Pergamon Mhdd I>~I’c/~)/)/~/(,~I/ Vol. 27. No. 1, pp. Y-65, 1999

0 1’)‘)s Elscvicr Scicncc Ltil All rights ~rcservccl. Printctl in Grcal Rritain

WY-750X/W/$ - see front maltel PII: s0305-750x(98)00120-x

The Role of Small Enterprises in the Household and National Economy in Kenya: A Significant

Contribution or a Last Resort?

LISA DANTELS ‘: Washington College, Chestertown, MD, USA

Summary. - J3ascd on a nalionwiclc survey, this article addrcsscs the contribution or micro and small cntcrpriscs (MSEs) to cmploymcnt, national income, and household income in Kenya. One-third of all working persons arc cmploycd in MSEs and the sector contrihutcs 1356 to national income. Despite their large contribution as II whole, trcturns to individual MSEI v;lq/ tremendously. Among those MSEs that rcprcscnt the sole source of incotnc for the houscholtl, 72% make lcss than the absolute poverty lint in urban ar-cas and none of the MSEs in ~rul-al arcas make above the ahsolulc povcrly line. Cornpat-ing hourly MSE rclurns lo avuagc earnings in the private sector, the majot-ity make hclow the avcragc earnings while a minority make signilicantly higher earnings. 0 Elscvict- Science Ltd. All rights I-cscrvcd.

1. INTRODUCTION

Micro and small enterprises (MSEs) are prevalent in many developing countries. Despite their large numbers, MSEs arc often seen as low-income activities that do not contribute to the economy. For example, Biggs, Grindle and Snodgrass (1988) report that “as agents of economic development, very small enterprises are, to put it bluntly, of littlc interest”. Others have emphasized that MSEs can play an important role in the development process (Pykc and Scngenberger, 1992). Recent studies in five sub-Saharan countries, for example, estimate that MSEs employ 22% of the adult population on average compared to only 15% in the formal sector. (Daniels, 1994; Daniels and Fisseha, 1992; Daniels and Ngw ira, 1992; Fisseha, 1991; Fisseha and McPherson, 1991).’ Furthermore, MSEs may contribute significantly to household and national income. Using data from a nation- wide survey, this study measures the level ol contribution to household and national income in Kenya.

Although many authors have acknowledged the contribution of the MSE sector in terms ot employment or poverty alleviation, few studies have quantified the overall contribution of the MSE sector to national income. For example, some studies have examined income earned in

specific industries and in particular localities (Bagachwa, 1991; Mugambiwa, 1991; Dossous, 1993; Maldonado, 1987). Other studies look at patterns of income carned across a range of

different types of MSEs, howcvcr, they arc not national in scope (Liedholm and Mead, 1987; Davies, Mead and Scale, 1992). Only one study from L,aos estimates the overall contribution of the MSE sector as h-c)% of Gross Domestic Product. The author points

‘-1 am gralcful Car the support or the US Agency fol Inlcrnational Dcvclopmcnl, Rurcail lor Arrica, Oflicc of Sustuinahle Dcvclopmcnt, Division of Strategic Analysis and the Global Rurcau, Microcntcrprisc Dcvclopment office. Thcsc two offices Cundcd the original study whose results are analyzed for this article. I would also like to thank the stall’ at the Kenya Rut-al Enterprise Programme (KREP), particularly Muli Musinga. Mr. Musing played an active role in planning and implementin g this study. Thanks al-c due to Susan Pietrzyk and the enumerators and supcrviaors involved in the data collection as well as Joan Parker i‘t-om Development Alter-native\, Inc., who provitlcd administrative hackstopping. Finally, a very special thanks to Donald Mud who played an active role in the design, analysis. and writing of the original sludy and furlher work on the dam set. His idea\ have heavily inllucnced my thoughts and writing ahout the microenterpriac sector in Kenya and other pal-b of Africa. Additionally, many of Ihe ideas in Ihis paper are extensions of another pap” that J have wriltcn with Dr. Med. Final revision acccptccl: June 29, 19%.

56 WORLD DEVELOPMENT

out, however, that this estimate was based on a subsample of 778 MSEs from a larger data set of 2,785 MSEs (Minot, 1996). Further- more, the Laos survey did not collect detailed information on non-labor costs, which leads to a less precise estimate of the MSE contribu- tion to national income.

