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Keywords:

  • Returns to human capital;
  • Micro business;
  • Economic transition;
  • Lao PDR
  • I28;
  • L26;
  • P25

Abstract

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

This paper examines the determinants of performance in 1,776 micro and small enterprises that represent all industry sectors and geographical regions in Lao PDR. Although considerable resources are being directed toward promotion schemes in the country, empirical research on this subject is very limited. This study provides concrete insights into development strategies, particularly investment in basic education. The impact of business experience is small and insignificant. The differences between urban and rural, and Lao and ethnic minorities are narrowing in the younger generation, but still remain very large. The difference between male and female entrepreneurs found in most reports is rejected regardless of region, groups, and generation. These findings would help to formulate further efforts attempting to promote this sector.


I. INTRODUCTION

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

It is argued that micro and small enterprises (MSEs) can play a role in poverty reduction, building the foundations of an expanding private sector and creating decent work for greater numbers of people in developing countries. By this token, Lao People's Democratic Republic (Lao PDR) presents an interesting case study of MSEs for both their significance in the economy and their priority in development policy. Since the introduction of a market-oriented economy in the late 1980s, various demands (business chances) have started to occur. In this paper, we define “entrepreneurship” as people who establish a new business to satisfythese new demands. The private sector, in particular MSEs, in Lao PDR has made significant strides in the generation of employment and the increase in household income. In 2004, MSEs dominated some 96% of the total number of 26,200 enterprises in Lao PDR, and large firms account for less than 1% (Lao PDR 2005). A recent national household survey showed that “28% of households operate at least one household business” (Lao PDR 2004, p. 47). It is more common among urban households (63%) compared to rural households (17%). Some households registered business income without having a family business. This is mainly the case in rural households where homemade products such as textiles and garments are informally for sale. If taking these households into account, about 46% of the households operate a micro or small business (68% of urban households and 38% of rural households).

The promotion of small and medium-sized enterprises (SMEs) is identified as one of the highest priorities in the National Growth and Poverty Eradication Strategy (NGPES), a Lao version of the Poverty Reduction Strategy Paper (PRSP), adopted in October, 2003. The Strategic Action Plan for SME promotion includes areas of: (1) enabling the legal, regulatory, and administrative environment for SMEs; (2) enhancing competitiveness of SMEs; (3) expanding the domestic and international markets for SMEs; (4) improving access to financing by SMEs; (5) improving access to appropriate work premises; (6) creating favorable conditions for the establishment of business organizations; and (7) encouraging entrepreneurial attitudes within the society (Lao PDR 2007, p. 107). Unfortunately, the concept of “micro enterprises” is neglected in this strategic paper. There is no agreed definition about the scale of such enterprises and, in many cases, micro enterprises are included in the group of small enterprises. The important point is that, although the NGPES takes into account equality issues such as “gender,”“Lao vs. ethnic minorities,”1 and “urban vs. rural,”2 the priorities for SME development do not specifically address these issues. This leaves the danger that these priorities could lead to “women, ethnic, and rural blind” actions, particularly as most of the small enterprises in these minority groups are micro enterprises.

Since the establishment of the National Small and Medium-Sized Enterprise Promotion and Development Committee in March 2005, the development projects for SME promotion are increasing dramatically. On the other hand, educational opportunity has expanded to the provinces at an accelerated rate following reforms beginning in 1991 (ADB 2000). The reforms in the education sector may affect entrepreneurial ability in various dimensions, especially for vulnerable groups of woman, ethnic, and rural entrepreneurs. Despite the urgent need to understand more about micro, small, and medium-sized enterprises in Lao PDR, there are very limited research data available, particularly for empirical research. Therefore, this paper aims to provide concrete insights into MSEs in Lao PDR based on an empirical study, particularly around the role of entrepreneurial human capital, so that key stakeholder agencies and groups can better understand this crucial sector, the constraints it still faces, and how best to promote its quantitative and qualitative development.

This paper is structured as follows: Section II reviews the relationships between human capital, entrepreneurship, and micro and small business. Section III provides the context of micro and small business in Lao PDR. Section IV presents the empirical models. Section V describes the data. Section VI analyzes the estimation results, and Section VII presents conclusions.

II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

Considerable efforts continue to be expended to promote entrepreneurship and micro and small business in developing countries through the resources of bilateral and multilateral agencies, as well as nongovernmental organizations such as the Grameen Bank (Hossain 1988; Khandker 1998, 2005). Many of the local government and international support activities target the “informal sector,” defined as micro and small businesses that employ minimal labor and often only one or two people, which contains many MSEs (Birks et al. 1992). Generally, it is argued that a large firm's performance is determined not only by the owner's talent, circumstances, and good luck, but also by his or her human capital, financial capital, and social capital. However, little is known about the determinants that influence the success of MSEs, particularly in developing countries. Is education important, or is practical experience more helpful? Is success governed by constraints on starting capital or access to credit? What, if any, is the influence of technology on the success of micro and small businesses? This is an important area of research, as employment in the informal sector represents the largest share of job growth in many developing countries.

A number of studies have argued that human capital can enhance entrepreneurial performance (Bosma et al. 2002; Cooper, Gimeno-Gascon, and Woo 1994; Kurosaki and Khan 2004; Gimeno et al. 1997; Honig 1998; Pennings, Lee, and van Witteloostuijn 1998; van Praag and Cramer 2001). This is easy to understand since entrepreneurship is a fundamental characteristic of modern knowledge-based economic activities. In this study, two types of human capital is distinguished: general human capital (education), and specific human capital (specific skills or experience). Education pertains to knowledge and skills that are applicable to a broad range of activities, whereas experience pertains to skills relevant to a particular context, e.g., skills relevant to a particular firm or industry.

