0 THE IMPACT OF MARKETING (VERSUS FINANCE) SKILLS ON FIRM PERFORMANCE:
EVIDENCE FROM A RANDOMIZED CONTROLLED TRIAL IN SOUTH AFRICA
This version:
30 September 2014
Stephen Anderson-Macdonald
London Business School
(Job Market Paper)
Abstract: This research seeks to address a significant constraint to performance among businesses in
emerging markets: marketing skills. Improvements in marketing skills offer the possibility of increased
growth and prosperity, however, there exists substantial evidence that it is not abundant among small
businesses. We present evidence from the first randomized controlled trial to examine the impact of
marketing skills, relative to finance skills, on firm performance. The empirical setting of the study is among
small business owners in urban and slum neighborhoods across Cape Town, South Africa. We offer
intensive marketing and sales training to one randomly selected group of firm owners, intensive finance and
accounting training to another randomly selected group of firm owners, and no training to a control group.
For the next eighteen months, we measure the effects of the interventions on the practices and performance
of these small businesses. Our findings are threefold. One, marketing skills and finance skills each have a
positive and significant effect on firm performance, including increases in: survival, employment, sales, and
profits. Two, the pathway to profits differs for marketing relative to finance: profit effects are roughly equal
across the two interventions, yet entrepreneurs who receive marketing training tend to achieve these gains by
increasing sales and hiring more staff (i.e. growth focus) while those who receive the finance training tend to
enhance profits by decreasing costs (i.e. efficiency focus). Three, the returns to business skills training differ
depending on individual characteristics. Consistent with a ‘growth focus’ explanation, marketing/sales
training appears to be most beneficial to small business owners who (ex ante) have been less exposed to
different business contexts. By contrast, and in line with an ‘efficiency focus’ explanation, entrepreneurs
who have been running more established businesses (prior to training) tend to benefit more from
finance/accounting skills.
Keywords: marketing and sales skills, growth focus, finance and accounting skills, efficiency focus,
business training, firm performance, job creation, economic growth, emerging markets, small business,
entrepreneurship, randomized controlled trial, heterogeneous treatment effects
1 What is the impact of marketing skills on business growth, prosperity and survival? Why do some
individuals and firms benefit more from marketing skills than others do? Answers to questions such as these
are important but scarce in the scholarly literature. They are important because they address something
fundamental to the field: the role of marketing in business and society (see Kotler and Levy 1969; Lehmann
et al. 2011; Lilien 2011; Reibstein, Day, and Wind 2009; Sheth 2011; Wilkie and Moore 1999; Winer 2000).
They are scarce because these questions are difficult. The effects of marketing exist among a cacophony of
other effects; isolating marketing’s voice and identifying its ultimate impact is not an easy task (Day and
Montgomery 1999; McAlister 2005).
This paper seeks to offer some initial answers to the questions noted above. We study the impact of
marketing among the most prevalent type of firm in the world: small businesses in emerging markets. Our
study represents the first randomized controlled trial of the economic impact of marketing skills. Our
conceptual arguments and empirical results suggest that improvements in marketing skills have large and
potentially transformative effects on small businesses. Moreover, we argue and show that improvements in
marketing skills offer a different pathway to profits than improvements in finance skills. Finally, we note
that the effects of improved marketing skills are not uniformly distributed among small businesses; we then
examine why some benefit more from an infusion of marketing skills than others do.
Small businesses, the majority of which are micro businesses with less than five employees (de Mel
et al. 2008), are especially common – and their role is especially crucial – in emerging markets. Compare,
for example, the size of the typical manufacturing firm in the US and in India. The average manufacturing
firm in the US has 45 employees; its counterpart in India has only 2.6 employees (Hsieh and Klenow 2014).
Not only are emerging market firms much smaller on average than those in rich countries, they also tend to
experience stunted growth. In the US, the average 40 year old manufacturing firm grows to have more than
7 times as many employees when compared to the average firm that is less than five years old (Hsieh and
Klenow 2014). In Mexico by contrast, the average 40 year old manufacturing firm has only twice as many
employees when compared to the average firm that is less than five years old. In India, the average 40 year
old manufacturing firm is no larger than the average firm that is less than five years old (Hsieh and Klenow
2
2014, p. 6-7; Jensen and Miller 2014). Although exceptions do exist, similarly dismal rates of growth and
profitability among small businesses in emerging markets have been documented across industry sectors and
across many countries (see Hsieh and Olken 2014; Nichter and Goldmark 1999; Tybout 2000).
The growth, prosperity and survival of small businesses matters in a rather obvious and personal
manner to the business owners themselves, and to the economies they operate in. They also matter to
marketing executives in much larger firms much farther afield. Self-employment rates (typically as business
owners) in emerging markets range from around 30% to 50% (and sometimes more, as in the case of India,
where the figure is 52%) – far higher than in advanced markets such as the US, where the self-employment
rate is less than 8% (National Sample Survey Office 2013; Parker 2004). As multinationals look to
emerging markets for new sources of growth, they discover that their fates are in many ways intertwined
with the fates of millions of tiny firms and business owners whom they have to rely on as customers,
suppliers, and distributors (Prahalad 2005; Viswanathan, Rosa, and Ruth 2007). In these markets, small
businesses are distributors for larger firms; they ensure that others’ goods reach otherwise hard to reach
markets (Viswanathan, Rosa, and Ruth 2010). For example, most of Nestle’s cocoa suppliers are tiny
businesses, as are most of its distributors in emerging markets. Procter and Gamble’s global distribution
channel includes over 20 million small businesses, mostly in emerging markets (Philippe 2014). A
consequence of this fact is that the much-discussed fortune at the bottom of the pyramid is unlikely to fully
manifest itself unless small business owners see improvements in their own prosperity. Even managers with
no interest in the human side of the lives of small businesses owners will recognize that their own ability to
operate efficiently and effectively in emerging markets is severely constrained if their potential customers,
suppliers, and distributors lead a precarious existence.
Not surprisingly given the scale and severity of the problem of stunted growth and prosperity among
small businesses, many scholars, many policy makers and NGOs, and indeed many corporations have sought
explanations and solutions to this problem. Adam Smith (1776) himself noted the importance of the butcher,
the baker, and the brewer to explain the workings of economies. Prominent explanations for the relatively
poor performance of small businesses in emerging markets are: (i) poor institutions (e.g. property rights, rule
3
of law); (ii) poor access to information (e.g. about government programs, markets); (iii) poor access to
finance (e.g. loans, equity financing); (iv) poor human capital (e.g. education, health); and, in more recent
years, (v) poor management skills (e.g. in operations, human resources, finance), (for extensive reviews, see
Acemoglu, Johnson, and Robinson 2005; Beck and Demirguc-Kunt 2006; Bloom and Van Reenen 2010;
Bruhn, Karlan, and Schoar 2010; Duflo and Banerjee 2012; Hsieh and Olken 2014; Jensen 1998; Karlan and
Appel 2012; Nichter and Goldmark 1999; Tybout 2000; Sachs 2006). The role of marketing skills in
promoting growth, prosperity and survival among small businesses in emerging markets is strikingly
underexplored (also see Ingenbleek, Tessema and van Trijp 2013; Viswanathan, Rosa and Ruth 2007).
Many marketing scholars may believe in their hearts that improvements in marketing skills should
by all reckoning lead to improvements in the performance of small businesses in emerging markets, just as
with any other types of businesses in any other market. But translating this belief into concerted action by
practitioners and policy makers requires rigorous evidence that identifies the causal effects of marketing
skills, after isolating the effects of all the other effects that exist in the noisy environment in which small
businesses in emerging markets exist. Such evidence is not easily available. In cross-sectional studies, for
example, endogeneity due to omitted variables can confound the researcher’s ability to quantify effects (see
Shugan 2004). Self-selection by business owners into formal or informal training programs can similarly
cause biased estimates of effects. And reverse causality concerns can preclude directional conclusions about
the impact of marketing skills (e.g. do greater prosperity and better performance offer a business owner the
luxury of taking marketing skills training courses?).
It is also worth noting that not all experts share the marketer’s belief in the power of marketing skills
to transform small businesses. No less a luminary than the Nobel Laureate Muhammad Yunus has referred
to skills development efforts for small business owners as a “waste of time” (Yunus 1999). The relative
popularity of financial skills development efforts among NGOs and governments suggests that they (at least
implicitly) deem financial skills to be more powerful drivers of small business performance than marketing
skills. A proper test of the effect of marketing skills therefore requires a comparison of the effects of
marketing skills relative to other skills (such as finance and accounting skills) that are currently (implicitly or
4
explicitly) deemed important for small business performance. Additionally, it requires an understanding of
the process (or pathways) through which improvements in business skills affect firm performance, and an
understanding of the contingencies when marketing skills (or finance skills) might matter more.
In this research, we make an initial attempt at addressing each of these requirements. We first test
whether an improvement in marketing and sales skills via a marketing training program impacts an emerging
market entrepreneur’s growth, prosperity and survival. Next, we compare the process through which
business skills influence performance for entrepreneurs who receive marketing and sales training versus
those who get finance and accounting training. Finally, we examine if the marginal effects of marketing
skills vary depending on an entrepreneur’s prior level of exposure to different business contexts, as well as if
the effects of finance skills vary depending on whether a firm owner is running a more established business.
From a substantive perspective, the results of this paper matter to managers and policy makers as
they offer the first formal evidence that marketing skills training can influence small business growth,
prosperity and survival. Indeed, to the best of our knowledge, this is the first study involving skills training
for small businesses to detect significant main effects on firm performance (i.e. average treatment effects).1
For the typical small business owner assigned to our marketing/sales training program, the average changes
in firm performance included: 9.7% higher probability of survival (i.e. greater chance of being in operation
after eighteen months); 0.95 increase in employees (62% gain relative to the control group); Rand 9,350
increase in sales per month (69% gain); and Rand 3,038 increase in profits per month (86% gain). Likewise,
in the case of the finance/accounting training program there were also positive returns to a small business
owner: 12.7% higher probability of survival; 0.53 increase in employees (33% gain); Rand 5,697 increase in
sales per month (39% gain); and Rand 2,590 increase in profits per month (75% gain). These effects are not
only statistically significant, but also substantively important. For example, a Rand 3,000 increase in
monthly profits equates to roughly the same salary earned by one full-time employee in a regular job with a
large corporate (e.g. KFC, Shoprite, etc.).
1
Throughout the paper, we only report the more conservative intention-to-treat (ITT) estimates which represent the
treatment effects averaged over all participants randomly assigned into a treatment group (regardless of whether they
attended the training program or not) compared against a control group.
5
From a theoretical perspective, the paper examines the mechanism of change in two distinct ways:
how and who. First, we introduce the concept of ‘pathway to profits’ to the literature and use it to explain
how different business training programs impact firm performance. Pathway to profits refers to the channels
through which small businesses can increase profits. Since we isolate (for the first time in the literature) the
dimensions of business training into marketing skills and finance skills, we are able to examine the effects of
each one independently. While we find that both training programs increase profits by roughly the same
amount, these outcomes seem to be achieved in different ways. Small business owners in the marketing
training program tend to take a ‘growth’ focus: they implement policies and practices related to increasing
overall sales and employees. On the other hand, those in the finance/accounting program tend to take an
‘efficiency’ focus: they adopt policies and practices linked to reducing costs and effectively managing
finances. Importantly for policy makers who seek employment-led growth, those in the marketing/sales
program hire almost twice as many employees as those in the finance/accounting program, and still achieve
equivalent levels of profitability. Second, we extend the marketing literature by exploring a new construct,
called ‘narrow exposure’, and using it to explain who may benefit more from marketing skills. We define
exposure as the variety of market contexts in which a business owner has had experience. Small business
owners, particularly in emerging markets, vary greatly in their level of exposure to business contexts that are
novel or different to what they are familiar with. We find that small business owners with narrow exposure
(versus broad exposure) tend to do better when they receive the marketing/sales program. Participating in a
training program that builds marketing skills appears to help individuals overcome potential exposure
deficits by encouraging them to look beyond their existing business context and to develop new perspectives
on products, customers, distributors and suppliers. In addition, firm owners vary in the extent to which their
businesses are established and have reached sufficient scale (e.g. formally registered, larger, more permanent
structure, etc.). Thus, developing finance/accounting skills may be especially worthwhile for firm owners
operating more established businesses as there exists greater opportunity for applying the skills to reduce
costs and increase efficiencies in the business.
6
From an empirical perspective, this study represents the first randomized controlled trial (RCT) to
explicitly evaluate the impact of marketing skills on the performance of businesses. The randomized
controlled trial research design is generally considered the gold standard for identifying causal effects in
field settings (Angrist and Pischke 2009; Imbens and Angrist 1994). It is designed to address some of the
empirical challenges noted earlier. This is because the randomization of firms into treatment and control
groups allows us to explicitly address endogeneity, self-selection, and reverse causality problems (e.g. de
Mel et al. 2008). By recruiting our own sample of small businesses and implementing primary data
collection methods to measure firm performance (among other variables) we are able to construct a novel
panel dataset from scratch. Further, combining these sampling and measurement steps with an RCT design
allowed us to cleanly test a set of hypotheses that would otherwise be near impossible to study. This paper
therefore adds to the literature on quantitative approaches to marketing strategy and inter-disciplinary
research in marketing (see Chintagunta et al. 2013; Desai 2011; Mela 2013).
THEORY AND HYPOTHESES
Traditionally, as we noted earlier, the literature on the growth challenges faced by small businesses
in emerging markets has focused on the constraints they face in terms of the quality and extent of institutions,
information, finance and human capital available to them. Among firm-level interventions (in contrast to
macro-level interventions around institutions, infrastructure, health and education) designed to improve
small business performance, particular attention has been given to enhancing financial capital (e.g. credit,
savings, cash or in-kind) and its impact on growth and prosperity (Banerjee and Duflo 2008, 2011; Bloom et
al. 2010; de Mel, McKenzie and Woodruff 2008; Dupas and Robinson 2012; Dupas et al 2012; McKenzie
and Woodruff 2008). However, after two decades of government and NGO efforts, the empirical evidence
on micro-finance solutions suggests they might not be the miracle pills (at least not in isolation) that can help
small businesses to scale-up their operations and transition into larger firms (Banerjee et al. 2010; Schoar
2010). Thus, researchers are now questioning what other forms of “capital” are missing in emerging markets
(Bruhn, Karlan and Schoar 2010).
