Firm-level evidence on public intervention and its impact on youth-owned MSEs in Ethiopia: a nonparametric preprocessing for parametric analyses

Transcription

1 Firm-level evidence on public intervention and its impact on youth-owned MSEs in Ethiopia: a nonparametric preprocessing for parametric analyses Wolday Amha * Tassew woldehan * Manex B. Yonis * Executive Director Associate Professor of Economics Research Fellow AEMFI/EIFTRI Addis Ababa University AEMFI/EIFTRI * The authors contributed equally for this study

2 Abstract The effectiveness of any government interventions to support micro and small enterprises (MSEs) is always a concern in achieving improvements in enterprise performances. In most developing countries, the impact of an intervention is measured by the value of the inputs. This study, independently and rigorously, studies the impact of the Ethiopian MSEs support programs on core intermediate and final outcomes of interest. The impact evaluation process employs a nonparametric matching procedure for parametric outcome analysis using the Propensity Score Matching (PSM) method. The data is from 565 youth-owned MSEs those who are established after the commencement of the micro and small enterprises development strategy. The nonparametric preprocessing data produces considerably low imbalance for all the covariates and almost zero for the distance balance. Aiming a doubly robust evaluation process, we conduct parametric analyses than nonparametric permutation-based tests to investigate the causal effects of the public intervention. The study confirms that the intervention encourages the MSEs to develop innovative business practices, and improve their human capital development process through on duty workers training. Moreover, it is likely effective on creating more employment opportunities in the urban areas of the country. Nonetheless, the evaluation study provides convincing evidence that the supported group MSEs are not better off in investment intensity. The lower level of investment intensity on fixed capital among the supported group MSEs resulting in inefficiency on the MSEs. Moreover, the impact analysis confirms that the intervention does not have an effect on the change in net-asset over time among the supported MSEs. Overall, the public intervention was effective on allowing youth-owned MSEs to create more jobs in urban areas. Contrariwise, the intervention was ineffective on enhancing the long-term performance of the MSEs. The results imply that the missing-gap, that is lack of growth phase support programs, was grown. Thus, there is a need for a new and dynamic intervention that leads to the creation of innovative high-growth MSEs. Key words: Ethiopia, Public intervention, MSEs, Evaluation study, PSM method, Nonparametric preprocessing for parametric analysis.

3 Contents 1. Introduction Overview of government MSE support programs in Ethiopia and theory of change Conceptual background and empirical literatures Public Intervention The rational of evaluating public interventions Empirical literature Econometric model specification Propensity Score Matching (PSM) Matching and parametric model estimation Statistical software and package Data and descriptive statistics Data Covariate selection Outcome variables Descriptive statistics Estimation results Estimation of propensity score Nonparametric preprocessing for parametric analysis Average treatment effect on the treated (ATT) Conclusion References Annexes... 46

4 1. Introduction Since the late 1980s, African countries have pursued political, economic, and financial reforms aiming economic growth, poverty reduction, and at large macroeconomic stability. Though outcomes have been far from satisfactory, in many countries, these reforms pave the way to productivity improvement and let the countries experiencing an impressive economic progress for the last two decades (Heidhues and Obare, 2011). Evidently, for the last one-decade the world s ten of the fastest-growing economies were in Sub-Saharan Africa. But, as many argue, the sustainability of the recent economic growth performance is highly depending on how the countries stimulate the private sector (United Nations, 2014). Whilst there are skeptics who argue that the extent of which small and medium enterprises (SMEs) contribute to bottom line economic growth is far less than is asserted (Nasar, 1994; Van Stel and Storey, 2004; and Shane, 2009), a detailed literature survey signifies that SMEs are part of booming an economy and are decisive as a major source of employment and income in many developing economies (Liedholm and Mead, 1988; Leegwater and Shaw, 2008; and Kang and Heshmati, 2008). In context of Africa, SMEs constitute the vast majority of the private firms and many are more labor intensive. The World Bank Enterprise Survey (2011) revealed that African SMEs employment share to be per cent out of the total employment in the formal non-agricultural private economy. The dynamic role of micro, small and medium enterprises (MSMEs) in the process of economic improvement has prompting developing countries to design various support programs that can encourage startups and strengthen them during their operational phases. However, concomitant with broadly existed incompetent structural and institutional frameworks, existing literatures revealed that MSMEs in Africa are experiencing several constraints. Access to factor of production, in general and finance, in particular are often presented as the most severe challenges followed by limited skill on business strategy management (Mousley, 2007; Boysana and Watson, 2011; and Quatraro and Varelli, 2013). Moreover, public intervention in favor of MSMEs, typically in developing countries, is justified by alleged market failure resulting from externalities, capital market imperfection and social justice responsibilities - relating to any active labor market policies (Foundation for SME development, 2002). To this end, many 4

