JUNE 1998 - World Bankdocuments.worldbank.org/curated/en/128931468049473167/... · Web viewJune...

JUNE 1998

Gender Earnings Differentials in the Microenterprise Sector:Evidence from Rural and Urban Mexico

Susana M. SánchezThe World Bank

Latin America RegionFinance, Private Sector and Infrastructure

Washington, DC 20433 Tel. (202) 473-1856 Fax (202) 522-2106

Email: [email protected]

JEL Classification:Keywords: Microenterprises; Women; Rural; Mexico

_____________________________I thank William F. Maloney, José A. Pagán, and Rodrigo A. Chaves for helpful discussions and Maria Correia, Wendy V. Cunningham, Arianna Legovini, Yira J. Mascaró, and Mark Schreiner for comments. The views and interpretations expressed in this article are solely of the author. They do not represent those of the World Bank or its member countries.

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Gender Earnings Differentials in the Microenterprise Sector

Evidence from Rural and Urban Mexico

by Susana M. Sánchez

Abstract

This study examines the sources of male-female earnings differentials in Mexico’s microenterprise sector. Using the 1994 Survey of Rural Entrepreneurs and Financial Services (conducted in the rural regions of Puebla, Guanajato, and Veracruz) and the 1992 National Survey of Urban Microenterprises, I found that female-headed microenterprises in rural and urban areas earn, respectively, 64% and 50% of less than male-headed microenterprises. About 35% (42% in rural areas) of the earnings gap in urban areas can be explained by differences in productive characteristics, whereas 62% (59% in rural areas) can be attributed to structural productive factors, such as household constraints, productivity, and access to education and credit services. Furthermore, a small share of the gender earnings gap is explained by gender differences in sectoral distributions. From a public policy perspective, the results suggest that policies attempting to reduce earnings disparities should focus on addressing gender differences in productive characteristics and structural factors across economic sectors because efforts to reallocate women’s microenterprises to other economic sectors would have a limited impact on increasing their earnings.

1. Introduction

Women are active participants in the microenterprise sector in both developed and less

developed countries accounting for about two-thirds of microenterprises in some countries

(Rhyne and Holt, 1994; Cohen, 1996; Evans and Leighton, 1989). Despite the role of women

in the microenterprise sector, women’s microenterpises earn substantially less than those

of men’s, are concentrated in the lower end of the size spectrum, have different sectoral

distributions than that of men’s, and are more likely to work from their dwellings (Rhyne

and Holt, 1994; Cohen, 1996). These gender differences in entrepreneurial patterns have

attracted the attention of policymakers and international organizations, especially because

women, who are likely to be affected by poverty, have increased their participation in the

microenterprise sector (World bank, 1994a). Although most countries have targeted

programs devoted to microenterprise development as well as the promotion of small-firm

formation (Wilson and Adams, 1994)1, only in recent years policymakers have started to

incorporate in their microenterprise development agenda that the male and female

segment of the microenterprise sector could face different constraints to growth and

profitability.

This study examines the sources of earnings differentials between male- and

female-headed microenterprises in Mexico’s rural and urban sectors. In particular, it

examines the roles of gender differences in size of operations, returns to productive factors,

and sectoral distribution. In the case of Mexico’s microenterprise sector, female owners of

microenterprises account for 26-44% of microenterprise, but their earnings (profits) are 36-

50% of what their male counterparts earn. The importance of the microenterprise sector as

a source of household income and employment makes understanding gender disparities in

the sector relevant for effective public policy formulation on microenterprise development,

poverty, income distribution, and labor market efficiency.

The determinants of gender earnings differentials in the microenterprise sector are

examined using the 1994 Survey of Rural Entrepreneurs and Financial Services (SREFS:

1 For example, United States, France, Germany, and the United Kingdom have self-employment programs for the unemployed (Wilson and Adams, 1994).

Encuesta Regional de Servicios Financieros a Unidades de Producción Rural) and the 1992

National Survey of Urban Microenterprises (ENAMIN: Encuesta Nacional de Micronegocios),

both conducted by the Mexican National Statistical Institute. The rural survey selected

1,944 households in localities with a population of 1,000 to 20,000 inhabitants in the states

of Guanajuato, Puebla and Veracruz. All owners of microenterprises (about 504) were

interviewed. The urban survey interviewed 9,036 microenterprise owners with less than six

employees (15 in manufacturing) that were selected from the last quarter of the 1991

National Urban Employment Survey. The microenterprise sector includes own-account

workers, partners, and employers. Both surveys examine the use of credit services, capital

structure, employment patterns, costs, and sales, among other issues.

The main results of this study are that: (a) gender differences in productive

characteristics (including owners and businesses) are more important in explaining gender

earnings differentials in rural than in urban settings; and (b) efforts to reallocate female-

headed microenterprises to other economic sectors could have a limited impact on

increasing women’s earnings. Hence, policies should focus on addressing gender

differences in productive characteristics and structural factors across economic sectors.

The study is organized as follows. Section 2 presents a profile of microenterprises and

examines gender differences in size, choice of economic sector, place of operations, and other

gender-specific issues. Section 3 investigates observable and unobservable factors that could

determine gender earnings differentials in the microenterprise sector. Section 4 presents the

empirical methodology used to evaluate the role of inter- and intra-sectoral factors in

explaining gender earnings disparities. Section 5 estimates earnings equations and evaluates

the role of differences in endowments. The last section provides some concluding remarks

and discusses public policy implications of the results.

2. Profile of Microenterprises in Mexico

In Mexico, female participation in the microenterprise sector has risen dramatically

during the first half of the 1990s. According to the Mexican National Employment Surveys,

in localities with less than 100,000 inhabitants female representation increased from 36% in

1991 to 44% in 1995, whereas in the rest of the country it only increased from 26% to 29%. 4

The urban and rural surveys show that women own about 24% and 39% of the

microenterprises in each area. While women figure prominently in the microenterprise

sector, female-headed microenterprises account for 16% of microenterprise earnings in

urban areas (27% in rural areas) and their earnings are 50% (36% in rural areas) as much as

their male counterparts across all economic sectors. In the context of microenterprises,

women’s businesses tend to have lower earnings than that of men as indicated by their

smaller share in earnings. Table 1 and Table 2 present descriptive indicators of

microenterprise activities for urban and rural microenterprises. The most important gender-

related differences are associated to the following variables:

Number of workers. Most microenterprises are operated by the owner in both urban

and rural areas (66% and 53%, respectively). In urban areas, the average number of workers

(including the owner) in male-headed microenterprises is 1.65 and ranges from a high of 2

workers in manufacturing to a low of 1.47 in the service sector. The average female-headed

microenterprise shows little variation in the number of workers across economic sectors

(about 1.52). In rural areas, microenterprises are a stronger source of employment generation

as both men’s and women’s businesses employ, on average, 2.11 and 1.59 workers,

respectively.

Home-based Operations. Most female-owned microenterprises operate from home,

although substantial urban-rural differences exist. In the rural areas studied, about 44% of

female-headed microenterprises (21% of male-headed) are located at home, while in urban

areas 33% (13%) of these businesses are home-based. The business location seems to be

closely linked to the sector of economic activity, and thus with earnings. The manufacturing

sector, which has the lowest female-to-male earnings ratio (Figure 1), has also a

disproportionate share of home-based microenterprises among female owners in both rural

and urban areas (88% and 81%, respectively).

Years of Operation. On average, female-headed microenterprises have been in

business for 6.7 years in both rural and urban areas. In contrast, male-headed

microenterprises are relatively older with 10.8 and 9.2 years of operation in rural and urban

areas, respectively. The survival rate of microenterprises—particularly those owned by women

5

— is lower in rural areas. In urban areas, only 36% of female-headed microenterprises (33% in

rural areas) have been in business for more than five years, compared to 51% of male-headed

microenterprises (50% in rural areas).

Hours Worked. In urban areas, women’s microenterprises operate 7 hours less per

week than those of men. However, this gender disparity in the hours of operation greatly

differs across economic sectors. In manufacturing, women’s microenterprises operate 20.2

hours less than men’s, but only 2.7 hours less in trade. 1

Plans to expand. In urban areas, about 67% of microenterprises will not change

their business/plant size in the near future. Interestingly, expansion plans appear to be

unrelated to the owner’s gender, with 21% (23%) of women’s (men’s) microenterprises

having plans to expand their activities by either making new investments, increasing the

number of employees, or developing new products. Only 11% of microenterprises reported

that they would close their operations in the near future.

Formality Indicators. Overall, microenterprises in both rural and urban areas

comply with tax legislation: about 42% of microenterprises are registered with the Ministry

of Finance. Registration with tax authorities seems to be related with the sector of

economic activity as well as the owner’s gender. For Indeed, gender differences in tax

registration is greater in manufacturing reaching 22 and 17 percentage points in rural and

urban areas, correspondingly. In this sector, female-headed microenterprises in

manufacturing report the lowest registration rates: about 15% and 12% in urban and rural

areas. Although compliance with tax legislation seems high, fewer microenterprises are

associated to chambers of activity and/or gremiales: 23% and 17% in urban and rural

areas, respectively.

