Compatibility of Policies and Social Capital

Abstract

In our previous study (Azis in Periphery and small ones matter: interplay of policy and social capital. Springer, 2022), the role of the interplay between public policies and social capital (SC) was clearly revealed to be very significant for MSME productivity. The majority of MSMEs perceived the contribution of social capital was either the same or greater than government policies in affecting productivity.

In our previous study (Azis, 2022), the role of the interplay between public policies and social capital (SC) was clearly revealed to be very significant for MSME productivity. The majority of MSMEs perceived the contribution of social capital was either the same or greater than government policies in affecting productivity. Any policies would have been more effective if they were consistent and compatible with the prevailing social capital. In particular, the two factors that reflect the social capital, i.e., participation and coordination, were considered by MSMEs as most important for achieving real improvements in their performance. If the set of policies (on linkages, structural, and technology) were put in the context of supporting the participation and coordination, they would have produced a profound impact on MSME productivity. Policies on linkages, more specifically those intended to create and strengthen interactions among MSMEs and with other stakeholders, were considered most crucial, followed by access to affordable financing. Some medium enterprises (MEs), particularly those involved in exports and had received supports and guidance from BI, were of the opinion that improving infrastructure including in digital technology would help enhance their productivity through information searching and increasing market share. It was also revealed during the discussions that simplifying rules and regulations and ensuring legal certainty could help streamline their business and reduce transaction costs. In turn, they would improve their efficiency.

On the social capital front, the importance of participation and coordination, especially for seeking knowledge and information, was ranked the highest, which also implies the imperative of having effective linkages among themselves and with others. In short, the key message of that study is to avoid making policies not compatible with the prevailing MSMEs’ social capital.

Fig. 2.1

Organization of this chapter

In the current study, we take a further step by focusing on the following questions: What are the social capital-compatible policies that can be implemented, in the sense that those policies will align the MSMEs’ preferences with the desired goals? Is there a mechanism that could be designed to implement those policies? This chapter focuses on the first question. The second is taken up in the next chapter. For both purposes, we construct a framework of analysis utilizing MSME perceptions where the resulting preference ranking is derived and used to identify the implementable policies. We also discuss some issues surrounding the mechanism to implement them.

Figure 2.1 shows the organization of this chapter. Based on the finding of our previous study (Azis, 2022) recapped in the upper box, our key question here is: What are the social capital-compatible (hereafter SC-compatible) policies perceived by MSMEs as the most important for improving their performance and productivity (Sect. 2.2). The expected result of the analysis is therefore a preference ranking of SC-compatible policies. In Sect. 2.3, a similar question is raised for joint policies: What are the SC-compatible joint policies most preferred by MSMEs? Before we try to answer those questions and show the analysis and the results, in the next section, we first describe the approach we used in the analysis, i.e., the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP).

2.1 Methodology: Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP)

It is well known that there are three logic-based approaches to understand and explain real phenomena on the ground and how human perceive those phenomena. First is the probabilistic approach in which a random process is utilized by taking the average and standard deviations of particular occurrences; second is the reductionist approach which is essentially using the cause and effect such as done in most impact studies; and third is the systems approach in which the interactions between parts within a system as well as the interactions of the system with its environment are central in the analysis. The Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP) fall under this latter category in which we look at the overall purposes (goals) governing the design and functions of a system to explain how and why it performs in a certain way. The fulfillment of the goals and objectives is the primary concern, based upon which prioritization or ranking in the fulfillment process becomes essential. The representation of the system itself takes on either a hierarchical or a network form consisting of the relevant objectives, criteria, constraints, and alternatives.

The starting point is to acknowledge that when applied to MSMEs with different “structural variables” or characteristics, the same policies can produce different outcomes (Ostrom, 2010). In the current study, the included structural variables are: location, type of activities, use of digital technology, level of supervision, financing status, gender, health conditions, and educational background of the owners and workers, etc. The outcome of implementing certain types of policy under those different structural variables depends on the identifiable environment and circumstances. They should therefore be evaluated on a case-by-case basis. This highlights the importance of acquiring firsthand information at different times, locations, and under different environments/settings. We did exactly that. We conducted a series of hybrid surveys—a mix of written, telephone, and online-based surveys to acquire firsthand information and perceptions of MSMEs.