7
What is the impact of business skills training on firm performance?
One recent suggestion for addressing this missing capital issue might, in retrospect, seem obvious:
provide business skills training that can enhance managerial capital, which we define as the skills associated
with the management of customers, money, operations and people within businesses (cf. Bloom et al 2010;
Bruhn, Karlan and Schoar 2010; Bruhn and Zia 2011). Managerial capital has been recently postulated as an
important complement to growth theory (Solow 1956). Specifically, Bruhn, Karlan and Schoar (2010)
theorize that managerial capital can increase output by working as an intercept shifter, A, in the firm’s
production function2: Y = A * [ (Kα) * (L(1–α)) ].
Managerial capital has been shown to be important in advanced markets, and also to be lacking
among firms in emerging markets (Bertrand and Schoar 2003; Bennedson et al. 2010; Bloom et al. 2010,
2013; Bloom and van Reenen 2010; Hambrick and Mason 1984). Yet surprisingly, there has been little
empirical research on the effects of improvements in managerial capital. Importantly, no study to the best of
our knowledge has examined – conceptually or empirically – the effects of marketing skills (or ‘marketing
capital’). Based on our field interviews,3 as well as the work of other marketing researchers (e.g.
Viswanathan Rosa and Ruth 2010), there exists descriptive evidence that ‘marketing capital’ is not abundant
among small businesses in emerging markets. Indeed, many small business owners have such few skills that
fundamental aspects of their customer targeting approach, product line choice, and pricing are demonstrably
sub-optimal (Hassan, Prabhu, Chandy and Narasimhan 2014).
Bruhn, Karlan, and Schoar (2010) propose two ways through which improved managerial capital
can lead to increased firm performance. Their ‘utilization’ argument suggests that managerial capital can
increase the marginal productivity of other inputs, such as increasing the efficiency of financial capital
investments or enhancing the motivation of employees. Their ‘allocation’ argument predicts that managerial
capital can lead to better strategic planning regarding inputs, including the type, amount and timing of capital
2
In a standard Cobb-Douglas production function, terms are defined as follows: Y= total output; L= labour input; K=
capital input; A= total factor productivity; α= output elasticity of capital; and (1-α)= output elasticity of labour.
3
Over the past several years, we have conducted over 500 interviews with small business owners in a variety of
emerging market contexts, including Brazil, Egypt, Ghana, India, Kenya, Rwanda, South Africa, and Uganda.
8
or labor used in firm activities. Thus, given these synergistic effects, increasing the managerial capital of
emerging market entrepreneurs can lead to expansion, productivity gains and efficiency enhancements in
small businesses. Based on this set of arguments, we provide the following hypothesis regarding the impact
of business training (marketing skills or finance skills) on changes in firm performance.
H1:
Business owners with higher managerial capital (marketing skills or financial skills) will
increase firm (a) survival, (b) employees, (c) sales, and (d) profits more than those with
lower managerial capital.
How do business skills influence the ‘pathway to profits’?
Further, given a business owner’s attention and actions likely differ for developing and executing on
marketing/sales skills, compared to finance/accounting skills, we propose that the pathway to profits for a
small business owner who receives marketing training will be different to that of an entrepreneur who
receives finance training. By pathway to profits we are referring to the channel through which a small
business owner increases her firm’s net income (or money ‘leftover’ after paying all expenses). Extending
the conceptual logic of Bruhn, Karlan and Schoar (2010), we propose that the intercept shifter, A, be
separated into two components: (i) growth focus, and (ii) efficiency focus.
In this way, we can view some utilization activities (e.g. changing sales staff incentives, expanding a
retail channel, or building new products from existing materials) and certain allocation activities (e.g.
planning how to adjust product lines, evaluating sources of competitive differentiation, or determining when
to target different customer segments with promotions) as being more focused on growth as a channel to
increase profits. A growth emphasis may involve firm policies linked with revenue expansion, which is
likely to have a positive effect on firm performance (Rust, Moorman and Dickson 2002). Focusing on firm
growth can also lead to investment in approaches that promote market research and the identification of new
product offerings and market contexts. Further, such an emphasis likely involves implementation of
different marketing activities and sales tactics aimed at attracting new customers or differentiating from
competitors. In addition, encouraging greater focus on top line growth (engendered by marketing skills
training) will likely highlight to the business owner the value of additional help in achieving sales goals and,
thus, lead to her hiring new employees. Taken together, we argue that having a growth focus will encourage
9
business owners to scale up sales and employees and, in turn, lead to gains in profits. These types of growth
oriented policies and practices are also closely linked to the skills one builds through training in marketing
and sales. Based on this logic, we provide the following hypothesis.
H2:
Business owners with higher ‘marketing’ managerial capital will increase firm profits by
implementing more growth focused policies and practices than other business owners.
By contrast, there exist different utilization activities (e.g. tracking the cost of goods, managing cash
flow, or purchasing supplies more effectively) and different allocation activities (e.g. separating personal and
business investments, using equipment at optimal periods to reduce costs, or shifting staff resources to
minimize expenses) that are more closely related to the skills developed during finance/accounting training.
Given their ‘cost and control’ emphasis, implementing these types of finance and accounting activities are
likely to have a direct impact on raising profits. Indeed, focusing on such throughput activities can increase
firm policies aimed at reducing costs or raising the output-to-input ratio, which in turn yield efficiency gains
(Hambrick and Mason 1984). A focus on efficiency (engendered by finance skills training) is also likely to
encourage greater implementation of firm practices related to tracking, analyzing and planning finances. In
this way, the channel through which a small business increases profits may differ depending on whether an
entrepreneur receives finance training (i.e. increased efficiency as the pathway to profits) or marketing
training (i.e. increased growth as the pathway to profits). Following this line of reasoning, we propose our
next hypothesis.
H3:
Business owners with higher ‘financial’ managerial capital will increase firm profits by
implementing more efficiency focused policies and practices than other business owners.
Who benefits more from business skills training?
There is most likely heterogeneity in the extent to which firm owners benefit from business skills
training, including individual level factors that make emphasizing a growth focus more applicable or other
situations when focusing on efficiency is particularly effective. First, we consider who might benefit more
from marketing training. It is quite common, especially in emerging markets, for individuals to start firms
because they cannot find jobs in the formal sector (Schoar 2010; Tokman 2007). Given their small,
uncertain and volatile incomes, most of these business owners are narrowly focused on basic survival – as
10
opposed to growth or expansion (Collins et al. 2009). Further, either because of mobility barriers (social and
geographic) or chronically limited resources (money and time), the majority of these small business owners
has rarely been exposed to novel business contexts. Thus, one factor that may lead some entrepreneurs to
realize greater returns from marketing training is their (lack of) prior experience with different business
contexts or markets. We propose that having a lack of exposure can limit the extent to which a small
business owner sees issues from others’ perspectives or views familiar situations through a different lens.
For example, a casual observer walking down the bustling retail streets of Chennai, Accra or Cape Town
will quickly notice row-upon-row of very similar shops selling the same merchandise to the same set of
customers – and they might ask: Why not do something different? The answer, for most of these business
owners, might be that they do not know what ‘different’ represents. That is, they have never had the
opportunity to leave their current milieu for great lengths of time or for great distances to learn that their
familiar surroundings (and approaches to business) are different from those experienced (and implemented)
by others. Or perhaps they’ve not had the chance to interact with people from different backgrounds and
understand that preferences might vary across customer types. Or maybe they’ve not held a variety of
professional experiences to learn that one could develop competitive advantages to stimulate growth by
sourcing unique or cheaper products from different suppliers.
We refer to this deficit in one’s experiences with different business contexts as narrow exposure.
More generally, we define exposure as the variety of market contexts in which a business owner has had
experience. Thus, the extent to which an entrepreneur experiences narrow (versus broad) exposure is a
function of his or her personal and professional background. We argue it is for those with narrow exposure
that marketing training may have a greater impact on business profits. Participating in a training program
that builds marketing skills can help individuals overcome potential exposure deficits. For instance, when a
business owner has only had a limited number of (different) experiences it is likely she will not have been
exposed to contexts, practices, and opportunities that fall outside the realm of her current business context.
Marketing skills training encourages business owners to put themselves “in someone else’s shoes” (e.g.
customers) and look beyond their own context, inducing more open-minded inquiry about market
11
information from multiple sources (Day 1994; Day and Schoemaker 2005). Developing broader
perspectives on customers, competitors, distributors and suppliers (via marketing training) can also lead to
greater external focus and attention to a wider set of activities and opportunities happening at the periphery
of the firm (Chattopadhyay, Glick and Huber 2001). Moreover, a lack of exposure to different geographical
locations and business sectors can decrease the likelihood a business owner participates in information
discovery activities that improve association skills, such as: questioning the status quo, observing new
behaviors, experimenting, and interacting with people from different backgrounds (Dyer, Gregersen and
Christensen 2009). However, by broadening exposure and introducing diverse views, marketing training can
provide entrepreneurs with mental models of ‘how things are done elsewhere’, new perspectives for
approaching problems, access to novel ideas and concepts, and the psychological readiness to accept ideas
from unfamiliar sources (Maddux and Galinsky 2009; Schooler and Melcher 1995). In sum, we expect
marketing and sales training to help business owners with an exposure deficit by encouraging them to look
outside their existing business context (i.e. shifting attention) and to develop new perspectives on managing
products, customers, competitors, distributors and suppliers (i.e. expanding associations). Based on these
arguments, we propose our next hypothesis.
H4:
Business owners with higher ‘marketing’ managerial capital will increase firm profits to a
greater extent when these owners also have narrow exposure.
Second, we consider who might benefit more from finance training and its ‘efficiency’ focused
policies and practices. The reality for most emerging market firms is that few manage to scale up into larger
businesses, formalize processes, operate out of more permanent structures or register with the government
(Hsieh and Klenow 2014; Schoar 2010). These firm owners vary in the extent to which they are running
established businesses and, thus, their opportunities to enhance performance by emphasizing efficiency
improvements (as encouraged in finance training). We define being established as the extent to which a
business owner has been operating her current business in a more permanent manner. We argue it is for firm
owners running more established businesses that finance and accounting training may have a greater impact
on profits. For one, there must be some threshold level of sales coming ‘in’ to the business before the owner
12
can learn how to manage this money more effectively (through finance training). Likewise, reaching a
sufficient scale of operations may be required before an efficiency focus is particularly valuable. Increased
size and structure provides greater potential for improvements in reducing costs, managing inventory, and
allocating inputs optimally. Indeed, there is research on medium and large sized firms in emerging markets
that suggests performance can be enhanced when professional consultants intervene (from the outside) to
improve operational efficiency, such as by reducing quality defects, machine downtime or inventory wastage
(Bloom et al. 2013; Bruhn, Karlan and Schoar 2012). It is therefore likely that by developing the finance
and accounting skills of individual firm owners, they can intervene in their own businesses (from the inside)
to implement policies and practices aimed at decreasing costs and increasing control. In sum, we propose
that building finance skills will be particularly useful to firm owners operating more established businesses.
These firms will have reached a minimum threshold in terms of sales or scale, and so the finance and
accounting skills developed by the owner can actually be put into practice to reduce costs and increase
efficiencies in the business. Based on this logic, we provide our final hypothesis.
H5:
Business owners with higher ‘financial’ managerial capital will increase firm profits to a
greater extent when these owners are also operating more established firms.
METHODOLOGY
Empirical Approach
There is very little research (to date) that examines the impact of marketing interventions on the
performance of small businesses in emerging markets. Data collection and measurement issues aside, this
lack of empirical work on small business growth may in part be due to endogeneity concerns that make it
difficult to isolate the effects of ‘marketing’ on changes in firm sales or profits. In the case of marketing
skills training there are three such concerns. First, omitted variables may confound the cause-effect
relationship of interest. Even if we could recruit a sample of emerging market businesses and then observed
some owners to be high in marketing skills and also high on firm performance, we could not claim this was
due to the influence of marketing. An unobserved factor, such as ability or access to money, could be
driving the positive correlation. Second, reverse causality issues loom large. In the above example, we
13
could not confirm that the owner’s marketing skills were accumulated before her firm increased its
performance. For instance, acquiring high marketing skills may require an entrepreneur to invest a lot of
time and possibly money, but these resources might only be available to a small business owner after her
firm reaches a certain threshold of performance. As such, the direction of the effect is ambiguous: higher
marketing skills could drive performance; or increased performance could lead to greater development of
marketing skills. Third, self-selection can bias interpretation of causal effects. Business owners who choose
to participate in a marketing training program may be quite different from those who do not participate. For
instance, they may differ in their interests regarding customers and products, or they could have a priori
expectations about their returns to training given unique personal or business circumstances. In either case,
they may self-select into a marketing training program and, in turn, the program could increase their
marketing skills and firm performance differently than business owners who did not choose to take such
training. These unobserved factors (e.g. personal interest, firm characteristics, expected returns) could
influence a business owner’s decision to participate in a market training program. However, since these
factors are unknown to the researcher (and therefore cannot be accounted for in the estimation model) it may
result in endogeneity problems. In order to overcome this type of selection bias, we need to construct a valid
counter-factual (i.e. we need to know what “would have” happened to the same business owner had she not
attended a business training program) and then compare changes in firm performance under each scenario.
Taken together, these three empirical challenges make it difficult to cleanly test the effects of
marketing interventions on the performance of small businesses. Fortunately, there is a solution. Given its
ability to address critical identification problems, the randomized controlled trial (RCT) methodology is
generally considered the gold standard for identifying causal effects in field settings (Angrist and Pischke
2009; Imbens and Angrist 1994). This is because the randomization of firms into treatment and control
groups allows one to address endogeneity concerns such as omitted variables that may confound the causeeffect relationship of interest, reverse causality issues that preclude directional conclusions, or firm owner
self-selection that can bias outcomes (e.g. de Mel et al. 2008).
