metadata: authors: 0: author: Fox,M. Louise
1: author: Kweka,Josaphat Paul
count: Tanzania
docna: 0: docna: The household enterprise sector in Tanzania
            : why it matters and who cares
docty: Policy Research Working Paper
subtopic: Labor Markets,Educational Sciences,Food Security,Inequality
teratopic: Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development
abstracts: cdata!: The household enterprise sector has a
            significant role in the Tanzanian economy. It employs a
            larger share of the urban labor force than wage employment,
            and is increasingly seen as an alternative to agriculture as
            a source of additional income for rural and urban
            households. The sector is uniquely placed within the
            informal sector, where it represents both conditions of
            informal employment and informal enterprise. This paper
            presents a case study on Tanzania using a mixed approach by
            combining both quantitative and qualitative analysis to
            examine the important role of household enterprises in the
            labor force of Tanzania, and to identify key factors that
            influence their productivity. Household enterprise owners
            are similar to typical labor force participants although
            primary education appears to be the minimum qualification
            for household enterprise operators to be successful. Access
            to location matters -- good, secure location in a
            marketplace or industrial cluster raises earnings - and
            access to transport and electricity is found to have a
            significant effect on earnings as well. In large urban
            areas, the biggest constraint faced by household enterprises
            is the lack of access to secure workspace to run the small
            business. Although lack of credit is a problem across all
            enterprises in Tanzania, household enterprises are more
            vulnerable because they are largely left out of the
            financial sector either as savers or borrowers. Although HEs
            are part of the livelihood strategies of over half of
            households in Tanzania, they are ignored in the current
            development policy frameworks, which emphasize
            formalization, not productivity. Tanzania has a large number
            of programs and projects for informal enterprises, but there
            is no set of policies and program interventions targeted at
            the household enterprise sector. This gap exacerbates the
            vulnerability of household enterprises, and reduces their productivity.
geo_reg_key: 517191
repnme: repnme: The household enterprise sector in Tanzania
            : why it matters and who cares
display_title: The household enterprise sector in
            Tanzania : why it matters and who cares
listing_relative_url: /research/2011/11/15477397/household-enterprise-sector-tanzania-matters-cares
topicv3: Agriculture and Food Security m1326205 435,Informal Sector m1326258 1168,Poverty m1326687 695,Education m1327048 470,Household Enterprises m1583295 4426
publishtoextweb_dt: 2011-11-01T00:00:00Z
historic_topic: Social Protections and Labor,Health, Nutrition and Population,Finance and Financial Sector Development
no_of_pages: 81
author_keys: 0: author_key: 91198
1: author_key: 798602
model_extractions: WPS5882 Policy Research Working Paper 5882 The Household Enterprise Sector in Tanzania Why It Matters and Who Cares _Josaphat Kweka Louise Fox_ The World Bank Africa Region Poverty Reduction and Economic Management Unit November 2011
Policy Research Working Paper 5882

# **Abstract**

The household enterprise sector has a significant role in the Tanzanian economy. It employs a larger share of the urban labor force than wage employment, and is increasingly seen as an alternative to agriculture as a source of additional income for rural and urban households. The sector is uniquely placed within the informal sector, where it represents both conditions of informal employment and informal enterprise.

This paper presents a case study on Tanzania using a mixed approach by combining both quantitative and qualitative analysis to examine the important role of household enterprises in the labor force of Tanzania, and to identify key factors that influence their productivity. Household enterprise owners are similar to typical labor force participants although primary education appears to be the minimum qualification for household enterprise operators to be successful. Access to location matters - good, secure location in a marketplace or industrial cluster raises earnings—and access to transport and electricity is found to have a significant effect on earnings as well. In large urban areas, the biggest constraint faced by household enterprises is the lack of access to secure workspace to run the small business. Although lack of credit is a problem across all enterprises in Tanzania, household enterprises are more vulnerable because they are largely left out of the financial sector either as savers or borrowers.

Although HEs are part of the livelihood strategies of over half of households in Tanzania, they are ignored in the current development policy frameworks, which emphasize formalization, not productivity. Tanzania has a large number of programs and projects for informal enterprises, but there is no set of policies and program interventions targeted at the household enterprise sector. This gap exacerbates the vulnerability of household enterprises, and reduces their productivity.

This paper is a product of the Poverty Reduction and Economic Management Unit, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jkweka@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Household Enterprise Sector in Tanzania: Why It Matters and Who Cares**<sup>**1**</sup>

## **Josaphat Kweka and Louise Fox**<sup>**2**</sup>

JEL Classification: O17, J23, D13, O55 Key Words: employment, micro enterprises, household enterprises, job creation, youth, poverty reduction, returns to education and training, self-employment, female employment, informal economy, Tanzania, 1 We are grateful to Alexandru Cojocaru, Rafael Novella, and Elizabeth Mehta for data analysis, and to Caterina Laderchi for initiating the project. Useful inputs were received from Flora Kessy, Deogratias Mushi, and Godwil Wanga in Tanzania. William Steel, Waly Wane, Yutaka Yoshino, and Paolo Zacchia provided helpful comments and support. We also acknowledge valuable comments received from a roundtable discussion with high-level government officials in August, 2010; and from a stakeholders‟ workshop in February, 2011. The paper also benefited from comments by participants in the workshop on Employment and Poverty, organized by ILO in collaboration with REPOA and the Ministry of Employment and Labor (MOLE). Further comments were received from Prof. Benno Ndulu (Governor of the Bank of Tanzania). The financial support by the Belgian Partnership for Poverty Reduction, the donors of the TFESSD and the SPMDTF, and the World Bank is highly appreciated. 2 The Authors are respectively, Senior Economist (AFTP2), World Bank Tanzania country office; and Lead Economist, (AFTP1), Poverty Reduction and Economic Management (PREM) unit in the Africa Region. All comments should be addressed to: jkweka@worldbank.org.
i
# **Table of Contents**

|1<br>Intro|duction ............................................................................................................................1|
|---|---|
|1.1|Background ...................................................................................................................1|
|1.2|Objectives and structure of the report ...........................................................................3|
|2<br>Ove|rview of the Economy and the Emerging Role of HEs in the Labor Market ..................5|
|2.1|Growth and Poverty Reduction in Tanzania .................................................................5|
|2.2|Trends in the Growth of Employment and HEs ...........................................................6|
|2.3|Motivation for Having HEs ..........................................................................................9|
|3<br>Key|Characteristics and Productivity Drivers of Household Enterprises ............................12|
|3.1|Characteristics of Household Enterprise Operators ....................................................12|
|3.2|Analysis of Productivity Drivers for HE Earnings .....................................................16|
|4<br>Con|straints and Risks Faced by Household Enterprises ......................................................20|
|4.1|Types of Constraints ...................................................................................................20|
||4.1.1 Constraints by Sector/Nature of Activity ....................................................... 20|
||4.1.2 Constraints by Spatial Location ..................................................................... 21|
||4.1.3 Seasonality Problem in the Rural Areas ........................................................ 21|
|4.2|Lack of Credit .............................................................................................................22|
|4.3|Business Premises and the Urban Planning Policy .....................................................27|
||4.3.1 Places for Conducting Business, by Location ................................................ 27|
||4.3.2 Implications of Urban Planning Policy of LGAs ........................................... 28|
||4.3.4 The Machinga Problem .................................................................................. 30|
|4.4|Operational Challenges and Livelihood Risks ...........................................................32|
|4.5|Coping Strategies ........................................................................................................34|
|4.6|Perception of Factors for Business Growth and Failure .............................................35|
|5<br>Poli|cies, Programs, and Institutions Affecting the HE sector ..............................................37|
|5.1|Policies and Institutions on the Informal Sector .........................................................37|
||5.1.1 Policy Dilemma.............................................................................................. 37|
||5.1.2 Coordination Failure ...................................................................................... 38|
|5.2|Licensing and the Regulatory Regime ........................................................................40|
||5.2.1 Laws and Regulations .................................................................................... 40|
||5.2.2 Existing Challenges and the New Licensing Regime .................................... 41|
|5.3|Existing Support Programs and Projects ....................................................................44|
|5.4|Informal Sector Training (IST) for HEs .....................................................................46|
|6<br>Con|clusions and Policy Recommendations .........................................................................49|

ii
# **List of Tables**

|Table 1.1: Typology of Concepts Used in the Informal Non-farm Sector ...................................... 2|
|---|
|Table 2.1: Selected Economic Indicators (percent*) ...................................................................... 5|
|Table 2.2: Poverty Incidence (percent) ........................................................................................... 5|
|Table 2.3: Percent of Households Engaged in HE, by Area, 2006 ................................................. 9|
|Table 2.4: Percent of Households Engaged in HE, by Asset Quintile, 2006 .................................. 9|
|Table 2.5: Main Reason for Having an HE, by Economic Activity ............................................. 10|
|Table 3.1: Distribution of Recent Migrant Population in Urban Areas, by Job Type, 2006<br>(percent) ......................................................................................................................................... 14|
|Table 3.2: Industry Distribution of HEs ....................................................................................... 15|
|Table 3.3: Types of HE Activities, by Sector, 2006 ..................................................................... 15|
|Table 3.4: Earnings Regressions (OLS) Model for HE Operators, by Gender ............................. 19|
|Table 4.1: Ranking of Constraints by Magnitude of Their Effect Across Different Groups of<br>HEs, 2009 ....................................................................................................................................... 20|
|Table 4.2: Overall Infrastructure Constraints for Operating Rural Non-farm Enterprises, 2005 . 21|
|Table 4.3: Reasons for Not Running Household Enterprise All Year round, by Area, 2006|
|(percent) ......................................................................................................................................... 22|
|Table 4.4: Credit Sources Among HE Operators and the Microenterprise Operators in the|
|Informal Sector, 2006 (percent) ..................................................................................................... 24|
|Table 4.5: Place of Conducting Business Among HEs, 2006 (percent) ....................................... 28|
|Table 4.6: Relative Importance of Eviction as Traders' Most Memorable Experience, 2007 ...... 30|
|Table 4.7: HEs' Perceptions of Factors for Business Growth and Failure .................................... 35|
|Table 5.1: Institutions and Policies on the Informal Sector with Overlapping Mandates ............ 39|

# **List of Figures**

|Figure 2.1: Tanzania Labor Force Pyramid, 2006 (percent) ......................................................... 7|
|---|
|Figure 2.2: Employment Distribution in Urban Areas, 2006 ........................................................ 8|
|Figure 2.3: Growth of Employment by Type of Job, National, 2000/01 – 2006 .......................... 8|
|Figure 3.1: Age Distribution, by Area and Employment Type, 2006 ............................................ 13|
|Figure 3.2: Incidence of Training Among HE Operators, by Education Level, 2006 (percent) .... 14|
|Figure 3.3: Normalized Earnings by Employment Type and Gender, 2006 ................................ 17|
|Figure 4.1: Formal and Semi-formal Access Lags, Tanzania and Other East African Countries,<br>2009* ............................................................................................................................................. 23|
|Figure 4.2: Growth of Commercial Bank Lending Against Personal and Household Access to|
|Loans .............................................................................................................................................. 24|
|Figure 4.3: Households that Took Out Loans, 2000/01-2007 ....................................................... 24|
|Figure 4.4: Gender Distribution of Credit Sources Among HEs and Microenterprises, 2006....... 25|
|Figure 5.1: Distribution of Apprentices in Dar es Salaam, by Field (percent) ............................. 46|
|Figure 5.2: Total Vocational Graduates, by Institutional Ownership ........................................... 47|

iii
# **List of Boxes**

|Box 1.1: Main Data Sources ............................................................................................................ 4|
|---|
|Box 2.1: Effects of the Decline in Agricultural Production on Rural Non-farm Enterprises ........ 11|
|Box 3.1: The Success Story of a Small Entrepreneur ................................................................... 16|
|Box 4.1: The Vodacom's M-PESA Financial Services .................................................................. 27|
|Box 4.2: Will the Machinga Complex Solve the Problem of Itinerancy in Dar es Salaam? ......... 31|
|Box 4.3: Main Constraints Identified by VIBINDO, 2008 ............................................................ 33|
|Box 4.4: Major Constraints Listed by the Sampled Enterprises in the Urban FGDs, 2009 ........... 33|
|Box 4.5: Major Constraints Listed by the Sampled Enterprises in the Rural FGDs, 2009 ............ 34|
|Box 5 1: Regulating Household Enterprises in Uganda ................................................................ 43|
|Box 5 2: Studying the Impact of Microfinance and Training on Small Enterprises ...................... 44|
|Box 5 3: Recent Media Report on the Massive Default Rate of the PEF (JK Billions) ................ 45|
|Box 5 4: Training of HE and MSE Women in Food Preparation .................................................. 48|
|Box 5 5: Government Financing of Rural Skills Development ..................................................... 48|

iv
# **ABBREVIATIONS AND ACRONYMS**

|ACB|Akiba Commercial Bank|
|---|---|
|BARA|Business Activities Registration Act|
|BDS|Business Development Service|
|BEST|Business Environment Strengthening for Tanzania|
|BOT|Bank of Tanzania|
|BRAC|Bangladesh Rural Advancement Committee, Tanzania|
|BRELA|Business Registration Licensing Authority|
|CEM|Country Economic Memorandum|
|CRDB|Cooperative Rural Development Bank|
|CSSC|Christian Social Services Commission|
|DDSDP|Demand-driven Skill Development Program|
|FBO|Faith-based Organizations|
|FDC|<br>Focal Development Colleges|
|FGD|Focus Group Discussion|
|FINCA|Foundation for International Community Assistance|
|FSDT|<br>Financial Sector Deepening Trust|
|GEMA|Gender Education Management Association|
|GTZ|_Deutsche Gesellschaft für Technische Zusammenarbeit_|
|HBS|Household Budget Survey|
|HE|Household Enterprise|
|ISIC|International Standard Industrial Classification|
|IST|Informal Sector Training|
|ILFS|Integrated Labor Force Survey|
|ITEP|Individual Training Evaluation Program|
|LGA|Local Government Authority|
|MDA|Ministries, Departments, and Agencies|
|MFI|Microfinance Institutions|
|MITM|Ministry of Industry and Trade, Marketing|
|MKUKUTA|_Mkakati wa Kukuza Uchumi na Kupunguza_Umaskini Tanzania|
||(National Strategy for Growth and Poverty Reduction)|
|MKURABITA|_Mpango wa Kurasimisha Rasilimali na Biashara za Wanyonge_|
||Tanzania (National Business and Property Formalization Program)|
|MSME|Micro- and Small and Medium Enterprises|
|MNRT|Ministry of Natural Resources and Tourism|
|M&E|<br>Monitoring and Evaluation|
|NACTE|National Council for Technical Education|
|NSGRP|National Strategy for Growth and Reduction of Poverty|
|NBS|<br>National Bureau of Statistics|
|NGO|Nongovernmental Organization|
|NMP|<br>National Microfinance Policy|
|PIN|<br>Personal Identification Number|
|PMO|Prime Minister‟s Office|
|PTF|Presidential Trust Fund|
|PPA|Participatory Poverty Assessment|

v
|PRIDE|Promotion of Rural Initiatives and Development Enterprises|
|---|---|
|RAS|Regional Administrative Secretary|
|ROSCA|Rotating Savings and Credit Association|
|SACCO|Savings and Credit Cooperative Society|
|SCCULT|Savings and Credit Cooperatives Union League of Tanzania|
|SEWE|Self-Employed with Employees|
|SIDO|Small Industry Development Organization|
|SMS|Short Message System|
|SME|Small and Medium Enterprises|
|SPMDTF|Social Protection Multi-donor Trust Fund|
|TFDA|Tanzania Food and Drugs Authority|
|TFESSD|Trust Fund for Environmentally and Socially Sustainable<br>Development|
|TFP|Total Factor Productivity|
|TGT|Tanzania Gatsby Trust|
|TIN|Tax Identification Number|
|TAZARA|Tanzania Zambia Railway Authority|
|TRA|Tanzania Revenue Authority|
|UDEC|University of Dar es Salaam Entrepreneurship Centre|
|VETA|Vocational Education and Training Authority|
|VIBINDO|_Vikundi vya Biashara Ndogondogo_|
|VICOBA|Village Community Banks|
|WBI|World Bank Institute|
|WDT|Women Development Fund|

vi
# **1 Introduction**

## **1.1 Background**

1. In the last decade, development policy has emphasized strategies aimed at attaining the twin objectives of increasing economic growth and reducing poverty. As an alternative to agriculture, the non-farm informal economy has become an important vehicle for economic participation by the poor, thereby contributing to both growth and poverty reduction agendas of many countries.<sup>3</sup> Thus, a better understanding of the dynamics, constraints, and potentials of informal enterprises is essential for designing policies and interventions that can turn them into an engine of employment and income growth, rather than simply a mechanism for coping with vulnerability and sharing poverty.

2. However, an analysis of the informal sector is less straightforward. The informal sector is an extensive, often misunderstood concept that refers to conditions of employment or of firm behavior. Informal employment is likewise subject to multiple interpretations as many „informal‟ jobs exist even within companies in the formal sector, some of which pay regular wages. In general, informal workers are those who do not have a contract that is in accordance with labor regulations. Meanwhile, the firm side of the informal sector can include a variety of different types of enterprises, but informal firms are typically small-scale and may or may not be registered with government agencies of any kind. Viewed from an enterprise perspective, the definition of the informal sector omits the informal workers, in contrast with a labor market definition that captures them. There are also other important dimensions of size, such as capital outlays or turnover, which show important variations within each category of informal enterprises. These are often ignored when the size of an informal enterprise is defined mainly on the basis of the number of its employees (see Table 1.1). All these imply that a “one size fits all” approach is less useful in analyzing the informal sector.