The acquired information and the list of preferences were subsequently processed by using the AHP for the hierarchy based, and the ANP for the network-based questionnaire, from which we explored and identified the alternative policy measures and policy-mix.

Fig. 2.2

A hierarchy in AHP

The first step in AHP and ANP is to establish the relevant hierarchy and network, respectively, before comparing the objectives with each other to find the importance of each objective over the other (the weight represents the degree of importance). We do similar comparisons for the criteria based on each objective, and for the alternatives based on each criteria. The following are examples of a simple hierarchy, a simple network, and a more complex network. It should be noted that the number of levels in a hierarchy and a network, as well as the number of element in each level, can vary depending on the scope of the issue at hand. Note also that the type of arrows distinguishes the hierarchy in Fig. 2.2 (one-way arrows) and the simple network in Fig. 2.3 (two-way arrows).

Survey story: Clay pottery products made by members of a woman cooperative in the village of ‘Banyu Mulek’ located in outside Lombok, West Nusa Tenggara. The saving-and-loan cooperative was set up to help women in the village to earn money by doing small business, mainly producing clay-pottery, and allow them to borrow money at a level of interest rate agreed by members. After paying a small fee to join the cooperative, members put a voluntary saving account. During years of operation, the cooperative was able to improve the livelihood of members and the village community in general, without any loans from banks or other parties. It was the strong social capital among members that played an important role in its success. Trust among members and the tightly-kept community norms were reflected in the effective social sanctions, making the cooperative’s non-performing loan negligible

It is the way to generate the weights that distinguish AHP and ANP from the other system approach models. In particular, the procedure is aimed at yielding neither cardinal nor ordinal scales, but ratio scales which can be derived from pairwise comparisons (comparing two elements in a pairwise way) such that we can include intangible elements in the system. Using ratio scales is necessitated by the condition we wish to meet, namely, to enable us to perform all of the arithmetic calculations (addition, subtraction, division, and multiplication).

Fig. 2.3

A simple network in ANP

Fig. 2.4

A more complex network in ANP

In AHP, the weights of the criteria are multiplied by the weights of the alternatives for those criteria to find the overall weights or priority ranking. In ANP, however, the process to generate the overall weights is by forming the so-called supermatrix that is raised to large powers (after making the matrix stochastic) to reach a converging point where the largest eigenvalue equals to one. Brief technical explanations and the mathematics of AHP and ANP are shown in Appendices B and C.

Two sets of questionnaire were used: one that includes policies and social capital to capture their interplay, and another that includes a set of policy-mix. The latter is important because the effectiveness of one policy is often influenced by the effect of another policy. In both sets, the polices and policy-mix were constructed based on what MSMEs perceived as being consistent with the conditions they believed reflecting their social capital. Hence, these measures are essentially SC-compatible policies. The first set involves a network, where some components are affecting—and be affected by—other components, and the second set uses a hierarchy in which the objectives, broad challenges, and more specific problems faced by MSMEs are ranked before making the priorities of policy-mix. In constructing the networks and hierarchies, we conducted a series of preliminary discussions with the prospective respondents to capture their perspectives with respect to the relevant components, the nature of the goals, objectives, and the consequences of the selected components on productivity.

2.2 Interplay of Policies and Social Capital

The main task of getting firsthand information through surveys is to generate policies that are compatible with social capital. For that purpose, we first constructed a questionnaire based on a network system where some levels and components in the system interact with each other. As displayed at the bottom of the network in Fig. 2.5, the first two components represent what MSMEs think necessary to get the information and support for the business, for which participation and coordination (in a cluster) are required, hence need to be facilitated. In turn, the acts of participation and coordination require the presence of two important elements of social capital, trust and norms. Both participation and coordination are essential to make a network, another element of social capital, more effective and useful for improvements of MSMEs’ performance. The high importance assigned by MSMEs to participation and coordination for information searching and business purposes indicates their preference to have some sort of linkages among themselves, for example, through a cluster for networking. We arrived at those SC-compatible measures based on the results from our earlier study as well as inputs from the pre-survey discussions with MSMEs and other relevant parties.