14
Thus, to test our hypotheses we implement a randomized control trial (RCT) with small businesses
as the unit of analysis. Our RCT consists of five parts: (i) sample recruitment; (ii) pre-treatment
measurement of outcome and control variables for all firms in sample; (iii) random assignment of firms into
either a treatment group or a control group; (iv) implementation of the training intervention which is offered
only to treatment firms; and (v) post-treatment measurement of outcome and control variables for all firms in
sample. In contrast to other firm studies that rely on secondary data sources, our empirical approach allows
us to conduct a field study with hundreds of real firms, collect detailed performance data on small businesses
in emerging markets, construct a valid counterfactual group, and manipulate the main theoretical variable of
interest (e.g. managerial capital). In this way, the randomly assigned interventions (e.g. business skills
training), which shift around the theoretical variables in an exogenous manner, are orthogonal to other
factors that could potentially drive changes in the outcome variables of interest (e.g. firm performance).
Research Design
As mentioned, the few empirical studies using training interventions to improve managerial capital
have been inconclusive as to whether ‘business skills training’ can have an impact on firm performance (e.g.
survival, employment, sales or profits). We seek to address the potential issues in prior work by focusing on
a more homogeneous group of established firms, using a more intense intervention, providing training
programs that focus on only one dimension of managerial capital per course, and measuring outcomes using
a novel electronic surveying approach (see Web Appendix A for a discussion on prior training studies).
First, we implemented screening and registration steps to identify an initial sample of 832 firms.
This set of firms represented a homogenous sample of slightly more established micro and small enterprises
operating across the greater Cape Town region of South Africa (refer to the Sampling Frame section for
details). Firms were recruited from urban, suburban and slum areas. All participants in this initial sample
attended a one-hour registration session and completed a baseline survey that measured pre-treatment levels
of the key firm performance variables, as well as other individual covariates and firm controls. After
registration was completed, the initial sample of 832 firms was randomly assigned into the control group or
one of the treatment groups (marketing or finance). The participants were not aware that two different
15
training programs existed and at each training center location the marketing/sales courses were always held
on different days from the finance/accounting courses. This was done to limit the chances of contamination
from participants attempting to switch classes if they were not satisfied with their initial assignment.
Next, participants in the treatment groups were exposed to a high quality training intervention
(www.thebusinessbridge.org). Given the quality of the training is an important factor in determining its
impact on business outcomes (Bloom et al. 2010), we were fortunate to have used a business training
intervention that combines world-class e-learning content with face-to-face classroom teaching sessions in
an innovative approach that reduces dropouts, enhances learning, and improves real-world implementation.
These Business Bridge courses are also intense, requiring roughly 10 hours of business owner time per week
over a two-month period. Thus, our research design attempts to deviate from prior training studies by using
a high quality and more intense intervention (McKenzie and Woodruff 2012). Further, the course structure
allowed us to study the impact of providing ‘specific’ business skills training programs (e.g. marketing skills
versus finance skills) as opposed to more general entrepreneurship or financial literacy training (see Web
Appendix B for a description of the two training interventions).
Under the guise of a ‘scholarship lottery’ participants were randomly assigned into one of three
study groups. 272 participants received the “Marketing and Sales training” intervention through an intensive
10-week course that covered topics such as value creation, customer segmentation, pricing, competitive
differentiation, and selling strategies. 261 participants received the “Finance and Accounting training”
intervention that included classes on separation of personal and business finances, cash-flow management,
business investments, and record keeping (e.g. income statements, balance sheets, etc). 299 participants
were assigned to a control group that did not receive any training during the study period.4 For the next 15
months, all participants had their business performance measured using a midline survey (six months post
intervention) and an endline survey (twelve months post intervention).
4
Note that all control group participants were provided with training, but only after the study finished (i.e. once
endline data collection was completed).
16
Sampling Frame
One stumbling block noted by other researchers conducting field studies with small businesses in
emerging markets is that there can be a lot of attrition (decreases power) and heterogeneity (increases noise)
in the sample which, in turn, can raise the chances of finding null effects. This is largely because
participants often claim to be running a real businesses during recruitment, but after being registered for the
program (and randomly assigned into a group) the research team discovers that many participants were in
fact: lying (as they had hoped for money from the foreign academics); not operational (they had an idea for a
business but nothing started yet); or not established (they had a basic survivalist business but no permanent
location or regular monetary exchanges, and so they were not suitable for a business training program). In
order to minimize these research design threats, our sampling process consisted of three stages (see Web
Appendix C for details). Following these steps, we obtained an initial sample (n = 832) that was generally
consistent with the firm characteristics and individual demographics of the population of small businesses in
South Africa according to the 2010 FinScope Small Business Survey.5
Randomization: Steps
After registration was completed, the initial sample of 832 firms was randomly assigned into three
groups: a marketing group (n= 272), a finance group (n= 261), and a control group (n= 299). The three
groups were not perfectly equal in size because we performed a stratified randomization in order to balance
our sample on several variables (e.g. gender, education, firm size, formalization, and reason for start-up).
The participants – as well as the research administrators and all staff at the training centres – were not aware
of three critical facts: (i) a randomized controlled trial was being conducted; (ii) two different training
interventions existed; and (iii) some people purposefully did not receive training (i.e. to construct a control
group). Participants only knew they were involved in a research project with the Business Bridge program
and, in addition to a training course, they would get their firm performance monitored over an eighteen
month period (receiving a free consulting report at the end). In addition, they did not know there was a
5
Trust, Finmark (2011), “FinScope South Africa Small Business Survey 2010”. The FinScope survey used a
nationally and geographically representative sample of 5,676 self-perceived business owners employing less than 200
people.
17
marketing/sales course and a separate finance/accounting course (so as to minimize the chances for
contamination). Finally, during the registration sessions the participants were told that due to popular
demand there were more people interested in the training than there were available seats. So some
participants would get the training this year and the others would get it in 18 months. They believed this
cover story and also agreed that a ‘lottery’ was the only fair way to decide who would get training now
(versus later). These steps were taken to maintain commitment throughout the eighteen months (especially
for control group participants) and to guard against any systematic attrition.
By the midline (six month post-intervention survey) there were 712 firms remaining in the study
sample. A total of 120 firms were not contactable or refused to participate further (14.4% attrition rate). As
at the endline (twelve month post-intervention survey) there were 607 firms remaining in the study sample
with 105 additional firms dropping out (14.7% attrition rate). Overall, 225 firms dropped out from the initial
sample at baseline (n= 832) to the final sample at endline eighteen months later (n= 607). This lead to an
attrition rate of 27% and was comparable to other small business training studies (McKenzie and Woodruff
2012). The attrition rates were roughly equal across the three groups with the final sample of 607 firms
distributed as follows: marketing group (n= 189), finance group (n= 192), and control group (n= 226). Also,
Tables 1 and 2 show the initial sample and final sample did not differ systematically across a range of
covariates; and further attrition checks demonstrate that dropout at midline or endline was not significantly
different between the three groups.
Finally, given our focus is on small businesses, we took one additional sampling step and removed
the largest firms from our analysis (i.e. 14 firms with monthly sales greater than Rand 150,000 as at
registration). For robustness, we re-ran all of our analyses with these 14 firms included and the pattern of
results is not only similar but also stronger, increasing the size of the effects on all four outcome variables
for both the marketing training group and the finance training group. However, throughout the paper we
report on the more conservative results and restrict our analysis to this set of 593 firms.6 The final sample
6
Treatment compliance was achieved for the large firms: those in the marketing group (n=4) and finance group (n=4)
graduated from the training program, whereas those in the control group (n=6) did not attend any training classes.
18
for analysis therefore included 593 firms divided into three groups: marketing group (n= 185), finance group
(n= 188), and control group (n= 220). We constructed a panel dataset that included 1,741 observations in
total with approximately three observations per firm: baseline (pre intervention), midline (six months post)
and endline (twelve months post).7 Tables 1 and 2 provide descriptive statistics of our final sample. At
baseline, the average firm in our sample had approximately 1.50 paid employees, Rand 13,500 in monthly
revenues, and Rand 3,500 in monthly profits. Also, from the summary in Table 3, we see there is substantial
variation in the types of industries represented in our sample.
Checks: Randomization and Attrition
Next, to ensure confidence in the randomization process, we checked if the three groups were
comparable with respect to individual and firm characteristics that could otherwise account for systematic
differences in outcomes (Gelman and Hill 2006, p.199-206). We did this by comparing the marketing
treatment group to the control group (Table 1), and the finance treatment group to the control group (Table
2), on 28 pre-treatment covariates, such as firm performance (e.g. employees, sales, profits), entrepreneur
demographics (e.g. age, gender, number of children, race/ethnicity, education), firm characteristics (e.g.
years in operation, start-up capital, working hours, access to credit, type of business structure), previous
business practices (e.g. separating finances, record keeping, developing products, marketing promotions),
and research administrator evaluations (e.g. English, literacy, numeracy). As can be expected, refer to Table
1 (for marketing versus control) and Table 2 (for finance versus control), the paired-samples T-test is not
significant for all but 3 of 112 independent t-tests (p > 0.10; tests control for unequal variances). Moreover,
the Hotelling test for ‘joint equality of means’ cannot be rejected for the randomization check on the
marketing group (F= 0.92, p= 0.59) or the finance group (F= 0.71, p= 0.86). We also plotted the propensity
score for each of the three groups (from a probit of treatment group assignment on these 28 covariate
variables) and the distributions were similar in shape and range (i.e. balance and overlap). Based on these
7
There were a small number of firms (n= 40) for which midline data could not be obtained. All of these firms were
included in the analyses, nonetheless, as effects could still be estimated using their baseline and endline data.
19
results, we conclude: (i) randomization holds; and (ii) the control group appears to represent a valid counterfactual for each of the two treatment groups (Angrist and Pischke 2009).8
Measuring Variables
The operationalization of each variable used in analyses is outlined in Web Appendix D. First, the
main outcomes of interest include four measures of firm performance: survival, employees, sales, and profits.
Second, to examine the “growth versus efficiency” mechanisms discussed previously, we construct measures
of multiple firm policy variables (e.g. the within firm percentage change in sales, employees, costs, and
outputs-to-inputs) as well as multiple firm practice variables (e.g. market research, marketing tactics, sales
tactics, tracking finances, analyzing finances, planning finances). Third, to study the heterogeneous
treatment effects proposed in hypotheses H4 and H5, we create composite variables that measure the extent
to which firm owners were less exposed (prior to training) and also whether they were running businesses
that were more established. Fourth, to control for additional sources of variance, and demonstrate our
treatment effects hold after accounting for other factors that may also influence firm performance, we
include 15 control variables and 15 industry fixed effects in our analyses.
ANALYSIS AND RESULTS
Our unit of analysis is the small business for which we have a total of 1,741 firm-period
observations. The data have been converted into a panel structure with 593 firms (randomly assigned into
either a marketing, finance or control group) and 3 time periods (one baseline, one midline and one endline
survey per firm). For the model-free evidence, we use all 1,741 observations to study the evolution of each
outcome. However, when estimating the regression models, we drop the midline data and only use 1,186
observations to examine the treatment effects from the pre-intervention to final post-intervention periods.
8
To ensure randomization also held for the initial sample (n=832), we ran an identical set of analyses to compare the
marketing group (n=272) against the control group (n=299), as well as the finance group (n=261) against the control
group. There were no differences detected between the groups using the same set of covariates, and the Hotelling
tests could not be rejected. In addition, to check against any systematic attrition we ran a similar set of analyses to
compare: (i) final sample (n=607) versus initial sample (n=832); and (ii) initial sample (n=832) versus invited sample
(n=1,500). In both cases, the pattern of results was similar and there was no evidence that the samples differed
systematically over time (on any of the 28 covariates listed in Tables 1 and 2).
20
Training Intervention: Checks
As an initial step, we check to see that the interventions worked as they should. Table 4 provides
descriptive statistics of the two training programs. First, we see that 75% of the training sessions occurred in
the morning (8:30am start) and participants had to travel roughly 4-5 miles to reach a training center from
their business location. These are not trivial hurdles and so it seems participants are reasonably motivated to
take part in the course – and despite having to travel slightly further on average, participants in the marketing
training group performed as well as those in the finance training group (i.e. attendance, graduation and
knowledge gains did not differ). Second, participants attended nearly six of their required eight training
sessions (5.86 classes for marketing vs. 5.59 for finance) with those in the marketing group graduating at a
slightly higher rate (79%) than participants in the finance group (74%). Third, we see that participation
scores were relatively similar – albeit those who received the finance training we more likely to complete
homework exercises and be engaged during class (which might also be an artifact of the experiment as the
finance course typically has more written exercises compared to the marketing course). Fourth, participants
in both groups were generally quite happy about their business skills training experience, giving relatively
high ratings on scales such as satisfaction, willingness to recommend, relevance to business, course and
instructor quality, value for time/money, etc. That said, participants in the finance training appeared to rate
their program slightly higher on average than those in the marketing training did (albeit course performance
did not vary between the two groups). Finally, both training programs lead to roughly 50% increases in
business knowledge (as measured by a pre-course test and a different but comparable post-course test).
Overall, it seems that both the marketing training intervention and the finance training intervention
worked as one would expect in terms of encouraging attendance, achieving high graduation rates (roughly
75%), and building new business knowledge (roughly 50% increase) – and this level of performance is
strikingly similar across the two intervention groups in our study (refer to the distributions for Attendance
and Knowledge in the bottom panel of Table 4). Such a high level of ‘treatment compliance’ is encouraging
and bodes well for detecting an impact from the training programs on firm performance.