In terms of the role of education, previous studies have mainly devoted attention to its effect on (new and existing) venture performance with respect to survival, profit, and generated employment, rather than on the likelihood of new venture creation (De Clercq and Arenius 2003). For example, Gimeno et al. (1997) found a positive association between the level of human capital, as measured by education level and work experience, and economic performance at both the entrepreneur's level and the firm's level. Cooper, Gimeno-Gascon, and Woo (1994) found that the unique and specific capabilities of the prospective entrepreneur are an important source of human capital to the new venture, and can contribute to its survival and growth. Furthermore, Pennings, Lee, and van Witteloostuijn (1998) found a negative effect of human capital on firm dissolution. In other words, firm-level human and social capital could be important sources of competitive advantage, especially when the capital was specific to a firm or was held by its owners.

Honig (1998) found the importance of considering heterogeneity when examining micro entrepreneurship and the influence of human capital variables. Whereas the returns to experience in current business were universally positive, different structural environments (with vs. without employees and low vs. high technological tier) may considerably alter the returns to schooling. Similarly, Kurosaki and Khan (2004) indicated that the educational level of enterprises and type of business (low-end vs. high-end) are positively correlated. High educational attainment seemed to enhance the ability to manage (valued-added) enterprises, which was necessary for the household to enter into high-end business.

Bosma et al. (2002) found that the endowed level of talent of a small business founder is not the unique determinant of performance. Rather, investment in industry-specific and entrepreneurial-specific human and social capital contributes significantly to the explanation of the cross-sectional variance in the performance of small firm founders. In addition, van Praag and Cramer (2001) used a unique measure of success (labor demand) which has some interesting policy implications compared to other measures in the literature (e.g., survival and profit/earnings). They concluded that education strongly influences successful entrepreneurship, particularly if it is at intermediate levels.

On the other hand, owners of MSEs in low-income economies rarely keep financial records and typically fail to distinguish between household and business transactions. Due to this lack of separation, detecting exactly how much, if any, return to capital has occurred over a specific period is very difficult. Because access to credit is the primary path of assistance to MSEs for bilateral and multilateral agencies, as well as governmental and nongovernmental organizations, a close examination of the owners of firms who received credit support is both warranted and useful. The constraint of insufficient financial capital for the informal sector has been well documented (Hashemi, Schuler, and Riley 1996; Holt and Ribe 1991; Stiglitz and Weiss 1981; and Von Pischke, Adams and Donald 1983).

III. MICRO AND SMALL BUSINESS IN LAO PDR

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

There is no agreed definition in Lao PDR for different enterprise sizes in different sectors. In this study, we define the size of a micro enterprise as 1–2 workers and a small enterprise as 3–19 workers. The classification system adopted here is based on the number of workers employed. On average, about 80% of MSEs in the total sample are in the “micro” group, and this share is a slightly lower in urban areas. The national household survey conducted in 2002/3 classified MSEs according to the International Industrial Standard Classification (ISIC). As shown in Figure 1, the commerce sector is the largest, accounting for 55% of all MSEs and generating 53% of MSEs based on employment in 2002/3. This sector has been the largest sector since the introduction of market liberalization. The biggest proportion of this sector, and indeed of all MSEs, is retailing. This trend remains the same as in the 1996 National Survey on Small and Medium Enterprises.3 It is worth noting that the results of different studies must be compared with caution due to possible different coverage. Nevertheless, in terms of number of establishments,4 the manufacturing sector decreased drastically from 34% in 1996 to 15% in 2002/3. This decrease occurred by the expansion of textile and garment factories that absorbed the individual workers. Manufacturing and agriculture are characterized by low levels of productivity. Most production, including textiles, garments, food/wood processing, and construction materials, is small-scale and many activities are in rural areas. By contrast, the transport sector jumped up to 8% resulting from the development of the economy and the increase of tourists. The construction sector also accounts for a relatively small percentage of MSEs.

image

Figure 1. Distribution of MSEs by Industry Sectors

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The characteristics of the business varied between female- and male-owned enterprises. Females tended to operate in the commercial sector (vending, retailing, and textiles). Males dominated most of the construction, transport, manufacturing sectors (food processing, wood processing, and metal products), and other services activities. The high proportion of male-owned enterprises in food processing may come as surprise. As can be observed, the dominant activities in this sector are rice milling and ice making, which involve the use of machinery and are often thought of as “male” occupations. Similarly, other services activities include mainly watch repair, and motorbike/bicycle repair.

In terms of employment creation, MSEs have played a very important role in providing employment opportunities during the process of economic transition in developing countries (ILO 2000). For the case of Lao PDR, the contribution by MSEs in terms of employments is about ten times greater than that of large enterprises. It is estimated that MSEs account for the most active nonagricultural labor force in rural areas (ILO 2002). In 2002/3, the average number of workers (the sum of household members and non-household members5) including the owners of the enterprises is only 1.95, although this varies across sectors and regions. The workforce in this case is mainly enterprise owners and unpaid family members. Unpaid family workers tend to support the business by preparing the materials and opening or closing the shop. Some entrepreneurs are purely micro family business, while some are in farmer households, but generally donot provide labor force in farming.6 Only 20% are paid employees. Characteristically, part-time workers are less common in Lao PDR. Two-thirds of the employed persons in Lao PDR work at least 40 hours per week and very few works less than 20 hours per week. These MSEs have the capacity to generate significant levels of employment if they could survive and grow. This is important because it has been difficult for Lao PDR to attract investment for larger level enterprises, mainly as a result of the country's lack of trained personnel, the extent of bureaucratic red tape, and its landlocked location that means there is no direct access to seaports. Specific impediments to growth include unsupportive regulatory environments and lack of access to credit and business skills.

The education level of micro and small entrepreneurs is still low, especially for vulnerable ethnic minorities in rural areas.7 Overall, the average schooling of owners is around five years or the primary level. Rural entrepreneurs have fewer educational attainments than those in urban areas in all categories. More rural entrepreneurs (41%) than urban (28%) have not attained a full primary education. They also have fewer attainments for tertiary education, 5% compared to 12%. Similarly, females have less educational attainments than males. 16% of female entrepreneurs receive no education, 46% have a primary education, and 13% have a secondary education or higher. The corresponding education levels for males were 10%, 51%, and 22%.