Fig. 2.5

Network structure for MSME

Other components shown at the bottom of the network are the set of policy measures. The first policy compatible with the social capital is on financing, particularly access to affordable funding to enable MSMEs to get involved and be active in a cluster or a network. Another SC-compatible measure is to simplify regulations and ensure legal certainty including legal supports for MSMEs. Simplifying regulations will free up MSMEs to focus on their actual operations without the need for additional subsidies or tax breaks. All these measures need infrastructure supports, hard and soft, to make the interaction and networking effective. For example, the availability of electricity and Internet access is inevitable for establishing an effective network. Another supporting infrastructure (the soft type) is to provide training and guidance to use web browsers and other Internet applications for communication. These measures are necessary for getting the maximum benefits from interactions.

But choices and preferences do not exist in a vacuum; they are predicated not on an ideal or normative world but on the world as it is. What governs them and drives their attainments are the objectives and challenges that MSMEs must face in their day-to-day activities.

To accomplish any objectives, one needs to know how to set them, not just state “what I want” and expect them to happen. Objective setting is a process that starts with a careful consideration of what MSMEs want to achieve, and ends with SC-compatible measures that social planners are expected to take. In the process, MSMEs consider what necessary actions they would take to make those policies work. In between objectives and policy measures, some well-defined challenges are identified. They transcend the specifics of each objective and provide implicit guides on what steps to take. Only after considering these challenges and the necessary steps can the objectives to be accomplished be formulated.

The three objectives displayed in Fig. 2.5 reflect that consideration, i.e., to meet the end needs and expand market share, to increase sales volume, and to utilize individual and local potentials. One of the common mistakes made in many studies on SMEs is to overlook the different characteristics of different sectors and sizes, even within the categories of micro and small enterprises (MSEs) and medium enterprises (MEs). Some of our respondents are very small scale, more properly classified as micro with a sole proprietor and fewer than three employees including members of their own family. For many of them, the activities they are doing are largely intended to meet the end needs, or earn incomes just to survive. For example, there are quite a number of cases in the outer islands where residents undertake fishing in wetlands, rivers, and seas, to earn their livelihood by consuming their fish catch for themselves or selling it in the traditional markets. Many poor fishermen have to rely on the patronage of boat owners and middlemen. Other microenterprises that produce foods, other agricultural products, or handicrafts earn enough money just to pay for the things they need to live, while others earn a little better than subsistence income.

For the more fortunate micro and small scale enterprises and the larger units, having an increased market share could be a stronger driver. Such an objective is at the same time supportive to meeting the end needs. In order to gain a higher profit margin, they need to increase their market share. This is especially the case for those selling products purchased infrequently by a fragmented customer group. Market share is also closely related to return on investment, as a bigger market share reflects economies of scale and efficiency. MSMEs that actively use e-commerce are a notable example, in which the “experience curve” is at work. But a more frequent and simpler explanation for the market share-profitability relationship that we observed from numerous interviews was with respect to maintaining existing customers and finding new ones, and improving the quality of management particularly in controlling costs and making workers to be more productive.

The second objective, raising sales volume, is closely related to productivity. To the extent sales increase in monetary value can be due to rising price or increased volume, and the channel of social capital affecting productivity can lead to either a positive outcome (e.g., increasing output through collective action) or a negative outcome (e.g., lowering output due to collusion), the more relevant one for productivity is the increase in sales volume.

The third objective reflects closely the element of social capital. It represents respondents’ intention to make personal changes of attitude by utilizing their potential and capacity as well as local opportunities through collective actions. In some cases, our respondents indicated their deliberate desire to serve as initiator, facilitator, or mentor to help the community do something useful for themselves, the surrounding areas and the community at large. Indeed, we found a number of micro and small enterprises (MSEs) and medium enterprises (MEs) in some regions whose activities were driven by concerns over the poor conditions of the community and wish to make improvements. Such activities are usually initiated and run by social entrepreneurs willing to empower others to solve their own challenges. Some of them are capable of catalyzing the potentials and bringing value to the fight against poverty and other social ills that the existing players and government programs are not. The overall results indicate that for both categories, ME and MSE, this third objective is ranked the highest, at 0.512 and 0.522, followed by the objective to increase sales volume, at 0.488 and 0.478, respectively.

