‑micro framework. What was novel regarding the early part of the pandemic outbreak and subsequent panic was that the co‑produced modelling work was the first such exercise of its kind in the country. Importantly, what the policymakers, empowered by this evidence, seemed to get right was the ability to have meaningful influence over government ministers and non‑governmental bodies, especially as non-governmental organs were playing evermore active roles in the delivery of services in mitigation of Covid-19 impacts.
But this successful experience begs the bigger question of what it is that facilitated the research team’s access to higher-level policymakers, as well as the translation of the research into actual policy. While many other factors may have been at play, including the overriding necessity of migrating rapidly to online electronic communication platforms, our interpretation is that the deep-rooted and collegial economic modelling team relationships with state actors and a culture of evidence use within the Zimbabwean government played a key role in opening doors for the research team to policymakers. The research team’s own positionality played a decisive role in this regard. The nationals in the research team derived legitimacy as respected academics amongst the Zimbabwean politicians and policymakers. They have previously conducted policy-relevant research over the years and been interacting with the Office of the Presidency and Cabinet (OPC) as part of stakeholder engagements, and the research has proved to be both objectively relevant and having practical application in relation to the issues of the day.
Furthermore, some of the OPC top officials who helped with access to government were former university students of PEP research team members. Such ties are very important, albeit often not amplified enough as useful entry points for knowledge co-production in such settings. As such, there have been long-standing professional relationships that have been maintained over many decades that we used effectively as an entry point. While this experience may not necessarily be widely applicable to many other countries or even other policy settings within Zimbabwe, the salient point for the practitioner community is that of knowing and defining clearly upfront one’s unique leverage points prior to embarking on the knowledge co-production process. This worked well in the Zimbabwean case.
While our case study experiences as outlined thus far may inadvertently signify that the Zimbabwean government favoured modelling as a key type of research to inform Covid-19 government responses, in reality, this perceived preference was just by coincidence and was a culmination of many other factors at play. Our case is perhaps unique in that it reflects a particular aspect of knowledge co-production, requiring a particular framing of the policy design to make it suitable for modelling analysis, at a very specific moment brought about by the pandemic. Indeed, as we elaborate in later sections, our co-producers in the research exercise – i.e. the policymakers – made many other suggestions that were important for policy but were not followed through because economic modelling was not the best approach to address those questions, important as they were. As the pandemic was unravelling, data on its evolution and economic consequences was scarce, and this drastically narrowed the choice of available methods. So naturally, simulation methodologies such as the ones we used in the economic modelling provided a logical route to follow.
Indeed, the Zimbabwean government does not appear to be inclined to any particular or unique approach to evidence and has embraced a variety of approaches in other policy settings from both local and foreign sources. The government, though, generally prefers any credible research evidence with the proviso that it speaks to issues in Zimbabwe and is conducted with the involvement of Zimbabwean stakeholders and researchers. The government, in the new dispensation following departure from office of the then President Robert Mugabe, has increasingly warmed to evidence-based research, and over the years, the government has been taking on board research findings and global evidence from institutions such as the International Monetary Fund (IMF), the World Bank, and the African Development Bank (AfDB), among other institutions that provide global evidence. National thinktanks such as the autonomous thinktank Zimbabwe Economic Policy Analysis and Research Unit (ZEPARU) have been in some instances commissioned by the government to conduct studies in a variety of areas.
For any such commissioned studies, sometimes even research design and analysis leading to study recommendations are presented to the Cabinet and the president of the country. This shows political will and at least some form of respect for the importance of research in generating evidence for policymaking at the highest level and generally permeates to other spheres of decision-making. Furthermore, some national thinktanks receive budget allocation annually from the country’s national budget, a rough indication of the importance attached to their contributions to policymaking architecture. The fact that the government has tacit influence over who does research and the nature of that research to find its way into policy could be construed as a form of subtle political expediency or more cynically as tokenism. However, this situation is perhaps better than the alternative. There is evidence to suggest (Zinyama 2021) that researchers are sometimes humiliated, bullied, and generally insulted in public when they venture into contested policy terrain with, at times, contrary views, sadly a feature that was not uncommon in the recent history of the country prior to the new dispensation.