Most micro and small business owners receive informal training from family members or friends. Indeed very few have formal vocational training. On the demand side, entrepreneurs want skills in management and marketing. The majority of training, however, is technical rather than managerial and specific to the certain kind of enterprise (e.g., textiles). The technological level of MSEs is very low. Many entrepreneurs have made no technology improvement in their business in the past year. On the supply side, there are serious concerns about the capacity of teachers (in both the formal and informal training systems) to match with regional investment and labor markets.

Access to credit in Lao PDR is generally poor and only a few MSEs could obtain some form of formal or informal credit. This information is not directly available in the survey used in this study. Nevertheless, indirectly, we find that about 15% of MSEs have access to credit, measured using the question whether any members of the household owe money to anyone. According to ILO (2002), only one enterprise in five has ever received a loan. Less than 2% of all MSEs have ever received a bank loan. 11% of MSEs, which is about half of those receiving any kind of credit, obtain their loans from family members. The pattern for rural and urban MSEs accessing credit is strongly similar. While female entrepreneurs manage to obtain credit more than male entrepreneurs, the average loan for a woman is substantially smaller than that for a man. MSEs often lack legally recognized assets as collateral for the bank. There appears to be a greater chance of finding finance from the informal sector in various forms, such as village revolving funds (VRF), household-to-household loans, rotating fund groups (or houay), rice/buffalo banks, and moneylenders (see UNDP/CDF 1997). These are easiest to obtain despite the fact that the interest rates charged could reach up to 20% a month or be as much as 60% a year.

In addition, it is commonly observed that a household might engage in plural nonfarm activities in other economies. However, in the Lao case, joint production is quite low, and perhaps much lower than one may expect. Only 17% of households do plural nonfarm business. In many cases, MSEs tend to combine commerce (retails) with other business activities by different owners from their family members. In this study, we specify the primary industrial classification by share of the sales.

IV. EMPIRICAL MODELS

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

The economics of the acquisition of entrepreneurial ability is still in its infancy. In pioneering work, Knight (1921) argued that uncertainty bedevils many economists in analyzing entrepreneurship. Leff (1999) provided a review of entrepreneurship and economic development. The entrepreneur does not appear as an explicit economic agent in nearly all of the production function literature. The abilities of entrepreneurs to deal with disequilibria that are pervasive in a dynamic economy are a part of the stock of human capital. Schultz (1980) argued that the reward (earnings) for the entrepreneurship of most human agents is small, but in the aggregate in a dynamic economy, entrepreneurship accounts for a substantial part of increases in national income. He suggested that investment in entrepreneurial ability implies the returns to education that actually occur are substantially undervalued.

In order to measure the returns to entrepreneurial human capital, this study applies the Mincerian human capital earnings function, which hypothesizes that education is an investment that yields higher earnings in return for individual variations in schooling and work experiences. Our basic model is taken from Mincer (1974):

  • image(1)

where Yi is average monthly sales for an entrepreneur i. Schi is a measure of his/her schooling (raw form or continuous number), and Exi represents a measure of current business experience. Xi are dummy variables indicating female, number of workers, ethnic minorities, type of businesses, business locations, operation months, and rural and regional areas. ui is a residual error. Most models rely on a proxy of age less schooling or potential work experience, but this study uses the exact number of years that micro and small entrepreneurs have engaged in current business experience .

The earnings function method is also used to estimate rates of return per year to different levels of schooling for entrepreneurs with a complete level of schooling. After fitting, the extended earnings function (dummy form) is given by:

  • image(2)

where PRIi, SECi, TECi are primary, secondary, technical and university education by individual i, respectively, and no education is the omitted category. The rates of return per year to different levels of schooling are then calculated as follows:

  • image(3)
  • image(4)
  • image(5)
  • image(6)

The typical route, primary 5 years + secondary 6 years (lower 3 years + upper 3 years) + tertiary (technical 3 years or university 5 years), is analyzed in this study. However, it is incorrect to assume that primary school graduates forego earnings for the entire duration of their studies. Two or three years of foregone earnings while in primary schooling have been used in some empirical literature, but this study applies only one year of foregone earnings for primary school graduates, which is often assumed in recent studies (see Psacharopoulos (1995) for the concepts and methods in estimating rate of returns to education).

V. DATA DESCRIPTION

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

Despite the urgent need for information on private sector development, the statistics in Lao PDR are very limited. In this paper, the authors apply the data of Lao Expenditure and Consumption Surveys (LECS 3) in 2002/3 to examine the influence of entrepreneurial human capital on the performance of micro and small business. The survey was conducted by the Swedish International Development Agency (SIDA) and the National Statistical Centre of Lao PDR. It was undertaken from March 2002 to February 2003 by interviewing 8,092 households, 49,790 persons from 540 villages. There are 1,956 samples reporting on the household business. 76 samples are dropped due to missing data. The LECS 3 provides the most comprehensive, currently available data on MSEs that represents all industry sectors and geographical regions in Lao PDR. It still lacks two important variables, however, namely profit (income) and financial capital. These factors are very good predictors because each industrial sector/business has its own specific characteristics, such as sales scale, cost of capital, and material cost.

Estimates of profit and financial capital, especially among informal labor markets in low-income economies, are often subject to speculation as to their accuracy, particularly regarding issues that are sensitive to regulation and taxation. Moreover, the proprietor of an informal firm will have little accurate conception of profit or income, as accounting and bookkeeping are rarely evident, and personal and business accounts typically mix within a single cash box. The dependent variable used in this study, average monthly sales,8 is determined through extensive interviews with business owners at the site of their activities, and the data ranges are also compared with a field survey of 250 MSEs conducted in a similar period by a different organization to establish more accuracy (ILO 2004). With respect to financial capital, most entrepreneurs have started their businesses at the age of 20–29 years old, followed by those who were 30–39 years old, together accounting for about 70% of total businesses. The majority of respondents (63% for women and 70% for men) claim that they establish the business alone. Only about 10% inherited the business from their family. Most entrepreneurs in Lao PDR running a small-scale business are the first generation to do so, and about half of them are doing retail with very low capital. These factors together suggest that the impact of difference in financial capital on sales performance is relatively limited.