5 governments in Africa have been responding with designing and implementing policies and support programs that focus on promoting MSMEs in Africa. Ethiopia is one of the developing countries that has implemented extensive government support programs to promote micro and small enterprises (MSEs) development to achieve sustainable economic growth, through employment creation and poverty reduction (FeMSEDA, 2011). The government has been committed to invest significant amount of financial resources to provide all-rounded support for the MSEs sector, which focused on employment generation, particularly for the youth in urban areas. The five-year MSE development strategy, enacted in 2011, is a mix of support programs targeted at each phases of development ranging from, offering finance, providing production and marketing places, creating local and international market linkages, training and advice, and technology, in addition to promoting a favorable legal framework. However, it is believed that structural and institutional challenges that are hampering the success and growth of MSEs in the country are widely observed since the start of the public intervention during GTP I (FeMSEDA, 2014). Though there are empirical and theoretical knowledge for the importance of supporting MSMEs, the effectiveness of government interventions on supporting SMEs is always a concern in achieving improvements in enterprise performances. Often, in most developing countries, the impact of intervention is measured by the value of the inputs (for e.g., the reports of FeMSEDA). It has been noted that designing and monitoring a support program and improving the participation rates cannot evaluate the effectiveness of a program. Typically, lack of thorough support program impact evaluation is a major drawback in developing countries. Although Ethiopia has been implementing a mega MSE support programs through the five-year MSE development strategy, there are hardly evidences and research outputs which assess the effectiveness of government interventions and the performance of those MSEs receiving the support services. Moreover, with the exception of the government secondary sources which indicate, the number of MSEs benefitting from the support program, value of inputs, participation rates, etc., there has been little effort on executing rigorous impact evaluation in terms of changes in desired outcomes. Although assessing impact is a difficult process, the support program which cost the government huge resources has to allocate resources to regularly monitor and evaluate the impact and effectiveness of the interventions. A modest attempt is 5

6 made in this paper, to study the impact of support programs during the first five-year MSEs development strategy on core intermediate and final outcomes of the interventions. The main objective of the study is to assess the effectiveness of the public intervention in terms of changes in business practices, business behavior, employment creation and improvement in the performance of MSEs. During the process of assessing the effectiveness of the public intervention, attempts were made to respond to the following key questions: Do youth MSE operators change their business practices and behavior as a result of receiving the government support programs? Does improvement in the business performance of youth owned MSEs exist that is attributable to the intervention of government? Does the public intervention allow youthowned MSEs to expand employment? On top of filling the gaps in the existing empirical impact assessment literatures in developing countries context, the paper makes a number of contributions which can assist the policymakers and MSE promoters/facilitators to revisit some of the support programs. First, the study can provide evidence-based information on the effectiveness of the support programs, by focusing on firm-level analysis. Second, it utilizes a new dataset emanated from a nationwide urban youthowned MSEs representative survey that is rich in information from micro and small scale enterprises, female and male entrepreneurs, and almost from all industry sectors. Moreover, we consider detailed start up, and pre and post-support information on socio-economic features of the MSE owners as well as firm and industry related characteristics. Third, although most empirical studies examine the impact of a particular intervention on participant MSEs, the dataset gives the researchers to understand and examine the effect of receiving any kind of support on the firms' performance. Finally, given the available dataset, the impact evaluation is performed with respect to various interim and final outcomes by using a nonparametric preprocessing for parametric analysis. Considering the fact that non-experimental evaluation method is expectedly highly exposed to selection bias, we specify a matching method called Propensity Score Matching (PSM) that has been widely applied in many intervention evaluation literatures in a wide range of disciplines (Rosenbaum and Runin, 1983; Heckman, Ichimura and Todd, 1998). The process how outcomes differ for support recipients relative to observationally similar non-recipients enables to estimate the effects of the support program with less bias and variance. The evaluation process uses a 6

7 nonparametric matching procedure for parametric outcome analysis that allows the study to enjoy the 'doubly robust' property. The data is obtained from a retrospective cross-sectional representative survey held in 2013/2014. In detail, the paper is organized as follows: Section 2 presents the rational for public intervention, the motive behind conducting a systematic evaluation study, and the results from relevant empirical literatures. Section 3 outlines technical details of the methodology and describes the specifications of the models. Section 4 provides an overview of the data and presents the key descriptive statistics. Section 5 discusses the main empirical results. Section 6 concludes Overview of government MSE support programs in Ethiopia and theory of change The MSEs contribution towards creating employment and reducing poverty in urban centers has been recognized in Ethiopia for the last seven decades. The sector has been institutionalized during the Emperor regime for the first time in During the Dereg regime, it was organized and restructured as Handicraft and Small Scale Industries Development Agency (HASIDA). Later in 1998, the Federal Micro and Small Enterprises Development Agency (FeMSEDA) was established by the regulation of the Council of Ministers and further structured in In the same year, the agency issued an MSEs development strategy aiming to address challenges of MSEs and promote the growth of the sector. Since then the sector has been recognizing as the priority sector in the country s industry development plan as it is one of the important vehicles improve production and productivity, particularly in the manufacturing sector. The existing definition of the MSEs in Ethiopia is developed based on the number of employees and the total amount of net-asset, categorizing differently in the service and industry sector (Table 1). Moreover, the value of the net-asset was fixed by considering the expected inflation rate for the first five years of the strategy. During the first five-year MSE development strategy, the manufacturing, construction, trade and service sectors, were the key sectors that received direct assistance through the government support programs. 7

