Introduction

Abstract

Consider this book as an accompanying guide to a journey. The journey is about listening to micro, small, and medium enterprises (MSMEs) across Indonesia, and designing mechanisms that could make the social capital-compatible policies implementable. The primary goal is to enhance the MSME productivity. In particular, the analysis highlights the importance for social planners and those who genuinely intend to help MSMEs to listen, feel, and understand the mental bandwidth of MSMEs, so that the gaps between what the policies are intended to achieve and what they actually accomplish can be narrowed. There are complex problems and multiple challenges faced by MSMEs. Some of those problems and challenges cannot be resolved by simply providing financial supports or allocating resources for training, promotion, digital technology, and infrastructure, without considering the prevailing institutions that guide how those resources are managed and influence each other. The way the whole processes of using those resources are organized could be key to achieving the intended goal, and it is crucially determined by the social capital as part of the institution’s continuum.

Consider this book as an accompanying guide to a journey.

The journey is about listening to micro, small, and medium enterprises (MSMEs) across Indonesia, and designing mechanisms that could make the social capital-compatible policies implementable. The primary goal is to enhance the MSME productivity. In particular, the analysis highlights the importance for social planners and those who genuinely intend to help MSMEs to listen, feel, and understand the mental bandwidth of MSMEs, so that the gaps between what the policies are intended to achieve and what they actually accomplish can be narrowed.

There are complex problems and multiple challenges faced by MSMEs. Some of those problems and challenges cannot be resolved by simply providing financial supports or allocating resources for training, promotion, digital technology, and infrastructure, without considering the prevailing institutions that guide how those resources are managed and influence each other. The way the whole processes of using those resources are organized could be key to achieving the intended goal, and it is crucially determined by the social capital as part of the institution’s continuum. Understanding the prevailing social capital to derive policies compatible with it is therefore critical, and so is designing the mechanism to implement those policies. It is shown in the book how a set of policies compatible with the social capital are elicited by directly listening to MSMEs and obtain their perceptions, and how to derive and design the mechanisms that could bring those policies align with the diverse preferences of respondents and social planners.

Before taking the journey into the analysis, let us first take a high-level view of some facts and myths about MSME, and the general mapping of MSME in Indonesia.

1.1 Facts, Myths, Productivity, and Social Capital

There are facts and myths about MSME.

What are the facts? Despite their smaller per-unit output compared to that of larger firms, MSMEs are an important contributor to growth and employment in many countries. The much bigger number of units makes MSMEs’ contribution to total GDP fairly substantial. The number of jobs created is also significant. Globally, it accounts for two-thirds of all jobs. It has been recorded that they created the majority of new jobs everywhere. All these numbers get higher when we include informal MSMEs under which many microenterprises are usually classified.

But it is also a fact that the inherent performance of most MSMEs has been less than favorable. Many of them operate in the informal sector, unregistered, and difficult to be reached by policies and programs intended to help them. Data sources did not always differentiate between formal and informal units, making it difficult to obtain the true and complete picture of their conditions. Majority of formal MSMEs are credit constrained and have a lower propensity to export. More importantly, their productivity is low and their network is poor. Of course one has to be aware of the variations in MSME definition and characteristics between countries, sectors, and status (formal or informal).

Then there are a lot of myths going around. When it comes to MSME resilience, for example, it is often conjectured that in a difficult time (crisis) MSMEs contribute to economic resilience. True that the nature of business operations of MSMEs are generally more flexible, partly because of their informality, but it is equally true that many governments tend to put greater efforts to support and rescue MSMEs in a crisis, either because of their large share in total business establishment (over 99% in Indonesia), or because it is politically risky for doing nothing about it. Crisis or no crisis, supporting small businesses that employ a large number of low-income workers is politically appealing. Some also believe that helping MSMEs can achieve other objectives such as reducing income inequality and poverty. The truth is, we cannot be certain about MSME resilience until we have reliable estimates about how their conditions would have been had there been no government supports.