3. This study focuses on the smallest informal firms, the household enterprises (HEs), as these entities are uniquely placed within the informal sector. HEs represent both conditions of informal employment and informal enterprise. For the most part, they are started by a single entrepreneur so they create employment for the owner and establish small businesses within the household and the economy.

4. Household enterprises, as defined in household and labor surveys, consist of own-account operators and unpaid family workers. From the standpoint of enterprise surveys, they are tiny firms consisting of a single entrepreneur, perhaps working with unpaid workers who are likely to be family members.<sup>4</sup> In practice, many people may participate in HEs as a secondary activity, including farmers, civil servants, and schoolchildren. Although this complicates the statistical picture, it means that HEs may be even more important for coping with and exiting poverty than would be suggested by data based only on primary occupations (Steel and Snodgrass, 2008).

> 3 See ILO (2002), Fox and Gaal (2008), and Heintz (2004).

> 4 The term “ _nano_ enterprises” may also be applied to indicate that these are the lowest rung on the ladder of enterprise sizes, with no wage workers.

1
**Table ‎1.1:  Typology of Concepts Used in the Informal Non-farm Sector**

|Concept|General Description|Statistical Descriptions|Comments|
|---|---|---|---|
|Non-farm Household<br>Enterprise without<br>employees (HEs)|Individuals who operate a<br>business by themselves or<br>with the support of other<br>members of the household,<br>but without hiring any<br>employees except on a casual<br>basis.<br>Unincorporated; this may<br>mean that the finances of the<br>enterprise are mixed with<br>those of the household.| Own-account operator<br> Self-employed in a non-farm<br>activity without employees<br> Unpaid family workers in a non-<br>farm activity|Helpful to include the family<br>employees in the definition as it is<br>a household activity.<br>Thus, the definition of “self<br>employed in a non-farm activity<br>without employees” is confusing<br>unless employees is defined to<br>exclude relatives.<br>May or may not have any<br>registration or license; may or<br>may not operate full-time all year;<br>may or may not pay taxes.|
|Non-farm Household<br>Enterprise with<br>employees|As above, but employ at least<br>one person outside of the<br>family| Self-employed with employees<br> Own-account operator with<br>employees<br> Unpaid family workers|As above. No size limitation. In<br>this study, mostly referred to as<br>microenterprises.|
|Family workers, non-<br>farm|People living in the household<br>who work in the business.<br>May also include close<br>relatives not living in the<br>household.| Unpaid family workers|The adjective “unpaid” is often<br>used without actually checking on<br>how earnings are shared<br>In our analysis, family workers<br>are combined with other non-<br>wage workers, either in as farmers<br>on in HEs|
|Employee|A person who is paid to do a<br>task for someone else in<br>exchange for payment.| Employee in a wage job, paid in<br>cash or kind|Synonym is “job;” “employ_ment_”<br>includes employee as well as<br>employer and anyone engaged in<br>nonwage economic activities,<br>with or without remuneration|
|Employer|Normally, the owner of<br>business (incorporated or<br>unincorporated)| Employer<br> Business-owner|In principle, an HE owner with<br>employees is an employer. But<br>mostly, the term employer is used<br>for a more established business.|
|Informal employee|Definitions vary across<br>countries, but normally<br>workers who do not have a<br>contract, or whose contract is<br>not in accordance with the<br>labor regulations and/or are<br>not covered by the national<br>social insurance system| Employee<br> Requires some information on the<br>nature of contract, and benefits<br>provided on the job|Not related to the characteristics<br>of the firm, only characteristics of<br>the job; informal workers may<br>work in informal firms|
|Micro, small, and<br>medium enterprises<br>(MSMEs)|<br>Enterprises which may be<br>incorporated and registered or<br>not.|Firm (in firm surveys)|Some include HEs with employee<br>in microenterprises. Size<br>categories varyacross countries.|
|Informal firm|Definition varies by country.<br>Usually a firm which is not<br>incorporated, licensed, or<br>registered. May or may not<br>have informal employees.|Can include HEs if:<br> data is collected in household<br>survey, and<br> survey instrument includes an<br>enterprise model<br>Some firm surveys (e.g., ICA) include<br>small (micro) firms, which may be<br>labeled as informal (criteria varies).<br>These are usually not HEs, as they<br>have nonfamilyregular employees.|Informality of firms has to be<br>defined with respect to national<br>regulations. An informal firm is<br>not in compliance with<br>regulations, regardless of size. But<br>there tends to be a correlation<br>with size of employment<br>(although not with revenues).|

2
5. The HE sector reflects the efforts of farmers, peri-urban, and urban households to earn more income. It is also their entry point into participation in private sector-led growth. Clearly, from the perspective of pro-poor growth policy, the sector presents a significant opportunity for policymakers to enhance complementarities, rather than tradeoffs, between poverty reduction and growth strategies.

6. Focusing on HEs triggers a key question relevant to pro-poor growth strategies: what measures can help poor households generate income and cope with vulnerability in a laborsurplus situation? In response, the World Bank in the Africa region has embarked on a regional multi-country study to provide policymakers with a concrete set of policies that will enable the HE sector to have a bigger role in the economy as a major source of productive employment and enterprise growth. This report presents a case study of Tanzania‟s HEs, which follows the methodology outlined by Steel and Snodgrass (2008).

# **1.2 Objectives and Structure of the Report**

7. Given its significant size in the non-agriculture labor market, the HE sector plays an important role in job creation, but the sector is largely ignored in Tanzania‟s policy and institutional framework, which generally focuses on the MSMEs in the informal sector. This gap weakens the potential of HEs to become major players in the country‟s poverty reduction efforts. Thus, the challenge is to understand the significance of HEs and to explore policy measures that recognize them as a strategic sector for achieving the MKUKUTA‟s twin objectives of increasing growth and reducing poverty.

8. In view of the above, this study has three main objectives. Since the overarching question is how to improve the productivity of HEs, the first objective is to profile them, i.e., we need to know who they are, where they are, what they do, and why they are formed. From a range of characteristics that define HEs, including the constraints and risks they face, the second objective is to determine the key productivity drivers in the HE sector in order to inform policy towards improving its overall performance. Finally, the study seeks to analyze existing policies, programs, and projects affecting HEs in order to identify and propose measures by which support for them can be improved. While this Tanzania case study followed as much as possible a diagnostic methodology framework, the actual design is mainly determined by available information and evidence from existing literature, taking into account the specific context in which HEs operate.

9. The report is structured as follows. Following the introduction, Section 2 provides a background on the HE sector, and describes its role in the economy. Section 2 profiles the HE operators and determines the key characteristics that influence their productivity and overall performance. Section 4 analyzes the different constraints and risks that HEs face, and the coping strategies they adopt in response to these problems. Section 5 reviews the policy and institutional environment within which HEs operate, including programs and projects that affect them directly or indirectly. Finally, Section 6 concludes with some recommended policy actions and next steps.

3
# **Box 1.1: Main Data Sources**

This study used both existing data and literature available in Tanzania, as well as a special qualitative analytical work commissioned for the report.

- The only household survey which contains data on HEs is the Integrated Labor Force Survey (ILFS). Using available data drawn from the two rounds of the ILFS survey conducted by the National Bureau of Statistics (NBS, 2000/01 and 2006), a quantitative analysis was conducted to identify the key features of the HE sector and its economic role, and to determine what drives or constrains their productivity. The two rounds of survey enabled understanding of the dynamics in the HE sector in general, and in several types of HEs differentiated by spatial location (Dar es Salaam, other secondary urban, and rural areas), gender (male- and female-operated), industry group (trade, manufacturing, services), and other key characteristics. However, the coding of the spatial locations is not consistent in both surveys, so comparisons between the two surveys by area are biased and should be regarded with care.

- To complement the quantitative analysis, a focus group discussion (FGD) survey was conducted to learn from HEs and from their experiences with running their business. The FGD survey was done in two phases. The first phase, conducted in March and April, 2009, focused on urban clusters in 9 regions -- Dar es Salaam, Morogoro, Dodoma, Singida, Kigoma, Mtwara, Kilimanjaro, Arusha and Mwanza.  The second phase, launched in September, 2009, covered the 3 districts of Kilosa, Kwimba, and Masasi located in Morogoro, Mwanza and Mtwara regions, respectively. The narratives from the in-depth interviews of individuals and groups during the FGDs provide rich insights into the HE operators‟ perception of their needs, constraints, and coping strategies, as well as the impact of government policies, programs, and projects on them (see Kessy, 2010).

- The study also drew on existing literature on the informal sector, as well as interviews with Government officials, donors and NGOs involved in the sector.

4
# **2 Overview of the Economy and the Emerging Role of HEs in the Labor Market**

## **2.1 Growth and Poverty Reduction in Tanzania**

10. Tanzania has achieved very modest gains in poverty reduction amidst sustained economic growth.<sup>5</sup> Over the last five years, the economy has grown at a rate of at least 5 percent per year (Table 2.1). However, the most recent data shows a weak impact on consumption of key commodities; the percentage of the population in poverty fell from 35.6 in 2001 to 33.6 in 2007 (Table 2.2).

**Table ‎2.1:  Selected Economic Indicators (percent*)**

||**2004 **|**2005**|**2006**|**2007 **|**2008**|**2009**<sup>**.**</sup>|
|---|---|---|---|---|---|---|
|GDP growth|7.8|7.4|6.7|7.1|7.4|5.0|
|Annual Inflation (CPI, period avg.)|4.7|5.0|7.2|7.0|10.2|11.9|
|Private credit to GDP|8.5|9.7|12.1|14.9|16.2|19.5|
|Current account balance to GDP|-2.9|-6.1|-8.0|-9.3|-12.3|-11.1|
|Exchange rate (TSh per USD)|1043|1165|1266|1132|1259|1319|
|Interest rate (T-Bond)|9.3|8.3|9.3|17.1|7.8|-|
|Domestic Revenue to GDP|11.9|12.4|14.1|16.0|15.9|16.1|
|Overall budget deficit to GDP (after grants)|-4.9|-4.9|-4.8|-1.6|-4.5|-6.1|
|Domestic borrowingto GDP|1.0|1.5|1.2|-1.5|0.8|1.6|

_Note: *unless otherwise specified Source: Ministry of Finance and Economic Affairs, (various years); World Bank, 2010_

**Table ‎2.2:  Poverty Incidence (percent)**

||**1991**|**2001**|**2007**|
|---|---|---|---|
|Dar es Salaam|28.1|17.6|16.4|
|Other urban|28.7|25.8|24.1|
|Rural|40.8|38.7|37.6|
|Tanzania Mainland|38.6|35.6|33.6|

_Source: World Bank, 2008b_ 11. Close to 850,000 new job-seekers enter the labor market every year. Recognizing this trend, job creation has become a flagship policy of the government. In the search for workable solutions, an analysis of the labor market is critical. With its growing share in the non-agriculture labor market, the household enterprise sector plays an essential role in job and income creation, and can provide a route out of poverty.

> 5 See Utz (2008).

5
12. However, the literature on the effectiveness of HEs in poverty reduction is limited as the focus of most existing studies on small enterprise development has been on MSMEs and the informal sector in general. Yet the number of households with an HE is remarkably large. This signifies the importance of HEs to poor households as a livelihood strategy and as a means to supplement their income, and therefore a practical option for increasing their welfare.

# **2.2 Trends in the Growth of Employment and HEs**

13. By far, agriculture remained the main economic activity of the labor force in Tanzania in 2006, employing 77 percent of women and 72 percent of men. However, within the nonagriculture sector, when family helpers are included, the HE share in the labor force increased to over 50 percent for male and 75 percent for female (Figure 2.1). In urban areas in 2006, HEs employed a larger share of the labor force than wage employment, i.e., 40 percent, the largest category (Figure 2.2).

14. Non-farm sector employment has been growing very rapidly in Tanzania as incomes in agriculture stagnate. However, despite a very rapid growth in non-farm wage employment, especially in urban areas, the supply of labor seeking non-farm employment outpaced the demand in the wage sector, leaving many labor force participants with no choice but to create their own employment. A comparison of data from the two rounds of the ILFS survey, 2000/01 and 2006, shows that employment in the HE sector grew by 13 percent, higher than the overall change in the labor force and faster than the growth of wage employment in both non-agriculture and agriculture sectors (Figure 2.3).

15. Since the labor force perspective considers only one activity per individual, it understates the economic role of HEs. Even more remarkable than the importance and growth of HEs as a primary employment source is their role as secondary employment as Tanzania transitions from an agrarian economy. For individuals who declared a secondary employment, the overwhelming majority cited an HE, including 36 percent of the labor force in rural areas (Table A1.5, Appendix A). Forty-two percent of all females in the labor force cited their HE as their secondary employment (Table A1.6). Therefore, although only 16 percent of labor force participants reported working in an HE as their primary employment, 66 percent of households in Tanzania ran some kind of a household enterprise, either as a primary or secondary activity (Table 2.3). As a livelihood source for households, on a full- or part-time basis, HEs are increasingly viewed as an alternative or complement to agriculture. In contrast, households in peri-urban areas rely much more on their HE as a source of livelihood given the limited wage employment opportunities, and less or no farming activities in these areas.

6
# **Figure 2.1: Tanzania Labor Force Pyramid, 2006 (percent)**

<!-- Start of picture text --> Figure 2.1a Tanzania Pyramid including Agriculture<br>employer 2.0 2.2<br>wage with secure contract 0.9 4.8<br>wage without secure contract 2.7 6.7<br>HE without employees outside of household 9.7 11.9<br>non-agri family worker 7.7 2.6<br>agri. 77.1 71.7<br>Female Male 80 60 40 20 0 20 40 60 80<br><!-- End of picture text --> <!-- Start of picture text --> Figure 2.1b Tanzania Non-agriculture Labor Force Pyramid<br>employer 3.9 7.9<br>wage with secure contract 8.8 17.1<br>wage without secure contract 12.1 23.5<br>HE without employees outside of household 43.8 42.2<br>non-agri family worker 31.4 9.3<br>50 40 30 20 10 0 10 20 30 40 50<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 7
# **Figure 2.2: Employment Distribution in Urban Areas, 2006**

<!-- Start of picture text --> 6%<br>33% 20% Public wage non-agr.<br>Private wage non-agr.<br>Household enterprise non agr.<br>Wage agriculture<br>Family farming<br>1%<br>40%<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data._

**Figure 2.3: Growth of Employment by Type of Job, National, 2000/01 – 2006 (percent)**

<!-- Start of picture text --> Total employment 4.0<br>Family farming 2.1<br>Wage agriculture 4.7<br>Household enterprise non agr. 12.9<br>Private wage non-agr. 11.2<br>Public wage non-agr. 3.2<br>0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0<br><!-- End of picture text --> _Note: Type of jobs refers to primary employment only. Source: Calculations based on the ILFS 2000/01 and 2006 data._ 8
**Table ‎2.3:  Percent of Households Engaged in HE, by Area, 2006**

|1.3|1.4||
|---|---|---|
||HE as primary activity*|All HE*|
|Dar|58.4|67.9|
|Other urban|49.1|74.2|
|Rural|13.0|63.5|
|Total|24.4|65.9|

_* Households where HE is the primary activity of at least one member.** Households with HE as a primary or secondary activity of at least one member. Source: Calculations based on the ILFS 2006 data._

**Table ‎2.4:  Percent of Households Engaged in HE, by Asset Quintile, 2006**

||HE non-agriculture*|HE non<br>-agriculture**|
|---|---|---|
|Quintile 1|9.4|66.1|
|Quintile 2|15.3|59.3|
|Quintile 3|20.4|69.2|
|Quintile 4|30.6|73.1|
|Quintile 5|51.1|61.9|
|Total|14.5|65.9|

_Note: * refers to HE as a primary activity ** refers to HE as a primary or a secondary activity Source: Calculations based on the ILFS 2006 data._ 16. Does running an HE reduce poverty? The evidence in Tanzania is not clear. The ILFS 2006 data showed that both poor and non-poor households participate in HEs. The one panel study for Tanzania which analyzed this question found that in rural Kagera, adding an HE to a farm-based activity is indeed a successful route out of poverty, but only for those with access to towns and markets (De Weerdt, 2008). What we can see from our cross-sectional evidence is that when the HE is the primary activity of a household member, ownership of HEs is strongly correlated with higher income -- half of the households in the top quintile in 2006 had a household enterprise as a full-time activity, compared with only 10 percent in the lowest quintile (Table 2.4). A separate study on non-farm rural household enterprises also came up with the finding that richer households own HEs (Jin and Deininger, 2008). What is not known is if households had a successful enterprise because they were already richer (i.e., had the needed start-up and working capital) and could devote full-time attention to their HE, or if a successful enterprise allowed them to work full-time and move up the income ladder.

# **2.3 Motivation for Having HEs**

17. If it is not clear that an HE is a route out of poverty (or a way to stay out of poverty), why do households start and maintain one? The reasons provided by survey respondents vary widely, but can be grouped into “pull” factors -- they were attracted into the business, or “push” factors – 9
they were pushed into operating an HE as they could not find adequate income-earning opportunities in either wage or agricultural employment. Although a combination of both may be at work, analysis of the ILFS data shows that push factors are more common, especially when an HE is a secondary activity (Table 2.5).