8 Table 1: The existing definition of MSEs in Ethiopia Level of the enterprise Sector No. Employees Net-asset Micro enterprise Industry < 5 < 100, 000($6,000 or 4,500) Service < 5 < 50,000($3,000 or 2,200) Small enterprise Industry 6-30 < birr 1.5 million and birr 100,001 ($6,000-90,000 or 4,55-70,000) Service 6-30 < birr 500,000 and birr 50,001($3,000-30,000 or 2,200-23,000) On top of addressing the challenges of the sector, the strategy aims at creating conducive business, policy and regulatory environment in order to ensure the expansion of MSEs in the country. The support programs, which cover all regions and sectors, are all- rounded: formation of MSEs, one-stop service centers, sub-contracting and outsourcing, access to finance support, provision of training, production and marketing premises, market linkage, technology support, business development service (BDS), availing information and counseling services to the MSE operators. The finance support emphases on smoothing access to finance, particularly credit to MSE operators. The intervention in terms of finance aims to improve the saving culture of MSEs, enhance access to credit, provide financial education, and avail lease financing and credit guarantee facilities. The credit service was provided through microfinance institutions (MFIs), and for the last four years (between 2010/ /14) MFIs disbursed a total of birr 9.87 billion throughout the country. The credit service has been targeted those MSEs engaged in export market and import substitution products. However, MSEs are required to save the 20 % of the loan amount as collateral before assessing the loan from MFEs. The MSE offices at various levels and TVETs provided training on business management and technical and vocational skill to more than 5 million MSE operators for the first four years of the strategy. Moreover, during the first four years of the strategy, the agency provided business development service (BDS) to 375,000 MSEs, aiming to enhance the competitive capacity of the enterprises. The strategy stipulated the provision of production and marketing premises by giving priority for MSEs engaged in manufacturing, export and import substituting activities. Accordingly, a total of 15,748 hectares of land, 9,741 sheds and 916 buildings were allotted to MSE operators, with the objective of addressing the problem of access to production and sales premises. 8

9 The MSE offices at various levels assisted the MSEs, operating with market incompetency, to create market linkages with domestic and global firms. According to the principle of the strategy, the market linkage support considers the enterprises stage of development. For example, public sub-contracting marketing opportunity has been provided mainly to MSEs at startup phase. The report of FeMSEDA reveals that MSEs throughout the country benefited from domestic market linkage a worth of birr 25.6 billion and global market a worth of birr 1.3 billion. The above government support programs were designed aiming to improve the efficiency of the MSEs, foster the progress of the sector through individual enterprises growth, and create employment opportunities in urban centers across the country. To understand and guide the structure of the study, the researchers used a framework focusing on the theory of change, which is consistent with five-year MSE development strategy (Figure 1). The objective of the theory of change is to explain how the intervention will improve or change institutions and structures, and lead the MSEs to desirable outcomes. Figure 1: Theory of Change: Supporting MSEs through various approaches Intervention (Input) - Provide factors of production - Create market linkage - Value chains and clustering - Training and technical assistant Outputs - MFIs provide financing - Number of jobs created increase - Raw material sales increase - Market expansion - Expand OSS service - Access to financial services - Increases innovation - Increases export - Improve strategic management capability - High investment intensity Interim Outcomes Final Outcomes (Impact) - Profitability (Efficiency) - Growth -Employment Source: Own presentation 9

10 2. Conceptual background and empirical literatures 2.1. Public Intervention Small and medium sized enterprises play a key role for economic and social development of a country and they account for high share of private sector employment, the rise and expansion of technological innovation, and regional development and social cohesion (Liedholm and Mead, 1999). Though it is far below from the expectations of policy makers and development partners, African countries have been benefiting from the expansion and growth of MSMEs in terms of employment creation and poverty reduction (WB, 2011). However, the positive contribution of MSMEs is mainly influenced by the structural and institutional frameworks existed in a specific country. In most developing countries, MSMEs are performing under unfavorable economic and business environment and they lack: economies of scale, collective voice so that influence policymakers, factor of production, and they incur high cost for information (Mousley, 2007 and Quatraro and Varelli, 2013). The classical socio-economic rationale for public intervention in favor of MSMEs, typically in developing countries, is linked to the alleged market failure - resulting from externalities and capital market imperfection, social justice responsibilities - including creating more employment opportunities - and SMEs' limited strategic management capability (GLA Economics, 2006). Despite the differences in the magnitude of the support programs, virtually all developing countries allocate significant amount of financial and physical capital to address the major institutional and structural failures and in turn foster the development of the MSME sector. Preliminary review of existed literatures reveals that there are various support intervention approaches designed aiming to address the institutional and structural constraints that impede MSMEs from contributing effectively to job creation and poverty reduction (Gonzalez, Piza, Cravo and Abdelnour, 2014). As the MSME sector is highly differentiated from country to country, from region and region, and as what the market structure is; the public intervention approaches are vary considerably in their scope and intention (OECD, 2004). Mostly, the public intervention frameworks have focused on the creation of efficient regulatory frame work and hence create conducive business environment; programs related to formalization of enterprises; access to factor of production and 10