Using data from a nationwide survey in Kenya, this paper goes beyond these previous studies. First, it is based on a random sample of MSEs in the country as a whole, including urban as well as rural areas. Second, all types of MSE activities are included. Finally, unlike previous studies, the national scope of this survey and the detailed questionnaire regarding MSE revenues and expenses offer the ability to provide a more precise estimate of the contribution of MSEs to national income. In addition, this data set can examine the MSE contribution to employment and household income. For example, are MSE returns sufficient to support a household? How do MSE returns compare to minimum wages in the formal sector?

This paper begins with a description of the survey methods in Section 2. Section 3 shows the overall contribution of MSEs to employ- ment and national income based on location, size of the MSE, industry, and gender of the proprietor. Section 4 examines the ability of MSEs returns to support a household and it compares MSE wages to formal sector wages. Finally, Section 5 offers conclusions and policy implications.

2. SURVEY METHODS

The 1995 survey of micro and small enter- priscs in Kenya was carried out by the Kenya Rural Enterprise Programme (K-REP) and Development Alternatives, Incorporated (DAI), in cooperation with Michigan State University (MSU). The survey was done in close consultation with the Central Bureau of Statistics of the Government of Kenya (1989). It was funded by the US Agency for Inter- national Development, via the Growth and Equity through Microenterprise Investments and Institutions (GEMINI) Project. A brief description of the survey method used for this study is described below. A more complete description can be found in the GEMINI technical report by Daniels, Mead and Musinga (1995).

(a) MSE deJirzitiom

The original survey covered all enterprises engaged in income-earning activities, other than agricultural or mineral production, with I-50 workers. This paper, however, limits the analysis to enterprises with ten or fewer workers. Workers include working owners, unpaid workers, paid workers, and apprentices.

(b) Sampling appoaclz

The country was divided into four strata: (i) Nairobi and Mombasa; (ii) all other urban areas with populations above 10,000; (iii) towns with population 2,000-10,000; and (iv) rural areas. Each of these strata was divided into enumeration areas, and a random sample of the enumeration areas was selected. In the selected localities, each household or place of business was approached and someone present was asked whether any non-primary economic activity took place at that locality. If the response was positive, the questionnaire was administered.

Overall, the survey teams visited 11,012 households or enterprise sites in 54 enumera- tion arcas. At these sites, 2,247 existing enter- prises with 10 or fewer employees were identified and enumerated. The remaining sites did not have a small-scale enterprise or no one was home to respond to the question- naire. In the latter case, the assumption was made that there were 25% fewer enterprises in closed households than in open households. This assumption was based on an earlier study in Zimbabwe where repeat visits were made to closed households to determine the number of existing MSEs at the location (Daniels, 1994).

Among the 2,247 existing enterprises, only 1,615 enterprises were able to provide enough information to estimate enterprise income per year. At some sites, for example, the owner was not available to give income information or the owner was not willing to provide the information. At the sites where income infor- mation was estimated, 35% of the estimates were negative. Although economic theory indicates that firms can operate with negative profits in the short run, this was an unusually high number. We, therefore, assumed that some of the negative estimates were inaccu- rate and may have been due to poor recall information. Two rules were then imple- mented to remove improbable income

SMALL ENTERPRISES IN KENYA 57

estimates. First, the average ratio of the sales price to the purchase price for goods sold by traders had to be greater than one. Second, for all enterprises the total costs in a reference month could not exceed the total value of sales in that month by more than 20%. Although both of these rules increased the average profit level of the remaining MSEs, the rules allowed for roughly seven percent of MSEs to operate at a negative profit level, which is plausible. Following the implemcnta- tion of the rules, information from 1,154 MSEs was used to estimate the contribution of MSEs to Gross Domestic Product (GDP).

(c) Iricon7e estimates

Because income estimates are difficult to measure, the survey began with an estimate of sales, which appears to be a relatively uncon- troversial measure and fairly easy to recall. Recognizing the issue of seasonality, respon- dents were asked to specify whether each month of the past year was one of high, medium, or low sales and to estimate the average monthly value of salts for each category. These numbers were then used to estimate of the total value of sales over the previous year.

To estimate net income, the approach focused on the most recent month to specify expenses. Although expenses should vary from month to month, particularly given the fluctuation in sales volume, the proprietor’s recollection of expenses over an entire year on a month-to-month basis may not be reliable. Since expenses from the most recent month should be the most reliable, it provided a rough estimate of monthly expenses.