Next are the challenges. These components act as the constrains MSMEs must face in their attempts to achieve the above objectives. The list of challenges in Fig. 2.5 is selected based on the findings of our previous study, particularly with respect to the components under “transaction costs” and “operating costs” in the benefit/cost framework. The operation costs include spending for inputs and raw materials, costs of capital (including bank loans), and labor costs. The latter are related to the quality of human resource and labor productivity that influence MSMEs’ ability to compete with domestically produced and imported goods of similar products. The effect on the latter is indicated by an arrow emanating from “human resource constrains” pointing to “competition & imports.” Constraints on human resource quality also have some effects on the effectiveness of using the supporting infrastructure, e.g., computers for accounting and reporting, the Internet for marketing, payment system, and information searching. Note that the supporting infrastructure in this case includes soft infrastructure as many MSMEs especially those run by older individuals with low Internet literacy encounter significant challenges to utilize online activities even when a relatively good Internet facility is available. They face a predicament of being largely disconnected from the world of digital tools and services, both physically and psychologically. This condition is depicted by the arrow connecting “Human resource constrains” and “Infrastructure constraints” in the network. A less-than-capacity usage of infrastructure could also prevent MSME to have lower operating costs and to compete with other products; hence, there are arrows connecting “Infrastructure constraints” with “Operational costs” and “Competition & imports.”

By incorporating all the interactions and feedback effects, overall results show that the most important challenges for our MSME respondents are the “Operational costs” (0.285) followed by “Competition & imports” (0.268) and “Institutional constrains” (0.233). Breaking down the respondents into MSE and ME, the ranking for ME is slightly different. Since some of their products have to compete with other products including from imports, they ranked “Competition & imports” slightly higher than “Operational costs” (0.267 versus 0.260).

Unarguably, the above complex interactions of challenges influence the preferences of MSMEs’ SC-compatible measures (bottom part of Fig. 2.5). However, in a network system the priority ranking of challenges could also be altered by the preference ranking of policy measures. That is, the feedback effects are at work. Consider the case of “operational costs.” For some MSMEs, especially those located in remote areas, this component may be the most binding of all challenges. Therefore, it is ranked the highest. But if efforts to facilitate participation and coordination for business purposes are not highly prioritized for these MSMEs, instead, they consider networking with other stakeholders (“interaction-network”) and having legal certainty and protection (“regulation & legal matters”) to be more relevant for productivity improvements, then the “Operational costs” may not be ranked high. This mechanism is captured by the arrow originating in “Facilitate business” pointing to “Operational costs.” A similar argument can be made for “Institutional constraints” in the list of challenges.

Another important point to note is that, choices of policy measures could also have some feedback effects that evoke different ranking of objectives. In particular, measures to simplify regulations and provide legal certainty to MSMEs could be so critical that they may be able to sway the importance of MSMEs’ different objectives. The arrows connecting “Regulation & legal matters” to “Increase sales volume” and “Utilize individual & regional potential” capture the mechanism of such influence. We learned that quite a number of respondents complained about the complexity of acquiring certain permits related to their business operations. Some were also concerned with the sporadic and unannounced inspections on certain matters requiring compliance which often ended up with bribing the inspectors. Since regulatory agencies are usually unable to conduct continuous inspections due to resource or technological constraints, they tend to choose sporadic and unannounced inspections. Although such a practice can be more effective in catching the violators off-guard, it could seriously disrupt MSMEs’ daily operations, not to mention create fears among them. This has the potential to cause severe financial difficulties and bring some into bankruptcy. Ironically, such an episode often occurs during an economic hard time, when many MSMEs are facing financial difficulties. As a result, the objectives of improving productivity and utilizing individual potential, let alone initiative to develop the community and boost the local economy are no longer on the priority list. Under such circumstances, survival becomes the overwhelming purpose of running the business (instead, “Meet the end needs” receive a higher weight).