As a result, the samples are carefully evaluated for consistency and developed into a standard format to make the data more reliable. Thus, the highest 1% and lowest 4% of data (outliers) are eliminated. Finally, 1,776 samples are analyzed: 920 for urban areas and 856 for rural areas. 857 observations are female entrepreneurs. Summarizing the data, Table 1 presents the basic characteristics of the MSEs in Lao PDR. The average schooling of micro and small entrepreneurs is 5.9 years, which is slightly above the national average. Urban entrepreneurs have a higher educational attainment than rural and ethnic entrepreneurs. Poor human capital is indicated in the rural entrepreneurs, of which as many as 40% have less than the primary level (16% have no schooling), and the ethnic entrepreneurs, half of which have less than the primary level (one-fourth are illiterate). At the primary level, education does not vary much among groups and regions. However, the higher the education level, the larger the gap is between male and female, and between urban and rural.

Table 1.  Characteristics of Micro and Small Enterprises in Lao PDR
Mean VariablesTotalUrbanRuralLaoEthnicsAge 15–39Age 40 or Over
  1. Source: LECS 3 (2002/3). Average market exchange rate in 2002 was at 10,056 kip/dollar.

  2. Note: We consider the regional (provincial) differences by grouping provinces from a perspective of market size as measured by population and density, rather than simply group them by north, central, and south regions. Region A includes Vientiane capital, Savannakhet (central), and Champasack (south); Region B includes Luangprabang, Xayabury (north), Vientiane Province, Khammuane (central) and Saravane (south); Region C includes Phongsaly, Luangnamtha, Oudomxay, Bokeo, Huaphanh (north), Xiengkhuang, Borikhamxay, Xaysomboun (central), Sekong, Attapeu (south).

Monthly sales (1,000 kip)1,8602,5541,1132,1159441,6412,079
Number of laborers (persons)1.952.121.772.041.671.752.15
Schooling (years)5.906.794.956.474.146.435.37
 Less than primary (%)34.027.640.828.849.928.939.0
 (No education)(13.0)(10.1)(16.0)(7.0)(24.5)(11.4)(14.5)
 Primary (%)48.447.349.549.644.649.447.3
 Secondary (%)9.212.95.111.23.013.64.7
 Tertiary (%)8.412.24.610.42.58.19.0
Age39.940.039.740.338.731.548.3
Current business experiences5.56.294.655.844.434.346.66
Female (%)48.353.243.048.846.755.441.1
Ethnic (%)24.414.634.9  27.321.5
Business location at traditional market, roadside, etc. (%) (baseline = home)44.651.037.744.943.746.342.9
Yearly operation (%) (baseline = seasonal operation)57.964.950.561.148.055.660.2
Rural dummy (%)48.2  41.569.148.547.9
Region A39.553.824.049.48.837.841.1
Region B39.824.856.035.852.241.038.6
Region C20.721.420.014.839.021.220.3
Observations1,7769208561,343433888888

The average age of micro and small entrepreneurs is about 40 years, regardless of groups or regions. The average business experience is roughly 4 to 6 years. Although potential experience of entrepreneurs is quite long, they seem to have multiple business experiences in the past. According to the report by ILO (2002), new MSEs were born at an annual rate of 25%. A death rate of 15% meant the average annual growth rate was 10%. About 55% of MSEs closed within 4 years. This means entrepreneurs are facing many specific impediments to the survival of their businesses.

The average monthly sales show significant differences between areas and sectors. The average monthly sales for owners in urban areas (2,554,125 kip) are over double that of owners in rural areas (1,113,259 kip). A female-owned enterprise has slightly lower monthly sales than a male-owned enterprise. The difference in sales figures could be a result of the women entrepreneurs' limited mobility and greater dependence on having a business location at home. Over half of the MSEs are based at home rather than a traditional market, roadside or being mobile. Moreover, over 70% of MSEs in the capital operated their businesses all year round, but nearly one-half of MSEs in provinces are seasonal, operating particularly in the off-farm season.

Moreover, we consider the regional (provincial) differences by grouping provinces into three regions from a perspective of market size as measured by population number and density, rather than simply grouping them by north, central, and south regions. Region A includes Vientiane capital, Savannakhet (central), and Champasack (south). These three provinces all have a population greater than 600,000. The density rates are 40 persons/km2 for Savannakhet and Champasack. Only the density in the capital is exceptionally high at 180 persons/km2. However, we group them together to avoid the capital alone being an insufficient sample in terms of rural and ethnics. Region B includes Luangprabang, Xayabury (north), Vientiane Province, Khammuane (central), and Saravane (south). The populations are between 300,000 and 4,000,000 and the densities are between 20 and 30 persons/km2. Region C includes Phongsaly, Luangnamtha, Oudomxay, Bokeo, Huaphanh (north), Xiengkhuang, Borikhamxay, Xaysomboun (central), Sekong, Attapeu (south). The populations are less than 299,999 persons in all provinces and the densities are less than 19 persons/km2, except for Bokeo with 23 persons/km2 but with a population size of only 145,000.

VI. ESTIMATION RESULTS

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

The results of OLS analysis on entrepreneurial human capital and micro and small business are presented in Tables 2 and 3. The analysis especially focuses on four aspects: (1) urban vs. rural; (2) males vs. females; (3) Lao vs. ethnic minorities; and (4) young entrepreneurs vs. old entrepreneurs (progress of economic transition). Overall, this research found that most variables for schooling years and educational levels are statistically significant at least at the 5% level. Education seemed to be a good predictor of success in terms of sales. All educational variables show the positive sign regardless of groups and areas. Entrepreneurs are capitalizing the most from secondary education, but failing to capitalize on practical experience. As expected, the study found significant differences among urban and rural, Lao and ethnic minorities, and young entrepreneurs and old entrepreneurs. These three aspects will be discussed in more detail in the following subsection. However, it is a very valuable contribution made by this research that the difference of sales performance between men and women is very small, i.e. insignificant, in all categories. We expected a larger gender gap as is often seen in most reports of this sector,9 due to the fact that female entrepreneurs generally have lower schooling, lack of access to credit and less mobility. It is clear, however, that female entrepreneurs could manage their business as well as male entrepreneurs. Thus, it is questionable for development policy to target only female entrepreneurs.