For traders, a trade margin was estimated for the five most important products sold. From this margin, other costs were deducted to provide an estimate of the gross profit margin before depreciation charges. A similar costs-to-sales ratio was calculated for the most recent month for non-trading enterprises.” Applying the ratio to the annual sales figure provided an estimate of gross profits per year of the enterprise.

The questionnaire also collected informa- tion on capital assets: buildings and equip- ment, vehicles, machinery, and hand tools. The respondent was asked the year of purchase and the amount spent for each item. The consumer price index was then used to inflate the numbers to reflect 1994 values.

Assuming straight-line depreciation, rough estimates of annual implicit depreciation charges were made and deducted as an additional cost.”

To estimate the contribution to GDP, payments to factors of production working in the enterprise were measured. These included: rental payments for land, interest on capital invested, wage payments to labor, and profits earned by the owners of the enterprise. Based on this information, the average annual contribution to GDP per enterprise was estimated within 5 I industries. This number was then multiplied by the estimated total number of MSEs in each industry group, which was derived from the survey itself.

3. AGGREGATE MSE CONTRIBUTION TO EMPLOYMENT AND NATIONAL

INCOME

The MSE sector contributes a significant amount to both employment and national income in Kenya. In 1994, the MSE sector employed over one million people or one-third of all working persons.” In terms of national income, the MSE sector contributed roughly 2.2 billion Kenya pounds or 13% to the GDP.j Table 1 shows the employment and GDP contribution by region. Due to the overwhelming majority of MSEs in rural areas, the overall contribution to GDP is greatest in these areas. In contrast, MSEs in the two largest cities (Nairobi and Mombasa) contri- bute the lowest amount to GDP due to the relatively smaller number of MSEs in these cities.” Considering the contribution to GDP per enterprise, rural MSEs contribute a lower amount due to lower profit levels.

Table 2 shows the MSE contribution to employment and GDP by the size of MSEs. Close to 90% of all MSEs have only one to two workers (including the proprietor). Given the large number of MSEs in this category, the largest contribution to both employment and GDP comes from these enterprises. Roughly three-quarters of all workers in the MSE sector are employed in MSEs with one to two workers and they represent about three-quarters of the MSE contribution to GDP. Although only 12% of all MSEs have three to 10 workers, combined they represent about one-quarter of the total MSE contribu- tion to GDP. Furthermore, considering the contribution per enterprise, MSEs with three to

N;lirohi/Momh;ls~I 54.990 104.622 237,236.562 4.314 othcl- large towns s7.5 16 153,504 ih9.66 I .u34 0.509 Smaller to\vns 36,993 64,020 244374.196 6,619 Rural arcas 527,772 S33.476 I. 1 1s,000,000 ?,I 18 Total 707.27 I 1.155.672 2, I70,000,000 3,ObS

10 workers contribute much more than MSEs with one to two workers.

Table 3 shows the contribution of MSEs to employment and GDP per industry. In terms of employment, groceries, retail of agricultural produce, and other manufacturing provide the highest levels of employment. The highest contribution to GDP, however, is from a different set of industries. Wholesale, bars, hotels, and restaurants, and wearing apparel contribute the greatest amount to GDP per enterprise due to much higher profit levels. Considering rural I/S urban areas in Table 4, Ihe same set of industries provide the greatest source of employment and the largest contribution to GDP.

Table 5 indicates the MSE contribution to employment and GDP by gender of the owner. The number of MSEs and employment in MSEs is roughly the same for male- and female-operated MSEs. The overall contribu- tion and average contribution to GDP, howcvcr, is higher for malt-owned MSEs. This reflects higher profit levels among male-owned MSEs. Using multiple regression analysis, Daniels and Mead (forthcoming) show that male-owned MSEs make higher profits even

when controlling for sector, proprietor educa- tion, location, size, and age of the MSE.

4. MSE CONTRIBUTION TO HOUSEHOLD INCOME

Section 3 illustrated that MSEs make a significant contribution to both employment and national income in Kenya. Although the aggregate contribution is large, it is important to examine MSE returns to individual house- holds as a source of income. There are several questions that should be addressed: Are MSE returns sufficient to support a household? How do MSE returns compare to wage earnings in the “modern” sector?’ Are MSEs survival activities that reflect a lack of oppor- tunities in the modern sector?

(a) AI-C MSE retwns su~cient to support n hoLl.rehold?