Table 2.1 Summarized ranking of policy preferences of ME, MSE, and MSME (ANP)

The point is, in stating their preferences toward SC-compatible policies, the MSMEs went through a series of questions related to alternatives, challenges, and objectives. The interrelations among those factors are complex and include some feedback effects, such that there is a possibility that the ultimate policy ranking could have been different had they been asked to rank the preferred policies directly without considering those complex interrelations. At this juncture, it is worth associating MSMEs’ response with the concept of “fast thinking” and “slow thinking” proposed by Kahneman (2011). In “slow thinking” (also labeled System 2), either consciously or unconsciously MSMEs incorporated the complex patterns of ideas and association involved in the questionnaire. To reveal their preferences, they proceeded through a sequence of steps using their cognitive program through an orderly process. This process is very different from revealing the preferences in a more automatic and direct manner (“fast thinking” or System 1) without considering objectives, challenges, or anything else. Under this system, their quick impressions and feelings are the main sources of their deliberate choices, based upon which they reveal the ranking of policies. The different results of using System 1 and System 2 are shown and discussed in the next chapter, particularly in Sect. 3.2.

Once the weight of each component is measured and included in the network, and the relevant method to find an equilibrium is applied, the final outcome of SC-compatible policies would be more consistent with the existing objectives and challenges. As shown in Table 2.1, of all six measures, the highest rank is “Interaction-network” (0.232) followed by “Supporting infrastructure” (0.195) and “Regulation & legal matters” (0.171). Broken down by MSE and ME, the ranking remains the same for both.

The importance of networking cannot be overstated. Our field observations and series of interviews also found such a ranking was quite overwhelming.¹ Evidence from the global experience is also supportive to the primacy of networking. Many studies have shown that interaction, relationship, or network play an important role in market exchange; see, for example, Greif (1994), Kranton (1996), Knack and Keefer (1997), Barr (1988), and Fafchamps and Minten (2002). Compared to other variables including human capital (e.g., years of schooling, school density), networking as part of social capital has a much stronger positive effect on productivity. Through interactions in a network, MSMEs are able to deal with each other and with stakeholders in a more trustworthy manner, be it in sharing information, economizing production, marketing, borrowing and financing, or utilizing digital technology, such that the transaction costs can be lowered.

The probability of bringing innovation into MSME operations is also higher if a network involving the right players works through inter-firm cooperation. A study using the case of MSMEs in China shows that there is a strong positive impact of network on the innovation performance of MSMEs, albeit with different degree depending on whom the relation and cooperation are made with. The most significant positive impact is when the cooperation through the network is with customers, suppliers, and other firms including other MSMEs. The impact is stronger than the horizontal cooperation with research institutions including universities, and government agencies (Zeng et al., 2010). The role of social capital in strengthening innovation and its dissemination and absorption is as important as direct investments in knowledge and hardware infrastructure (Fountain, 1998).

But in many cases, the social planners and policymakers may have different perspectives. They tend to think that most problems faced by MSMEs are due to a lack of financing. Having a different mental bandwidth than MSMEs’, therefore, they are more inclined to “solve” MSME problems by providing funds through various programs, regardless of the prevailing conditions and social capital around which the MSME operates. The ranking of policy preferences for MEs, MSEs, and MSMEs according to social planners is displayed in Table 2.2. Clearly, for the social planners providing financial support is considered the most important policy to help MSMEs solve their problems. The same applies for MSEs. Reconciling the differences of preferences of MSMEs and social planners is therefore imperative. This issue is discussed in detail in the next chapter.

The consistency of survey results from applying the ANP for the interplay of policies and social capital is summarized in Table 2.3. In all cases, the inconsistency index is less than 0.10. Similarly, the consistency results from applying the AHP for the policy-mix are shown in Table 2.4, from the ANP for Social Planner-MSME in Table 2.5, and from the AHP for Social Planner-MSME in Table 2.6. In all cases, none of the inconsistency index is greater than 0.10 (see Appendix B for the measurement of inconsistency index).