The elements of power dynamics and political expediency also play out in the insistence on the involvement of local researchers and on Zimbabwean issues, even if these conditions are neither unique to Zimbabwe nor did they constitute an impediment to our work. The preference for local researchers and research questions generated by and with locals resonated well with the PEP objective of involving Southern researchers and voices in Southern research issues. Thus, by default, the research team already satisfied government conditions of a research team led by Zimbabwean researchers and consisting of both national and international researchers. This PEP team worked closely and collaboratively with policymakers in coming up with the inclusive intervention proposals to subject to quantitative analysis before translating these proposals into policy actions (Mabugu et al. 2021).
Finally, we think Zimbabwe provides an interesting approach for a much more resource-constrained African country when dealing with the impacts of the pandemic. How does such a country undertake mitigation and recovery from a position where finances were always tightly squeezed as a result of both severe international economic sanctions and economic decline? Here, we argue that even when foreign aid dwindles and the country is subjected to such a severe negative shock as the pandemic, having state actors with sound research literacy at various government levels significantly ameliorates the negative consequences of shocks. There is capacity in various Government of Zimbabwe ministries and state-owned enterprises. On economic issues, these government institutions are staffed with technical people with academic qualifications being mostly at master’s and doctoral levels. Furthermore, there are other government officials who have even worked outside the country in other jurisdictions, hence they have international experience.
With regard to structured support connecting evidence with policy, the fact that the Cabinet sometimes makes decisions using evidence from research on any particular issue is one connection that exists between evidence and policy. That said, despite Zimbabwe’s turbulent past and difficult economic situation, this nascent culture of evidence use in government has facilitated the acceptance and use of the evidence generated in this project for policymaking. Other than the evidence itself, admittedly, many other factors influenced decision-making, including political expediency, ideology, and short-term priorities. As an example of the latter, although it might not have been explicitly stated, Zimbabwean policymakers would probably have perceived this project as one way of re-engaging with external donors to fund even larger interventions aimed at groups left behind by the devastating effects of the pandemic. It is quite conceivable this may in part explain why policymakers played a large role in facilitating the modelling team’s access beyond the Cabinet to civic society and the donor community to share the results of the work (see section 3 for further elaboration).
As discussed in section 2, building enduring relationships with policymakers proved important in opening doors to them and subsequently for the government to use evidence generated in policy. In our view, it was not just that the findings were timely and could be translated into policy user formats, though this was important, but that at the root of it were partnerships. We document and discuss three other main elements regarding partnership arrangements that were pivotal for the successful execution of the process.
The first is that the entry point matters for researchers regarding partnership formation for collaborative modelling; in particular, how one goes about setting up the arrangements. Our experience constitutes what can be characterised as PEP’s niche insider status with policymakers during the Zimbabwe project. From the very outset, we as the PEP team were very clear in our strategy that developing a strong PEP team with the ability to not only forge but also sustain enduring relationships with the OPC in Zimbabwe was going to be a critical condition for project success. As described previously and reiterated here, the positioning, influence, and leverage PEP and the local team enjoys is built on a combination of the legacy of PEP researchers being respected academics, their personal relationships, and their networks. The personal relationships local researchers had with state actors is what is often referred to in the policy literature as niche insider status, wherein access to OPC in this case is facilitated by these relationships (Maloney, Jordan and McLaughlin 1994).
For our study, just before we commenced our research, we sought approval from the Chief Secretary to the president and the Cabinet to conduct the study. The request was not only granted but also other relevant government arms were directed to cooperate with us on the study. The same request was also a way of informing the Government of Zimbabwe about the study and this created a sense of eager anticipation of the research evidence as it unfolded. What cemented the relationship over and above the niche insider status enjoyed by the researchers in our assessment was also: (1) a shared sense of urgency and desire to seize the opportunity to understand on both sides the impacts and rethink gender-focused development during a crisis, and (2) the centralised decision-making system in Zimbabwe alluded to earlier in section 2 through the Cabinet.