This first question, are MSE returns sufficient to support a household, is most relevant in the category where MSEs provide the only source of income for the household.” Table 6 shows

Six OF MSE Percent of (Numlm of all MSEs Wet-kcrs) (%)

Percent or all MSE wol-kcrs cmploycd hy

MSE sire (‘X)

Percent Conlrihutcd to GDP oi” Total MSE Contribution (%')

Avg. Contribution to GDP per enterprise

(Kenya pounds)

I worker 57 35 43 2,319 2 wet-kers 31 is 33 3,207 3-S workers I I 23 I8 5,015 O- 10 workers I 4 6 18,335

Total IO0 100 100 3,068

“Thcsc numbcra XI-C lxwxi on extrapolated estimates Cram the survey, which was conducted in May and June, 1995.

SMALL ENTERPRISES IN KENYA 59

that about one-quarter of the MSEs in opera- tion provide all or almost all of household income. Considering urban and rural areas, almost half of MSEs in urban areas provide all of the household income whereas only 15% of MSEs in rural areas provide all of the house- hold income.

The earnings of MSEs that provide all of household income can be compared to the absolute poverty line developed by the World Bank (1995). The absolute poverty line is defined as “the minimum level of expenditure

deemed necessary to satisfy a person’s food requirement plus the consumption of a few non-food necessities” (World Bank, 1995 p. 8). Taking the 1992 poverty line figures provided in the study and adjusting for infla- tion and household size in urban and rural areas, the absolute poverty line in 1995 for urban and rural households was k Sh 6,415 and k Sh 4,531 respectively.”

In urban areas, 18% of MSEs that provide all of the household income generate earnings above the poverty line. Within this group, 42%

Table 3. MSE contvihution to onployment rrncl rlutional income /q industry, 199.5”

Industry Percentage MSEs % of MSE Percentage Contributed Average Contribution in the industry workers employed lo GDP of Total to GDP per MSE

(%I in the MSE Contribution within the industry (%) industry

(%/o) (Kenya pounds)

Beer Brewing Other food, bev, & tobacco Wearing apparel Shoes, tcxtilcs & leather Wood and cane products Other manufacturing Wholesaling Retail: ag produce Retail: Charcoal & Fuel Retail: Hardware Retail: Ready-made clothes Retail: Second-hand clothes General Grocery/kiosk Retail: all other Processed food sales Bars, hotels, and restaurants Repairs: shoes Repairs: all others Barbers, beauly salons All other services

3.9 4.1

2.0

4.3

4.7 4.6

1.9

3.7

0.6 9.9

11.1

1.3

493 I,49 I

16,704

896

7.0

11.1 0.4

20.5

7.4

11.8 .7

16.6

4.7

1.4 15.6

9.4

2,212

2,079 136,750

1,474

857

2,944 2,840

1,698

1,243

17s

1,805

4.4 5.5

0.8 1.5

.h 1.4

2.9 2.6

2.0

0.3 0.7

1.3

20.2 19.2 9.2

3.8

5.0

4.0

5.5

1.7

3.0

1.2 2.7 14.4 37,973

822

3,463

1.0

1.8

.8

2.1

0.3

2.1

0.5 .5 0.5 3,113

3.3 3.3 4.3 4,023

Total 100.0 100.0 100.0 3,068

“These numbers are based on extrapolated estimates from the survey, which was conducted in May and June, 1995.

60 WORLD DEVELOPMENT

make at least two times the poverty line and MSEs that provide all of the household 23% make at least three times the poverty income do not generate sufficient earnings to line. On average, MSEs in this category make meet the absolute poverty line. 6.8 times the amount of the poverty line. Although the earnings are high in this group, a full 72% make below the poverty line. (b) How do MSE returns compare to wage

In rural areas, none of the MSEs that earnings in the modern sector? provide all of the household income generate earnings above the poverty line. Combined, As noted above, only one quarter of all MSEs these results indicate that the majority of provide the sole source of income for the

Table 4. MSE contribution to employment und national income by industr): urban und rwal areas, 199.~

% of MSEs in the industry

% of MSE workers

employed in the

industry

% Contributed to GDP of Total MSE

Contribution

Average contribution to GDP per MSE within the industry

(Kenya pounds)

