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The Journal of Development Studies
ISSN: 0022-0388 (Print) 1743-9140 (Online) Journal homepage: https://www.tandfonline.com/loi/fjds20
Understanding the Role of Rural Non-Farm
Enterprises in Africa’s Economic Transformation:
Evidence from Tanzania
Xinshen Diao, Eduardo Magalhaes & Margaret Mcmillan
To cite this article: Xinshen Diao, Eduardo Magalhaes & Margaret Mcmillan (2018)
Understanding the Role of Rural Non-Farm Enterprises in Africa’s Economic Transformation:
Evidence from Tanzania, The Journal of Development Studies, 54:5, 833-855, DOI:
To link to this article: https://doi.org/10.1080/00220388.2018.1430766
© 2018 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Published online: 22 Feb 2018.
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Understanding the Role of Rural Non-Farm
Enterprises in Africa’s Economic Transformation:
Evidence from Tanzania
XINSHEN DIAO*, EDUARDO MAGALHAES** & MARGARET MCMILLAN*
*Development Strategy and Governance Division, International Food Policy Research Institute, Washington, DC, USA,
Department of Economics, Tufts University, Boston, MA, USA
ABSTRACT Tanzania’s recent growth boom has been accompanied by a threefold increase in the share of the
rural labour force working in nonfarm employment. Although households with nonfarm enterprises are less likely
to be poor, a substantial fraction of these households fall below the poverty line. Heterogeneity in the labour
productivity of rural nonfarm businesses calls for a two-pronged strategy for rural transformation. Relatively
unproductive enterprises may be part of a poverty reduction strategy but should not be expected to contribute to
employment and labour productivity growth. Failure to account for this heterogeneity is likely to lead to
Since the beginning of the twenty-first century, Tanzania’s economy has grown more rapidly than at
any other point in recent history. Between 2000 and 2015, the average annual GDP growth rate was
6.8 per cent and the average annual labour productivity growth rate was more than 4 per cent. Between
2002 and 2012, more than three quarters of this labour productivity growth was accounted for by
structural change; the remainder of the growth is largely attributable to within sector productivity
growth in agriculture. The growth attributable to structural change is almost entirely explained by a
rapid decline in the agricultural employment share and an increase in the non-agricultural private
sector employment share (Diao, Kweka, McMillan, & Qureshi, 2017).
Despite these changes, Tanzania remains heavily rural; between 2002 and 2012, the share of the
population living in rural areas declined by only 6.5 percentage points: from 76.9 per cent to 70.4 per
cent (Table 1). Living in rural areas is traditionally associated with farming. However, the census and
household survey data also shows that between 2002 and 2012, the share of the rural population
engaged in agricultural activities decreased by almost 14 percentage points (Table 1). The data also
show that growth in rural nonfarm employment has been very rapid at between 11.1 per cent and 13.5
per cent per annum depending on the data source (Table 1). Thus, while the agricultural sector still
employs the majority of the rural population in Tanzania, the rural nonfarm economy is becoming
The purpose of this paper is threefold. The first is to describe the characteristics of the households
that make up the rural nonfarm sector in Tanzania. The second is to describe the businesses run by
households in the rural nonfarm sector; we also describe characteristics of the owners of these
businesses and their self-reported motivations for running a business. The final purpose of this
paper is to assess the productivity of rural nonfarm businesses. This last exercise is meant to inform
the following question: does the rural nonfarm sector have the potential to contribute to long-run
Correspondence Address: Xinshen Diao, Development Strategy and Governance Division, IFPRI, 2033 K Street, NW,
Washington DC, 20006, USA. Email: email@example.com
The Journal of Development Studies, 2018
Vol. 54, No. 5, 833–855, https://doi.org/10.1080/00220388.2018.1430766
© 2018 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecom
mons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
productivity growth in Tanzania. The importance of this question is highlighted in recent work by
Diao, McMillan, and Rodrik (2017) who show that: (i) the pattern of growth in Tanzania is common
across Africa and; (ii) without labour productivity growth in nonfarm enterprises, labour productivity
growth in Africa is likely to stall.
Using Tanzania’s 2012 Household Budget Survey (HBS) –we classify rural households into three
groups: (i) households uniquely engaged in farming; (ii) households uniquely engaged in the nonfarm
sector and; (iii) mixed households. Our analysis shows that more than 10 per cent of Tanzanian rural
households engage only in the nonfarm economy. The heads of these households tend to be younger
and better educated than the heads of households in the two comparison groups. Gender of the
household head does not seem to affect the likelihood of being a nonfarm household. Households in
Table 1. Rural population and agricultural employment shares and annual growth rates
Rural areas National total
Share of rural population (percentage)
Share of agricultural employment in total employment (percentage)
2000/01 (HBS) 91.8 82.6
2002 (Census) 93.2 81.1
2006 (ILFS) 76.5
2011/2012 (HBS) 77.4 63.8
2012 (Census) 79.5 65.8
2014 (ILFS) 66.9
Annualised growth rate in population, total employment and employment in agriculture and non-agriculture
Population (2002–2012, Census) 1.8 2.7
2000–2011 (HBS) 1.3 2.4
2002–2012 (Census) 1.6 2.5
2006–2014 (ILFS) 0.3 2.4
Employment in agriculture
2000–2011 (HBS) −0.3 0.1
2002–2012 (Census) 0.1 0.4
2006–2014 (ILFS) 0.7
Employment in non-agriculture
2000–2011 (HBS) 11.1 9.4
2002–2012 (Census) 13.5 8.8
2006–2014 (ILFS) 6.8
Notes: For the Census employment, data for current employees aged 10 years old and above is used. Agricultural
employment is based on the industry classification. For the Household Budget Survey (HBS), employees are for
aged 10 years old and above. The definition of employment differs between the two rounds of HBS. In HBS 2000/
2001, ‘unpaid family helper’counts for 7.8 per cent of total employment and is all considered as non-agricultural
employment. This causes two problems: (1) total employment of HBS 2000/2001 is more than that of Census
2002, which was conducted two years later; and (2) share of non-agricultural employment in HBS 2000/2001 is
too high. Moreover, given that population calculated from HBS 2000/2001 is lower than that in Census 2002, the
ratio of employment to population in HBS 2000/2001 becomes too high compared with that of Census 2002. For
these reasons, we decided to exclude ‘unpaid family helpers’from total employment in HBS 2000/2001. After this
adjustment, the ratio of total employment to population is similar between HBS 2000/2001 and Census 2002, as
well as the shares of agricultural and non-agricultural employment. Employees in the Integrated Labor Force
Survey (ILFS) are for age 15 years old and above, and the data for rural employment is not available in the survey
report published by Tanzania National Bureau of Statistics (NBS) (2015).
Source: Most numbers in the table are calculated by authors using the micro data downloaded from the NBS
website as well as from official government documents. For Census 2012 and ILFS 2006 and 2014, the micro data
is not available, and the calculation for these surveys is solely based on official government documents published
by NBS (NBS, 2006, 2011a, b, 2014a, b, 2015). The micro data for Census 2002 is downloaded from IPUMS
834 X. Diao et al.
communities with access to daily public transportation are found to have a higher probability of
participating in rural nonfarm activities.
In order to analyse business in the rural nonfarm sector, the Tanzania National Baseline Survey of
Micro, Small and Medium Enterprise (MSME) has been employed. The survey has been conducted by
the Tanzania government, and it was the first nationally representative survey of MSME. It is also one
of the only comprehensive nationally representative surveys of MSME types in sub-Saharan Africa.
covers roughly three million businesses and five million people; around 75 per cent of these businesses
are in rural areas. 20 per cent of rural nonfarm businesses operate in the manufacturing sector while the
remainder operate in various service sectors.
Our firm level analysis begins with a description of the characteristics of the owners of these
businesses; roughly half say they would not quit their business for a full-time salaried position. We
then document the enormous degree of productive heterogeneity in both rural and urban enterprises.
Using the information about the productive heterogeneity of the rural nonfarm enterprises, we identify
a group of small firms which appear to have the potential to contribute significantly to rural
transformation. A probit analysis is conducted to better understand which readily observable char-
acteristics of these firms and their owners are correlated with labour productivity. The analysis is an
attempt to search for firm and/or owner attributes that might be used by policy-makers for targeting
services to a select group of firms.
This research contributes to a large and growing literature on the rural nonfarm economy in Africa.
One strand of this literature focuses on farm/nonfarm linkages and the estimation of multipliers (see
for example Haggblade, Hazell, & Brown, 1989). A second strand of this literature focuses on rural-
urban linkages and stresses the importance of reducing barriers to the movement of labour and
products from rural to urban areas (see for example, De Brauw, Mueller, & Lee, 2014 on migration;
and Gollin & Rogerson, 2009 on transportation costs). A third strand of this literature studies the
effects of farm/nonfarm linkages and rural/urban linkages simultaneously; (see for example, Hagglade,
Hazell, & Reardon, 2010). Unlike these papers, our work focuses on the productivity of rural nonfarm
businesses and their potential to contribute to economy-wide labour productivity growth.