21
Model-Free Evidence
To ensure exogenous variation in the theoretical variables of interest (managerial capital), our
identification strategy relies on the random assignment of training interventions to some participants and not
others. Given the treatment variables (marketing training and finance training) are orthogonal to other
factors that may also influence firm performance, we can conduct an initial analysis using a basic
differences-in-differences (DID) approach (Angrist and Pischke 2009). First, we can compare the ‘within’
group change in the mean value of a firm performance variable (e.g. profits) from its pre-treatment level
(measured at baseline) to its post-treatment level (measured at endline). Second, we can compare these preto-post changes ‘between’ the groups. For instance, we can compare the difference between the marketing
group’s change in firm performance (pre-to-post) to the change realized by the control group (pre-to-post).
This differences-in-differences on the mean values of a performance variable (e.g. profits) provide us with
an unbiased estimate of the impact that marketing training has on firm performance – above and beyond any
macro-level changes in the business environment as well as any unique individual or firm characteristics.
Tables 5-8 provide a summary of our main effect results, including basic differences-in-differences
(DID) estimates using the mean values of the firm performance variables. Without any additional structure
on the analyses, we can detect significant effects for each training program on firm performance. Overall,
there is support for hypothesis H1 as the marketing training intervention and the finance training intervention
each leads to an increase in firm survival, employment, sales and profits.
First, both marketing training and finance training lead to an improvement in survival for the firms
assigned to either of these intervention groups (see Table 5). Said differently, firms in the marketing group
were 9.7% more likely (than their counterparts who did not get training) to continue business operations
after eighteen months. An even greater improvement in survival rate (12.7%) was realized by the
participants in the finance training group. Moreover, as shown in the top panel of Table 5, the local average
treatment effect (for the sub-population of compliers) is higher than the basic DID estimate for the marketing
group (12.2% vs. 9.7%) as well as the finance group (15.9% vs. 12.7%). This suggests that attending more
22
training sessions and obtaining a graduation certificate (i.e. compliance with the treatment) leads to greater
improvements in firm survival.
Second, in the case of employment, we see that participants who received the marketing training
increased their total number of paid employees by 0.55 (from 1.53 at pre-intervention measurement to 2.08
at post-intervention measurement) while those in the control group, who did not receive any training, tended
to decrease paid staff by 0.40 employees (see Table 6). Thus, by comparing the changes between these two
groups, we calculate that marketing training had an average effect of 0.95 employees (with respect to
increasing employment over time relative to the control group). Moreover, the finance intervention resulted
in employment gains as well. Compared to the control group, the participants in the finance group tended to
increase their total number of employees by 0.53 over time. It is also encouraging to see that these estimates
increase for the participants who comply with treatment and attend more training sessions.
Third, both training interventions lead to increased sales. As shown in Table 7, marketing training
tended to increase the monthly sales of firms assigned to this group by Rand 9,350 (USD 1,039).9 There was
also a positive and significant effect of finance training on sales, resulting in a Rand 5,697 (USD 644)
increase per month compared to the control group – from their pre-intervention levels (at baseline) to their
post-intervention levels eighteen months later. Both of these trends are displayed in the middle panel of
Table 7 where we can visualize the gains in monthly sales made by participants who received business
training versus those who did not get any training during the same period. In addition, we see that attending
more training sessions (i.e. treatment compliance) resulted in greater sales increases over time for the
marketing group (Rand 11,767 vs. Rand 9,350) and the finance group (Rand 7,705 vs. Rand 5,697).
Fourth, a review of Table 8 also provides evidence that business skills training can result in greater
profits for the participating businesses. In the case of marketing training, there is a Rand 3,038 increase in
profits per month (USD 338) from the pre to post periods (compared against the performance changes of the
9
The US Dollar to South African Rand exchange rate fluctuated during our study period: USD 1.00 ~ Rand 8.50 at
Baseline (August to September 2012); USD 1.00 ~ Rand 9.00 at Midline (May to June 2013); and USD 1.00 ~ Rand
10.00 at Endline (October to November 2013). All analyses were conducted using the raw data provided by
participants in SA Rand. However, for consistent presentation throughout the paper, we use the average exchange rate
during our two-year study period when reporting on firm outcomes in US Dollars (USD 1.00 = Rand 9.00).
23
control group). A similar increase in profits is achieved by the participants in the finance group where the
training lead to an average gain of Rand 2,590 per month (USD 288). This pattern of results is also depicted
in the middle and bottom panels where the contrast between treatment and control groups is quite apparent.
Main Effects: What is the impact of business training on firm performance?
Given our research design includes a randomized control trial, we can use the ‘intervention offer’
(i.e. training offer) as an orthogonal treatment variable and calculate the intention-to-treat (ITT) estimates to
examine the impact of marketing training (or finance training) on firm performance. In the case of
employment, sales and profits, we use OLS to regress the dependent variable on the intervention variables
and control variables. We also estimate our models using robust standard errors clustered at the training
cohort level to take into account that the observations within a given training classroom are non-independent
(see Bertrand, Duflo and Mullainathan 2004).
To test hypothesis H1 we first estimate the model specified in Equation 1 without any controls. Yit
denotes an outcome variable (e.g. employment, sales, profits) for business owner i at time t. The five
intervention variables are as described previously. And εit denotes the error term, assumed to be i.i.d. and
normally distributed. Thus, β2 is the treatment estimate of the marketing training’s impact on outcome Y.
Said differently, β2 measures the difference between the marketing group and the control group in the
evolution of outcome Y (i.e. change from the pre-intervention to final post-intervention periods). It is an
unbiased estimate of the average impact of being assigned to the marketing treatment group on the outcome
variable Y. Similarly, β1 measures the treatment estimate of the finance training’s impact on outcome Y.
In addition, we can estimate the same model but with several control variables included to capture
variation in business owner characteristics and environmental factors.10 As per Equation 1, ω is a vector of
10
Note that because the treatment was assigned randomly, the inclusion of these controls into the estimated models
would not affect the consistency of parameters. Nonetheless, for robustness we also analyze all of the main models
24
the two Training controls, γ is a vector of the six Individual controls, and λ is a vector of the seven Firm
controls. All fifteen of these controls vary by business owner i, but are time invariant since they represent
pre-treatment covariates (hence, no subscript t). Also, δ is a vector of the fifteen industry fixed effects, one
for each industry k containing 10 or more firms. Finally, since the two treatment variables (Finance dummy
and Marketing dummy) are randomly assigned – and assumed to be orthogonal to all of the 30 control
variables in the model – we would expect to obtain similar coefficients (size and sign) for β1 and β2 as those
obtained from estimating Equation 1 without any controls.
Table 9 provides a summary of the estimation results, which closely resemble the model-free
estimates (as per Tables 5-8). First, both interventions lead to an increase in the total number of paid
employees. For the finance treatment, there is a positive and significant coefficient on β1 in models A1 and
A2. Likewise, there is a positive and significant coefficient on β2 in the same models, providing support that
the marketing treatment also had an effect by increasing employment for the firms assigned to this group.
Note also that the impact of marketing training on employment (β2) is nearly double in size to that of finance
training (β1). Moreover, when the 14 largest firms – originally removed due to sampling criteria – are
included in the estimation (see model A3), there is an even stronger employment effect for both the finance
treatment (β1 = 0.55) and the marketing treatment (β2 = 1.02). Second, the results in Table 9 provide
additional evidence that marketing training leads to an increase in monthly sales (β2 is positive and
significant in models B1 and B2). These effects are not only similar to the model-free estimates, but they
also increase substantially when the 14 largest firms are included in the analysis (refer to model B3). By
contrast, in the case of the finance treatment, the positive sales effects are not significant in models B1 and
B2 (and are also smaller than the impact that the marketing training appears to have on sales). However, this
may be related to statistical power since the finance treatment effect is significant when the 14 largest firms
are included in estimation (refer to β1 in model B3). Finally, as displayed in models C1, C2 and C3, the
evidence suggests that business skills training (in marketing or in finance) results in greater profits on
with these 30 control variables included to improve precision, control for chance differences in pre-treatment
characteristics that may exist between groups, and account for nonrandom attrition in the midline and endline surveys.
25
average for the participating business owners. In the case of marketing training, β2 equates to about a Rand
3,038 increase in profits per month regardless of whether controls are excluded or included in the analysis
(models C1 and C2). In addition, we see that a similar increase in profits is achieved by participants in the
finance group (β1 = Rand 2,590 per month in models C1 and C2). It is also encouraging to see that the profit
effect, for either treatment, is enhanced when the 14 largest firms are included in the estimation (model C3).
Overall the results shown in Table 9, as well as in Tables 5-8, provide evidence in support of
hypothesis H1. Being assigned to the marketing training group, or the finance training group, lead to
significant increases in firm performance – as measured by several outcomes, including firm survival,
employment, sales and profits. The results continue to hold regardless of whether model-free approaches or
more structured models are used to identify the effects of training on firm performance.
Finally, despite the fact that the finance training lead to a greater improvement in survival (relative
to marketing) whereas the marketing training lead to a greater improvement in employment and sales
(relative to finance), the two treatments increased monthly profits by roughly the same amount.11 This
pattern of results therefore suggests that the ‘pathway to profits’ may have differed for participants in the
finance group compared to those in the marketing group. Indeed, these main effect results lend some initial
support to the predictions of hypotheses H2 and H3, with the marketing training leading to profit gains
through a growth focus (H2) and the finance training enhancing profits through an efficiency focus (H3).
For instance, in the case of H2, we find that the increases in employment (models A1-A3) and sales (models
B1-B3) are greater for the marketing group than for the finance group: β2 is positive and larger than β1 (refer
to Table 9). We delve deeper into these different mechanisms of change in the next sections.
Mechanism (process): How do firm ‘policies’ influence the pathway to profits?
To thoroughly test hypotheses H2 and H3, we consider additional process evidence. With each
firm’s financial data collected at the baseline and endline survey rounds, we can examine the impact of the
two training interventions on firm policy outcomes related to ‘growth’ (e.g. the within firm percentage
11
Note that although Rand 3,038 (marketing group) and Rand 2,590 (finance group) appear to be different (as per
Tables 8 and 9), an independent samples t-test comparing the means is not significant.
26
change in sales or employees) versus policy outcomes related to ‘efficiency’ (e.g. the within firm percentage
change in costs or outputs-to-inputs). We can analyze the percentage change in a policy outcome (from the
pre-to post intervention periods) for firms in the marketing group (or finance group) compared to those in the
control group by estimating Equation 2 via OLS, where Pi denotes the policy outcome variable for business
owner i. Further, for each of the four policy outcomes we estimate Equation 2 using the entire sample of
firms (n=593), as well as using only the surviving firms (n=446) to check for consistent patterns.
Table 10 summarizes the estimation results. In the case of the two Growth Focused policy outcomes
(top panel), we find support for hypothesis H2: the coefficient for β2 is positive and significant (and larger
than β1) across the models. First, the firm owners in the marketing training group tended to realize a 79.7%
increase in sales from the pre-to-post intervention periods (relative to the control group) while those in the
finance group did not achieve a significant increase in their percentage change in sales over time. Further, as
expected, we find a stronger impact of marketing training on sales growth (85.1%) when the analysis is
conducted only on the surviving firms (n=446). Second, we obtain similar results when the policy outcome
is the percentage change in employees over time – there is a positive and significant coefficient for β2 when
the analysis includes all firms (41.1%) or only the surviving firms (44.1%). The marketing training effects
on employment growth (within firm) are also greater than those of the finance training (β2 is larger than β1).
Taken together, the pattern of results in the top panel of Table 10 suggests that marketing training may work
though a ‘growth focused’ mechanism to impact profits (as predicted by H2).
By contrast, in the case of the two Efficiency Focused policy outcomes (bottom panel of Table 10),
we find support for hypothesis H3 (i.e. an efficiency focused mechanism for finance training). First, the firm
owners in the finance training group tended to decrease their costs over time compared to the control group
(-33.2%), although the coefficient is not significant (p=0.109), whereas those who received the marketing
training tended to increase costs from the pre-to-post intervention periods (70.1%). This differential change
in costs is even greater when the analysis includes only the surviving firms: a 68.9% decrease in costs on
average for finance training, but a 68.3% increase in costs for marketing training. Second, we also examine
27
the impact of each training program on a typical measure of operational efficiency: the outputs-to-inputs
ratio. Here we expect the within firm percentage change in outputs-to-inputs would increase if a business
owner was becoming more efficient over time. Consistent with our expectations, Table 10 shows that firms
in the finance training group tended to substantially increase their output-to-input ratio (relative to the
control group) from the pre-to-post intervention periods, regardless of whether the analysis included all firms
(115.8%) or only the surviving firms (134.2%). This effect is also greater for the finance training group than
for the marketing training group. In sum, the results provided in Table 10 suggest that marketing training
enhances firm policies related to ‘growth’ (e.g. the within firm percentage change in sales or employees), but
finance training improves firm policies related to ‘efficiency’ (e.g. the within firm percentage change in
costs or outputs-to-inputs ratio).
Mechanism (process): How do firm ‘practices’ influence the pathway to profits?
Next, we examine the impact of marketing training on growth related practices (i.e. a firm owner’s
implementation of marketing and sales activities in her day-to-day business), as well as the impact of finance
training on efficiency related practices (i.e. the implementation of finance and accounting activities). Recall
that we constructed three ‘marketing practice’ composites (e.g. market research, marketing tactics, sales
tactics) each of which was converted into a dichotomous outcome variable (coded ‘1’ if the business owner
conducted three or more of the five practices in the composite; ‘0’ otherwise). We also constructed one
Marketing Overall composite that consisted of all 15 practices (coded ‘1’ if the business owner conducted
ten or more of the marketing practices; ‘0’ otherwise). In a similar fashion, we constructed three ‘finance
practice’ composites (e.g. financial tracking, financial analyzing, financial planning) and one Finance
Overall composite. Since these marketing and finance practices are only measured once and at the midline,
we can compare the post-treatment differences of the marketing group (or finance group) against the control
group by estimating Equation 2 via probit, where Pi denotes the dichotomous practice outcome variable for
business owner i (at midline). In addition, since these practices are measured six months before the final
measurement of profits (at endline), we can examine the impact of marketing practices (growth focus) or
finance practices (efficiency focus) on a firm’s monthly profits. We obtain this correlational evidence by
28
estimating Equation 3 via OLS, where ΔYi denotes the change in monthly profits (from baseline to endline)
for business owner i. Finally, we can examine the process further by conducting a mediation analysis to
determine how much of the marketing training effect on profits operates through a given marketing practice
composite. Similar process evidence can be obtained for the finance training’s impact on profits.