Urban Rural Urban Rural Urban Rural Urban Rural

Beer Brewing 0.6 3.3 0.7 4.0 0.1 0.5 493 493 Other food, bev, & tobacco 0.8 3.3 1.2 3.5 3.3 6.6 12,989 6,185 Wearing apparel 0.8 1.2 0.9 1.0 4.5 6.7 16,704 16,704 Shoes, textiles, & leather 0.3 4.1 0.2 3.5 0.8 0.5 9,102 373 Wood and cane products 0.8 6.1 1.1 6.2 0.9 3.8 3,390 2,046 Other manufacturing 0.5 10.6 0.8 11.0 1.8 5.6 16,652 1,621 Wholesaling 0.4 0.0 0.7 0.0 15.6 0.0 136,751 0 Retail: ag produce 6.6 13.9 5.1 11.5 3.0 6.3 2,041 1,062 Retail: Charcoal & fuel 1.2 3.3 1.0 4.5 0.5 1.5 539 931 Retail: Hardware 0.4 0.4 0.4 0.2 0.2 0.2 2,944 none Retail: Ready-made clothes 0.7 0.8 0.7 0.7 0.3 0.4 4,410 742 Retail: Second-hand clothes 0.9 2.0 0.8 1.7 0.4 2.3 5,648 662 General Grocery/kiosk 5.1 15.1 5.2 14.0 2.3 0.6 3,399 543 Retail: all other 1.3 2.4 1.5 2.5 0.6 1.0 4,766 444 Processed food sales 1.8 3.3 2.2 3.2 1.0 9.2 1,805 1,805 Bars, hotels, and restaurants 0.8 0.4 2.2 0.5 9.2 0.1 37,721 38,439 Repairs: shoes 0.5 0.4 0.5 0.2 0.1 1.1 822 822 Repairs: all others 0.6 1.2 1.1 1.0 1.1 0.5 5,484 2,4 35 Barbers, beauty salons 0.5 0.0 0.5 0.0 0.5 0.0 3,114 00 All other services 0.8 2.4 1.0 2.2 2.0 2.2 7,818 2,776

Table 5. MSE contribution to employment and national income by gender ofproprietor; 1995”

Gender of proprietor

Percentage of MSEs

(%)

Percentage Total MSE Workers

Employed (%)

Percentage contributed to GDP of Total

MSE contribution

(%)

Avg. contribution to GDP per enterprise

(Kenya pounds)

Female 43 39 30 2,112 Male 41 40 50 3,752 Joint Ownership 16 21 20 3,815

Total 100 100 100 3,068

“These numbers are based on extrapolated estimates from the survey, which was conducted in May and June, 1995.

SMALL ENTERPRISES IN KENYA 61

Contribution to household income Percentage of as estimated by proprietor all MSEs (%)

Percentage of MSEs in urban areas (%)

Pcrccntagc of MSEs in rural arcas (%)

All or almost all More than half About half Less than half Negligible amount

24 49 15 17 14 IX 20 15 22 29 14 34 IO 8 II

household. The remainder of MSE activities are undertaken in households that have other sources of income. This does not necessarily mean, however, that most MSEs operate on a part-time basis. In fact, the majority of MSEs work more than the full-time equivalent of 45 hours per week. On average, MSEs operate 55 hours per week and a full two-thirds of MSEs work more than 4.5 hours per week. Due to the variation in the number of hours worked per week or per month, it is not possible to directly compare total monthly MSE returns with one month’s wages in the modern sector. Instead, MSE returns must be compared to modern sector wages on an hourly basis. Taking this hourly profit and multiplying by 195 (45 hours per week times 4.3 weeks per month) will give the full-time equivalent returns for MSEs for one month. These numbers are discussed below for the net return per person per month.

The lowest monthly minimum wage in Kenya is for general laborers. At the time of the survey, these wages were KSh 1,904 for Nairobi and Mombasa, KSh 1,755 for other municipalities and a few larger townships, and KSh 1,070 for the rest of the country. Table 7

shows that roughly one-quarter of MSEs make above the minimum wage on a full-time equiv- alent basis. Among the MSEs that make more than the minimum wage, there is a wide varia- tion in MSE returns as illustrated in Figure 1. Two-thirds of MSEs in this category make at least two times the minimum wage and 41% make at least three times the minimum wage. Among the top ten percent of enterprises, MSEs make 17 times the minimum wage on average. Again, this indicates that among the minority that earn above the minimum wage, the earnings are relatively high. The majority of MSEs, however, earn very low returns.