Thus, while there was no binding agreement developed or signed between us and the Cabinet, what was important in this process at the time was our niche status, a ‘coincidence of wants’ at that opportune moment, prior ‘institutionalised’ knowledge on both sides of the policy and research divide and what would be brought to the table, and that the Chief Secretary to the president and the Cabinet took it upon himself to send formal letters to all relevant ministries and policy organs at very high local level, introducing and endorsing the project. Maloney et al. (1994) refer to this as the rules of the game already factored in between the two sides, given the long association described earlier in this section and section 2. The information sharing at high policy level with other organs of state by the Chief Secretary further created the opportunity for developing a mutual agenda between the policy experts, policy contacts, and the research team.
Since different priorities often limit the establishment of mutual agendas in research–policy partnerships, such an invitation to collaborate within the process was essential for building mutual agendas as much as possible (Georgalakis and Rose 2019; Newman, Bharadwaj and Fransman 2019) in the context of collaborative economic modelling. Admittedly, it is unlikely that all groups that produce knowledge relevant to the response to the pandemic can enjoy such niche access as we did, nor should it be a general expectation, but a general lesson here would appear to be that these relationships can be built through long-term investment. This is especially relevant for stakeholder engagements including deliberate actions by researchers to involve policymakers in knowledge co-production and always safeguarding researcher independence and objectivity. It does appear from the Zimbabwean case that safeguarding objectivity and yet being deliberate about knowledge co-production also enhances researcher credibility in the eyes of policymakers. This no doubt has wider or growing relevance for other country contexts.
The second feature of the work was that in order to corroborate results from the quantitative analysis, get buy-in from other key stakeholders (not necessarily those with previous relations or connections with researchers, i.e. policy experts and policymakers), and ensure recommendations are used, strong relationships had to be forged with the implementing ministries and agencies in government. Notwithstanding the well-documented risks involved in using modelling analysis for policy, generally addressed in section 4, and how these risks were minimised during the modelling approach, the following institutions in the process at the coalface of the Covid-19 policy response particularly stand out and were enduring: the Ministry of Finance and Economic Development, Ministry of Health and Child Care, Ministry of Industry and Commerce, Ministry of Tourism, Ministry of Small to Medium Enterprises, the Zimbabwe Tourism Authority (ZTA), Zimbabwe National Chamber of Commerce (ZNCC), Confederation of Zimbabwe Industries (CZI), and the Chamber of Mines.
As will be discussed further on in this section and in section 4, the approach taken with these central agencies followed the introductory letter sent from OPC and consisted of interactive workshops and one-on-one engagements facilitated via online platforms. It also comprised in-person follow-up by locally based researchers, where necessary, with the respective experts and policymakers. This ensured that these experts were informed and also able to influence the design of policies to respond to the Covid-19 crisis as it unfolded. The specific modelling innovations for the project included modelling more scenarios as they were motivated and requested by government itself (see elaboration in section 4).
The third and final feature was the interaction with civil society and the donor community. What is unique about this aspect for Zimbabwe is perhaps that the policy partners facilitated our research team engagement with civil society and donors. This is interesting because often we see development researchers engaging with civil society and not government or using donors as leverage to engage their findings with policy. In our case, the government brokered our own engagement with civil society as we were located closer to the government and further away from civil society than the government was when it came to the specific issue of policy response to the ongoing pandemic.
The Coordinator of the National Covid-19 Technical Task Force made it her role to facilitate our engagement and presentation of study findings in these other fora, such as groupings of development partners and civil society where Covid-19 issues and policy programming were discussed. She was in any case the main coordinator of these stakeholder engagements with the government. Given the panic and vacuum of credible information surrounding the onset of the Covid-19 crisis, the government was well positioned to champion this role as this was a completely new issue, in that none of our researchers had dealt with civil society and donors before.