Educational Attainment. Gender disparities in educational attainment are greater in

the rural areas studied than in urban areas. About 25% (11% in urban areas) of female rural

entrepreneurs have not received formal education, compare to 11% (7% in urban areas) of

1 Unfortunately, the rural survey does not have information regarding the number of hours worked. Although the surveys did not ask for hours worked in the wage-and-salary sector, the National Employment Survey in Urban Areas reveals the gender gap in hours worked is not substantially larger in the self-employment sector. Indeed, gender gap in hours worked between wage-employment and self-employment are 9 and 11 hours on average, respectively (I thank Wendy V. Cunningham for providing this information)

6

their male counterparts. This indicates that urban-rural differences are substantially greater

among female entrepreneurs.

3. Gender Earnings Differentials in the Microenterprise Sector

Earnings differentials between male- and female-headed microenterprises are likely

to be influenced by observable and unobservable factors. Within each economic sector,

(the log of) microenterprise earnings, I, can be seen as a function of observable productive

characteristics, X, (number of employees, capital, level of formality, firm life, human capital

variables, market conditions, household demographics, and so on), the market return to

each of these characteristics, , and a residual, , capturing unexplained individual-specific

effects; namely,

(1) I X

An examination of (1) suggest that the average earnings of self-employed women

and men may differ because of differences in their levels of productive characteristics, X,

for example, capital, and/or they may vary due to gender differences in the return for each

added unit of capital, . Thus, overall or within each sector, gender differences in earnings

can be explained by endowment disparities and/or differences in returns to productive

characteristics. The latter will be referred as structural productive factors. The “structural”

component encompasses various unobservable variables and/or structural factors that are

detailed below.

First, unobservable variables, such as entrepreneurial ability, could be differently

distributed between male and female entrepreneurs and would tend to show up statistically

as differences in returns to observed characteristics or factors. Variance in entrepreneurial

abilities may result in differential returns to human and physical capital among possible

types of microenterprises (Maloney and Cunningham, 1997). Moreover, differences in

entrepreneurial abilities will determine the success of microenterprises, and thus, their size

(Lucas, 1978). If self-employed women have lower levels of entrepreneurial ability, then

their business will be smaller and their earnings lower.

Second, labor distortions and/or gender-based household responsibilities may also

influence differing returns to measurable characteristics or factors. For example, if women 7

are unable to get salaried work because of employer discrimination or because of

household responsibilities, then those who are not entrepreneurially talented might be

forced into self-employment and will, thus, depress the mean earnings of self-employed

women. Indeed, using the same rural survey in Mexico, Pagán and Sánchez (1998) found

that structural supply and demand factors, such as differences in employment preferences,

employer discriminatory behavior, or household constraints, induce rural women into self-

employment.

Third, the need to balance home and market responsibilities is a major constraint on

productivity and earnings (Blau, 1998; World Bank, 1994a). Family responsibilities may have a

direct effect on earnings by reducing the amount of energy and effort available to devote to

income generating activities (Hersch, 1991). Because household responsibilities generally

have predetermined schedules, women’s microenterprises are more likely to be home-

based which allow them greater flexibility in hours worked, have lower growth rates and

earnings than market-based firms. Moreover, unobservable job characteristics, such as

working schedules, may justify lower earnings as a compensating differential for favorable

working conditions (to combine family and job responsibilities). This indicates that we

should also find differing returns to factors between men’s and women’s microenterprises.

Fourth, credit constraints might be an important impediment to growth among

microentrepreneurs (Evans and Jovanovic, 1989; Rhyne and Holt, 1994). The presence of

credit constraints would affect microentrepreneurs’ abilities to smooth consumption over

time and undertake profitable investments. The investment behavior of microenterprises—

and, therefore, their growth potential—could be restrained by limited access to credit

services (Tybout, 1983; Nabi, 1989). If female-headed microenterprises face lower access

to credit services than male-headed small firms, this would reduce their growth

possibilities, and thus affect earnings. However, participation in credit markets is not

skewed by gender in urban areas. About 12% of male- and female-headed microenterprises

have received a production loan since starting their businesses. Moreover, there is

evidence that gender does not influence borrowing activity from formal and informal

lenders among urban microenterprises in Mexico (Sánchez, 1998). However, in the rural

8

areas studied, 45% of women have received some type of financing (for consumption and

production purposes) during the two years prior to the survey compared with 56% of men.

Fifth, women and men may have different goals with respect to the microenterprise

activity, which influences their business strategies and, thus, their earnings. For example,

women’s business strategies weight more heavily on their ability to supplement family income

and emphasize risk management (Rhyne and Holt, 1994; Rothstein, 1995). Under this

hypothesis, women may engage in low-risk, low-return activities to provide a stable source of

funds for the family. For example, in Mexico’s urban areas, the main reason for women to

enter the microenterprise sector is to supplement family income (57%), while men reported

their desire to become independent (47%). Another evidence of differences in risk preferences

between male- and female-headed microenterprises can be found in the reported reasons for

not requesting a loan. Overall, about 23% and 27% of microenterpreneurs in rural and

urban areas, respectively, perceive borrowing as a risky endeavor and prefer to use

internally generated funds.

When analyzing the overall gender earnings gap (including all economic sectors), an

observable determinant of the gender earnings gap is the difference in the sectoral

distributions of female- and male-headed microenterprises. For example, if women’s

microenterprises tend to be over-represented in low-returns sectors and under-represented

in high-returns sectors, then some of the difference between the average female and male

business earnings may result from different sectoral distributions.

Gender disparities in sectoral distributions can be analyzed by using the index of

dissimilarity (or Duncan Index), which equals 0.5j|PjF - PjM | (Psacharopoulos and Tzannatos,

1992). PjF and PjM represent the proportion of females and males in the jth economic sector.

The index compares the existing sectoral distribution of men and women to the distribution

that would exist if “assignment” to sectors was unrelated to gender. If men and women

were equally distributed across sectors, the index would equal zero, while if all sectors were

completely dominated by one gender it would equal 100%. An estimation of the Duncan

Index using a detailed classification of sectors is about 51% for both urban and rural

microenterprises.

9

Male-female differences in sectoral choices are likely to be influenced by several

factors. They include individual preferences or by barriers to entry into certain sectors

resulting from gender differences in household responsibilities, high levels of required

initial investments, lack of business skills, or consumer’s perceptions that the quality of

services or products depend on the gender of microentrepreneurs. For example, the

absence of women providing construction services may indicate that consumers prefer

male providers or that women lack the necessary skills or desire to work in the sector.

Moreover, pre-market factors, such as differential treatment by parents, schools, and

gender differences in educational access and business experience also channel

entrepreneurial women into a narrower range of economic sectors. Female

microenterprises are concentrated primarily in economic sectors associated with household

activities, such as food processing or domestic services, often less dynamic sectors or more

competitive ones (World Bank, 1994a).

A glance at Figure 1 suggests that across broadly defined sectors, women earn

substantially less than men; the female-to-male earnings ratios in the microenterprise

sector lay in the ranges of 30% to 58%, and 25% to 55% for urban and rural

microenterprises, respectively.1 Thus, given the above discussion, any meaningful analysis

1 Calculations across narrowly defined sectors result in similar figures.10

Figure 1 Microenterprises: Female-to-Male Earnings Ratios in Rural and Urban Areas

of gender earnings differentials should consider the role of both intra- and inter-sectoral

factors in determining the causes for the male-female disparities in earnings. Moreover, the

analysis should also incorporate the endogeneity of the sectoral decision to explicitly take

into account gender differences in sectoral-specific barriers to entry or preferences.

The separation of the role of inter- and intra-sectoral factors has important

implications for the design of public policies. This distinction sheds light on whether public

policy measures should concentrate on increasing women’s earnings within each sector or

simply redistributing self-employed individuals (or redirecting potentially self-employed

individuals) across sectors. Moreover, this disaggregation highlights whether policies to

increase earnings in each sector should focus on reducing unequal distributions of

productive endowments, including human and physical capital, and entrepreneurial

abilities or addressing structural factors, such as household or labor and credit markets

distortions.

4. Empirical Methodology

This study employs the Brown, Moon and Zoloth (1980a, 1980b) decomposition

technique to evaluate the impact of inter- and intra-sectoral factors on gender earnings

differentials among rural and urban microenterprises in Mexico. Brown et al. extended the

traditional Oaxaca (1973) decomposition technique to account for endogenous selection of

occupations in the wage-and-salary sector. This technique has two attractive features that

aid in identifying the sources of gender earnings differentials. First, the method takes into

account that gender differences in sectoral distributions could result from barriers to entry

into certain sectors (e.g., minimum capital investments or household responsibilities) and

permits for endogenous determination of the sectoral distribution. Second, the technique

takes into account that endowment disparities vary by economic sector, and affect the

overall differences in earnings.