In addition to the returns to all MSEs, Table 7 shows the percentage of MSEs that make above the minimum wage based on proprietor and MSE characteristics. Considering the different characteristics of the proprietor, there was no statistically significant difference in the percentage of male-owned vs female-owned MSEs that made above the minimum wage. Higher levels of proprietor education training also did not result in a significantly higher proportion of MSEs that make above the minimum wage.

Considering characteristics of the MSE, enterprises with paid workers are more likely

Table 7. Percentage of MS3 that make above the monthly minimum wage on a full-time equivalent basis Kenya, 1995

All MSEs Gender

Male-owned MSEs Female-owned MSEs

Education Primary or less Some secondary or more

Paid workers None Some

Credit received from formal institution None Some

26%

260/o 17s ,I 23% ns

24% ns 38% ns

25 % ” 38% ”

26%! ns 23% ns

‘Ins = Not a significant difference bctwccn the two categories. “Significant difference between the two categories at x = 0.10

62 WORLD DEVELOPMENT

Figure 1: Returns to MSEs Compared to Minimum and Average Wages

/ (ml-tune eqLuvalmt earmngs)

30 40 50 60 70 80 90 100 Cumulative % of MSEs

--F Ln of MSE returns + Ln of min wage - Ln of avg earnings

Figure 1. Returns to MSEs compared to minimum and average wages.

to make above the minimum wage. In the case of credit received by the MSE, there was no significant difference in the number of MSEs that make above minimum wage.

Comparing MSE returns to the average earnings in the private sector instead of the minimum wage gives different results. According to the 1995 Economic Survey, the average wage earnings per employee in the private sector in 1994 was 4,270 Kenya Shill- ings per month.‘” Only 9% of MSEs make above the average earnings as illustrated in Figure 1. Within this group, 55% make two times the average earnings and 44% make more than three times the average earnings. The top 10% of MSEs in this category make eight times or more than the average earnings.

Although the full-time-equivalent measure used above gives a better comparison of the earnings on an hour per hour basis in the informal and modern sectors, it is also interesting to compare the earnings made during the actual number of hours worked compared to the modern sector wages. This is particularly true since entrepreneurs considering a job in the modern sector may compare their current wages in the MSE based on actual hours with the monthly wage in the modern sector. In this case, roughly 30% of MSEs make above the monthly minimum wage based on their actual hours

worked. Among those that make above the minimum wage, 62% make two times the minimum wage while 38% make three times the minimum wage. Among the top 10% of wage-earning MSEs, on average MSEs make 18 times the minimum wage based on their actual hours worked.

Comparing earnings based on actual hours worked to the average earnings in the modern sector, 10% of MSEs make above the average earnings. Among the MSEs that make above average earnings, 61% make two times the average earnings while 35% make at least three times the average earnings. Among the top 10% of wage-earning MSEs, on average MSEs make 4.6 times the average wage in the modern sector.

(c) Are MSEs survival activities that reflect a luck of opportunity in the modem sector?

Due to the large variation in MSE returns discussed above, there is no clea r cut answer to this question. Some general observations, however, can be made.

For the majority of activities that make below the minimum wage, these MSEs probably reflect a lack of opportunities in the modern sector. It is important to keep in mind, however, that in some cases proprietors

may not want to work outside of the home. Women, for example, may want to combine child care with their business activity and may not have the option of taking a job outside the home.

For the minority of activities that make above the minimum wage and above the average earnings in the modern sector, these MSEs clearly do not reflect a lack of oppor- tunity in the modern sector. Proprietors in these MSEs are making just as much, if not much more than they could make in the modern sector. Overall, however, the majority of MSEs do appear to reflect a lack of better opportunities while a minority of MSEs generate income well above the average earnings in the modern sector.

5. CONCLUSIONS

Based on a nationwide survey, this article addresses the contribution of the micro and small enterprise sector to employment, national income, and household income in Kenya. As a whole, the MSE sector contri- butes substantially to employment and national income. One-third of all working persons are employed in MSEs and the sector contributes 13% to the GDP. These statistics indicate that the MSE sector is an integral part of the economy. These statistics also call

into question the suggestion that the MSE sector is “of little interest.” Even if the indivi- dual contribution of many MSEs is small, the sheer size of the sector and its overall contribution cannot be ignored.