**Table ‎2.5:  Main Reason for Having an HE, by Economic Activity**

||_Primary_<br>|_Activity_<br>|_Secondary_<br>|_Activity_<br>|
|---|---|---|---|---|
|**PUSH FACTORS**|_2000_<br>%|_2006_<br>%|_2000_<br>%|_2006_<br>%|
|Can‟t find other work|44.9|37.2|21.2|18.5|
|Released from employment|3.6|1.7|1.1|1.1|
|Retirement from employment|1.1|0.9|1.2|0.5|
|Family need for more income|24.7|31.6|43.5|55.9|
|**PULL FACTORS**|||||
|Good business opportunities|6.6|11.8|8.5|9.0|
|Does not require much capital|7.8|7.4|10.2|7.2|
|Low production cost|0.6|0.4|0.3|0.5|
|Desire for independence|2.1|2.0|0.9|1.4|
|Free choice of work hours and place|2.3|1.6|3.2|1.4|
|Can combine business w/ housework|2.4|3.2|5.0|2.9|
|Traditional line of business of family|2.3|1.4|3.0|1.5|
|Other|1.7|0.9|2.0|0.4|
|Total|100|100|100|100|

_Source: Calculations based on the ILFS 2000/01 and 2006 data_ 18. For those engaged in HEs as their secondary activity, the primary reason for doing so was their family‟s need for additional income. This is not surprising as one would expect this need to be a strong motivation for seeking a second job generally. Informal sector arrangements are also more conducive to part-time work, which requires lesser time commitment than a primary occupation. The share of households driven into the HE sector as a secondary source of income rose by 12 percentage points during the 2000-2006 periods, indicating its increasing role as a source of livelihood.

19. More than two-thirds of HEs in the urban areas were formed because of lack of any other job opportunities, in particular wage employment providing adequate remuneration. The dominant factor in the rural areas, on the other hand, was the need for a secondary source of income by households whose main activity was farming (Box 2.1).

10
**Box 2.1: Effects of the Decline in Agricultural Production on Rural Non-farm Enterprises**

# 1.6

In the rural areas, agricultural and non-agricultural enterprises are so complementary that capital and labor for either activities flow reciprocally between the two groups of enterprises. Almost every operator of a non-farm enterprise is also a farmer. The entrepreneurs of Lukuledi Village gave evidence to this by saying: _“During the rainy season, every villager here is farming. We, who have non-farm enterprises, have to go to farm in the morning and do our non-farm activities in the afternoon. Even if we don‟t farm, we don‟t get anyone to buy from us since all our potential customers are away from the village center where we run our non-farm enterprises. In the afternoon some farmers buy from us, but we don‟t expect many of them since they hardly have money. Others are away from the village for several weeks to farm.”_ The complementary relationship between agricultural and non-agricultural enterprises was made clearer by one respondent who owned a shop and said: “ _I expect to circulate my money through agriculture so that my shop can flourish. Now that agriculture is limping, my shop is limping too_ .” Another one added: “ _Siku hizi, hasa 2007-09, pesa imepotea sana, ”which_ means: “ _These days, especially from 2007 until now (2009), cash is not available_ .” The discussants further explained that rural entrepreneurs depend on farmers. They have to earn cash from agriculture in order for them to buy their goods. They added that some entrepreneurs divert money from their enterprises and use it on agricultural production, ending up losing money from both activities in times of bad weather. They noted that rains had been less than in some years, resulting in a decline in agricultural productivity, income, and food security.

One respondent who was selling petroleum products said: _“These days, most people do not even buy kerosene; they use grass to light their houses. This is because they do not get good harvests for selling and boosting their household income.”_ That famine was looming was made evident by a concurrent open meeting while the focus group discussion was being held, to discuss how to distribute maize about to be received by the village as part of the food relief operation of the government.

_Narrated by a male participant, FGD, Masasi District_ 20. In sum, the primary drive behind the growth of HEs in both rural and urban Tanzania is income diversification in an economy with limited option in the non-farm sector, rather than a latent wellspring of entrepreneurship which spots a great business opportunity. This, combined with evidence showing that very few graduate beyond HE status into a small business employing people outside the family, has led some authors to dismiss them as “survivalist” (see Kinda and Loening, 2009, for Tanzania; and Mead and Lindholm, 1998, for a survey of experiences in Africa in the 1990s). However, their importance in absorbing labor in the economy, and therefore helping people to work more hours and earn more cash, cannot be understated. Previous studies have found that overall economic growth brings more HE and microbusiness activities, which are especially important for women as the flexibility of these activities allows them to produce household goods and earn income at the same time. It is expected that when more panel data become available (like those collected for Kagera as cited above), stronger evidence on the role of HEs in poverty reduction and economic development will emerge.

11
# **3 Key Characteristics and Productivity Drivers of Household Enterprises**

## **3.1 Characteristics of Household Enterprise Operators**

21. **Demographic profile** . Owing to high fertility and therefore high rates of population growth, the majority of HE operators are young, though they tend to be slightly older relative to the country‟s labor force in general (Tables A1.7 and A1.8). Over half falls in the age group 1534 years. Between 2001 and 2006, however, the share of HE operators in the age group 15-24 declined by three percentage points, while those in the 25-34 age group rose by about 3 percentage points. As will be noted later, this youngest group is also associated with low levels of education, most likely because they left school early. Urban areas have a high density of labor force in the age range 25-34 because of migration of this age group from rural areas. In Tanzania, women and men are equally likely to start an HE.

22. **Education and training profile.** The level of education of HE operators is similar to that of the labor force in general (Table A1.9). Over 60 percent of them completed primary education only. Consistent with an overall low level of education in rural areas, a higher share of HE operators in the rural areas had no education (24 percent), or completed primary education only (17 percent), compared to Dar es Salaam with 10 percent and 11 percent, respectively (Table A1.7).

23. ILFS data show that more than 80 percent of HEs have never had any training of any kind, with a mere 4 percent having attended on-the-job (onsite) training. Vocational and apprenticeship training is the main form of training for about 12 percent of HEs. The proportion of HEs without training increased, but only in the rural areas, even as the share of those who received on-the-job training rose between 2000 and 2006.

24. More educated HE operators tend to acquire training as well, indicating that training is not a substitute for lack of education. .Only 5 percent of HE operators with no education report having had training, whereas the share of those with training increases up to 68 percent for the 5 percent of HE operators with advanced secondary or university-level education (Figure 3.2). Recent research suggests that even in the informal apprenticeship system where the formal education requirements may appear less stringent, formal education is an important determinant of HE operators‟ acceptance into training programs.<sup>6</sup> This raises concerns about their opportunities for further skill acquisition, let alone their prospects for formal sector employment. Fox and Gaal (2008) argue that at least some post-primary education is generally required to obtain a job in the formal sector or to upgrade their skills.

> 6 See Kahyarara and Teal (2008)

12
# **Figure 3.1: Age Distribution, by Area and Employment Type, 2006**

## **Urban**

<!-- Start of picture text --> 0.04<br>0.03<br>0.02<br>0.01<br>0<br>15 22 31 39 48 57 66 75 84 92<br><!-- End of picture text --> <!-- Start of picture text --> 0.04<br>0.03<br>0.02<br>0.01<br>0<br>15 24 32 41 50 59 68 77 86 95<br>public wage non-agri private wage non-agri HE wage agri family farming<br><!-- End of picture text -->

**Rural**

_Note: Employment type is based on primary employment only. Source: Calculations based on the ILFS 2006 data_ 13
# **Figure 3.2: Incidence of Training Among HE Operators, by Education Level, 2006 (percent)**

<!-- Start of picture text --> Incidence of training by education level, HEs<br>0 .2 .4 .6 .8<br>No education<br>Incomplete primary<br>Completed primary<br>Incomplete ord. secondary<br>Completed ord. secondary<br>Advanced secondary / univ<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 25. **Migration profile.** HEs provide recent migrants with economic opportunities in the urban areas. Although many migrants find private sector wage and salary jobs (they have a higher proportion in these jobs because they are, on average, more educated that non-migrants), in 2006, about 40 percent of HE operators in Dar es Salaam and in other urban areas were migrants (Table 3.1).

**Table ‎3.1:  Distribution of Recent Migrant Population in Urban Areas, by Job Type, 2006 (percent)**

||||Dar<br>||Oth<br>|er urban<br>|
|---|---|---|---|---|---|---|
||Not migrant|Migrant|Total|Not migrant|Migrant|Total|
|Public wage non-agri|6.8|4.3|6.4|5.5|9.9|6.1|
|Private wage non-agri|25.0|36.0|27.0|13.7|27.2|15.3|
|HE non-agri|47.6|39.6|46.2|34.4|40.9|35.2|
|wage agri|0.7|3.0|1.1|0.7|1.9|0.8|
|Family farming|19.9|17.2|19.4|45.8|20.2|42.7|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

_Note: Job type refers to primary employment only .Migrant indicates a migration in the last 5 years only. Source: Calculations based on the ILFS 2000/01 and 2006 data_ 26. **Sectoral and Occupational Distribution of HEs:** Although HEs operate in different sectors, there is a high concentration in certain sectors or activities. In 2006, over 70 percent of HEs in the urban areas and more than 60 percent in the rural areas were in small businesses related to trade, hotels, and restaurants. These activities have no or low barriers to entry, and are highly competitive. However, HEs are increasingly diversifying out of the trade sector. Between 2001 and 2006, the share of HEs engaged in trade declined (Table 3.2). Following a transformation in the economy, more changes in the sectoral distribution of HEs can be expected as HEs develop new expertise and operate in other areas.

14
**Table ‎3.2:  Industry Distribution of HE**

|1.7||2006||Change 20|01-2006(Percen|tagepoints)|
|---|---|---|---|---|---|---|
|||All|||||
||Rural|Urban|Total|Rural|All Urban|Total|
||%|%|%|%|%|%|
|Agric., Hunting, Forestry, Fishing|2.6|1.0|1.9|-1.0|0.4|-0.4|
|Mining, Manufacturing, Energy|26.6|17.5|23.0|9.5|8.9|9.5|
|Construction|2.4|2.9|2.6|-3.1|-3.1|-3.1|
|Trade, hotels, restaurants|62.1|71.1|65.6|-6.7|-4.5|-6.0|
|Transport,storage,real estate|2.2|2.8|2.4|1.7|1.5|1.6|
|Public and social/person. Services|4.2|4.7|4.4|-0.4|-3.2|-1.6|
|Total|100.0|100.0|100.0||||

_Source: Calculations based on the ILFS 2000/01 and 2006 data._ 27. Although not based on a representative sample, Table 3.3 enriches the sectoral picture by showing data from the FGD study. HEs are categorized according to three major non-agriculture sectors (manufacturing, trading, and services) on the basis of the participants‟ description of their activities. Not surprisingly, a substantial number of HEs are engaged in some form of trade. Next in importance are manufacturing enterprises. This sectoral structure is in part a reflection of the ease of entry into informal trading (see Box 3.1), but also of the constraints HEs faced in undertaking production and other activities requiring electricity, fixed premises, labor, and skills.

**Table ‎3.3:  Types of HE Activities, by Sector, 2006**

|Trading||Manufacturing||Services||
|---|---|---|---|---|---|
|_Activity_|_Incidence_|_Activity_|_Incidence_|_Activity_|_Incidence_|
|Sellingroasted/fried cashew nuts|2|Tailoring|8|Motorcycle repair|2|
|Sellingwater|3|Makingsnacks|2|Video shows|1|
|Sellingsugarcanejuice|1|Restaurant|18|Decorations|1|
|Sellingbelts for clothing|1|Food vending|4|Women‟s hair saloon|2|
|Sellingsocks and wallets|1|Juice makingand selling|1|Motor vehicle mechanics|<br>1|
|Sellingfruits and vegetables|9|Masonry|1|Transport(_bodaboda_)|3|
|Dealer in used clothes|7|Carpentry|2|Pharmacy|2|
|Selling cellular phone SIM cards<br>and air-time vouchers|4|De-husking cashew nuts|1|IT equipment and video<br>cassette hiring|3|
|Sellingcookingstoves<br>|1|Foodprocessing|2|||
|A small shop/_genge_<sup>_7_</sup>|20|Local brew making|1|||
|Fish retailing|3|Millingmachine|4|||
|Vendingwater|1|Carpentry||||
|Sellinglocal chicken/guinea fowls|2|||||
|Sellingsoft and alcoholic drinks|4|||||
|Selling petroleumproducts|1|||||
|Importing maize from Mozambique<br>and selling|2|||||
|Selling _upupu_<sup>_8_</sup><br>_Source: FGD report, 2009_|1|||||

> 7 Note that in a _genge,_ food products like sardines, tomatoes, onions, as well as other items like soap are sold.

> 8 _Upupu_ is a wild legume cooked for 10 to 15 hours and is consumed during periods when there is shortage of food. It is poisonous, if improperly cooked.
28. When grouped by occupation, a similar distribution of HEs is also evident. Using the ILFS data, results suggest that more than 75 percent of HEs are in just two categories of activity: service shopkeepers and craftsmen. Owing to lack of education, the number of HEs providing professional services is low, i.e., hardly 2 percent of the total (Table A1.10). Most workers with high levels of education prefer to work in firms.

## **Box 3.1:  The Success Story of a Small Entrepreneur**

The 25-year old Hafsa Hassan from Matwara used her TSh 150,000 savings to start a business buying and selling cashew nuts. In 2003, she joined the Matwara Small Entrepreneurship Development Association (MSEDA) from where she was able to get a TSh 500,000 loan to expand her business, and avail of various training opportunities, including study tours with SIDO, ILO, and other NGOs. With only 25 kilogram of cashew nuts to sell at start-up, Hafsa is currently trading on more than 250 kilograms of cashew nuts and has a working capital of more than TSh 1.5 million. Commenting on the reasons why her business is doing well, she remarked: “ _Unahitaji kuwa mbunifu na mtafutaji wa misaada kama unataka kufanikiwa kama mjasiriamali mdogo. Nilianza kidogo kidogo, lakini nilihakikisha natafuta taarifa muhimu kuhusu maendelea ya biashara kokote nilikosikia. Nilipoingia MSEDA ikawa ndiyo kama Mungu kanifungulia....nilikutana na wenzangu, tukabadilishana mawazo na uzoefu. Tulipata mafunzo kutoka sehemu mbalimbali. Kwa sasa naweza kusema mimi ni kati ya kinamama ambao ukija miaka michache ijayo utakuta nina kampuni yangu ya kubangua na kuuza korosho_ .” This means, “You need to be creative and look for where you can get assistance if you want to succeed as a small entrepreneur. I started with a small capital, but I was constantly looking for important information and opportunities that could help me succeed. When I joined MSEDA, it was like God opening the door for me. I met several entrepreneurs and we exchanged ideas and also got training from several sources. I can go so far as to say that I am one of few women who, when you come back here a few years from now, will be owning a factory, and in my case, my own cashew nut processing factory.” _Source: Narratives from the FGD report, 2009._

# **3.2 Analysis of Productivity Drivers for HE Earnings**

29. HE operators enter the business to gain income. Does running an HE pay off? Unfortunately, the ILFS data set does not include agricultural income so a comparison cannot be made between earnings from HEs and from the alternative for most of the labor force in rural areas -- agriculture. However, when comparing HE earnings with those from other nonagricultural employment options, HE is not a poor choice. Although public employment is obviously a better choice in Tanzania, 95 percent or more of HE operators do not have the qualifications for these jobs (see Table A1.9). Not surprisingly, if an HE is able to graduate to an MSE, reported earnings tend to rise. For women, the median hourly earnings appear to be higher than those from a wage job in the private sector, even though HE earnings tend to be undermeasured compared to wage earnings (Figure 3.3). Moreover, if agricultural wage earnings approximate the alternative of agriculture, both men and women do better in non-agricultural activities, including HEs.

30. To determine in more detail which of the various characteristics of HE operators support increased earnings, a multivariate analysis was done separately for men and women. Table 3.4 16
reports the regression of the natural logarithm of hourly earnings<sup>9</sup> on a range of HE operators‟ characteristics (using this specification means that the coefficients can be interpreted as the percentage effect on earnings).

**Figure 3.3:   Normalized Earnings by Employment Type and Gender, 2006**

<!-- Start of picture text -->
1.5<br>1.25<br>1<br>0.75<br>Male<br>0.5 Female<br>0.25<br>0<br>Public wage non- Employer Private wage  Household  Wage agriculture<br>agr. non-agr. enterprise non<br>agr.<br><!-- End of picture text -->

_Note: Reference category = public wage employment, non-agriculture_ _Source: Calculations based on the ILFS 2006 data._ 31. Not surprisingly, education is found to have a strong effect on earnings, although there are clear diminishing returns. HE operators without complete primary education are clearly disadvantaged.<sup>10</sup> For example, for a male HE operator, completing primary education increases earnings by 23 percent at the mean, but completing ordinary secondary education adds only one percent more. This is not surprising, as few HE operators have reached this level of education. The returns to highest levels of education are significantly greater, but very few HE operators have education beyond primary school (less than one percent has gone beyond ordinary secondary). Age (a proxy for experience) adds much more to the earnings of male HE operators than of female HE operators. Migration status, however, has no effect on earnings.

> 9 Hourly earnings are obtained by taking the net earnings of enterprises divided by the hours reported by all those who worked in these enterprises.  One concern regarding the measure of hourly earnings for the self-employed is that earnings are qualitatively different from wages, i.e., they include remuneration for capital and for entrepreneurial risk. The data does not allow us to address the latter concern. With regard to earnings, the potential bias is likely to be minor.