This research also contributes to a large body of work on SMEs in developing countries. Numerous
studies in this field focus on documenting and analysing the heterogeneity of SMEs in both rural and urban
areas (Banerjee, Breza, Duflo, & Kinnan, 2015; Bezu & Barrett, 2012; Fafchamps, McKenzie, Quinn, &
Wood ru f f , 2014; Grimm, Krüger, & Lay, 2011; La Porta & Shleifer, 2011,2014;McKenzie,2015;
McKenzie & Woodruff, 2008; Nagler & Naudé, 2017; Nordman, Rakotomanana, & Roubaud, 2016;
Rijkers & Costa, 2012; Rijkers, Soderbom, & Loening, 2010). Some other studies in the SME literature
focus on assessing employment growth in SMEs (McPherson, 1996;Mead,1994; Mead & Liedholm,
1998), while others focus on the factors that constrain SME development (Beck, 2007; Gibson & Olivia,
2010; Jin & Deininger, 2009; Raj & Sen, 2013).
Most of this previous work relies on small samples and where samples are nationally representative,
labour productivity is not examined since the nationally representative surveys do not include
This point was made recently by Li and Rama (2015) in an article published in the
World Bank Research Observer. According to Li and Rama (2015), in most African countries
enterprise censuses or surveys generally cover formally registered firms, therefore excluding the
vast majority of micro- and small-enterprises, which are typically informal (Li & Rama, 2015). The
surveys that do cover SMEs are often small in sample size.
It follows that the role of small, largely
informal firms in the growth and development of poor economies is likely not well known using data
from such small sized surveys, given the large heterogeneity among SMEs in developing countries. In
addition, analyses of the rural nonfarm economy typically do not compare the productivity of rural
enterprises to the productivity of urban enterprises.
The remainder of this paper is organised as follows. Section 2 demonstrates the growing importance
of the rural nonfarm economy in the context of the broader Tanzanian economy. Section 3 describes
the data and methods used in the analyses. Section 4 focuses on the household level analysis and
examines the characteristics of households with and without rural nonfarm activities. Section 5 turns to
the firm data and describes the characteristics of rural entrepreneurs and their businesses. This section
The role of rural enterprises in Tanzania’s transformation 835
also identifies a group of firms –the ‘in-between’firms –with the potential to contribute to rural
transformation. Section 6 studies the characteristics of the ‘in-between’firms to better understand how
policy-makers might target these firms. Section 7 concludes with a summary of the main points and a
brief discussion of policy implications.
2. The role of rural nonfarm enterprises in Tanzania’s economy
Three nationally representative surveys are used to describe the changes in shares of rural population and
rural employment. These are the 2000/2001 and 2011/2012 rounds of the Household Budget Surveys
(HBS), the 2002 and 2012 Population Censuses, and the 2006 and 2014 Integrated Labor Force Surveys
(ILFS). According to the two rounds of the census, which capture the change in population structure, the
share of Tanzania’s population living in rural areas declined from 76.9 per cent in 2002 to 70.4 per cent in
2012 (Table 1, first panel). Thus, Tanzania is urbanising but still heavily rural.
The second panel of Table 1 contains agricultural employment shares from the three different
surveys in their different rounds. While different surveys cannot be directly compared because of
differences in the definition for agricultural employment, there is a clear trend in which the share of
agricultural employment declines much more rapidly than the share of rural population. Between the
two rounds of the Census (2002–2012), the agricultural share of employment declined by 15.3
percentage points nationally and 13.7 percentage points in rural areas. The two rounds of HBS
conducted in 2000/2001 and 2011/2012 and the two rounds of ILFS conducted in 2006 and 2014
all show a similar trend (Table 1). Comparing these large declines with the modest changes in the share
of rural population discussed above indicates that growth in rural nonfarm employment outpaced
growth in agricultural employment over this period.
Indeed, the annualised employment growth rates in the recent 15 years presented in the third panel
of Table 1 clearly indicate this pattern. These employment growth rates are computed from each of the
three surveys’two most recent rounds. In general, employment in agriculture has been growing at a
slower pace than total employment nationwide and in rural areas. According to the census data, the
growth rate of agricultural employment is almost zero between 2002 and 2012 in rural areas. By
contrast, growth in rural non-agricultural employment has been in the double-digit range growth rate
over this period (the bottom of Table 1).
To help better understand the changing structure of Tanzania’s employment, Table 2 displays the
structure of net increases in employment across different economic sectors between 2002 and 2012 for
total employment and formal and informal employment. While the agricultural sector still accounts for
two-thirds of total employment in Tanzania, as shown in Table 1, it has played a relatively minor role
in the net job increase as shown in Table 2. In fact, almost 90 per cent of the net increase in jobs
occurred in the non-agricultural sector over the period of 2002–2012. Considering that agricultural
employment made up more than 80 per cent of total employment nationwide in 2002 (Table 1), this
rapid non-agricultural employment growth is remarkable.
As is evident from Table 2, about 73 per cent of the net increase in total employment has taken place in
the informal non-agricultural sector, accounting for 88 per cent of the increase in private non-agricultural
employment. We do not have access to detailed employment data disaggregated by rural and urban among
different sectors. However, we know from Table 1 that nonfarm employment increased significantly in
rural areas. If one assumes that formal non-agricultural employment is more likely to take place in urban
than in rural areas, which is a realistic assumption, then the 88 per cent of increased private sector’snon-
agricultural employment created by the informal sector nationwide can be taken as the lower bound for
informal employment growth in rural areas. Section 4 of this paper will use the MSME data to further
investigate the nature of this rural nonfarm employment.
836 X. Diao et al.
3. Data and methods
The two datasets used for the analyses are briefly discussed in this section, followed by a description
of the methodologies employed to analyse two distinct research questions. We provide a more detailed
description of the HBS data in the Appendix since the data are fairly standard and have been used
frequently. We devote more space here to the description of the MSME data which is unlikely to be
familiar to readers both because of its’limited use and because these kinds of nationally representative
firm level surveys of mostly informal firms are much rarer. First, what are the characteristics of
households which participate in the rural nonfarm economy compared to other types of households?
And second, can the firm level data be used to assess the characteristics and potential of rural
enterprises in comparison with those in urban areas?
3.1. The HBS data
The 2011/2012 Household Budget Survey (HBS) is a nationally representative survey, which is designed
to provide estimates of household income and expenditures for poverty assessments, similar to how the
Living Standard Measurement Surveys (LSMSs) are conducted routinely in many other countries. The
2011/2012 HBS surveyed 4130 rural households and 6056 rural households, and is also representative at
three geographic locations –rural, Dar es Salaam and other urban. Similar to a standard LSMS, the HBS
has an occupational module that provides information for all household members’primary employment
by industries (including farming). This is the module used for the rural employment analysis in this
paper. The survey also asked whether the households have their home enterprises, a question also used in
the discussion in Section 3. However, the HBS only covers mainland Tanzania and excludes Zanzibar.
Like the MSME survey to be discussed later, the sampling framework used to conduct the HBS survey is
based on the 2002 Census, which could possibly oversample rural households given that, as discussed in
Section 1, the 2012 Census has shown a decline of 6.5 percentage points in the share of rural population
from the 2002 Census. A set of summary statistics based on the 2011/2012 HBS for the variables used in
our analysis is presented in Appendix Tab le A1.
Table 2. Contribution to new increases in employment by sector, total, formal and informal in 2002–2012
Total Formal Informal
employment net increase employment net increase employment net increase
Agriculture 446,677 11.2 −3,865 −0.1 450,542 11.3
Mining 404,212 10.1 9,021 0.2 395,192 9.9
Manufacturing 313,882 7.8 103,049 2.6 210,833 5.3
Utilities 194,960 4.9 194,960 4.9 –0.0
Construction 281,864 7.0 21,185 0.5 260,679 6.5
Trade services 966,807 24.2 1,304 0.0 965,503 24.1
Transport services 182,383 4.6 18,497 0.5 163,886 4.1
Business services 105,635 2.6 56,924 1.4 48,711 1.2
Public sector 224,579 5.6 224,579 5.6 0.0
Personal services 881,289 22.0 0 0.0 881,289 22.0
Total private non-
3,331,032 83.2 404,940 10.1 2,926,093 73.1
Total private economy 3,375,978 84.4 845,077 21.1 2,530,901 63.2
Total non-agriculture 3,555,611 88.8 629,519 15.7 2,926,093 73.1
Notes: Employment is defined by the current employment status with age 10 or more years old.