What about the increased number of MSME units in the midst and after the crisis? In Indonesia, during the period of pre-COVID (2018) to COVID (2021) the number of MSME unit increased by over 5%, and the number of employee increased by a whopping 27%. Isn’t that an evidence demonstrating the sector’s flexibility and resilience? One needs to untangle the demand-side and the supply-side motivations. The supply side is rarely analyzed as it is more difficult to identify, let alone measure, the degree of “willingness” of the incumbent MSMEs to absorb newcomers. The “free entry” nature of MSME market makes it less relevant to investigate the issue. It is the demand side that is more relevant to analyze. Some segments of society had no choice but to do something to earn money during the COVID crisis, and many of them ended up doing small businesses. Hence, the increase in the number of MSME unit during and after the crisis should not be interpreted as a sign of resilience, rather it was more likely driven by a necessity to survive for many people whose socioeconomic condition had worsened during the pandemic. We should be wary in declaring something about MSME resilience in times of crisis. Anecdotal stories may support the case, but the preponderance of evidence is still lacking. At the very least, the conjecture is premature if not disputable.

What is certain is that, MSMEs are often impacted more than large businesses during a crisis. In the 2008 Global Financial Crisis (GFC), for example, export-oriented MSMEs and those relying on imported inputs suffered the most as the impact of the slow-down in many countries worked through the trade channel, e.g., falling global demand including tourism, and increased production costs in trading partners. Compared to large businesses, MSMEs had to face greater difficulties to overcome those problems as they did not have a network to rely on for help. During the COVID pandemic, the simultaneous occurrence of supply and demand shocks also impacted the MSMEs more than large businesses. Although necessary, the restrictive policies (e.g., lock down, physical distancing, etc.) simply exacerbated the impact. As many MSMEs suffered globally, the living standards of millions of those involved in their operations got worsened.

As far as the living standard is concerned, productivity matters the most. The link between the two and between both of them and growth is undisputable. Acceleration or deceleration of one affects the other in the same direction. Greater productivity enables higher profits for firms and higher consumption spending for consumers. It also means lower working hours at either the same level or higher level of income earning. Keeping factor proportions unchanged, a simple arithmetic suggests that the growth of labor productivity is the only source of lasting growth of per capita income. This applies to all activities of MSMEs. For the farm sector, Timmer (2015) argued that higher productivity for smallholder farmers is even more important because it will have a significant and sustainable impact on food security. Alas, it is precisely on productivity that most MSMEs had an upsetting record. Many of them suffered from a much lower productivity compared to that of larger firms, and the gap widened overtime despite all efforts directed to help them. As a result, MSME contribution to the national growth was smaller than the potential.

The evidence from the past also shows that in each crisis the shock was always followed by falling investment and lasting labor productivity losses. This occurred clearly during and after the GFC in 2008 where the growth of labor productivity fell steeply. In emerging market and developing economies, the decline was prolonged and broad-based, reflecting the weakness in investment and a deceleration of total factor productivity (Dieppe, 2021; Dieppe et al., 2021). The post-COVID trend is expected to show a similar pattern. However, the severity and broad-based nature of the shock warrants a careful interpretation of the published data. Many MSME during the COVID pandemic suffered from shortage of cash flow, outright losses of employment, and a sharp decline in work-hours, more than what had been experienced by the larger firms. The decline in work-hours reached 14–17% compared to less-than 9% in larger firms (ILO, 2021). To the extent the average productivity is measured by the ratio of value-added over work-hours—or over the number of workers—that ratio went up significantly during the pandemic, giving a false impression that productivity had increased. The fact is, the higher ratio was driven mainly by a sharp decline in the denominator, not due to improvements in efficiency.

Enhancing MSME resilience to ensure growth and sustainability of their productivity is a huge challenge for social planners. It requires policy measures that could leverage the main drivers of MSME productivity at all levels, macro, meso, and micro.