> 10 Obviously, the coefficient on “complete primary” could be picking up other factors than education;  in other words, there may be a selectivity issue. Clearly, there is a selectivity issue into operating an HE, but we were unable to pick up anything other than education as a selection variable. Education is by far the strongest predictor of occupational choice in Tanzania, but it also exerts a strong impact on earnings.

17
32. Trading, the most popular sector, is still a good choice for HE operators. Only two sectors -- mining and construction -- appear to generate more earnings for male-run HEs, and one sector – hotels and restaurants -- for female-run HEs. Note that with training, however, hotels and restaurants generate positive returns for men.

33. Other authors have found that access to infrastructure matters a lot for HE profitability (e.g., Jin and Deininger, 2008). However, these correlations are difficult to interpret since clearly infrastructure comes first to richer areas (e.g., Dar es Salaam). In our regression analysis, local conditions are very important in determining earnings. The coefficients on Dar es Salaam and on Other Urban areas are positive. in addition, the fixed effects variables account for the majority of the variance explained by the regression. This may be why we did not find access to electricity, to be significant, but we did find that being near a market matters to female HE operators. As noted above, De Weerdt (2008) found that rural households living near towns were much more likely to be successful in using an HE as a route out of poverty.

34. Surprisingly, apprenticeship training does not help earnings, and more formal types of training tend to help only male HE operators. The effect of training on earnings is also difficult to isolate as it involves a certain element of omitted variable – what is it that causes people to go get training in the first place? Some authors have found that training comes up negative in these types of regression because only poorly educated people go for training (Kahyarara and Teal, 2008). Testing this with the ILFS 2006 data, training is found to be a fairly rare event in Tanzania – only about 10 percent of female HE operators and 18 percent of male HE operators had any training at all. Those who received training tended to be concentrated in a few sectors. To clarify the effect of training on earnings, interaction terms between training and the sector of activity were included in the regression. The results show that indeed in some sectors training can increase earnings, but not for women HE operators. This is most likely because in Tanzania, the training sector is still quite underdeveloped. In Ghana, where the apprenticeship sector is more developed, similar analysis showed strong positive results for informal training for both male- and female HE operators.

35. What do these findings mean for policy efforts to increase the productivity and earnings of HE operators? The picture is less clear since the information available from the ILFS can only explain about 30 percent of the variation in HE earnings (compared with over 60 percent for wage-earners in Fox and Novella, 2011). Obviously, other variables not measured in the data set matter a lot for HEs‟ success. What is clear is that for now, wage and salary employment in Tanzania is mostly not available for those without at least secondary school qualifications. Those with these qualifications tend to find a wage and salary job, and thus for them staying in the HE sector does not pay off relative to the costs of school. For primary school leavers, however, operating an HE is likely to be the only alternative to agriculture. Since they make up the majority of new entrants to the labor force, and given the shortage of placements in secondary schools, the crucial question is how to make this economic activity pay off. The next sections will examine this issue using information mainly from other data sets, such as those collected from the qualitative FGD study.

18
**Table ‎3.4:  Earnings Regressions (OLS) Model for HE Operators, by Gender**

|**1.8**|**Males**|**Females**|
|---|---|---|
|Age|0.056***|0.028***|
||(0.007)|(0.007)|
|Age2|<br>0.065***|<br>0.034***|
||(0.009)|(0.008)|
|_Education (reference: no education)_<br>|||
|Incomplete Primary|0.070|-0.020|
||(0.074)|(0.066)|
|Completed Primary<br>|0.226***<br>(0.064)<br>|0.104*<br>(0.055)<br>|
|Incomplete Secondary|0.247*|0.220|
||(0.129)|(0.150)|
|Completed Ordinary Secondary|<br>0.245**|<br>0.315***|
||(0.097)|(0.117)|
|Some or completed Adv. Secondary and Tertiary|0.644***|0.459|
||(0.195)|(0.390)|
|Recent migrant|-0.011|0.071|
||(0.061)|(0.064)|
|Market within 30 min|-0.027|0.163**|
||(0.086)|(0.081)|
|# Months business operates|<br>-0.014*|<br>-0.003|
||(0.007)|(0.007)|
|_Training (Omitted category: no training)_<br>|||
|On job/informal apprenticeship|0.004|0.031|
||(0.068)|(0.098)|
|Other(vocational/college/diploma/other)|0.213**|0.138|
||(0.097)|(0.129)|
|_Industry (Omitted category: wholesale/retail trade)_<br>|||
|Manufacturing, Energy|0.334***|-0.129**|
||(0.070)|(0.059)|
|Mining, Construction|0.298***|0.276*|
||(0.088)|(0.144)|
|Hotels / restaurants|-0.314***|0.100**|
||(0.088)|(0.049)|
|_Transport storage real estate_|-0.007|-0.099|
|_, ,_|<br>(0.086)|<br>(0.807)|
|Public, social, personal services|0.159*|0.256*|
||(0.095)|(0.131)|
|_INTERACTIONS_|||
|Training*manufacturing|0.219**|0.114|
||(0.109)|(0.131)|
|Training*Construction mining|-0164|-|
|,|.<br>(0.143)|<br>-|
|Training*Hotels / restaurants|<br>0.522**|-|
||(0.257)|-|
|Training* Public, social, personal services|-|0.137|
||-|(0.253)|
|_Area of Residence (reference: rural)_|||
|Dar|0456*|1011*|
||.<br>(0.274)|.<br>(0.549)|
|Other urban|<br>3093***|<br>4957***|
||.<br>(0.315)|.<br>(0.593)|
|Constant|3.380***|3.366***|
||(0.310)|(0.563)|
|Observations|3526|3207|
|R-squared<br>_Notes: The regression takes includes (i) EA s and (ii) quarter of the survey_<br>_not reported. Robust standard errors reported in parentheses. Significanc_<br>_Source: ILFS, 2006._|0.28<br>_, as fixed effects, however thes_<br>_e level: * p<0.1 ** p<0.05_|0.32<br>_e coefficients are_<br>_*** p<0.01._|

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# **4 Constraints and Risks Faced by Household Enterprises**

36. Household enterprises face constraints and risks that inhibit their growth and trap them at a low level of productivity. When analyzing them, several important points should be borne in mind. First, HEs differ by location, nature of activity, and characteristics of operators, and may therefore be affected differently by different constraints. While recognizing these differences, it is certain that some constraints are most binding across all HEs. Second, while the study seeks to identify the many constraints HEs face in operating their business, the focus is more on highlighting the underlying differences in how they are affected by these constraints. Third, some operational difficulties faced by HEs are cited as constraints, but may in fact be a reflection of the HE operators‟ weak capacity to do business, the unfavorable business environment in which they operate, and/or their frustration with the government‟s failure to support them. Finally, the analysis of the constraints, risks, and coping strategies of HEs are based primarily on data drawn from the ILFS and the FGD surveys, but also made use of the limited literature available, most notably the studies conducted by Jin and Deininger (2008) and by Lyons and Msoka (2007).

37. Lack of access to credit (hence the lack of capital) was the most often reported constraint by HEs. This is not surprising, as it also applies across the MSME sector (Skof, 2008). Another most commonly cited constraint is the lack of workspace to conduct business. Because of their pervasiveness, these two issues are given greater prominence in the analysis that follows.

## **4.1 Types of Constraints**

## 4.1.1 **_Constraints by Sector/Nature of Activity_**

38. Some of the most binding constraints faced by HEs are specific to the nature of their activity. Business premise, power, and technology constraints are most binding among HEs engaged in manufacturing. HEs in the trading sector contend with constraints imposed by market competition, regulation (or corruption in the regulatory system), and the risk of theft (Table 4.1). How market location, corruption, and lack of workspace adversely affect the hawkers ( _Machingas_ ) will be discussed later in more detail.

**Table ‎4.1:  Ranking of Constraints by Magnitude of Their Effect Across Different Groups of HEs, 2009**

|Constraint|Hawking<br>(_Machinga_on street)|Manufacturing/Skill based<br>(car_penters/h_air dressers)|General Merchandise<br>(kiosk)|
|---|---|---|---|
|Businesspremise|3|1|4|
|Market location/competition|1|4|1|
|Power(electricity)|5|2|5|
|Technology|6|3|6|
|Crime/theft|4|5|3|
|Regulation/corruption|2|6|2|
|**Key issue**|Profitable to look for spot<br>customers,mostlyon street|Earnings are drained by high<br>rentals andpower shading|Located in-house, limited<br>customers,often visited byofficials|

_Note: 1 means most important; 6 means least important. Source: FGD report, 2009_ 20
39. Constraints imposed by market competition affect HEs in two different ways. First, a large number of HEs sell similar products or services, and compete for the same limited number of customers, hence their turnover is low. Second, as is the case with the rest of the economy, increased imports of products (mainly from Asia) threaten the survival of HEs manufacturing similar products locally, although HEs involved in trading as well as all Tanzanian consumers gain. Thus, while the first type of competition adversely affects HEs in the trade and distribution sector, the second type has opposite effects on production- and trade-related HEs.

# 4.1.2 **_Constraints by Spatial Location_**

40. When location and availability of infrastructure are considered, some HEs are more disadvantaged than others. Access to electricity and transport differ significantly by region. In the Kilimanjaro region, 79 percent of households have access to electricity, compared to 15 percent in Kigoma and 21 percent in Mtwara.<sup>11</sup> While close to half of the households in Kilimanjaro have public transport to markets, no such facility is available in Kigoma and only 3 percent were served in Mtwara. Half of the households in Kilimanjaro are connected to mud external road, compared to 77 percent in Mtwara and 75 percent of households in Kigoma (Table 4.2). Overall, about half of all rural households are connected to mud roads only, indicating that the country‟s road infrastructure remains underdeveloped (Table A1.12). The share of rural nonenterprise households thus connected is higher (55 percent) relative to rural households with HEs (50 percent).

**Table ‎4.2:  Overall Infrastructure Constraints for Operating Rural Non-farm Enterprises, 2005**

||Distance to<br>bank (km)|Electrified<br>(%)|Public<br>transport to<br>markets(%)|Mud<br>external<br>road(%)|Distance to<br>city (km)<br>|Distance to<br>market<br>(km)|Average<br>tax rate<br>(%)|Days to<br>register|Number of<br>observations|
|---|---|---|---|---|---|---|---|---|---|
|Total by<br>Region|39.1|50.0|14.4|48.2|15.5|6.1|19.3|20.9|1230|
|Kilimanjaro|18.4|78.8|44.9|49.8|12.2|6.9|18.1|20.2|113|
|Morogoro|84.0|40.2|27.4|37.1|19.4|7.7|20.6|19.2|238|
|Mtwara|59.2|20.7|3.3|76.9|16.5|7.6|28.5|19.5|199|
|Mbeya|19.3|67.5|8.3|30.8|12.7|5.8|13.8|17.3|285|
|Tabora|27.1|48.7|0.0|45.0|21.1|6.6|20.2|27.1|142|
|Kigoma|53.3|15.2|0.0|75.0|11.9|3.2|17.2|20.2|130|
|Kagera|46.9|42.6|9.9|33.2|20.5|2.8|17.1|25.2|123|

_Source: Calculations based on the NBS/World Bank 2005 RIC Survey data_ 41. For urban HEs, shortage of land or business premise is one of their most critical constraints, a factor that led many of them to hawking as a last resort. Moreover, as rental price increases with the size of potential market, most HEs are pushed into smaller markets or temporary structures in unauthorized locations.

# 4.1.3 **_Seasonality Problem in the Rural Areas_**

42. Seasonality is a critical feature of HEs, especially those in rural and peri-urban areas. In 2006, only about 40 percent of HEs who had been in existence for at least one year reported operating all 12 months. Many of those who operated less than 12 months are in rural areas.

> 11 Note that lack of access to electricity is a key constraint, and a more strategic area for intervention to increase the productivity of HEs. Jin and Deininger (2008) report that rural and semi-urban households with access to electricity are more likely to have an HE (44 percent), against 32 percent for those without.

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These are HEs run by households whose primary activity is agriculture, and thus have to adapt to the seasonality of business activity and income (see Box 2.1 in Section 2). However, the proportion of households affected by their HE‟s seasonal operation vary, depending on whether or not it is the main activity of the HE operator or the secondary occupation. For those whose HEs are their secondary activity, 31 percent did not run their business year-round because of their farm work, compared to only 12 percent of those whose main activity is running their HEs (Table 4.3).

43. Perhaps reflecting their different motivations for engaging in HEs, the two groups of HE operators also differ in their other reasons for operating seasonally. Those who‟s HEs are their primary activity contend more with the problem of limited market especially in the rural areas (22.4 percent of them, compared to 15 percent of those engaged in HEs as a secondary activity). They also attribute their seasonality to personal reasons (e.g., illness, sad or happy social events) more than those engaged in HEs as a secondary activity (18.3 percent and 5 percent, respectively). Note that some HEs reported operating less than a full year because they are less than a year-old.

**Table ‎4.3:  Reasons for Not Running Household Enterprise All Year round, by Area, 2006 (percent)**

|||_Main econom_<br>|_ic activity_<br>||_S_<br>|_econdary econ_<br>|_omic activit_<br>|_y_<br>|
|---|---|---|---|---|---|---|---|---|
||Rural|Secondary<br>Urban|Dar es<br>Salaam|Total|Rural|Secondary<br>Urban|Dar es<br>Salaam|Total|
|Business created within 12 months<br>|21.3<br>|25.1|28.7<br>|24.0|17.9|17.3|19.9|17.9<br>|
|Limited market|16.6|27.0|28.5|22.4|13.8|24.1|17.1|15.0|
|Lack of inputs|9.4|8.8|6.5|8.7|11.8|5.5|18.1|11.2|
|Seasonal / temp activity|18.8|9.3|7.7|13.4|20.0|19.4|11.1|19.7|
|Other work (agric.)|18.5|8.2|1.3|11.8|31.5|28.2|28.3|31.1|
|HH demands|1.8|0.7|1.7|1.4|0.4|0.0|1.3|0.4|
|Personal,other reasons|13.7|20.9|25.5|18.3|4.7|5.4|4.0|4.7|
|Total|100|100|100|100|100|100|100|100|

_Source: Calculations based on the ILFS 2006 data._ 44. Regardless of causal factors, it is worth noting that seasonality sometimes affects the earning capacity of HEs (Table 3.4 in Section 3 finds a very small negative effect, but significant for males only). Among rural HE operators, poor agricultural performance was cited as one of the main reasons why they operate their HEs seasonally (see Box 2.1 in Section 2). Not surprisingly, their business turnover is very small, if any, during the agricultural season. When the rains are erratic and they do not produce enough, their cash flow is reduced to a minimum, which in turn adversely affects the operations of their HEs. Conversely, rural HEs thrive during good agriculture season when farm income augments HEs‟ capitalization and local demand for farm goods is high.

# **4.2 Lack of Credit**

45. Although lack of credit is a common problem across all enterprises in Tanzania, HEs are particularly vulnerable,<sup>12</sup> _mainly because they are left out of the financial sector and policy interventions have been less effective_ **_._** Overall, access to finance is stunted in Tanzania. Data

> 12 In many studies, lack of credit is found to be the most stringent constraint binding HEs from growing and being more productive. For instance, during the FGD survey, participants in every session mentioned lack of capital or credit as the most important constraint they face, at times associating the visit by the study team with assistance that is forthcoming.

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from the FinScope survey show that, compared to other neighboring countries, access to financial services in Tanzania is notably low. In 2009, 56 percent of households had no access to any banking or savings/credit services at all in any form despite the sharp growth in credit lending to households in recent years (Figures 4.1 to 4.3).

46. Reforms in the financial sector have had only marginal impact on improving household access to financial services overall. While formal access to finance increased by less than 4 percentage points (from 9 percent of the population in 2004 to 12.4 percent in 2009), the proportion of financially excluded households increased from 54 percent to 56 percent, respectively (Figure 4.1). Among HEs, access to banks was limited to only about 14 percent of them. Of those without bank access, about 80 percent were in the urban areas and 90 percent in the rural areas (Table A1.13). Lack of bank access was attributed by respondents mainly to economic reasons, such as not having a regular income or employment, or having very little income. However, this also reflects the low access of Tanzanian households to cheap financial services. The high bank charges prevalent among commercial banks, the only ones which can take deposits, dissuade HEs from using their services. Most HEs reported in 2009 that they depended on informal sources, such as rotating savings scheme (e.g., merry-go-round or ROSCA), family or clan, and welfare groups, rather than on formal and semi-formal sources of finance (Table A1.14). This implies that ten years after the National Microfinance Policy (NMP) was created (2001) and twenty years since the liberalization of Tanzania‟s financial sector, government interventions to increase access to financial services (including through microfinance), especially for Tanzania‟s rural population, remain largely ineffective.

**Figure 4.1:  Formal and Semi-formal Access13 Lags, Tanzania and Other East African Countries, 2009***

<!-- Start of picture text --> 100%<br>100%<br>80%<br>80%<br>54.0% 56.0% 60%<br>60%<br>40%<br>40% 20%<br>35.0% 27.3%<br>20% 0%<br>4.3%<br>2.0%<br>9.0% 12.4% Kenya Uganda Malawi Rwanda Tanzania Tanzania East<br>0% (2006) (2009) Africa<br>Tanzania (2006) Tanzania (2009)<br>Formal Semi Formal Informal Financially Excluded<br>Formal Semi Formal Informal Financially Excluded<br>Note: *unless otherwise specified<br> Source: FinScope Survey, 2009<br><!-- End of picture text -->

> 13 People with “formal” access include those who hold an account in or have some sort of relationship with a financial institution, such as a commercial bank, community bank, or insurance company that is supervised by a financial services regulator; and also people who use products from formal institutions such as pension fund or hire purchase companies which are not supervised by either the Bank of Tanzania or the Insurance Commissioner. “Semi-formal” access includes people who use products from SACCOs and MFIs which are formally registered, but not supervised by a financial service regulator; and people who use products from institutions offering financial services other than SACCOs and MFIs.  These institutions have a formal character, but are not supervised by a financial service regulator.  This category includes M-Pesa and government loans for housing and education. “Informal” access includes people who use products offered by informal associations or groups, e.g., ROSCAs, VSLAs, and other community-based savings groups, family and friends, small businesses and moneylenders. “Financially excluded” are those who keep their savings in a secret hiding place (e.g., under the mattress) and/or who save in the form of nonmonetary items (e.g., livestock, jewelry) and/or who use loyalty card from a supermarket or a petrol station.