11 markets; value chains and clustering; and training and technical assistant. The interventions could be indirect or direct support to the beneficiaries (Gonzalez et al., 2014). The indirect support main aim is enabling firms to practice smooth business activity, this includes, provide incentives for formalization to those informal SMEs and provide one-stop service. The direct support programs directly target the firm itself, focusing firm's shift from low level business performance to high level equilibrium. As BIS (2013) presents, business intervention can also be classified as programs, policies and projects The rational of evaluating public interventions As many researchers argue, many of the public interventions to support MSMEs have not been subjected to rigorous evaluation. Often, in most developing countries, the impact of public interventions is measured by the value of the inputs - financial, physical and social resources used to implement the project or policy or program. However, the hub of an impact assessment for a program should be based on the performance of the enterprises and market development. Intervention impact measurements can be held into two ways: i) assessing the achievement and the progress of an intervention on an explanatory fashion, which is called monitoring studies ; ii) assessing the outcomes from a program by studying the extent to which the intervention produced the intended impacts, which is called evaluation studies (Storey, 2000). The aim of intervention monitoring is gathering information on whether a program is on target to meet the intended objective and whether revisiting a program process are required and make improvement. Impact evaluation studies in contrast relay on measuring the degree of an outcome change that is attributable to the intervention (BIS, 2009). In other words, the effectiveness of public interventions is often determined only through evaluation studies based on through quantitative approaches (Khandker, Koolwal, and Samad, 2010). Both qualitative and quantitative impact evaluation methods are widely used in existing literatures. Understanding the intervention details and the participants socio-demographic and economic characteristics provide valuable information on the institutional context and norms guiding behavior in the intended sample and is essential to a profound quantitative assessment (Khandker et al., 2010). However, impact assessment based on qualitative information cannot identify what might happen in the absence of the intervention, i.e. counterfactual outcomes. This 11

12 implies, impact evaluation seeks to contrast the performance of a beneficiary with and without an intervention. The core intention of any public intervention is to change the state of a pre-defined outcome of the potential beneficiaries. Once an intervention is conducted and if the desired change is observed, the main concern in evaluation studies is whether the change is directly attributable to the intervention. Idyllically, the intervention causality effect can be measured by identifying and contrasting what would have been the outcome with and without the intervention. However, at a given point in time the beneficiary cannot be in the treated group - those who are the program recipients - and the control group those who are not the program recipients. The main challenge of an impact evaluation, thus, is to identify the counterfactual outcome - what would have happened to the outcome for a beneficiary without the intervention. Given that the counterfactual is unobservable, the main task in impact evaluation is to create a convincing observationally similar comparison group that is a close counterfactual of intervention program recipients. Quantitative evaluations measure real impacts attributable to the intervention (Khandker et al., 2010). It can estimate the counterfactual outcomes using statistical models and it is important to handle potential statistical biases related to intervention impact evaluations. Figure 2 depicts a graphical presentation for a real impact of an intervention. Assume that the average total capital value of enterprises at the time of the support program is outset is. Since then there will be support program beneficiaries (treated group) and non-beneficiaries (control Y 0 group); and Y 1 is the value of the average total capital that would be observed for the control group and group. Y 2 is the value of the average total capital that would be observed for the treated 12

13 Figure 2: MSE support program and impact evaluation: the nonrandomization case Y 2 Treated group MSE support impact Y 1 Control group Y 0 Counterfactual MSE support program Time Source: Own presentation Unless the two samples (treated and control groups) are similar, it will be difficult to produce realistic impact assessment. In other words, without persuasive information about the control group that it is similar to the treated group, impact comparison could be unreliable and the capital variance ( Y 2 Y 1 ) could not measure the real program s causal effect. This implies that the pre and post-intervention characteristics of the treated and control firms could lead to a difference in the average total capital between the two groups. Hence, the real program effect would be measured as Y2 Y 1, if and only if Y1 is found to be the right counterfactual outcome Empirical literature Though they are mostly in industrial countries context, there exist empirical evidences aimed at analyzing the impact of public intervention on firms performance, particularly on small and medium enterprises. The empirical researches were undertaken on either macro perspective (Huggins and Williams, 2011; Murdock, 2012), or a micro perspective - a firm-level analysis (Lenihan and Hart, 2006; Mole, Hart, Roper and Saal, 2009; Morris and Stevens, 2010). Most interventions are mainly designed to address the key challenge on the MSME sector, i.e. providing access to finance in the forms of subsidy, loan and guarantee. There are also studies that investigate the impact of non-finance support programs on business performance. The 13