The analysis in this book attempts to delve into the root causes of MSMEs’ low productivity in Indonesia, taking into account the country’s heterogeneity in several dimensions, for which the prevailing social capital cannot be ignored. By zeroing on productivity improvement as the main goal for MSMEs, the analysis is intended to answer the question “what are the key drivers for improvement in MSME productivity, and why were there gaps between what policies sought to achieve and what they actually accomplished.” According to the analysis throughout the book, the answer to the first question is to create network(s) as a critical component of social capital, and to the second question it is because of a lack of serious efforts to listen carefully to MSMEs such that the design of many policies was not compatible with the prevailing social capital. Elaboration of those answers, how they are derived, and how to generate ranking of policies and identify the implementable ones take up the bulk of the book.

Social capital is the factor that we suggest policies ought to be compatible with. What is social capital? It is referred to as features of social organization. To the extent the interactions between communities and institutions in which social capital is part of could determine the prospects for development in a given society, social capital provides opportunities for mobilizing growth-enhancing resources (e.g., through social relations). It also implies that social capital does not exist in a political vacuum. Woolcock and Narayan (2000) argued that by incorporating different levels and dimensions of social capital, and recognizing its positive and negative outcomes (e.g., to promote or to undermine the public good), one could gather the empirical support to come up with comprehensive and coherent policy prescriptions.

Norms, trust, and networks that facilitate participation, coordination, and cooperation for mutual benefit are the main components of social capital (Six et al., 2015).

Social norms are actions regarded as either proper or improper by a particular community (e.g., a cluster of MSMEs). Manifested in rules, beliefs, mores, and habits, they regulate behavior, and are socially defined and enforced through social sanction. They therefore are essential to the functioning of the cluster, community, or society in general. As an important component of social capital, norms help create an enabling environment for MSME to improve productivity by way of encouraging prosocial actions from which cooperation for collective actions can be formed (Ostrom, 2007; 2010), and discouraging exploitation or other depraved practices.

Closely related to norms is trust. It reflects both an outcome and an antecedent of social capital, for it is built from—and also a basis for—relationship. That is, trust and norms have a two-way relation. Trust enables the free flow of information. And since all transactions rely on it, trust could also reduce transaction costs and lower the level of risk, both of which could stimulate more transactions. Its potential to reduce the transaction costs makes individuals invest in trust (e.g., to gain reputation).

The third component of social capital is network.¹Network can be formed along many dimensions of community (a group of MSMEs) in which related members of the community (stakeholders) interact—customers, suppliers, lenders, social planners, and other MSMEs. It can promote and strengthen the social capital by facilitating transfers of information and resources to improve productivity. This component of social capital is strongly emphasized by MSMEs in our survey as the primary driver for productivity improvements. Extensive networks of contacts can reduce the costs of searching (e.g., for markets, inputs, credit) and improve the flow of information (e.g., regarding opportunities, permits, new techniques, innovations, quality of clients), while narrowing the range in which moral hazard exists. Another line of reasoning could be made that networks allow members to use as supplementary activities to exploit monitoring devices not otherwise available, and to guard against market failure caused by asymmetric information. Although in general case social networks are not always built up for the economic value to members, the survey results in our previous study revealed that most MSMEs utilized the networks for business purposes; see Azis (2022), particularly the analysis of Fig. 4.4 (replicated in this book in Fig. 3.4).

Studies after studies have shown the wealth of evidence of pervasive and profound effects of social norms on the effectiveness of policies, because social norms influence the nature of human actions including reactions toward policies. We endorse this premise. The social capital should not be treated like other forms of capital because its existence does not reflect a deliberate sacrifice, and it is not transferable. There is also a risk of making overgeneralization if we see its function like that of other capital where some quantitative return can be derived from (e.g., the rate of return on social capital changed from x percent a year to y percent a year does not convey any clear picture). Instead, we see social capital as a preexisting social and institutional conditions to be analyzed in the context of organizational theory, i.e., as a social means of coping with moral hazard and incentive problems. Social capital can be understood through the rational choice theory, where improving performance requires finding and designing a better mechanism to change the incentives system that alters agents’ behavior. All these imply that social capital can adapt, albeit partially and in an evolutionary way, to a new environment such as interpersonal networks being partially replaced with formal institutions.