Source: Authors’calculation based on data from the Formal Employment and Earnings Survey and the Census
2002 and 2012 (NBS, 2006; 2007, 2014b, c, d, e).
The role of rural enterprises in Tanzania’s transformation 837
3.2. The MSME data
As mentioned in Section 1, the Micro, Small, and Medium Sized Enterprise (MSME) survey is
Tanzania’s first nationally representative survey of small businesses. The data was collected during
interviews with 6134 small business owners identified in a three-step sampling process. In the first
step, a sample of 640 representative enumeration areas were selected. A complete listing of all
households was carried out to identify households that currently owned and ran small businesses or
had recently closed businesses. In the second step, about nine to 12 households with currently
operating businesses (and two to three households with closed businesses) were selected. Finally, if
more than one member in a selected household owned and ran a small business, a Kish Grid was
applied to select the interviewee. There are three questionnaires for the survey. The main question-
naire, which is used for this study, includes 192 questions on 20 topics that are asked to owners of
currently operating enterprises. Based on the enterprise’s main activities, main products and services,
all enterprises in the survey were assigned to an industry according to the International Standard for
Industrial Classification (ISIC). There are 80 unique ISIC industries, of which many have few sampled
firms. Thus, in this study, the 80 industries are aggregated into 24 subsectors, six of which are in the
manufacturing sector and the rest in service sectors.
A set of summary statistics of the MSME survey data is reported in Table 3 for the three areas
separately: rural, other urban area and Dar es Salaam. Among the 6134 sampled firms, a total of 5609
firms have all the information required for the analysis. Based on the information that is available,
there is no reason to believe that the firms with missing information are significantly different from the
rest of the sample; for example, they are dispersed across regions and firm size.
As shown in the first row of Table 3, most MSMEs are extremely small: mean employment is 1.5 in
rural areas, 1.65 in urban areas outside Dar es Salaam and 1.7 in Dar es Salaam. The very small size of
MSMEs is at least in part possibly due to a sample selection bias. The sampling framework is
household based rather than enterprise based, and is based on the 2002 Population and Housing
Census. This could possibly lead to oversampling rural households given that, as discussed in Section
1, the 2012 Census has shown a decline of 6.5 percentage points in the share of rural population from
the 2002 Census. The household-based sampling means that the survey probably under-sampled
businesses outside households, which in practice translates into under-sampling relatively larger
sized firms. The fact that more than 40 per cent of firms with five or more employees in the survey
are sampled in rural areas might support this concern, as it is known that most larger sized firms are
often in urban areas instead of rural areas. Among the 6134 enterprises sampled in the survey, there are
only 96 firms with five or more employees. While for the largest firm in the sample there are 80
employees, the second and third largest ones have employees of 34 and 33 respectively. In fact, there
are only four firms in the survey with employees more than 20. While the larger sized firms are usually
assigned larger weights than the smaller ones in the data, this potential issue of possibly under-
sampling larger urban firms is unlikely to be fully corrected by the assigned sample weights given that
the sampling weights are derived from the relationship between listed households and households
currently operating with businesses in selected representative enumeration areas.
As expected, few small firms are registered with Tanzania’s Business Registration and Licensing
Agency (BRELA) and there is little difference between rural and urban firms in this regard.
contrast, more urban enterprises (8%) have tax identification numbers than rural enterprises (3%).
While the MSME survey is a household based survey, 49 per cent of rural firms report that their
businesses actually operate out of their homes and the number in urban areas is almost identical at 47
per cent. Therefore, the shares of nonfarm enterprises reported in Table 4 calculated from HBS 2011/
2012 data could significantly underestimate the importance of MSMEs in rural areas, given that HBS
captures only businesses run out of the home.
Table 3 also reports average monthly value-added and average monthly sales per firm. Firms in the
MSME database report sales on a monthly basis and also provide their own judgement on whether a
particular month is a good, bad or normal month. By taking the possible seasonality into account,
value added is then computed as the firm’s average monthly sales minus the firms’average monthly
838 X. Diao et al.
Table 3. Rural and urban MSME summary statistics
Observations Mean S.D. Observations Mean S.D. Observations Mean S.D.
Names of variables
Business characteristics Value unit or range Rural Other urban Dar es Salaam
Number of employees per firm Person 4,160 1.40 1.01 1,093 1.64 1.94 353 1.74 1.52
Number of full-time employees per firm Person 4,160 1.23 0.69 1,093 1.38 1.30 353 1.52 1.07
Annual employment growth [-.09,.25] 4,150 0.02 0.18 1,090 0.02 0.21 352 0.01 0.18
% of firms registered with BRELA [0,1] 4,160 0.03 0.16 1,093 0.03 0.18 353 0.05 0.22
% of firms with tax ID [0,1] 4,160 0.03 0.18 1,093 0.11 0.31 353 0.10 0.31
% of firms with business run out of home [0,1] 4,160 0.08 0.27 1,093 0.09 0.28 353 0.05 0.21
Average monthly value added per firm 1,000 TZS 4,160 199 429 1,093 252 496 353 256 661
Average monthly sales per firm 1,000 TZS 4,160 383 979 1,093 463 1,4 21 353 466 797
Firm’s age Year 4,121 6.46 6.08 1,079 6.24 5.98 348 5.43 5.35
% of firms with business as full-time [0,1] 4,160 0.80 0.40 1,093 0.80 0.40 353 0.83 0.38
Keeps accounts in ledger [0,1] 4,160 0.39 0.49 1,093 0.51 0.50 353 0.46 0.50
Hires paid workers [0,1] 4,160 0.07 0.26 1,093 0.16 0.37 353 0.23 0.42
>20 customers per day [0,1] 4,160 0.28 0.45 1,093 0.29 0.46 353 0.32 0.47
Firms powers business with electricity [0,1] 4,160 0.09 0.28 1,093 0.35 0.48 353 0.42 0.50
Owner saves in formal bank account [0,1] 4160 0.05 0.22 1093 0.15 0.36 353 0.16 0.37
Age of owner Year 4,160 36.92 10.53 1,093 37.07 11.01 353 36.97 10.62
Whether owner is female [0,1] 4,160 0.47 0.50 1,093 0.67 0.47 353 0.68 0.47
% firms with business as main source of income [0,1] 4,160 0.43 0.49 1,093 0.35 0.48 353 0.26 0.44
% firms with farming as main source of income [0,1] 4,160 0.24 0.43 1,093 0.06 0.25 353 0.00 0.00
% firms with business as only source of income [0,1] 4,160 0.28 0.45 1,093 0.46 0.50 353 0.55 0.50
% of firms’households that are not poor [0,1] 4,160 0.41 0.49 1,093 0.53 0.50 353 0.61 0.49
% of firms’households that are moderately poor [0,1] 4,160 0.37 0.48 1,093 0.32 0.47 353 0.27 0.45
% of firms’households that are very poor [0,1] 4,160 0.22 0.41 1,093 0.15 0.36 353 0.12 0.32
Notes: Full-time employees include business owners and their family members if they work full-time in the firms. BRELA is Tanzania’s Business Registration and Licensing
Agency opened in 1999. Household poverty was reported in the survey by an indicator variable equal to zero if the household is not poor, one if the household is moderately
poor and two if the household is very poor. The measure of poverty was computed using monthly household income as reported by survey respondents, and the poverty
measured in the HBS is considered. TZS denote Tanzanian Shillings and 1000 TZS equivalent to $0.7 US dollars in 2010.
Source: Authors’calculations using the MSME Survey 2010
The role of rural enterprises in Tanzania’s transformation 839
costs of production. The mean value-added of rural firms reported in Table 3 is about 20 per cent lower
than the mean value-added of urban firms in both Dar es Salaam and other urban areas. However, there
is significant variation among surveyed firms in monthly value-added in both rural and urban areas,
indicated by the high value of the standard deviation (s.d.) in Table 3. We will return to this point in
detail later in this paper.
Most MSME firms are young, with a mean age of 6.9 years for rural firms, 6.1 years for urban firms
outside Dar es Salaam, and 5.5 years for firms in Dar es Salaam. This is consistent with the findings in
Section 1 that most nonfarm jobs created in Tanzania between 2002 and 2012 were created by the
informal sector. Table 3 also indicates that 76 per cent of rural businesses operate full time, compared
to 82 per cent in urban areas outside Dar es Salaam and 87 per cent in Dar es Salaam. More than 40
per cent of rural business owners report that the business is the owners’main source of income with a
significantly lower share of rural business owners (28%) reporting that the business is the owners’only
source of income. By contrast, 44 per cent and 51 per cent of business owners in other urban and Dar
es Salaam report that the enterprise is their only source of income. Only about a quarter of rural
business owners report that farming is their main source of income, a fact that supports the finding
from the 2011/2012 HBS, that many households (about 10%) in rural areas only participate in the
nonfarm economy as their primary employment.
