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\title{Analysing the Factors Influencing Financial Records Keeping amongst Small and Medium Entreprises (SMEs) in Ilala Municipal Council}
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             \author[1]{Kochecha S.  Herieli}

             \author[2]{Evod J.  Rimisho}

             \affil[1]{  Eastern Africa Statistical Training Centre}

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\date{\small \em Received: 10 December 2018 Accepted: 4 January 2019 Published: 15 January 2019}

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\begin{abstract}
        


Globally SMEs have been known as engines of growth. However, the performance of SMEs has been seriously challenged, especially in developing countries such as Tanzania. This study aimed at analyzing the factors influencing financial records keeping amongst SMEs in Ilala municipality using mainly primary data from 130 respondents. Both descriptive analysis and regression analysis using a binary Probit model were employed. Descriptive analysis shows that, about 90% of the respondents were keeping financial records for their business and the commonly records kept by SMEs were cash books, debtors books, and sales day books while purchase day books, creditor's books, and petty cash book were rarely kept. The probit regression results indicate that, membership, age of the firm, location of the firm, the size of the firm (capital invested and number of employees) and years of schooling are significant factors that affect financial records keeping in SMEs. All in all, financial record keeping is still a major challenge for many SMEs in Tanzania, therefore measures such as awareness raising and provision of education and training on financial records keeping should be taken so as to improve the capacity and level of understanding on records keeping, use and report preparation. The Government and private sector should collaborate to improve and enhance the capacity of SMEs in record keeping, business management, accessing market and venturing into existing and new opportunities.

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\let\tabcellsep& 	 	 		 
\section[{Introduction a) Background of the Problem}]{Introduction a) Background of the Problem}\par
mall and Medium Enterprises play a pivotal role in African countries economy through job creation, income generation which in turns leads to economic growth and development. They provide support to larger firms by providing inputs and other essential services. In a nutshell, SMEs are engine of growth \hyperref[b14]{(Fjose et al., 2010)}. Globally, SMEs constitutes about 95\% of all firms, contributes to 50\% of GDP and accounts to 60\%-70\% of total employment \hyperref[b23]{(ITC, 2015)}.\par
Specifically, in Tanzania, recent survey report of MSMEs shows that MSMEs employs more than 5.2 million people and it contributes to 27 per cent of the national GDP (MIT, 2012). It is also indicated that, in households with MSMEs, small business generated income of about 62.7\% and were the only source of and developing small scale miners and promotes the private sector investment in the mining sector. (URT-MEM, 2009).\par
On the other hand, the Government initiated various other institutions which in one way or another influence SMEs development. These include Tanzania Industrial Research Development Organization (TIRDO) which aims at supporting and enhancing local resources utilization, Centre for Agricultural Mechanization Rural Technology (CAMARTEC) aiming at smooth promotion of technology for rural agricultural development, Tanzania Engineering and Manufacturing Design Organization (TEMDO) aiming at machines or engines designing and Tanzania Bureau of Standards (TBS) aiming at strengthen the supporting institutional infrastructure for the industry and commerce sectors of the economy undertake measures for quality control of products of all descriptions \hyperref[b48]{(URT, 2003)}.\par
Along the same line, numerous funding mechanisms and schemes were initiated so as to address unemployment problems through SMEs promotions, these includes as stipulated in the SME Development \hyperref[b48]{Policy 2003}; National Entrepreneurship Development Fund (NEDF), Youth Development Fund (YDF), Women Development Fund (WDF), Small Entrepreneurs Loan Facility (SELF), National Income Generating Programmes (NIGP), Presidential Trust Fund and Community Development Trust Fund. 
\section[{v. Mineral Development Policy}]{v. Mineral Development Policy}\par
The mineral development policy targets to support the large, medium and small scale mining operators and artisanal. The policy has numerous objectives as stipulated herein; promote economic environment and attract more investors both local and international, to invest in the mining sector, strengthening legal and regulatory framework in the mining sector, support the development of small scale miners, so as to integrate their activities into the mainstream economy, develop and assist small scale miners in accessing market through improving the marketing system both locally and internationally. 
\section[{vi. Small Industries Development Organization (SIDO)}]{vi. Small Industries Development Organization (SIDO)}\par
The small industries development organization was developed by the Government of United Republic of Tanzania in 1973 with the sole aim to support and develop the small industry sector. However, currently, SIDO supports the whole private sector in various undertakings, especially business and technical expertise development.\par
SIDO receives supports, including technical and funding from various international organizations and partners such as the World Bank, SIDA, Netherlands, India, Hungary and United States of America (USA). SIDO helps SMEs in business development training, technical trainings and marketing to mention a few. 
\section[{vii. Vocational Education and Training Authority (VETA)}]{vii. Vocational Education and Training Authority (VETA)}\par
The authority, VETA was initiated through the vocational education and training act of 1994. The authority has created various training centers and branches all over Tanzania so as to develop, coordinate and roll out vocational trainings that cater the needs of the market. The agency through its activities supports social and economic development in Tanzania. The agency provides business trainings and short courses to SMEs in a bid to help them improve their modus operand on business matters. 
\section[{b) Challenges Faced by SMEs in Tanzania}]{b) Challenges Faced by SMEs in Tanzania}\par
SMEs in Tanzania are faced with numerous challenges that hamper their smooth development, these include but not limited to limited access to finance, unfavorable legal and regulatory framework, poor infrastructural facilities, poor business development services, inadequate access to information and poor financial records keeping. (URT, 2003) Therefore, speaking of the roles that SMEs play in the entire economy, such as employment creation, income generation, provision of inputs to larger industries, equitable distribution of income and contribution to Gross Domestic Product (GDP) increase in general which leads to poverty reduction; it is important to note that SMEs ability depends largely on how they perform in their business activities (Musah and \hyperref[b38]{Ibrahim, 2014)}.\par
Ineffective and non-productive of numerous funding mechanism and schemes established affected the performance of SME's in Tanzania. Most of the SMEs produces competing with imported cheap, fake, dumped and poor quality products while no mechanism in place for control, incubate and mentoring these local producers.\par
Financial records' keeping is very important for easy management and growth of the firm. They assist the organization to know whether the business is worth or not by determining the profit or loss made, assets they owned, liabilities they owe and values of their debtors, names of creditors and how much cash they have. (Tulli and \hyperref[b43]{Lyanga, 2011)}. Financial records help to demonstrate the stewardship of the firm's management for resources entrusted to it. Generally, financial records provide the basis on which the management can make decisions regarding the firm's performance and operations. These decisions include decisions whether to borrow, open new branch, employ new staffs or retrench. Effective managers rely on the past and present financial records to predict the future and evaluate the financial activities of the firms (Ntim, 2014).\par
However, despite the stipulated reasons and essence of financial records keeping above, majority of SMEs in Tanzania are reported to have poor financial records keeping attitudes. The Tanzania development policy 2003 document didn't shed light on the poor financial records keeping practices amongst SMEs in Tanzania.\par
However, the policy review draft of 2012 recognized poor financial records keeping as a challenge amongst SMEs in Tanzania (UNIDO, 2012). On the other hand, various authors have hinted poor financial records keeping being among the serious challenges amongst SMEs in Tanzania \hyperref[b41]{(Danford et al., 2014;}\hyperref[b30]{Marwa N, 2014;}\hyperref[b32]{Mashimba and Kühl 2014)}. Therefore, this study is set to identify factors influencing financial records keeping amongst SMES in Ilala municipality. 
\section[{d) Statement of the Problem}]{d) Statement of the Problem}\par
The role played by SMEs in Tanzanian economy cannot be ignored, inter alia employment creation, income generation, provides support to larger firms by providing inputs and other essential services, poverty reduction and great contribution to economic growth and development. The National MSME reports indicate that SMEs employs more than 5.2 million people, contribute to 27\% of the national GDP (MIT, 2012). The number of MSMEs in Tanzania has been increasing significantly. For instance, from 1990 to 2010 MSMEs have increased from 1.8 million to 3.1 respectively.  {\ref (Mori, 2014)}. However, various researchers have repeatedly pinpointed about the small growth rate of micro enterprises into small and medium enterprises. (MIT, 2012; Olomi and  {\ref Mori, 2015)}.\par
Small and Medium Enterprises are faced with numerous challenges which hinder them from exploiting the existing potentials to create employment, wealth and reduce the poverty trap to millions of Tanzania  {\ref (Olomi, 2006}). These challenges include; limited access to finance, unfavorable legal and regulatory framework, limited and poor business development services, poor infrastructural facilities, inadequate access to information and poor financial records keeping \hyperref[b48]{(URT, 2003;} {\ref Olomi, 2006;}\hyperref[b41]{Danford et al., 2014;}\hyperref[b34]{MIT, 2012)}.\par
However, out of all the above-mentioned challenges; poor financial records keeping amongst SMEs has attracted special attention to various researchers. Studies have been done in various parts of the world pertaining to financial records keeping and challenges involved amongst SMEs.\par
For instance,  {\ref Amoako (2013)} in their study in Sunyani Municipality, Ghana found that, majority of SMEs were not keeping records due to numeruous factors such as inadeqaute book keeping skills amongst owners and managers and high costs involved in adopting a good accounting system. Also, a study by, \hyperref[b19]{Haron et al (2013)} in Malaysia, found that, lack of good track of financial records among SMEs is a major reason behind banks and other financial institutions reluctance to disburse funds to SMEs.\par
Specifically, in Tanzania, the MSME National Baseline Survey Report shows that only 44.7\% of SMEs met were keeping financial records (MIT, 2012). On the other hand, Richard and Geoffrey (2012) in their study on SMES access to financial services, reports that most of SMEs in Tanzania have failed to access finance from various financial institutions due to poor financial records keeping among other things. Bank and securities wary to lend to SMEs as they perceive the sector be a risky investment because of poor accounting practices. \hyperref[b24]{(Kofi et al., 2014)}.\par
SMEs have negative attitude towards keeping financial records. Most of SMEs believe that, financial record keeping using accounting standards does not add any value to their firm's operations. It largely results into unnecessary costs and time consuming \hyperref[b41]{(Danford et al, 2014)}. However elsewhere, it is emphasized that, Firms depend entirely on records kept so as to take decisions on various business matters (Abdul-Rahamon and Adejare, 2014).\par
Despite providing useful insights and information, past studies have some weaknesses which necessitate further empirical research. At first, studies of the likes conducted in Tanzania were conducted in small areas like the one conducted in Dodoma region at Madukani ward where there are limited volume of business composition and size compared to big cities such as Dar es Salaam and Mwanza. On the other hand, most of the studies didn't show how firms' characteristics such as age, capital, tangible assets and location influence financial records keeping amongst SMEs specifically on the Tanzanian context.\par
Therefore, taking to account that, poor and inadequate financial record keeping amongst SMEs in Tanzania has been critical, this study is set to analyze the factors influencing financial records keeping amongst SMES in Ilala municipality. ? How firm are characteristics such as age, capital and tangible assets influence financial records keeping amongst SMEs? 
\section[{g) Scope of the Study}]{g) Scope of the Study}\par
The study analyzed the factors influencing financial records keeping amongst SMEs with particular reference to Ilala Municipality. The study was conducted in Ilala Municipality in four wards namely, Gerezani, Kariakoo, Kimanga and Buguruni between the months of April to October, 2016.\par
The major reason behind the selection of Ilala Municipality is that, much of the commerce, banking, and national offices are located in the area, therefore it could be easy to access adequate and relevant data for the study. The study focused to both registered and non-registered SMEs in Ilala Municipality mainly Min supermarkets, Hardware, Agro shops, Phones and accessories shops so as to capture adequate and impartial information. 