In contrast to the effect on individual agent or MSME, however, social capital can be the cause of, rather than a corrective response to, market failure. They can be detrimental to the overall productivity. Social capital-driven collusion and self-interest behavior are notable examples. They can lead to a price increase due to production cut or stockpiling at the cost of society’s interest. In some cases, the policy response to it could also make things worse when it favored politically connected agents in the name of restricting competition. Another example is with respect to group participation as part of social capital that could paradoxically result in higher social costs (costs incurred by society as a whole). It could occur when trust, despite its ability to stimulate broader elements of civic cooperation, does not correlate with group membership (Varshney, 2002), or when participation in one locality/group causes a “crowding out” effect by imposing external costs on other localities/groups as highlighted in Wade (1988) and Alatas et al. (2002).

In retrospect, our study considers social capital as features of social organization, such as networks, norms, and trust that facilitate coordination and cooperation for mutual benefit (Putnam, 1993; Ostrom and Ahn, 2003). Put in the context of the effects on performance, there is a close association between trust, norms, and network. When attempts are made to define how those components can be strengthened, however, disputes about the role of social capital arise. Using membership in formal groups as a measure of social capital, Putnam (1993) argued that horizontal networks reinforce trust and norms, and through that relation the social capital is strengthened and it eventually affects performance. Others, however, suggest that it is trust and cooperation rather than membership that affects performance. Membership is not associated with trust or with improved performance (Knack, 1992; Knack and Keefer, 1997).

What about institutions? How does social capital relate to institutions? Simply defined, institutions are systems of established social rules that structure social interactions, and social institutions form an element in social structure. Institutions in general and social institutions in particular are useful. They create stable expectations of society behavior, and can both constrain and enable behavior, e.g., existence of rules can open up possibilities and actions that otherwise would not exist.

Survey story: Traditional market in Wamena, Papua. Like many other traditional markets, it faces a certain degree of competition from a growing number of modern market stores that have better facilities and are able to offer lower prices, guaranteed product quality, shopping convenience, and choices of payment methods. However, since the goods sold in this type of traditional market are mostly produced by the traditional micro farmers, and they are different from goods sold in the modern market, there is some kind of market segmentation, where certain customers shop in modern market stores, but other segments of customers continue to go to this traditional market

By using the trust-driven cooperation (e.g., a bottom-up cluster), participation, and coordination as the examples of social capital, Azis (2022) argued that there is a two-way relation between social capital and institutions. In governing the behavior and activities of agents, social capital is constrained by the prevailing institutions. The formation of social capital is sensitive to the political and social forces as a result of the prevailing institutional arrangement. On the other hand, institutions are shaped by history and social choice, implying that they are also influenced by social capital. Hence, there is a deep complementarity between institutions and social capital. Their nexus could transmit greater influence on performance.

In the context of our study, insofar institutions and social capital are shaped by the characteristics of the society, the interaction between policies, institutions, and social capital plays a crucial role for understanding the productivity performance of MSMEs. The interaction could determine how and why policies were responded by the community in such a way that their effectiveness became limited.

The use of the conceptual analysis presented here can be tested in any countries, but the specific findings from the journey reported in this book are based on the case of MSMEs in Indonesia, the overview of which is given next.

1.2 Overview of Indonesia’s MSMEs

What is MSME? One may argue that finding the precise definition is not too important because what matters is designing policy intervention that will help small businesses to improve their performance. I disagree. That argument may sound reasonable and practical, but inconsistencies among several definitions of MSME can lead to serious distortions in policy intervention (e.g., targeting fund allocation). Finding consensual definitions is not only possible but also necessary to avoid some biases from overgeneralizing the category and coverage of MSMEs.