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**Figure 4.2:  Growth of Commercial Bank Lending Against Personal and Household Access to Loans**

**Figure 4.3:  Households that Took Out Loans 2000/01-2007**

<!-- Start of picture text --> Commercial Bank Lending to Personal 2001 - 2008 Households that took Out Loans<br>9,000.00 30.0%<br>6<br>8,000.00<br>25.0%<br>7,000.00 5<br>6,000.00 20.0%<br>4<br>Urban<br>5,000.00<br>4,000.00 15.0% 3 y = 2.1x - 1.5 Rural<br>3,000.00 10.0% 2 Tanzania<br>2,000.00 Linear (Tanzania)<br>5.0% 1<br>1,000.00<br>- 2.13 0.0% 0<br>00/01 2007<br>% Households<br>In Tshs 10 bn (2001 Prices)<br>% of Total Private Sector Lending<br>2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source: Bank of Tanzania, 2009 Source: Household Budget Survey, 2007_ 47. The ILFS 2006 data corroborate the finding that access to finance by HEs (especially those in the rural areas) are severely limited. Only 7 percent of operators with HEs as their main activity reported having received credit from anyone – bank or relative - in the last 12 months, against 13 percent of microenterprise owners. Only 9 percent of all households had access to formal credit, most of whom are urban households.

48. Of the HEs who received credit, 48 percent obtained it from relatives, compared to 22 percent of microenterprises. While borrowing from relatives is much more common in rural and secondary urban areas, it is less so in Dar es Salaam, where only 28 percent of HE operators reported it as a source of credit over the last year. For another 30 percent of HEs in Dar es Salaam, credit sources are business associations, NGOs, or donor projects, a much higher proportion than in rural or secondary urban areas. Credit from banks and other financial institutions is considerably lower among HEs, even in Dar es Salaam, than among microenterprises Table 4.4).<sup>14</sup>

**Table ‎4.4:  Credit Sources Among HE Operators and the Microenterprise Operators in the Informal Sector, 2006 (percent)**

||||_HEs_<br>||_M_<br>|_icroenterprises_<br>|
|---|---|---|---|---|---|---|
||Rural|Secondary|Urban|Dares Salaa|m Total|Total|
|Relative or friend|55.6|49.1||27.7|48.2|21.8|
|Rotating savings & credit group (UPATU)|11.3|4.8||9.1|8.6|5.4|
|Savings and credit co-operative (SACCO)|14.1|13.3||15.6|14.1|16.6|
|Business association, NGO, donor project|9.8|13.2||29.2|14.6|11.9|
|Bank or financial institution|2.2|7.4||10.2|5.6|18.9|
|Other sources|6.9|12.1||8.3|9.0|25.3|
|Total|100|100||100|100|100|

_Source: Calculations based on the ILFS 2006 data_ 14 The sample size of non-HEs in the informal sector does not allow for a meaningful disaggregation by area of residence.

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**49.** The incidence of receiving credit is higher among women-operated HEs (11 percent versus 5 percent among male-operated HEs). A larger share received credit from business associations, NGOs, or donor projects, i.e., 17 percent of women-operated HEs, compared to 11 percent of male-run HEs (Table A1.15), which likely reflects the focus of microfinance institutions on lending to women. The share of those with access to loans from savings and credit cooperatives is also larger among women (Figure 4.4).

**Figure 4.4: Gender Distribution of Credit Sources Among HEs and Microenterprises, 2006**

<!-- Start of picture text --> HEs<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br>Micro enterprises<br>0 .2 .4 .6 .8 1<br>Relative or friend<br>Rotating saving & credit group (UPATU)<br>Saving and credit co-operative (SACCO)<br>Business association, NGO, donor project<br>Bank or financial institution<br>Other sources<br>Male Female<br><!-- End of picture text --> _Source: Calculations based on the ILFS 2006 data_ 50. Lack of access to credit for HEs is often caused by lack of a successful business strategy or investment, which would yield returns high enough to pay back the loan. In FGDs, HE operators reported that they did not apply for credit in part because they were not confident about their ability to repay the loan. This finding was corroborated by the results of the FinScope 2009 survey, which show that among the majority of HEs who have never applied for a loan (87 percent), more than a third did not do so out of fear of not having enough money to repay the loan, while 17 percent said they actually did not have enough money. Taken together, these two groups constitute a little more than half of all HEs who did not apply for a loan, implying that their underlying problem is poor cash flow. Another 20 percent did not seek credit because of tough loan conditions (Table A1.16). HEs are therefore subjected to a vicious circle. Their high credit risk associated with their weak business performance makes it difficult for them to access bank credit to grow their business.

1. Even when microcredit companies (such as PRIDE or FINCA) and NGOs (such as VICOBA/DUNDULIZA) manage risk by lending to groups of HEs, the interest rate they charge is high. This is due not only to the high unit costs of providing credit to small borrowers, but also to high risk of default. Some lending companies charge as high as 40 to 100 percent interest per year along with short repayment periods. Weaknesses in the business and regulatory environment of the microfinance sector have constrained their effectiveness in addressing this

25
gap.<sup>15</sup> Despite government interventions, FGD participants reported that many governmentsupported credit programs have not reached them at all.

51. Anecdotal evidence from the FGD study shows that managing the loaned money is a daunting task for HEs owing to business risk, household risk, and fungibility problems, which create the potential for delayed repayment or default. In such cases, the response from some of these creditors can be disproportionately adverse. Some FGD participants narrated their ordeal when the credit-lending organization raided the house of one of the defaulters and parted with belongings worth more than the value of the credit. Managing a large lump sum of borrowed money also poses risks to borrowers and creditors alike. As noted by participants in the FGD study: “If you give a loan of TSh 200,000 to an individual who needs only TSh 20,000 for capital, he/she will not be able to use it. He/she will use TSh 150,000 for other purposes unrelated to the business.” _Male participant, Mwanza_ .

“I borrowed TSh 900,000 one year ago from Akiba Commercial Bank (ACB), and one of the group members borrowed TSh 2,000,000. She disappeared. We are paying for her loan and can‟t get any new loans until we pay up her debt. I would rather do _kibati_ (merry-go-round) from the neighborhood than to be part of a group whose fate can be this devastating.” _Youth participant, FGD, Arusha_ .

52. The “soft” loans provided under most microfinance programs are often viewed not to be so. The additional conditions attached to them, including the need for some form of collateral, or a business registration certificate, or reference from a group lending association, work against the expectation of many HEs. Both the quantitative and qualitative analyses revealed that the youth are more vulnerable to this credit constraint because they are under-age to qualify for a loan. Compared to older traders, they are also more mobile, but with less unmovable assets, hence they are less trusted. This is a common predicament among the _Machingas_ , over 95 percent of whom are young people (Lyons and Msoka, 2007).

53. Most NGO and government microfinance programs consider women more trustworthy and effective in using loaned money for intended impact.<sup>16</sup> Women may be constrained from borrowing from other sources because the assets they seek to use as collaterals are legally owned by men. But these donor- or NGO-assisted projects target women not only because they lack

> 15 However, it is interesting to note that most FGD participants had some broad knowledge of loan conditionality, including the setting of interest rate. This was attested by their response to hypothetical test question during the FGD. For example, they were asked: If you were given a loan of TSh 5 million, would you accept it? If not, why? If yes, what would you do with it? Those whose sales turnover was much higher said they will accept it, as opposed to those with less who said they will not. Those in between had plans to expand their business, mostly to buy equipment or move to another more lucrative business. These were often the ones who had applied for but were denied loans earlier.

> 16 Some microfinance institutions, such as BRAC and PRIDE, have programs that are biased towards women. Also, there are specific government programs for women, for instance, the Women Development Fund (WDF) and Equal Opportunity Trust Fund (EOTF), but none for men. Globally, the Microcredit Summit Campaign reports that 80 percent of microfinance clients are women, with the proportion varying by region. The larger shares were in Asia, followed by Africa and Latin America (Karlan and Goldberg, 2007).

26
collateral, but also because the models used (such as group lending) are from South Asia, where experience showed that women were better at loan repayment.

54. M-PESA could become a real game-changer for HEs. The M-PESA (and similar technologies by other mobile phone operators, such as Zain and Zantel) provides money transfer by short message service (SMS). More importantly, it is accessible to anyone, even to those with no bank account, and does not charge any fee other than the small fee for using the service. The system operates just like banks where financial services can be bought and sold. Local shops, which are mostly HEs, deposit cash in with local agents. The M-PESA system also offers substitutes to savings in the form of pre-paid phone credits, which are liquid (Box 4.1). They are not sources of credit, however. Banks have been quick to see this as an opportunity by creating mobile phone accounts and linking them to their services.

## **Box 4.1: The Vodacom's M-PESA Financial Services**

The VODAFONE M-PESA system provides financial services like a commercial bank. The customer can deposit, transfer, or withdraw cash with the M-PESA agent, who serves like a bank agent. The customer is required to complete a form with his/her personal information (name, number of cellular or mobile handset, and phone number) to be filled in the M-PESA communication system. After registration, a registered customer receives an SMS indicating his/her Personal Identification Number (PIN), which is a 4-digit secret code. The PIN number is to be used whenever the customer wishes to access account information or perform financial-cum-banking transaction under the control of the M-PESA service network.

The M-PESA system can provide information on account balances, transfer funds from a registered customer‟s account to another, and receive notification of the transaction through a message on the mobile phone. It also allows the customer to change his/ her PIN number. Most of the VODAFONE customers are automatically registered by the M-PESA communication system for financial services. Any holder of a mobile or cellular phone qualifies to be „a recipient end‟ to receive messages from the M-PESA network, even without being its customer. For instance, anybody registered with either ZANTEL, ZAIN, or TIGO can also receive VODAFONE-based messages and cash from an M-PESA agent, but only if adequate information from the accountholder is provided.

The customer can access his account balance, using a cellular or mobile phone screen in any location where network is available, to make transactions. For instance, a customer with an M-PESA account can instantly buy or pay for goods or services, which belong to another M-PESA customer, e.g., BP (for fuel), TANESCO (for LUKU electricity), SHOPRITE (for goods), using their cellular handsets or mobile phones with money balances.

The charges for M-PESA services are very low and affordable to low-income households (about 2 percent of the transacted value). The integrity of the system, however, is still an issue that calls for a regulatory oversight by the Bank of Tanzania to ensure the sustainability of the service, and guard against the risk of mass default.

The rate of M-PESA utilization remains low despite the credit needs of a large number of households without access to financial services. this may reflect limited knowledge, or since the service is new, an incomplete network of agents. It is hoped that as more companies enter the business, - giving Vodacom some competition – usage will take off.

# **4.3 Business Premises and the Urban Planning Policy**

# 4.3.1 **_Places for Conducting Business, by Location_**

27
55. Having a fixed premise for conducting business is a prerequisite to being formally registered or granted a business license. However, only a very small share of HEs (9 percent) is located in a permanent workspace other than their home (Table 4.5). Because of lack of access to credit coupled with the significant growth of the informal sector, it has become very difficult for HEs to afford a rented premise. On average, over 35 percent of them conduct their business in their own homes. Others are forced to be mobile (e.g., the _Machingas_ ). Most rural HEs operate from home and are less mobile than urban HEs.

56. For urban HEs, however, operating from one‟s home is often not optimal, as it limits access to bigger markets. This strengthens their motivation for hawking, an option which runs them into constant fights with local authorities (as discussed in the next subsection).

**Table ‎4.5:  Place of Conducting Business Among HEs, 2006 (percent)**

||Rural|SecondaryUrban|Dar es Salaam|Total|
|---|---|---|---|---|
|Own home - with business space|19.5|16.4|16.9|18.4|
|Own home - no special business space|17.2|13.8|13.5|15.8|
|Structure attached to house|0.8|0.9|1.4|0.9|
|Perm. building other than home|7.4|11.1|10.3|8.8|
|Fixed kiosk/stall – market|5.5|10.8|9.0|7.3|
|Vehicle, cart, temp stall – market|4.0|9.6|3.8|5.3|
|Fixed stall – street|3.5|4.2|7.8|4.4|
|Vehicle, cart, temp stall – street|1.1|3.7|6.4|2.6|
|Other temporary structure|8.0|10.9|9.6|8.9|
|Construction site|1.3|1.1|2.0|1.4|
|Customer's/employer's house|1.1|0.6|0.8|0.9|
|No fixed location / mobile|30.6|16.9|18.4|25.4|
|Total|100|100|100|100|

_Source: Calculations based on the ILFS 2006 data_

# 4.3.2 **_Implications of Urban Planning Policy of LGAs_**

57. The town planning policy of the Local Government Authorities (LGAs) has not been proactive in tapping the opportunity presented by HEs to support its job creation agenda. With rapid urbanization fuelled mainly by rising rural-urban migration, the LGAs are under increasing pressure to allocate legitimate areas for conducting businesses.<sup>17</sup> Large urban areas, however, have limited business premises in the more economically active and lucrative urban locations, and the rent in these areas is usually high. In the case of Dar es Salaam, the largest city in Tanzania and the fastest growing in Africa, this has become a major challenge because the growth of traders has far exceeded the ability of city authorities to respond effectively. As a result, traders congest in populated areas for assured market. Since they cannot afford the cost of renting, most end up as mobile traders.

17 During its Innovative Cities Global Dialogues (June 22-23, 2010), the World Bank Institute (WBI) argued that, although urbanization presents many challenges, it provides enormous economic opportunities for growth, poverty reduction, and a better quality of life. This argument is based on the fact that most wealth is created in cities, which account for some 70 percent of global GDP; and that no advanced country has achieved its levels of development without urbanizing. As shown in this report, the performance of urban HEs is significantly better than those of rural HEs. This shows that, if well managed, urbanization will be a key driver of productivity in HEs, with enormous potential for the poor to participate in economic growth, improve welfare, and fight poverty.

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# 4.3.4 **The** **_Machinga_ Problem**

58. In order to curb the problem of congestion of informal sector activities in towns and cities, the city authorities, especially in Dar es Salaam, Arusha, and Mwanza, regularly engage in “clean-up” operations to which the mobile traders or hawkers (the _Machingas_ ) are especially vulnerable. The loss of goods and potential earnings often proved costly, with adverse impact on HEs. It is therefore not surprising that harassment by LGAs and law enforcement officers was cited in the FGD study as the biggest problem of those who are engaged in hawking. When asked why they are chased by city authorities, hawkers acknowledged that they are doing business where the customers are. These are often areas close to business centers or markets in which existing zoning laws prohibit them from operating: “The authorities are harassing us and confiscating our assets. I have decided to open my business at night. I do business when the government is asleep. I earn a living for my family by doing this.” _Urban female participant, FGD, Kilimanjaro_ .

59. Forceful eviction from the more lucrative business areas in which traders make spot sales presents a compelling case for the failure of LGAs to support HEs. Even though the local authorities are simply enforcing the law, their operations have largely been counterproductive by increasing rather than reducing poverty. Such actions also serve to heighten the sense of insecurity and vulnerability among HEs. This was borne out by a report popularly known as the “ _Machinga Study,”_ which examined this particular issue (Lyons and Msoka, 2007).<sup>18</sup> Over 60 percent of the 622 operators interviewed for the study considered eviction as their most memorable experience relative to other forms of government interventions, such as taxes, fines, and other forms of harassment (Table 4.6).

**Table ‎4.6:  Relative Importance of Eviction as Traders' Most Memorable Experience, 2007**

||Eviction|Other intervention*|Other experience|Total|
|---|---|---|---|---|
|No direct experience|115 (49.6%)|45 (19.4%)|72 (31%)|232 (100%)|
|Direct experience|113 (81.3%)|18 (12.9%)|8 (5.8%)|139 (100%)|
|Total|228(61.5%)|63(17.0%)|80(21.6%)|371(100%)|

_Note: *include fines, confiscations, prosecution, and harassment. Source: Lyons and Msoka, 2007._ 60. The response of HE operators to the eviction plan has been mixed. While some abide by it, most make a temporary retreat, while others abandon their business altogether. On those who resist eviction, a disproportionate amount of force is deployed by LGA officials to drive them out, with adverse consequences. As noted by the _Machinga_ Study: “The impact of eviction and relocation policies has been profound and far-reaching, affecting vendors, their livelihoods, their dependents and anyone dependent on them for business.<sup>19</sup> The evictions have involved loss of physical capital such as kiosks, loss of operating capital through fines and stock confiscations, loss of customers through relocations, loss of supply

> 18 Other previous studies on the impact of eviction include Liviga and Mekacha (1998) and Sisya R.M (2005).

> 19 The report does not contain quantitative estimates of the losses or impact of eviction, but rather detailed narratives from the respondents.