14 empirical review attests, there is no consistence result on the impact of public interventions. Some researchers argued that public intervention has had a negative effect on firm performance; in contrast, others proofed that government support programs have played a key role in making SMEs capable to compete in the dynamic contemporary market and deliver intended performances. Not few researchers found that the business performance of MSMEs is not responsive to interventions at all. We present some of the empirical works in Table 2. Table 2: Empirical analyses on the impact of public intervention on the performance of MSMEs Authors Type of intervention Outcomes of interest Results Lerner (1999) Access to finance Sales and job creation Little effect on sales and employment growth, but Chrisman and McMullan (2000) Consultancy adivce Innovation, survival rate, employment, and sales growth higher growth and survival rate Wren and Storey (2002) Consultancy advice Sales turnover, employment and firm survival rate Increase survival rate for medium enterprises and increase growth Boocock and Mohd Shariff(2005) Credit guarantee Financial and economic additional No significant impact Honjo and Harada (2006) -Subsidies -Loans -Tax breaks Sales growth, number of employee, and asset value Asset growth Kang and Heshmati (2008) Credit guarantee Sales, productivity and employment growth Oh, Lee, Heshmati, and Choi (2009) Mole et al. (2009) Credit guarantee Consultancy and business advice Productivity, sales, employment, investment, research and development, wage level, and firm survival Sales growth and employment growth Tan (2009) Any support Training, adaptation of technology, sales, production, labor productivity, wage, and export Enhance productivity, improve performance and increase survival rate Employment growth Increase sales; Raise production; Enhance labor productivity; Higher wage; and Increase export 14

15 Zecchini and Ventura (2009) State funded guarantee Credit supply to SMEs and cost of borrowing Cost of borrowing reduced by 16-20% and Bank credit supply reached at 12.4% Pergelova and Angulo-Ruiz (2014) Government financial support (loan, guarantees and equity) Innovative, marketing, human capital and licensing comparative advantage Increase competitive advantage in all aspects 3. Econometric model specification The ultimate objective of impact evaluation or causal inference is to measure the effect of causes on specific outcomes, i.e. finding out the value of Y2 1 Y2 Y 1 (see figure 1). However, it is not possible to observe and Y for the same firm at the same time. As Holland (1986) described, the fundamental problem of causal inference is the existence of such missing data. Thus, the best impact assessment should rely on identifying the missing data through statistical estimation of the counterfactual from a pool of non-recipients. Moreover, the reliability of a causal effect is also conditioned on how the created comparison group is statistically similar to the treated group. The relatively reliable approach to measure accurately the effects of interventions is designing randomized experiment. In a random approach, all the characteristics of the treated and untreated groups are equally distributed, implying the untreated group is the right comparison group for the treated group. This reflects, random assignment, conditioned on proper implementation, provides an unbiased and consistent estimate of treatment effects. However, randomized approach is not always feasible; particularly, when the impact evaluation is intended after the intervention has already been given to participants (Steiner, Shadish, Cook and Clark, 2010). From the very nature of the Ethiopian MSEs support program structure and intention, the nonrecipients of the program are unlikely to be the right comparison group for the recipients, and the impact evaluation process based on a survey data conducted after the program has already been started is highly subscribed to selection bias. For example, the evaluation study could be tainted by self-selection bias since the support program is in voluntary base; administrative selection bias could also result due to the implementation process, i.e. such as, different MSE promoters could follow undocumented criteria in selecting potential recipients; the process, type and value 15

16 of the support could probably vary from region to region and office to office. These are not the only factors expected to result selection bias in the impact evaluation, there could be many, observable and unobservable, socio-cultural, economic and political facts that may led to produce biased impact measurement. Due to such expected structural gaps and nature of the support intervention process and limited resources (time and finance), as well as the timing of the research, the present study applies a nonexperimental evaluation approach to assess the impact of the government support programs on the MSE operators. The econometric model used in the study is specified as follows: The linear form of a capital level with intervention and without intervention for a micro or smallscale firm i can be presented as: y i i n i j j 1 X i j D i i 1 where, y is the capital level of firm i, X is a multi-dimensional vector of observed i characteristics of the owner and firm, where j= 1,, N and N denotes different socio-economic, and business and industry related characteristics of the owner and firm, respectively, and D is a dummy variable equal to 1 if the firm receives any support type from the government and 0 otherwise. Let Y 2 represents the average value of the capital for the MSEs who received any support type, and Y 1 represents the average value of the capital for the non-recipient firms. Based on the framework of potential outcomes approach of Roy (1951) and Rubin (1974), the average impact of the program on those MSEs who received any support type; i.e. the average treatment effect on the treated (ATT), will be, ATT E( Y2 Y1 D 1) 2 The value E( Y 1 D 1), the average capital level for firms received the support would have registered in absence of the program, is not observed (is the missing data). However, we do have the value ( Y 1 D 0), and if we consider as a comparison outcome for Y and measure the E Y1 2 average capital difference between the two groups, we will have, E( Y2 D 1) E( Y1 D 0) 3 16