Various arguments about firm size classification have been made. It is generally suggested that the proper criterion to use is the firm’s volume of turnover rather than the number of employees or the value of assets as commonly adopted in many countries. And when such a criterion is applied in different regions or localities, it needs to be adjusted by regional differences in the level of economic development (Gibson and van der Vaart, 2008).

What is the definition in Indonesia? Until recently, the official reference for classifying MSME was based on Law Number 20, 2008. The criteria rely on both the turnover and the asset size. More precisely: microbusinesses are those with assets below Rp 50 million and turnover below Rp 300 million; small businesses are those with Rp 50 million to Rp 300 million assets and a turnover of Rp 300 million to Rp 2.5 billion; and medium businesses have assets of Rp 500 million to Rp 10 billion and turnover of Rp 2.5 billion to Rp 50 billion. During the COVID pandemic, the parliament passed a controversial “Omnibus Law” (officially known as Law No. 11/2020 on Job Creation) with a massive 1187 pages that came into effect on November 2, 2020. One of the legal directives of that Law was the Government Regulation No. 7/2021, in which changes are made in the classification of MSME as follows: microbusinesses are those with maximum assets Rp 1 billion and sales of Rp 2 billion, small businesses are those with assets between Rp 1 billion and Rp 5 billion and sales between Rp 2 billion and Rp 15 billion, and medium businesses are those with assets over of Rp 5 billion but below Rp 10 billion and sales between Rp 15 billion and Rp 50 billion (see Table 1.1).

Table 1.1 Comparison of MSME classification criteria

Even using the above references, data availability and consistency are problematic. Most microenterprises and some small enterprises in Indonesia operate in the informal sector. The proportion with formal registration for that category is generally lower than in other developing countries, and so is the incentive to become formal (longer years spent in the informal sector). The problem is, data sources did not clearly differentiate between formal and informal microenterprises. One estimate shows that the ratio between informal and formal MSMEs in Indonesia was about 22% World Bank-IFC (2017).

Virtually all of Indonesia’s MSMEs have the status of sole proprietorships, and some are gathered in co-operatives. In the microenterprise category, less than a quarter are run or owned by woman, and in the small and medium categories the female proportion is even lower (less than 17%). Majority of MSME have a lower propensity to export than larger firms, although part of their output may be exported indirectly through subcontracting arrangements. The recorded contribution of MSMEs in Indonesia’s total exports was only 15.65% (2019 data), barely improved from the previous years. For 2024, the government set a target of 17%, which is still much lower than in other neighboring countries (60%, 41%, and 29% in China, Singapore, and Thailand, respectively). In general, MSMEs represent a significant part of the country’s economy especially in terms of their share in business units, output, and labor absorption. The information on output varies according to the sources. The latest available data from the Ministry of Cooperatives and SMEs show that the contribution of MSME in Indonesia’s GDP in 2019 was about 54.2%. But according to the Coordinating Ministry of Economic Affairs, the post-COVID number already reached 61%. The same predicament applies to data on the number of unit. According to the Coordinating Ministry for Economic Affairs (Press Release in 2022), there were 64.2 million MSME, far higher than the data from the National Survey of Labor Force (SAKERNAS), i.e., 50.6 millions. Although the COVID pandemic accelerated the growth of MSME unit, such a difference is hard to reconcile. The sectors in which most MSMEs operate are: Agriculture, Trade, Manufacturing, and Accommodation (Fig. 1.1), and one estimate indicates that women own about 65% of the total MSMEs. Around 54% of MSMEs operate in Jawa, and urban location slightly dominates the distribution country wide. This translates into a density of 185 unit per-1000 population in average, which is high by international standard. The inequality between regions is also evident: 178 in Jawa and 195 in non-Jawa. When we look at the distribution by provinces, the inequality is even starker. The highest density is recorded in East Nusa Tenggara (242.7), and the lowest is in Jakarta (143). All the above are based on 2021 data. Prior to 2020, most MSMEs operated in the agricultural sector, but since the pandemic the service sector took over the dominance.