The results of these analysis steps are provided in Table 11. In the case of the Growth Focused
practices (top panel), we find broad support for hypothesis H2. First, for the Marketing Overall composite,
the results suggest that assignment to the marketing training group (treatment dummy turned ‘on’) leads to a
53.7% greater likelihood of the business owner implementing at least ten marketing practices (β2 is positive,
significant and larger than β1). We also see there is a correlation between the Marketing Overall practice
variable and firm profits (α1 is positive and significant), which equates to an increase in monthly profits of
Rand 3,813 (USD 424). Moreover, evidence from the mediation analysis suggests that 54.9% of the effect
of marketing training on firm profits operates through the Marketing Overall practice variable (Z = 2.70;
fully mediated). Second, a similar pattern of results is obtained for the Marketing Research, Marketing
Tactics, and Sales Tactics composites. For each of these ‘growth focused’ composites, we find: (1)
assignment to the marketing training group (versus finance training) leads to a greater likelihood that at least
three of the individual marketing activities are implemented; (2) the marketing practice composite is
positively correlated with profits; and (3) a large portion of the effect (of marketing training) on profits is
mediated through the marketing practice composite. Overall, the results provided in Table 11 (top panel)
suggest marketing training not only encourages business owners to implement more firm practices related to
‘growth’, but also that these practices (and having a growth focus) represent an important channel through
which marketing training can influence firm profits.
By contrast, in the case of the Efficiency Focused practices (bottom panel of Table 11), there is
general support for hypothesis H3. First, for the Finance Overall composite, assignment to the finance
training group tends to result in a 64.0% greater likelihood that the business owner will implement at least
ten finance practices (β1 is positive, significant and larger than β2). In addition, there is a correlation
29
between the Finance Overall practice variable and firm profits (α1 is positive and significant), which roughly
equals an increase in monthly profits of Rand 3,892 (USD 432). The mediation analysis also suggests that
36.5% of the effect of finance training on firm profits operates through the Finance Overall practice variable
(Z = 2.73; fully mediated). Second, a similar pattern of results is obtained for the Financial Tracking,
Financial Analyzing, and Financial Planning composites. For each of these ‘efficiency focused’ composites,
we find: (1) assignment to the finance training group (versus marketing training) leads to a greater likelihood
that at least three of the individual finance activities are implemented; (2) the finance practice composite is
positively correlated with profits; and (3) a large portion of the effect (of finance training) on profits is
mediated through the finance practice composite. These results are consistent with the view that finance
training can induce business owners to implement firm practices related to improving ‘efficiency’ and, in
turn, impact profits through this channel.
In sum, the analyses in Table 11 provide additional process evidence that business skills training
encourages firm owners to change behaviors, with marketing training leading to more growth focused
practices and finance training inducing more efficiency focused practices. In both cases, the mechanism of
change (growth focus or efficiency focus) tends to result in firm owners achieving higher profits. Taken
together, the set of results presented in this section provide support for hypotheses H2 and H3.
Mechanism (heterogeneous effects): Who benefits more from business training?
To examine the heterogeneous treatment effects predicted in hypotheses H4 and H5, we estimate
Equation 4 using OLS. This allows us to test whether the business training interventions are effective for a
particular subset of the population. In the case of H4, we argued that marketing training would result in
greater profits for firm owners who (ex ante) have been less exposed to different business contexts. Recall
that we constructed a dummy variable to indicate prior level of exposure (1 = low; 0 = high) based on an
individual owner’s personal and professional background experiences. In the case of H5, we predicted it
would be the firm owners running more established businesses (ex ante) who benefit more from finance
training. We created a dummy variable to measure a firm owner’s level of establishment prior to training (1
= high; 0 = low). Each of the Characteristici dummy variables (less exposed or more established) can then
30
be interacted with the Financei, Marketingi, and PostTrainingt variables as shown in Equation 4. Estimation
of this model is first carried out using ‘less exposed’ as the Characteristic variable, and then it is estimated
(separately) using ‘more established’ as the Characteristic variable. Thus, the parameters of interest are θ1
and θ2, which measure the treatment effect for firm owners with the given characteristic.
Table 12 provides a summary of these heterogeneous treatment effects. In the case of Exposure
(Characteristic 1), we first observe that 60.0% of the firm owners in our sample were ‘less exposed’ to
different business contexts prior to training – and the distribution in exposure scores was similar across the
three intervention groups (bottom panel). From the estimation results (top panel), we see that θ2 is positive
and significant in models D1-D4 (while θ1 is not significant and is smaller than θ2). This supports H4 and
the view that marketing training, on average, tends to be more beneficial for owners with narrow exposure.
In addition, [β2 + θ2] measures the overall impact of marketing training on profits for firm owners who (ex
ante) were ‘low’ on the exposure characteristic. This term, which combines the main effect and the
interaction effect of marketing training, is positive and significant across the models (middle panel). For
instance, in model D1 we see that assignment to the marketing group leads to a Rand 5,450 (USD 605)
increase in monthly profits, on average, for firm owners who had been less exposed to different business
contexts prior to training. And this result continues to hold if the 30 control variables are included in the
estimation (model D2) or the 14 largest firms are included (models D3-D4). Taken together, this pattern of
results suggests that firm owners who have had fewer experiences with different business contexts and
markets (i.e. narrow exposure) are likely to see a greater return to marketing training as it expands one’s
attention beyond the firm’s current boundaries and existing products, customers, distributors and suppliers.
31
In the case of Established (Characteristic 2), we see that 60.2% of our sample consisted of firm
owners who were operating ‘more established’ businesses prior to training – and the established scores were
distributed similarly across the three intervention groups (bottom panel of Table 12). Next, the estimation
results (top panel) show that θ1 is positive and significant in models E1-E4 (while θ2 is not significant and is
smaller than θ1). These results support H5 and are consistent with the prediction that finance training, on
average, tends to be more beneficial for owners with established businesses. In addition, [β1 + θ1] measures
the total effect of finance training on profits for firm owners who (ex ante) were ‘high’ on the established
characteristic. This combined term is positive and significant across the models (middle panel). For
example, in model E1 we see that assignment to the finance group leads to a Rand 5,283 (USD 587) increase
in monthly profits, on average, for firm owners who had been running more established businesses prior to
training. Further, this result holds when the 30 control variables are included (model E2) or the 14 largest
firms are included in the estimation (models E3-E4). Overall, the results in Table 12 suggest that firm
owners operating more established businesses (e.g. formally registered, larger, more permanent structure)
are likely to see a greater return from finance training as the skills can be applied to enhance efficiency.
DISCUSSION
In this study, we implemented a randomized controlled trial with 832 small business owners in
South Africa, randomly assigning one third of them into a marketing training group, one third into a finance
training group, and one third into a control group. We followed the entire sample for eighteen months and
measured firm performance (amongst other variables) prior to the launch of the training intervention as well
as multiple times after it had finished. The results reported in this paper support the five hypotheses and can
be summarized in three main findings (what, how, who) that are consistent with our proposed “growth
versus efficiency” explanation of the influence of business skills training on firm performance.
First, in contrast to prior research on managerial capital and small firms in emerging markets, we
find that both marketing training and finance training can lead to positive and significant effects on firm
performance. For the typical small business owner assigned to our marketing/sales training program, the
32
average changes in firm performance were quite large: 9.7% greater survival rate; 62% increase in
employees (0.95 paid staff); 69% increase in monthly sales (USD 1,039); and 86% increase in monthly
profits (USD 338). In addition, the small business owners participating in the finance/accounting training
program also realized positive returns: 12.7% greater survival rate; 33% increase in employees (0.53 paid
staff); 39% increase in monthly sales (USD 633); and 75% increase in monthly profits (USD 288). It is
worth highlighting again that these effect sizes are particularly striking given the emerging market context in
which these businesses operate: survival, growth and prosperity are famously elusive among micro and small
businesses in emerging markets (Hsieh and Klenow 2014).
Second, whilst both marketing training and finance training lead to roughly the same increase in
monthly profits (~USD 300), the paths through which these profit changes occur are different. Marketing
training tends to shift a firm owner’s focus onto business ‘growth’ by encouraging the implementation of
marketing/sales activities that increase scale (e.g. sales, employees) and, in turn, lead to greater profits. For
instance, firm owners in the marketing group demonstrated higher performance on growth related policies
(e.g. the within firm percentage change in sales or employees) and were more likely to implement practices
connected to top-line business growth (e.g. market research, marketing tactics, sales tactics). By contrast,
finance training tends to shift a firm owner’s focus towards greater ‘efficiency’ in the business through more
finance/accounting activities that decrease costs and subsequently increase profits. For example, firm
owners in the finance group tended to perform better on efficiency related policies (e.g. the within firm
percentage change in costs or outputs-to-inputs). They were also more inclined to conduct practices focused
on enhancing business efficiency (e.g. financial tracking, financial analysing, financial planning). These
patterns suggest that the mechanism of change may differ for marketing training (growth focus) versus
finance training (efficiency focus) but both can increase profits for small business owners.
Third, consistent with a ‘growth focus’ explanation, we find that small business owners who have
narrow exposure (ex ante) tend to do better when they receive the marketing/sales training program. In fact,
for firm owners with narrow exposure, the marketing training led to an increase in monthly profits that was
not only significant (USD 605), but also larger than the average effect of the marketing treatment (USD 338).
33
Level of exposure, however, did not seem to matter for firm owners in the finance group. Overall, we
conjecture that participating in a training program to build marketing skills can help small firm owners in
emerging markets overcome exposure deficits by encouraging them to look beyond their existing business
context and to develop new perspectives on products, customers, distributors and suppliers.
In addition, and in line with an ‘efficiency focus’ explanation, we find that firm owners who have
been running more established businesses (prior to training) tend to achieve greater profit gains when they
receive the finance/accounting training program. Indeed, the finance training resulted in large increases in
monthly profits for firm owners with more established businesses (USD 587) – these gains were also greater
than the average effect obtained for the finance treatment (USD 288). Further, operating a more (versus less)
established business did not seem to influence the profit changes of those in the marketing training group.
Considered together, it appears that building finance skills can be particularly useful to firm owners
operating more established businesses. These firms will have reached a minimum threshold in terms of sales
or scale, and so the finance/accounting skills developed by the owner can actually be put into practice to
reduce costs and increase efficiencies in the business.
Implications for Practice
The paper’s evaluation of the impact of marketing skills (and finance skills) on the performance of
emerging market businesses has several implications for practitioners, including managers of multinationals
and domestic firms. First, providing larger firms with tools to measure and systematically examine
differences across micro and small businesses can assist management decisions related to identification of
partners in emerging markets. Such partnerships can be invaluable for successfully entering new markets,
distributing goods, competing on tiny margins, and investing in new ventures. Second, better understanding
the heterogeneity in managerial capital (as well as individual backgrounds, practices, and firm characteristics)
of small business owners can also enhance the success of market expansion and customer segmentation
strategies. Third, our findings suggest that managerial capital can play a critical role in the growth and
prosperity of small businesses. As such, it is essential that multinational firms learn how best to develop
these business skills among their distributors and suppliers in emerging markets. The Business Bridge
34
model represents one such training program that multinationals can use to support the management and
business growth of their local partners.
Implications for Policy
This research is important to policy makers wishing to stimulate growth and prosperity in emerging
markets. Research on small business development and entrepreneurship is central to the goal of poverty
alleviation. The first reason has to do with the sheer numbers involved. “Vast armies” (de Mel, McKenzie
and Woodruff 2010, p.1) of micro businesses populate the poor parts of the world. But few appear to grow
to a level that allows them to escape poverty. The frustrations of the vast armies of micro and small business
owners can easily explode into chaos and conflict. Yet the energies of these tiny firms can also yield growth
and prosperity. Second, improvements in economic outcomes would provide a way of “helping people help
themselves” (Nopo 2007, p.2). Managerial capital, particularly marketing and sales skills, could represent
an intangible asset developed, owned and implemented by an individual firm owner. Thus, investments in
business skills training at a micro-level, as opposed to macro-level aid efforts or access to finance programs,
may offer an alternative for effective use of scarce development funds, and for economic transformation
more generally. Third, in the absence of systematic research, potentially promising approaches to improve
the lives of micro and small business owners may fail to get implemented. As de Mel, McKenzie, and
Woodruff note, “we need a much more nuanced and detailed understanding of [micro and small businesses]
before appropriate policies can be devised” (2010, p.25). This research may offer a voice for marketing at
the policy making table: marketing appears to offer a path to employment-led increases in growth, prosperity,
and business survival.
Implications for Research
This research can also provide useful insights for marketing academics. First, by giving small
businesses in emerging markets a central role in our research efforts, this paper highlights the opportunities
that marketers have to solve the challenges of the “other 99%” of firms (in contrast to the large Western
firms that are often the focus of academic research). Micro and small businesses in emerging markets differ
from businesses in developed countries. Crucially, most small business owners in emerging markets suffer
35
from stunted growth (Collins et al 2009; Jensen and Miller 2014). We hope that this research offers a
glimpse of the many opportunities that exist for marketing researchers to be agents of change – in areas
where change can have a huge social and economic impact – through their ideas and their consequences.
Second, while an increasing number of economists are examining how small businesses can scale-up and
transition into larger firms (see de Mel, McKenzie and Woodruff 2010), extant research has typically
focused on reducing constraints related to financial capital and institutions, but ignored the role of marketing.
Thus, there are opportunities for marketing academics to study firm behaviour in emerging markets and not
only extend the knowledge in our field, but also that of other disciplines. Third, this study represents the
first randomized controlled trial (RCT) of the impact of marketing skills on the performance of businesses.