\section[{II.}]{II.} 
\section[{Literature Review a) Introduction}]{Literature Review a) Introduction}\par
This chapter discusses theoretical and empirical research relating with the factors influencing financial records keeping amongst SMEs in Ilala Municipality. This chapter involves the following sections; definition of SMEs, theoretical and empirical literature, the essence of training in accounting amongst SMEs, Customizing Financial Records and Report to Suit SMEs needs And Conceptual framework. 
\section[{b) Definition of SMEs}]{b) Definition of SMEs}\par
There is no single and standard acceptable definition of the term SMEs (Dababneh and Tukan, 2007; Amoako 2013;  {\ref Gibson and Van der Vaart, 2008)}. Countries and organizations all over the world set their specific definitions of the term SMEs according to their own objectives. However, in defining the term SMEs there are key yardsticks used everywhere, these include size, total number of employees or headcounts, sales turnover or assets and total investment (URT, 2003;Dababneh and Tukan, 2007; Amoako 2013;  {\ref Gibson and Van der Vaart, 2008)}.\par
According to OECD (2004), SMEs are a heterogeneous group with diversity in the locality (urban and rural), country, nature and type, legality, skills, capital and sophistication. According to \hyperref[b12]{Dalberg (2011)}, the Inter American Development Bank defines SMEs as an enterprise with less than \$3 million revenue and employs maximum of 100 employees, while Bank pinpoints SMEs as enterprise with \$15 million assets and revenue and employs maximum of 300 people.\par
According to \hyperref[b48]{URT (2003)}, SMEs in Tanzania mean micro, small and medium enterprises and it covers nonfarm economic activities mainly activities such as services, commerce, mining and manufacturing.\par
The definition uses the same benchmarks (employees' number, total investment and sales turnover) as used in other countries. See the  i. Record Keeping Record keeping and Bookkeeping are terms used interchangeably in most cases. The term bookkeeping or record keeping can be defined as an art and science of recording all the financial transactions taking place on a day to day basis in the business. Firms depend entirely on records kept so as to take decisions on various business matters (Abdul-Rahamon and Adejare, 2014). There are two types of book keeping methods, namely Single entry and double entry method as explained below; 
\section[{ii. Single Entry Book Keeping}]{ii. Single Entry Book Keeping}\par
This is an informal accounting system or a method in which all business transactions are recorded once. It entails one sided entry of accounting transactions. It may include; cash, accounts payable, accounts receivable and taxes. The information obtained from single entry method allows the preparation of income statement only. This method is known worldwide due to its advantages, mainly simplicity in preparations and less costs involved (Wood F and Sangster A, 2005).\par
On the other hand, the method is said to have various disadvantages mainly; it may lead inefficient decision making due to inadequate availability of data, losses of assets cannot be easily detected and inefficient management at large.\par
Other authors such as Eric and Gabriel (2012) explains that that, the single entry method is allowed in tax matters by all revenue authorities, however for the firms it does not provide with detailed financial and business information required for making various business decision hence posing challenges to the growth and operation of enterprises.\par
iii. Double Entry Book Keeping Under this method every transaction of an account is recorded twice in the books of accounts. It is simply defined as an accounting method in which every transaction in the business is recorded twice in the books of accounts. It is known worldwide for its two major effects in the books of accounts, namely; one side of an account increases while the other side of an account decreases. Once the recording of transactions is done perfectly without any error, the credit side of an account and the debit side of an account will have the same aggregate balance.\par
Various authors such as Eric and Gabriel (2012) and (Wood F and Sangster A, 2005) have figured out a double entry method to be the best one to use for effective management of the business firms as explained below:\par
? At first, the double entry method is regarded as the complete method as it records both transactions and their associated aspects. It was further argued that apart from recording transactions and its aspects, it also records them systematically and in a more concrete chronological order.\par
? Double entry book keeping helps to detect and avoid arithmetic errors due to its golden principle of recording every transaction with its corresponding debit or credit entry. In line with this, accountants are required to ensure that, every credit entry has its corresponding credit entry with the same balance. Therefore, in case of any deviation, the transaction needs to be rechecked. 
\section[{?}]{?}\par
The method helps to detect and identify frauds, errors or any embezzlement of the organization's funds. It provides detailed and precise information regarding the occurrence of transactions and how the money has been used including the disbursement.\par
? Since the double entry method allows the preparation of all accounting and financial reports such as income statement, statement of financial position, balance sheets and statement of equity, it is therefore the best method to ascertain the financial position of the business.\par
? The system is well known worldwide for providing detailed information that can be used in decision making in the organization of owners and managers. On the other hand, the information obtained through double entry system can be used by external people who have interest in the organization such as creditors and investors and other potential shareholders.\par
? The method follows and comply with Generally Accepted Accounting Principles (GAAP), Therefore the information and reports prepared can be used and well understood by anyone in the world without any problem. 
\section[{c) Theoretical Review}]{c) Theoretical Review}\par
Financial Records keeping amongst SMEs is mainly for providing information, which in the future is used as an aid to decision making basing on the facts and figures recorded. Decision making in this angle entails finding solutions to all problems facing the firm and arriving into conclusions efficiently and effectively. Therefore, taking that into account, the study reviews two theories, namely; Decision theory and information theory.\par
i 
\section[{. Information Theory}]{. Information Theory}\par
The central function of accounting and financial record keeping is communication. Accounting and financial records are used to relay information to managers, shareholders or business owners and external users of the financial aspects of the entities such as tax authorities and government. If data are measured and comply with the criterion of relevance, quantifiability, unbiased and verifiability they can be termed as accounting information (Holmes, S. \& Nicholls, D., 1988). It is within this theory where information generation process is associated with costs. The costs increase with the increase in the volume of collecting information.\par
ii. Decision Theory Financial records keeping provide a base for decision making to managers, accountants and SMEs owners. Various decisions in business are carried out basing on the past and current reports obtained from the respective firm's operations and transactions  {\ref (Simon, 1978)}. Various ratios analysis measures are calculated so as to arrive into the best business decisions. These include but not limited to current ratio, liquidity ratio, profitability ratio and return on investment ratio \hyperref[b2]{Agbemava et al., 2016)}. 
\section[{d) Empirical Review}]{d) Empirical Review}\par
Numerous researches have been undertaken by scholars in the area of financial records keeping amongst SMEs. The key areas of focus have been understanding SMEs' financial records keeping practices, challenges encountered in preparing and maintaining financial records and the impact of financial records keeping with SMEs business performance. Mixed evidences have been presented as to whether SMEs are aware of financial records keeping, keep records and use them in their day to day business decision making for their growth.\par
For instance, \hyperref[b3]{Amoako et al. (2014)} reports that about 63\% of SMEs in Sunya Municipality in Ghana do not prepare any formal accounts with retail shops being the most in numbers. The main reasons figured out behind non-preparation of accounts were; high associated costs, inadequate accounting skills amongst proprietors, on the other hand owners needed privacy of their business undertakings and the last one was the fact that the owners didn't find the essence of keeping records and preparing accounts. The same findings were reported by Olukotun et al.  {\ref (2012)} where they found that 87\% of SMEs do not keep proper written records in Ijumu Local Government Area of Kogi State in Nigeria. Along the same line, \hyperref[b32]{Mashimba and Kühl (2014)} in their study on the Performance of Micro and Small-Scale Enterprises (MSMEs) in Tanzania reported that most of small holder businesses do not keep the business records of their firms.\par
It is obvious that poor record keeping is among the major reasons behind the failure of startup and existing firms \hyperref[b50]{(Yohannes, 2015)}. This is caused by number of factors ranging from inadequate business management skills, lack of education and training on financial records keeping and low attachment of prioritization on financial records keeping. The study found that most firms end up losing the tracks of their records, hence operating without knowing if they are making profit or not. However, it was recommended that, SMEs, owners, managers and other staffs should be trained on records management and if possible where and when necessary they should seek assistance from accounting professionals.\par
On the other hand, \hyperref[b9]{Chelimo and Sopia (2014)} in their study at Kabarnet Town, Baringo County in Kenya found that 75\% of SMEs surveyed maintained books of accounts. Along the same line Musah and Ibrahim (2014) pinpointed in their study in Tamale Metropolis of Ghana that about 65\% of SMEs respondents met keep and maintain records for tracking their business progress.\par
Another interesting result was captured by Maseko and Manyani (2011) who asserted that SMEs do not prepare a complete set of financial statements and others do not even prepare a single statement.\par
They further pinpointed that even those SMEs with well-maintained books fail to prepare financial statements as the task requires thorough accounts skills and knowledge. However, despite the existing mixed evidences as to whether SMEs keeps records or not, it is quite true especially in the developing countries that not all SMEs are well equipped to keep and maintain financial records of their businesses.\par
SMEs are faced with numerous challenges in keeping financial records and these factors have made some SMEs to totally abandon keeping financial records. Several factors have been put forward to explain these challenges. \hyperref[b3]{Amoako et al. (2014)} explain the major challenges faced by SMEs in keeping financial records to be cost constraints, inadequate accounting skills of SMEs manager and owners, involvement of family members in financial matters and inadequate availability of skilled personnel. This study recommends regular trainings to SMEs on financial record keeping, records keeping consultation services at reduced fees and control of family interference into business matters. Olukotun and Olore (2012) in Kogi State of Nigeria identified the factors to be limited knowledge, SMEs assume they can keep the records in their heads, limited awareness and time consuming.\par
Apart from financial records keeping being perceived to be time consuming, costly and requiring more staffs to be employed \hyperref[b2]{Agbemava et al. (2016)} added one important point that SMEs avoid keeping financial records as it make their business firms pay more tax. Therefore, most SMEs abandon preparing financial records for a purpose of evading taxes.\par
Maseko and \hyperref[b31]{Manyani (2011)} in their study in Zimbabwe, found that Lack of accounting knowledge, cost and time constraints and lack of guiding accounting rules to be the major challenges faced by SMEs in preparing and keeping financial records.\par
On the other hand, Okoli (2011) pointed out that most of small scale entrepreneurs fail to keep a good track of records due to beliefs and perceptions. He further argued that most of small scale entrepreneurs believe that financial records keeping is a timeconsuming activity, tedious, costly and requiring high level of skills.\par
In line with this he figured out hat, SMEs are afraid of keeping records with the sole reason being the belief that, records keeping exposes the financial position of their firms.\par
Various researchers have recommended various techniques to address the above mentioned challenges facing SMEs in preparing and maintaining financial records as explained here below. Maseko and \hyperref[b31]{Manyani (2011)} in their study in Bindura, Zimbabwe recommended Book keeping training, having specific guidelines or regulations for accounts preparation and improvement, record keeping should be mandatory and hiring consultancy services to be the panacea to SMEs challenges towards financial records keeping and maintaining. \hyperref[b41]{Danford et al. (2014)} in their study in Dodoma suggested that the challenges faced by SMEs in financial record keeping can be addressed through the Government and respective stakeholders in the SMEs sector developing a special and simple step by step record keeping guidance for all SMEs. They further appealed for regular training programmes for SMEs owners and their staffs on financial records keeping. Along the same line \hyperref[b3]{Amoako et al. (2014)} in their study in Sunyani Municipality Ghana appealed to Government, regulatory bodies and education and financial institutions to conduct periodic training and regulation of consultancy fees for easy access to consultancy services amongst SMEs.\par
According to \hyperref[b1]{Adom et al (2014)} in their study conducted in Ghana, showed that the completion of VAT monthly returns form resulted into increased costs to traders compared to the use of invoice book.\par
However, on the other hand, the results depicted the best practice on record-based controls of VAT as quoted "record-based controls of VAT significantly improved SME's record keeping skills and accounting system, albeit it is found to be stringent relative to other record keeping types kept by SME's".  