Like their businesses, the owners of these small businesses are also relatively young. For the full
sample, the mean age of business owners is roughly 37 years in rural and 36 years in urban areas. In
contrast, according to the HBS data, the average age of a rural household’s head is 47 years old and
42 years old for an average urban household’s head.
Finally, the last three rows of Table 3 report the distribution of business owners by the three
categories of their household’s income. The measure of these three income categories (very poor,
modestly poor and not poor)
was computed using monthly household income reported by survey
respondents. Poverty appears to be higher among households with MSME owners than among overall
rural and urban households. Based on the poverty assessment profile reported by the government
(NBS [National Bureau of Statistics], 2013), there are 66.7 per cent of the rural population, 78.3 per
cent of the urban population outside of Dar es Salaam and 95.8 per cent of Dar es Salaam’s population
lives in nonpoor households. In contrast, in the MSME survey, only 45 per cent of rural MSME
owners, 54 per cent of urban MSME owners outside of Dar es Salaam and 62 per cent of MSME
Table 4. Distribution of rural households with and without nonfarm activities (2011/2012)
Farm/nonfarm mixed Nonfarm only
Share by household
61.4 22.1 5.4 5.8 5.4
Share by youth-
56.9 20.5 4.7 8.7 9.2
Share by population 58.4 25.8 6.3 5.1 4.4
Share by employment 59.4 26.6 6.6 3.9 3.5
Share by youth
58.1 24.1 5.3 6.1 6.4
39.3 27.3 31.8 14.2 15.3
Notes: The HBS asked individual households whether they have a home business and an ISIC rev4 code is used
for assigning sectors to the business. We consider ISIC non-agricultural sectors only as nonfarm enterprises. The
employment of non-agriculture is defined by the current primary employment that is not in agriculture.
Source: Authors’calculations based on the data of HBS 2011/2012 (NBS, 2014a).
840 X. Diao et al.
owners in Dar es Salaam live in nonpoor households. This seems to confirm that in both urban and
rural areas, small businesses are often part of a coping strategy for many poor households, while rich
households are less likely to choose such small businesses as their main livelihoods.
The empirical strategy employed in this paper aims to answer to two questions: (1) What determines
whether households participate in the nonfarm economy? And (2) what determines whether nonfarm
enterprises have the potential to contribute to employment and labour productivity growth? To answer
these questions econometric analyses are used.
Descriptive statistics for the HBS and MSME data provide a glimpse of the heterogeneity that is
observed across households and firms. The means and standard errors presented in all the descriptive
tables of this paper were generated using the sampling design of the two surveys (HBS and MSME).
To address the first question, the rural households in the HBS data are classified into three types
based on their family members’primary employment in the econometric analysis, while more
subgroups of households are further classified in the descriptive analysis. The three types of rural
households are: (1) farm, in which all family members’primary employment is agriculture; (2) mixed,
indicating that in the same household some family members work in agriculture and others in rural
nonfarm economy; and (3) nonfarm, in which all family members work in the rural nonfarm economy
as their primary employment. A multinomial probit model is used in the analysis. The choice of a
multinomial probit over a multinomial logit arose from the fact that the multinomial probit is better
suited to handle correlations and is not bound by the independence of irrelevant alternatives like the
multinomial logit. The farm household group is chosen as the comparison category in the regression.
For the MSME dataset, the left-hand side variable is binary which under normal circumstances
could be addressed with a simple probit model. However, we must modify our approach due to
possible endogeneity issues in some of the right-hand side variables. Dealing with endogeneity in non-
linear models (particularly in a probit model) is a straightforward exercise if the endogenous variable is
continuous. In such cases, endogeneity is dealt using a control function approach as explained in
Wooldridge (2010) and StataCorp (2015).
The endogenous variables in our dataset are binary in
nature, however. This eliminates the possibility of using a control function approach but raises the
possibility of using a bivariate probit model. However, this last approach is also ruled out because it
only allows for one endogenous variable while our dataset contains multiple endogenous variables. To
address this issue, we have therefore resorted to estimating the probit model using a generalised
method of moments (GMM) approach which allows for the endogeneity of multiple variables.
Estimating probit models using GMM is straight-forward if only exogeneous variables are present
in the right-hand side, as is shown in StataCorp (2015). However, in the presence of endogeneity the
estimation becomes considerably more complicated, as instruments cannot simply be added to the
moment conditions of a GMM instrumental variable approach (as one would do for linear models).
Doing so for probit or logit models is not possible since neither the conditional expectations nor the
linear projections assumed for the linear model apply in the case of probit models. We have therefore
followed Wilde (2008) to estimate a two-stage generalised methods of moments (GMM) model which
accounts for both the non-linearity of the model and the binary nature of the endogenous variables.
The GMM estimation as proposed by Wilde takes the following form. In the first stage, a reduced
form probit model is estimated for each endogenous variable and the residuals are calculated. Having
estimated stage 1, a two-step GMM approach is used to estimate the parameters using the correct
moment conditions and the necessary adjustments to the standard errors. We refer the reader to Wilde
(2008) for details on the specification of the moment conditions. The method proposed by Wilde and
as applied in this paper leads to an exactly identified estimation, meaning that the number of
instruments equal the number of parameters. Thus, no tests for overidentification of instruments
could be conducted for the final estimation. The left-hand side variable for the structural model was
defined as follows. It took the value of one if the firm’s value-added per worker (which is used to
measure the firm level labour productivity) is greater than the economy-wide labour productivity in the
The role of rural enterprises in Tanzania’s transformation 841
trade sector at the national level and zero otherwise. The small firms with high potential are defined as
the ‘in-between’firms following Lewis (1979).
The specifications of the models described above are provided in Sections 3.2 for the HBS survey
and Section 4.4 for the MSME survey. Average marginal effects, which can be interpreted as the
change in the predicted probability given a one unit change in the right-hand side in the case of
continuous variables or a discrete change in the case of categorical variables, are reported. With the
exceptions of two variables (firm age and number of employees), all our right-hand side variables are
binary in nature. Thus, the marginal effects should be interpreted as discrete changes and, as such, we
report average marginal effects. All estimations are done using robust standard errors in accordance to
the sampling design. Since the survey was not in any way stratified and subnational units are not
representative, we have not clustered the standard errors to any specific subnational location.
4. Characteristics of households with rural nonfarm activities
We begin this section using the 2011/2012 HBS to assess the size of the rural nonfarm economy by the
number (or share) of rural households that participate in the nonfarm economy and the differences
between households with and without rural nonfarm participation. We then analyse the characteristics
of the three categories of households using a multinomial probit model.
4.1. How large is the rural nonfarm economy?
As a starting point, we first classify rural households using the HBS data into with and without
nonfarm activities. The classification is based on household members’primary employment. Like
other low-income countries in Africa, most states in Tanzania are predominantly rural (Davis, Di
Giuseppe, & Zezza, 2014). Farm activity dominates rural Tanzania –61.4 per cent of rural households’
members engage only in agricultural activities in 2012 (Table 4), and less than 40 per cent engage in
nonfarm activities. Households engaging in rural nonfarm activities can further be classified as farm/
nonfarm mixed and nonfarm only households. As shown in Table 4, about 27.5 per cent of total rural
households are farm/nonfarm mixed households and about 11 per cent are nonfarm only households.
We also further categorise rural households with nonfarm activities according to whether they have
their own nonfarm businesses. This helps us to establish a link between the HBS survey and the
MSME survey to be analysed later. According to the HBS, fewer mixed households have their own
nonfarm businesses, while almost half of the nonfarm only households own nonfarm businesses.
These numbers are comparable to the statistics drawn from the firm data of the MSME survey in
Table 3, which shows that 43 per cent of rural nonfarm enterprises are the household’s main income
source, but only 28 per cent with businesses report that the business is the only source of income.
Finally, we also show in Table 4 that rural households with nonfarm businesses are less likely to be in
4.2. Characteristics of households in the rural nonfarm economy
Characteristics of the different types of households are explored using a multinomial probit model as
discussed in Section 2.3. Equation (1) below describes the specification used in the estimation
is the choice of a given household and takes three values (1 = farm, 2 = mixed, 3 = non-
is a vector of household characteristics, C
is a vector of infrastructure or other community
level factors, D
is a set of regional dummies. The variables in the vector H
include a dummy equal to
one if the household is headed by a young person (age 15–34); a dummy equal to one if the household
head is female; dummies for the levels of education of the household heads (less than primary as the
842 X. Diao et al.
comparable variable) and; dummies for farm size defined by cultivated area categorised into four
groups: no-land, farms with less than two ha, farms with two to five ha, and farms with more than
five ha (no land is the comparison group). Vector C
contains a set of variables related to access to
infrastructure at the community level and other community level variables including daily public
transportation to the regional capital, electricity, mobile phone signal, internet, banks, informal finance,
cooperatives, a large employer (for example, a factory), and a weekly market. εiis the iid error term.