Using an RCT provides three key benefits that can help to overcome challenges that otherwise make it
difficult to study firm-level marketing phenomena. One, researchers can actively shape the intervention and
its implementation. For instance, we were able to work with the partner organization to design the rollout of
the training intervention in a way that let us separate the development of marketing/sales skills from
finance/accounting skills. Thus, we were able to examine the research questions of interest and could
control (a priori) how the theoretical variables of interest were manipulated, thus maintaining construct
validity and ensuring that exogenous variation was created where and when it was needed. Two, researchers
can control randomization (and data collection) more closely, including the random assignment of firms into
treatment and control groups to address potential endogeneity concerns. Three, the RCT approach allows for
field studies with hundreds of real firms as the unit of analysis. While experimental approaches are common
in marketing, such studies typically focus on consumers or at most a few firms (to the extent that field
studies have been conducted at the firm level). This paper shows the promise of using RCTs with a large
number of firms to address theoretically and substantively important marketing questions.
36 REFERENCES
Acemoglu, D., Johnson, S., & Robinson, J. A. (2005), “Institutions as a fundamental cause of long-run growth,”
Handbook of economic growth, 1, 385-472.
Angrist, J. D., J. Pischke. 2009. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton, NJ,
USA: Princeton University Press.
Banerjee, A. and Duflo, E. (2007) “The Economic Lives of the Poor,” Journal of Economic Perspectives, 21(1):
141-167.
Banerjee, Abhijit, and Esther Duflo (2008). “Do Firms Want to Borrow More? Testing Credit Constraints Using a
Directed Lending Program”, mimeo, MIT.
Banerjee, Abhijit, Esther Duflo, Rachel Glennerster, and Cynthia Kinnan (2010). “The Miracle of Microfinance?
Evidence from a Randomized Evaluation,” BREAD working paper no. 278.
Banerjee, Abhijit, and Esther Duflo (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global
Poverty, Public Affairs: New York, NY, USA.
Belsley, D., E. Kuh, R. Welsch. 2004. Regression Diagnostics: Identifying Influential Data and Sources of
Collinearity. Cambridge, MA: John Wiley & Sons.
Bertrand, Marianne, and Antoinette Schoar (2003). “Managing with Style: The Effect of Managers on Firm
Policies,” Quarterly Journal of Economics, 118 (4): 1169-1208.
Bennedsen, Morten, Francisco Perez-Gonzalez, and Daniel Wolfenzon (2006). “Do CEOs Matter?” NYU
Working Paper No. FIN-06-032. Accessed at: http://ssrn.com/abstract=1293659
Beck, Thorsten, and Asli Demirguc-Kunt (2006). “Small and medium-size enterprises: Access to finance as a
growth constraint,” Journal of Banking and Finance, 2931-2943.
Bertrand, Marianne, Esther Duflo and Sendhil Mullainathan (2004). "How Much Should We Trust Differencesin-Differences Estimates?" Quarterly Journal of Economics, 119(1), pp. 249-75.
Bloom, Nick and John van Reenen (2010) “Why do management practices differ across firms and countries?”,
Journal of Economic Perspectives, 24(1): 203-24.
Bloom, Nick, Aprajit Mahajan, David McKenzie, and John Roberts (2010) “Why Do Firms in Developing
Countries Have Low Productivity?” American Economic Review: Papers & Proceedings, 100:2, 619-623.
Bloom, Nick, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts (2013). “Does management
matter? Evidence from India”, Quarterly Journal of Economics, 128(1): 1-51.
Bruhn, Miriam, and Bilal Zia (2011). “Stimulating Managerial Capital in Emerging Markets: The Impact of
Business and Financial Literacy for Young Entrepreneurs”, The World Bank working paper no. 5642.
Bruhn, Miriam, Dean Karlan, and Antoinette Schoar (2010). “What Capital is Missing in Developing Countries?”
American Economic Review: Papers & Proceedings, 100:2, 629-633.
Bruhn, Miriam, Dean Karlan, and Antoinette Schoar (2012). “The Impact of Offering Consulting Services to
Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico”, mimeo, MIT.
Chattopadhyay P., W.H. Glick and G.P. Huber (2001). “Organizational actions in response to threats and
opportunities.” Academy of Management Journal, (44): 937-955.
Chintagunta, P., D. Hanssens, J. R. Hauser, J. S. Raju, K. Srinivasan, R. Staelin (2013). Marketing Science: A
Strategic Review. Marketing Science, forthcoming.
Collins, Daryl, Jonathan Morduch, Stuart Rutherford, Orlanda Ruthven (2009). Portfolios of the Poor: How the
World’s Poor Live on $2 a Day, Princeton University Press: Princeton, NJ.
Day, G.S. (1994). “The Capabilities of Market-Driven Organizations.” The Journal of Marketing, 58(4): 37-52.
Day, G.S. and Paul J.H. Schoemaker (2005). “Scanning the Periphery.” Harvard Business Review: 135-148.
Day, G. S., & Montgomery, D. B. (1999), “Charting new directions for marketing,” Journal of Marketing, 3-13.
De Mel, Suresh, David McKenzie and Christopher Woodruff (2008). “Returns to capital in microenterprises:
Evidence from a field experiment”, Quarterly Journal of Economics, 123(4): 1329-72.
De Mel, Suresh, David McKenzie and Christopher Woodruff (2009). “Measuring microenterprise profits: Must
we ask how the sausage is made?” Journal of Development Economics, 88: 19-31.
De Mel, Suresh, David McKenzie and Christopher Woodruff (2010). “Who are the microenterprise owners?
Evidence from Sri Lanka on Tokman v. de Soto,” in International Differences in Entrepreneurship, J.
Lerner and A. Schoar (Eds.), University of Chicago Press.
37
De Mel, Suresh, David McKenzie and Christopher Woodruff (2012) “Business Training and Female Enterprise
Start-up, Growth, and Dynamics: Experimental evidence from Sri Lanka”, Mimeo. World Bank.
Desai, P. S., D. Bell, D. Soberman, G. Lilien. 2012. The Science-to-Practice Initiative: Getting New Marketing
Science Thinking into the Real World. Marketing Science 31(1) 1-3.
Dupas, Pascaline and Jonathan Robinson (2012). “Savings Constraints and Microenterprise Development:
Evidence from a Field Experiment in Kenya,” NBER working paper no. 14693.
Dupas, Pascaline, Sarah Green, Anthony Keats, and Jonathan Robinson (2012). “Challenges in Banking the Rural
Poor: Evidence from Kenya’s Western Province,” mimeo, Stanford.
Dyer, J.H., H.B. Gregersen, and C.M. Christensen (2009). “The Innovator’s DNA.” Harvard Business Review,
61-67.
Fafchamps, Marcel, David McKenzie, Simon Quinn and Christopher Woodruff (2012). “Using PDA consistency
checks to increase the precision of profits and sales in measurement panels.” Journal of Development
Economics, 98(1): 51-57.
Galinsky, Adam D. and Gordon B. Moskowitz (2000). “Perspective-Taking: Decreasing Stereotype Expression,
Stereotype Accessibility, and In-Group Favoritism,” Journal of Personality and Social Psychology, 78:4,
708-724.
Galinsky, Adam D. and Thomas Mussweiler (2001). “First Offers as Anchors: The Role of Perspective-Taking
and Negotiator Focus,” Journal of Personality and Social Psychology, 81:4, 657-669.
Gelman, A., J. Hill. 2007. Data Analysis Using Regression and Multilevel/Hierarchical Models. New York, NY,
USA: Cambridge University Press.
Hambrick, Donald C., and Phyllis A. Mason (1984), “Upper Echelons: The Organization as a Reflection of Its
Top Managers,” Academy of Management Review, 9 (2), 193–206.
Hassan, Magda, Jaideep Prabhu, Rajesh Chandy and Om Narasimhan (2014). “The Lives of Micro-marketers,”
Cambridge University, working paper.
Hsieh, Chang-Tai and Benjamin A. Olken (2014). “The Missing ‘Missing Middle’”, working paper, University of
Chicago, version February 2014.
Hsieh, Chang-Tai and Peter J. Klenow (2014). “The Life Cycle of Plants in India and Mexico”, working paper,
Stanford, version February 2014.
Imbens, G.W. and J. D. Angrist (1994). “Identification and Estimation of Local Average Treatment Effects,”
Econometrica, 62(2): 467-475.
Ingenbleek, P., Tessema, W. K., & van Trijp, H. (2013). Conducting field research in subsistence markets, with
an application to market orientation in the context of Ethiopian pastoralists. International Journal of
Research in Marketing, 30 (1), 83-97.
Jensen, Robert, and Nolan Miller (2014), “Market Size, Demand and the Growth of Firms: Evidence from a
Natural Experiment in India,” working paper, Wharton School.
Karlan, D., and J. Appel (2011), “More Than Good Intentions: Improving the Ways the World's Poor Borrow,
Save, Farm, Learn, and Stay Healthy,” Penguin.
Karlan, Dean S, and Martin Valdivia (2011). “Teaching Entrepreneurship: Impact of Business Training on
Microfinance Clients and Institutions.” Review of Economics and Statistics, 93(2): 510–527.
Lehmann, D. R. (2004), “Metrics for making marketing matter,” Journal of Marketing, 68 (4), 73-75.
Lehmann, D. L. McAlister, R. Staelin. 2011. Sophistication in Research in Marketing. Journal of Marketing 75:4,
155-165
Lilien, Gary L. (2004) "Special Section Introduction by the ISMS Practice Prize Competition Chairman."
Marketing Science 23 (2),180-191.
Lilien, G. L. (2011) “Bridging the academic-practitioner divide in marketing decision models,” Journal of
Marketing, 75(4), 196-210.
Lilien, G. L., Roberts, J. H., & Shankar, V. (2013), “Effective Marketing Science Applications: Insights from the
ISMS-MSI Practice Prize Finalist Papers and Projects,” Marketing Science, 32(2), 229-245.
Maddux, W.W. and A.D. Galinsky (2009). “Cultural Borders and Mental Barriers: The Relationship Between
Living Abroad and Creativity.” Journal of Personality and Social Psychology, 96(5): 1047-1061.
McAlister, L. M. (2005) “Toward insight and relevance,” Journal of Public Policy & Marketing, 24(1), 131-132.
38
McKenzie, David, and Christopher Woodruff (2008). “Experimental Evidence on Returns to Capital and Access
to Finance in Mexico.” World Bank Economic Review, 22(3): 457-482.
McKenzie, David, and Christopher Woodruff (2012). “What Are We Learning from Business Training and
Entrepreneurship Evaluations around the Developing World?”, Mimeo, World Bank.
Mela, C. F. 2013. A key word history of Marketing Science. Marketing Science, 32, 1, (January-February).
Nichter, Simeon and Lara Goldmark (2009), “Small firm growth in developing countries,” World Development,
37 (9).
Nopo, Hugo (2007). “Becoming an Entrepreneur,” Inter-American Development Bank working paper no. 605.
Parker, Simon C. (2004), The economics of self-employment and entrepreneurship, Cambridge University Press.
Philippe, Laurent (2014), "How is P&G building winning brand equities in Emerging Markets,” presentation at
London Business School.
Prahalad, C.K. (2005). The Fortune at the Bottom of the Pyramid: Eradicating Poverty Through Profits, Upper
Saddle River, NJ: Wharton School Publishing.
Reibstein, D. G. Day, Y. Wind. 2009. Is Marketing Academia Losing Its Way? Journal of Marketing 73 (4) 1-3.
Rust, Roland T., Christine Moorman, and Peter R. Dickson (2002), "Getting Return on Quality: Revenue
Expansion, Cost Reduction, or Both?," Journal of Marketing, 66 (October), 7-24.
Sachs, J. (2006) The end of poverty: economic possibilities for our time, Penguin.
Schoar, Antoinette (2010). “The Divide between Subsistence and Transformational Entrepreneurship.” in
Innovation Policy and the Economy, Vol. 10, Chicago: National Bureau of Economic Research, p. 57-81.
Schooler, J.W. and J. Melcher (1995). “The ineffability of insight.” In S.M. Smith, T.B. Ward and R.A. Finke
(Eds.), The creative cognition approach (p. 97-133). Cambridge, MA: MIT Press.
Shadish, W. R., T. D. Cook, D. T. Campbell. 2005. Experimental and Quasi-Experimental Designs for
Generalized Causal Inference. Boston: Houghton Mifflin.
Sheth, Jagdish N. (2011). “Impact of Emerging Markets on Marketing: Rethinking Existing Perspectives and
Practices”, Journal of Marketing, 75(July): 166-182.
Smith, Adam. [1776] (1937), An Inquiry into the Nature and Causes of the Wealth of Nations, New York:
Random House
Solow, Robert M. (1956). “A Contribution to the Theory of Economic Growth”. Quarterly Journal of Economics,
70(1): 65-94.
Tokman, Victor (2007). “Modernizing the Informal Sector,” DESA working paper no. 42, United Nations
Department of Economic and Social Affairs.
Tybout, J. R. (2000), “Manufacturing firms in developing countries: How well do they do, and why?” Journal of
Economic Literature, 38(1), 11–44.
Viswanathan, Madhubalan, and Jose Antonio Rosa (2007). “Product and Market Development for Subsistence
Marketplaces: Consumption and Entrepreneurship Beyond Literacy and Resource Barriers,” in Advances
in International Management Series, Vol. 20, Jose Antonio Rosa and Madhubalan Viswanathan (Eds.),
Oxford: Elsevier, 1-17.
Viswanathan, Madhubalan, Jose Antonio Rosa, and Julie A. Ruth (2010). “Exchanges in Marketing Systems:
The Case of Subsistence Consumer–Merchants in Chennai, India,” Journal of Marketing, 74: 1-17.
Wilkie, W. L., & Moore, E. S. (1999), “Marketing's contributions to society,” Journal of Marketing, 198-218.
Yunus, Muhammad (1999), Banker to the Poor: Micro-Lending and the Battle Against World Poverty, Public
Affairs.