\section[{e) The Essence of Training in Accounting amongst}]{e) The Essence of Training in Accounting amongst}\par
SMEs Training of accounting skills to Small and Medium Enterprises (SMEs) is an issue which cannot be ignored. Numerous studies call for training of accounting skills to SMEs especially in developing countries. For instance, according to (Schwarze, 2008) in their study in South Africa, found that most of micro ntreprise do not posses finanial management skills required and this is attributed by lack of accounting and book keeping skills.\par
Accoridng to \hyperref[b37]{Msoka (2013)} in their study in Tanzania recommended that, SMEs should be given training speifically on four key areas namely; Business planning, marketing skills, and accounting knowledge and customer care skills to enable them conduct businesses more efficient and effectively. Accounting and book keeping skills help SMEs to be well equipped with the essential knowledge to effectively record and maintain their business records. It helps SMEs owners prepare their business budgets, pricing, proper record keeping, prepare financial statement and use them in making their own business decisions such as increasing or reducing the size of operations. It further provides an aid to SMEs in tracking their debtors and stock taking aspects \hyperref[b3]{(Amoako, 2014;}\hyperref[b48]{URT, 2003;}\hyperref[b37]{Msoka 2013)}  
\section[{f) Customizing Financial Records and Report To Suit}]{f) Customizing Financial Records and Report To Suit}\par
SMEs needs Various authors have hinted that, financial records keeping and reporting is paramount for SMEs performance and growth, however, they should be customized to suit to the needs and demands of SMEs. The study by the International Federation of Accountants (IFAC) indicated that there is a need to develop well suited International Financial Reporting Standards that conforms to the needs and demands of SMEs  {\ref (IFAC, 2006)}.\par
On the other hand, McMahon (1998) in his study on Australian Manufacturing firms stressed that there is a need to develop financial standards that suits the need of SMEs as the existing ones seems to be complicated to SMEs and irrelevant taking to account their level of understanding, operations and volume of business. 
\section[{g) Summary}]{g) Summary}\par
Despite the few existing literature in this area of study, review of literature has shown that, financial records keeping is vital towards SMEs decision making and growth. However most of the studies conducted especially in developing countries such as Ghana, Kenya, Zimbabwe, Nigeria and Malaysia shows that most of SMEs do not keep their financial records and some do not prepare a complete set of financial statements. The major identified reasons behind poor financial records keeping amongst SMEs includes, but not limited to costs associated issues, time consuming, Tax avoidance and lack of book keeping skills amongst SMEs owners and managers.\par
However, there are gaps which still need to be filled by this study. The important identified gaps in this study include; studies of the likes conducted in Tanzania were conducted in small areas like the one conducted in Dodoma region at Madukani ward where there are limited volume of business composition and size compared to cities such as Dar es Salaam and Mwanza. Most of the studies reviewed didn't show how firms' characteristics such as age, capital, tangible assets and location influence financial records keeping amongst SMEs specifically on the Tanzanian context. Therefore, this study is set to bridge the gap by analyzing these factors. On the other hand, other studies focused only to SMEs owners and neglected SMEs staffs during the data collection process. Along the same token, the study uses other methodological framework in its design (Random Sampling) compared to those used by other researchers such as stratified sampling and snow balling techniques and purposive sampling and test if the results obtained are the same. With respect to recommendations, almost all studies suggested education and training as the key panacea towards SMEs challenges in financial records keeping and maintaining, however they didn't unfold on who bear the costs. Also, the studies have been carried out in developing countries context; therefore, there is a need to test if the results obtained comply with LDCs' environment, specifically Tanzania. 
\section[{h) Conceptual Framework and Research Model i. Conceptual Framework}]{h) Conceptual Framework and Research Model i. Conceptual Framework}\par
A conceptual framework can be defined as a set of broad ideas and principles taken from relevant fields of enquiry and used to structure a subsequent presentation \hyperref[b42]{(Tromp, 2006)}. Following the empirical and theoretical literature reviews, it is possible to conceptualize and visualize diagrammatically how firms' characteristics such as age, capital and tangible assets influence financial record keeping in Ilala Municipality. ii 
\section[{. Explanation of Variables and Measurement}]{. Explanation of Variables and Measurement}\par
The variables in ths study can be explained as follows (More variables are given in Chapter four, Table \hyperref[tab_11]{4}.5). 
\section[{Yi = (}]{Yi = (} 
\section[{Yi = Financial Records Keeping}]{Yi = Financial Records Keeping}\par
This is a dependent variable in this study. It refers to probability on how firms characteristics such as age, capital and tangible assets influence financial records keeping amongst SMEs in Ilala Municipality. 
\section[{X 1 = Size of the Firm}]{X 1 = Size of the Firm}\par
This was captured through the capital invested in the firm, operations, technology used and the human resources employed. This was continuous, measured by capital investments and human resources employed. Firms with huge capital were expected to keep proper records compared to others with little capital investment and human resources. 
\section[{X 2 = Location of the Firm}]{X 2 = Location of the Firm}\par
This was captured through direct observation of the firm locality that is either at the city center or suburbs (sub streets).\par
1 if a firm is located at the commercial area and 0, if otherwise. Firms located in commercial areas were expected to keep proper records compared to those in the suburbs. 
\section[{X 3 = Age of the Firm}]{X 3 = Age of the Firm}\par
This involves the number of years the firm has been in operations since its establishment. This was gathered through asking the owners or staffs of the respective firms using administered questionnaires. Other supporting documents such as license certificate was used where and when necessary. 
\section[{X 4 = Tangible Assets of the Firm}]{X 4 = Tangible Assets of the Firm}\par
This variable was ascertained through the value of tangible assets owned by the respective firm. The information was collected through direct observation and administered questionnaires, taking note of the indicative value of the assets owned by the firm. 
\section[{X 5 = Membership}]{X 5 = Membership}\par
Membership is the variable representing 1 if a respondent is a member of SACCOS, TCCIA or TPSF and 0 if the respondent is not a member. An enterprise is expected to keep financial records if the respondent is a member of SACCOS, TCCIA or TPSF compared to when he/she is not a member. 
\section[{III.}]{III.} 
\section[{Methodology a) Introduction}]{Methodology a) Introduction}\par
The purpose of this study was to analyze the factors influencing financial records keeping amongst SMEs with particular reference to Ilala Municipality. This chapter represents the blueprint or design of the   
\section[{c) Research Design}]{c) Research Design}\par
Research design is a framework that constitutes the blueprint for sample selection and sampling procedures, data collection, measurement and analysis of data collected. It aims at smooth facilitation of various research activities and operations yielding utmost information efficiently and effectively \hyperref[b25]{(Kothari, 2004)}. This study opts to use cross sectional research design to analyze the factors influencing financial records keeping amongst SMEs with particular reference to Ilala Municipality. The quest behind the selection of cross sectional research design lies on the fact that; generalizability of such studies is easy because they are representative of given populations, it is quick, cheap and less resources are utilized \hyperref[b29]{(Mann, 2003)}. 
\section[{d) Targeted Population and Unit of Analysis}]{d) Targeted Population and Unit of Analysis}\par
The study deployed SME's owners as the unit of analysis. SMEs owners being the operators of the entity are equipped with all the related information regarding business operations and activities. The study targeted population covers all the registered and non-registered SMEs in Ilala Municipality operating in Min supermarkets, Hardware, Agro shops, Phones and accessories shops. 
\section[{e) Sample Size}]{e) Sample Size}\par
The study deployed a sample size of 130 respondents randomly selected from; Kariakoo, Buguruni, Gerezani and Kimanga wards. This sample size was selected basing on the fact, the model used (Probit model) is more appropriate to small sample size even between 40 and 100 (Cakmakyapan and \hyperref[b4]{Goktas, 2013)}. Also, it is difficult to reach the whole population due to limited resources especially time, financial and human resources.\par
Along the same line, it is easy to manage the sample selected compared to studying the whole population. The study could not capture all the groups. Therefore, surveyed respondents were convenient enough to represent the entire population. 
\section[{f) Sampling Methods}]{f) Sampling Methods}\par
The study used both Probability and Non-Probability sampling techniques to select respondents operating in Min supermarkets, Hardware, Agro shops, Phones and accessories shops. Non-probability sampling was used to select four wards and owners of the enterprises for the study (SMEs). A sample of 130 Small and Medium Enterprises (SMEs) operating in Min supermarkets, Hardware and Phones and accessories shops were selected using probability and nonprobability sampling. The probability sampling was used to select respondents to be included in the sample without considering their status or title in the respective business.\par
The targeted firms employed between 1 to 5 employees with capital investment ranging from Tsh. 500,000/= to Tsh. 80 million respectively. 
\section[{g) Sampling Procedures}]{g) Sampling Procedures}\par
The research study was carried out in Ilala municipal council, Dar es Salaam region covering four wards namely; Gerezani, Kariakoo, Buguruni and Kimanga. A total of 130 respondents were interviewed in the entire research study, in which 54 were female and 76 were male. Out of 130 respondents interviewed, 54 respondents were from Gerezani which constituted 41.54\%, 35 from Buguruni ward constituting 26.92\%, 30 respondents from Kimanga ward constituting 23.08\% and the remaining 11 respondents were from Kariakoo constituting 8.46\%. A total of 75 firms in Min supermarkets, Agro shops Hardware and Phones and accessories shops that are directly supervised by owners themselves in each ward (Kariakoo, Buguruni and Ilala) were selected to ensure the unit of analysis is well captured and valid information is collected. The remaining 55 firms' respondents were randomly selected amongst SMEs staffs in Min supermarkets, Hardware, agroshops and Phones and accessories shops regardless of their positions or status. 