Table 5 reports the average marginal effects for the three types of households. We begin with the
household variables. Being a young household head has a significant and positive effect on being a
nonfarm household, with the predicted probability increasing by 3.1 per cent, and has a negative effect
at a similar scale on being a mixed household. The level of the household’s head education shows a
Table 5. The average marginal effects (predicted probabilities) calculated from the multinomial probit regression
using 2012 HBS data
Variable Farm Mixed Nonfarm
Youth (age 15–34) as head of a household 0.00622 −0.0370*** 0.0308***
(0.0153) (0.0127) (0.00913)
Female headed household 0.0149 −0.0217 0.00679
(0.0197) (0.0145) (0.0109)
(Compared to less than primary)
Primary education −0.0639*** 0.00566 0.0583***
(0.0156) (0.0103) (0.0127)
Secondary & higher −0.299*** 0.103*** 0.196***
(0.0259) (0.0177) (0.0196)
(Compared to no land)
Less than 2 ha 0.249*** −0.0283 −0.221***
(0.0296) (0.0254) (0.0162)
2–5 ha 0.273*** −0.0264 −0.247***
(0.0318) (0.0259) (0.0185)
More than 5 ha 0.206*** 0.0309 −0.237***
(0.0377) (0.0273) (0.0235)
Daily public transport to the regional capital −0.0298 −0.00986 0.0397**
(0.0290) (0.0179) (0.0201)
Electricity access −0.0285 0.0258 0.00277
(0.0289) (0.0200) (0.0171)
Mobile phone signal 0.0678** −0.0682*** 0.000398
(0.0288) (0.0188) (0.0187)
Internet access −0.0511 0.0365* 0.0145
(0.0337) (0.0211) (0.0198)
Formal banks −0.0261 0.0236 0.00249
(0.0492) (0.0321) (0.0401)
Informal financial services 0.0667*** −0.0282 −0.0385***
(0.0248) (0.0170) (0.0139)
Cooperatives −0.0253 0.0331** −0.00781
(0.0255) (0.0145) (0.0175)
A major state employer (business or factory) −0.0517 0.00614 0.0455**
(0.0353) (0.0220) (0.0223)
Weekly market −0.0756*** 0.0409** 0.0346**
(0.0246) (0.0144) (0.0154)
Observations 4,053 4,053 4,053
Notes: Standard errors in parentheses. *p < 0.05; **p < 0.01; ***p < 0.001. The average marginal effects
(predicted probabilities) based on the multinomial probit regression are reported. In the multinomial probit
regression, the farm household group is chosen as the comparison category. See Table 5 in Sosa-Rubi,
Galárraga, and Harris (2009) for a similar way to report the result.
Source: Authors’calculation from their estimation results of multinomial probit regression using 2012 Tanzania
The role of rural enterprises in Tanzania’s transformation 843
distinct and contrasting effect on being a farm or a nonfarm household. While both primary and
secondary/higher education matter for being a nonfarm household, the more educated the household’s
head, the larger the effect. Only the higher level of education affects the probability of being a mixed
household, as the effect of primary education is insignificant. These results seem to indicate that higher
levels of education may be required to obtain nonfarm jobs in rural areas.
Next, we look at farm size. As expected, having a larger farm size decreases the probability of being
a nonfarm household by 22–24 per cent. Likewise, a larger farm size is associated with a higher
predicted probability of being a farm household by 20.6–27.3 per cent.
Differences among the three types of households are less pronounced for community level variables,
perhaps due to the decreased variability that is inherent in these variables. Indeed, public transportation
to the regional capital, a proxy for road access, only positively affects the probability of being a nonfarm
household. Having a mobile phone signal has the opposite effect on being a farm and a mixed
household, positive for the former and negative for the latter, while the effect on being a nonfarm
household is insignificant. While the use of electricity for doing business, especially in the manufactur-
ing sector, is important, the variable is not significant for any type of household; this is also true for
internet access. This may be because access to electricity or internet at the community level does not
necessarily imply access at the household level. Access to informal financial services is associated with a
higher probability of being a farm household and a lower probability of being a nonfarm household,
suggesting that informal financing is the main channel to borrow money for farm households. The
presence of cooperatives is only significant for the effect on being a mixed household with a 3.3 per cent
greater predicted probability. As expected, the presence of a large employer is associated with a higher
predicted probability of being a nonfarm household by 4.5 per cent, but does not influence being a mixed
household. Finally, having access to weekly markets is the only variable that is significant across all
types of households. Access to markets reduces the predicted probability of being a farm household by
7.5 per cent and increases the predicted probability of being mixed and nonfarm households by 4 per
cent and 3.4 per cent respectively. The mixed results regarding the role of infrastructure are puzzling. As
noted, this may be because the community level variables are too ‘rough’a proxy for access at the
household level. However, the lack of significance of these variables may also be associated with the
small scale of rural nonfarm enterprises. According to Tybout (2000), low levels of economic density
and interaction may lead to small, diffuse pockets of demand, which in turn result in small, localised
production and services. We revisit this issue in the next section using the MSME data.
5. Characteristics of rural nonfarm enterprises and their owners –an analysis at the firm level
using MSME survey data
A vibrant rural nonfarm sector can play an important role in rural transformation. To understand the
extent to which the rural nonfarm sector can play a role in labour productivity growth and poverty
reduction in rural areas, the MSME survey data is used to examine the motivations of business owners
in the rural nonfarm sector as well as the characteristics of their businesses. In a previous paper, Diao,
Kweka, McMillan and Qureshi (2017) identify a group of MSMEs that can be considered members of
what Arthur Lewis (1979) referred to as the in-between sector. According to Lewis (1979), these firms
play an important role in the transformation process. Lewis (1979) uses the term in-between to signal
that these firms are not just petty traders, rather, they often look more like formal firms and provide
important goods and services. Diao, Kweka, et al. (2017) show that rural enterprises are on average
slightly less productive than their urban counter-parts (this is confirmed in Table 3 of Section 2 in this
paper) but they do not explore in detail the characteristics of rural enterprises or rural entrepreneurs.
This section begins with a description of the location and industrial composition of MSMEs. It
follows with an exploration of the extent to which rural entrepreneurs appear to be subsistence or
growth-oriented. To analyse this issue, we use the following data from the MSME survey: (i) self-
reported motivations for business ownership; (ii) the productive heterogeneity of MSMEs and; (iii)
employment growth in MSMEs.
844 X. Diao et al.
5.1. Industrial and geographic composition of MSMEs
Table 6 reports the distribution of employment and the number of MSMEs by rural, other urban and
Dar es Salaam, compared with the distribution of population in the three locations. While more than 67
per cent of the population lives in rural areas, rural MSMEs account for 52 per cent of total MSME
employment. In urban areas, the distribution of MSME employment/firms and distribution of popula-
tion seem to be similar in Dar es Salaam and other urban areas. 15.8 per cent of MSME employment
and 17.3 per cent of MSME firms are in Dar es Salaam, where 12.2 per cent of the national population
resides. Likewise, 32.6 per cent of MSME employment and 30.7 per cent of MSME firms are in other
urban areas, which contain 20.4 per cent of the population (Table 6).
Table 7 reports the industrial distribution of MSMEs by rural, other urban and Dar es Salaam.
Although the MSMEs operate in a wide range of activities, the bulk of these activities can be classified
as trade services (80%) and manufacturing (15%). However, more rural firms (19.8%) engage in
manufacturing than urban firms (10.1% in other urban and 7.2% in Dar es Salaam). Seventy-two per
cent of manufacturing MSMEs are in rural areas while 52 per cent of trade service MSMEs are in rural
areas. This is an expected pattern, as small manufacturing firms operate mainly in food processing,
which has strong links to agriculture. Without further information, however, it is not possible to
identify exactly what these linkages are and how they work. This is an important area for future
research. More firms are in the trade services in Dar es Salaam (87.6%) than in other urban (83.0%),
which is clearly driven by demand for tradable goods.
5.2. Self-reported motivations of small business owners
The MSME survey includes three questions designed to elicit the reasons for opening a business.
Responses to such self-reported motivations for a business could help us assess the extent to which
rural entrepreneurs are in business solely for the purposes of survival or aiming to grow. The responses
to these questions are tabulated using sample weights in Tables 8–10.
Table 6. Distribution of population and MSMEs (weighted, percentage)
Population MSME employment Number of business
All All MSMEs
Rural 67.4 51.6 52.1
Other urban 20.4 32.6 30.7
Dar Es Salaam 12.2 15.8 17.3
Total 100 100 100
Source: Population is from HBS (2012) and MSME employment and number are from MSME survey (2010).