The methodology has two steps: first, a model of sectoral choice is estimated,

separated by gender, to treat the sectoral choice as an endogenous variable.1 Then, for

1 The level of sectoral aggregation could affect the decomposition of intra-sectoral factors into explained and unexplained components. Kidd and Shannon (1996) showed that when occupation is treated as endogenous, the level of occupational aggregation has little effect on the size of the “explained” component of the wage

11

each sector and for each gender, microenterprise earnings equations are estimated

separately to decompose the earnings gap into inter- and intra-sectoral factors.

Sectoral Choice. Assume that self-employed individuals select their earnings-

generating sector according to a set of variables ( Zi ) that represent their socioeconomic

status, personal characteristics, household demographics, business opportunities in the

region, and individual preferences. The probability that the ith self-employed individual is

conducting his/her business in the jth sector is thus a function of a set of variables ( Zi )

capturing supply-and demand-side factors determining the observed sectoral distribution.

The reduced form specification of the model is given by:

(2) P Sector j Z Zi j i jj

J

( ) exp( ) / exp( )'

1

where represents a vector of coefficients for sector j. P follows a logistic conditional

probability function and the parameters are estimated by maximum likelihood [Greene,

1997].

Equation (2) is estimated for the male sample. Using the explanatory variables from

the female sample, the estimated male coefficients (M~

) could be used to predict self-

employed women to be in the jth sector and generate a predicted sectoral distribution for

females under male-specific demand/supply factors determining the sectoral choice. Thus,

the predicted probability that the ith self-employed woman participates in the jth sector

given male preferences is:

(3) ~ exp(~

) / exp(~

)'P Z ZijF

iF

jM

iF

jM

j

J

1

To obtain a female predicted sectoral distribution, the predicted probabilities for

each observation and sector are added across the female sample. This procedure assumes

that in a world of gender equality women would be distributed across economic sectors

according to the male allocation decisions. Hence, the predicted female probabilities

simulate a gender-neutral sectoral distribution for women.

income gap. Hence, a similar result could be expected on how sectoral aggregation changes the size of the “explained” components of the earnings gap in the microenterprise sector.

Earnings Decomposition. To estimate inter and intra-sectoral factors, the

decomposition requires the estimation of self-employed earnings equations for each sector

(separated by gender). Let the log of earnings of the ith self-employed individual of gender

g conducting business in sector j ( I gij ) be given by:

(4) I Xijg

ijg

jg

ijg

where X ijg

is a vector of variables posited to affect self-employment earnings, jg

is a vector

of parameters and ijg

is a random error term. The parameters of (4) are estimated using

ordinary least squares. Hence, the mean log of earnings in sector j can be written as

I Xjg

jg

j

g

^

and the aggregate sample mean is given by I P Xgjg

jg

j

J

jg

(

~)

1 where Pj

g

represents the proportion of entrepreneurs in the jth sector. The gender earnings gap can

then be decomposed as:

(5) I I P X X P X I P P I P PM FjF

jM

jM

j

J

jF

jF

jF

j

J

jM

jF

jM

jM

j

J

jF

jM

j

J

jF

jF

~

( ) (~ ~

) ( ~ ) ( ~ ) 1 1 1 1

where M and F refer to males and females, and ~PjF represents the predicted proportion of

female entrepreneurs in sector j if women were distributed across sectors according to the

male sectoral choice model (predicted probabilities using equation (3)).

The intra-sectoral component is composed of the first and second terms of (5). The

first term represents the “explained” component in the sense that it captures earnings

differentials attributed to gender differences in both owners’ human capital as well as

microenterprise characteristics. The second term on the right-hand side of equation (5)

represents the “unexplained” component of the gender earnings gap that can be attributed

to differences in the male-female compensation for a given set of characteristics, keeping

the sectoral distribution constant. This latter term should be attributed to differences in

productivity between men and women or other unobservable variables. For example, if

female-headed microenterprises face greater growth constraints, this component will be

influenced by their lower access to markets, networks, technology, and credit.

Similarly, the inter-sectoral component, which captures the effect of differences

between male and female sectoral distributions, has two parts: the third and fourth terms in

equation (5). To isolate the importance of differences in sectoral distributions, the

decomposition implicitly assumes that female and male entrepreneurs differ only in their

sectoral distributions. The third term captures the effect of differences in the values of

characteristics between men and women on their sectoral distributions, the explained

portion of the inter-sectoral component. The fourth term represents the effect of gender-

specific tastes for economic sector or barriers to entry, structural factors, on the gender

earnings gap.

5. Empirical Results

To analyze gender earnings differentials among microenterprises in rural and urban

Mexico, I estimated equations (2) and (4) using microdata from the 1994 Survey of Rural

Entrepreneurs and Financial Services and the 1992 National Survey of Urban

Microenterprises. The empirical results indicate that gender earnings differentials in the

microenterprise sector are largely due to men’s microenterprises being larger than

women’s in each economic sector and having differential constraints to profitability. Gender

differences in sectoral distribution account for a small share of the gender earnings gap in

both rural and urban areas.

The explanatory variables utilized in the analysis follow the standard human capital

model of earnings. Table 10 and Table 11 report the variables utilized in the analysis and

the basic sample descriptive statistics by gender and economic sector. These variables can

be grouped in three categories:

Microenterprise characteristics: years in operation, number of hours worked (only for

urban areas), number of workers, capital endowments, place of operation, registration with

the ministry of finance, and involuntary entry into the microenterprise sector (only for

urban areas)

Personal characteristics of the owner: marital status, years of education, ethnicity

(only for rural areas) and potential labor market experience (defined as age minus years of

education minus six).

Market conditions: number of inhabitants in the locality of residence (only for rural

areas).

As mentioned earlier, an important limitation of the rural survey is that the number

of hours worked is not available and not considered for the estimation. Thus, the

decomposition results will overestimate the unexplained portions of the intra-sectoral and

inter-sectoral components. In the urban sample, for example, the share of the unexplained

parts more than doubled by eliminating the number of hours worked from the current

specification (see Table 5 and Table 6). Before proceeding with the decomposition of

gender earnings differentials, a discussion of the main empirical findings of the earnings

equation may be instructive.

Determinants of Earnings. The results from estimating sector-specific earnings

equations are reported in Table 8, Table 9, and Table 12. When heteroscedasticity was

detected, the standard errors were adjusted using White’s (1978) consistent estimator of

the variance-covariance matrix. The specification has high explanatory power as the

explanatory variables account for 33-50% of the total variation in earnings. The results

from these regressions are consistent with other human capital studies:

Years of formal education have a positive impact on earnings, although

substantial differences appear across economic sectors. Owners in manufacturing show the

lowest returns to education in urban areas and they are even negative for males in the

rural areas studied.

Earnings increase with experience but at a decreasing rate among urban

microenterprises. Surprisingly, for rural microenterprises experience is not statistically

significant.

Earnings are positively related to the number of hours worked, especially

among female owners in urban areas. Indeed, ceteris paribus, a one percent increase in the

number of hours worked is estimated to increase earnings by 39% among men, but by 41%

among women.

Microenterprises that have been longer in business have higher earnings,

particularly those owned by men. This finding might be a result of older microenterprises

having learned the nature of the business and discovered their efficient scale of operations

(Jovanovic, 1982) or having higher expected returns than their younger counterparts. The

larger coefficient for male-headed microenterprises could be partially explained by their

higher survival rates.

The number of workers in the firm is positively related to earnings, especially

among microenterprises in the trade sector.

Involuntarily entry in the microenterprise sector comes at a substantial

reduction on earnings among urban microenterprises.1 The estimated percentage reduction

on earnings due to involuntary entry is about 17%.2

Married male owners have an earnings premium across all sectors, especially

in urban areas. There, the estimated earnings premium ranges from 8% in manufacturing

to 21% in services. In the rural areas studied, the positive marriage coefficient is only

significant for female owners in the service sector (51%).

Surprisingly, among rural microenterprises, market conditions—proxied by

the size of the locality—do not seem to be related to earnings.

Decomposition of Earnings Differentials. Table 5 and Table 7 present the

results from the decomposition analysis [equation (5)] of gender earnings differentials for

the rural and urban sample. The gender earnings differential is about 0.70123 log-points for

the urban sample (1.01767 for the rural): that is, the average female-to-male earnings ratio

is 50% (36%). The main results are:

Inter-sectoral factors explain a relatively small proportion of the earnings gap: about

3.2% and -1.1% in urban and rural areas, respectively. This finding indicates that efforts to

reallocate female-headed microenterprises would not increase their mean overall earnings

significantly. Hence, policies that attempt to increase women’s earnings within an economic

sector would be far more successful.

1 This variable is not available for the rural survey.2 For the semi-logarithmic functional form the percentage impact on earnings of a change in a dummy variable from zero to one status is e -1, where equals the dummy coefficient.