\section[{h) Data Types and Collection Methods}]{h) Data Types and Collection Methods}\par
The study deployed both Primary and Secondary data so as to ensure a thorough analysis of the facts collected which in turn lead to bold conclusion and recommendations. Primary data were collected directly from SMEs (Min supermarkets, Hardware. Agroshops, Phones and accessories shops owners and staffs in Kariakoo, Gerezani, Buguruni and Kimanga wards using structured and unstructured questionnaires with both open and closed ended questions.\par
The primary data, answers the question regarding the factors influencing financial records keeping amongst SMEs in Ilala Municipality. On the other hand, Secondary data, such as journal articles, newspapers, research journals and financial reports were used to supplement the information collected from primary sources. On the other hand, Observation and Interview methods were used to get some insights directly from the respondents.\par
Key information from secondary sources included but not limited to number of registered and non-registered SMEs in Ilala Municipality, experience of financial records keeping from other researchers and financial institutions reports shedding light on issues related to records keeping amongst SMEs. The study uses the Probit Model (PM) to analyze the factors influencing financial records keeping amongst SMEs in Ilala municipality. The probit model was chosen taking to account that, the dependent variable in the study was dichotomous in nature taking the value 1 or 0. The value 1 indicates that an SME keeps financial records and 0 indicates otherwise (SME is not keeping financial records). Also, the Probit model constrain the estimating probability to lie between 1 and 0. Cakmakyapan and Goktas (2013) pinpoints that, logit model is more appropriate to the larger sample size at least 150 while the Probit model is more appropriate to small sample size even between 40 and 100. In this study, the sample size was 130 and that lead the option of Probit model to be chosen.\par
Linear Probability Models (LPM) was not taken into account in this study due to its limitations, namely; it has the problem of generating predicted values which may fall outside 0,1 intervals, thereby violating the basic principle of probability (it has the defect that the conditional probability is not constrained to lie between zero and one). Another problem of LPM is heteroscedasticity; that is sub-populations may have different variabilities from others (Cameron and \hyperref[b6]{Trivedi, 2005)}. Linear Probability Model (LPM) is given by the following equation: The response variable Y is binary, that it only takes two possible outcomes which we denote as 1 and 0. Y is 1 if an SME keeps financial records and Y=0 if an SME does not keep financial records. Xi are explanatory variables. Specifically, we assume that the model takes the form:\par
Where Pr denotes probability that SME keeps financial records or not, and ? is the Cumulative Distribution Function (CDF) of the standard normal distribution. The parameters ? are typically estimated by maximum likelihood.\par
(2) It is possible to motivate the probit model as a latent variable model. Suppose there exists an auxiliary random variable.\par
()\textbf{3}\par
Where ? \textasciitilde  N (0, 1). Then Y which represents the SME influenced to keeps financial records or not can be viewed as an indicator for whether this latent variable is positive:\par
(4)\par
The use of the standard normal distribution causes no loss of generality compared with the use of an arbitrary mean and standard deviation because adding a fixed amount to the mean can be compensated by subtracting the same amount from the intercept, and multiplying the standard deviation by a fixed amount can be compensated by multiplying the weights by the same amount. \hyperref[b17]{Gouriéroux (2000)} noted that for each explanatory variable, there are two types of marginal effects in binary dependent variables models. The first type of marginal effect is marginal Index effects which is the partial effects of each explanatory variable on the probit index function . If X J is a continuous explanatory variable, marginal index effect of variable Xj is given by: (5) X J is a binary explanatory variable which is a Dummy or indicator variable. The marginal index effect of a binary explanatory variable equals to the value of the index function when and the other regressors equal fixed values minus the value of the index function when and the other regressors equal the same fixed values. The marginal index effect of the dummy variable X J is marginal index effect of Xj\par
The limitation of the Marginal index effects are difficult to interpret because it is difficult to interpret and impossible to measure the latent dependent variable which whether people might be influenced to keep records or not. The other effect is Marginal probability effects which are the partial effects of each explanatory variable on the probability that the observed dependent variable Y i = 1, in probit models are given below: If X J is a continuous explanatory variable Using the chain rule of differentiation, we can obtain a general expression for the marginal probability effect of a continuous explanatory variable:\par
Where; If X J is a binary explanatory variable (a dummy or indicator variable), the marginal probability effect of a binary explanatory variable equals to the value of when X ij = 1 and the other regressors equal fixed values minus the value of when X ij = 0 and the other regressors equal the same fixed values. 
\section[{j) Data Analysis Techniques}]{j) Data Analysis Techniques}\par
This research study examines how SMEs firms 'characteristics influence financial record keeping in Ilala Municipal council. It entails to bridge the knowledge gap on the challenges faced by SMEs in financial records keeping; the different types of financial records used by SMEs, the importance of financial records and how do SME keep financial records in Ilala Municipal council.\par
Firms were asked about whether they keep financial records or not. For those who were keeping record were asked to figure out how they use the records kept, types of records they keep and the challenges they face and the respective solution. For those who were not keeping any record, were asked to stipulate the reasons behind.\par
Other key information collected from the respondents includes; the age of the firm, sales turnover, employees, value of tangible assets, location and capital invested.\par
Data analysis and processing involved data entry into the Statistical Package for Social Sciences (SPSS), data cleaning and verifications, transformation and analysis using figures, charts and tables.\par
Taking to account that, the influence was based on firms' socioeconomic characteristics, SMEs provided "Yes" and "No" answers which were analyzed using a Probit model. Data were coded using SPSS 16.0 then transferred into a STATA file for major analysis.\par
The final results were produced using Statistical Package for Social Sciences (SPSS) and STATA. Microsoft excel was used as an aid in drawing tables, figures and charts.\par
Descriptive statistics for the data are provided to show SMEs' characteristics. Descriptive analysis explains how firms'' characteristics influence financial record keeping amongst SMEs in Ilala municipal council. Relevant tests for the models and interpretation of results are provided. The Probit model was evaluated by performing model specification test, goodness of fit test, and multicollinearity test. 
\section[{IV. Data Analysis and Presentation of Findings a) Introduction}]{IV. Data Analysis and Presentation of Findings a) Introduction}\par
This chapter presents the analysis and discusses the results following the methodology outlined in chapter three. It is divided into two sections. The first section present and discusses the descriptive statistics, that includes the characteristics and features of the sample used. The subsequent section entails to present the econometric results. 
\section[{b) Socio Economic Characteristics of the Respondents}]{b) Socio Economic Characteristics of the Respondents}\par
The social-economic characteristics of the respondents include marital status, age, sex and level of education. These socio-economic characteristics have paramount implication towards financial record keeping amongst SMEs. Table \hyperref[tab_11]{4}.1 presents some of the descriptive statistics.  
\section[{i. Age of the Respondents}]{i. Age of the Respondents}\par
The analysis was carried out to ascertain the age of respondents (owners, managers, human resources officers and sellers). The results show that, the youngest respondent had an age of 19 years while the oldest had an age of 50 years and 32 years on average. The age at turning point is 30 years. This indicates that, the respondents who were taking part in the SMEs as owners, managers, sellers and human resources officers are matured enough and understand the essence of keeping records in the business.\par
ii 
\section[{. Marital Status of the Respondent}]{. Marital Status of the Respondent}\par
The results regarding the marital status of the respondents met shows that, 71\% (n=92) of respondents were married and 29\% (n=38) of respondents were not married. The married category constitutes the largest percentage over the single category. This implies that, family responsibility increases the seriousness of the people in handling various issues business stuffs inclusive so as to earn profit and avoid incurring losses. The study also corresponds to the recent national population census where marriage was cited as 62\% (NBS, 2012).\par
iii. Education of the Respondents Concerning the level of education, 42\% (n=55) of respondents met attained secondary education, 22\% (n=29) attained tertiary or college education, 18.46\% (n=24) attained primary education and 16.92\% (n=22) attained university level education. The dominance of secondary, tertiary level and university level education amongst respondents shows that, majority of respondent met at least had a clue on preparation and essence of keeping financial records. However, the only challenge with this is that each and every respondent was keeping financial records as per his or her wishes without following the set standards and guidelines. The same findings were reported by Richard (2003) that SMEs lack a clear and concrete guidance on financial records keeping.\par
iv. 
\section[{Sex of Respondents}]{Sex of Respondents}\par
The results show that, 58.46 \% (n=76) of the respondents met were male, while 41.54 \% (n=54) were female. This implicates that, there is higher male dominance in ownership and operation of business firms compared to female. The result corresponds to study conducted by \hyperref[b27]{Kuunibe (2012)} which showed that male participation in Small and Medium Enterprises (SMEs) is higher compared to female. Therefore, the government in collaboration with other stakeholders should join hands to empower women so that they can equally participate in all sectors of the economy (SMEs inclusive) as men.\par
v. Financial Records Trainings to SMEs It was noticed that the majority of respondents constituting about 71.54\% (n=93) received training on financial records from various public and private institutions such as Small Industries Development Organization (SIDO), Savings and Credit Cooperative Society (SACCOS) and faith based organizations (FBOs). However, despite the fact that respondents received trainings from different agencies, time and location but they all stressed to have been trained on issues, inter alia; record keeping practices, tools, rules and regulation, tax matters and essence of records keeping to SMEs. On the other hand, the remaining 28.46\% (n=37) of the respondent didn't receive any training related to financial records keeping. The findings conform to the study conducted by \hyperref[b8]{Chachage (2006)} which stressed that education and trainings are essential for skills development in multifaceted arrays of life. Along the same line, Rugumyamheto et al  {\ref (1997)} stresses that, management skills and trainings, especially record keeping are essential for both formal and informal sector operators to venture and utilize the new opportunities.\par
Along the same line, descriptive statistics shows that, 78.7\% of shareholders or owners received trainings on financial records keeping while only 21.3\% didn't receive training, 76.9\% of general managers received training on financial record keeping while the remaining percentage of about 23.1\% didn't receive, for human resources it was 50\% for those who received and those who didn't and for sellers 59.4\% received training while 40.6\% didn't receive any training on financial records keeping. 
\section[{vi. Title or Position of the Respondents in a}]{vi. Title or Position of the Respondents in a}\par
Respective SME Approximately 57.69\% (n=75) of respondents met were the owners and shareholders of the enterprises, 24.62\% (n=32) were sellers, 10\% (n=13) were general managers, 7.69\% (n=10) were in the administrative department covering finance, accounts and human resources. The study shows that, higher percentage of the interviewees were the targeted population or unit of analysis (Owners and shareholders), therefore the findings obtained represent the real picture of records keeping situation in Ilala Municipality. 
\section[{vii. Sources and Amount of Capital Invested}]{vii. Sources and Amount of Capital Invested}\par
The minimum amount of capital invested by entrepreneurs met by the researcher was Ts.500, 000/= while maximum amount was Tsh.80, 000,000/= with average of Tsh.10, 100,000/= (see Table \hyperref[tab_11]{4}.1). The major source of finance or capital which amounts to 68.46\% (n=89) amongst respondents interviewed was from their personal sources and grants. Respondents used their personal savings, grants and charities from their family members to earn enough capital for investments. On the other hand, 31.54\% (n=41) of the respondents cited financial institutions such as Banks, SACCOS and VICOBA as the key sources of their capital base.\par
It was noted that, most of the respondents didn't prefer to ask for a loan from financial institutions due to high interest rate, lack of collateral securities and requirement of financial reports of at least three years past. The same results were highlighted by \hyperref[b5]{Calice et al (2012)} and Padachi (2009) that SMEs fail to access finance due to limited availability of attractive and affordable finance. One of the respondent met dealing with agro-shop business hinted that, as quoted "it is quietly cumbersome to wait for loan from financial institutions due to high interest rate, bureaucracy and unnecessary requirement such as collateral securities and financial reports" 
\section[{viii. Sales Turnover of the Firm}]{viii. Sales Turnover of the Firm}\par
The researcher was interested to ascertain the sales turnover of the firms on annual basis and how it influences financial records keeping amongst SMEs in Ilala Municipality. The findings show that, the minimum sales turnover was Tsh.1, 900,000/= and the maximum sales turnover was Tsh.70, 000,000/=. The results show that, the sales turnover coefficient is positive as expected and statistically significant at 5 percent significance level. This implies that, the probability of an enterprise to keep financial records increases as the annual sales turnover increases. 
\section[{ix. Value of Tangible Assets Owned by the Firm}]{ix. Value of Tangible Assets Owned by the Firm}\par
Value of tangible assets of the firm was one among the independent variables in this study. All respondents interviewed were asked about the value of tangible assets they possess in their firms. All respondents met in this study possessed various assets all valued in Tanzanian shillings (Tsh.). Study findings show that the minimum value of the tangible asset owned by the firms is Tsh.1, 000,000/= and the maximum value is Tsh.30, 000,000/= and Tsh.6, 807,692/= on average. 