Table 7. Sectoral distribution of rural and urban MSME firms in the survey (weighted, percentage)
Percentage of total in each location
Percentage in each sector
(National total by sector = 100)
Rural Other urban Dar es Salaam National Rural Other urban Dar es Salaam
Total 100 100 100 100 54.2 31.2 14.6
Manufacturing 19.8 10.1 7.2 14.9 71.9 21.1 7.1
Trade services 76.5 83.0 87.6 80.2 51.7 32.3 16.0
Others 3.8 6.9 5.2 4.9 41.1 43.5 15.4
Source: Authors’calculations using the MSME Survey 2010.
The role of rural enterprises in Tanzania’s transformation 845
The first question is: ‘What was your main occupation before you started this business?’As shown
in Table 8, the biggest difference between rural and urban entrepreneurs is that 56.5 per cent of rural
entrepreneurs report that their main occupation prior to starting the business was farming compared to
19.3 per cent in urban areas outside Dar es Salaam. Very few respondents (4.8%) in rural areas report
that they were unemployed prior to starting the business; this is not true in urban areas where 11.3 per
cent and 9.7 per cent of MSME owners in other urban and Dar es Salaam report that they were
unemployed before starting their business. Unlike in rural areas, urban business owners are much more
likely to report that they were previously employed in a private company or running a similar sized
business in another line of business. It is also much more common for urban business owners to report
that they were previously a housewife or homemaker (26.6% in other urban and 34.1% in Dar es
Salaam) than for rural respondents (12.3%).
The second question is: ‘For what reasons did you choose your line of business?’In Table 9, the
firms responding to this question are grouped into three broad sectors: manufacturing, trade services
and other services, by rural, other urban and Dar es Salaam. In rural areas, half of all business owners
say that the reason they chose their line of business is because they saw a market opportunity. This
response is similar for firms in manufacturing and trade services. However, this response is less
common in Dar es Salaam and other urban areas. The second most common reason for operating in a
line of business in rural areas is that the owners’capital could only finance that line of business; this
response is more common in urban than rural areas possibly indicating that capital constraints are more
severe in urban areas. The third most common reason for choosing a line of business in rural areas was
prior experience in that line of business, although shares for this reason are much lower than the two
The third question is: ‘If you were offered a full-time salary paying job, would you take it?
‘Responses to this question are reported in Table 10 and indicate that only 46.6 per cent of all small
business owners would leave their current business for a full time salaried position, but the share is
higher in rural areas (47.8%) and other urban areas (48.6%) than in Dar es Salaam (37.7%).
Approximately 64 per cent of all respondents who would prefer a full time salaried job say they
Table 8. Occupation prior to starting business of MSMEs (weighted, percentage)
All MSMEs Rural
Unemployed 4.8 11.3 9.7 7.6
Housewife (home maker) 12.3 26.6 34.1 20.0
In education, at various levels 3.2 5.5 5.4 4.2
Employed in large private enterprise in similar business 0.4 1.7 2.9 1.2
Employed in large private enterprise in a different business 1.7 4.7 6.9 3.4
Employed in a similar sized private business in the same line of
0.6 1.5 1.9 1.0
Employed in a similar sized private business in another line of business 0.6 0.8 3.0 1.0
Ran a similar sized business in the same line of business 0.9 2.0 1.9 1.4
Ran a similar sized enterprise in another line of business 9.5 16.4 20.0 13.2
Civil servant/employed by the government 1.8 2.1 6.0 2.5
I was employed by some individual 0.6 2.2 1.9 1.3
Rearing of cattle 0.5 0.4 0.2 0.4
Farming 56.5 19.3 0.7 36.7
I was selling food 0.6 1.4 1.1 0.9
Others 4.8 3.5 2.4 4.0
None 1.2 0.6 2.0 1.1
Notes: This table is prepared based on the question ‘what was your main occupation before you started this
business?’in the MSME survey, and a unique answer is provided by individual MSME owners. The sum of each
column in the table is 100.
Source: Authors’calculations using the MSME Survey 2010.
846 X. Diao et al.
Table 9. Reasons for business choice by broad sector in MSME survey (weighted, percentage)
Rural enterprise Other urban enterprises Enterprise in Dar es Salaam
Dar Es Salaam
I had previous experience in this line 25.0 15.2 18.3 39.6 15.5 19.5 37.0 9.7 18.5
Friends/relatives are in this line 20.6 13.4 14.8 21.0 19.4 17.8 13.2 16.4 12.8
I saw a market opportunity 48.2 51.6 50.0 36.3 43.1 41.6 14.6 46.4 39.2
My capital could only finance this
36.1 42.1 41.8 26.2 47.4 43.3 46.8 46.8 47.7
No apparent reason 2.9 6.0 4.5 4.3 2.9 4.2 3.2 5.2 4.7
I could start business gradually 0.0 0.1 0.1 0.0 0.1 0.5 0.0 1.0 0.6
Goods are easy to manufacture and
1.4 2.0 2.0 0.2 1.5 1.6 0.0 0.9 1.0
I just wanted to be near my house 0.8 1.0 0.9 0.0 1.8 1.2 0.0 0.0 0.4
I have been trained in it, I am an
1.6 0.4 0.6 6.2 0.1 1.1 9.4 0.4 0.9
Goods are available 0.3 0.4 0.5 0.0 0.4 0.2 0.0 2.7 1.4
I perceived it to be profitable 1.3 1.6 1.7 0.0 2.6 1.8 0.0 0.2 0.1
I liked it 0.7 1.0 1.3 3.5 1.5 1.6 1.8 1.1 1.1
Business does not have many
1.4 0.6 0.7 0.0 0.3 0.3 0.0 1.6 1.2
Other 1.0 2.5 2.2 5.7 2.4 3.1 1.3 3.2 2.4
None 0.3 1.3 0.8 2.0 0.9 1.2 1.3 0.0 1.1
Notes: Multiple answers are allowed for individual MSME owners.
Source: Authors’calculations using the MSME Survey 2010.
The role of rural enterprises in Tanzania’s transformation 847
would like to work for the government, with 68.6 per cent and 62.9 per cent in rural areas and other
urban areas respectively but only 44.6 per cent in Dar es Salaam, where more government jobs are
concentrated. The responses from rural and other urban MSME owners are consistent with results
reported in Banerjee and Duflo’s analysis of the economic lives of the poor (Banerjee & Duflo, 2007).
Large private companies are more attractive to small business owners in Dar es Salaam than in other
places. The predominant reason for preferring a full time salaried position is better security of income.
5.3. The productive heterogeneity of rural enterprises
The kernel densities of the log of value added per worker, which is defined as firms’labour
productivity, is used to examine the productive heterogeneity of MSMEs. Value added is computed
as the firm’s average monthly sales minus the firms’average monthly costs of production, and
seasonality is taken into consideration in the calculation. Only full-time employees (including owners
of the firms) are considered in calculating value-added per worker or labour productivity for individual
firms. The kernel densities of labour productivity reveal two important features of the MSME firms.
First, there is a significant degree of productive heterogeneity among both rural, urban and Dar es
Salaam enterprises. This can be seen by examining the density of the log of value added per worker in
Figure 1. Surprisingly, the distribution of the log of value added per worker or labour productivity for
rural firms is almost identical to the distribution for urban firms. In fact, stochastic dominance test
rejects the hypothesis that the rural and urban distributions are not identical.
One reason for this may
be the fact that medium sized enterprises that are mainly in urban areas appear to be under-sampled in
the MSME survey discussed in Section 2.
In Figure 1 the vertical lines represent average labour productivity in Tanzania’s economy in 2010 in
the agricultural sector (the far-left line), the trade services sector (the middle-line) and the manufactur-
ing sector (the far-right line). Economy-wide labour productivity is calculated using national accounts
data and census data; since 1997 national accounts data make every attempt to include the informal
Table 10. Job satisfaction in MSME survey (weighted, percentage)
All MSMEs Rural Other urban Dar es Salaam Total
If you were offered a full-time salary paying job, would you take it? 47.8 48.6 37.7 46.6
Who would you rather work for?
A large private company 17.9 27.4 43.1 24.0
Government 68.6 62.9 44.6 63.9
Someone else’s business 10.5 6.2 10.8 9.1
Anywhere 3.0 3.4 1.5 3.0
And why do you say that?