16

Most of the gender earnings gap can be explained by intra-sectoral factors, about

96.9% in urban areas and 101.1% in rural areas.3 Hence policies should consider sectoral

policies to reduce gender disparities in earnings.

Differences in productive and personal characteristics are more important in

explaining the male-female earnings gap in rural than in urban areas. Among urban

microenterprises, about 35% of the earnings gap is explained by unequal distribution of

endowments, whereas in the rural areas studied, the lower-bound estimate of the effect

“endowments disparities” explains 42% of the earnings gap.

Differing returns to factors (explanatory variables) explains 62% of the earnings gap

in urban areas and 59% in the rural areas studied (upper-bound estimate). Among urban

microenterprises, this component fluctuates from a low of 60% to a high of 68%. In the

rural areas studied, the service sector has the largest size of this component, about 68%.

The magnitude of this component illustrates that unobservable variables, such as

entrepreneurial abilities, and/or structural factors, such as labor and credit markets

distortions and gender-based household constraints, are responsible for a substantial share

of earnings differentials between men’s and women’s microenterprises.

6. Concluding Remarks and Public Policy Issues

Microenterprises are particularly active in both rural and urban areas of Mexico, and

they substantially contribute to household earnings, employment creation, and overall

economic growth. There are significant gender differences in earnings in the

microenterprise sector, particularly in the rural areas studied. Because women are

particularly active in this sector—and they participate mostly to supplement family income

—reducing gender disparities can potentially increase their ability to increase household

income.

The results from the study illustrate that policies attempting to reduce earnings

disparities within each economic sector should focus on two aspects: (a) reducing the

unequal distribution of productive characteristics, and (b) eliminating differential growth

3 The intra- and inter-sectoral factors must add to 100 percent, however, each one can be larger than 100 percent if the other is negative.

17

constraints that influence returns to productive and personal characteristics. Moreover, the

small share of inter-sectoral factors indicates that efforts to reallocate women’s

microenterprises to different economic sectors would not significantly increase women’s

earnings.

Endowments Disparities. Public policies should aim at reducing endowment

disparities, especially in rural areas. The results indicate that these disparities are mostly

responsible for gender earnings differentials in the microenterprise sector. An indirect

approach to skill development among entrepreneurs may revolve around broad-based

education and training systems, both formal and informal. Because about 20% of workforce

in Mexico is self-employed, the formal education and training systems are the main

vehicles that prepare future entrepreneurs to succeed through the acquisition of business

skills, literacy, numeracy, among others. Public policies may target better access to formal

and non-formal education for females. Access to formal and non-formal education expands

individual’s choices, increase their capacity to respond to market opportunities, and

increase their productivity.

The provision of direct enterprise development efforts can increase and facilitate

entrepreneurial activities, although they cannot substitute for adequate macroeconomic

conditions, infrastructure, and business environment. Such services should assist

microenterprises to manage their business more effectively, increase their access to

information and technology, and manage risk (USAID, 1995b). The problem is that the

development of non-financial assistance methodologies remains somewhat unexplored.

The design of a successful strategy to provide non-financial services should: charge market

prices (recover costs), be customer oriented, be market driven, work within specific sectors,

and deliver services in a timely fashion (USAID, 1995b).

Structural Factors. Because structural factors or external constraints explain a

substantial share of gender earnings differentials, strategies dealing with structural issues

could have a desirable long-term impact in increasing women’s earnings in the

microenterprise sector. Although, investigating the impact of constraints to growth and

profitability in the microenterprise sector is beyond the scope of this study, these findings

18

stress the need to examine differential growth constraints among both male- and female-

headed microenterprises. Another puzzling question is why women’s microenterprises will

stabilize at a smaller size of operations than that of men’s. As mentioned earlier, both male-

and female-headed microenterprises plan to stay in business without expanding their

operations.

Business Environment and Macroeconomic Policies. Most constraints to

entrepreneurship result from the business environment and macroeconomic conditions in

which microenterprises operate. Public policy should promote a business environment in

which microenterprises can enforce contracts and have lower costs of accessing

professional services, output and input markets, information, training and technology. De

Soto (1989) argues that many firms operating outside formal institutions are willing to pay

to become formal, but that excessive transaction costs and bureaucracies keep them

outside of the formal economy. By increasing the participation of microenterprises in the

institutions of civil society —such as federal and local treasuries, social security, and the

legal system— microenterprises can augment their productivity and growth potential as

their participation becomes increasingly important in the development process (Maloney

and Levenson, 1997).

Household constraints. Gender roles within the family may be a fundamental source

of distinct gender structural barriers to economic parity which influence women’s work,

productivity, and earnings (Blau, 1998). Although changing gender roles within the

household may not be feasible in the short-term, it is necessary to explore best practices to

raise women’s social and intra-household status which could have a positive impact on

their businesses and growth.

Increasing the Availability of Financial Services. Access to financial services,

especially to loans is often identified as one of the most important constraints to

microenterprise growth and profitability. Although only 3% of microenterprenurs in urban

areas reported lack of credit as their main constraints, most microenterprises operate in

financial autarky. Increasing the availability of credit services to microenterprises may be

an effective measure to allow microenterprises to manage risk better, finance investment,

19

keep businesses afloat when cash flow is tight, exploit profitable opportunities, and smooth

household consumption. Increasing the availability of financial services only, without

corresponding improvements in access to new markets and technologies, may have a small

effect in the long run to promote growth and profitability in the Mexican microenterprise

sector.

20

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Table 1 Characteristics of Urban Microenterprises (% of each column heading)All MEs Total Female Male

Indicators Female Male Service Commerce Manufacture Service Commerce Manufacture

All MEs (% all MEs) 100.0 24.0 76.0 8.1 12.4 3.4 37.4 22.4 16.2

Reasons to enter ME Sectora

Want to be independent 40.5 21.4 46.5 29.0 17.6 17.4 48.4 42.0 48.3Family tradition 8.5 6.8 9.0 4.2 8.8 5.8 7.6 9.9 11.2Complement family income 23.4 57.0 12.9 48.6 60.3 65.2 12.1 15.0 11.7Get higher income than as a worker 19.7 7.9 23.4 9.3 7.9 4.8 22.8 20.7 28.9Did not find salaried work 11.6 5.5 13.4 6.1 5.4 4.5 12.9 16.8 10.1Flexible hours 4.0 6.1 3.4 6.3 6.2 5.8 3.7 3.5 2.3Lost previous job 2.6 0.7 3.1 1.0 0.7 0.3 3.0 3.8 2.7Other 6.4 8.0 5.9 10.9 5.9 8.7 6.5 5.8 4.8

Sector of economic activityServices 45.6 33.9 49.2 100.0 - - 100.0 - -Commerce 34.8 51.7 29.4 - 100.0 - - 100.0 -Manufacturing 19.6 14.3 21.3 - - 100.0 - - 100.0

Main problem facedLack of clients 35.0 32.8 35.7 35.8 29.6 37.1 38.3 27.3 41.4Lack of credit 2.7 1.7 3.0 0.7 2.3 1.6 2.2 4.1 3.7Lack of economic resources 6.3 7.7 5.9 5.4 9.5 6.8 4.2 8.6 6.1Low earnings 10.2 11.9 9.7 8.7 15.1 7.7 8.4 13.3 7.6Problems with authorities 2.5 2.1 2.6 1.9 2.5 1.3 2.7 3.3 1.3Excessive Competition 15.5 15.2 15.7 13.5 18.6 6.8 15.7 17.9 12.6Problems with workers/partners 0.5 0.4 0.5 0.4 0.2 1.0 0.6 0.1 0.8Customers pay with delays 3.8 4.2 3.7 3.9 3.9 6.1 3.4 3.5 4.8Problems with merchandise, inputs, and raw materials

0.8 0.6 0.9 0.3 0.9 0.6 0.4 1.6 1.0

Other 3.5 2.9 3.8 4.2 1.8 3.5 4.0 3.4 3.8No problem 19.1 20.6 18.6 25.2 15.6 27.4 20.3 17.0 17.0

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Problems faceda Lack of clients 56.3 55 56.7 52.1 56.5 56.5 58.1 51.2 61.3Lack of credit 9.1 7.1 9.7 5.2 8.8 5.8 7.6 12.0 11.3Lack of economic resources 20.4 23.3 19.4 21.0 25.6 20.3 16.7 22.6 21.3Low earnings 45 49.9 43.4 43.5 55.7 43.9 41.9 49.1 39.2Problems with workers/partners 1.3 0.6 1.5 0.8 0.3 1.3 1.5 0.9 2.5Customers pay with delays 11.9 10.2 12.4 8.6 11.0 11.0 12.3 9.1 17.2Problems with authorities 7.1 4.9 7.7 4.6 5.3 4.2 8.4 9.3 4.1Excessive competition 42.3 40.4 42.8 35.4 48.3 23.9 42.4 45.6 40.2Problems with merchandise, inputs, and raw materials