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lines through increased distance to suppliers, and loss of trading time through jail sentences or time taken outside the business to rebuild starting capital.” In response to the problem of lack of workspace in Dar es Salaam, the LGA recently built a multi-story building for use by itinerant traders. Dubbed the “ _Machinga_ Complex,” the project was estimated to cost TSh 13 billion (approximately US$13 million), but has not been operational because of some governance issues (Box 4.2). Nonetheless, initial assessment shows that the intervention falls short of addressing the needs of the _Machingas_ .

# **Box 4.2: Will the Machinga Complex Solve the Problem of Itinerancy in Dar es Salaam?**

The “ _Machinga_ Complex” project, started in 2008 with funding from the National Social Security Fund (NSSF), is estimated to cost around TSh 13 billion. The complex is owned by the Dar es Salaam City Council, which borrowed money for the project from the NSSF. The regional administration (the Regional Commissioner and the Regional Administrative Secretary) participates in the process to ensure that the project addresses the problem of the _Machingas._ However, building operations were delayed due to governance issues.

The building was designed to provide 10,000 workspaces (compartments or stalls). However, only 6,500 rooms were built, prompting the government to order an investigation. Fewer rooms increased average cost, raising the price of rent to cover the loss and repay the NSSF loan. No parking space is provided for motorists, lowering the market value of the building. That the building contractor was alleged to have been paid over and above the contracted amount complicates the problem with the project.

The _Machinga_ complex is a 7-storey building. Since there is no elevator and few customers will walk all the way to the 7<sup>th</sup> floor (nor do merchants want to haul their goods up that many floors either), it is likely that the uppermost floors may end up being used for a different purpose (e.g., for storage or office use), which run counter to the nature of the _Machingas_ ‟ business. Being multi-storeyed, it is also feared that potential customers might be discouraged from shopping in the complex and might prefer to go to more accessible shopping locations.

. The main concern of the public though is whether the building will solve the _Machinga_ problem. A rapid survey by a local media reveals a rising public skepticism over who will actually benefit from the project. There are those who believe that the richer and larger enterprises will be the beneficiaries instead of the _Machingas_ . One _Machinga_ interviewed by a local media was reported to have revealed that the application form for a slot in the complex is being sold at an expensive price of TSh 50,000, but does not guarantee that the applicant will get the slot. There is, however, a mixed reaction to the project. Some consider it a positive step as the complex can accommodate a large crowd and hence a bigger market. Others feared that they not make the same amount of money they currently earn by moving into the complex. Some _Machingas_ were quoted as saying: “It is okay. We will have a big crowd, and that‟s where we can make more money.” “There is no sign of us going there. It seems there are people already prepared to occupy the slots meant for us.” The most furious complained that the process for selecting operators to occupy the building is not transparent. The problem with the project stems to a large degree from how it was planned. When the project was designed, there was little consultation with the _Machingas._ 61. In other instances, LGA officials conduct inspections of HEs to check their compliance with license, tax, and various other regulations even though most of these requirements apply only to formal enterprises. HEs have little knowledge of the tax code or registration requirements, and no place to complain. Many reported having paid large sums to unscrupulous officials or that the latter confiscated their merchandise, causing them tremendous losses and more hardship – an increasing the poverty and deprivation of their household members.

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62. What mainly deter small and unincorporated businesses, including HEs, from applying for a license or getting registered are the cumbersome registration and licensing procedures that LGAs require, which involve visits to several government departments. The licensing regime is due to change following the enactment of the new Business Activity Registration Act (BARA) of 2009 that is provisionally planned to take effect from September 2010 (see Table 1.19). It is not clear how BARA can effectively address the challenges faced by small enterprises. More importantly, its provision of abolishing the itinerant trade license appears to be a lost opportunity to improve the _Machinga_ situation in large urban areas. The _Machinga-_ run enterprises will not disappear just because they do not qualify as a business under BARA.

63. In addition to business licensing or registration, LGAs have also been administering public microcredit programs. An example is the Youth Empowerment Fund, which was established in 1993 to provide youth with small capital to start a business. The central government provides the seed money, with contributions from the respective LGAs from their own revenues. Each district council is required to select two SACCOs to administer the fund. Many SACCOs, however, are found to be too small and suffer from weak operational capacity. For example, they receive more loan applications than they can accommodate. Moreover, SACCOs have not been brought under a clear financial regulatory regime, which makes working with a SACCO risky for an HE operator.

64. In summary, a change in the LGAs‟ regulation of and attitude towards HEs is necessary to resolve major issues concerning workplaces and the legality of HEs. Regulatory changes need to be accompanied by efforts at positive collaboration between the LGAs and the HEs themselves to find a compromise solution that facilitates rather than restricts HE operations. LGAs need to see HEs as a solution to their employment problems, as well as contributors and not temporary impediments to local economic development.

# **4.4 Operational Challenges and Livelihood Risks**

65. Boxes 4.3 to 4.7 list the many constraints HE operators grapple with in running their business. Apart from these difficulties, HEs, by their very nature of being a household business, are also vulnerable to both household as well as business risks. The most frequently cited in the literature, from the FGD study and from the recent FinScope survey (Table A1.17), is the risk of serious illness or death in the family. Faced with such shock, HEs often struggle to meet the immediate cost of treatment or burial by borrowing money from relatives and/or friends or drawing down their limited savings (Table A1.18), thereby depleting their working capital.

66. Rural HEs, in particular, are exposed to risks that stem from their overreliance on agricultural produce as a source of capital. In bad weather, low production reduces their farm earnings. In turn, their operations suffer, resulting in low turnover and further reduction in earnings. Even in times of favorable weather and good harvest, HEs are exposed to potential risks, such as instability in the market and/or prices of their products. Aggravating these risks is the perishability of some agricultural produce.

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# **Box 4.3: Main Constraints Identified by VIBINDO,**<sup>**20**</sup> **2008**

1. Lack of reliable and suitable location for doing business

2. Lack of recognition in local town planning 3. Lack of start-up and working capital 4. Lack of basic management and business development skills 5. Lack of laws that recognize the informal sector 6. Inadequate implementation of the national SME development policy 7. Lack of institutional framework (within specific ministry) for the promotion of the informal sector 8. Lack of representation in national policymaking 9. Humiliation and insecurity resulting from the negative attitude of city council officials 10. Official view of informal sector activities as illegal 11. High cost of legal services and court procedures 12. Unfavorable tax assessment, based on size of capital and not on profit 13. Language discrimination as most laws are written in English 14. Unfair market competition due to imports of low quality cheap goods 15. Lack of linkage with large-scale enterprises 16. Lack of representation and recognition in the national business council.

_Source: VIBINDO, 2008_

# **Box 4.4: Major Constraints Listed by the Sampled Enterprises in the Urban FGDs, 2009**

1. Lack of capital because of high interest rates charged on loans and unfavorable repayment conditions, which hinders expansion of the enterprises and/or start-up of new ones

2. Unreliable markets/unpredictable demand 3. Lack of skills to organize and conduct business 4. Middlemen ( _madalali_ ), who take home most of the profit (e.g., this was a major problem in the Keko cluster of carpentry).

5. Inadequate machinery, equipment, and tools; lack of maintenance 6. Poor quality of packaging materials for enterprises engaged in food processing 7. Fluctuation in prices of inputs and products; sharp rise in commodity prices 8. Poor condition of transport/infrastructure for businesses that have to transport commodities from one region to another (for instance, bananas from Kagera to Mwanza region).

9. Payment of unrealistic taxes (based on estimates by local and central government agents) 10. Competition among entrepreneurs doing the same type of business over few customers 11. Business premises subject to demolition ( _bomoa-bomoa_ ) by municipal and central government authorities; poor location with weak customer base 12. Corruption (involving tax assessment and in accessing loan from President Kikwete‟s Fund, in particular.

13. Lack of teamwork (not organized); lack of collaboration, for example, among enterprises doing the same kind of business, causing heavy competition and often tension 14. Seasonal businesses (e.g., food vendors get a large number of customers in construction sites only at particular times, etc).

15. Difficulties in getting commodities, for instance, timber for furniture industries 16. Absence of industries to process agricultural products like tomatoes 17. Imbalance between expenditure and earnings (low profits) 18. Unethical or untrustworthy employees 19. Perishability of products (e.g., fruits and vegetables resulting in loss of earnings).

20. Family members diverting money allocated to business for luxury goods.

20 VIBINDO is an organization of informal sector operators based mainly in Dar es Salaam. Its main function is advocacy for informal traders.

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## **Box 4.5: Major Constraints Listed by the Sampled Enterprises in the Rural FGDs, 2009**

|1. Seasonality of income from agriculture, resulting in business stagnation (especially businesses dependent<br>on farmers‟ seasonal incomes).|
|---|
|2. Poor road infrastructure to the villages (hence high transport cost for goods)|
|3. Low capital which hinders expansion or start-up of businesses.<br>4. Lack of business skills; lack of training|
|5. Poor harvest (due to bad weather), high price of agricultural inputs, poor agricultural markets, poor<br>market information, and lack of irrigation facilities; hence less money from agriculture to invest in<br>business.|
|6. Market competition among entrepreneurs in the same village and/or entrepreneurs from other villages<br>(e.g., auction markets for selling clothes attract traders from several areas, including from the nearby<br>towns).|
|7. Security (risk of theft), coupled with lack of secure places to keep money (e.g., banks which are easily<br>accessible)|
|8. Remote location of some business enterprises|
|9. No saving culture|
|10. Selling by credit with possible risk of default by borrower|
|11. High income tax charged bythe Tanzania Revenue Authority (TRA)|

# **4.5 Coping Strategies**

67. While much of the studies conducted so far have generated a wealth of information about the many constraints faced by HEs, little is known about how they cope with these difficulties. Narratives from the FGD participants shed light on some of the coping strategies HEs adopt to overcome these constraints or mitigate their effects. Some of these findings are corroborated by the results of the recent FinScope survey (Table A1.18).

68. Most HEs survive from a variety of risks and constraints with resilience, hard work, and good use of a network of family and community support. Some draw on their limited household savings and switch between working on farm and running their HEs to augment low income. Others sell on credit, which is made possible by previous business relationships. Urban HEs cope with the risk of theft and burglary by making their own security arrangements. For example, HE operators sharing a business premise with other operators make a monthly contribution to pay for hired security guards. In the rural areas, it is common to find a group of HEs guarding their property on a rotation basis.

69. Although most HEs adopt coping strategies that are useful in mitigating risks, others resort to deceptive, sometimes illegal practices. For example, some operators admitted during interviews to charging wealthier-looking customers higher than the regular price for services rendered (e.g., TSh 500 instead of TSh 200 for shoe-shining), or tampering with their weighing machines such that customers pay more for lesser amount of the goods they buy. Hawkers reported to have paid bribes to be able to operate in restricted areas. In some extreme cases, HE owners resorted to operating at night to avoid harassment by local officials.

70. Support from relatives and friends is very useful for HEs, especially as a source of capital. Since access to credit from MFIs is found to be biased in favor of urban areas, the only option for HEs in the rural areas is to rely on the generosity of relatives and friends, or to draw 34
down their savings, when available (Table A1.18). As mentioned above, SACCOs are reported to be the second best alternative for businesses to access credit, but their level of service is limited (only 15 percent of HEs use SACCOs; see Table 4.4 above). Assistance received from donor and NGO projects are limited to Dar es Salaam (30 percent of HEs), compared to only 10 percent in the rural areas. HEs also make extensive use of family workers (paid or unpaid) to avoid paying wages, and also as a source of trustworthy labor.

2. Most HEs are vulnerable to risks associated with sudden family or social events (e.g., illness or death in the family, weddings, etc.). To mitigate such risks, they invest a considerable amount of effort, time, and resources in group networks (clan, community, ethnic, etc.), or join self-help or welfare associations.<sup>21</sup> Such networks offer some sort of group insurance to lessen the burden of members in times of need. Self-help networks have also become a useful channel for group lending, especially under various NGO- and/or donor-assisted microfinance projects that aim to reduce the potential risk of default by beneficiaries. Other associations, like VIBINDO, play the role of advocacy to demand supportive action from authorities to address their problems, or build solidarity among affected groups to protest against unfavorable policies.

# **4.6 Perception of Factors for Business Growth and Failure**

71. Although predominantly engaged in relatively low-earning activities, HEs have a good sense of the many factors that underpin a successful business, as well as those that lead to failure. Table 4.7 lists some of the indicators for business success, as well as failure, most often cited during the FGDs. A successful business to many means: (a) one with a capital that grows over time; (b) one that earns substantial profit; (c) one that attracts more customers over time; and (d) one that generates enough cash for consumption and acquisition of more assets (including a house and private education for the HE operators‟ children).

**Table ‎4.7:  HEs' Perceptions of Factors for Business Growth and Failure**

|**Factors for Business Growth**|**Factors for Business Failure**|
|---|---|
| Economical use of income from business whereby<br>expenditures are less than earnings;<br> Increased capital which enables firms to procure<br>larger consignments of goods at a time;<br> High demand for goods produced and services<br>provided;<br> Good business management; and<br> Good sales of agricultural produce, which generate<br>income to boost capital| Theft of traded goods by untrustworthy employees;<br> Incurring loss, such as losing goods or cash in an<br>accident<sup>22</sup><br> Spending more than what one earns from the business;<br> Seasonality<br>of<br>businesses<br>done,<br>for<br>instance,<br>agribusiness depends on rainfall;<br> Poor roads, which lead to high transport cost<br> Perishable goods that are not sold out, such as fruits<br>and vegetables|

> 21Wenga _et al_ . (1995) observed that self-help organizations in the informal sector are critical to the success of HEs, but are incapable of participating effectively in dialogue with the government to defend their members‟ interests. The study estimated that in 1995, there were 300 self-help organizations in Dar (current estimate is more than twice that number).

> 22 <mark>Note that traders have to travel to nearby towns to buy goods from wholesale shops and transport them back to the village.</mark>

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_<mark>Source: FGD report, 2009</mark>_ 72. Narratives collected from FGDs provide further insights into what HE operators perceive as the main contributing factors for their success or failure in business (Appendix B). The entrepreneur from Arusha cited the experience he had accumulated over the years in running his HE and his good cash management skills as the reasons why his business is doing well. He took his first loan to expand his business, and a second loan to have a secure workspace where he could grow it further. His HE enabled him to build up his assets. His is a success story.

73. Similarly, the woman entrepreneur from Mtwara used her savings to start her business of trading cashew nuts. She joined an association of small entrepreneurs and made full use of her membership to avail of loans, entrepreneurship training, and important business and market information. She attributed her success to her constantly seeking and seizing every opportunity to improve her trading enterprise and hone her business skills.

74. The food vendor from Mtwara, however, shows the risks and vulnerability of HEs which operate in difficult, often unfavorable conditions. She started her food-vending business with a small capital from her husband, but suffered an initial loss because of the seasonality of demand for her goods and the demolition of her _banda_ (shed) by thieves. To rebuild, she moved to another rented premise, but her business hardly recovered: “The business was not paying! Unlike in the early days, a big chunk of the cooked food remained unsold and I had to take it home or give it to neighbors.” As a fallback and to reduce the cost of running her business, she left her rented workplace and started to operate from home where her HE continued to struggle to survive.

75. The story of the entrepreneur from Mwanza started out as a success, but ended in failure. He began as a trader in rice, and later opened his own place to sell rice, which proved profitable. Having earned enough capital, he diversified into another more high-risk business, a butchery, which was equally lucrative in the beginning and which helped him build his assets. However, the butchery did not stay profitable for long. He eventually suffered a loss because of lack of demand for his meat products, and was not able to recover: “I could not sell all the meat. For example, when I slaughtered a cow worth TSh 100,000, I could sell about Tsh 70,000 worth on the first day. When meat remains unsold, one has to sell at a lower price the following day. I was losing my capital.” 76. The above narratives show the potentials of household enterprises as a source of livelihood and a pathway out of poverty, but they also reveal some of the risks and vulnerabilities of small HE operators that lock them in it.

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# **5 Policies, Programs, and Institutions Affecting the HE Sector**

77. A review of policies, programs, projects, and institutions that affect the HE sector shows that the government has no specific policy and institutional framework for promoting this sector. Instead, the various policy statements that purport to promote the informal sector are not matched by the government‟s actions on the ground. Although there are a number of programs and projects that seek to address some of the key constraints faced by HEs, they suffer from lack of coordination, monitoring and evaluation, and ineffective targeting, all of which limit their impact as well as potential for scaling up. More importantly, they fail because they are conceived without adequate knowledge of the problems of the HE sector, and without direct input from HEs.

## **5.1 Policies and Institutions on the Informal Sector**

78. Despite a notable recognition of the HEs as an integral part of the informal sector,<sup>23</sup> and of its importance in job creation and poverty reduction, Tanzania has no specific policy framework for promoting the HE sector. Even when policy intentions exist, there is no single institution responsible for coordinating the various initiatives aimed at promoting the informal sector in general and the HE sector in particular. The government‟s objectives remain too broad and its interventions mostly piecemeal to effectively address the specific needs of HEs. In some cases (e.g., the MKURABITA program), policy interventions differ from the priority needs of HEs.

79. Although many factors contribute to this indifference, this subsection focuses on two important policy issues: the policy dilemma faced by the government in dealing with the informal sector, and the failure of coordination among concerned agencies tasked with programs directed to the sector.

## 5.1.1 **Policy Dilemma**

80. One explanation for the lack of an institutional environment favorable to HEs is the policy dilemma faced by the government on whether to promote it on the basis of its positive role in the economy, or to thwart it on the basis of its illegitimacy (Bangasser, 1996). Some policy documents ( _inter alia,_ the SME Policy of 2003 and the Employment Policy of 2008) contain specific statements in support of the sector, but adverse practices on the ground (e.g., forceful eviction of hawkers) negate them.