As already alluded to, Zimbabwean policymakers would probably have perceived and realised that the reputation of PEP itself and links to funding from the International Development Research Centre (IDRC) on this project offered an opportunity for them to re-engage with donors to fund larger-scale interventions for groups left behind by the devastating effects of the pandemic. This in part may explain why policymakers played a significant role in facilitating the modelling team’s access to civil society and the donor community to share the results of the work.
The Covid-19 crisis has challenged policy analysis with its multidimensional impacts on productivity and trade (Kim et al. 2022; McKibbin and Fernando 2020a, 2020b; van Heerden and Roos 2021). We start this section by explaining why computable general equilibrium (CGE) with microsimulation were chosen as the main tools for the analysis. At the time of the study, surveyed data as empirical data were not yet available as a base for impact analysis. As the Covid-19 crisis impacted multiple channels in the economy, an economic policy simulation model was the appropriate tool to simulate and analyse impacts and policies.
The experimental framework of an economic policy simulation model requires model development based on statistical data. Since the pandemic created economy-wide impacts, we chose a CGE model as a suitable simulation tool to analyse economic shocks on economic growth, economic sectors, and different agents. The main advantages of CGE models are the relatively small data requirements, the scientifically rigorous methodological framework based on microeconomic and macroeconomic theory, and the high flexibility in application. These advantages make CGE models a flexible quantitative analysis tool applicable for relatively low development cost in countries with small data availability (Devarajan and Robinson 2013; Lemelin and Savard 2022). However, using CGE models (or economic models in general) as a policy decision support tool has its limits and risks for the policy decision process (Devarajan and Robinson 2013; Lemelin and Savard 2022).
These include:
To overcome limits and reduce the risks for policy decision‑making, and to provide the most representative analysis as possible we (PEP researchers) followed a stakeholder-oriented economic modelling approach. A stakeholder-oriented application of the model required participatory development and validation by policy experts and makers (hereinafter referred to together as policymakers or state actors). The participation of policymakers in the development process can lower the burden to understand the model and the simulation results (Benfica 2021; van Bruggen, Nikolic and Kwakkel 2019). By involving the policy experts directly and interactively in the modelling process, a second aspect to learning on the part of the modellers can be achieved; i.e. the model can be designed according to stakeholders’ perceptions and their subsequent further input (Süsser et al. 2021). As a participatory approach, we followed PEP’s approach, ‘which is premised on the belief that evidence produced from an in-country perspective, by empowered and engaged local researchers, results in better policy choices, more sustainable development outcomes, and more inclusive policy debates. As a network, PEP facilitates collaboration between local researchers and stakeholders to produce contextualised, policy-relevant evidence’ (de Haan and Sanchez-Swaren 2022: 48).
In this stakeholder-oriented approach to economic modelling, we designed impact and recovery scenarios of interest and allowed for revisions of the analysis after the discussion of results. Figure 1 presents schematically the approach and processes that this project followed in that regard. In phases 1 and 2, we developed the economic policy simulation model as a CGE model linked to a microsimulation model (Decaluwé et al. 2013). We combine the CGE model with a distributional analysis (microsimulation) to analyse the impact on poverty at household level (data obtained from Davies, Kwaramba and van Seventer 2018; ZIMSTAT 2018, 2019; ZIMSTAT and World Bank 2020). We then developed the model for Zimbabwe by consulting academic studies and reports (ZIMSTAT 2018, 2019; ZIMSTAT and World Bank 2020), as well as including home-based researchers from the local university in the modelling team.
Source: Authors’ own.
Next, we designed first impact scenarios based on research studies on other African countries which provide estimates on the expected economic impacts (e.g. Fofana and Sall 2020; Djiofack, Dudu and Zeufack 2020; Escalante and Maisonnave 2022; Maisonnave and Cabral 2021). Based on the official reports of the Government of Zimbabwe (2020a, 2020b) we defined first mitigation scenarios, which we presented in Phase 3 to the Zimbabwean policy experts. After revisions (see Mabugu et al. 2023 for how such revisions played out in practice) in which we redefined the impact and policy scenarios according to the experts’ knowledge, we presented in Phase 4 the ‘new’ results to the policymakers. This interactive two-way learning process built not just co-production and co-learning and much-needed transparency around what economic models could or could not do, but importantly cemented the much-needed trust between researchers, local policy experts, and policymakers which was pivotal in ensuring the analysis linked to the specific policy needs of users.