17 There is a mathematical difference between equation 2 and equation 3, and adding and subtracting the value E( Y 1 D 1) in equation 3 results as follows: E( Y2 D 1) E( Y1 D 1) E( Y1 D 1) E( Y1 D 0) ATT E( Y1 D 1) E( Y1 D 0) ATT SB 4 where, SB is a difference between the average capital for the treated firms would have registered in absence of the program and the observed average capital for the untreated firms, and it captures the extent of selection bias. This implies that a simple mean capital difference between the treated and untreated firms will not necessarily measure the accurate impact of the support program, unless and otherwise SB is equal to zero, i.e. the mean capital for the observed untreated firms is equal to the counterfactual mean capital. Since we are applying a nonrandomized approach for the evaluation study, SB is hardly to be zero, and it is expected that the impact analysis with the available raw data is exposed to selection bias. Nonrandomized evaluation studies require statistical methods that fully correct for imbalances in pretreatment covariates between the treated and untreated firms (Khandker et al., 2010). Thus, to undertake a sound impact evaluation, we apply a nonparametric way to condition on, which is the Propensity Score Matching (PSM) method that enables us to construct a counterfactual group statistically similar to the treated group from the pool of untreated group data Propensity Score Matching (PSM) X j For the last more than two decades, matching has been a widely used method in microeconometric impact evaluation studies to address the basic problem of selection bias (Khandker et al., 2010). The basic idea behind the matching approach is to find individuals from a pool of untreated group who have similar distribution as the treated group in all relevant preintervention observable characteristics - along a set of (see equation 1). Matching preprocesses the data prior to outcome analysis so that the effect of X on D is controlled. In nonexperimental studies, matching likely to work efficiently under theoretical assumptions, 17 X j j

18 called strongly ignorable treatment assignment, and with data that justifies these assumptions. One basic assumption is Conditional Independence (CIA), which states that given a set X of covariates, observable to the researcher and are not affected by the treatment, the potential outcomes Y are independent of treatment assignment D (Rosenbaum and Rubin, 1983). ( Y, Y ) D X 2 1 The assumption is also known as unconfoundedness, implying that treatment assignment is entirely based on observed characteristics. The other assumption is the Common Support or Overlap Condition which states that for each value of X j there is a positive probability of being both treated and untreated: 0 P( D 1 X ) 1 i i j (Rosenbaum and Rubin, 1983). This property ensures that there is sufficient overlap in X j for the treated and untreated groups to find matches. However, the assumption of overlap condition is hard to proof empirically, and it is a common problem for observational studies. As Rosenbaum and Rubin (1983) suggested, propensity score, i.e. the probability of receiving a support conditioned on observed characteristics X, noted P( D 1 X ) P( X ), is a possible balancing score bx ( ). Heckman et al. (1998) describes that matching on PX ( ) is better as it involves only one dimensional parametric or nonparametric estimation compared to X which involves multi-dimensional nonparametric estimation. Rosenbaum and Rubin (1983) proof that if CIA holds for X, the potential outcome Y is also independent of treatment assignment D conditioned on the propensity score ( ) : ( Y, Y ) D P( X ), implying conditioning on the PX 2 1 propensity score is statistically equivalent to conditioning on X. If CIA holds and if there is comfortable overlap between the treated and untreated groups, the PSM estimator for ATT can be specified as: ATT E { E[ Y D 1, P( X )] E[ Y D 0, P( X )]} PSM P( X ) D ATT PSM is a simple mean difference, weighted by the propensity score distribution of participants, between potential outcomes Y over the common support. 18

19 3.2. Matching and parametric model estimation little guidance is available in the existing empirical literatures on what to be the functional form to estimate the propensity score. During a binary treatment case, most empirical works have applied a discrete choice model, normally a logit or probit function - logit and probit models usually produce similar results (Steiner, Shadish, Cook and Clark, 2010). To minimize bias related with model selection and reduce the preprocessing data inefficiency, we employ the MatchIt package in R software which reduces model dependence via preprocessing data with semi-parametric and nonparametric matching estimation method (Ho, Imai, King and Stuart, 2011). Given that the treatment variable is dichotomous in the intended evaluation study, we estimate a logit model of MSE support reception on various pre-treatment socio-demographic characteristics of the owner and business related characteristics. D i f ( X i ) 6 where, D i is a dummy variable that equals 1 if the firm received any support and 0 otherwise, X i is a vector of observable pre-treatment covariates; i.e. owner and business related characteristics for firm i. Followed the propensity score estimation, we apply the best straightforward and widely used matching estimator called Nearest Neighbor (NN) matching, where a firm from the pool of nonsupported group MSEs is chosen as a matching partner for the supported firms with the closest propensity score (Caliendo and Kopeining, 2005). Many empirical works suggested allowing the 'replacement' variant of the NN, particularly when the propensity score distribution for the treated and comparison groups is significantly difference, as it enhances the quality of matching and reduces the sampling bias (Ho et al, 2007). Since the data set utilized for this particular study have a lot of support recipient firms with high propensity scores compared to comparison firms and vice-versa, we employ matching estimator with replacement (see Figure 4). Moreover, to avoid a risk of bad matches associated with an existence of a very high distance gap between the closest neighbors, we specify a threshold on the maximum propensity score distance (caliper) of 0.05 by which a match is made. Moreover, as Dehejia and Wahba (2002) suggested, we realize a radius matching as it uses as many 19