\section[{x. Temporary and Permanent Employees}]{x. Temporary and Permanent Employees}\par
Regarding the number of employees, SMEs interviewed were found to have both permanent and temporary employees. The minimum number of employees found in the study was 1 while the maximum number of employees was 5 with mean and standard deviation of 2.384615 and 0.943231 respectively. The study corresponds to study conducted by the International Finance Company (IFC) of the World Bank (2012) which estimated that there are 2.7 million enterprises in Tanzania and the majority of them are micro entrepreneurs employing less than 5 people. 
\section[{xi. Form of Business Ownership}]{xi. Form of Business Ownership}\par
Sole proprietorship was the dominant form of business ownership constituting about 83.08\% (n=108) while partnership covered the remaining 16.92\% (n=22). This was attributed by the fact that, sole proprietorship is easy to manage, easy in tax preparation and it does not require a lot of procedures to form and register compared to partnership and company.\par
xii. Location of the Firm About 80.77\% (n=105) of the respondents interviewed located their firms in the designated commercial areas such as Kariakoo market, Buguruni and Ilala area while 19.23\% (n=25) located their firms in residential areas. When asked the reasons behind locating their firms in residential areas, firm owners stressed that, it is difficult to acquire plots in designated commercial areas due to high rent charges and municipal levies which affect their profit and operations. It was further narrated that, locating your firm in the residential areas is a strategy for winning the market. Therefore, for the sake of ensuring that, all SMEs owners locate their firms in designated commercial areas and undertake their business operations transparently including keeping proper financial records in compliance to laws, regulations and financial standards, the government and its respective authorities should create conducive business environment including lowering rental charges and other city levies so as to avoid affecting SMEs' profit margin to a great extent. 
\section[{xiii. Registration and Age of the Firm}]{xiii. Registration and Age of the Firm}\par
Out of 130 firms reached, 93.8\% (n=122) firms were registered with Business Registration and Licensing Agency (BRELA) and only 6.15 \% (n=8) were not registered. The reason behind failure to register their firms were highlighted to be fear of tax costs in terms of time and money and limited knowledge on registration procedures. The youngest firm has an age of less than 6 months since in business operations while the oldest firm has an age of 26 years in operations.\par
The average age of all firms reached is 7.5 years while the age of the firm at turning point is 11.6 years. In line with this, firms interviewed were registered and started their operations between 1976 and 2016. 
\section[{xiv. SMEs' Membership Affiliation}]{xiv. SMEs' Membership Affiliation}\par
The study was also interested to ascertain the membership affiliation of the respective SMEs interviewed. It was found that, 43.85\% of SMEs met were members in Savings and Credit Cooperatives Societies (SACCOS) and 5.38\% were members of the Tanzania Chamber of Commerce Industry and Agriculture (TCCIA). However, the majority of SMEs about 50.8\% had no any membership association and when asked the reasons behind, they asserted that most of membership associations do not deliver effective services as per their needs and challenges despite charging high membership fees and other related contributions.  
\section[{) Examination of the Types of Financial Records Kept by SMEs in Ilala Municipal Council}]{) Examination of the Types of Financial Records Kept by SMEs in Ilala Municipal Council}\par
Before presenting the types of financial records kept, the Researcher presents first the number of respondents who were keeping financial records and those who were not keeping any record and the reasons behind. About 90\% (n=117) of the respondents met were keeping financial records for their business while 10\% (n=13) of the respondents met were not keeping any financial record. Despite the fact that majority of SMEs were keeping records but their records were not complying with the national and international standards set rules and regulations including bookkeeping standard procedures.\par
They were just maintaining records as per their own wishes and understanding mainly to determine whether they are operating in loss or profit. Other researchers also shed light on this such as Chelimo (2014) who asserted that, even those SMEs claiming to keep records they do not keep proper records and skip following the standards and set procedures in records keeping. To identify the reasons for not keeping financial records, those 13 respondents who were not keeping cited various reasons. All 13 respondents were not keeping financial records on the opinion that it is a timeconsuming endeavor. Other reasons cited by 12 respondents out of 13 were costs factor, limited knowledge and fear of tax assessment by tax officials.   The Researcher also investigated which financial statements are prepared using the financial records that they keep. Table \hyperref[tab_11]{4} 
\section[{.4 below clearly shows the financial statements prepared by SMEs in Ilala}]{.4 below clearly shows the financial statements prepared by SMEs in Ilala}\par
Municipal. The majority of SMEs prepares income statement (96.9\%) and very few respondents prepared cash flow statement (4.27\%) and statement of financial position (2.56\%). Note: * This is the number of respondents reporting a particular type of record they keep as a percentage of 117 respondents who were keeping records. Total percentage is greater than 100 because some of the respondents kept more than one type of financial record.\par
Volume XIX Issue V Version I Finally, the Researcher explored more information regarding financial records keeping and financial reports preparation. The respondents were asked to state who prepare their financial reports if any. It was revealed that, 65.8\% of respondents (n=77) were personally preparing financial reports such as income statement of their business firms. On the other hand, about 26.5\% (n=31) were using hired consultants with professionalism in accounts preparation, which conforms to findings by \hyperref[b2]{Agbemava et al. (2016)} who suggested that in some cases, SMEs hire professional people to prepare accounts on their behalf. Only 7.7\% (n=9) of the 117 respondents who kept records, used their own employed accountants to prepare financial statements. 
\section[{d) Analyzing the Importance of Financial Records Keeping and Exploring the Existing Methods Used for Recording and Settling Transactions}]{d) Analyzing the Importance of Financial Records Keeping and Exploring the Existing Methods Used for Recording and Settling Transactions}\par
The second objective of this study is to analyze the importance of financial records keeping and to explore existing methods used for recording transaction and settling debts. The findings regarding this objective are presented below: 
\section[{i. Methods Used for Recording Transaction and Settling Debts}]{i. Methods Used for Recording Transaction and Settling Debts}\par
Here the Researcher focused on exploring the use of single entry or double entry in preparation of financial records. About 53\% (n=62) of respondents who were keeping records cited to use Single entry and the remaining 47\% (n=55) were using double entry, where each financial business transaction is recorded twice in the books of accounts (see Figure  {\ref 4}.3). Single entry was the dominant method used by the majority of SMEs in their bookkeeping which is similar to the outcome that was also figured out by \hyperref[b9]{Chelimo (2014)} in his study based in Kenya. Further findings were based on how financial obligations were settled by consumers in the respective SMEs firms. It was found that, 97.69\% of the respondents allowed settling transactions by Cash, 38.46\% accepted transaction settling on credit and only 3.08\% accepted the use of cheque 1   1 Total percentage is greater than 100 because some SMEs settled financial obligations using more than one method.\par
. 
\section[{e) Analyzing the Challenges Faced by SMEs in Financial Records Keeping}]{e) Analyzing the Challenges Faced by SMEs in Financial Records Keeping}\par
The third objective of this study was to analyze the challenges faced by SMEs in financial records keeping. As it applies in other areas in the world, SMEs in Ilala Municipal council face numerous challenges in financial records keeping. The most cited challenge in the course of this study was inadequate recording keeping skills (44.62\%), cost constraints in terms of time and money (34.62\%) and inadequate availability of skilled personnel such as accountants and cashiers (21.54\%). One of the respondents at Gerezani ward contended that as quoted "Record keeping and financial statement preparation requires your enterprise to have enough staff, time and money and it's difficult for a person like me who is just standard seven leaver (primary school) to prepare records on my own. Big enterprises are the ones to keep records and prepare financial statement". The findings of this study conform to the findings of \hyperref[b2]{Agbemava et al. (2016)} and \hyperref[b3]{Amoako et al. (2014,)} who stressed that when it comes to bookkeeping and preparation of financial statements, SMEs are faced with several challenges namely; costs, time and inadequate staffs. When SMEs were asked about the solutions to above mentioned challenges facing them in financial records keeping, respondents argued the provision of accounting trainings specifically on financial records keeping and management (58.46\%) and reduction of financial report preparation consultancy fees (40.77\%). 
\section[{f) Challenges Faced by SMEs in Ilala Municipal}]{f) Challenges Faced by SMEs in Ilala Municipal}\par
Council This section extends from the above section to cover the challenges facing SMEs in Ilala District. Various reports show that, SMEs face numerous challenges in their day to day operations, inter alia, inadequate access to finance, limited entrepreneurial knowledge and education and access to markets. However, the most cited impending challenges facing SMEs in Ilala Municipal council were heavy taxes and levies constituting about 65.38\% (n=85), inadequate access to finance 26.15\% (n=34) and difficulties in financial records keeping constituting about 16.15\% (n=21) as shown in figure  {\ref 4}.4 in subsequent chapters.\par
Regarding taxes and levies it was asserted that, apart from tax collected by the Tanzania Revenue Authority (TRA), the Dar es Salaam city imposes other charges such as cleanness, security, development and market charges on a daily basis which results into increased costs. The same results were figured out by Fjose (2010) who stressed that getting rid of inefficient taxes levied upon SMEs may encourage them to enter into formal sector and operate legally. Respondents claimed that sometimes the city subject them into penalties in case of any delay in paying the respective levies.\par
With respect to difficulties in accessing finance, high interest rates, strict conditions and requirements by financial institutions such as collateral security were cited as the key causes towards SMEs failure to access finance. The same results were also found by Fjose (2010) who stressed that SMEs in Africa have huge opportunities for growth, however they are hindered by inadequate access to finance and power shortage especially electricity.\par
On the hand, the study by Calice et al (2012) with African development bank conducted in Kenya, Tanzania, Uganda and Zambia figured the same results that hinder SMEs access to finance inter alia; SME must be a client for at least three years before requesting loan, SME must be accessing deposit and build a good track record before loan aplication.\par
On the other hand, SMEs perceived financial record keeping as a challenge due to the fact that, they have inadequate skills and knowledge on records keeping and use of the records kept and consultancy fees for preparation of financial statement is very high coupled with inadequate availability of accounting professionals. The chart below shows the challenges. This section shows how data were evaluated to see their validity and reliability if applied to probit regression. We first provide the independent variables in Table \hyperref[tab_11]{4}.5 that were used in the model in investigating the factors affecting Financial Records Keeping in SMEs followed by probit regression diagnostic tests. Our model went through various diagnostic tests to avoid making invalid statistical inferences if the assumptions of probit regression are not met. The probit regression requires no omission of important variables; no inclusion of irrelevant variables; the independent variables are measured without error; the observations are independent, and the independent variables are not linear combinations of each other. The following tests were conducted to check the reliability of the estimates.\par
i. Multicollinearity Test Multicollinearity is a linear combination of two or more explanatory variables in the model and can have different effects on the model depending on the degree of multicollinearity. For example, it is impossible to find a unique estimate of regression coefficients with all the explanatory variables in the model when perfect multicollinearity occurs. Any correlation among the explanatory variables is a sign of multicollinearity; however moderate multicollinearity is not a problem. Severe multicollinearity causes the standard errors for the coefficients to be very large (inflated), and the estimated probit regression coefficients are likely to be unreliable.\par
The Researcher conducted a correlation analysis to identify if there is moderate or severe multicollinearity. The correlation of 0.8 or above between two explanatory variables is an indication of severe multicollinearity. The results from Table \hyperref[tab_11]{4}.7 show that there is no severe multicollinearity since the correlation is less than 0.8. Therefore, this implicate that, the standard errors for the coefficients are not inflated and the probit regressions in this study are reliable.\par
ii 
\section[{. Model Specification Test}]{. Model Specification Test}\par
The Researcher tested whether the model has all the relevant variables and if the linear combination of them is sufficient. To detect a specification error, the test uses the linear predicted value (\textunderscore hat) and their squares (\textunderscore hatsq). The variable \textunderscore hat has to be statistically significant predictor, since it is the predicted value from the model while variable \textunderscore hatsq should be insignificant for the model to be correctly specified. If \textunderscore hatsq is significant, then either relevant variable(s) are omitted or the link function is not correctly specified. Table \hyperref[tab_11]{4}.6 shows the result of model specification test. From Table \hyperref[tab_11]{4}.6, it is shown that the model is correctly specified since \textunderscore hat is statistically significant at 1\% significance level as reflected by a probability value of 0.00 and the variable \textunderscore hatsq is statistically insignificant with probability of 0.454. 