Table 2.  Determinants of Micro and Small Enterprises Performance in Lao PDR: Use of Schooling Years (Dependent Variable: Log (Average Monthly Sales) in Kip)
VariablesTotalUrbanRuralLaoEthnicsAge 15–39Age 40 or Over
  • Notes: 1. t-statistics in parentheses.

  • 2. The omitted category for type of business is other service activities.

  • 3. White heteroskedasticity consistent covariance is applied for equations in columns (1), (2), (3), and (6).

  • ** and * 

    represent statistical significance at the 1% and 5% level, respectively.

Schooling0.0487**0.0358**0.0660**0.0437**0.0704**0.0557**0.0397**
(7.26)(4.09)(6.29)(5.94)(4.42)(5.81)(4.11)
Business experience0.0072−0.01260.03260.00790.00530.03720.0078
(0.59)(−0.81)(1.72)(0.58)(0.16)(1.47)(0.49)
Experience-squared/1000.02110.0900−0.09740.01730.0128−0.22100.0216
(0.39)(1.46)(−1.04)(0.29)(0.07)(−1.40)(0.34)
Female−0.01520.0085−0.0800−0.0178−0.01060.0297−0.0671
(−0.26)(0.10)(−1.03)(0.26)(−0.09)(0.34)(−0.83)
Ethnics−0.4075**−0.2976*−0.4689**  −0.2905**−0.5545**
(−6.09)(−2.52)(−5.75)  (−3.10)(−5.97)
Log (labor)0.6930**0.7640**0.5649**0.6924**0.6672**0.5828**0.7401**
(12.51)(9.87)(7.12)(11.13)(5.32)(6.68)(10.01)
Non–family-labor input−0.1297−0.19760.0269−0.1229−0.0565−0.0558−0.1516
(−1.53)(−1.71)(0.21)(−1.28)(−0.30)(−0.44)(−1.30)
Access to credit−0.0239−0.0155−0.0430−0.05290.0734−0.14130.1473
(−0.35)(−0.16)(−0.44)(−0.69)(0.46)(−1.59)(1.40)
Second business−0.3716**−0.4405**−0.2603*−0.4078**−0.2812−0.2565*−0.4735**
(−5.27)(−4.72)(−2.43)(−5.14)(−1.83)(−2.50)(−5.06)
Manufacturing−0.2883**−0.2956*−0.2872*−0.3080**−0.2630−0.1994−0.4132**
(−3.32)(−2.30)(−2.42)(−3.02)(−1.54)(−1.53)(−3.37)
Construction0.23250.21660.37870.21060.36060.45000.0626
(1.37)(0.98)(1.58)(1.09)(1.35)(1.74)(0.27)
Commerce0.2737**0.2664*0.2884**0.2754**0.23740.3709**0.1825
(3.83)(2.54)(2.93)(3.39)(1.53)(3.38)(1.91)
Transport0.4784**0.2875*0.7466**0.3626**0.8577**0.6654**0.3133*
(4.38)(2.03)(4.36)(3.00)(3.54)(4.18)(2.05)
Business locations0.1530*0.1998*0.06570.1375*0.20960.03020.2631**
(2.74)(2.54)(0.83)(2.13)(1.86)(0.38)(3.30)
Yearly operation0.2734**0.3730**0.1699*0.3308**0.15080.2483**0.2825**
(5.09)(4.62)(2.31)(5.32)(1.34)(3.29)(3.67)
Rural area−0.4017**−0.3618**−0.5185**−0.3699**−0.4400**
(−7.18)  (5.71)(−4.21)(−4.43)(−5.75)
Region B−0.1172−0.2093*−0.0191−0.1371*−0.1340−0.0257−0.2210**
(1.91)(−2.36)(0.22)(−2.06)(−0.64)(−0.28)(−2.64)
Region C−0.2325**−0.2610*−0.1983−0.1889*−0.3550−0.1626−0.3033**
(2.99)(−2.32)(−1.79)(−2.07)(−1.61)(−1.44)(−2.82)
Constant12.638613.400012.230513.135312.515012.481812.6934
(98.97)(69.98)(70.88)(100.45)(41.48)(62.44)(75.48)
Adjust R20.3180.2500.2520.2610.2380.2380.395
F-test46.88**19.06**17.98**28.89**8.92**16.38**33.24**
Observations1,7769208561,343433888888
Table 3.  Determinants of MSEs Performance in Lao PDR: Use of Educational Level (Dependent Variable: Log (Average Monthly Sales) in Kip)
VariablesTotalUrbanRuralLaoEthnicsAge 15–39Age 40 or Over
  • Note: t-statistics in parentheses.

  • ** and * 

    represent statistical significance at the 1% and 5% level, respectively.