Regarding the three questions addressed by this article, the results show that returns to MSEs vary tremendously. In a minority of cases where the MSE provides the sole source of income, 72% of MSEs in urban areas and all of the MSEs in rural areas earn returns that are below the poverty line. In these cases, MSEs may be seen as a last resort or the best of a number of poor options. Among the MSEs that make above the poverty line and the minimum wage, however, returns are quite high. This suggests that some MSEs represent a dynamic source of income. Finally, the majority of MSEs that fall in between these two categories may provide a supplemental source of income for proprietors who cannot find employment in the modern sector. For others, proprietors may choose the conveni- ence of operating from the home in order to combine their MSE activity with household responsibilities instead of finding work in the modern sector. Overall, further research needs to be done to determine the source of the dynamic growth among the minority of MSEs that do make well above the minimum wage and how to assist other MSEs to become more profitable.

SMALL ENTERPRISES IN KENYA 63

NOTES

1. An MSE is defined as a business activity that employs 50 or fewer workers and markets at least 50% of its output. The adult population is defined as 1.5 years or older. The five countries include: Zimbabwe, Botswana, Malawi, Lesotho, and Swaziland. The formal sector is defined as income-earning activities that are registered with the government and counted in national statistics.

2. For both trading and non-trading enterprises, the questionnaire included a list of twelve different expense categories. Respondents were asked about recent expenses for each, leaving them the option of answering in terms of expenses per day, per week, per month or per year. For non-trading enterprises, there were similar options for expenses for inputs/supplies; up to eleven could be separately specified. All of these were then converted to a monthly basis, and the resulting figure expressed as a percentage of sales for that month. This ratio of expenses to sales was assumed to hold throughout the year. Further details are given in Daniels, Mead and Musinga (1995).

3. Buildings wcrc depreciated over 20 years, while machinery and equipment, hand tools, vchiclcs, and other fixed assets were depreciated over five years.

4. The 1995 Economic Survey estimated that 3.4 million persons were employed in Kenya, including the informal scctol and excluding rural small-scale agriculture and pastoral activities (Central Bureau of Statistics, 1995). Based on these statistics, the 1.2 million persons employed in the MSE sector according to the GEMINI survey represent over one-third of all employment in Kenya. It should be noted, however, that the Central Bureau of Statistics estimates an even higher number of persons employed in the informal sector (1.8 million persons). These differences may be due to data collection procedures or differences in the definition of an MSE. No information is given in the 1995 report about how the Central Bureau of Statistics numbers were collected on the informal sector.

5. The exchange rate at the time of the survey as KSh 50.6 = US $1.00; 1 I<enya pound is equal to 20 Kenya shillings. The GDP in 1994 was estimated at lb.1

64 WORLD DEVELOPMENT

billion pounds. If all MSE activities are included in official statistics, the MSE sector contributed 13.7% to GDP. If no MSE activities arc included in official statistics, the MSE sector contributed 12%. Since some activities must be captured by official statistics, the MSE sector contributed roughly 13%.

8. Although respondents indicated that MSEs wcrc the only source of income in 25% of all activities, transfer payments may represent an additional source of income that were not reported.

The 1995 GEMINI study in Kenya was a follow-up to a previous nationwide survey conducted two years earlier (Parker, lY94). It was cxpccted that the MSE sector would have expanded during the two-year period between the surveys. Instead, the 1995 survey showed a decrease in the number of MSEs of 22%. The authors felt that this was decrease was overestimated and offcrcd some possibilities for the dccrcasc. Although the estimation of the number of MSEs would not affect the individual income estimates of MSEs in the lYY5 survey, it could affect the estimation of the overall contribution to GDP. In this case, the contribution may be higher if the number of MSEs in Kenya is higher than the sulvcy suggests.

6. The estimate of the number of MSEs is gcneratcd from the GEMINI survey itself. It is not take from the Central Bureau of Statistics.

0. The 1995 World Bank report gave the absolute poverty line for urban and rural areas per adult equiva- lent unit in 1992. These numbers were first adjusted for inflation to provide lY95 figures. The 1995 figures were then multiplied by the number of adult equivalent units in urban and rural households. The number of adult equivalent units was estimated by first taking the average household size in urban and rural areas taken from the 1989 census. These numbers were then adjusted for age groups within the household and their estimated cost to the household in terms of adult equivalent units based the numbers used in the World Bank report. These numbers were reported in Mukui (1993). In particular, children aged O-4 and 5-14 were estimated as 24% and 65% of the cost of one adult to the household, respectively. Any person that was 15 years old or above was counted as one adult.

7. The modern sector is defined in by the Kenyan Central Bureau of Statistics as modern establishments in urban and rural areas. It includes wage employees, self-employed individuals, and unpaid family workers. It does not include the informal sector, rural small- scale agriculture and pastoralist activities.