> 23 One might plausibly argue that informal sector is inclusive of HEs, but as indicated earlier, within the informal sector, wide variations between categories of businesses and types of employment are observed. Thus, many suggest a blanket focus on “formalizing” the informal sector, but what they really mean is enforcing the labor regulations on firms which hire employees. while this policy focus may be justified, it has nothing to do with HEs who do not hire labor. Thus, policies should take into account such differences, and design appropriate interventions specifically for the HE sector.

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81. Recognizing the faster growth and increasingly important role of the informal sector in the economy, the national employment policy (2008) focuses on its formalization (through licenses, VAT, and business registration) as though it was a panacea to addressing the problem of low productivity in the sector. The policy aims “to transform the informal sector so that it provides decent employment and increased productivity” and “to empower the informal sector to become formal in order to be able to access finance, training or any other BDS.” However, the policy also cautions that the informal sector is not in conformity with regulations, thus it is limiting its potential to create more jobs and increase its productivity. Further, the policy states that its aim is “to empower the informal sector to become formal in order for them to access finance, training, or any other business development service.” 82. These policy statements raise several questions. First, how would conformity with regulations help a sector that is almost regulated out of business? Second, is this approach in line with HEs own aspiration to not be transformed into a large employer, but rather to survive and bring sufficient cash income into the household? This “formalization” approach has not been useful, since neither the different programs supporting the sector nor the drive to formalize it has had significant tangible impact. Instead, the sector continues to grow amidst weak policy environment. Given their _nano_ size, formalizing HEs can be too costly and is generally less useful in improving their productivity. Moreover, it is important to note that in the specific context of HEs, formalization is not a priority objective. This disconnect between the policy framework and the sector has further limited the needed government support for HEs.

# 5.1.2 **Coordination Failure**

83. Reflecting its crosscutting nature, there exists a wide range of policies influencing the informal sector, with accompanying programs and projects being executed by various government agencies at the national and sector level (Table 5.1). Not surprisingly, the implementation of such policies raises significant coordination challenge, since there is no single institution solely responsible for the promotion and development of the informal sector.

84. One of the main efforts by the government aimed at promoting the informal sector, including the HEs, is the MKURABITA program. The program was established in 2007 to support the formalization of informal activities, focusing mainly on land registry and titling to increase the value of assets in the sector. To improve program implementation, a number of baseline studies were conducted. The results of these and other related studies, however, reveal that the needs of the informal sector are different from those identified by the program (Lyons and Msoka, 2007; VIBINDO, 2008). Moreover, the program is found to have little, if any effect in addressing the main constraints facing the HE sector. This comes as no surprise as the target beneficiaries from the informal sector know little or nothing about the program. A major finding of the VIBINDO study, for example, shows that over 96 percent of informal sector enterprise operators never heard of MKURABITA (VIBINDO, 2008). Based on this finding and the results of other related studies, various stakeholders recommend the need for rethinking the scope and implementation of the program.

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**Table ‎5.1:  Institutions and Policies on the Informal Sector with Overlapping Mandates**

|**S.n.**|**Implementing**<br>**Institution or Agency**|<br>**Policy**|**Associated**<br>**Agency/Program**|**Relevant Issue for**<br>**the HE Sector**|**Key**<br>**Observation/Comment**|
|---|---|---|---|---|---|
|1|Ministry of Industry,<br>Trade and Marketing|The SMEs<br>Development Policy| BRELA<sup>24</sup><br> SIDO|Licensing and<br>regulation, and<br>support to SMEs|Focus mainly on<br>formalization of SMEs,<br>less on HEs|
|2|Ministry of Finance<br>and Economic Affairs|The National<br>Microfinance Policy| Bank of Tanzania<br> Cooperative Soc.|Access to credit|Weak regulation of the<br>microfinance sector|
|3|Ministry of Labor,<br>Employment and<br>Youth Development| The Employment<br>Policy<br> Youth<br>Development<br>Policy| Tanzania<br>Employment<br>Services Agency<br> Directorate of<br>Youth<br>Development<br> Program for<br>elimination of<br>Child labor| Youth<br>unemployment<br> Job creation<br> Elimination of<br>Child Labor|Primarily focused on the<br>segment of the economy<br>where the employment<br>relationship is between<br>two unrelated<br>individuals. Not focused<br>on the top non-farm<br>economic activity, the<br>HE.|
|4|President‟s Office -<br>Planning Commission|National Economic<br>Empowerment Policy|<br>MKURABITA|Formalization|Need to enhance<br>relevance to HEs|
|5|Ministry of Gender,<br>Women and Children| Child Development<br>Policy<br> Women and<br>Gender<br>Development<br>Policy|<br> Focal Education<br>and Training<br>Program<br> Community-based<br>development<br>program<br> Women<br>Development Fund| Gender issues,<br>including women<br>empowerment<br> Community-<br>based initiatives<br> Access to soft<br>loans and credit<br> Establishment of<br>Tanzania Women<br>Bank|Support is biased<br>towards women and<br>children|
|6|Ministry of<br>Agriculture, Food Sec.<br>and Cooperatives|<br>The National<br>Cooperative<br>Development Policy|Cooperatives<br>Association of<br>Tanzania|Access to credit<br>through the SACCOs<br>model|<br>Not an effective<br>advocate or regulator of<br>SACCOs.|
|f|Ministry of Energy|The new Mineral|Development of|Artisanal miners||

> 25 This amount applies to businesses with a yearly turnover not exceeding Tsh 20 million.  For those with a turnover of more than Tsh 20,000, the fine is between Tsh 200,000 and Tsh 500,000.

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||and Minerals|Policy and Act|artisanal miners|||
|---|---|---|---|---|---|
|8|Prime Minister‟s<br>Office – Local<br>Government and<br>Regional<br>Administration| Town Planning<br>Policy<br> BARA Act<br> Decentralization<br>Policy| Councils/Municipa<br>lities<br> Regional /City<br>Authorities|Availability of<br>business premises,<br>licensing and<br>regulation of HE<br>activities|The problem of the<br>_Machingas_still needs to<br>be resolved; Capacity of<br>LGA to implement<br>BARA Act is unclear|
|9|Ministry of Education<br>and Vocational<br>Training| Education Policy<br> PEDP and SEDP<br> Vocational<br>Education and<br>Training Policy|VETA<br>NACTE|Vocational education<br>and technical training<br>(trade test and<br>apprenticeship) for<br>the informal sector|<br>Training for the informal<br>sector is limited and not<br>given priority<br>Evidence on<br>effectiveness?|
|10|Ministry of Home<br>Affairs| NGO/CSO/CBO<br>Policy and<br>Regulation<br> Safety and crime<br>prevention| RITA<br> Police force| Regulation of<br>self-help<br>organizations and<br>NGOs working<br>for HEs<br> Crime and theft|Crime and theft remains<br>one of the key risks<br>facing HEs|

85. Despite its ambitious nature, the program also faces a critical shortage of resources, which limit the scope and effectiveness of its operations. The first phase was supported by a government budget of about US$7 million. Of this amount, US$4 million was from the Norwegian government, but this donor funding ended in 2008. Since then, funding relied entirely on the government, which aims to double it from US$3 million to US$6 million in the 2010/11 budget.

# **5.2 Licensing and the Regulatory Regime**

# 5.2.1 **Laws and Regulations**

86. The licensing regime is regulated by the Business Licensing Act (Cap 208) of 1972. The Act is administered by the Ministry of Industry and Trade and Marketing (MITM) and LGAs (councils, district, towns, municipalities, and cities), which is under the Prime Minister‟s Office (PMO). Business licenses are categorized and issued according to the size and nature of the business. While the MITM administers and collect fees, and issues Class “A” licenses, the LGAs issue class “B” licenses. The class A category refers to business licenses administered by policies and regulations on businesses of national and international nature. The Class “B” licenses are issued to businesses, which are administered by local government laws, bylaws, and other regulations.

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87. All the business licenses are not renewable except those administered by specific pieces of regulation or regulatory body. Examples of renewable business licenses under Class B include the Intoxicating Liquor License No. 28 of 1968, which is administered by the PMORegional Administration and LGAs, and the hotel and restaurants licenses administered by the Tourism Act No. 11 of 2008. These authorities issue business licenses biannually, i.e., the first licensing period is between April and September and the second, between October and March each year. The fees differ between urban and rural areas. For businesses in urban areas dealing with manufactured beers and other alcoholic drinks, the license fee is TSh 40,000; for rural areas, the fee is TSh 30,000 for a six-month period. Those businesses dealing with local brew in both urban and rural areas pay a six-monthly fee of TSh 12,000. Hotels and restaurants, on the other hand, are required to pay an annual license fee of TSh 20,000.

88. In addition, small businesses by individuals and traders in local markets are administered by some bylaws under the Treasury Department of the Councils of LGAs. Payment receipts (not business licenses) are issued to these businesses upon payment of local market levies, ranging between TSh 200 and TSh 300 per operator per day and TSh 3000 per cattle head in abattoirs or auctions. The collected revenues are deposited to the general fund under the control of the Treasurer and the Councils‟ Executive Director, part of which are used for cleaning and maintenance, security, and infrastructure services in the marketplace.

89. It is important to note that the process of obtaining a license is long and complex. For instance, the Business Licensing Act Cap 208 requires all businesses to get approval to operate from village or ward executive officers, and a tax identification number (TIN) from the Tanzania Revenue Authority (TRA) before obtaining any business license.

# 5.2.2 **Existing Challenges and the New Licensing Regime**

90. Table A1.19 provides an overview of the major changes in the licensing regime with the passing of the new BARA Act of 2009. Despite these changes, some key challenges persist. These are outlined below.

91. _Business Premises:_ Since business activities are required by the Business Licensing Act to operate in fixed locations, only a small proportion of HEs will be covered by the law. As noted earlier, only 9 percent of them operate in fixed premises. Therefore, over 80 percent of HEs are not eligible to obtain a business license.

92. _Multiplicity of Business Permits and Licenses:_ Since 2004, reforms on business licensing have ushered in changes that have reduced red tape (UN, 2004). These reforms are reinforced by the new BARA Act of 2009. The implementation of the BARA Act requires that all business activities be registered at the LGA or Council levels before being issued a license, that is, even if a business is already registered with BRELA, it has to be re-registered under BARA. In addition, any business entity moving from one location to another are required to re-register with the council controlling the local area where it will operate. Given the multiple documents required by registrars of business activities at the LGA level, registration under BARA, company registration under BRELA, or licenses to be obtained from MITM, in addition to sector-specific permits, the problem of red tape persists. Professionals, such as engineers, medical doctors, 41
lawyers, and tourism specialists, for example, have to have a professional permit or certificate before they can practice their respective profession, and in addition a professional business license if they open their own professional business (e.g., private hospital, an advocate‟s office, as tour operators, etc.). The time spent in obtaining permits, and the many agencies to be visited, give rise to many opportunities for corrupt practices. The rationalization of the regulatory framework is further compromised by the different categories of licenses required under the old regime, while the different roles by various actors in the current licensing process pose a new set of challenges in implementing the reforms.

93. _Weak relationship between the Tanzania Revenue Authority (TRA) and the business licensing authorities._ A case in point is the inability of Trade Officers to issue a business license to a business person without a TIN. The TRA is not concerned whether or not the particular business is licensed as long as it has a TIN, which is issued upon payment of a presumptive tax. As a consequence, some businesses opt to pay tax without complying with the licensing requirements. Under the new regulatory regime, it is not clear what the new role of the trade officers is in dealing with this issue.

94. _Payment of presumptive income tax:_ As mentioned above, the TRA requires small businesses to pay an income tax of at least TSh 35,000 each month in advance (even before they start operation) before issuing them a TIN. This tax is considered regressive because it is levied regardless of whether or not the business operator actually earns an income, and also because some larger enterprises pay an income tax equal to that imposed on small enterprises. While the TIN is granted free of charge, the TRA requires proof of nationality and location from village and ward executive officers before issuing it. The process of certification is therefore long and costly, and can be more so for businesses that operate in more than one locality or region.

_95. The value of registration certificate versus business license:_ The legal standing of a the registration certificate offered under BARA is perceived to be much lower than a business license in facilitating business operations. The certificate merely identifies from the registry the person conducting the business, but a license grants recognition to the particular business activity. It is noted that: _“with a license, your business can easily be recognized by other business including foreign trade partners, but this may not be the case with registration certificate.”_ Male FGD participant, Arusha 96. _Tax evasion:_ Informal consultations with municipal trade officers in Dar es Salaam reveal that many of them expressed doubt that the abolition of license fees is enough of a motivating factor for enterprises to register their business. With the new regime, however, a business operator can register under different names in the same or different locations every after three months, without paying any tax upfront since an advance payment certificate is not required, only a TIN is required.

97. _The itinerant trade license_ . Under the old regime, the itinerant trade license effectively covered the _Machinga_ HEs, but it is not clear if this coverage holds under the new regime. In practice, the licensing officers regulated the _Machinga_ HEs by granting a license, allowing a 42
particular trader to operate in a designated locality. As a consequence, the _Machingas_ were encouraged to organize themselves for self-policing against new traders without a proper license. How effective the itinerant trade licensing is in regulating _Machinga_ HEs, however, is not clear.

98. _Enforcement:_ Under the new BARA Act, businesses found doing business illegally are fined between Tsh 50,000 and Tsh 100,000,<sup>25</sup> a steep penalty rate to which HEs are likely to be subjected. BARA also effectively excludes about 90 percent of HEs as part of its simplification process. This begs the question: How will HEs exempt from BARA‟s provisions (because they have no fixed premises) know that LGAs have no authority to penalize them over their lack of a registration certificate? There is no explicit campaign to inform HEs not required to get a certificate of their rights as a business entity.

99. Considering the above challenges with the new national regulation, it is tempting to suggest that the problem of formalizing the HE sector can be resolved by focusing on regulations (i.e., registration, licensing, and taxation) by local governments. It is important to emphasize, however, that such an approach is not necessarily a panacea and requires a much more in-depth analysis of the costs and benefits of formalizing HEs. The experience of HEs in Uganda sheds light on the complex issues involved and how the system of licensing and taxing HEs can be easily abused by local authorities (Box 5.1).

# **Box 5 1: Regulating Household Enterprises in Uganda**

Under Uganda‟s decentralization program, the registration, licensing, and taxation of larger businesses is the responsibility of the national government, while regulation for small businesses rests with local governments, which are also responsible for providing services such as police protection, market places, etc. Businesses with a monthly income below USh 500,000 or an annual income of USh 5 million (about USD 250 and USD 2200, respectively) are considered small, and these are exempt from the national business tax and national registration.

Placing the responsibility for registration and taxation with LGAs has not simplified the process for small businesses. Applications for registration and for obtaining a trading license are two separate steps that require multiple visits to various offices. Businesses also have to pay user fees upfront (e.g., to use market facilities), as well as various other types of local government fees (“operating permits”), which differ by location. Because of the high transaction cost involved, more than half of household enterprises opted not to register, but obtained a trade license and/or paid some other local fees in one form or another.

Compounding the problem of multiple tax payments faced by HEs is the large variation in the number and amount of fees paid by them. For example, within the same sector or turnover category, some HEs ended up paying a higher amount than others simply because they paid more types of fees. Moreover, HEs in the high turnover category were more likely to report paying two or more fees than HEs in lower turnover categories. This suggests that high turnover HEs were targeted for multiple fee collection though they were not necessarily engaged in more activities. Not only does this practice discourage HEs from diversifying their activities or growing their enterprise; it undermines their viability.

Most important, the taxation of HEs by local governments is extremely regressive. The average fee paid by them was USh 83,400 per year. On the basis of their median income this amounted to a tax rate of 30-50 percent, while the national tax rate for large businesses is only 3.2 percent. Close to a third paid more than one type of fees, which indicates that HEs were taxed multiple times. Since the fees are fixed amounts, the poorest HEs pay the most. Fees and taxes on HEs went up dramatically after the abolition of the other main source of tax revenue for LGAS -- the individual tax (graduated tax) – following the elections.

> 25 This amount applies to businesses with a yearly turnover not exceeding Tsh 20 million.  For those with a turnover The lack of standardization and transparency in the setting and collection of fees paid by HEs presented many of more than Tsh 20,000, the fine is between Tsh 200,000 and Tsh 500,000. opportunities for corruption and abuse of authority by local government officials.  Even if the overwhelming majority chose to remain in the informal sector by not registering, the case of Uganda demonstrates that HEs face 43 the burden of paying a large share of local taxes in order for them to start and continue to operate their business.

_Source: World Bank, 2009._
# **5.3 Existing Support Programs and Projects**

100. With very limited available information, a comprehensive list and description of programs and projects targeted at the informal sector, including HEs, could not be drawn. Nevertheless, the stocktaking undertaken for this report reveals useful information on a number of programs and projects by various agencies, both government and nongovernment. Table A1.20 shows a list of programs and projects on which information was available, albeit with several gaps that limit an assessment of their impact.<sup>26</sup> Consequently, this study relies on useful insights gained from the FGD study to highlight key issues in general, while providing a detailed assessment of some of the programs and projects, whenever possible.