Primary0.3263**0.01880.5059**0.4438**0.14730.4160**0.1687
(3.85)(0.14)(4.77)(4.30)(0.97)(3.14)(1.53)
Secondary0.6825**0.5134**0.6247**0.7457**0.6861*0.7742**0.7398**
(5.95)(3.35)(3.31)(6.01)(1.97)(4.96)(3.52)
Technical0.5860**0.24340.6995**0.6513**0.5300*0.8081**0.2940
(4.34)(1.44)(3.22)(4.51)(1.16)(4.31)(1.50)
University0.6839**0.46190.6275*0.7028*0.56580.9874*0.3789
(2.68)(1.37)(2.21)(2.49)(1.66)(2.34)(1.20)
Business experience0.0012−0.00870.01930.0128−0.01950.00040.0270
(0.07)(−0.35)(0.79)(0.64)(−0.37)(0.01)(1.20)
Experience-squared/1000.02990.0738−0.0909−0.00690.0418−0.0954−0.0584
(0.36)(0.71)(−0.77)(−0.08)(0.12)(−0.44)(−0.63)
Female0.02980.0263−0.03370.0384−0.02060.1506−0.1080
(0.38)(0.23)(−0.33)(0.41)(−0.14)(1.31)(−0.98)
Ethnics−0.3985**−0.3044*−0.4235**  −0.3185*−0.4960**
(−4.52)(−1.98)(−3.92)  (−2.46)(−4.21)
Log (labor)0.7892**0.8433**0.6493**0.7888**0.6636**0.7258**0.7685**
(10.17)(7.64)(5.95)(8.96)(3.63)(5.99)(7.51)
Non–family-labor input−0.2411*−0.2528−0.1115−0.1689−0.3505−0.2087−0.1821
(−2.12)(−1.59)(−0.65)(−1.31)(−1.48)(−1.16)(−1.16)
Access to credit0.11530.10390.08700.06130.21740.01820.3103
(1.14)(0.68)(0.67)(0.54)(0.95)(0.14)(1.91)
Second business−0.3991**−0.4467**−0.3676**−0.4804**−0.2838−0.3825**−0.4042**
(−4.25)(−3.53)(−2.76)(−4.48)(−1.38)(−2.85)(−3.09)
Manufacturing−0.3119**−0.3856*−0.3021−0.3486*−0.2314−0.0937−0.4831**
(−2.69)(−2.26)(−1.90)(−2.55)(−0.99)(−0.53)(−3.06)
Construction0.0011−0.0074−0.14710.0220−0.03470.2849−0.2177
(0.00)(−0.02)(−0.30)(0.06)(−0.10)(0.56)(−0.54)
Commerce0.2524*0.2791*0.22390.2404*0.34560.3644**0.2022
(2.58)(1.96)(1.64)(2.14)(1.54)(2.60)(1.46)
Transport0.3476*0.04880.7218**0.26980.8361*0.7027**0.0367
(2.28)(0.26)(3.00)(1.69)(2.04)(2.99)(0.18)
Business locations0.1574*0.2175*0.09110.10980.29160.10580.1860
(2.12)(−2.01)(0.87)(1.27)(1.90)(1.00)(1.75)
Yearly operation0.3280**0.3623**0.3490**0.41090.21210.2864**0.3380**
(4.41)(3.21)(3.59)(4.69)(1.39)(2.74)(3.15)
Rural area−0.4332**−0.3872**−0.4727**−0.3526**−0.5296**
(−5.68)  (−4.45)(−2.79)(−2.98)(−5.20)
Region B−0.1143−0.2117−0.0325−0.1004−0.2579−0.0476−0.1697
(−1.40)(−1.78)(−0.29)(−1.13)(−1.00)(−0.38)(−1.57)
Region C−0.2229*−0.2961−0.1983−0.1639−0.4023−0.0464−0.4342
(−2.06)(−1.83)(−1.31)(−1.26)(−1.62)(−0.29)(−2.82)
Constant12.641413.489312.379512.971212.854012.525012.6165
(73.36)(55.15)(52.56)(70.55)(32.51)(48.01)(54.53)
Adjust R20.3720.2930.2970.3210.2200.2900.448
F-test27.35**11.12**10.34**17.43**4.32**9.89**19.49**
Observations935488447697238456479

A. Rural and Ethnic Entrepreneurs under the Economic Transition

Factors that are thought to be related to MSEs' performances can be divided into two categories: “human capital” and “others.” Starting from the results of all factors except for human capital, this study finds that there is no significant regional difference in MSEs sales performance between north, central, and south, but the disparity within the region is very large. Whereas entrepreneurs in Region B and C earn less than those in Region A by 12–23%, rural entrepreneurs earn nearly one half less than urban entrepreneurs (Table 2, column 1). It should be noted that MSEs in rural areas face significant disadvantage due to the small scale of their market, and many MSEs run their business as a side job in the off-farm season. Rural enterprises also have disadvantages in access to roads and electrical power. According to the National Statistical Centre, about 84% of rural villages with roads are accessible in the dry season and 65% in the rainy season (Lao PDR 2004). The conditions are worse for rural villages without roads, as only 35% of them are accessible in the dry season, and 17% in the rainy season. Although most urban villages have electricity, less than one-third of rural villages have access to electricity. Access to infrastructure in rural areas is generally lower in the northern part. Moreover, taxes and bureaucratic obstacles also impede the movement of goods within the country. An entrepreneur needs to obtain a transit permission document and pay a certain amount of taxes, depending on the type of goods and their value.

A 1% increase in the number of workers results in an increase in monthly sales of 76% for urban enterprises and 57% for rural enterprises, due to the difference of market scale. A small number of MSEs employed non-household members (mainly unpaid labor). The estimation results show that this factor is statistically insignificant in all categories. Some previous studies on MSEs in Lao PDR, for instance ILO (2002, 2004), claimed that access to credit is an obstacle for micro and small entrepreneurs. However, this study finds that although few MSEs have access to credit, this factor does not have significant impact on their performances in all categories. With respect to joint production, it is perhaps surprising to observe that in the sample survey applied in this paper only about 17% of households do plural business. The estimation results show that second businesses (measured by share of sales) have significantly lower sales in all categories, varying from 26% to 47% less than the primary/single business.

In terms of specific industry, micro and small entrepreneurs in commerce activities earn more than other sectors in urban areas, whereas rural owners earn the most in the transport sector. Entrepreneurs in manufacturing earn less than other sectors in both urban and rural areas. It is worth noting again that the manufacturing sector in Lao PDR is characterized by low levels of productivity. Most production is home-based and small scale, e.g., textiles and garments, food and wood processing, and construction materials. The micro and small manufacturing sector plays an important role in producing goods for the domestic market. Considering the concerns of business survival, growth and new birth rate of this sector, the government and related agencies should pay more attention on finding ways to support them.

Furthermore, ethnic minorities face a significant disadvantage compared with the Lao majority. Many ethnics are located in rural areas running seasonal MSEs. Some ethnic subgroups recently migrated from the highlands to the plains and the valleys, and thus lack business networks/customers. They have difficulties in speaking Lao language properly and, in some cases, they also face discrimination. Over half of MSEs are based in homes rather than in a traditional market, roadside or being mobile. Enterprises based on home business earn about 15% less than those located in a business location. Nearly one half of MSEs in rural areas are seasonal, operating particularly in the off-farm season. They earn about 17% less than firms operating year-round. In contrast, most MSEs in urban areas operate the business all year and the monthly sales are roughly 37% higher than the businesses run seasonally.

