Patterns of Constrains in Access to Finance for SMEs in the Western Balkans Region

What is the best model for growth and if we have it, what is the best way to implement it? These are fundamental questions in economic growth theory. Even when the same economic model is implemented in different counties the results we get are not always the same. This is so because there are many more factors influencing the model at the time when the ceteris paribus condition is removed. In order to get a glimpse of these other factors that may make a country perform better or worse than others the best approach is to make a comparison between them. Using four categories for classifying and measuring creditconstrained status for firms, this study is focusing on comparing constrains patterns in access to finance between countries in the Western Balkans region. For the analysis are used data from the World Bank-Enterprise Survey. We found that financial constrains are one of the top constrains for SMEs in the region. Also the level of integration doesn’t seem to influence the financial constrains level. For some of the countries in our analysis the perception does not reflect their comparable constrain level. Size, as expected, results negatively correlated with financial constrains and this pattern is consistent for all countries. And at last the financial deepening doesn’t seem to influence on easing financial constrains for any of the groups. This study is not focused in explaining the difference in the SMEs financial constrains patterns. The aim is to identify the best performing models and to set a track for further studies which will seek to understand the factors which characterize those countries whose models seem to have worked better.


Introduction
What is the best model for economic growth and development?This is the question haunting every economist.Even when the same economic model is implemented in different counties the results we get are not always the same.This is so because there are many more factors influencing the model at the time when the ceteris paribus condition is removed.In order to get a glimpse at these other factors that may make a country perform better or worse than another the best approach is to make a comparison between different models and/or their implementation.The best way to do that is to compare countries that share similar characteristics and backgrounds.This is usually found within same regions.This study focus is on the Western Balkan region because of the similar historical and economical background of these countries and also because all of these countries aspire to join the European Union, the economic model and legislation which they are trying to implement is similar.The definition Western Balkans comes from European Union institutions and includes the south-east European area composed of non-member states of the EU.Western Balkans is a neologism coined to describe the countries of "ex-Yugoslavia (minus Slovenia) and Albania" (Pond, 2016).Thus, the region would include: Croatia (now an EU member), Serbia, Bosnia and Herzegovina, Montenegro, Kosovo, Macedonia and Albania.Even though Bulgaria is not part of the Western Balkan countries, because of the similar background and also because Bulgaria is being a member state of the European Union since 2007, we decided to include it in our analysis.We expect that this will give a way to gauge EU integration effect on easing business constrains.
Several data and statistics show that developing countries even when they share the same geographic region and/or historical and economical background they face different constrains and they experience different growth rates.This study focuses on constrains that enterprises, more specifically Small and Medium Enterprises (SME), face which may influence the rate of their growth.Because of the important role that SMEs play in the economy, they have been the subject of many studies and researches.One of the reasons that SMEs are considered so important is the fact that they usually are the larger employer in a country.This is confirmed in many different studies and statistical databases.According to World Bank (SMEs Finance, 2015) in emerging countries, most formal jobs are with SMEs, which create 4 out of 5 new positions.The European Commission for example considers SMEs as a key to ensuring economic growth, innovation, job creation, and social integration in the EU (Entrepreneurship and SMEs, n.d.).E-ISSN 2281-4612 ISSN 2281-3993 Academic Journal of Interdisciplinary Studies MCSER Publishing, Rome-Italy Even though there is no unanimously accepted definition about how to classify SME, the classification based on the number of persons employed is the most commonly used one, especially in the statistical context.By this criterion the large group of SMEs usually refers to companies with less than 250 employees.This group accounts for about 99.8 percent of the enterprises in almost every European country and employees between 53 % of the working force in United Kingdom and 86% of the working force in Greece (Eurostat, 2016).The numbers are within the same patterns for the Balkan region too, where SMEs account for 98.7 to 99.9 % of all enterprises and their employment rate varies from 60.3% in Albania to highest 80.7% in Kosovo (EIB, Synthesis Report, 2016).
This study objective is to compare similarities and discrepancies between the patterns every country in our analyses developed on several SMEs indicators.The focus is on financial constrains that SMEs face.Based on World Bank Enterprise Survey data we divided the firms into four credit constrained groups and analyzed them by size and in relation to macro level financial development indicators.
This study is not focused in explaining the difference in the SMEs financial constrains patterns.The aim is to identify the best performing models and to set a track for further studies which will seek to understand the factors which characterize those countries whose models seem to have work better.