101. Most of the programs for HEs and MSMEs have overlapping objectives and target beneficiaries, reflecting weak coordination among implementing authorities. A large number of them focus on issues that involve access to credit, training on entrepreneurship skills, and socioeconomic empowerment of women and youth. A few others are much broader in scope and seek to address larger issues related to the business environment. Such programs include the MKURABITA (the formalization of properties owned by HEs), BARA (simplification of registration and licensing), and BEST (simplification and rationalization of regulations affecting businesses). The study of Johanson and Wanga (2009) on informal sector training discussed the limitations of the impact of SIDO and UDEC in entrepreneurship development, and VETA on vocational training and technical education. Another recent study analyzed the effects of both training and microfinance on small enterprises (Box 5.2).

**Box 5 2: Studying the Impact of Microfinance and Training on Small Enterprises**

In order to understand the needs of MSEs, an experimental study was conducted in Dar es Salaam in 2008-2009, which involved randomly selected male and female entrepreneurs who sought loans from PRIDE, the biggest microfinance institution in Tanzania. The members, all of whom had already had one successful loan from PRIDE, were divided randomly into two groups: one was given training in book-keeping, investment, and marketing; the other was not trained. Later, a sub-set of members from both groups were given business grants. The objective was to determine the effects of training and microfinance on the business performance of small entrepreneurs.

**_Results show that microfinance alone did not have any effect on the business performance of the small entrepreneurs.  However, a combination of microfinance and training resulted in an increase in business sales and profits, particularly among male entrepreneurs._** Training also improved the business knowledge of 26 ~~both female and male entrepreneur~~ including separating the business accounts from the household accounts, and in their mindset in terms of their  The lack of information with which to measure impact or a complete lack of the monitoring and evaluation s and caused a positive change in how the organized their finances – (M&E) aspects of these programs is a sign of weak accountability.  self-confidence and attitude towards risk. However, the finding that these effects were greater for male entrepreneurs than for female entrepreneurs suggests that females faced other obstacles which affected their 44 adaptation of new business practices conducive to business growth.  Supporting women to overcome these obstacles requires more efforts.
102. While no adequate information is available to explain the scope and location choice of different projects, their limited coverage affects their impact. There is also a serious lack of basic information on which to evaluate or monitor the effectiveness of many informal sector-related programs. Most programs either do not have an M&E component, or they do not implement it even if they have. Missing information includes, for instance, the programs‟ timeframe, the number and type of target beneficiaries, financial resources and allocation, the cost per beneficiary, and key target outcomes and associated quantitative indicators. This gap precludes drawing of important lessons to be learnt, as well as determining with confidence the impact of these programs and the feasibility for scaling-up.

**_103._** Although some programs have a clear focus on HEs, their outcomes on the ground are less evident. However, it is also true that the needs on the ground are far bigger than a reasonable program can accommodate. What is most revealing is the widespread notion that if a government program provides credit, the money is believed to be a grant or is some sort of a patronage. This serves as a disincentive to responsible borrowing and actual repayment.

104. The Presidential Trust Fund (PTF), better known as the _JK Billions,_ is a case in point. The fund is countrywide, where each region was allocated TSh one billion administered by the CRDB bank via the network of SACCOs. Established in 2006, its objective is to make credit available for poor households that do not have access to the formal credit market, and to support income-generating activities to improve their livelihood. While it appears to be a perfect example of the type of intervention that the HE sector needs, as well as a good indicator of the political will to address their problems, the effectiveness of the fund is far less noticeable. An assessment of the fund‟s impact is yet to be done, but anecdotal evidence suggests that its benefits have not reached its intended beneficiaries as much they have the local officials.

# **Box 5 3: Recent Media Report on the Massive Default Rate of the PEF (JK Billions)**

The Savings and Credit Cooperative Societies (SACCOs) in Mwanza are yet to repay a total of TSh 200 million loan, issued by the government through its citizens‟ economic empowerment initiative. The loans, popularly known as the „JK Billions,‟ was in its second phase. The 12 Mwanza-based SACCOS in Misungwi and Kwimba districts received a total of TSh 227 million, but only TSh15 million has been repaid. The loans, which were disbursed in September during the 2009/2010 financial year, were supposed to have been repaid within six months.

The officials of SCCULT attributed the difficulties in repaying the loans to the “dole” mindset of the people. “There are people who believe that the loans were just grants, while others are treating the project as a political issue, believing that the money is given by the ruling party (CCM) so as to continue garnering support from them,” said the officer, adding that there were others who had invested in agriculture and were waiting for the harvesting season. According to him, in the 2009/2010 financial year, SCCULT has failed to issue loans worth TSh283 million to various SACCOS as they did not qualify for these loans.

_Source:Media report by Jane Kajoki, Mwanza, Monday, 05 July 2010._ 45
# **5.4 Informal Sector Training (IST) for HEs**

105. It is difficult to obtain a comprehensive view of informal sector training (IST) in Tanzania because of the fragmentation and dispersal of its coverage. Government policies recognize the importance of IST, but these policy pronouncements have not been translated into concrete programs or investments **_._** Moreover, no single agency has the responsibility for collecting information about IST programs. The only evaluation identified was an early assessment of the ITEP pilot training program conducted by VETA/GTZ. This evaluation did not systematically determine the costs and benefits of the program, and was conducted too early to ascertain labor market outcomes and impact on income. This section relies on a review done by Johanson and Wanga (2009).

106. Demand-driven traditional apprenticeship is the most common form of IST, but it is not well organized in Tanzania, compared to those offered in other countries in the region, particularly in Kenya, Zimbabwe, or West Africa. Traditional apprenticeship has weaknesses – it is generally regarded as static and does not keep up with advancement in technology, although instances were found of innovation in the content of apprenticeship, particularly in areas where technology is changing fast (e.g., in vehicle maintenance). Apprenticeship training in the informal sector, however, has been effective in imparting trade skills to thousands of mostly male youth, but less to female workers. Figure 5.1 shows the result of a 10-year old survey of apprenticeships in Dar es Salaam, which found that only about one in four apprentices were female, half of whom were trained in tailoring, catering, childcare, and hairdressing (Nell and Shapiro, 1999). In the 2006 ILFS, 8 percent of women, compared to 16 percent of men, reported having participated in apprenticeship training programs. In general, apprenticeship training was found to perpetuate traditional gender-based occupational segregation.

**Figure 5.1:  Distribution of Apprentices in Dar es Salaam, by Field (percent)**

<!-- Start of picture text --> Informal sector apprentices in Dar es Salaam by field - 1999<br>(% of total surveyed)<br>25<br>20<br>15<br>10<br>5<br>0<br>Motor Vehicle Welding Carpentry Tailoring Masonry Hairdressing Catering Childcare<br>Mechanics<br><!-- End of picture text --> _Note_ : _Dark bars are mainly male; white bars are mainly female; striped bar is both male and female. Source: Johanson and Wanga, 2009, based on Nell and Shapiro, 1999._ 46
107. Few public institutions in Tanzania offer training for HEs. IST training is part of the core mandate of VETA, but the latter‟s own corporate plan has little to say about HE training. VETA also does not distinguish between training for wage-earners and for entrepreneurs, but its funding base (payroll taxes) favors wage-earners. Its trainees are mostly wage earners from both the informal and formal sectors, including the government. Moreover, a cursory review of information from VETA shows that demand for their programs far exceed their existing training capacity.

108. Among the limited number of training providers in Tanzania, nongovernment training institutions from the private, FBO, and NGO sectors produced about three-fourths of the total vocational graduates in 2007, compared to only one-fourth trained by public institutions funded by the central government, local governments, and VETA (Figure 5.2). Other training providers are business services and credit providers, including SIDO and SIDO-VETA, and other business development agencies such as the Tanzania Gatsby Trust (TGT).

109. _There has been no rigorous evaluation of these programs to show their impact on the prospects for employment and income generation of trainees._ Apprentices and trainees of government programs are expected to contribute to production as soon as possible, often after one to six months. However, no records are kept on trainee progress. Most trainees do not receive certification upon completion of training, except those trained in VETA or other similarly bigger and more formal training institutions (see Box 5.3 for an example of a successful women‟s training supported by VETA). Box 5.4 describes a _g_ overnment program to build skills in the rural areas, but one that suffers from weaknesses in quality and results monitoring.<sup>27</sup> Another training program showing promise for HEs include the CSSC program, which has heightened awareness of the importance of training for jobs in the informal sector based on local market assessments.

**Figure 5.2:  Total Vocational Graduates, by Institutional Ownership**

<!-- Start of picture text --> Tanzania- Total Vocational Training Graduates by Ownership<br>35000<br>30000<br>25000<br>2005<br>2006<br>20000 2007<br>15000<br>10000<br>5000<br>0<br>Central Gov. FBO Local Gov. NGO PRIVATE VETA<br>Number of Graduates<br><!-- End of picture text --> _Source: Calculations based on Johanson and Wangu, 2009._ 27 The UDEC program, one of the training programs on entrepreneurship in the University of Dar Business School, has addressed the problem by customizing the training program to fit the business needs of specific entrepreneurs. This program is not targeted towards HEs, and needs to be rigorously evaluated.

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110. Training staff (including master craftsmen offering apprenticeships) involved in informal sector training tend to lack basic business skills themselves in such areas as record-keeping, marketing, and pricing of goods and services. This limits the productivity of graduates. On the other hand, in spite of their multiple training needs, most informal sector operators are unwilling to invest in training, and have to be convinced before participating. The low willingness to participate is most likely explained by the HEs‟ perception of opportunity costs. In FGD discussions, HE noted the difficulty in taking time away from their business.

# **Box 5 4: Training of HE and MSE Women in Food Preparation**

In 2001, a project under the Poverty Reduction Strategic Plan helped organize and train women engaged in food preparation in the livestock trading area of Pugu Mnadani near Dar es Salaam. With ILO support, an NGO, the Gender Education Management Association (GEMA), formed an association of 60 women food vendors. GEMA collaborated with VETA to train them in food preparation, cooking, and hygiene for two months, and bread baking with SIDO for two weeks. As a result, the members were able to dramatically increase their sales of prepared food to livestock traders in the locality, and expand business into neighboring communities. The group also formed a savings association (SACCO). Before they were trained, members reported difficulties in repaying a loan of TSh 1000, but now they can afford to borrow more than TSh 100,000 at a higher interest rate. Members have also been able to identify new market opportunities for their juice and fresh milk products, and for their processed peanut butter and garlic products. Still, lack of capital imposes a constraint on business expansion. The group won a tender to supply 390 loaves of bread daily to neighboring secondary schools, but could not meet the terms of the tender due to lack of non-charcoal cooking facilities.

# **Box 5 5: Government Financing of Rural Skills Development**

The Demand-Driven Skill Development Program (DDSDP) for informal sector training was established under the Ministry of Labor and Youth Development in 2001. Since then, it has received about TSh 100 million from the government budget every year, drawn partly from HIPC funds. The purpose is to promote self-employment and income generation in rural areas. The DDSDP is a countrywide program designed to deliver skills development training to improve the employability of adults engaged in productive activities in sectors where the districts have a comparative advantage. Under the program, the Rural District Councils identify training needs and collaborate with local training institutions in overseeing the training. About 2,670 people have been trained thus far in courses ranging from 14 to 30 days. Sectorwise, about 31 percent of the trainees received training in agriculture and animal husbandry, followed by 19 percent in business management, 17 percent in tailoring, and 14 percent in carpentry. Other informal sector operators received training in food processing (7 percent) and construction (6 percent). The DDSDP delivered training mainly through the FDCs (52 percent), VETA (20 percent), and other public institutions, including SIDO (17 percent). The private sector training institutions trained only about 6 percent of the total trainees. Recently, the program has not received the planned government allocations and operations had to be scaled back. The DDSDP suffers from several other basic issues: lack of quality screening of the training providers; lack of evaluation of the results, lack of tracer study on the value added and performance of graduates in self- employment, and lack of linkage between training and supporting services, credit, and markets.

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# **6 Conclusions and Policy Options**

111. **This study concludes that household enterprises play an increasingly important role in‎Tanzania’s‎economy‎as‎a‎major‎livelihood‎source.** The HE sector has registered the most rapid increase in primary employment, growing faster than wage employment in both agriculture and non-agriculture sectors. The continuing uptrend of employment growth in the HE sector underscores the potential of HEs for non-farm job creation. Employment in HEs is found to be particularly important for primary school leavers, as they lack the necessary skills to secure a formal sector job.

112. **Yet despite its capacity to provide employment and income opportunities, HEs face several major constraints that inhibit their growth and productivity.** In rural areas, HEs cite the lack of infrastructure, particularly roads and electricity, as the main barrier that undermines their ability to become more productive. For urban HEs, the lack of fixed location to do business is a more binding constraint. Without fixed business premises, they are not eligible to be formally registered or licensed, hence they operate outside the country‟s legal framework. Given the current focus of LGAs on formalization, the widespread official view that HEs lack legitimacy has made these enterprises vulnerable to excessive regulation and its punitive impact, in the form of harassment, eviction, and demand for bribes by government or police officials, particularly in large urban areas.

113. **In both urban and rural areas, lack of access to credit is cited by majority of HEs as a critical constraint that limits their earning capacity.** For them, family and friends are the main source of credit for their HE‟s start-up and working capital. Available sources of more formal financing from both the government and private sectors either have limited coverage or offer high cost of credit. In addition to the constraints they face, HEs are also vulnerable to risks, the most common of which are sudden illness or death in the family, which also limits their creditworthiness.

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114. **In spite of its growing importance in creating jobs and sustaining livelihood, the HE sector‎is‎not‎included‎in‎the‎government’s‎policy‎and‎institutional framework.** This is partly due to the policy dilemma the government faces as to whether to promote HEs on the basis of their positive role in the economy, or to thwart them because of their “illegitimacy.” A large number of existing programs and projects have been created to provide support to the informal sector in general, but do not address the specific needs of HEs in particular. With several implementing ministries, departments, and agencies (MDAs) involved, these programs suffer from lack of coordination, have overlapping activities and target beneficiaries, and therefore have limited impact.

## **Policy options to improve productivity and incomes of HEs**

115. **Recognizing its vital role in income and employment generation is an important first step** towards **improving the institutional environment within which HEs can operate productively** . The need is for an explicit policy and institutional framework in support of HEs 49
and for such framework to be integrated in the broader policy agenda of Tanzania, such as the MKUKUTA. In line with this new framework, a focal point within the government should be established with the primary tasks of promoting and building support for HEs, including granting them legitimacy and supporting services, and coordinating government activities directed to the HE sector.

116. The government should also promote an advocacy group for HEs (e.g., VIBINDO or other such group), which will provide them with a political voice to articulate their needs; facilitate their participation in the design, implementation, monitoring, and evaluation of policy interventions specifically targeted at them; and represent their interest in policymaking at the national and local levels. Such advocacy group should also facilitate the flow of information to HEs on matters that have a direct influence on their operations (e.g., the new licensing regime under the BARA Act) and be able to organize and mobilize them for appropriate actions, whenever necessary.

117. The immediate focus should be the creation of a conducive regulatory environment that supports HE operations. This requires a reversal of the current policy bias that weighs heavily against HEs. In the urban areas, LGAs are the key players in efforts to improve the productivity of HEs as they administer land and private sector activities. They could play a major role in facilitating the operations of HEs, which would involve focusing on finding solutions to the problems of HEs, including legality and workspace. In doing so, they need to hold dialogues and collaborate with HE operators. They should also recognize the important contributions of HEs in the local economy and should enlist their active participation in urban planning and policy formulation.

118. **Tanzania’s‎financial‎sector,‎which‎provides‎almost‎no‎access‎to‎financial‎services‎for‎ middle** class **and poor households, appears stunted in its growth, and this hurts the productivity of HEs.** It is beyond the scope of this report to analyze the reasons for this and prescribe solutions. In other fora, the government has committed itself to addressing this issue and developing an action plan. Consultation with target groups would improve the effectiveness of the proposed strategy. Meanwhile, the technological breakthrough in reducing the costs of providing current account services to small savers through mobile phone banking services such as M-Pesa offers exciting possibilities. Financial regulators led by the Bank of Tanzania could play an active role in promoting the entry of more service providers to encourage greater competition, as well as integration of mobile phone services into financial service delivery.

119. Programs designed to provide access of HEs to financial services are likely to be more effective if they have a built-in training component to promote not only financial literacy, but also better business operations and management skills. For this purpose, existing large MFIs such as FINCA, PRIDE, and TGT can play major roles, as they are already providing some of these programs to their borrowers. The government and donors should support experiments in this area, provided that they include effective impact evaluation to identify the best candidates for scale-up. Programs that do not work should be discontinued, and their funds rechanneled to those found to be more effective in reaching and improving the productivity of HEs.

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120. **Although many HEs required training, developing programs directed specifically to them is not simple** owing to the vast number of HEs spread out all over Tanzania. Moreover, the opportunity cost HEs face when attending a training program of unknown quality and value impedes their participation, especially once their HE is up and running. Program options featuring training experimentation and evaluation are appropriate at this stage. Given that majority of the new entrants to the labor force in the next 5 to 10 years will be primary school leavers who are most likely to enter this sector, the government may wish to look at the role primary schools could play in meeting the initial needs of HEs (such as financial literacy) as well as directing graduates into effective NGO- or public training programs. Designating a nodal training institution tasked with organizing, coordinating, and evaluating training programs for HEs might be an effective step towards improving the programs offered. A small share of the funds from the “development levy” could be earmarked for this purpose. Given the need for flexibility in training schedules and modules, the NGO sector (as a contractor) may be more effective than the public sector in addressing this need as it has shown this type of responsiveness thus far in Tanzania. Regardless of the ownership of the service provider, the programs need to have a strong demand-driven component and accountability to the funding agency through adequate results monitoring and evaluation.

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