In terms of entrepreneurial human capital, an additional year of schooling would yield 3.6% and 6.6% more earnings in urban and rural, respectively (Table 2, column 2 and 3). Theses rates are similar to the returns to wage earners found in Onphanhdala and Suruga (2007). For ethnic minorities, the rate of returns to education is higher at 7.0% (Table 2, column 4).10 Practical experience is an important factor in operating a business in rural areas, but it is not significant in urban areas and among ethnic minorities. Since the research on micro and small entrepreneurial human capital, especially for developing countries, is still scarce, it is difficult to compare these results directly with other studies. The estimated rates of return to schooling in this study are approximately the same as other findings, for example, Gimeno et al. (1997) found 6.8% for the case of the United States. However, Psacharopoulos and Patrinos (2002) studied returns to investment in education worldwide and concluded that the mean rate of returns to schooling of wage earners for low-income countries is higher than that of high-income countries (10.9% vs. 7.4%). Considering this pattern, the benefits of investment in education might not be very high for Lao PDR. Nevertheless, these findings show that there is a fairly high demand for schooling among micro and small entrepreneurs (self-employed) in this country.

On the other hand, a cross-sectional sample may not be very instructive in a rapidly changing economy. For the countries under economic transition, it is very interesting to examine the changes in the returns to human capital and other factors during the transition. Thus, our samples are divided into two groups: entrepreneurs that are 15–39 years old and 40 years old or over. Younger entrepreneurs are expected to be more effected by recent changes as they enter directly into a free market economy.

The analysis reveals that the rate of returns to schooling for younger entrepreneurs at 5.6% is considerably higher than that for older entrepreneurs at 4.0% (Table 1, columns 5 and 6). The high rate of returns observed for younger generations is one bright sign that the returns might increase more as the market reforms take full effect. The high returns to schooling could lead to a higher demand for education for the next generation. We also found that very important changes occurred during the economic transition. It can be observed that the difference between Lao and ethnic minorities within the group of older entrepreneurs compared to the group of younger entrepreneurs is narrowing from 55% to 29%. Similarly, the disparity between rural and urban within these two groups is decreasing from 44% to 37%. These results indicate that younger rural and ethnic entrepreneurs tend to have better opportunities to utilize their abilities in this economic transition and the integration of domestic market.

B. Investment in Entrepreneurial Ability

In the previous sections, the rates of return to schooling have been estimated for the entire samples. In this section, the rates of return to schooling for various education levels (completed levels of schooling only) will be addressed. The full estimated results are presented in Table 3, and the summary of rates of return per year is shown in Table 4. By estimating the equations in the form of educational levels, we found information that is very useful in development policy planning for different groups and areas. For the entire sample, the rates of return to primary, secondary, technical, and university education are at 33%, 6%, –3%, and 0%, respectively. Primary education seems to be the most beneficial in increasing a firm's performance. These results indicate that the returns to post-secondary education both at the technical level and university level appear to be over-education for MSEs' entrepreneurs in Lao PDR.

Table 4.  Summary the Rates of Return to Schooling by Level of Education
 TotalUrbanRuralLaoEthnicsAge 15–39Age 40 or Over
  1. Notes: 1. Based on the earnings functions results presented in Table 3.

  2. 2. All variables are significant at the 5% level or better, except where indicated by parentheses.

Primary (vs. No education) (%)32.63(1.88)50.5944.38(14.73)41.60(16.87)
Secondary (vs. Primary) (%)5.948.241.986.0410.787.1611.42
Technical (vs. Secondary) (%)−3.22(−9.00)6.45−3.15−5.201.13(−14.86)
University (vs. Secondary) (%)0.03(−1.03)0.06−0.86(−2.41)4.26(−7.22)

When we look in detail at the differences between groups and areas, however, we can observe a different picture. Overall, younger entrepreneurs tend to receive a larger benefit from investment in education, particularly for the primary level. More precisely, for rural entrepreneurs, the best investment in education is obviously the primary level, and the rate of returns is as high as 51% compared to the reference point of no education. Even if two or three years of foregone earnings while in primary schooling is used in calculating the profitability of investment in schooling, the rate of returns to primary education is still higher than other educational levels. Since many rural entrepreneurs are illiterate, the finding suggests that having basic literacy and numeracy skills is very important to an entrepreneur's day-to-day operations, and this would bring on economic advantage for them. In contrast, for urban entrepreneurs, the rate of returns to primary education is very low and statistically insignificant. In urban areas, having a basic literacy and numeracy are already common, and higher education and better management skills has become a necessary condition.

A general result of this study is that the rates of return to technical and university education are low. These results are contradictory to the findings by many researchers. For example, Bosma et al. (2002) and van Praag and Cramer (2001) found that the returns to higher education are the largest for Dutch entrepreneurs. Kurosaki and Khan (2004) came to the same result for the case of Pakistan. It is not easy to explain this reverse phenomenon. It could be interpreted in various ways. In urban areas, many entrepreneurs are seeking higher returns to human capital by self-selection into businesses that have a certain amount of market potential. Those with higher education degrees may not be able to fully utilize their ability due to underdeveloped legal and institutions, or they may simply have been waiting for a better opportunity, whereas those with basic education degrees may have made a quicker response to market environments. Moreover, the quality of knowledge and skills provided by technical schools may be insufficient, and in many cases, the higher education they obtained is mismatched with what they are doing. On the other hand, in rural areas, higher education may be somewhat esoteric and provide little advantage for the small-scale, simple business activities.