Methodology and Data
This paper methodological approach is based on a descriptive analysis of compared data on SMEs financial constrains patterns for several countries of the Western Balkan region.Row data are taken from the Enterprises Survey conducted by the World Bank.This is a firm-level survey of a representative sample of an economy's private sector.The survey covers a broad range of business environment topics including access to finance, corruption, competition, infrastructure, performance measures, etc. (World Bank Enterprise Survey, 2013).It implements a global methodology which makes the data between different countries comparable to each-other.The Enterprise Surveys implemented in Eastern Europe and Central Asia is jointly conducted by the World Bank and the European Bank for Reconstruction and Development (EBRD) and is also known as Business Environment and Enterprise Performance Survey (BEEPS).BEEPS is being answered by business owners and top managers which gives more accuracy to the collected data.Based on the model proposed by Kutchev et. al. (2014), we focused on the finance section of the Enterprise Survey to construct four groups that measure the extent firms were credit constrained.The first group includes firms that are classified as Fully Credit Constrained (FCC).They meet the following conditions: they applied for a loan and were rejected or did not even bother to apply for one even though they needed external financing because of terms and conditions.The second group includes firms classified as Partially Credit Constrained (PCC) who by definition have used external sources of finance during previous fiscal year and/or have an outstanding loan at the time of the survey and did not applied for a loan even though they needed one, or applied but were rejected.The third group is called Maybe Credit Constrained (MCC) and includes firms that used external sources of finance during the previous fiscal year and that was obtained from an approved bank loan.They are classified under this category because we do not have information if they were partially rationed on the terms and conditions of their external finance.The last group is that of Non Credit Constrained (NCC) and includes firms that did not apply for a loan during the previous fiscal year because they were having enough capital for the firm's needs.By grouping firm responses on these four categories we will look into the different patterns shown in each country and will try to analyze the factors influencing this behavior.Another regrouping is done based on SMEs size using the size classification used in the survey which defines micro enterprises those with less than 5 employed persons, small ones those with more than 5 and less than 19, medium enterprises those with more than 20 and less than 99 and large enterprises those with more than 100 employees.For comparing macroeconomic financial development influence on SMEs credit constrains within a country, data from the World Bank Financial Development Indicators database were used.More specifically the Domestic Credit Provided to Private Sector as a ratio to GDP.This indicator measures the financial deepening in a country.

Analysis
We begin our analysis by looking at the main constrains reported from the different countries in our analysis.Based on the responses given to the question about the main constrains a firm faces we notice that the perception of the biggest constrains perceived by firms differs by countries.By arranging the top 5 answers given in every country in a matrix as is shown in figure 1, we can see that the most common perceived constrains are: Practices of the Informal Sector, which is a top constrain for four out of eight countries in our analysis and also present in the top 5 constrains of three more countries; Tax Rates is top constrain for two out of eight and present in the top 5 of five more; and Access to Finance is among the most mentioned constrains being present in the top 5 of seven out of eight countries and ranking at number two for four of them and number three for three of them.

Source: BEEPS, table build by author
Observing each country separately we notice that Albania ranks access to finance as third most important constrain, right after Informal sector and electricity.Serbia ranks it as fifth, right after political instability, taxes, corruption, and informal sector.For Bosnia and Herzegovina access to finance is the second most important constrain after political instability, followed by taxes and corruption.Macedonia also ranks access to finance in second place right next to practices of the informal sector.Kosovo perception is same as Macedonia regarding the first two, differing in the third constrain which for Kosovo is corruption.Bulgaria differs from the other countries in the region by ranking access to finance in the six place leaving the first places to practices of the informal sector, political instability, corruption, taxes and labor regulation.In Croatia access to finance is reported as the second most important constrain after tax rates, while for Montenegro tax rates and informal sector practices are topping the list leaving access to finance at number three.