39 TABLE 1:
COMPARISON OF GROUPS – MARKETING VS. CONTROL
40
TABLE 2:
COMPARISON OF GROUPS – FINANCE VS. CONTROL
41 TABLE 3:
SUMMARY OF INDUSTRIES
42
TABLE 4:
INTERVENTION CHECKS
43
TABLE 5:
IMPACT OF MARKETING / FINANCE SKILLS ON SURVIVAL (H1)
44
TABLE 6:
IMPACT OF MARKETING / FINANCE SKILLS ON EMPLOYMENT (H1)
45
TABLE 7:
IMPACT OF MARKETING / FINANCE SKILLS ON SALES (H1)
46
TABLE 8:
IMPACT OF MARKETING / FINANCE SKILLS ON PROFITS (H1)
47
TABLE 9:
IMPACT OF MARKETING / FINANCE SKILLS ON FIRM PERFORMANCE (H1)
48
TABLE 10:
RESULTS SUMMARY – FIRM POLICIES (H2 & H3)
49
TABLE 11:
RESULTS SUMMARY – FIRM PRACTICES (H2 & H3)
50 TABLE 12:
EXPOSURE / ESTABLISHED & FIRM PERFORMANCE (H4 & H5)
A1
WEB APPENDIX A:
POTENTIAL LIMITATIONS OF PRIOR TRAINING STUDIES
Prior studies on ‘business skills training’ have tended to fail in demonstrating that the training interventions used have
an impact on firm survival, employment, sales or profits. There could be several reasons why these training programs
did not lead to positive and statistically significant effects, one of which is that an effect truly does not exist. We offer
four additional explanations (see also McKenzie and Woodruff 2012), and do our best to address each of these
potential limitations in our own research design.
First, one potential issue is that existing studies typically involve offering business skills training to a broad mix of
entrepreneurs, the majority of whom are self-employed out of necessity and would prefer jobs in the formal sector (i.e.
subsistence entrepreneurs). Greater selectivity in the recruitment of candidates for management skills training might
offer greater potential for impact. Thus, we obtain a sample of firms that is more homogeneous at baseline, especially
with respect to the key outcome variables (e.g. employees, sales, profits). From a research design perspective, this can
reduce noise and thereby improve the signal-to-noise ratio. In addition, we also focus on identifying ‘more established’
firms with some operational history and some commitment to improve their business. These types of small businesses
are more likely to participate in the training program and apply the new skills in their business activities, which in turn
should increase the strength of the signal (i.e. the intervention’s impact on outcomes). Taken together, decreasing
noise and increasing signal can enhance statistical power and our ability to detect effects if they exist.
Second, to the best of our knowledge, no study has delivered an intense training program in which the participants
concentrate on just one set of management skills for the duration of the program (ensuring these skills are mastered and
practically applied to one’s business on a regular basis). For instance, there has yet to be a training intervention that
uses a combination of teaching methods (in-class, online, social and homework sessions) and requires participants to
invest 10-plus hours per week (for two months) learning only one business function. Most interventions tend to deliver
material in a condensed format that results in information overload and limits the chance of application to an
entrepreneur’s day-to-day business activities. Our study implements a more intense intervention than those used so far,
by increasing and condensing the time investment on the part of entrepreneurs in a flexible and engaging manner.
Such an intense intervention might help entrepreneurs overcome the inertia that is inherent whenever there are
preexisting habits and methods of engaging in business.
Third, none of the previous studies has separated the dimensions of managerial capital into different interventions such
that one training program provides entrepreneurs only with marketing and sales skills, while a completely separate
course focuses entirely on finance and accounting skills. Most interventions so far examine the effect of ‘general’
entrepreneurship training. Further, these programs typically spend more time emphasizing finance and accounting
skills that can lead to lower costs and improvements in bottom-line profits, and have neglected the specific role of
marketing and sales skills in increasing top-line revenues and creating jobs. In our study, we isolate the effect of
different dimensions of managerial capital by focusing on building expertise in one management skill at a time. This
has the advantage of allowing participants to better master one management skill set and focus on applying concepts in
their business. Also, separating the ‘specific’ dimensions of managerial capital is important for theoretical reasons so
that: (i) we can better understand how finance skills, relative to marketing or operations skills, affect thinking and
actions by entrepreneurs; and (ii) we can get a better sense of which skills matter when (i.e. in what contexts). In other
words, by separating finance training from marketing training, we can better understand the processes and mechanisms
involved.
Fourth, there is growing acknowledgment that using traditional paper survey tools and unaided recall questions to
measure firm performance outcomes can result in noisy data. With such measurement error comes the potential for
attenuation bias and higher standard errors, which can make it difficult to detect true effects in the field. This issue is
particularly salient in an emerging market context because the owners of these micro and small businesses do not
typically keep business records. Thus, as an initial attempt to address these measurement challenges, we implement a
novel electronic survey tool that has been designed to improve the precision of estimates on key outcomes such as firm
sales and firm profits (see Web Appendix E for details on this new approach).
A2
WEB APPENDIX B:
OVERVIEW OF MARKETING / FINANCE INTERVENTIONS
To exogenously manipulate “managerial capital” in our study, we used two business skills training interventions.
Business Bridge offers two courses: Making Sales, focusing on improving entrepreneurs’ marketing and sales activities,
and Managing Money, which provides entrepreneurs with basic accounting skills to improve their record-keeping
practices and financial management. Each course runs for 10 weeks, with entrepreneurs attending one four-hour class
per week. Seven of the modules have 2-4 hours’ worth of Application activities that entrepreneurs are expected to
complete between classes; this “homework” aims to generate a habit amongst entrepreneurs of thinking about,
gathering and recording data on their customers, competitors, product sales, expenditures, profits, etc. The table below
provides additional information on the content of each course.
Courses are delivered by volunteer business professionals who have academic and corporate experience in marketing,
finance and/or running a business. These instructors are recruited through a variety of business schools and forums, and
attend a one-day training course provided by Business Bridge introducing them to the course materials and a number of
past instructors (and entrepreneurs) who share their experiences of teaching (or taking) a course. Instructors typically
volunteer to teach at least four modules (of the same course), but some facilitate more sessions. Instructors are
provided with handbooks for each course, covering the content as well as advice on successful facilitation strategies.
A3
WEB APPENDIX C:
SAMPLING STEPS
Our sampling approach consisted of three stages. In stage one, using a systematic and geographically exhaustive
sampling plan, a team of 12 research administrators (RAs) worked for ten weeks and approached approximately 10,000
businesses in the greater Cape Town area. The only requirement for recruitment was that the business had to be
operating out of a physical structure (e.g. small shop, shipping container or larger retail space). The RAs were
instructed to exclude businesses operating in mobile street stand, roadside cart, small tent-like structures or no location
at all. Each entrepreneur approached was given a sales pitch for the training program and the opportunity to apply for
the program by participating in a short screening survey conducted by the RA. 2,168 of these small businesses opted to
complete the screening survey. Next, we examined basic financial and operating questions, as well as open-ended text
responses describing the business and its customers and products, to assess whether a firm was in fact operational and
running a business in which money exchanges hands (i.e. real customers currently pay for the products/services). 116
observations were dropped because the businesses were non-operational and another 117 observations were dropped
due to missing or incomplete data (or the participant was deemed to be lying). Our sampling frame therefore included
1,935 small businesses operating out of a physical structure in and around Cape Town, South Africa.
In stage two, we used the data collected in the screening survey to further narrow our sampling frame. This survey tool
included potential ‘screening’ questions on education, years in operation, formal registration, motivation and
commitment, as well as several ‘covariate’ questions (e.g. age, gender, race, etc.) and ‘interviewer impression’
questions evaluated by the RA (e.g. aspirations, understanding, English level, literacy, and numeracy). We developed
a scoring procedure to rank the entrepreneurs by variables we believed were representative of how established their
business was as at sign up. This procedure was developed through conversations with other researchers, local
practitioners, and the staff at enterprise development centers, as well as using insights we gained from cluster analyses
and logistic regressions performed on the screening survey data. The 1,935 participants were then ranked according to
their raw score on this Established Business composite variable.
In the third stage, the top 1,500 (more established) businesses were invited to attend a registration session to learn more
about the next steps for their training and complete additional forms. This number was chosen for two reasons. One,
based on statistical power calculations, we were aiming for an initial sample of 750 businesses (approximately 250 in
each of the three groups). Two, we only anticipated a 50% take up rate between the invitations and registration
attendance so it was important to ensure the program was oversubscribed (by sending out 1,500 invitations). During a
notification call, each of the 1,500 invited participants was told that they had qualified for a free scholarship to receive
a two-month Business Bridge training course, but they had to attend a registration session in person to pick up their
scholarship letter. In total, 832 small business owners attended these registration sessions and completed the baseline
survey.
Finally, the firms in our sampling frame (n = 1,935) and initial sample (n = 832) appear to be representative of the
private enterprise landscape in South Africa. Our sampling frame is generally consistent with the firm characteristics
and individual demographics of the population of small businesses in South Africa according to the 2010 FinScope
Small Business Survey (which used a nationally and geographically representative sample of 5,676 self-perceived
business owners employing less than 200 staff). Using a range of firm background and individual entrepreneur
variables from the screening survey, we were able to compare our sampling frame (and initial sample of firms) to the
population of businesses in South Africa. There were no substantive differences between our initial sample and the
FinScope sample on a range of factors (e.g. gender, race/ethnicity, age, firm founders, firm age, etc.). However, and as
expected given our research design, our initial sample did contain firms that were slightly ‘more established’ than the
average small business in South Africa (e.g. a higher percentage of firms that operated out of a physical structure).
A4
WEB APPENDIX D:
MEASURING VARIABLES
Dependent Variables: Firm Performance
The main outcomes of interest include four measures of firm performance: survival, employees, sales, and profits. We
also require a measure of firm costs to test some hypotheses.
Firm Survival. We measured firm survival using a dichotomous variable that assigned a value of “1” when the firm was
in operation (i.e. still exchanging products/services for money) and a value of “0” when it was no longer in operation (i.e.
it had failed or closed down). At each survey round, all participants in the study sample were contacted and the
surveyors visited them at their current business location. This occurred regardless of whether a business was still
operating or not. For instance, a field interview was scheduled even if a business owner told the survey coordinator over
the phone that she had since “gone out of business” or “closed up shop” or “found a salary job” or “returned to school”.
No matter what the reason provided, if we were able to get in contact with a participant then an interview was conducted
in person to ensure confidence in the responses, especially when determining that a firm had become non-operational.
Further, in situations where business was slow (so sales were reported as zero for the prior month) if the participant
demonstrated that she was still operating and trying to exchange products/services for money then the business was
coded as a “1” (operational) on the Firm Survival variable.
Firm Employees. At each survey round, participants were asked to provide the number of staff that they currently
employed in their business. To reduce misunderstandings, we separated employees into five categories and asked for
the current count on each of the following employee types:
i.
Full-time (paid, permanent employees who work more than 30 hours per week).
ii.
Part-time (paid, permanent employees who work less than 30 hours per week).
iii.
Temporary (paid, but not permanent employees who typically work via a seasonal or contract arrangement);.
iv.
Partners (individuals who share in the ownership and running of the firm).
v.
Unpaid (do not receive any money and typically include family or friends helping under an informal
arrangement).
Based on these responses, we constructed a composite variable to measure total paid employees as follows:
[ Full-time*(1.00) + Part-time*(0.50) + Temporary*(1.00) ].
Firm Sales. Sales were always reported for the most recent month. We obtained this monthly sales estimate for all
money collected ‘in’ to the business during the previous month (see Web Appendix E).
Firm Costs. Costs were calculated for the most recent month. An estimate of all money that went ‘out’ of the firm in
the prior month was obtained by aggregating over 12 categories (see Web Appendix E).
Firm Profits. Profits were always reported for the most recent month. The monthly profit estimate was calculated
automatically using an electronic survey tool: [ (final estimate of Total Sales) – (final estimate of Total Costs) ]. This
estimate was obtained through an iterative process (see Web Appendix E).
Other Dependent Variables: Firm Policies
Change in Sales. To further test our argument that marketing training leads to greater profits through a ‘growth’ channel,
we used the firm sales data to construct a change in sales ratio. Since this measure represents a ‘within firm’ percentage
change in sales over time (bounded between 0 and 1.00), it allows us to check sales growth across firms in a more
comparable way (and despite any variation in ex ante sales levels). The variable was computed as follows:
[ (TotalSales_endlinei – TotalSales_baselinei) / (TotalSales_baselinei) ] *100%.
Change in Employees. Similarly, as an alternative check of the ‘growth’ mechanism for marketing training, we used the
firm employment data to construct the following change in employees ratio:
A5
[ (TotalEmployees_endlinei – TotalEmployees_baselinei) / (TotalEmployees_baselinei) ] *100%.
Change in Costs. In addition, to more closely examine whether finance training leads to greater profits through an
‘efficiency’ channel, we used data on costs to create a ‘within firm’ change in costs ratio. We expect a negative sign on
the estimate for finance training if these firms are decreasing costs to increase profits. It was computed as follows:
[ (TotalCosts_endlinei – TotalCosts_baselinei) / (TotalCosts_baselinei) ] *100%.
Change in Outputs-to-Inputs. We also included a measure of operational efficiency. Here, we expect a positive sign on
the estimate for finance training if these firms are improving efficiency (e.g. utilizing fewer inputs to produce the same
or more outputs) as they increase profits. The variable was computed as follows:
[ ((Sales_endlinei / Costs_endlinei) / (Sales_baselinei / Costs_baselinei)) / (Sales_baselinei / Costs_baselinei) ] *100%.
Other Dependent Variables: Firm Practices
We measured thirty different business practices at the midline survey round. These individual practices were grouped
into six categories, three representing growth focused activities (e.g. marketing/sales practices) and three representing
efficiency focused activities (e.g. finance/accounting practices). Each category or composite included five practices,
for which the participant could respond ‘yes’ (scored 1 point) or ‘no’ (scored 0 points). Thus, each composite variable
ranges from 0 (if the participant did not implement any of the practices during the prior six months) to 5 (if the
participant implemented all five practices in the category during the prior six months).