3.2 2.8 3.3 1.1 4.4 1.3 1.7 5.7 3.6

Other 6.9 5.1 7.5 7.1 3.6 5.8 8.3 6.4 6.8No problem 19.1 20.6 18.6 25.2 15.6 27.4 20.3 17.0 17.0

Future PlansWill not continue in business 10.9 10.8 10.9 10.3 11.4 9.7 11.5 10.6 9.8Without important changes 66.8 68.1 66.4 68.3 66.0 75.5 68.2 62.3 67.5Expanding line of products 7.8 9.1 7.4 4.6 13.2 4.8 3.1 14.7 7.3Making new investments 10.7 9.1 11.2 12.1 7.5 8.1 12.3 10.8 9.4Expanding number of employees 1.4 0.2 1.7 0.5 - 0.3 1.7 0.3 3.5Other 1.7 1.8 1.7 3.8 0.4 1.6 2.2 0.8 1.8Do not know 0.7 0.8 0.7 0.3 1.4 - 0.8 0.4 0.8

Place of OperationTemporary establishment 10.0 14.9 8.5 17.7 16.3 3.2 4.1 21.3 1.0Ambulant 4.5 2.5 5.1 1.1 4.2 - 4.6 9.6 -Carts 3.4 0.9 4.2 2.0 0.4 - 4.4 6.8 -Clients' home 23.3 8.8 27.9 10.1 9.9 1.6 28.6 12.2 48.1At home 17.4 32.7 12.5 30.6 20.6 81.0 11.2 8.1 21.8Without business premises 0.8 0.5 0.9 1.0 0.4 - 1.2 0.6 0.3Fixed business premises 34.2 39.4 32.5 36.9 48.1 14.2 28.8 41.4 28.7Transportation 6.4 0.2 8.4 0.7 - - 17.1 - -

26



Owned fixed business premisesNo 83.5 77.7 85.3 86.7 67.9 92.3 90.5 76.6 85.4Yes 16.5 22.3 14.7 13.3 32.1 7.7 9.5 23.4 14.6

Assets (Pesos)NE 0.2 0.1 0.2 - 0.2 - 0.2 0.1 0.20-345 25.0 28.6 23.8 32.1 24.3 36.1 24.3 19.4 28.7346-2146 24.9 29.0 23.7 28.4 25.2 44.2 24.3 17.8 30.32147-15650 25.0 23.8 25.3 17.7 31.3 11.3 24.5 31.6 18.4other 25.0 18.5 27.0 21.8 19.1 8.4 26.6 31.0 22.3

Owned assets (Pesos)NE 0.1 0.0 0.1 - 0.1 - 0.0 0.1 0.20-345 27.8 30.9 26.9 34.7 26.7 37.1 28.5 21.7 30.2346-2146 26.8 31.5 25.3 31.4 27.9 44.5 26.5 19.2 31.32147-15650 26.4 24.5 27.0 20.8 30.6 11.3 26.6 32.8 20.1other 18.8 13.0 20.7 13.1 14.6 7.1 18.4 26.2 18.2

Inventory (Pesos)NE 5.1 6.3 4.8 6.3 6.9 4.5 3.6 6.9 4.6Zero 50.9 34.3 56.2 60.4 11.0 56.5 74.0 21.6 62.8positive 43.9 59.4 39.1 33.3 82.1 39.0 22.4 71.6 32.6

Assets / workers (Pesos)NE 0.2 0.1 0.2 - 0.2 - 0.2 0.1 0.20-293 25.0 29.3 23.6 34.0 25.3 32.6 24.1 19.1 28.7294-1590 24.9 28.2 23.9 27.6 24.4 43.2 24.1 18.8 30.61591-10275 25.0 24.8 25.0 16.7 32.3 16.5 22.6 32.1 21.010275-51000 19.9 14.5 21.6 16.7 15.0 7.1 23.0 23.7 15.7other 5.0 3.2 5.6 4.9 2.9 0.6 6.0 6.2 3.9

Monthly earnings (Pesos)

27



NE 4.4 4.9 4.2 3.8 5.4 5.5 3.3 6.4 3.40-387 19.3 39.4 13.0 33.7 38.5 55.8 12.5 17.6 7.8388-801 26.8 28.7 26.2 27.9 30.2 25.2 26.8 25.5 25.7802-1440 26.7 14.9 30.4 19.6 13.9 7.4 30.8 24.0 38.11441-2733 15.0 7.9 17.3 9.5 8.4 2.6 17.8 16.2 17.5other 7.8 4.2 8.9 5.4 3.6 3.5 8.8 10.3 7.4

Monthly income (Pesos)NE 1.8 1.6 1.9 1.8 1.5 1.3 1.5 2.5 1.80-345 8.2 18.1 5.1 17.0 12.1 42.6 5.9 4.8 3.5346-2146 50.4 50.3 50.4 52.2 50.9 43.9 55.5 35.5 59.02147-15650 36.1 28.1 38.6 28.3 33.0 10.0 35.9 48.7 30.8other 3.6 1.8 4.1 0.7 2.5 2.3 1.2 8.6 4.9

Monthly expenses (Pesos)NE 1.1 0.7 1.2 1.2 0.2 1.3 1.3 0.6 1.80-345 34.3 36.9 33.5 40.3 25.0 71.9 40.5 12.6 46.3346-2146 40.6 44.2 39.4 43.8 51.6 18.7 41.4 43.5 29.22147-15650 21.4 17.1 22.8 14.4 21.8 6.5 16.1 36.5 19.2other 2.6 1.1 3.1 0.3 1.4 1.6 0.7 6.8 3.5

Monthly profits (Pesos)NE 2.5 2.1 2.7 2.6 1.6 2.6 2.5 2.7 3.2Negative 4.3 5.0 4.1 3.1 7.1 1.6 2.7 7.4 2.90-387 20.1 38.9 14.2 35.0 36.7 56.5 13.8 18.6 8.9388-801 24.3 24.2 24.3 25.0 23.8 23.9 26.2 21.5 23.7802-1440 24.4 14.9 27.4 18.0 14.8 8.1 28.2 19.7 36.01441-2733 14.6 8.9 16.4 9.9 9.7 3.5 17.3 14.7 16.7other 9.7 5.9 10.9 6.4 6.2 3.9 9.3 15.5 8.6

AgeNE 0.0 0.0 - 0.1 - - - - -12 -32 25.5 24.9 25.7 29.7 21.5 26.1 27.5 23.1 25.233-41 25.0 25.6 24.8 29.7 22.4 27.4 25.4 22.7 26.242-52 25.0 26.0 24.6 23.5 27.9 25.2 24.5 23.0 27.153-68 19.7 18.7 20.0 14.8 21.8 16.8 19.0 22.9 18.6

28



other 4.8 4.7 4.8 2.2 6.4 4.5 3.6 8.3 2.9Years in operation

NE 0.0 0.0 0.0 - 0.1 - 0.1 - -0-2 years 31.1 42.4 27.5 43.4 44.1 33.5 27.0 30.8 24.12-5 years 21.9 21.2 22.1 22.2 20.4 22.3 21.2 24.5 21.05-12 years 23.8 20.0 25.0 20.3 19.2 21.9 25.4 23.2 26.612-30 years 19.7 13.9 21.5 11.8 14.2 18.1 22.4 18.3 23.8other 3.5 2.4 3.8 2.3 2.1 4.2 3.9 3.2 4.5

Number of workers (including owner)1 66.0 68.1 65.4 67.5 65.6 78.7 71.3 59.0 60.32-3 27.5 27.5 27.4 27.3 31.0 15.5 23.9 33.7 27.04-6 5.9 4.1 6.5 5.2 3.4 3.9 4.8 7.3 9.57-16 0.6 0.3 0.7 - - 1.9 - - 3.2

Number of non-paid workers 0 83.6 77.1 85.6 81.5 71.2 88.1 91.2 72.8 90.61 11.6 15.9 10.2 13.1 19.6 9.0 6.7 19.0 6.12-3 4.5 6.6 3.8 5.2 8.8 2.3 2.0 7.6 2.94-6 0.4 0.4 0.4 0.3 0.4 0.6 0.2 0.7 0.3

Number of salaried workers 0 80.7 89.4 77.9 83.9 93.3 88.4 78.9 83.6 67.51 10.4 6.2 11.7 9.0 4.6 5.5 12.8 8.6 13.62-3 6.6 3.2 7.7 5.7 1.7 2.9 6.7 6.2 12.24-6 1.9 0.9 2.3 1.4 0.4 1.9 1.5 1.6 4.87-16 0.4 0.2 0.4 - - 1.3 - - 1.9

Monthly hours of operationsNE 0.1 0.1 0.1 - 0.1 0.3 0.1 0.0 0.14-151 24.7 41.6 19.4 49.0 28.9 69.7 23.1 19.3 11.1