\section[{iii. Goodness of Fit Test}]{iii. Goodness of Fit Test}\par
Goodness of fit of the model shows how well the model fits the data. There are numerous goodnessof-fit measures such as percent correctly predicted, pseudo R-square, and other.\par
The log likelihood chi-square is an omnibus test that tells if the model as a whole is statistically significant. It matches the current model with an intercept model. The results show that, the model is statistically different from the intercept model as reflected by the probability of 0.0004 and hence the model as a whole is statistically significant.\par
The Hosmer and Lemeshow's goodness-of-fit test can be used to test model fit under the null hypothesis that, there is no difference between the observed and predicted values of the outcome variable. The Hosmer and Lemeshow test results (? 2 = 0.58, 8 degrees of freedom, P= 0.9998) indicate that the goodness of fit is adequate.\par
A Classification table gives the predictive accuracy of a probit regression model. It involves cross classifying the dependent variable with the categorical variable coming from the fitted probabilities. The overall correct percentage was 94.62 percent, which shows that, the model's overall explanatory strength is good.\par
The discrimination of a model indicates how well the model distinguishes respondents who keep financial records and those who do not keep. This can be examined using the Area under the Receiver Operating Characteristic Curve (AUROC). The value of the AUROC is the probability that a respondent who keeps financial records has a higher predicted probability than the one who do not keep. We calculated the AUROC and got a value of 0.9658, indicating that the model discriminates well.\par
Given the above tests of goodness of fit, it is generally observed that the model fits the data well. The Researcher also examined Tolerance and Variance Inflation Factor (VIF), which are the two regularly used measures of the strength of the interrelationships among the variables. Tolerance is an indicator of the extent of multicollinearity that a regression analysis can tolerate and VIF is an indicator of the extent of the inflation of the standard error which might be caused by multicollinearity. If all of the explanatory variables are completely uncorrelated with each other, then both the tolerance and VIF are 1. If a variable is very closely related to another variable or variables, the tolerance approaches to 0, and the VIF is very large. From the result presented in Table \hyperref[tab_11]{4}.8, the tolerance is close to 1 and the mean VIF is 1.22 which is very small indicating no multicollinearity problem.\par
Volume XIX Issue V Version I 60 ( E ) The results of the probit model suggest that the probability of an SME to keep financial records increases with membership in SACCOS; the age of the firm; location of the firm; capital invested, and number of employees. The results also suggest that the probability of an SME to keep financial records decreases with years of schooling.\par
The probit model parameters are estimable up to a scaling factor. The coefficients of the probit model give the change in the mean of the probability distribution of the dependent variable associated with the change in one of the explanatory variables, but these effects are usually not of primary interest. The marginal effects on the probability of possessing the characteristic can be of more use. The marginal effects vary across individuals and, in this case, indicate by how much the probability of an SME to keep financial records alters with changes in the explanatory variables.\par
Volume XIX Issue V Version I61 ( E ) i. Membership\par
The marginal effect results for membership is 3.6\%. This implies that an enterprise that is a member of SACCOS, TCCIA or TPSF has a 3.6 percent higher probability to keep financial records above the base case. This implies that lack of access to credit and membership in trade unions are significant constraints preventing some enterprises from keeping financial records. Financial institutions such as SACCOS and VICOBA and member based organizations or trade unions such as TCCIA or TPSF are therefore potentially effective institutions in empowering enterprises to reduce the impact of not keeping financial records. 
\section[{ii. Size of the Firm}]{ii. Size of the Firm}\par
Here the Researcher considers two factors, namely, Capital Invested and Number of employees. These factors are interpreted below:a. Capital Invested: The results show that, the coefficient of the Logarithm of Capital Invested is positive as expected and statistically significant at 5\% significance level. This suggests that, the probability of keeping financial records increases as capital invested increases. A unit percentage increase in capital invested increases the probability of keeping records by 0.4 percent, keeping other factors at their mean. This implies that, lack of access to adequate capital is a significant constraint preventing some enterprises from keeping financial records. Therefore, financial institutions such as banks, SACCOS and VICOBA should create a conducive lending environment so as to improve accessibility of capital to SMEs. 
\section[{b. Number of Employees:}]{b. Number of Employees:}\par
The results show that, the coefficient of the logarithm of number of employees is positive as expected and statistically significant at 1\% significance level. This implies that, the probability of keeping financial records increases as numbers of employees' increase. The probability of keeping financial records increases by 0.7 percent when employees increase by one person, keeping other factors at their mean. This suggests that, inadequate human resources in the enterprise is a significant constraint preventing some enterprises from keeping financial records. Therefore, SMEs are urged to employ adequate human resources relative to the size and operations of their enterprises for ease of record keeping. 
\section[{iii. Location of the Firm}]{iii. Location of the Firm}\par
The marginal effect for location of the firm is 10.5\%. This implies that an enterprise located in a commercial designated area has 10.5\% higher probability to keep financial records compared to those located in residential areas. This shows that, enterprises located in designated commercial areas can easily be reached with awareness campaigns and trainings provided by both public and private institutions regarding record keeping hence improved record keeping practices compared to those located in residential areas where it is difficult to reach or trace their where about and provide capacity building regarding record keeping. Therefore, locating firms in designated commercial areas is an effective mechanism or strategy towards improved record keeping amongst SMEs. 
\section[{iv. Age of the Firm}]{iv. Age of the Firm}\par
The marginal effect for age of the firm or enterprise is 0.1\%. This shows that, the probability of keeping financial records for an enterprise increases by 0.1\% when the age of the firm increases by one year, keeping other factors at their mean. This appeals that, the probability of keeping financial records increases as the age of the firm increases. Enterprises' lack of business experience is a significant hurdle preventing some firms, especially new ones from keeping financial records. Therefore, new firms should learn the tips and essence of record keeping from the experienced (older) firms as they have already encountered a lot of challenges other factors being constant. 
\section[{v. Years of Schooling}]{v. Years of Schooling}\par
The results show that, the coefficient of Years of Schooling is negative and statistically significant at 5\% significance level. This implies that, the probability of keeping financial records decreases as years of schooling of the respondent increases. The probability of keeping financial records decrease by 0.2 percent when the respondent achieves one extra year of schooling, keeping other factors at their mean.\par
This suggests that, most of enterprises do not find the real and practical implications of the records they keep using the existing guidelines in relation to the operations and growth of their enterprises'. Therefore, there is a need for the ministry of industry, trade and investment (MIT) in collaboration with other stakeholders such as SIDO, TCCIA and TPSF to develop and design a more consolidated, simple and practical record keeping guideline (tailor made) that will add value to SMEs operations and business performance at large.\par
In a nutshell, the Probit model examines the factors affecting financial records keeping in SMEs. The goodness of fit test indicates that the model is correctly specified. The model fits the data well as various tests of goodness of fit such as the log likelihood chi-square, Hosmer and Lemeshow's test, Classification table and AUROC suggest. The correlation analysis, VIF and tolerance measures, suggest that the independent variables are free from multicollinearity problem. The probit regression results indicate that, membership, age of the firm, location of the firm, the size of the firm in term of capital invested and number of employees, and years of schooling are significant factors that affect financial records keeping amongst SMEs in Ilala Municipality.\par
V. 
\section[{Summary, Conclusion, Policy Implications and Recommendations a) Introduction}]{Summary, Conclusion, Policy Implications and Recommendations a) Introduction}\par
This chapter presents the summary of the study, policy implications and recommendations, and areas for further research. Section 5.2 gives a summary of findings and conclusion, section 5.3 points out policy implications and recommendations based on the findings, and lastly, section 5.4 presents areas for further research. 
\section[{b) Summary of the Findings and Conclusion}]{b) Summary of the Findings and Conclusion}\par
The performance of SMEs can only be traced, monitored and evaluated through adequate financial records keeping. A financial record keeping involves capturing, organizing, classifying, maintaining and ensuring data quality of all financial related information of the business activities. This study aimed at analyzing the factors influencing financial records keeping amongst SMEs in Ilala municipality. Specifically, the study intended to examine the types of financial records kept by SMEs, to analyze the importance of financial records keeping and to explore existing methods used for recording transaction and settling debts, to analyze the challenges faced by SMEs in financial records keeping, and to examine how firms' characteristics such as age, capital and tangible assets influence financial records keeping amongst SMEs in Ilala Municipality.\par
To achieve these objectives the study used mainly primary data from 130 respondents selected using both purposive and non-purposive sampling techniques. The study employed both descriptive analysis and regression analysis. A binary Probit model was used to examine how firms' characteristics such as age, location, capital invested, human resources and other factors influence financial records keeping amongst SMEs in Ilala municipal council.\par
Descriptive analysis shows that, about 90\% of the respondents met were keeping financial records for their business while 10\% of the respondents met were not keeping any financial record. Despite the fact that majority of SMEs were keeping records, but their records were not complying with the accepted standards set, rules and regulations including bookkeeping standard procedures. They were just maintaining records as per their own wishes and understanding mainly to determine whether they are operating in loss or profit. To identify the reasons for not keeping financial records, those 13 respondents who were not keeping records cited various reasons.\par
The 13 respondents were not keeping financial records on the opinion that it is a time-consuming endeavor. Other reasons cited by 12 respondents out of 13 were costs factor, limited knowledge and fear of tax assessment by tax officials. In identifying the types of financial records kept by SMEs in Ilala Municipal Council, the results indicated that the commonly records kept by SMEs were cash books, debtors books, and sales day books while purchase day books, creditors books, and petty cash book were rarely kept. It was further found that the majority prepares income statement (96.9\%) and very few respondents prepared cash flow statement and statement of financial position. Regarding financial report preparation, it was revealed that, about 65.8\% of respondents were personally preparing financial reports such as income statement of their business firms, 26.5\% were using hired consultants with professionalism in accounts preparation, and the remaining 7.7\% of 117 respondents who kept records, used their own employed accountants to prepare financial statements.\par
In assessing the importance of keeping financial records it was revealed that the majorities of respondents, approximately 86\% pinpointed to use financial records for profit determination, followed by accessing finance (6\%), as a base for tax calculation purposes (4\%) and lastly for financial decision making (4\%). About 53\% of respondents who were keeping records claimed to use Single entry and the remaining 47\% were using double entry. Single entry was the dominant method used by the majority of SMEs in their bookkeeping.\par
Further findings were based on how financial obligations were settled by consumers in the respective SMEs firms. It was found that, 97.69\% of the respondents allowed settling transactions by Cash, followed by credit and very few accepted the use of cheque.\par
The most cited challenge faced by SMEs in financial records keeping in the course of this study was inadequate recording keeping skills (44.62\%), cost constraints in terms of time and money (34.62\%) and inadequate availability of skilled personnel such as accountants and cashiers (21.54\%). Generally, the most impending challenges facing SMEs in Ilala Municipal council were heavy taxes and levies reported by 65.38\% of respondents, inadequate access to finance by 26.15\% and difficulties in financial records keeping claimed by 16.15\% of the respondents. When asked about the solutions to the mentioned challenges facing SMEs in financial records keeping, respondents argued the provision of accounting trainings specifically on financial records keeping and management (58.46\%) and reduction of financial report preparation consultancy fees (40.77\%).\par
The probit regression results indicate that, membership, age of the firm, location of the firm, size of the firm in term of capital invested and number of employees, and years of schooling are significant factors that affect financial records keeping in SMEs. The probability of an enterprise to keep financial records is higher by 3.6\% if the respondent is a member of SACCOS, TCCIA or TPSF compared to when the respondent has no membership. A unit percentage increase in capital invested increases the probability of keeping records by 0.4\%. The probability of keeping financial records increases as numbers of employees' increase. The probability of keeping financial records increases by 0.7\% when employees increase by one person. Also, an enterprise located in a commercial area is more likely to keep financial records than the one located in a residential area. An enterprise located in a commercial area has a higher probability of keeping financial records by 10.5\% than the one located in a residential area. The probability of keeping financial records increases as the age of the firm increase. The probability of keeping financial records increase by 0.1\% when the age of the firm increases by one year.\par
Finally, it was found that the probability of keeping financial records decreases as years of schooling of the respondent increases.\par
Financial records keeping is the heart and engine of the business as it provides the direction and foundation for taking decision on various matters, including business expansion or narrowing down the size of operations. The government requires SMEs to keep records for tax matters while financial institutions such as Banks accept lending to SMEs after assessing the worthiness and viability of the business through its financial records. In a nutshell, financial record keeping is still a major challenge to many SMEs in Tanzania, therefore measures such as awareness raising and provision of education and training on financial records keeping should be taken so as to improve the capacity and level of understanding on records keeping use and reports preparation. The Government and private sector should collaborate to improve and enhance the capacity of SMEs in record keeping, business management, accessing market and venturing into existing and new opportunities. 