With the closely oriented collaboration and inclusion of experts’ knowledge, we addressed concurrently several limitations and risks as outlined in section 4. Through extensive explanation and presentation, we reduced the burden for policymakers, helping them to understand and trust the ‘black box model’. With the model validated by experts and the corresponding revision, we progressively nudged the model closer to reality at the same time, taking into account the more relevant aspects. Through presenting and explaining the results, we bring them into the policy debate and reduce the risk of selective bias or misinterpretation. By offering several different scenarios, we can outline a larger set of possible impacts and policy options than focusing on only a small set of scenarios.
By means of further explanation, from an initial two policy simulations, we ended up with 11 policy simulations run through the modelling tool. Most of the new policy suggestions from policymakers were also motivated by the fact that during the life of the project, different Covid-19 variants began to emerge, resulting in more serious illness and a greater number of deaths. These variants were envisioned neither during the planning period of the project nor at the inception and carrying out of the research study. Also, from their side, participants from the government had to be agile and responsive to new priorities and pressures from their political managers. This had a bearing on what they would prioritise or hope to get from the participation, even if at times it would prove unfeasible to accommodate some of the ‘demands’, given the nature of the research design.
The approach to gender modelling greatly improved as a result of the interaction with policymakers and representatives from the gender, labour, and agriculture ministries. Particular attention and emphasis were subsequently placed on the labour market as well as the agricultural sector in order to capture the realities of the Zimbabwean labour market and therefore, gender inequities. For instance, in large-scale farming, two thirds of the wage bill is allocated to permanent male workers, while this type of labour is absent from small-scale farming where female workers are more prevalent. Zimbabwe women are particularly prevalent in the agricultural sectors, especially in the smallholder farming sector and have less access to capital than men. We modelled this at the behest of the policy partners and went on to include a capital subsidy policy simulation for the agricultural sectors with a subsectoral emphasis on the smallholder farming sector as a simulation (Mabugu et al. 2023).
With hindsight, having different voices in the teams raising concerns and participating in the modelling was critical, not only to arrive at solutions that could be implemented but also as a mechanism that reduced some of the concerns around the limitations of risk-based modelling in a crisis. The strategy facilitated knowledge co-production, and better assimilation and use for both researchers and state actors, and ultimately at Cabinet level. However, the process was not always smooth and at times even became chaotic as would be expected when a team of policymakers, academia, and civil society organisations collaborate.
Quite often, we would receive many good suggestions on interventions, but these were usually made based on the area of expertise, patronage, or what could be described as the topicality or ‘flavour of the moment’. These suggestions did not always prove to be feasible with regard to the pandemic and recovery, or lend themselves to the economic modelling tool. Some of the suggestions were also at times generic in nature – a classic example being ‘we need more funding’ or requests for better implementation from each of the state actor participant’s specific constituencies.
While important, many of these issues could not be addressed using the tool developed for the specific exercise, and it was important to be frank and honest about this without undervaluing the issue or giving the impression of being dismissive. On self‑reflection, our view as lead researchers on the project is that the combination of such a study driven by modelling with in-depth interviews with selected relevant actors may have been helpful in advancing the design of these policies and accommodating some of the issues stakeholders raised that were not compatible with our type of modelling approach. Filling these gaps is important going forward. It may also be useful to select some specific policies and attempt to design and implement a more rigorous economy-wide modelling evaluation study complemented with the aforementioned in-depth interviews.
Finally, at times, it was clear to us that the modelling design and results from such modelling is too complex for the policymakers, even having taken into consideration the online and face-to-face follow-ups that took place as previously outlined. It may have been useful to design some more innovative ways of informing the main aspects of the research, including results interpretation (such as short interactive video). Thus, our own learning as economic modellers from this experience would be that an innovative communication, advocacy, and dissemination strategy should accompany the modelling from the outset.