Fig. 1.1

Source SAKERNAS, own calculation

Share of MSME unit, 2021

On labor absorption, it is often argued that MSME is more labor intensive than larger firms. Although it is true that MSMEs absorb a large number of employees, it is misleading to use enterprise scale as a reliable guide to identify the labor intensity of MSMEs. Many MSMEs are in fact more capital intensive than larger firms in the same industry. By implication, policies designed to help them should not be confused with targeting employment creation.

How large is the MSME labor absorption in Indonesia? According to SAKERNAS data, some 67.5 million Indonesians (more than half or 56% of total employment) work in MSME. The COVID pandemic not only raised the number but also changed the distribution between large and small enterprises. After the shock, the employment share declined in the former (through labor shedding and/or bankruptcy) and increased in the latter. Such a shift was accompanied by changes in the regional and sectoral distribution too. Prior to COVID, there were more MSME employees in non-Jawa than in Jawa, and the reverse occurred after COVID. Most of the added MSMEs after the pandemic apparently happened in Jawa, where the economic hardship forced many in this most populated island to open small businesses. Before 2020, the MSME sector that employed the largest number of workers was in the agriculture, and after COVID the highest number was in services. It is important to note, however, that by sub-sector, the largest number was in trade, part of the services sector (Fig. 1.2). Many of these businesses were involved in small retail activities with low productivity.

Fig. 1.2

Source SAKERNAS, own calculation

Share of MSME employment, 2021

A lack of access to finance is another important characteristic of MSMEs. It is a perennial topic for social planners, and is frequently identified as a critical barrier for growth. Like in many developing countries, MSMEs in Indonesia are credit constrained, facing a substantial financing gap. Credit rationing, more than demand, hampers the growth of loans. Policy measures to relieve the constraint were often ineffective as most of them failed to address the prevailing social capital and other on-the-ground challenges faced by the MSMEs.

Figure 1.3 shows that the gap between credits allocated to MSMEs and total credits has been widening, where the ratio of the former to the latter is hovering around 20%. Even with the falling BI’s policy rate and a series of government’s efforts to support MSMEs during the COVID pandemic, the gap continued to widen in 2022. That figure conceals the skewed distribution of lenders. About 65% of all credits to MSMEs were issued by only four biggest banks in the country (Bank Rakyat Indonesia or BRI, Bank Negara Indonesia or BNI, Bank Mandiri, and Bank Central Asia or BCA), and only two of them (BRI and BNI) allocated more than 20% of their credits to MSMEs. Compared to the case in most countries around the world, measured as a percentage of GDP the total credits for MSMEs in Indonesia have been among the lowest (less than 7%), while the density of MSMEs is among the highest.

Fig. 1.3

Source Department of MSME Development and Consumer Protection, Bank Indonesia

Gap of total and MSME credit 

Fig. 1.4

Source Department of MSME Development and Consumer Protection, Bank Indonesia

Sectoral MSME credit allocation, 2018 and 2021

The credit allocation by sector shows that trade has been always the largest recipient. For most people who wish to start doing business or selling something, it is easier to establish small retail trade than manufacturing activities at the beginning. Some may be able to expand the business or switch to manufacture something, others may fail and close the business altogether, and many stay in the same retail trade business. The first and the last categories have better access to bank credit. Getting credit approval is easier as banks’ propensity to lend to them is larger than the propensity to lend to newcomers who do not have credit record or sufficient collateral. Yet, even though the required size of credit is small, the number of retail trade MSME across the country is huge, that the overall sum of bank’s credit allocated to retail trade sector is the largest, close to 7% of all MSME credit (Fig. 1.4). The COVID pandemic seemed to cause a structural change in terms of a relative decline in manufacturing activities (lockdowns and other restrictions) and an increase of agricultural sector (may have been related to an increase in the health conscious behavior). Such a change is also reflected in the credit allocation before COVID and 2 years after COVID.