\section[{c) Policy Implications and Recommendations}]{c) Policy Implications and Recommendations}\par
The study shows that, SMEs in Ilala Municipal council are faced with numerous challenges, however high taxes, levies and other city charges were mentioned to be the most impending ones. In line with this, the study suggests the following policy recommendation so as to improve the performance of SMEs in general, recording keeping inclusive.\par
Government and other related stakeholders such as Non-Government Organizations should join hands to collaboratively initiate various training programs on business management and administration including proper records keeping and use. This will help both business owners and respective employees to improve their capacity, skills and knowledge in running enterprises efficiently and effectively.\par
Taking to account that, taxes and levies imposed by the city council on SMEs has been figured out as the major challenge, Tanzania Revenue Authority (TRA), Business Registration and Licensing Agency (BRELA) and the city council should harmonize the charges so as to reduce the burden and continuously create an awareness campaign through trainings and media campaigns to reach the entire business community.\par
Member based organizations (MBOs) such as Savings and Credit Cooperative Societies (SACCOS), Tanzania Chamber of Commerce, Industry and Agriculture (TCCIA), Tanzania Private Sector Foundation (TPSF) and Confederation of Tanzania Industries (CTI) should expand their network and develop tailor made products and services that match to the demand and needs of micro enterprises such as records keeping, accessing to finance so as to improve their performance and capacities. This involves charging affordable membership fees.\par
The government of United Republic of Tanzania with involvement of other key stakeholders such as members based organizations, financial institutions and the business community at large should review the SMEs development policy and align it with recent and future opportunities, including development in science and technology. Taking to account that, SMEs sector is not well researched and there is still some missing information, the National Bureau of Statistics (NBS) should enhance its research and documentation on SME sector so as to help the government, financial institutions, policy makers and other actors have thorough information to guide them in making informed decisions. 
\section[{d) Recommendations for Further Research}]{d) Recommendations for Further Research}\par
The study was conducted at the right time taking to account that the fifth phase government under President John Pombe Magufuli is undertaking various fiscal measures in a bid to improve the performance of the economy and expand the Tax base of the nation which for a long time has been claimed to be very narrow.\par
Therefore, if the government needs to improve its tax base, there is a need to invest much of its efforts and focus on ensuring that every business firms keep proper records. In line with this the government, academicians and other stakeholders in the SMEs sector should investigate the following issues which the study didn't capture detailed information and call for further studies; ? The study recommended on provision of financial records training, however it didn't shed light on how the trainings will be organized and mode of delivery including costs. Therefore, there is a need to undertake research on designing and developing the best framework and programs on provision of records keeping and business management education and training to SMEs. ? Designing effective financial reporting standards that suit the needs of Micro enterprises. This includes designing a tailor made and simple national guideline in Swahili language that will be easily understood by small entrepreneurs.\par
? Perceptions of SMEs towards joining Member based organizations such as Tanzania Chamber of Commerce, Industry and Agriculture (TCCIA), Confederation of Tanzania Industries (CTI) and Tanzania Private Sector Foundation (TPSF).  
\section[{List of abbreviations and acronyms}]{List of abbreviations and acronyms} 
\section[{Introduction}]{Introduction}\begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-2.png}
\caption{\label{fig_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-4.png}
\caption{\label{fig_2}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{22}\includegraphics[]{image-5.png}
\caption{\label{fig_4}Figure 2 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_5}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{31}\includegraphics[]{image-7.png}
\caption{\label{fig_6}Figure 3 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-8.png}
\caption{\label{fig_7}( 1 )}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43}\includegraphics[]{image-9.png}
\caption{\label{fig_9}Figure 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{44}\includegraphics[]{image-10.png}
\caption{\label{fig_10}Figure 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45}\includegraphics[]{image-11.png}
\caption{\label{fig_11}Figure 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46}\includegraphics[]{image-12.png}
\caption{\label{fig_13}Figure 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47}\includegraphics[]{image-13.png}
\caption{\label{fig_14}Figure 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{25621252313456}\includegraphics[]{image-14.png}
\caption{\label{fig_17}I 2 5 6 2 . 1 2 5 ?????????? 2 .FACED 3 . 1 3 4 5 6}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{}
\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{21} \par 
\begin{longtable}{P{0.29131455399061035\textwidth}P{0.11572769953051643\textwidth}P{0.4429577464788732\textwidth}}
Category\tabcellsep Employees\tabcellsep Capital Investment in Machinery (Tsh.)\\
Micro enterprise\tabcellsep 1 -4\tabcellsep Up to 5 mil.\\
Small enterprise\tabcellsep 5 -49\tabcellsep Above 5 mil to 200 mil.\\
Medium enterprise\tabcellsep 50 -99\tabcellsep Above 200mil to 800 mil.\\
Large enterprise\tabcellsep 100 +\tabcellsep Above 800 mil.\end{longtable} \par
  {\small\itshape [Note: Source:\hyperref[b48]{URT (2003)} ]} 
\caption{\label{tab_1}Table 2 . 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{32} \par 
\begin{longtable}{P{0.019833333333333335\textwidth}P{0.6658333333333333\textwidth}P{0.16433333333333333\textwidth}}
S/No.\tabcellsep Research Objective\tabcellsep Instrument used\\
\tabcellsep To examine how firms' characteristics such as age, capital and\tabcellsep Questionnaires\\
1\tabcellsep tangible assets influence financial records keeping amongst\tabcellsep \\
\tabcellsep SMEs in Ilala Municipality.\tabcellsep \\
2\tabcellsep To analyze the challenges faced by SMEs in financial records keeping.\tabcellsep Questionnaires and Interviews\end{longtable} \par
  {\small\itshape [Note: 3To find out the kinds of financial records kept by SMEs and what they are used for.QuestionnairesSource: Author (2016) i) Model Specification i. Specification of Probit Model]} 
\caption{\label{tab_2}Table 3 . 2 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{43} \par 
\begin{longtable}{P{0.39258474576271185\textwidth}P{0.06242937853107345\textwidth}P{0.11285310734463276\textwidth}P{0.11885593220338982\textwidth}P{0.08884180790960451\textwidth}P{0.07443502824858757\textwidth}}
Variable\tabcellsep Obs.\tabcellsep Mean\tabcellsep Std. Dev.\tabcellsep Min\tabcellsep Max\\
Financial Records Keeping\tabcellsep 130\tabcellsep 0.9\tabcellsep 0.3011\tabcellsep 0\tabcellsep 1\\
Membership\tabcellsep 130\tabcellsep 0.492\tabcellsep .502\tabcellsep 0\tabcellsep 1\\
Age of the Respondent\tabcellsep 130\tabcellsep 32\tabcellsep 7.44\tabcellsep 19\tabcellsep 50\\
Age of the Firm\tabcellsep 130\tabcellsep 7.127\tabcellsep 5.262\tabcellsep 0\tabcellsep 26\\
Squared Age of the Firm\tabcellsep 130\tabcellsep 78.274\tabcellsep 106.510\tabcellsep 0\tabcellsep 676\\
Gender\tabcellsep 130\tabcellsep 0.585\tabcellsep 0.495\tabcellsep 0\tabcellsep 1\\
Years of Schooling\tabcellsep 130\tabcellsep 11.946\tabcellsep 3.200\tabcellsep 7\tabcellsep 17\\
Source of Capital\tabcellsep 130\tabcellsep 0.315\tabcellsep 0.466\tabcellsep 0\tabcellsep 1\\
Logarithm of Capital Invested\tabcellsep 130\tabcellsep 15.719\tabcellsep 0.879\tabcellsep 13.12236\tabcellsep 18.19754\\
Total employees\tabcellsep 130\tabcellsep 2.385\tabcellsep 0.943\tabcellsep 1\tabcellsep 5\\
Location of the Firm\tabcellsep 130\tabcellsep 0.808\tabcellsep 0.396\tabcellsep 0\tabcellsep 1\\
Logarithm of Value of Tangible Assets\tabcellsep 130\tabcellsep 15.533\tabcellsep 0.634\tabcellsep 13.81551\tabcellsep 17.21671\\
Sales Turnover\tabcellsep 130\tabcellsep 1.58E+07\tabcellsep 9803512\tabcellsep 1900000\tabcellsep 7.00E+07\\
Value of Tangible Assets\tabcellsep 130\tabcellsep 6807692\tabcellsep 4691318\tabcellsep 1000000\tabcellsep 3.00E+07\\
Capital Invested\tabcellsep 130\tabcellsep 1.01E+07\tabcellsep 1.18E+07\tabcellsep 500000\tabcellsep 8.00E+07\\
Squared Age of the Respondent\tabcellsep 130\tabcellsep 1078.923\tabcellsep 505.123\tabcellsep 361\tabcellsep 2500\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author (2016)}\end{longtable} \par
 
\caption{\label{tab_3}Table 4 . 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{44} \par 
\begin{longtable}{P{0.6281446540880502\textwidth}P{0.03742138364779874\textwidth}P{0.18443396226415096\textwidth}}
\multicolumn{3}{l}{Analysing the Factors Influencing Financial Records Keeping amongst Small and Medium Entreprises}\\