29



151-207 23.7 17.0 25.8 21.4 14.8 14.2 26.4 19.0 33.9207-242 24.9 14.7 28.1 16.2 15.3 9.0 27.4 21.8 38.2242-425 26.6 26.7 26.6 13.5 40.9 6.8 23.0 39.8 16.7

Type of accountingFormal accounting 23.1 19.7 24.1 22.4 21.3 7.4 23.8 27.5 20.3Income & expenses notebook 6.6 8.0 6.2 7.5 9.7 2.9 5.8 8.8 3.4Personal notes 19.8 22.3 19.0 19.2 27.6 10.6 19.3 21.6 14.7None 50.5 49.9 50.6 50.9 41.3 78.7 51.0 42.0 61.6Other 0.1 - 0.1 - - - 0.1 0.1 -NE 0.0 0.0 0.0 - - 0.3 - - 0.1

Registered with SHCPNo 57.9 58.5 57.7 58.9 50.9 85.2 57.1 50.6 68.7Yes 42.1 41.5 42.3 41.1 49.1 14.8 42.9 49.4 31.3

Registered with SHCP or TesoreríaNo 52.6 51.7 52.9 50.5 43.6 83.9 52.4 43.5 67.0Yes 47.4 48.3 47.1 49.5 56.4 16.1 47.6 56.5 33.0

Member of chambers and gremialesNo 77.5 80.1 76.7 86.3 72.4 93.2 76.4 69.5 87.0Yes 22.5 19.9 23.3 13.7 27.6 6.8 23.6 30.5 13.0

Plan to continue with significant changesNo 78.4 79.8 77.9 78.9 78.8 85.2 80.6 73.4 78.1Yes 21.6 20.2 22.1 21.1 21.2 14.8 19.4 26.6 21.9

Involuntary entry into self-employmentNo 86.1 93.8 83.7 93.1 93.9 95.2 84.4 79.8 87.4Yes 13.9 6.2 16.3 6.9 6.1 4.8 15.6 20.2 12.6

MarriedNo 30.2 49.2 24.2 50.6 46.9 54.5 26.3 21.9 22.4Yes 69.8 50.8 75.8 49.4 53.1 45.5 73.7 78.1 77.6

Years EducationNE 0.2 0.2 0.2 0.1 0.1 0.6 0.2 0.1 0.10 8.1 10.6 7.3 10.5 11.3 8.7 6.1 8.8 8.11-3 16.2 16.9 16.0 12.4 19.5 18.1 14.3 16.4 19.6

30



4-6 33.8 35.6 33.3 30.5 38.0 39.0 32.7 30.7 38.37-9 20.1 20.6 20.0 19.7 21.1 20.6 20.1 20.7 19.010-12 8.4 7.6 8.6 8.0 6.7 9.7 8.1 11.1 6.513 and more 13.1 8.6 14.5 18.8 3.4 3.2 18.6 12.2 8.4

Source: Encuesta Nacional de Microempresas en Areas Urbanas, 1992a This question allows for multiple responses

31

Table 2 Characteristics of Rural Microenterprises (% of each column heading)All MEs Total Female Male

Female Male Service Commerce Manufacture Service Commerce ManufactureAll MEs (% all MEs) 100.0 39.0 61.0 12.4 20.2 6.4 14.6 23.6 22.8Sector of economic activity

Services 27.0 31.8 24.0 100.0 - - 100.0 - -Commerce 43.9 51.8 38.7 - 100.0 - - 100.0 -Manufacture 29.1 16.3 37.3 - - 100.0 - - 100.0

Place of OperationTemporary establishment 7.2 8.6 6.4 15.8 6.9 - 3.4 13.1 1.3Ambulant 4.7 4.1 5.0 1.7 6.9 - 3.7 10.7 -Carts 0.6 - 1.0 - - - 2.6 0.9 -Clients' home 13.0 10.1 14.9 26.4 3.3 - 17.9 4.6 23.6At home 29.9 43.6 21.1 24.3 41.5 88.0 18.4 10.7 33.7Without business premises 5.4 1.5 7.9 4.1 0.4 - 1.0 3.5 16.8Fixed business premises 35.8 32.2 38.2 27.8 41.2 12.0 29.6 56.5 24.7Transportation 3.4 - 5.6 - - - 23.4 - -

Owned fixed business premisesNo 79.3 82.7 77.2 92.0 75.2 88.0 89.6 61.6 85.3Yes 20.7 17.3 22.8 8.0 24.8 12.0 10.4 38.4 14.7

Assets (1992 Pesos) )NE 3.1 4.5 2.1 2.8 7.0 - 2.5 1.5 2.50-345 34.7 42.2 29.9 45.9 39.7 42.6 25.0 31.6 31.4346-2146 23.6 25.1 22.7 22.6 20.2 45.4 23.7 19.3 25.52147-15650 25.6 19.5 29.5 15.7 25.5 8.1 25.9 26.9 34.5other 13.0 8.7 15.8 13.1 7.6 3.8 22.9 20.7 6.1

Assets / workers (1992 pesos)NE 3.1 4.5 2.1 2.8 7.0 - 2.5 1.5 2.50-293 37.0 44.2 32.5 44.8 42.4 48.5 24.0 32.9 37.5294-1590 27.2 27.1 27.3 30.3 20.3 42.2 26.5 23.0 32.31591-10275 22.1 16.7 25.5 11.0 24.6 2.7 22.9 30.6 21.810275-51000 10.3 7.3 12.2 10.3 5.7 6.5 23.0 11.6 5.9other 0.4 0.3 0.4 0.8 - - 1.2 0.4 -

32


Female Male Service Commerce Manufacture Service Commerce ManufactureMonthly income (1992 pesos)

NE 3.0 2.4 3.4 2.4 3.2 - 5.5 2.5 2.90-345 22.5 39.8 11.5 43.9 28.5 67.4 18.0 7.8 11.2346-2146 46.7 40.1 50.9 37.2 45.1 29.9 56.7 34.6 64.12147-15650 24.9 15.0 31.3 12.2 20.5 2.7 19.8 47.5 21.8other 2.9 2.8 3.0 4.3 2.7 - - 7.6 -

Monthly expenses (1992 pesos)NE 3.3 2.4 3.9 1.2 3.9 - 5.8 3.7 2.80-345 39.6 52.5 31.3 64.1 34.3 87.7 37.4 12.0 47.5346-2146 39.3 33.3 43.1 23.2 47.0 9.6 48.6 44.1 38.62147-15650 15.5 10.3 18.8 9.9 12.9 2.7 7.1 33.5 11.1other 2.4 1.5 2.9 1.5 2.0 - 1.2 6.8 -

Monthly profits (1992 pesos)NE 3.0 2.4 3.4 2.4 3.2 - 5.5 2.5 2.9Negative 5.8 5.2 6.1 - 9.3 2.7 4.7 6.7 6.40-387 42.9 64.5 29.1 69.0 55.8 82.8 34.1 26.5 28.5388-801 22.8 14.4 28.2 13.6 15.8 11.7 23.9 26.1 33.2802-1440 13.0 6.2 17.4 2.6 9.5 2.7 19.9 13.7 19.71441-2733 6.3 3.9 7.9 6.4 3.7 - 7.0 11.1 5.1other 6.2 3.4 8.0 6.0 2.8 - 5.0 13.4 4.3

Age16 -32 25.6 24.2 26.6 36.4 11.3 41.3 36.4 19.2 27.933-41 22.3 21.9 22.6 17.6 25.3 19.4 17.3 29.4 19.042-52 26.9 26.0 27.5 26.6 28.0 18.7 27.9 25.8 29.153-68 18.8 22.6 16.3 16.3 28.0 17.6 13.8 20.8 13.4other 6.3 5.3 6.9 3.1 7.4 3.0 4.6 4.9 10.6

Years in operationNE 0.8 0.4 1.1 1.4 - - - 1.9 0.90-2 years 32.6 43.0 25.9 40.2 46.4 37.4 28.9 31.9 17.92-5 years 23.3 24.0 22.9 21.4 26.9 20.1 27.3 25.8 17.05-12 years 19.8 14.7 23.2 15.6 16.4 7.5 26.3 24.3 20.012-30 years 17.7 15.6 19.1 19.9 9.6 26.2 13.5 14.0 27.9other 5.7 2.3 7.9 1.6 0.7 8.8 4.1 2.2 16.4

Number of workers (including owner)1 53.4 64.1 46.5 74.7 53.6 76.6 65.3 40.7 40.5

33


Female Male Service Commerce Manufacture Service Commerce Manufacture2-3 36.6 30.8 40.3 20.8 39.2 23.4 31.3 43.1 43.34-6 8.4 4.2 11.1 1.7 7.2 - 3.5 12.4 14.67-16 1.6 0.9 2.0 2.8 - - - 3.8 1.5

Number of salaried workers 0 85.4 94.2 79.8 89.0 95.6 100.0 87.8 85.0 69.21 8.0 3.4 11.0 6.7 2.4 - 12.2 8.2 13.22-3 4.6 1.5 6.5 1.5 2.0 - - 4.1 13.24-6 1.7 0.9 2.3 2.8 - - - 1.6 4.47-16 0.3 - 0.4 - - - - 1.1 -