Table 2.2 Summarized ranking of policy preferences of Social Planner-ME, Social Planner-MSE, and Social Planner-MSME

Table 2.3 Inconsistency index, ANP representative sample

Table 2.4 Inconsistency index, AHP representative sample

Table 2.5 Inconsistency index of Social Planner-MSME using ANP

Table 2.6 Inconsistency index of Social Planner-MSME using AHP

As indicated earlier, in this study we used several structural variables. For each of those variables we generated the ranking for all elements under objectives, challenges, and alternatives. We display the results of the ranking under the digitalization-related variable (using and not using digital technology) in Table A.2 and those under the financing gap-related variable (above and below median loan size) in Table A.1, both on Appendix A. The results in both cases do not change the supremacy of “Interaction-network,” suggesting that even after controlling for the financing gap and digitalization, the preferred SC-compatible policy remains “Interaction-network.” The results for other structural variables are not shown; they are available upon request. But in general they maintain the “Interaction-network” choice as the top priority. Appendix E shows the radar charts for total MSME, ME, and MSE under the following structural variables: duration of firms operations (Fig. E.1), duration under BI supports (Fig. E.2), urban-rural (Fig. E.3), size of profit change (Fig. E.4), digital use (Fig. E.5), and exports-non exports (Fig. E.6).

To the extent the revealed ranking is derived after taking into account the complex interrelations among components in a network, the above results are robust, and they represent the consistent preference ranking of ME and MSE with respect to SC-compatible policy measures.

2.3 Preferred Policy-Mix

The idea of interaction between policies is central to the concept of policy-mix in which one policy may either reinforce the effect of, or create a trade-off with, other policies. When duplication and its significant implications on administrative costs could reduce not only the effectiveness but also the efficiency of the policy-mix, it is important that the choice of the mix takes into account the complexity and dynamics of SC-compatible requirements. The attempt should go beyond just finding a combination of interacting policies. It should include a strategy and the accompanying processes to achieve the policy-mix as part of the interplay. Unlike in unilateral actions, in joint actions one needs to consider the consequences of one action as well as of those of others, and integrate them to infer the joint consequences. It therefore involves a coordination. This requirement is important for the policy-mix to be implementable if social planners and MSMEs are to develop mutually satisfactory views.

Consider the case of providing funds for MSMEs, and establishing or strengthening MSME network with other stakeholders. When the two are combined, for example, directing financial assistance toward developing interactions through a network, the effectiveness is likely higher than if each is implemented separately. The criteria and conditions for allocating the funds are clearer, able to reduce the transaction costs, and, more importantly, MSMEs could benefit a lot more from such a policy-mix.² Nonetheless, a careful selection of the mix ought to be made to ensure that the outcome is indeed superior to that resulting from the case where each policy is taken independently. For this purpose, a preliminary step was taken prior to constructing the final network that contains the policy-mix. We first explored the critical elements capturing the prevailing social capital in the alternative policy measures.

From the earlier study (Azis, 2022), participation and coordination for information searching and business purposes are key elements of the social capital. Based on those elements, interactions among MSMEs emerged as key in our analysis of SC-compatible policies. As we expanded the analysis by considering the specific objectives and challenges faced by MSMEs, the associated SC-compatible policy is one that includes establishing a network for interactions. Furthermore, to meet the stated objectives and deal with the complexity of challenges that are relevant to the prevailing social capital, we broadened the scope and span of coverage to include interactions with other stakeholders. This led to the “Interaction-network” as one of the SC-compatible policy choices in the network-based analysis. At any rate, we developed a separate hierarchy for ME and MSE in which the objectives and challenges for each are carefully studied before coming up with the list of relevant SC-compatible policies (the hierarchies for these preliminary steps are available upon request).

Only after taking such steps we constructed a relevant hierarchy in which a set of choices of relevant policy-mix is listed at the bottom of the hierarchy in Fig. 2.6 for MSE and in Fig. 2.7 for ME. The objectives are re-adjusted from the ones used in the network analysis in order to make them relevant to the policy-mix applicable for MSE and ME. For example, the stated objective of utilizing individual and regional potential in the preceding case is now separated for ME and MSE. More specifically, in addition to the objective of catalyzing community’s potential, MSE is also concerned with improving individual potential, hence “Catalyze community and individual potential” is listed as one of their objectives. For ME, the objective to catalyze the community potential is sort of expanded to include developing the region’s economy. Somewhat related to it is the MSE’s objective to “Meet end needs” whereas the corresponding objective for ME is to enlarge “Market share.” Despite this distinction, however, the objective of catalyzing the potential of community activities received the highest priority for both, MSE and ME, for which the weight including catalyzing the individual potential for MSE equals to 0.452, and for ME equals to 0.406. As expected, the weight for the second highest rank is larger for MEs than for MSEs (0.325 compared to 0.292).³