\multicolumn{3}{l}{(SMEs) in Ilala Municipal Council}\\
Financial Statement Prepared by the firm\tabcellsep Frequency\tabcellsep Percentage*\\
Income statement\tabcellsep 113\tabcellsep 96.6\\
Statement of Financial Position\tabcellsep 3\tabcellsep 2.56\\
Cash Flow Statement\tabcellsep 5\tabcellsep 4.27\\
\tabcellsep \tabcellsep Source: Field Data, 2016\\
\tabcellsep \tabcellsep © 2019 Global Journals\end{longtable} \par
  {\small\itshape [Note: Note: * This is the number of respondents preparing a particular financial statement as a percentage of 117 respondents who were keeping records. Total percentage is greater than 100 because some of the respondents prepared more than one kind of financial statements.]} 
\caption{\label{tab_5}Table 4 . 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{45} \par 
\begin{longtable}{P{0.6454887218045112\textwidth}P{0.12462406015037593\textwidth}P{0.07988721804511277\textwidth}}
Challenge Encountered\tabcellsep Frequency\tabcellsep Percentage\\
Inadequate skills\tabcellsep 58\tabcellsep 44.62\\
Inadequate availability of skilled personnel\tabcellsep 28\tabcellsep 21.54\\
Cost Constraints\tabcellsep 45\tabcellsep 34.62\\
\multicolumn{3}{l}{Note: Total percentage is greater than 100 because some of}\\
\multicolumn{2}{l}{the respondents cited more than one challenge.}\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Source: Field Data, 2016}\end{longtable} \par
 
\caption{\label{tab_6}Table 4 . 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{46} \par 
\begin{longtable}{P{0.23896226415094338\textwidth}P{0.11386792452830188\textwidth}P{0.4570754716981132\textwidth}P{0.040094339622641514\textwidth}}
Variable\tabcellsep Units\tabcellsep Description\tabcellsep Expected Sign\\
Membership\tabcellsep 1 or 0\tabcellsep 1=Member of SACCOS/TCCIA/TPSF, 0=Otherwise\tabcellsep +\\
Age of the Firm\tabcellsep Years\tabcellsep Age of the Firm\tabcellsep +/-\\
Gender\tabcellsep 1 or 0\tabcellsep 1=Male, 0=Female\tabcellsep +/-\\
Years of Schooling\tabcellsep Years\tabcellsep Number of years in School\tabcellsep +\\
Source of Capital\tabcellsep 1 or 0\tabcellsep 1= Financial Institutions, 0= Personal Source/Grants\tabcellsep +\\
Capital Invested\tabcellsep Log\tabcellsep Logarithm of Capital Invested\tabcellsep \\
Total employees\tabcellsep \multicolumn{2}{l}{Number Number of employees}\tabcellsep +\\
Location of the Firm\tabcellsep 1 or 0\tabcellsep 1=Commercial area, 0=Residential area\tabcellsep +\\
Value of Tangible Assets\tabcellsep Log\tabcellsep Logarithm of Value of Tangible Assets\tabcellsep +\\
\tabcellsep \tabcellsep \multicolumn{2}{l}{Source: Author (2016)}\end{longtable} \par
 
\caption{\label{tab_7}Table 4 . 6 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{47} \par 
\begin{longtable}{P{0.2973995271867612\textwidth}P{0.060283687943262415\textwidth}P{0.24917257683215127\textwidth}P{0.03817966903073286\textwidth}P{0.04018912529550827\textwidth}P{0.09042553191489361\textwidth}P{0.0743498817966903\textwidth}}
Financial Records Keeping\tabcellsep Coefficient\tabcellsep Err.\tabcellsep Std. z\tabcellsep P>|z|\tabcellsep \multicolumn{2}{l}{[95\% Conf. Interval]}\\
\textunderscore hat\tabcellsep 0.9067\tabcellsep 0.2535\tabcellsep 3.58\tabcellsep 0.000\tabcellsep 0.4098\tabcellsep 1.4035\\
\textunderscore hatsq\tabcellsep 0.1871\tabcellsep 0.2497\tabcellsep 0.75\tabcellsep 0.454\tabcellsep -0.3023\tabcellsep 0.6765\\
\textunderscore cons\tabcellsep -0.1977\tabcellsep 0.3844\tabcellsep -0.51\tabcellsep 0.607\tabcellsep -0.9511\tabcellsep 0.5557\\
Probit regression\tabcellsep \tabcellsep \multicolumn{3}{l}{Number of observations}\tabcellsep =\tabcellsep 130\\
\tabcellsep \tabcellsep LR chi2(2)\tabcellsep \tabcellsep \tabcellsep =\tabcellsep 50.91\\
\tabcellsep \tabcellsep Prob > chi2\tabcellsep \tabcellsep \tabcellsep =\tabcellsep 0.000\\
Log likelihood = -16.805\tabcellsep \tabcellsep Pseudo R2\tabcellsep \tabcellsep \tabcellsep =\tabcellsep 0.6023\\
\tabcellsep \tabcellsep \multicolumn{5}{l}{Source: Author's Computation from Field Data, 2016}\\
© 2019 Global Journals\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_8}Table 4 . 7 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{48} \par 
\begin{longtable}{P{0.15623946037099493\textwidth}P{0.02293423271500843\textwidth}P{0.053035413153457\textwidth}P{0.011467116357504215\textwidth}P{0.025801011804384485\textwidth}P{0.03153456998313659\textwidth}P{0.025801011804384485\textwidth}P{0.048735244519392915\textwidth}P{0.054468802698145025\textwidth}P{0.07883642495784147\textwidth}P{0.07166947723440135\textwidth}P{0.08313659359190556\textwidth}P{0.10750421585160203\textwidth}P{0.07883642495784147\textwidth}}
\multicolumn{4}{l}{Total Location Logarithm of Value of Tangible employees of the Firm Assets}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep 0.034 1\tabcellsep -0.110 0.420 1\tabcellsep Author's Computation from Field Data, 2016\\
Logarithm\tabcellsep \multicolumn{2}{l}{of Capital}\tabcellsep Invested\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep 0.074\tabcellsep 0.282\tabcellsep 0.355\tabcellsep Source:\\
\multicolumn{2}{l}{Source}\tabcellsep \multicolumn{2}{l}{of Capital}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep 0.255\tabcellsep -0.049\tabcellsep 0.163\tabcellsep 0.326\\
\multicolumn{2}{l}{Years of}\tabcellsep \multicolumn{2}{l}{Schooling}\tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep 0.173\tabcellsep 0.218\tabcellsep 0.189\tabcellsep 0.108\tabcellsep 0.112\\
\tabcellsep \multicolumn{2}{l}{Gender}\tabcellsep \tabcellsep \tabcellsep \tabcellsep 1\tabcellsep 0.118\tabcellsep 0.068\tabcellsep 0.064\tabcellsep -0.054\tabcellsep -0.134\tabcellsep -0.123\\
\multicolumn{2}{l}{Age of}\tabcellsep \multicolumn{2}{l}{the Firm}\tabcellsep \tabcellsep 1\tabcellsep -0.273\tabcellsep 0.076\tabcellsep 0.162\tabcellsep 0.109\tabcellsep -0.033\tabcellsep 0.139\tabcellsep 0.309\\
\multicolumn{2}{l}{SACCOS}\tabcellsep \multicolumn{2}{l}{Membership}\tabcellsep 1\tabcellsep -0.070\tabcellsep 0.018\tabcellsep 0.041\tabcellsep 0.060\tabcellsep 0.049\tabcellsep -0.223\tabcellsep 0.129\tabcellsep 0.156\\
\tabcellsep \tabcellsep \tabcellsep \tabcellsep SACCOS Membership\tabcellsep Age of the Firm\tabcellsep Gender\tabcellsep Years of Schooling\tabcellsep Source of Capital\tabcellsep Logarithm of Capital Invested\tabcellsep Total employees\tabcellsep Location of the Firm\tabcellsep of Value of Tangible Logarithm\tabcellsep Assets\end{longtable} \par
 
\caption{\label{tab_9}Table 4 . 8 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{49} \par 
\begin{longtable}{P{0.5203125000000001\textwidth}P{0.23124999999999998\textwidth}P{0.0984375\textwidth}}
Variable\tabcellsep VIF\tabcellsep Tolerance\\
Membership\tabcellsep 1.11\tabcellsep 0.9043\\
Age of the Firm\tabcellsep 1.22\tabcellsep 0.8203\\
Gender\tabcellsep 1.16\tabcellsep 0.8634\\
Years of Schooling\tabcellsep 1.14\tabcellsep 0.8797\\
Source of Capital\tabcellsep 1.18\tabcellsep 0.8442\\
Logarithm of Capital Invested\tabcellsep 1.26\tabcellsep 0.7953\\
Total employees\tabcellsep 1.14\tabcellsep 0.8759\\
Location of the Firm\tabcellsep 1.28\tabcellsep 0.7822\\
Logarithm of Value of Tangible Assets\tabcellsep 1.54\tabcellsep 0.6487\\
Mean VIF\tabcellsep 1.22\tabcellsep \\
\tabcellsep \multicolumn{2}{l}{Source: Author's Computation (2016)}\\
h) Interpretation of the Results from Probit Model\tabcellsep \multicolumn{2}{l}{used to assess the factors affecting financial records}\\
The coefficients and marginal effects of the\tabcellsep keeping in SMEs.\tabcellsep \\
Probit Model are given in Table 4.9. The Probit model is\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_10}Table 4 . 9 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.4401972872996301\textwidth}P{0.2347718865598027\textwidth}P{0.1750308261405672\textwidth}}
Explanatory Variables\tabcellsep Coefficient\tabcellsep Marginal Effects (dF/dx)\\
Constant\tabcellsep -12.629 [9.370]\tabcellsep \\
Membership\#\tabcellsep 2.015*** [0.642]\tabcellsep 0.036*** [0.030]\\
Age of the Firm\tabcellsep 0.126** [0.054]\tabcellsep 0.001** [0.001]\\
Gender\#\tabcellsep 0.408 [0.394]\tabcellsep 0.004 [0.006]\\
Years of Schooling\tabcellsep -0.221** [0.087]\tabcellsep -0.002** [0.002]\\
Source of Capital\#\tabcellsep 0.547 [0.525]\tabcellsep 0.004 [0.006]\\
Capital Invested\tabcellsep 0.554** [0.283]\tabcellsep 0.004** [0.005]\\
Total employees\tabcellsep 0.861*** [0.292]\tabcellsep 0.007*** [0.008]\\
Location of the Firm\#\tabcellsep 1.913*** [0.548]\tabcellsep 0.105*** [0.081]\\
Value of Tangible Assets\tabcellsep 0.222 [0.479]\tabcellsep 0.002 [0.004]\\
Number of observations\tabcellsep 130\tabcellsep \\
Wald chi2(9)\tabcellsep 30.55\tabcellsep \\
Prob > chi2\tabcellsep 0.0004\tabcellsep \\
Pseudo R2\tabcellsep 0.5943\tabcellsep \\
obs. P\tabcellsep 0.900\tabcellsep \\
pred. P\tabcellsep 0.997\tabcellsep (at x-bar)\\
Note: Robust standard errors in brackets\tabcellsep \tabcellsep \\
\multicolumn{3}{l}{*** significant at 1\%, **significant at 5\% * significant at 10\% level}\\
\multicolumn{3}{l}{(\#) dy/dx is for discrete change of dummy variable from 0 to 1}\\
\tabcellsep \multicolumn{2}{l}{Source: Author's Computation (2016)}\end{longtable} \par
 
\caption{\label{tab_11}Table 4 .}\end{figure}
 			\footnote{© 2019 Global JournalsAnalysing the Factors Influencing Financial Records Keeping amongst Small and Medium Entreprises (SMEs) in Ilala Municipal Council} 			\footnote{© 2019 Global Journals} 		 		\backmatter   			  			  			  				\begin{bibitemlist}{1}
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\end{document}