20 comparison cases as are available within the caliper, but not those that are poor matches. Our matching strategy, thus, involves a nearest neighbor matching estimator with replacement among propensity scores within a preferred caliper of 0.05 and radius of 2. By doing so, we create the counterfactual group statistically similar to the treated group. The matching procedure expected to provide a good balance and it will result in model dependence reduction, bias reduction in all dimensions of the covariates,, and less variance. Following the preprocessing data, we conduct the outcome analysis using the matched sample that we consider as generated through randomization. The parametric model analysis to compute Equation 5 is conducted in a way that is quite robust. We first fit either a weighted linear least square or a weighted logistic regression, based on the nature of the outcome variable, to the counterfactual group only. X j y i f ( X i ) 7 where y is an outcome variable for MSE i and X is multi-dimensional vector of observed i characteristics for MSE i from non-supported group. Once the coefficients are estimated from the counterfactual group, we impute the missing outcome E( Y 1 D 1) through simulation using weighted least square or weighted logistic regression. The ATT is then be obtained by E( Y2 D 1) E( Y1 D 1). Applying the ATT weakens the overlap condition assumption as it evaluates the average effect in the sub-sample of the treated group. Moreover, given the matching strategy that is based on matching with replacement and radius matching, we found it important to incorporate weights in the analysis that reflect the number of control group MSEs that they were used as a match. Consequently, the weights created by preprocessing procedure estimate the ATT, with the control group MSEs weighted to be like the treated group MSEs (Ho et al., 2011) Statistical software and package Though there are many statistical software and packages that work fine for causal inference analysis, we found MatchIt package in R statistical software a very flexible matching method and it is easy to link it in other methods. Moreover, it is one of the preferable packages for non- 20

21 experimental studies (Ho et al., 2011). MatchIt works for causal inference in a dichotomous treatment variable case with a set of pretreatment covariates. The method includes most existing approaches to matching, however unlike to others, it reduces model dependence through preprocessing data with semi-parametric and nonparametric matching methods (Ho et al., 2011). Most causal inference analyses use a simple mean outcome difference following preprocessing the data, unlikely, MatchIt analyses are "doubly robust", implying that the impact analysis will be statistically consistent if either the balance is good or the parametric analysis model is correct (Ho et al., 2007). 21

22 4. Data and descriptive statistics 4.1. Data This study utilizes a primary data from a representative cross-sectional survey conducted by AEMFI/EIFTRI in July, 2013/14. The intention of the survey was to obtain fresh information and evidence on the progress of micro and small enterprises development in Ethiopia, particularly towards addressing youth unemployment. The survey focal point was also to assess the MSE support program under the GTP I period. The survey definition of micro and small enterprises was solely based on the Federal MSEs development agency classification (see Table 4.1). The survey was conducted in five regional states (Oromia, Amhara, SNNPR, Tigray and Harari) and two city administrations (Addis Ababa, Dire Dawa) on 1109 randomly selected youth-owned MSEs (age between 18 and 34), of which 909 of them are existing enterprises and 200 are closed enterprises. Stratification was made by gender of the owner, enterprise size (Micro and Small) and enterprise type (manufacturing, construction, urban-agriculture, service, and trade). This implies that the data set can be considered as a representative of the youthowned MSE sector found in Ethiopia (Tassew et al., 2014). The structured questionnaire used in the survey had detailed start up, and pre and post-support retrospective information on socio-economic features of the MSE owners (recipients and nonrecipients of government support programs) as well as firm and industry related characteristics. The questionnaire had nine different modules, including owner background, the enterprise history, business activity, finance, training and BDS, infrastructure, laws and regulations, marketing and government support service. Each module has detailed retrospective and on-duty information. The key questions in MSE support program module include: getting information from the respondents whether they had information about the MSE development strategy; whether they were received any support program between January, 2010/11 and June, 2013/14; the bureaucracy in accessing government support; barriers in accessing the support program; and the quality of the support program. Overall, the questionnaire had ample information and variables that can explain the probability of treatment assignment, and the intermediate and final outcome variables. 22

