Overlapping assistance distribution of the Indonesian government ’ s scheme for small and micro - scale enterprises during COVID - 19

ABSTRACT


INTRODUCTION
The COVID-19 pandemic has had a significant global impact, socially and economically.
The economic downturn affected business communities, regardless of their size, but smaller businesses (small and microscale enterprises/SMEs) may have been at a greater disadvantage due to higher levels of vulnerability (OECD, 2020).Although Asian countries had different policy responses to maintain their SMEs' sustainability, most countries applied income and profit tax deferrals, loan guarantees, direct lending to SMEs, and wage subsidies.According to the OECD (2020), only a few countries responded by applying special grant/cash transfer schemes.The Malaysian Prihatin Rakyat Economic Stimulus Package (PRIHATIN) provided cash transfers to compensate for income loss (Ahmad et al., 2020;Hasin et al., 2021;Mustapa & Mohamad, 2021).Singapore implemented the Self-Employed Person Income Relief Scheme (SIRS) and the Productivity Solutions Grant (PSG) (OECD, 2020;Chow & Ho, 2021).In developed countries such as the United States, assistance was provided to small businesses through the Coronavirus Aid, Relief, and Economic Security program, which comprised the Paycheck Protection Program (PPP), Small Business Administration (SBA) loan forgiveness, and Economic Injury Disaster Loan (Bartik et al., 2020;Humphries et al., 2020;Li, 2021).
The Government of Indonesia introduced the Productive Assistance for Microenterprises (Bantuan Produktif Usaha Mikro, BPUM) program to help SMEs survive during the COVID-19 pandemic.The BPUM fund was sourced from the State Budget (Ishak et al., 2022).In 2020, approximately 9.11 million SMEs were BPUM recipients, which increased to more than 12 million SMEs in 2021 (Nasution et al., 2020).Each SME with specific criteria received IDR 1.2 million in 2021 (Ishak et al., 2022).The budget for the BPUM in the second year (2021) reached IDR 36 trillion, proving the program's priority status and underscoring the government's commitment to economic restoration during the pandemic (Nasution et al., 2020).Furthermore, because SMEs in Indonesia are the backbone of the national financial system, capital assistance is critical (Digdowiseiso & Hamrin, 2022;Rinaldi et al., 2022;Ishak et al., 2022).
Some studies on the BUPM, such as Kaawoan et al. (2022), Wajong et al. (2022), Imelda (2022), Kusumawati (2022), Fauziah et al. (2022), also Mustofa and Yunita (2021), highlighted the effectiveness of the BPUM, its impact on business actors, and the characteristics of business actors at the regional level.However, few studies examined the characteristics of households receiving BPUM with a large sample.A large sample will help minimize the sampling error rate as this study applies to it.
Information about the characteristics of BUPM recipient households is essential for evaluating the program, for example, whether there is any overlap in the distribution of government aid programs.According to the Fiscal Policy Agency (BKF, 2020), the challenge of providing the BPUM is that BPUM recipients also received other assistance due to the lack of integrated and validated data (Ishak et al., 2022;Wijayaningsih et al., 2021).Various kinds of assistance prevent the impact of the pandemic at the household level; for example, the Ministry of Social Affairs (Kemensos)