Number of partners 0 97.2 99.1 95.9 97.2 100.0 100.0 93.6 97.4 96.01 2.1 0.9 2.8 2.8 - - 6.4 1.1 2.42-3 0.6 - 1.0 - - - - 0.8 1.74-6 0.2 - 0.3 - - - - 0.7 -

Number of non-paid workers 0 65.8 67.5 64.8 81.3 56.1 76.6 80.8 49.9 69.91 19.5 22.0 17.9 13.9 30.3 11.3 12.2 24.5 14.72-3 12.0 9.7 13.4 4.8 11.9 12.1 5.3 21.2 10.54-6 2.5 0.9 3.6 - 1.7 - 1.7 3.4 4.97-16 0.3 - 0.4 - - - - 1.1 -

Type of accountingFormal accounting 22.3 17.8 25.2 18.5 22.1 2.7 24.8 35.4 14.8Income & expenses notebook 3.2 5.1 2.0 4.3 7.2 - 1.6 1.5 2.8Personal notes 18.2 16.8 19.1 17.3 19.8 6.5 13.9 24.0 17.2None 55.8 59.4 53.5 57.2 50.9 90.7 59.7 38.4 65.3Other 0.5 0.9 0.3 2.8 - - - 0.7 -

Registered with SHCPNo 57.6 62.2 54.8 63.7 53.1 88.0 52.6 45.5 65.8Yes 42.4 37.8 45.3 36.3 46.9 12.0 47.4 54.6 34.2

Registered with SHCP or TesoreríaNo 49.9 53.8 47.3 56.7 43.8 79.9 44.3 35.8 61.3Yes 50.2 46.2 52.7 43.3 56.3 20.1 55.7 64.2 38.7

Member of chambers and gremialesNo 83.4 89.6 79.5 95.4 84.8 93.3 82.7 72.5 84.6Yes 16.6 10.4 20.5 4.6 15.2 6.7 17.4 27.5 15.4

34


Female Male Service Commerce Manufacture Service Commerce ManufactureMarital Status

Single 13.9 18.8 10.7 31.9 7.4 29.7 11.8 7.7 13.1Married 61.6 44.5 72.5 34.5 50.6 44.8 69.8 79.4 66.9Common law marriage 11.6 8.0 13.9 5.2 10.3 5.7 14.3 11.1 16.5Divorced 1.0 2.5 - 4.4 2.2 - - - -Separated 4.0 8.1 1.3 7.2 10.4 2.7 2.5 1.2 0.8Widow(er) 8.0 18.0 1.6 16.8 19.1 17.2 1.6 0.6 2.7

MarriedNo 38.4 55.5 27.6 65.5 49.4 55.3 30.2 20.6 33.1Yes 61.6 44.5 72.5 34.5 50.6 44.8 69.8 79.4 66.9

Education IndexIlliterate 14.5 22.2 9.5 16.8 29.3 10.3 6.6 8.4 12.6Did not go to School 2.1 2.6 1.8 1.4 3.6 1.5 2.6 3.0 -Some Primary 31.6 33.2 30.6 32.2 33.5 34.1 19.3 26.0 42.6Completed Primary 26.3 21.5 29.3 20.9 22.4 19.9 33.7 31.7 24.1Some Secondary 4.7 2.0 6.4 3.2 0.7 3.9 6.0 6.7 6.4Completed Secondary 11.0 11.4 10.7 15.6 6.8 18.1 11.7 11.8 8.8High School, or College 9.9 7.2 11.7 9.9 3.8 12.3 20.3 12.4 5.5

Years Education0 16.6 24.8 11.3 18.2 32.9 11.8 9.2 11.4 12.61-3 31.6 33.2 30.6 32.2 33.5 34.1 19.3 26.0 42.64-6 26.3 21.5 29.3 20.9 22.4 19.9 33.7 31.7 24.17-9 15.7 13.4 17.1 18.7 7.5 21.9 17.6 18.5 15.210-12 5.5 3.7 6.7 3.4 2.4 8.5 8.8 7.9 4.013 and more 4.4 3.5 5.1 6.6 1.5 3.8 11.4 4.5 1.5

EthnicityNon-indigenous 86.1 85.8 86.3 88.1 88.1 74.0 91.7 82.3 86.9Indigenous 13.9 14.2 13.7 11.9 11.9 26.0 8.3 17.7 13.1

Source: Encuesta Regional de Servicios Financieros a Unidades de Producción Rural, 1994 a This question allows for multiple responses

35

Table 3 Urban Microenterprises: Participation in Credit MarketsAll MEs Total Female Male

Indicator Female Male Service Commerce Manufacture Service Commerce ManufactureAll MEs (% all MEs) 100.0 24.0 76.0 8.1 12.4 3.4 37.4 22.4 16.2

Have received credit from:a

Formal lenders 5.0 3.5 5.5 2.6 4.6 1.6 3.9 7.9 5.9Banks 4.3 2.6 4.9 2.2 3.3 1.3 3.4 7.2 5.0Cajas de Ahorro 0.7 0.8 0.7 0.4 1.3 0.3 0.6 0.7 0.9Informal lenders 7.4 8.8 6.9 6.1 12.0 3.9 5.7 10.9 4.1Friends & Relatives 3.0 3.3 2.9 3.4 3.3 2.9 2.9 3.8 1.8Moneylenders 0.8 0.9 0.8 1.4 0.9 - 0.9 0.7 0.6Supplier Credit 3.3 4.7 2.8 1.6 7.8 1.0 1.5 6.1 1.2Other 0.4 0.1 0.4 - 0.2 0.3 0.4 0.4 0.5

Have not requested credit because:Prefer to use own resources 25.7 27.2 25.2 24.8 29.2 25.5 24.6 27.1 24.1High Interest Rates 6.9 7.1 6.8 7.6 7.2 5.2 6.7 7.3 6.5Too many requirements 5.5 3.8 6.0 3.7 4.1 2.9 5.9 5.8 6.6Do not know how to obtain credit 2.4 1.9 2.5 1.8 2.3 0.7 2.3 2.4 3.1Do not that credit exist 0.4 0.2 0.4 - 0.1 1.3 0.5 0.2 0.6Other 1.2 1.5 1.1 1.1 1.7 1.9 1.0 1.0 1.2Have not needed credit 44.3 45.3 44.0 51.7 38.1 55.8 48.1 35.6 46.1

Have requested and did not received it 1.7 1.1 1.9 1.0 1.1 1.3 1.5 2.2 2.1Have used the credit to:a

Buy business premises 1.2 0.3 1.5 0.4 0.4 - 2.1 0.9 0.7Repair premises or vehicles 1.5 0.6 1.7 0.8 0.5 0.6 2.8 0.8 0.6Expand premises 0.4 0.3 0.4 0.3 0.4 - 0.2 0.5 0.5Buy inputs or merchandise 7.3 9.4 6.7 4.2 14.6 2.9 1.9 16.2 4.7Pay debts 0.5 0.6 0.5 0.7 0.6 0.3 0.4 0.8 0.5Buy Equipment 1.4 1.2 1.5 2.0 0.4 1.9 1.5 0.3 3.1Buy Tools 0.5 0.1 0.6 0.1 0.1 - 0.9 0.1 0.8Consumption 0.4 0.4 0.4 0.8 0.3 - 0.4 0.2 0.7Other 0.3 0.2 0.3 0.4 0.1 - 0.4 0.1 0.3

36

Table 3 Urban Microenterprises: Participation in Credit MarketsAll MEs Total Female Male

Indicator Female Male Service Commerce Manufacture Service Commerce ManufactureSource of Funds to Start-upa

Formal lenders 2.3 1.8 2.4 1.8 2.1 1.3 2.0 2.9 2.7Banks 1.7 1.5 1.8 1.5 1.5 1.3 1.5 2.3 2.0Caja de Ahorro 0.5 0.4 0.6 0.3 0.5 - 0.5 0.6 0.8Informal lenders 22.9 30.6 20.4 27.9 35.6 19.0 18.0 28.7 14.7Friends & Relatives 17.4 23.9 15.3 24.6 25.8 15.2 14.7 19.4 11.0Moneylenders 1.8 2.1 1.8 2.0 2.3 1.3 1.8 1.9 1.4Credit from clients 0.7 0.6 0.7 0.3 0.9 0.6 0.7 0.7 0.8Credit from suppliers 3.4 4.6 3.0 1.4 7.5 1.9 1.0 7.2 1.6Own Resources 57.2 51.7 58.9 46.8 57.1 43.9 56.5 64.4 57.0Personal Savings 50.7 46.9 52.0 42.9 51.6 39.4 51.0 53.6 51.9Liquidation from previous employment 5.4 2.2 6.4 1.4 3.4 - 5.0 10.0 4.6Other 2.3 3.6 1.9 3.3 3.5 4.5 1.7 2.5 1.4Did not need it 22.8 20.2 23.6 27.2 11.3 36.1 28.3 10.6 30.5