23 The MSEs development strategy was fully implemented on January, 2010/11. The strategy encompasses a range of different support programs for the MSE operators throughout the country, particularly in urban centers. The objective of the present study is however, to estimate the impacts of participating in twelve extensively available support programs; naming: i) production premises, ii) marketing premises, iii) infrastructure support, iv) market linkage to access raw materials, v) market linkages to sell products, vi) access to sub-contracting, vii) technology support, viii) access to finance, ix) technical training, x) BDS trainings xi) one-stopshop (OSS), and xii) extension service. To account for potential survey bias, the data for both the treated and untreated firms are drawn from the 909 existing enterprises sampling, ensuring that the logit model estimation for the probability of treatment assignment utilizes similar covariates across the groups. Hoverer, out of the total 909 firms, there are firms which are established earlier the new MSE development strategy is enacted, reflecting that some firms are likely benefited from the previous support programs, such as programs during the PASDEP period. To avoid such confusion and focus only on the impact of MSE support program designed during the GTP I, we anchor our focal point on firms established after the support program during PASDEP period was phased-out. In this regard, we select 565 MSEs that are established after June 2010/11 from the 909 existing sampling, noting that the new data set is dropping its representativeness of the youth-owned MSE sector of the country. Out of 565 MSEs, we drop 42 MSEs which are established between September, 2012/13 and June, 2013/14 and received the government support program during the same period. We drop them because the post-treatment outcome information for this study covers a period between September, 2012/13 and June, 2013/14. Hence, our impact evaluation is solely designed based on the baseline information from 523 MSEs; of which the treatment group has 309 firms that are beneficiaries of one or more public interventions between January, 2010/11 and June, 2013/14, and the control group has 214 firms that are never participated in any programs. In the treated sampling, the most commonly used support programs were technical training (66%), extension service (46%), infrastructure (45%) and production premises (40%) supports. The least support services accessed by the firms were one-stop-shop service (8%), followed by 7% for technology 23

24 support service. Note that the probability of appearing more than once for a firm is high, as MSEs are allowed to access multiple programs during their business operation. The main drawback of the survey is, however; it does not have information on the year the treated owners started using each program. This creates a challenge on the researchers on defining the pre-treatment baseline information. To this end, we find the MSEs startup information suitable to apply it as pre-treatment information for the impact analysis due the following reasons. i) Out of the 565 youth-owned MSEs, 125 of them were already operating in their perspective industry before the support program was fully operated in January, 2010/11. This implies that we are able to use the six months startup information, between June 2010/11 and January 2010/11, of the MSEs as pre-treatment information. ii) According to the country s MSEs intervention principle, eligible MSEs for each support program are those which are formally registered and started the business. This reflects that prior to accessing any government support program the MSEs were operating with given personal, firm and industry features. In this regard, we find it logical to assume the startup features as the baseline covariates for the rest 398 MSEs, which are established after January, 2010/ Covariate selection Sample selection can bias the treatment effect estimation. The basic concern in a nonrandomized analysis is, thus, establishing what information regarding owner, firm and industry related characteristics determine whether to receive government support or not. Given the matching strategy of the paper is built on Conditional Independence assumption, we choose observable pre-participation covariates for the propensity score estimation that are unaffected even by the anticipation of the support program. The paper attempts to include important socio-economic characteristics of the business founder as well as firm and industry related characteristics, aiming to capture all the possible explanatory variables for the probability of receiving the support program. The variables are selected based on the information on the country's MSE support program framework and principle, and knowledge from theories and previous empirical studies. Consistent with the proposals of Augurzky and Schmidt (2001), the researchers restrained from including unrelated covariates in the participation equation. To this end, the participation equation includes explanatory variables that strongly influence simultaneously the participation 24

25 and the outcome equation. Moreover, we also incorporate some variables that do have only weak influence on the outcome variables (e.g. Ownership form). Gender: The gender variable is mostly included in the participation equation as a determinant factor for receiving a support program (Lerner, 1999; Honjo and Harada, 2006; Hopp and Stephan, 2012). Gender is also directly or indirectly associated with the performance of a business. The Brush et al. (2003) and Robb (2002) studies revealed that female-owned firms' performance in terms of economic and business outcomes is lower compared to male-owned. Tassew et al. (2014) showed that being female-owner reduces the probability of business survival in Ethiopia. In the present paper, gender is a dummy variable with a value of 1 if the MSE owner is female and 0 otherwise. Age: Access to the government support could be specific to the age of the owner, so it is important to control for age variable (Lerner, 1999; Honjo and Harada, 2006; Hopp and Stephan, 2012). The results of many empirical studies indicated that age is highly associated with the performance of a firm (Davidsson, 1991; Amran, 2001; Oh et al., 2008; and World Bank, 2010). Hence, we include age as a continuous variable in the participation and outcome analysis. Owner education: Many of the impact evaluation studies used the educational attainment of a firm owner as a covariate in the participation estimation (Wren and Storey, 2002; Pergelova and Angulo-Ruiz, 2014). Moreover, various empirical studies in different countries indicated education as a significant factor in determining the business performance in different countries (Bates, 1995; Lussier and Pfeifer, 2001, Tassew et al., 2014). The education variable in this paper is proxyed by the number of years of education of the owner. Previous labor market and business experience: It has been reiterated by various studies that it is essential to control for the previous employment and business experience in the participation equation (Lerner, 1999; Wren and Storey, 2002; Honjo and Harada, 2006; Mole et al., 2009; and Hopp and Stephan, 2012). Existed empirical studies also revealed that the owner's prior labor market experience is highly associated with the likelihood of a firm performance and survival (Honig, 1998; Taylor, 1999; and Pena, 2002). Previous labor market/business experience of the founder is measured by a dummy variable that includes 1 if the owner responds that he/she has the labor market experience prior to establishing the new business and 0 otherwise. 25