Better security of income 81.7 83.8 81.4 82.3
Shorter hours 5.1 5.7 3.5 5.1
Less risk 1.8 1.8 3.0 1.9
To get pension 1.5 1.6 . 1.4
I am less educated 2.2 0.9 1.9 1.8
They listen to the opinions of the employees 1.1 1.0 . 1.0
As long as I get a living 0.6 0.4 . 0.5
Job security 2.1 0.6 . 1.4
Others 2.2 2.3 9.9 3.1
None/Nothing 1.7 2.1 0.2 1.6
Notes: This table is prepared based on three questions: (1) ‘If you were offered a full-time salary paying job,
would you take it?’(2) ‘Who would you rather work for?’and (3) ‘Why do you say that?’A unique answer is
provided by individual MSME owners to each of the last two questions. Rural, urban and national total MSMEs,
MSMEs owned by youth and MSMEs owned by other adults for the sum of these two questions are 100
Source: Authors’calculations using the MSME Survey 2010.
848 X. Diao et al.
sector (GGDC, Africa Sector Database, 2015). However, in practice it is difficult to accurately
measure informal sector activity and so it is likely that economy-wide estimates of labour productivity
are biased toward the formal sector.
Figure 1 reveals that a little over half of the firms in the MSME sector have labour productivity
levels higher than the average labour productivity in the agricultural sector and this is true in all three
locations. This is not surprising and is consistent with evidence presented by Diao, Kweka, et al.
(2017), who show that labour productivity for many MSMEs is consistently higher than average
labour productivity in the agricultural sector. It is also true that around 25 per cent of rural and urban
MSMEs have labour productivity higher than average labour productivity in the services sector –a
sector most MSMEs belong to. In fact, as shown in Diao, Kweka, et al. (2017), this group of 25 per
cent of small firms accounts for 77 per cent of total value-added produced by the whole MSME sector.
In other words, the remaining 75 per cent of MSMEs account for less than 25 per cent of the value
added generated by the MSME sector. These results underscore the productive heterogeneity of
MSMEs in both rural and urban areas. They also raise the possibility of a growth strategy focused
on these most productive firms. This is not to say that the remaining firms should not be part of a
strategy for alleviating poverty, perhaps they should. Our point is that the productive heterogeneity
most likely calls for different strategies for different types of firms.
6. Using the MSME survey to identify ‘high potential’rural enterprises
If we accept that some rural MSMEs have more potential to contribute to rural transformation in
Tanzania than others, we are left with the question of how to identify those with potential. This is a
complicated problem not least because a properly designed mechanism should be immune from
manipulation. We do not pretend to solve it. Instead, what follows is meant to be illustrative of the
way in which we are thinking about the problem. We use a productivity cutoff to distinguish in-
between MSMEs from the rest of the MSMEs and then we look for readily observable characteristics
of these highly productive MSMEs that might be used for targeting. In practice, it would be important
to use readily observable characteristics that cannot be manipulated or are too costly to be manipulated
For the purposes of this exercise, we define the in-between firms as those with labour productivity
greater than economy-wide labour productivity in trade services. Using this criterion, we identify 1334
rural firms in the MSME sample that can be classified as belonging to the in-between sector. Having
Figure 1. The distribution of the log of value added per worker among MSMES in 2010 by location.
The role of rural enterprises in Tanzania’s transformation 849
selected our in-between firms, we use the GMM probit analysis previously described to identify
characteristics of in-between firms. We use a host of business and owner characteristics that have been
used and tested in the literature.
Prior to discussing the results of the GMM probit regressions, we must first identify the endogenous
variables and present the instruments used for the reduced form probit estimations. Three endogenous
variables were identified: the owner views the business as growing, the firm has regional customers, and
the number of daily customers is more than 20. The first two variables were instrumented using the following
variables: whether the business was located at home, whether the firm advertised, whether the firm had a
business plan, and whether the firm regularly sends or receives money. The model was run using a two-stage
GMM with robust standard errors. Results of the first-stage estimations are available upon request.
Tab l e 11 presents the GMM probit results of the increases in predicted probability (the average marginal
effect) of being an in-between sector firm in rural and urban areas. Results for the owners’personal
characteristics suggest that a female-headed business is associated with a decrease in the probability of
being in-between in both rural and urban areas; probabilities decrease by 6.4 and 9.5 per cent, respectively.
Owners that perceive their businesses to be growing observe gains in the predicted probability of being in-
between by 4.8 per cent and 6.4 per cent nationally and in rural areas, respectively. These results are
intuitive; business owners who are optimistic about their firm’s future and potential are more likely to be
driven to achieve success and to use resources productively. Being a member of a business association
increases the probability of being an in-between firm nationally and in urban areas, but not in rural areas,
possibly due to the low participation rate of rural firms in such associations. Education was not found to be
significant anywhere, likely due to lack of variation in education among business owners.
The second panel of Table 11 shows varying levels of significance between locations, which is
expected. A one-year increase in a firm’s age has a small effect on the probability of being an in-
between firm in the country as a whole and in rural areas, but not in urban areas. A one-unit increase in
the number of employees reduces the probability of being an in-between firm by around 5 per cent
consistently in both rural and urban areas. On the other hand, operating full time is associated with an
increase in the probability of being in-between by 6.2 per cent nationally and 6.7 per cent in rural
areas, but is not significant in urban areas possibly for two reasons. First, the survey is designed to
capture small businesses, of which many in urban areas may be part-time. Second, unless the
businesses are large enough, running a full-time small business in an urban area probably carries a
higher opportunity cost if it means not finding a job. Keeping written accounts is significant with an
increase in the predicted probability of being in between of around 6.5 per cent in all locations. There
are increases in the predicted probability of being in-between for firms which have licenses, with a
larger marginal effect in urban areas (6%) than in rural areas (3.4%). The increases in the predicted
probability of being in the in-between sector from having regional customers is significant only in
rural areas. However, firms that have a daily number of customers greater than 20 are between 7.4 per
cent (rural areas) and 6.2 per cent (urban areas) more likely to be in the in-between sector.
The variables associated with the external conditions of doing business, infrastructure and technol-
ogy, are presented in the third panel of Table 11. Using a mobile phone increases the predicted
probability of being in-between by 4.3 per cent in rural areas and 6.2 per cent in urban areas. Whether
the business uses electricity to light their businesses is important in rural areas, increasing the predicted
probability by 5.6 per cent, but not in urban areas, possibly again due to the lack of variability in
electricity access in urban areas. We also include three financing variables in the regression (the last
panel of Table 11), and all three variables are related to the ways business owners allocated their
profits. It turns out all three variables are insignificant, possibly due to lack of variability in these
variables, or they fail to accurately capture firms’investment behaviour.
7. Summary and policy implications
Although Tanzania remains heavily rural, the composition of economic activity in rural areas has
changed significantly over the past decade and a half. Between 2002 and 2012, the share of the rural
850 X. Diao et al.
Table 11. GMM probit results for probability of being in-between rural and urban enterprises –average marginal
Variable National Urban Rural
Education (completed secondary or higher) 0.00852 −0.00511 0.0261
(0.0220) (0.0302) (0.0307)
Female −0.0740*** −0.0954*** −0.0649***
(0.0148) (0.0276) (0.0161)
Owner would leave for salaried job −0.0330** −0.0484** −0.0206
(0.0139) (0.0241) (0.0145)
Owner is a Member of a Business Association 0.0777*** 0.120*** 0.00183
(0.0280) (0.0382) (0.0340)
Saw Business as a Market Opportunity 0.0198 0.0374 0.00821
(0.0137) (0.0239) (0.0144)
Views business as growing 0.0485*** 0.0326 0.0645***
(0.0149) (0.0270) (0.0154)
Firm’s age 0.00244** 0.00132 0.00309***
(0.00100) (0.00185) (0.00106)
Firm Size (number of employees) −0.0508*** −0.0550*** −0.0494***
(0.00990) (0.0124) (0.0172)
Business runs as Full time 0.0622*** 0.0438 0.0673***
(0.0187) (0.0332) (0.0211)
Firm keeps Written accounts 0.0643*** 0.0627** 0.0678***
(0.0154) (0.0271) (0.0160)
Firm has a License 0.0467*** 0.0600** 0.0337*
(0.0169) (0.0287) (0.0197)
Firm has Regional Customers 0.0188 −0.00993 0.0421**
(0.0176) (0.0285) (0.0197)
Number of daily customers is more than 20 0.0686*** 0.0622*** 0.0742***
(0.0138) (0.0241) (0.0146)
Firm’s suppliers are small traders 0.0151 0.0269 0.00865
(0.0139) (0.0245) (0.0150)
Firm’s Suppliers are Nationwide 0.0223 0.0523 −0.0514
(0.0302) (0.0390) (0.0496)
Infrastructure and technology
Owner using Mobile to Conduct Business 0.0529*** 0.0617** 0.0427***
(0.0157) (0.0282) (0.0156)
Fir Owner has a Calculator 0.0235 0.0370 0.00891
(0.0182) (0.0312) (0.0201)
Business uses Electricity to Light Business 0.0493*** 0.0433 0.0560**
(0.0187) (0.0275) (0.0233)
Owner uses profit to Expand Business 0.0204 0.0287 0.0194
(0.0178) (0.0329) (0.0173)
Owner uses profits to Buy Stocks in Advance 0.00483 −0.0100 0.0150
(0.0145) (0.0256) (0.0145)
Owners uses Profits to Invest in Buildings and Land 0.0291 0.0148 0.0360
(0.0262) (0.0498) (0.0289)
Observations 5,548 1,427 4,121
Notes: Standard errors in parentheses; ***p < 0.01, **p < 0.05, *p < 0.1. Dependent variable is a binary variable
which takes the value of one if the firm is in-between and zero otherwise. Firms in the ‘in-between’category
satisfy the following conditions: labour productivity is higher than economy-wide labour productivity in trade.