RESEARCH METHOD
This study used secondary raw data from the March 2021 National Socioeconomic Survey (Survei Sosial Ekonomi Nasional, the Susenas) conducted by Statistics Indonesia (Badan Pusat Statistik, BPS).The unit of analysis is all households holding SMEs recorded in the Susenas.Binary logistic regression is used to identify the factors distinguishing BPUM recipients from non-BPUM recipients.The method produces a tendency (odds ratio).SPSS.20 is used for processing.In addition to using inference analysis with binary logistic regression, the study uses descriptive analysis, visualizing data with tables and graphs that are further discussed.
In binary logical regression processing, the response variable is access to BPUM, with a value of "1" assigned for households receiving the BPUM; otherwise, "0."Meanwhile, the explan- businesses and never previously received BPUM funds, not currently receiving KUR, and not government employees.Therefore, the accessibility of KUR is the main explanatory variable in this study apart from other assistance.According to the BKF ( 2020), one problem that may occur in the distribution of BPUM is the duplication of assistance.Other assistance programs studied included PKH, BPNT, and local government assistance.Thus, this study assumes that BPUM recipients did not receive further assistance.
In addition, this study used explanatory variables in the form of control variables, which are SME owners' demographic characteristics, such as education, gender, age, place of residence, and internet access.Although no literature associates these variables with BPUM accessibility, other studies can be referenced.BPS (2018a) examines the tendency to access capital for SME entrepreneurs according to gender and level of education, with the result indicating that males and entrepreneurs with higher levels of education are more likely to obtain credit assistance from financial institutions.According to this assumption, for productive aid such as the BPUM that requires that entrepreneurs not get KUR, it is assumed that male and loweducated SME owners are more likely to obtain BPUM.This is also supported by the fact that most SME actors did not complete higher education, are female, and are older than 40 years (BKF, 2020).Meanwhile, the BPS data show that more than 40% of SME managers have an elementary school education or did not finish elementary school (BPS, 2018b).In terms of gender, COVID-19 had a greater impact on women in terms of job loss and income decline ), as well as empowerment (Kania et al., 2021).
Another variable associated with BUPM accessibility is the type of residence area.Based on the BPS study (BPS, 2021b), residents who were temporarily unemployed or unemployed due to COVID-19 were likelier to live in urban areas.This is understandable because more activity restrictions are imposed in urban areas while implementing pandemic mitigation policies (Istiqomah & Suherningtyas, 2022;Nissa et al., 2020;Romdiati & Noveria, 2022).In Indonesia, COVID-19 spread from the epicenter of Jakarta to other urban areas near other outer Java Island areas (Olivia et al., 2020).
This study also included internet access as a factor influencing the acquisition of BPUM.
Overlapping assistance distribution of the Indonesian government's...

Tusianti, Abdurrahman, Simanjuntak
This is based on the problem of the BPUM promotion/communication, which is not optimal (Ishak et al., 2022;Dzakiyati & Astuti, 2021), such that few people registered themselves as prospective BPUM recipients with local governments (Nasution et al., 2020).The information related to the BPUM was accessed more often by those with internet access.This assumed that the online media information also influenced the decision of SME owners to register.The form of the logistic regression model with logit transformation per Agresti (2018) that will be formulated is as follows: Where ρ is the opportunity for SMEs to obtain the BPUM; X1, ...X8 are explanatory variables as written in Appendix; β0 is constant coefficient value; and β1,...β8 are coefficients on each explanatory variable.Meanwhile, the odds ratio value is an exponential function for each coefficient.For example, the odds ratio of local government assistance acquisition is exp X1 = 2.This illustrates that the tendency of SME owners to obtain the BPUM in the case of households that also receive local government assistance is twice that of SME owners who do not receive local government assistance.
It is necessary to conduct a G test or Likelihood Ratio Test to examine the role of explanatory variables to response variables simultaneously or as a whole (Agresti, 2018).Furthermore,

