Anàlisi de Conversa familiar sobre el regionalisme

Microcredit is critical for the growth of SMEs because SMEs require sustained investment of working capital. But, at low income levels, the accumulation of such capital may be challenging. Under such circumstances, microcredit may enable SMEs to improve their income and accumulate capital (Atieno, 2001). This section reviews the impact of microcredit at the enterprise level using different techniques. Empirical studies on the impact of microcredit can be classified into two groups: those that neglected the selection bias problem and those that did not.

Table 3.4 shows some studies on the impact of microcredit did not take into account the issue of selection bias (Atmadja et al., 2016; Dunn & Arbuckle, 2001; Hartarska & Nadolnyak, 2008; Ouma & Rambo, 2013). For instance, Dunn and Arbuckle (2001) used analysis of covariance (ANCOVA) to evaluate the impact of microcredit on participants of microcredit in Peru and found it substantially increases microenterprise net income, assets and employment. Even though the study was based on panel data to measure the impact variables between 1997 and 1999, methodological limitations to address possible selection bias may provide unreliable results. Hartarska and Nadolnyak (2008) used

10 _{Refers to a group of women who participate in microfinance} 11 _{Refers to a group of women who do not participate in microfinance}

54 the credit constraints approach to study the impact of microfinance on access to credit for

microenterprises in Bosnia and Herzegovina. Their results indicate that microfinance can alleviate financing constraints faced by businesses. The logit model used in the study evades methodological challenges typical of impact assessment and thus leads to underestimation or overestimation of its results.

Table 3.4 Summary of Previous Studies on Impacts of Microcredit at the Enterprise Level

Author(s) Area/country Unit of analysis Outcome indicator Approach

Dunn &

Arbuckle (2001) Peru Enterprise Net income Assets Employment ANCOVA Hartarska & Nadolnyak, (2008) Bosnia and

Herzegovina Enterprise Income Logit model Ouma & Rambo

(2013)

Kenya Enterprise Sales volume Net profits Number of paid workers Frequency distributions, percentages and cross- tabulations Johansson & Pettersson (2014)

El-Salvador Enterprise Sales Total assets Equity

OLS regression Ferdousi, (2015) Bangladesh Enterprise Business Income Simple

regression Atmadja et al.,

(2016) Indonesia Enterprise Profit (increased, decreased, or unchanged)

Ordered probit Tedeschi, (2008) Peru Enterprise Profits Fixed effects

Instrumental variable Peprah & Ayayi

(2016)

Ghana Enterprise Sales Stock Expenses Profit Propensity score matching Quaye & Hartarska, (2016)

Ghana Enterprise Amounts of

investment Propensity score matching

Numerous studies have endeavoured to correct for selection bias and have found positive impacts of microcredit using various econometric techniques (see Table 3.4). For instance, Tedeschi (2008) attempted to solve selection bias using fixed effects and instrumental variables and emerged with the evidence to support the positive effect of microfinance on business profits. Similarly, Peprah and Ayayi (2016) found a positive impact of microcredit on clients’ sales, profits, and expenses compared

55 with their non-microcredit counterparts. The authors addressed the issue of selection bias by using propensity score matching and nearest neighbour matching. The PSM approach has the advantage of solving the selection bias problem that may be posed by self-selection of clients. The main pillars of this impact model are individuals, treatment and potential outcomes. Quaye and Hartarska (2016) also employed PSM to investigate the effect of microcredit access on the amount of investment made by the small enterprises in Ghana and concluded that unconstrained enterprises make higher investments than constrained enterprises because unconstrained enterprises acquire funds from microcredit providers.

With regard to the impact of microcredit on small business in Malaysia, Mahmood and Mohd Rosli (2013) used regression analysis to investigate 756 micro and small enterprises and found a

microcredit scheme significantly impacts a firm’s performance across AIM and TEKUN. The authors

concluded that a microcredit scheme helps to bridge the capital gap and enhances the performance of SMEs in Malaysia. Hassan and Ibrahim (2015) studied the impact of microcredit on 350 enterprises in Penang, Malaysia. They found 175 respondents (50%) felt that the microcredit programme was very helpful; 152 respondents (43.43%) felt that the programme increased their business income. Only two respondents (0.57%) said that the programme was not helpful, four (1.14%) said the programme had no impact, and three (0.86%) were unsure whether the programme was helpful. The

authors conclude that microcredit programmes have a positive impact on a firm’s business. Similarly,

(Abdul Wahab et al., 2014) employed an average effect of treatment to study the impact of microcredit on women empowerment in urban Peninsular Malaysia. The authors’ results show

microcredit is a powerful tool in promoting women’s empowerment in various aspects including their role in household economic decision making, economic security, control over resources and family decisions, mobility and legal awareness in urban Malaysia.

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Table 3.5 Impact Microcredit in Malaysian Studies

However, the study on the impact of microcredit in Malaysia is inconclusive because most of the studies focus only on AIM (see Table 3.5). Besides, the AIM and TEKUN microcredit schemes are also offered by the Central Bank of Malaysia and channelled through 10 financial institutions including commercial banks and development financial institutions. The present study grasps the essential need to investigate the impact of microcredit scheme not only for AIM and TEKUN but also other microcredit providers. Additionally, the impact of microcredit on Malaysian SME performance has been less explored using econometric techniques thus leaving room for further investigation. The details of the impact methodologies will be discussed in Chapter 4.

3.6

Chapter Summary

This chapter reviewed the relevant theories and practices regarding credit markets, accessibility and the impact of microcredit. SMEs are constrained in access to formal credit because financial

institutions fail to grant credit because of information asymmetry, high processing cost and

insufficient valuable collateral. Therefore, microcredit was introduced to cater for the credit needs of poor people and small businesses. Understanding the key factors to access microcredit among SMEs is important because SMEs contribute greatly to income and employment creation.

Previous empirical studies have focused on household-level factors in microcredit accessibility, but relatively few studies are of enterprise-level factors. In addition, to the best of our knowledge, the

Author Topic Sample Institutions Analytical

techniques Ahmed et al.

(2011) Impact of a microcredit programme for the rural poor: Evidence from Amanah Ikhtiar Malaysia

Members of AIM and TEKUN AIM Descriptive Mamun, Malarvizhi, Hossain, & Wahab (2011)

Examining the effect of participation in

microcredit programmes on assets owned by hard core poor households in Malaysia

Members of

AIM AIM Structural equation modelling

Chan & Abdul

Ghani (2011) The impact of microloans in vulnerable remote areas: evidence from Malaysia

Beneficiaries

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present study is the first attempt at an impact evaluation study of microcredit on SMEs’ performance

based on a quasi-experimental using the PSM and DID methods to mitigate selection bias.

This chapter also reviews impact evaluation methodologies and the problem of impact evaluation. In Malaysia, although few empirical studies have documented a positive impact of microcredit

programmes, the findings are inconclusive because the studies neglect econometric issues in impact evaluation, i.e., selection bias and endogeneity. Therefore, it is necessary to evaluate the impact of

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