Source: BEEPS, charts build by author
Using the responses given in the finance section of the BEEPS we sorted the data in order to get the four groups defined in Kutchev et. al. (2013).These indicators give a measure to the extent firms were credit constrained.In contrast to the results reported in the first part of the analysis, the indicators used here are based on actual results and not on the perceptions of the interviewers.By defining the weight that each group holds in the respective country, in order to have a base of comparison between the different countries in our analysis we used the data to build Figure 2. What's obvious by looking at the chart is that Albania has a larger share of NCCs compared with the other countries while having a smaller portion of MCCs, this means that 72% of the enterprises didn't need any external finance and only 9% did get a bank loan.The pattern we see in the case of Albania differs from that of the other countries which are showing for the group of NCCs an interval of 37% for Serbia to 57% for FYR of Macedonia, and an interval, for the group of MCC, of 18% for Bulgaria to 38% for Serbia.But if we look at these two groups together, which represents the non-credit constrained part, we can notice that the patterns are much similar with smaller differences.While comparing the patterns of the remaining two groups, we notice that the most credit constrained firms are in Montenegro with 23 % for the FCC and 18% for the PCC and the less credit constrained firms are reported in Bosnia and Herzegovina with respectively 9% for the FFC and PCC groups.
Comparing this results with the perception that firms reported about their biggest constrains we find that even though Montenegro results with the biggest credit constrains, firms rated it in third place after tax rates and practices of E-ISSN 2281-4612 ISSN 2281-3993 Academic Journal of Interdisciplinary Studies MCSER Publishing, Rome-Italy the informal sector.While in Bosnia and Herzegovina despites their actual credit constrains are comparably the lowest in the region their perception ranks this constrain in second place.Bulgaria is having the second highest credit constrained group and still in the rating of the biggest constrains it did make it to the top five.
In Figure 3 1 we are looking at credit constrains patterns first within the country between firms of different sizes and then this patterns are compared with those of the other countries.In grouping the enterprises by size we used the employed person's criteria used in the survey with intervals of: 0-5 employed persons for micro enterprises; 5 -19 for small enterprises; 20 -99 for medium; and 100 plus for large.For Albania, Kosovo and Croatia there are no data of micro enterprises.By the charts we can tell that size is negatively correlated with credit constrains.Small enterprises report more constrains and more of them fall into the groups of FCC and PCC.The size of this group diminishes as the enterprises grow in size.This pattern looks consistent for all the countries in our analysis (the only exception being FYRM which results with higher constrained large enterprises compared to medium ones) and also consistent with theory and literature (Beck et. al, 2006) which suggest that as firms grow bigger their ability to provide collateral increases thus making it easy on them to find financing.
As suggested by Kutchev et. al. (2013) we also tested our data against macro level variables looking for correlations suggesting influences on firm level indicators.For the purpose we used Domestic Credit Provided to the Private Sector as a ratio to GDP (DCPPS) as is reported by the World Bank, Financial Development Indicators database which is an indicator of financial development level of a country.We compared it against our four groups of financial constrained indicators.The results are presented in figure 4. We find that the level of domestic credit provided to the private sector is not having a significant effect on credit constrains with R squared of 0.12 and 0.14 for the FCC and PCC group respectively and with even lower ones for the MCC and NCC.Also as can be seen the trend lines suggests that more credit in the economy is not giving more access to finance to the constrained groups.This results even though not very common they are conforming with previous other studies results which suggest that financial development in some developing countries may result in negative effect on economical growth (Musta, 2016;Mehl et. al, 2005;Rioja and Valev, 2004).

Conclusions
SMEs play a very important role in a market economy and they are the backbone of economic growth.In the Balkan region SMEs are the biggest employer in the economy and they may be the cure for fighting the high youth unemployment rates.In order for this to happen the business environment must be welcoming and offering good growth odds and support on entrepreneurship initiatives.This study focus was on analyzing the biggest constrains that SMEs face and among them we account financial constrains as the biggest threat to entrepreneurship and SMEs growth and survival.We compared the different patterns of SMEs reported constrains in the Western Balkan Region to find that by perception, the unfair competition coming from practices of the informal sector are a bigger concern to SMEs followed by financial constrains which even though didn't hit the top were consistent to all countries.By looking at the patterns of financial constrained enterprises when grouping them in four categories based on actual reported data we notice that Bosnia and Herzegovina is showing a better situation comparing to the other countries.
When compared by size, SMEs in almost all the countries showed a similar trend pattern suggesting that financial constrains are negatively related to the size.This is also commonly found in the literature where smaller firms are usually the one who have more difficulties to find external finance because they face more information asymmetries and thus having higher monitoring costs for lenders.All this translates in higher collateral demands and higher credit costs.
Also financial development measured by the amount of domestic credit given to the private sector as a ration of GDP didn't show to have a influence on levitating access to credit constrains which suggest for underdeveloped financial system operating only on low risk lending which is confirmed by the less constrains patterns for bigger size firms.
Comparing Bulgaria and Croatia2 against the other non member states we do not find any improvement in their patterns which we can credit on their EU integration stage.

Figure 3 .
Figure 3. Financial constrains of SMEs grouped by size