Fig. 2.6

Hierarchy structure for MSE policy-mix

Fig. 2.7

Hierarchy structure for ME policy-mix

For the challenges, we broke them down into two parts to include specific problems into which some challenges can be subsumed. More specifically, since facing competition with other products including imports implies the need for the government or social planners to implement some sort of trade regulation and competition policy, the role of social planner is paramount. Hence, such a possibility is covered under the specific components of institutional constraints, particularly “Lack of government support” and to some extent also “Lack of interaction.”

The two most important challenges are “Financial constraints” (0.306) and “Human resource constraints” (0.263). Broken down into MSE and ME, the weights are 0.325 and 0.273, and 0.268 and 0.252, respectively. Translating those challenges into more specific problems, the kind of financial constraints considered most serious are associated with difficulties repaying loans (cost of financing: 0.422) and paying for activities related to sales (sales financing: 0.367). For MSE, the weights are 0.416 and 0.433, and for ME they are 0.373 and 0.358, respectively. In terms of institutional challenges, they ranked a lack of government supports at the top (0.521) with a greater weight for ME compared to MSE (0.551 versus 0.503), followed by a lack of interaction. In practice, the two can be interrelated in the sense that they expect that social planners will help establish a network for them to interact better with the relevant stakeholders. The interaction is also expected to help them compete with domestically produced and imported products.

Having considered the challenges and the specific problems, we list the preferences of MSE and ME for the alternative policy measures at the bottom of the hierarchies in Figs. 2.6 and 2.7, respectively. The preferred alternatives consist of strengthening the promotion (P), providing liquidity supports (Q), giving the necessary information and tools available to assess the environmental impact of their activities (E), and making the linkages work more effectively (L).

For the promotion (P), most respondents expressed their preference to actively—and directly—involve (either physically or virtually) in exhibitions, trade fairs, and workshops. They also expect that the public and private sectors use more MSME products in various events such as meetings, seminars, and other gathering. For liquidity supports (Q), MSMEs expect that lending institutions especially banks could provide better access for low-cost lending to support the liquidity and refinancing needs of MSME end-borrowers. Given the prevalence of credit rationing and high transaction costs (discussed in greater details in Chap. 5), however, achieving this expectation may require special efforts and interventions from the lenders and the authority in charge of lenders’ operations (financial regulators). Since lending to MSME in Indonesia is very low by international standard (see Figs 5.2 and 5.3 in Chap. 5), there should be room for improvements in this area.

As for the environmental impacts (E), many discussions we had with the respondents clearly indicated that most of them were aware of climate change because their activities had been impacted by it. For those MSMEs operating in the agricultural sector, the impact had been more direct and severe. To adhere with the environmentally sustainable goal, most of them also realize that they need to adopt practices that are less harmful to the environment.⁴ This applies particularly to MSMEs that are involved in some supply chain networks as they are required to do so by other parties in the supply chain.

But for many MSMEs, accessing the information about changes to be made and meeting the required costs for such changes are too challenging. They expect the government or other relevant institutions can provide tools and guide them to obtain those information, including the explanation on how to access green financing. We found a lot of issues they raised were similar with those expressed by MSMEs in other countries reported by the WTO based on their 2021 and 2022 surveys on over 35,000 MSMEs across 30 countries (WTO, 2022).

On making the available linkages more effective (L), most respondents expressed their strong wish to have a better coordination with other stakeholders such as larger firms, suppliers, lenders, regulators (including security apparatus), customers, and other MSMEs. Their interactions with each of those stakeholders could have, at a varying degree, profound impacts on their operations.

In the policy-mix, the above alternative measures are individually paired with the following set of policy measures: Interaction-network, affordable loan, regulation, and supporting infrastructure (see again the bottom level of the hierarchies in Figs. 2.6 and 2.7).