10. The average wage earnings may be skewed by the earnings of high-level executives, particularly in trans- national companies. For example, the highest average wages were reported in “finance, insurance, real estate, and business services” followed by “transport and communications” according to the Economic Survey, 1995.

REFERENCES

Bagachwa, M. S. D. (1991) Cl~oice of Technology in Industry: The Economics of Grain Milling in Tanzania. International Development Research Center, Ottawa, Canada.

Biggs, T., Grindlc, M. and Snodgrass, D. (1988) The informal sector, policy reform, and structural trans- formation. In Beyond the Informal Sector: Including the E.xclicluded in Developing Countries, ed. J. Jenkins. Institute for Contemporary Studies Press, San Francisco.

Central Bureau of Statistics (1995) Economic Survey 1995. Central Bureau of Statistics, Nairobi, Kenya.

Daniels, L. (1994) Changes in the small-scale enter- prise sector from 1991 to 1993: Results of a second nationwide survey in Zimbabwe. GEMINI Technical Report No. 71. Development Alternatives, Inc., Bethesda, MD.

Daniels, L. and Fisseha, Y. (1992) Micro- and small- scale enterprises in Botswana: Results of a nation- wide survey. GEMINI Technical Report No. 46, Development Alternatives, Inc., Bethesda, MD.

Daniels, L. and Mead, D. (forthcoming) The contribu- tion of small enterprises to household and national inCOme in Kenya.

Daniels, L., Mead, D. and Musinga, M. (1995) Employ- ment and income in micro and small enterprises in Kenya: Results of a 1995 survey. GEMINI Technical

Report No. 92, Development Alternatives, Inc., Bethesda, MD.

Daniels, L. and Ngwira, A. (1992) Results of a nation- wide survey on micro, small, and medium enter- prises in Malawi. GEMINI Technical Report No. 53, Development Alternatives, Inc., Bethesda, MD.

Davies, S., Mead, D. and Seale, J. L. (1992) Small manufacturing enterprises in Egypt. Economic Development and Cultural Change 40(2), 381-412.

Dossous, A. (1093) Etude monographique de quelques activites economiques du secteur informel au Benin (Study of some economic activities in the informal sector in Benin). Department of Enterprise Development and Cooperative, International Labour Organization, Geneva.

Fisseha, Y. (1991) Small-scale enterprises in Lesotho: Summary of a country-wide survey. GEMINI Technical Report No. 14, Development Alternatives, Inc., Bethesda, MD.

Fisseha, Y. and McPherson, M. (1991) A country-wide study of small-scale enterprises in Swaziland. GEMINI Technical Report No. 24, Development Alternatives, Inc., Bethesda, MD.

Government of Kenya. (1989) Kenya Population Census, 1989. Central Bureau of Statistics, Nairobi, Kenya.

SMALL ENTERPRISES IN KENYA 6.5

Liedholm, C. and Mead, D. (1987) Small scale indus- tries in developing countries: Empirical evidence and policy implications. International Development Paper No. 9, Michigan State University, East Lansing, MI.

Maldonado, C. (1987) Petits producteurs urbains d’Afrique francophone, (Small urban producers in francophonc Africa). World Employment Programme, International Labour Organization, Geneva.

Minot, N. W. (1996) Small and medium entcrpriscs in the Lao P.D.R.: The results of a national survey. German Agency for Technical Cooperation and the Ministry of Industry and Handicraft, Laos.

Mugambiwa, T. (1991) A business analysis of r-ural small-scale bread, beer and brick-making industries.

Working Paper No. 21, Zimbabwe Environmental Research Organization, Harare, Zimbabwe.

Mukui, .I. T. (1993) Kenya: Poverty profiles, 1982-1992. Office of the Vice-President’ and Ministry of Planning and National Development, Nairobi, Kenya.

Parker, J. (1994) Micro- and small-scale enterprises in Kenya: Results of the 1993 National Baseline Survey. GEMINI Technical Report No. 75, Development Alternatives, Inc., Bethesda, MD.

Pyke, F. and Scngenberger, W. (1992) Incinstrinl Districis and Local Ecorzornic Regenemtiorl. Inter- national Institute for Lahour Studies, International Lahour Organization, Geneva.

World Bank. (1YOS) Kenya poverty assessment. Report No. 13152-KE, The World Bank, Washington, DC.