RESULT AND DISCUSSION
The 2021 increase in the BPUM budget was evidence of the Indonesian Government's commitment to help SME business actors survive during the pandemic.In 2021, as many as 15 million business actors were targeted as BPUM recipients (Nasution et al, 2020).When calculated on a proportional basis, the BPUM target recipients represented 46% of all SMEs in Indonesia, with the number of SMEs as many as 26 million, according to BPS data (2018a).
Meanwhile, based on the 2021 Susenas data processing results, of approximately 75 million households, 9% (6.8 million) owned SMEs (Figure 1).Thus, based on the household criterion, all these households should have obtained the BPUM.However, the Susenas data tend to indicate fewer SMEs than the number observed in the data based on a business approach because it is possible for one household to have several SMEs.The data of the BPUM recipients also shows this.Sixteen percent of the 9% of households that owned SMEs were BPUM recipients.
Compared to the overall target mentioned above (46% of SME actors), the Susenas SME data are fewer.Employing the household approach is a limitation of this study.
The absolute number of households owning SMEs and obtaining the BPUM by province is shown in Figure 2. The more households with SMEs, the greater the number of BPUM recipients (Pearson correlation = 0.91).The province with the highest number of households owning SMEs is Central Java, but the largest BPUM recipient is West Java.This is understandable because the impact of COVID-19 is more pronounced in West Java Province.This is also in line with the BPS (2022a) Small Micro Industry (Industri Mikro dan Kecil, IMK) study in 2020 that shows the 1.9 million businesses in Java Island affected by COVID-19 are mainly in the Central Java, East Java, and West Java Provinces.According to DJPK (2021) and Ishak et al.
(2022), 45% of BPUM was distributed in Java.Therefore, the distribution of the BPUMrecipient households by province was based on the provincial severity of the COVID-19 impact.Because Java Island had a high rate of spread of COVID-19, social distancing restrictions were also greater, especially in urban areas (Olivia et al., 2020).This aligns with the data in Table 1, showing that urban households receive more BPUM than those in rural areas (19 ver- The critical finding in this study is that social assistance by households overlapped.More households obtaining the BPUM received local government assistance than those who did not receive local government assistance.In addition, more households obtaining the BPUM also received PKH and BPNT social assistance.This situation is paradoxical, positive, and negative.While it is positive if the BPUM recipient is an impoverished household, it may be negative if many other households received no assistance from the government.That is, various types of assistance are received by certain households only.However, analyzing this potential requires further studies.
Another interesting finding is that obtaining the BPUM was more common in households whose household members also obtained the KUR.Obtaining the BPUM was prohibited for households that received the KUR benefit.Nevertheless, this study had limited access to KUR data to determine whether these BPUM recipients are similar to those who received KUR.
However, from the distribution of data obtained, nearly all households had only one SME.
Thus, it is assumed that the BPUM recipient who is the KUR recipient is the same individual.
The province with the most BPUM and KUR recipients is Central Sulawesi.In addition to Central Sulawesi, 11 other provinces have cases above the national level, with a causal number of 2.15% (Figure 3).Therefore, these provinces require extra attention.Furthermore, BPUM recipients were predominantly male.There were also more BPUM recipients with junior high school education/equivalent than other educational categories.In addition, more BPUM recipients accessed the internet than those who did not.Meanwhile, data analysis using logistic regression produced exciting results.First, the age and gender variables of the BPUM recipients did not significantly distinguish household accessibility to the BPUM.Households receiving local government assistance were 1.5 times likelier to obtain BPUM than households not receiving local government assistance.The odds ratio or tendency is the highest compared to other variables.
According to BPS (2021c), local government assistance is money/goods from local governments to individuals, families, groups, and/or communities that aim to protect against possible social risks.Thus, various types of local government assistance exist, including routine and nonroutine, cash and noncash.Due to the lack of integrated data, there is a potential for duplication of assistance.Different budget allocations, data on beneficiaries that are not integrated, and social assistance distribution systems are the main problems in mapping beneficiaries during the pandemic (Rahmansyah et al., 2020).Moreover, the definition of people affected by Covid-19 is still unclear (Mufida, 2020).
When mapped by province, as seen in Figure 4, the households with SMEs in DKI Jakarta received the most local government and BPUM assistance simultaneously.The cases were 22.74% of all households with SMEs in that province.In contrast, Papua and West Sulawesi were the provinces with the fewest cases of SME households that obtained the least assistance from the local government and the BPUM.
The tendency of households receiving social assistance and BPUM simultaneously was also higher than that of households that did not receive social assistance (odds ratio 1.3).As mentioned above, this may be negative if it turns out that there were still many poor households that did not receive any assistance.In contrast, many households obtained various types of assistance simultaneously.This will be more unfortunate if the recipients of social assistance Overlapping assistance distribution of the Indonesian government's... Tusianti, Abdurrahman, Simanjuntak (PKH and BPNT) also tend to be less on target, as revealed by Sofianto (2020), Beni and Manggu (2020), BPS (2022b), Hasimi (2020), Aisyah et al. (2021), andKurniawan (2020).
The provinces with the most cases of SME households that received social assistance in the form of PKH or BPNT and BPUM at the same time are Central Sulawesi and North Sulawesi (Figure 5).Meanwhile, the fewest cases were recorded in Papua.When linked to the poverty rate in the province, although North Sulawesi had a relatively low poverty rate, many households received social assistance and the BPUM at the same time; a similar situation occurred in Banten.Therefore, in these provinces, attention must be paid to assessing whether the various assistance programs are given to the appropriate parties.
The use of the internet in daily activities also served as a differentiating factor in the accessibility to the BPUM.The tendency of internet users to obtain the BPUM was 1.2 times that of those who did not use the internet.This is quite concerning, as internet access was not only one of the supporting factors of business development but also to obtain government assistance (Alwendi, 2020;Roosdhani et al., 2012).In Indonesia, many SMEs still do not have access to information technology, as shown by the results of the BPS study (2018a).
Level of education is also a variable that distinguishes SMEs that receive the BPUM and those that do not.However, the results of this study are interesting.BPUM recipients tended to have a lower level of education than college/university graduates.The tendency of SME owners with a junior high school education/equivalent and high school education/equivalent to obtain the BPUM was nearly 1.2 times that of highly educated ones.However, the chances of SME owners who were elementary school graduates or had no schooling to obtain the BPUM were not much different from those with a college/university education (this is not significant, as shown in Table 2).Table 2 shows that the percentage of the BPUM recipients with an elementary level of education is only 14%, while those with a college education level are approximately 16%.
Finally, the residence type is also a determining factor of BPUM access.SMEs in urban areas are 1.6 times more likely to receive the BPUM than those in rural areas.This is consistent with the previous explanation that the impact of Covid-19 is greater in urban areas.
The research findings show that BPUM tends to be allocated to beneficiaries with levels of education below the university level, who are male, who use the internet, and who live in urban areas.Unfortunately, BPUM is also distributed to beneficiaries who are KUR recipients and other social protection programs from the State Budget and Regional Revenues and Expenditures Budget or Special Autonomy Funds.This indicates that the same households receive several social protection programs simultaneously and further reveals that the data are