Market Research (growth focused)
i.
Visited one of my competitor’s businesses to see what products/services they sell and the prices they charge.
ii.
Asked one of my suppliers about which products are selling well in my industry or in a similar business sector.
iii.
Asked existing customers if there are any other products/services they would like my business to sell or
produce.
iv.
Talked with one of my former customers to find out why he has stopped buying from my business.
v.
Researched the needs of an existing or potential customer (e.g. by asking 'pain' or 'gain' questions, reading
online).
Marketing Tactics (growth focused)
i.
Improved the quality or design of an existing product/service so it provides more benefits to customers.
ii.
Advertised my business in any form (e.g. flyers, posters, billboards, street signs, radio ads, online, social media,
etc).
iii.
Expanded my sales reach by opening a new store or by distributing my products/services in a different way.
iv.
Changed the pricing of my products/services to increase sales.
v.
Developed at least one new product or service that creates additional value for customers.
Sales Tactics (growth focused)
i.
Written a list of my business capabilities, including the benefits and value my business creates for customers.
ii.
Offered advice to one of my customers on what would be the most suitable product/service for his needs.
iii.
Matched the body language, voice level or facial expressions of a customer to build rapport when I interacted.
iv.
Uncovered and ranked a customer's buying criteria so I could show him that my product/service was better
positioned.
v.
Contacted a customer after he bought my product/service to ask if he was satisfied and received what was
promised.
Financial Tracking (efficiency focused)
i.
Kept my business finances separate from my personal finances.
ii.
Created business records (written or electronic) to track my business finances.
iii.
Recorded the total amount of assets owned by my business (e.g. buildings, vehicles, equipment, tools,
inventory).
A6
iv.
v.
Recorded the total amount of liabilities owed by my business (e.g. bank loans, debts with family/friends,
unpaid bills).
Recorded all money that comes in (sales) and goes out (expenditures) of my business for an entire one-month
period.
Financial Analyzing (efficiency focused)
i.
Used my business records to see how much cash was available in my business at any point in time.
ii.
Used my business records to check if the sales of a particular product were increasing or decreasing.
iii.
Identified the fixed costs and variable costs in my business, and used them to set the price of products/services.
iv.
Compared the actual performance of my business against the targets, and looked for areas to improve in.
v.
Kept track of the Working Capital available in my business and used it to ensure there was enough cash so
daily operations could continue.
Financial Planning (efficiency focused)
i.
Made a business budget (written or electronic) stating how much is owed each month for costs (e.g. rent,
transport, electricity, etc).
ii.
Analyzed budgeted vs. actual spending, and updated my budget to reflect variances or changes in my business.
iii.
Made (or updated) an income statement to report on my business monthly or annually.
iv.
Made (or updated) a balance sheet to report on my business monthly or annually.
v.
Used my business records and budget to predict if there exists enough money each month (after paying
expenses) to afford a new bank loan or asset purchase.
Intervention Variables
Finance. The finance/accounting intervention was randomly assigned to one third of the firms in our sample. Since this
treatment offer was randomized, it is orthogonal to other factors that may also be correlated with the performance
outcomes. We constructed a dummy variable that provided a value of “1” if participants were offered the finance
training intervention (n= 188) and a value of “0” if not (n= 405).
Marketing. Likewise, we created a dummy variable that had a value of “1” if participants were offered the marketing
training intervention (n= 185) and a value of “0” if they were not (n= 408).
Post Training. To indicate the pre-intervention versus post-intervention time period, we used a dummy variable that
was assigned a value of “1” if an observation occurred in the post-intervention period (i.e. after the training program had
finished) and a value of “0” if it occurred in the pre-intervention period. Thus, the Post Training dummy was turned ‘on’
for firm observations at midline and endline, but it was turned ‘off’ for firm observations at baseline.
Finance x PostTraining. An interaction term was created by multiplying the Finance Training dummy with the Post
Training dummy. Thus, when a business owner had been assigned to the finance intervention and the firm observation
occurred at midline or endline, this interaction term was turned ‘on’ and took a value of “1” (otherwise it took a value of
“0”).
Marketing x PostTraining. In the same way, we constructed an interaction term to indicate firm observations at midline
or endline in which the owner had also been assigned to the marketing intervention.
Interaction Variable: Exposure
Exposure. We introduced the concept of narrow exposure to describe situations when marketing training might be
especially beneficial for increasing firm profits. We make a first attempt at measuring this new variable by constructing
a composite based on a variety of professional and personal background factors that can proxy for one’s exposure to
different contexts. The composite variable ranges from 0 (if the participant was not exposed to any of the background
factors) to 5 (if the participant was exposed to all five factors in her prior background). There were five questions, to
which the participant could respond ‘yes’ (scored 1 point) or ‘no’ (scored 0 points).
A7
The five questions included:
i.
Previously held a salaried job in a company with at least 20 different products/services.
ii.
Previously held a salaried job in at least five different companies.
iii.
Previously held a salaried job in a company with at least 50 employees.
iv.
Lived outside my current state/province for longer than five years.
v.
Speak more than two languages fluently.
Next, to construct our ‘Less Exposed’ proxy measure, we recoded this composite using a median split by assigning a
value of “1” if participants had narrow exposure in their prior background experiences (i.e. below the midpoint on the
composite variable: score of zero to one) and a value of “0” if they had broad exposure (i.e. above the midpoint: score of
two to five). This Less Exposed dummy variable (based on proxy measures) was therefore turned ‘on’ when an
entrepreneur had narrow exposure to different contexts.
Interaction Variable: Established
Established. In addition, we argued that finance training would be more beneficial to firm owners who are already
running more established businesses. That is, emphasizing an efficiency focus can lead to greater profit gains in a
business that has reached a minimum threshold in terms of sales or scale because the finance and accounting skills
(learned during training) can actually be applied to reduce costs. Thus, we construct a composite based on firm
characteristics that can proxy for the extent to which a business is more established (prior to the owner starting a training
program). The composite variable ranges from 0 (if the participant’s business was considered less established on all five
factors) to 5 (if the participant’s business was more established on all five factors).
The five factors included:
i.
Years in operation (1= above median, 0= below).
ii.
Capital at start-up (1= above median, 0= below).
iii.
Structure of business location, measures physical size and permanency of the premises (1= above median, 0=
below).
iv.
Firm size, as per total paid employees (1= above median, 0= below).
v.
Registered business (1= if participant’s business had been formally registered with the government, 0= if not).
Next, to construct our ‘More Established’ proxy measure, we recoded this composite using a median split by assigning a
value of “1” if participants were running more established firms (i.e. above the midpoint on the composite variable:
score of two to five) and a value of “0” if firms were less established (i.e. below the midpoint: score of one to two).
Thus, the More Established dummy variable was turned ‘on’ when a firm owner was running a more established
business prior to training.
Control Variables
To control for additional sources of variance, and demonstrate that our treatment effects hold after accounting for other
factors that may also influence firm performance, we include several control variables in our analyses. Importantly, all
of these control variables are “pre-treatment covariates” and represent the levels or scores as measured at baseline (prior
to randomization or implementation of the interventions).
First, we included 2 training variables to account for potential variation in exposure to the training interventions:
i.
Distance to Training (continuous), which was constructed using the GPS coordinates of each participant’s
business location and measured the distance in miles to the training centre location where she took the course or
attended registration (if in the control group).
ii.
Attendance at Training (continuous), which was a count of the total number of class sessions attended by a
participant.
Second, we used 6 demographic variables to control for individual level differences between the small business owners:
A8
i.
ii.
iii.
iv.
v.
vi.
Age (continuous), measure of total years old.
Gender (dummy), coded “1” if the participant was female.
Children (continuous), measure of total dependants under the age of 18 for which the participant was
responsible.
Education (dummy), coded “1” if the participant reached the matriculation level or higher (e.g. graduated high
school).
Ethnicity: SA Black/Coloured (dummy), coded “1” if the participant was a South African national with black or
coloured ethnicity.
Ethnicity: Foreigner, coded “1” if the participant was not a South African national (typically these were
immigrants from another country in Africa).
§ Ethnicity: SA White (dummy), coded “1” if the participant was a South African national with white
ethnicity (this was the ‘base’ case for Ethnicity and not included in the estimation models).
Third, we also included 7 firm variables to control for other business level sources of variance such as:
i.
Years in Operation (continuous), measure of total years the business has been running.
ii.
Capital at Startup (continuous), measure of total amount of money (in Rand) invested to initially get the
business running.
iii.
Hours Worked (continuous), measure of the average number of hours worked each week by the participant.
iv.
Accessed Credit (dummy), coded “1” if the participant obtained a formal loan during the prior year.
v.
Structure of Business Location (continuous), measure of the physical size and permanency of the participant’s
business premises on a scale of 1 (no physical structure) to 10 (operating out of a large, standalone structure
such as an office building or factory).
vi.
Firm Size (continuous), measure of the total number of paid employees working in the business as at baseline.
vii.
Registered Business (dummy), coded “1” if the participant’s business had been formally registered with the
government. Tables 1 and 2 provide descriptive statistics for the majority of these control variables (in both the
initial and final samples).
Fourth, we included 15 dummy variables to account for industry fixed effects. We created a dummy for each industry
(3-digit SIC code) that represented 10 or more of the firms in our sample:
i.
152 (Residential building construction).
ii.
179 (Miscellaneous special trade contractors).
iii.
233 (Women’s, misses’ and juniors’ outerwear).
iv.
238 (Miscellaneous apparel and accessories).
v.
349 (Miscellaneous fabricated metal products).
vi.
541 (Grocery stores).
vii.
565 (Family clothing stores).
viii.
569 (Miscellaneous apparel and accessory stores).
ix.
581 (Eating and drinking places).
x.
723 (Beauty shops).
xi.
724 (Barber shops).
xii.
734 (Services to buildings).
xiii.
737 (Computer and data processing services).
xiv.
762 (Electrical repair shops).
xv.
835 (Child day care services).
§ 999 (All other industries: ‘base’ case for industry and not included in estimation models).
A9
WEB APPENDIX E:
MEASURING FIRM PERFORMANCE
We designed and implemented a new electronic survey tool that, through multiple iterative processes, narrows in on a
more precise (and true) estimate for firm sales, firm costs, and firm profits – possibly overcoming some of the
measurement challenges that have limited the study of small firms in emerging markets (de Mel, McKenzie and
Woodruff 2009; Fafchamps et al 2012). Moreover, by default our electronic surveying approach offers certain
advantages (over a similar paper survey tool), including: automating calculations, effectively transitioning through
survey logic, aggregating estimates in a more comprehensive way, clearly summarizing information for confirmation
decisions, and allowing additional iterations for adjusting estimates.
Firm Sales (money in). Firm sales were always reported for the most recent month. We obtained this monthly sales
estimate for all money collected into the business during the previous month through an iterative process. First, to
reduce recall bias and overcome the general lack of financial records in these research contexts, we asked participants
to provide three separate estimates of monthly sales: (i) Aided recall estimate (of all money collected into the business
last month); (ii) Averaged sales estimate (of best and worst months over prior year); (iii) Aggregated sales estimate
(based on aggregating up from a typical day in the last week to a monthly total). Second, these three different sales
estimates were calculated, stored and presented to the participant using an electronic survey tool. The participant then
used the three estimates to guide her final sales estimate (for the prior month’s total revenues). Third, after completing
the cost and profit estimates (see below), the participants were able to return to the sales section (of the electronic
survey tool) and adjust their final sales estimate as needed. Triangulating and adjusting on a monthly sales figure
through this iterative process has the advantage of increasing measurement precision (de Mel, McKenzie and Woodruff
2009; Fafchamps et al 2012). When reporting the mean differences in Sales (Table 4) we use the raw values, but for
estimation purposes we transform this variable and use log(Sales) in our main analyses.
Firm Costs (money out). Firm costs were calculated for the most recent month. A total estimate of all the money that
went out of the business in the previous month was obtained by aggregating up over 12 major cost categories: (i) loans
(for business only); (ii) purchases of stock/inventory; (iii) purchases of supplies/materials; (iv) employees (v)
location/rent; (vi) energy and electricity; (vii) transport and travel; (viii) equipment rentals and repairs; (ix) food and
water (while at work); (x) phone and communication; (xi) services; (xii) fees and taxes. First, this aggregation occurs
within a given cost category (e.g. energy and electricity) whereby the overall cost is divided into its component parts.
The component parts (which could be provided daily or weekly) are automatically converted into a monthly estimate.
The component estimates are then aggregated up to calculate a monthly total for the major cost category. Each of these
major cost categories is represented as a separate section in the electronic survey tool. Second, all 12 of the major cost
categories are aggregated (added together) to calculate Total Costs, which represents the total money that left the
business in the prior month. This becomes very clear to the participant on the summary page – and for most it is the
first time they have ever tracked their business outgoings or viewed them on a single display.
Firm Profits (money left-over). Firm profits were always reported for the most recent month. The monthly profit
estimate was calculated automatically using the electronic survey tool: [ (final estimate of Total Sales) – (final estimate
of Total Costs) ]. This estimate of Total Profits, or the money left-over after paying all expenses and bills in the prior
month, was obtained through an iterative process. Once the participant finished providing her sales and cost estimates,
the electronic survey tool presented her with a Summary page, which looks like a simple income statement and lists her
Total Sales estimate followed by each of the 12 major cost estimates. At the bottom of this ‘income statement’, the
firm’s Total Profits were displayed (calculated automatically by the electronic survey tool). After reviewing the sales
estimate and each of the cost estimates one-by-one, the participant was able to ‘adjust’ any of the individual line items
by returning to the relevant section in the survey tool. Once a change was made, the Summary page updated
automatically and displayed the new values, including an adjusted Profit estimate. At the end of this iterative process,
the participant confirmed her final estimates and they were stored by the survey tool. The final estimate of total profits
is used for reporting mean differences in Profits (Table 4) and this raw value is also used in the main analyses.