This article makes the point that the process of generating evidence-based recommendations for policy should be as important as their content. An important dimension of the article is the learning about how collaborative economic modelling as a form of co-production of evidence may reduce some of the concerns around the limitations of risk-based modelling in a crisis. Partnerships in this respect were pivotal and, it goes without saying, there is a need for researchers to be deliberate in investing in activities that forge enduring partnerships with state actors. The article highlights several messages.
First, country context, particularly the underlying socio‑political conditions and culture of using research, plays a key role in shaping government responses to Covid-19 research and translating that into policy. Together with state actors, we exploited the paucity of evidence on the unanticipated shock of the pandemic and the centralised nature of policy decision‑making. This then allowed us to rapidly formulate a number of mitigation and recovery policy measures and to find an audience at the highest level of policy decision-making.
Second, the importance of designing an interactive yet structured framework with internal consistency to enhance co-production, co-learning, and transparency, and to build the much-needed trust of policymakers in the results arising from technical economic modelling. The approach chosen needs to be flexible and adaptable enough to provide evidence-based input with very short turnaround times.
Third, while, as elsewhere, public institutions in Zimbabwe need official channels to gain meaningful cooperation, the process of building mutual respect involves enduring relationships that may be forged through long-term investments in knowledge co-production. In addition, understanding how key decision makers and relevant stakeholders react to evidence-based policy responses during feedback sessions and then meaningfully implement those responses is key to having policies established.
Fourth, involving policymakers, particularly in simulation design and interpretation of the model results, is pivotal to minimising risks around engaging modelling with policy. Complementing this is a need for economic modellers to invest in developing innovative communication and dissemination strategies that reinforce learning amongst state actors in the intricacies of economic modelling.
* This IDS Bulletin was produced as part of the Covid-19 Responses for Equity (CORE) Knowledge Translation Programme, led by the Institute of Development Studies (IDS), which supports the translation of knowledge emerging from the CORE initiative. Supported by the International Development Research Centre (IDRC), CORE brings together 20 projects to understand the socioeconomic impacts of the pandemic, improve existing responses, and generate better policy options for recovery. The research is being led by local researchers, universities, thinktanks, and civil society organisations across 42 countries in Africa, Asia, Latin America, and the Middle East. The views expressed herein are those of the authors and do not necessarily represent those of IDRC or its Board of Governors, or IDS. For further information, please contact: c19re.org.
† This work was carried out with financial and scientific support from the Partnership for Economic Policy (PEP), with funding from the Government of Canada through the IDRC. The authors gratefully acknowledge the assistance and guidance during the engagements with policymakers provided by Misheck Sibanda, V.K.J. Makaya, Pius Mabugu, Agnes Mahomva, and Esau Chiroba. We gratefully acknowledge the insights from anonymous reviewers of this article, and thank the Issue Editor, James Georgalakis, and the editorial team for suggestions and comments that improved the article.
1 Ramos E. Mabugu, Professor, School of Economic and Management Sciences, Sol Plaatje University, South Africa and Research Fellow, PEP, Kenya.
2 Hélène Maisonnave, Professor, Le Havre Normandy University, France.
3 Martin Henseler, independent researcher, Le Havre Normandy University, France and PEP, Kenya.
4 Margaret Chitiga-Mabugu, Dean, Faculty of Economic and Management Sciences, University of Pretoria, South Africa.
5 Albert Makochekanwa, Professor, Department of Economics and Development, University of Zimbabwe, Zimbabwe.
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© 2023 The Authors. IDS Bulletin © Institute of Development Studies | DOI: 10.19088/1968-2023.131
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International licence (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited and any modifications or adaptations are indicated.
The IDS Bulletin is published by Institute of Development Studies, Library Road, Brighton BN1 9RE, UK. This article is part of IDS Bulletin Vol. 54 No. 2 October 2023 ‘Knowledge in Times of Crisis: Transforming Research‑to-Policy Approaches’; the Introduction is also recommended reading.