CONCLUSION
The study results show that BPUM assistance distribution is inaccurate, and duplication occurs.This could be due to the beneficiaries' data not being integrated.Therefore, it is urgent to establish one complete and comprehensive SME database.Based on the integrated and complete data, the government can develop better-informed, targeted policies and avoid overlapping assistance.An integrated SME database will also reflect SMEs grouped by category, for example, by amount of capital, household categories, those that have never received capital assistance, those that have not received training, and those that have not utilized information technology.
In terms of aid distribution, a complete database would facilitate the government's distribution of various types of targeted assistance effectively.Thus, the principle of effective and efficient budget distribution would be implemented.
The following important finding is that there are indications of suboptimal socialization.
This can be seen from the number of internet users who received the BPUM compared to those who were not.Therefore, the government must provide more intensive promotion by targeting those who do not have access to sufficient information and technology.This also applies to other types of assistance for SMEs.
This study has the limitation of using the household as the unit of analysis; the variable type of work was not included in the analysis due to unavailable data.Therefore, further studies are recommended that use a business approach and include the type of work to identify whether BPUM is distributed to civil servants, members of the National Army/National Police, local government-owned enterprises' or state-owned enterprises' employees, as required in the Regulation of the Minister of Cooperatives and SMEs.
atory variables are categorized into two main variables: the KUR access and the acquisition of other assistance.Based on the Regulation of the Minister of Cooperatives and SMEs (Peraturan Menteri Koperasi dan UKM) Number 2 of 2021 concerning General Guidelines for BPUM distribution, the requirements for BPUM recipients are Indonesian citizens who have partial testing is carried out to test whether an explanatory variable is worthy of inclusion in the model.The hypotheses for each variable were tested using the Wald test (W) on Formula 2, with i is the first variable, valued at 1 through 8 (see Appendix); S(βi) = default error conjecture for the coefficient βi; and βi is presumptive value for the parameter .The rejection criterion (reject H0) on the Wald test is if the P-value < α. ..................... (2)

Figure 2 .Figure 1 .
Figure 2. Distribution of the number of households that have SMEs and the number of BPUM Recipients by province Source: Raw Data Susenas 2021, processed

Figure 3 .
Figure 3. SME Households receiving BPUM and KUR at the same time by province (%) Source: Raw Data Susenas 2021, processed

Figure 4 .
Figure 4. SME Households receiving BPUM and local government assistance at the same time by province (%) Source: Raw Data Susenas 2021, processed continues routine social protection programs such as the Family Hope

Table 1 .
Percentage of households that have SMEs according to various characteristics

Table 2 .
The degree of significance and influence of research variables on BPUM access Note: This table is obtained based on a fit model, with a Hosmer and Lemeshow Test P-value of 0.017** Description: *Significant at α = 10%, **significant at α = 5%, ***significant at α = 1% Source: Susenas 2021, processed not integrated, and the budget allocation is ineffective and inefficient.