Results of the survey show that the ME preference is clearly toward a policy-mix that includes establishing a network for interaction (“Interaction-network”). For them, that policy is better combined with “Linkage requirement” and “Promotion.” For MSE, however, the “Linkage requirement” is ranked the highest when combined with “Infrastructure,” and the second highest when combined with “Interaction-network.” Note that interaction is associated with the number of network (quantity) while linkages are more related to the quality—hence the effectiveness—of network. Interestingly, MSEs also put a policy-mix of “Infrastructure” and “Liquidity” at a relatively high ranking (third highest), given the fact that compared to the medium enterprises MSE often encountered cash flow or liquidity problems in conducting their activities. At any rate, looking at the collective ranking of ME and MSE, the policy-mix involving “Interaction-network,” “Linkage requirement,” “Infrastructure,” and “Liquidity” were at the top three of the ranking.

Survey story: Listening to the community in Galo Galo village, off Morotai island, North Maluku, about their problems and challenges. The main sources of their income are from seaweed and salted fish production, and also tourism. Attempts have been made by the local government and other ministries, as well as researchers from a local university in Morotai, to help provide vocational empowerment program, counseling on seaweed cultivation, ways to improve the quality of salted fish production, guidance to utilize mangrove roots for producing soap, and training to prepare financial report as part of the requirements to apply for loans. From those and other efforts, it was clear that opportunities to raise productivity were there, but a lack of networking made the community unable to utilize such opportunities

Looking at ME and MSE separately, MSE’s preference is overwhelmingly toward a mix of facilitating a network and improving the infrastructure (.123). On the other hand, the most preferred policy-mix for ME is overwhelmingly for network and linkages. Looking at the ranking further, the difference between the preference of MSE and that of ME is fairly stark. Although both clearly point to the importance of network for interaction, the next policy-mix preferred by MSE is infrastructure improvement combined with liquidity provision, and creating a network mixed with conducting promotion. On the other hand, the next policy-mix preferred by ME is to improve the infrastructure combined with strengthening linkages, followed by a combination of improving the infrastructure and providing liquidity support. It is therefore clear that the most highly ranked pairs of policies always include creating a network, improving the infrastructure, supporting liquidity conditions, and conducting the promotion. Table 2.7 summarizes the final ranking of the SC-compatible policy-mix based on the perceptions of ME and MSE discussed above. The ranking results under two structural variables—digitalization (using and not using digital technology) and financing gap (above and below median loan size)—are shown in Appendix A, Tables A.3 and A.4, respectively. The results under other structural variables are not shown here; they are available upon request. There are essentially no meaningful differences in the preference ranking under those structural variables and those discussed above.

Table 2.7 Summarized ranking of policy preferences of ME, MSE, and MSME (AHP)

The resulting preference ranking of the social planners, however, is very different. The hierarchy for the social planners’ views toward MSE and ME is shown in Figs. 2.8 and 2.9, respectively. The dominant joint-policy according to them, summarized in Table 2.8, is to provide affordable loan and to strengthen the financial liquidity of MSMEs. The combined policy of providing loan and strengthening the linkage is only ranked the next. Interestingly, although the same applies to MSEs, the results for MEs are slightly different. While social planners continue to place affordable loan as the top priority, they seem to realize that for MEs such a policy needs to be combined with strengthening the linkage.

Fig. 2.8

Hierarchy structure for social planner and MSE policy-mix

Fig. 2.9

Hierarchy structure for social planner and ME policy-mix

Nonetheless, similar to the survey results discussed in the preceding chapter, in general social planners tend to consider providing funds to MEs and MSEs as the “solution” to almost all problems. On the other hand, facing day-to-day challenges and working under conditions within the prevailing social capital, MSMEs think differently. As revealed in this chapter and the preceding one, for them networking is the highest priority.

Table 2.8 Summarized ranking of SC-compatible policies of social planner-ME and social planner-MSE

The question now is, are those preferred SC-compatible policies and the policy-mix of ME and MSE implementable in the sense that they are aligned with their joint incentives and the desired objectives? What if the respondents were not entirely truthful in conveying the information, and were only interested in their own preferences but not those of others including the social planners? If those preferred SC-compatible policies are the Nash equilibrium outcomes, is there a mechanism that implements the so-called social choice rule (SCR) in such an equilibrium? We will explore these fundamental questions next.