Fig. 1.5

Source SME Finance Forum (2017). MSME Finance Gap 2017, WB-IFC, Washington DC

Percentage share of microenterprises in total MSMEs in selected countries

Another important feature of MSMEs in Indonesia is the dominance of microenterprises in total MSME unit. While in many countries the share of small and medium enterprises is typically lower than that of the microenterprises, the proportion of the latter in Indonesia is far higher than the average in developing countries (see Fig. 1.5). Based on the IFC data taken from the Ministry of Planning 2017, almost 99% of total MSMEs in Indonesia are of the microtype. This number is higher than the average in Asia Pacific countries (85.3%), the average in Sub-Saharan African countries (97.5%), and the average of Latin America and Caribbean countries (94.9%). Hence, to get a better and truer picture about the problems and challenges of the country’s MSMEs and the appropriate policies to improve their performance, it is necessary to cover a disproportionately larger share of microenterprises in the study. It is for this reason more than 80% of our survey respondents were of the microtype.

In short, Indonesia’s MSMEs are large in number and dominated by microenterprises, having low productivity, and credit constrained. Efforts to enhance their productivity thus far have not been effective. Policies to relieve their credit constraint may require a different approach than what had been taken. Whether it is to use movable collateral registries to cover the perceived risks of lending, or to apply other alternatives to traditional collateral-based lending such as supply chain finance, or to pair financial support with advisory services, a good network between MSMEs and the stakeholders is required to make such alternatives possible.

1.3 Book’s Outline

We begin our journey in Chap. 2, where we identify and rank the list of social capital-compatible policies and policy-mix from the perspective of our respondents. Applying the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) techniques as we did in our previous study, the analysis in Chap. 2 is attempted to test whether the results continue to reveal the dominance of social capital. By capturing more detailed interrelations between objectives, criteria and alternatives with some feedback effects, and also by identifying the policy-mix, however, in the current study, we are able to specify which among social capital components that is most effective for productivity improvements.

Survey story: Women-run cooperative in Aceh, selling handicrafts produced by women villagers. Having limited network at early stage, they struggled to find market, and were unable to improve their productivity. By forming groups or clusters, and developing a network with other institutions (with the help of the local BI office), the sales increased and the skill of the members improved

To the extent not all policies preferred by one group are in line with those of others, hence they are not implementable from the social welfare perspective, attempts are made to scrutinize the derived social capital compatible policies in Chaps. 3 and 4. We adopt a novel approach to dissect the preferred policies into non-implementable and implementable ones, and derive the mechanism (endogenously) to implement the latter. The approach is to combine the eigenvector-based ranking—derived from the AHP and the ANP—with the application of monotonicity test based on the mechanism design theory (MDT).

In Chap. 6, we verify the results by using hybrid data (combined interviews and secondary data) applied to the instrumental variable regression. We used a set of control variables, including the non-economic type, and we assign a culture-related variable, i.e., the presence of indigenous communities or masyarakat adat, as the instrument. The analysis is intended to test the significance of the component of social capital derived from the perception survey reported in Chaps. 2–4, and challenge the conventional wisdom that size is the key determinant for productivity improvements (larger firms tend to have higher productivity).

Among several control variables, two are highly relevant for the analysis of MSME growth and productivity: financing and digitalization. To the extent these two are among the top agenda of social planners in Indonesia, issues surrounding them are discussed in Chap. 5. For the financing part, the extent of Indonesia’s MSMEs being credit constrained is shown, followed by the discussion on the results of a disequilibrium model where the presence of credit rationing and high transaction costs is substantiated. On digitalization, the opportunities and challenges for MSMEs to use digital technology are analyzed. Using the trend since the COVID pandemic, our interest is to find out whether the increased use of digital technology since the pandemic is cyclical or structural. And our journey and findings are summarized in Chap. 7.

Let us begin the journey.