VII. CONCLUDING REMARKS

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES

At present, the Lao government aims to reduce poverty through agriculture-related businesses and to target rural entrepreneurs, ethnic minorities, and family businesses. However, the existing legal and policy framework favors large enterprises. For Lao PDR, MSEs dominate the private sector with high potential growth in income generation and job creation at the household level. This sector is undergoing dynamic change during the process of economic transition. Existing studies on this subject provide mainly statistic and descriptive analyses, often based on a small number of samples. Most reports insist upon the improvement of regulations, access to credit, and business skills, whereas the necessity of entrepreneurs' education has received little attention. There is an urgent need to have updated statistics for MSEs in general, and for empirical analysis in particular. Thus, thispaper contributes significantly to the very limited knowledge base in order to promote this sector.

This research found that education of entrepreneurs is very important for increasing the sales performance of MSEs. The rates of return to schooling are ranging from 4% to 7% for different groups and areas. The advantages to conventional formal education have outweighed the returns to practical business experience. Primary education demonstrates a very high benefit in enhancing the performance of MSEs in rural areas, and secondary education yields favorable returns for those in urban areas, but post-secondary education is found to be over-education, having little impact on increasing sales for entrepreneurs in all categories. Therefore, policy makers should target the provision of a suitable level of education to the groups of micro and small entrepreneurs, especially primary education (basic literacy and numeracy) for illiterate entrepreneurs in rural areas. Basic education is also a fundamental element for further study in accounting and bookkeeping. Further education may be helpful for improving business efficiency and, perhaps, innovation in the future. Most entrepreneurs run their businesses in isolation, without extensive interaction with business associations and networks. So far, skills development is insufficiently linked to market demand and there are also ongoing problems with the basic quality of and capacity building for teachers and trainers. Thus, local and international organizations should improve the delivery of business-skills training programs suitable for MSEs.

Furthermore, this research found that entrepreneurs in certain sectors and locations have more disadvantages. It is worth noting again that many existing studies claim that gender disparity is an issue, but the empirical work provided here shows that female entrepreneurs could perform well, and it is misleading to focus on this issue. Initiatives to develop MSEs by related agencies should be made more sensitive to entrepreneurs in the manufacturing sector, rural areas and to the ethnic minorities. In addition, this analysis reveals that the return to schooling for younger entrepreneurs is considerably higher than that for older entrepreneurs. The differences between Lao vs. ethnic minorities and rural vs. urban within the group of older entrepreneurs compared to the group of younger entrepreneurs are narrowing. These are strong indications that the Lao government and concerned agencies should allocate more effort and resources to promote this sector.

REFERENCES

  1. Top of page
  2. Abstract
  3. I. INTRODUCTION
  4. II. HUMAN CAPITAL, ENTREPRENEURSHIP, AND MICRO AND SMALL BUSINESS
  5. III. MICRO AND SMALL BUSINESS IN LAO PDR
  6. IV. EMPIRICAL MODELS
  7. V. DATA DESCRIPTION
  8. VI. ESTIMATION RESULTS
  9. VII. CONCLUDING REMARKS
  10. REFERENCES
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Footnotes
  • 1

    Lao PDR is a very ethnically diverse country (49 different subgroups), especially in the north and the west (Lao PDR 2004). In view of this diversity, we subdivide the geographical and gender groups by ethno-linguistic affiliation: “Lao,” the majority group accounting for 55% of the population; and “ethnics,” which includes the Tai-Kadai (10%), Mon-Khmer (23%), Tibeto-Burman (3%), and Hmong-Mien (9%). We exclude other smaller groups (1%) from the analysis.

  • 2

    According to the National Statistical Centre, a village is categorized as “urban” if it has three out of the following five conditions: (1) located in major city of the province, (2) more than 70% of households use electricity, (3) more than 70% of households use piped water, (4) accessible by road in all seasons, and (5) having a market open daily.

  • 3

    See the details of the 1996 MIH-GTZ (Ministry of Industry and Handicraft and the German Development Agency) survey in the report of ILO (2002).

  • 4

    Note that the differences between the number of establishments and the number of employments in both surveys are considerably small.

  • 5

    Only about 17% of MSEs use non-household members. It is worth noting that this variable cannot be directly equated to paid labor input, because a number of them do not receive a salary.

  • 6

    To a Laotian, it is natural to refer to MSEs rather than rural nonfarm activities. Rural entrepreneurs have only started practicing nonfarm income generating activities in the early 1990s following the introduction of the market-oriented economy. It is uncommon to consider that entrepreneurs engage both in farm and nonfarm activities. Most members of farmer households who run MSEs are purely full-time micro business entrepreneurs. Moreover, the probability of sample selection bias (bias estimates of returns to schooling) caused by a member with relatively high or the highest educational attainment engaging in rural nonfarm activities is very low for the Lao case. Typically, the schooling gap among household members is not large (due to a limited school supply), and there is no practice of selecting a member having a higher education attainment to engage in nonfarm activities. In contrast, children who have the ability to study are often chosen to continue their studying, whereas children who cannot study well are often chosen to work in their own family business as unpaid family members.

  • 7

    If household income is highly correlated with education levels of household members, it may cause spurious correlation between sales and education levels. However, this correlation is very low: sales vs. schooling, no education, primary, secondary, technical, and university education = 0.184, −0.134, 0.007, 0.154, 0.036, and 0.054, respectively.

  • 8

    It is more preferable to use variables such as profit or value added, but this information is not available for our case. Moreover, seasonal operation is a common trait of MSEs, especially in the rural areas. Average monthly sales used in this study are obtained from average sales during actual operating months.

  • 9

    For example, the average sales and value added for a male-owned enterprise were almost twice as high as the average for a female-owned enterprise in the 1996 survey (ILO 2002, p.13). In the 2003 survey, the average monthly sales for a male-owned enterprise were higher by 73% than the average for a female-owned enterprise (ILO 2004, p. 29).

  • 10

    We also ran regressions for only the largest sector, commerce. The results are generally consistent with the total samples and reiterate the importance of education on MSEs' performance. The rates of returns to education are 4.9% (total), 3.0% (urban), 7.2% (rural), 4.8% (Lao), 6.4% (ethnics), 6.0% (young), and 3.5% (old), with all coefficients statistically significant at least at the 5% level.