Source: Authors’estimation using MSME data.
The role of rural enterprises in Tanzania’s transformation 851
labour force working in nonfarm employment tripled going from 6.8 per cent to 20.5 per cent,
Moreover, in 2011/2012, more than one-third of rural households participated in the rural nonfarm
economy and 11.2 per cent of rural households reported that working members of the household had
primary employment only in the nonfarm economy.
The heads of ‘nonfarm only’rural households tend to be younger and more educated, while the
heads’gender does not appear to influence the likelihood of being a nonfarm household. Education of
the household head is also a determinant of the likelihood that a household participates in the nonfarm
sector; a primary education increases the probability of engaging in nonfarm activities by 5.8 per cent
and a secondary education increases the likelihood of engaging in the nonfarm sector by 16.9 per cent.
Among a set of selected community level variables, households in communities with access to daily
public transportation or a weekly market are more likely to participate in rural nonfarm activities.
Consistent with these results, we find that rural households with nonfarm activities are less likely to be
poor. However, it is still true that around 15 per cent of rural households whose primary source of
income is the nonfarm economy have incomes that place them below the poverty line. The implication
is that some nonfarm activities must be very unproductive. By extension, although these activities help
families to survive, it would be unrealistic to expect them to contribute significantly to rural
To explore the nature of the nonfarm businesses owned by rural households in Tanzania, we use
Tanzania’s first nationally representative survey of micro, small and medium sized enterprises.
Roughly 20 per cent of these businesses operate in the manufacturing sector –more than double the
share in urban areas –the rest of the businesses operate in the services sector. Labour productivity
among these businesses is extremely heterogeneous with roughly half having labour productivity
lower than average labour productivity in agriculture. Using a probit specification we show that
operating full time, keeping written accounts and using electricity to run the business are all positively
correlated with labour productivity.
We conclude that policies designed to stimulate rural transformation must take into account the
heterogeneity of the rural nonfarm sector. Unless this heterogeneity is understood, policies designed to
stimulate rural transformation are likely to disappoint. Of course, rural nonfarm activities help to
generate income and reduce the risks associated with agricultural production for many rural house-
holds. These activities should be supported as part of a poverty reduction strategy. But we should not
expect the large majority of these activities to transform rural livelihoods. For this to happen, it will be
important to target the firms with the potential for employment and labour productivity growth.
This work was undertaken as part of the CGIAR Research Program on Policies, Institutions, and
Markets (PIM) led by IFPRI. Funding for the study was provided by PIM. We are grateful to two
anonymous reviewers for useful comments on previous versions of the paper, and to Peixun Fang
and Jed Silver for excellent research assistance. Finally, Xinshen and Margaret would like to
dedicate this article to the memory of Eduardo Magalhaes who sadly and prematurely passed
away in August 2017.
This paper has benefited from funding from the CGIAR Research Program on Policies, Institutions,
and Markets (PIM) led by IFPRI.
No potential conflict of interest was reported by the authors.
852 X. Diao et al.
1. Although other nationally representative surveys of MSMEs exist, the data collected in these surveys is minimal and do not
allow for the calculation of labour productivity.
2. For example, the nationally representative GEMINI surveys conducted in the early 1990s and funded by the United States
Agency for International Development in a handful of African countries only collected location and employment data for the
nationally representative sample. For a much smaller group of around 250 firms per country, more detailed information was
collected but still not enough information to calculate value added and thus labour productivity.
3. For example, the World Bank Enterprise Survey conducted in Tanzania in January 2013–August 2014 interviewed 813 firms’
owners and top managers including 514 small firms.
4. The survey used in Jin and Deininger (2009) covers only rural enterprises (1239) and rural households (1610) in Tanzania and
cannot be used for a comparison to urban enterprises.
5. BRELA is Tanzania’s Business Registrations and Licensing Agency. It is a Government Executive Agency and was
established on the 28 October 1999. The aim of the agency is to ensure that businesses operate in accordance with regulations
and to ensure that businesses follow ‘sound principles’.
6. See the definition in the notes of Table 3.
7. Stata estimates these models using the ivprobit.
8. A stochastic dominance test compares the cumulative density function of the log of the valued added in rural and urban areas.
To establish whether the two curves originate from the same distribution, we have used the Komolgorov-Smirnov test of
equality of distribution. We were not able to reject the null hypothesis that the distributions of urban and rural value added
were the same (p-value = 0.9).
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Table A1. Summary statistics of main variables of 2012 HBS used in the regression –rural
Variable Number of HHs or EAs Means SE LL UL
Youth headed (between ages 15–34) 4,130 0.268 0.010 0.249 0.287
Female headed 4,130 0.241 0.009 0.222 0.259
No Primary Education 4,130 0.270 0.013 0.243 0.296
Completed Primary Education 4,130 0.673 0.012 0.649 0.697
Completed Secondary Education or more 4,130 0.058 0.008 0.043 0.073
Number of Youth (15–34) 4,130 1.577 0.051 1.477 1.677
No Cultivated Land 4,130 0.053 0.009 0.034 0.072
0-2ha of cultivated land 4,130 0.565 0.019 0.528 0.602
2–5 ha of cultivated land 4,130 0.296 0.015 0.267 0.326
>5 ha of cultivated land 4,130 0.086 0.010 0.066 0.105
Household paid loans to bank or family friends in past year 4,130 0.023 0.005 0.013 0.032
Public Transportation to Regional HQ in EA 157 0.756 0.038 0.682 0.830
Electricity in the EA 157 0.238 0.037 0.165 0.311
Mobile Signal in the EA 157 0.821 0.034 0.753 0.889
Internet in the EA 157 0.086 0.024 0.039 0.133
Bank in the EA 157 0.024 0.012 0.000 0.047
Cooperative Primary Society in the EA 157 0.381 0.042 0.298 0.464
Informal Financial Service in the EA 157 0.450 0.043 0.366 0.535
Major Employer (that is business, factory) in the EA 157 0.161 0.032 0.098 0.225
Weekly Market in the EA 157 0.295 0.040 0.217 0.373
Notes: The estimates account for survey sampling design.
Source: Authors’calculation using data of 2012 HBS.
Table A2. Summary statistics of main variables of 2012 HBS used in the regression –urban
Number of HHs or
EAs Means SE LL UL
Youth headed (between ages 15–34) 6,056 0.343 0.010 0.324 0.362
Female headed 6,056 0.249 0.009 0.231 0.267
No Primary Education 6,056 0.084 0.007 0.071 0.098
Completed Primary Education 6,056 0.632 0.014 0.605 0.659
Completed Secondary Education or more 6,056 0.283 0.017 0.251 0.316
Number of Youth (15–34) 6,056 1.747 0.042 1.663 1.831
No Cultivated Land 6,056 0.702 0.034 0.635 0.769
0-2ha of cultivated land 6,056 0.217 0.024 0.170 0.265
2–5 ha of cultivated land 6,056 0.054 0.010 0.034 0.074
>5 ha of cultivated land 6,056 0.026 0.011 0.004 0.049
Household paid loans to bank or family friends in past
6,056 0.039 0.006 0.027 0.052
Public Transportation to Regional HQ in EA 223 0.943 0.025 0.894 0.992
Electricity in the EA 223 0.852 0.044 0.764 0.939
Mobile Signal in the EA 223 0.846 0.031 0.786 0.907
Internet in the EA 223 0.292 0.037 0.219 0.366
Bank in the EA 223 0.155 0.024 0.108 0.203
Cooperative Primary Society in the EA 223 0.188 0.033 0.124 0.252
Informal Financial Service in the EA 223 0.650 0.040 0.572 0.729
Major Employer (i.e. business, factory) in the EA 223 0.416 0.044 0.329 0.503
Weekly Market in the EA 223 0.207 0.039 0.129 0.284
Notes: The estimates account for survey sampling design.
Source: Authors’calculation using data of 2012 HBS.
The role of rural enterprises in Tanzania’s transformation 855