Source: Encuesta Nacional de Microempresas en Areas Urbanas, 1992a This question allows for multiple responses

37

Table 4 Rural Microenterprises: Participation in Credit Markets (% of each column heading)All MEs Total Female Male

Indicator Female Male Service Commerce

Manufacture Service Commerce Manufacture

All MEs (% all MEs) 100.0 39.0 61.0 12.4 20.2 6.4 14.6 23.6 22.8Have received credit from:a

Formal lenders 6.1 2.2 8.6 1.5 3.4 - 7.6 9.0 8.9Banks 2.7 0.9 3.8 - 1.8 - 1.2 6.5 2.5Development Banks 0.4 - 0.6 - - - 1.6 - 0.7Cajas de Ahorro, UC, and SAP 3.3 1.3 4.5 1.5 1.6 - 4.9 2.5 6.4Informal lenders 48.9 44.1 51.9 32.6 48.4 52.8 37.9 57.3 55.3Moneylenders 5.2 3.9 6.0 1.5 4.8 5.5 5.2 2.5 10.1Friends & Relatives 10.0 7.4 11.7 1.9 9.0 13.0 11.8 12.7 10.7Trader 0.7 0.9 0.6 1.1 1.0 - - 0.8 0.8Commercial credit 36.2 34.8 37.0 29.9 39.0 30.9 25.8 47.3 33.6Sales with Advances 6.5 4.5 7.8 - 1.8 21.7 1.2 1.5 18.6None 48.3 55.5 43.7 66.0 51.6 47.2 60.3 36.9 40.0

Market for Cash LoansReceived 21.2 14.3 25.6 6.0 18.1 18.5 23.0 24.1 28.8Both Sectors 0.6 . 1.0 . . . 1.6 0.8 0.7Formal sector only 5.6 2.2 7.7 1.5 3.4 . 6.0 8.2 8.2Informal sector only 15.1 12.1 17.0 4.5 14.7 18.5 15.4 15.1 19.9Rejected 2.1 1.5 2.6 . . 8.9 1.4 3.1 2.8Did not apply because: 76.7 84.2 71.8 94.0 81.9 72.7 75.6 72.8 68.4Would not be approved 7.7 8.4 7.3 5.3 9.8 10.1 5.2 8.4 7.5Many requirements 9.2 6.6 10.9 3.3 10.6 . 15.8 9.6 9.0Afraid to request 8.2 8.9 7.7 6.0 11.9 5.4 7.2 7.5 8.1Do not need credit 26.2 29.4 24.2 46.2 21.4 21.9 25.1 26.4 21.3Too risky 19.3 23.4 16.6 20.8 21.7 33.7 18.3 14.3 18.0Other 5.9 7.6 4.9 12.4 6.5 1.5 3.9 5.9 4.5NE 0.2 . 0.3 . . . . 0.7 .

38

Table 4 Rural Microenterprises: Participation in Credit Markets (% of each column heading)All MEs Total Female Male

Indicator Female Male Service Commerce

Manufacture Service Commerce Manufacture

Source of Funds to Start-upa

Formal lenders 2.9 0.8 4.2 - 1.5 - 3.0 5.9 3.2Banks 1.0 0.8 1.1 - 1.5 - 1.2 1.5 0.7Caja de Ahorro 1.9 - 3.1 - - - 1.8 4.4 2.6Informal lenders 18.6 22.3 16.2 20.3 25.1 17.2 13.7 23.5 10.2Friends & Relatives 11.9 16.6 8.9 18.0 15.5 17.2 8.0 12.2 6.1Moneylenders 0.4 - 0.6 - - - - 1.6 -Credit from clients 0.8 0.5 1.1 1.6 - - 1.4 - 1.9Credit from suppliers 5.9 5.7 6.0 2.3 9.6 - 5.4 10.1 2.2Own Resources 65.4 56.7 70.9 43.2 68.3 46.6 67.1 73.7 70.5Personal Savings 56.2 50.4 59.9 40.8 58.4 43.5 51.2 62.0 63.4Liquidation from previous employment 4.5 1.3 6.6 2.3 1.2 - 12.0 6.9 2.8Sale of assets 1.3 0.9 1.6 - 1.8 - 2.2 2.7 -Other 6.5 7.0 6.3 2.4 10.1 5.8 4.8 6.4 7.0Did not need it 19.9 23.6 17.5 38.2 9.3 40.1 22.3 10.0 22.3

Source: Encuesta Regional de Servicios Financieros a Unidades de Producción Rural, 1994 a This question allows for multiple responses

39

Table 5 Urban Microenterprises: Male-Female Earnings Decomposition (with hours worked)

All Sectors % Services Commerce ManufactureDecomposition of Male-Female Earnings Differential by Economic Sector (in log-points)

Earnings Male 6.8260 6.8364 6.7567 6.8952 Female 6.1251 6.2869 6.1347 5.7027Male-Female Earnings differentials 0.7009 0.5495 0.6220 1.1925 Attributed to differences in: explanatory variables (X's) 0.2163 0.1992 0.4796 estimated parameters (unexplained) 0.3332 0.4227 0.7129

Decomposition of Male-Female Earnings Differential (in log-points) 0.70127Intra-sectoral Earnings Effect: 0.6790 96.9% Attributed to differences in: explanatory variables (explained) 0.2455 35.0% 0.0747 0.1018 0.0690 estimated parameters (unexplained) 0.4336 61.9% 0.1150 0.2160 0.1025Inter-sectoral Earnings Effect: 0.0222 3.2% Attributed to differences in: explanatory variables (explained) 0.0026 0.4% -0.1882 -0.0506 0.2414 estimated parameters (unexplained) 0.0196 2.8% 1.2282 -1.4701 0.2615

Actual and Predicted Sectoral Distribution of Microenterprises (in percentages)Females Predicted 52.5% 29.3% 18.2% Actual 34.5% 51.1% 14.4%Males Actual 49.7% 28.6% 21.7%

Source: Calculated using estimated earnings regression in Table 8

40

Table 6 Urban Microenterprises: Male-Female Earnings Decomposition (without hours worked)

All Sectors % Services Trade ManufactureDecomposition of Male-Female Earnings Differential by Economic Sector (in log-points)

Earnings Male 6.8260 6.8364 6.7567 6.8952 Female 6.1251 6.2869 6.1347 5.7027Male-Female Earnings differentials 0.7009 0.5495 0.6220 1.1925 Attributed to differences in: explanatory variables (X's) 0.0602 0.1284 0.1642 estimated parameters (unexplained) 0.4893 0.4936 1.0283

Decomposition of Male-Female Earnings Differential (in log-points) 0.70127Intra-sectoral Earnings Effect: 0.6790 96.9% Attributed to differences in: explanatory variables (explained) 0.1100 15.7% 0.0208 0.0656 0.0236 estimated parameters (unexplained) 0.5690 81.2% 0.1689 0.2522 0.1479Inter-sectoral Earnings Effect: 0.0222 3.2% Attributed to differences in: explanatory variables (explained) 0.0030 0.4% 0.0519 -0.1682 0.1193 estimated parameters (unexplained) 0.0192 2.7% 0.9881 -1.3525 0.3837


Source: Calculated using estimated earnings regressions without hours worked

41

Table 7 Rural Microenterprises: Male-Female Earnings Decomposition

All Sectors % Services Commerce ManufactureDecomposition of Male-Female Earnings Differential by Economic Sector (in log-points)

Earnings Male 6.3886 6.1131 6.6502 6.2927 Female 5.3709 5.5221 5.4611 4.9116Male-Female Earnings differentials 1.0177 0.5910 1.1892 1.3810 Attributed to differences in: explanatory variables (X's) 0.6013 0.3052 0.4076 estimated parameters (unexplained) -0.0103 0.8840 0.9734

Decomposition of Male-Female Earnings Differential (in log-points) 1.01767Intra-sectoral Earnings Effect: 1.0284 101.1% Attributed to differences in: explanatory variables (explained) 0.4245 41.7% 0.2004 0.1421 0.0820 estimated parameters (unexplained) 0.6039 59.3% -0.0034 0.4115 0.1958Inter-sectoral Earnings Effect: -0.0107 -1.1% Attributed to differences in: explanatory variables (explained) 0.0152 1.5% -0.2424 0.1506 0.1070 estimated parameters (unexplained) -0.0259 -2.5% -0.3811 -0.6905 1.0457


Source: Calculated using estimated earnings regression in Table 12

42

Table 8 Urban Microenterprises: Males Regression of Log (Earnings)

MalesAll Sectors Services Commerce Manufacture

Explanatory variables Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statisticConstant 4.0812 30.3800 *** 4.1117 22.9880 *** 3.9024 15.8960 *** 4.0858 10.6500 ***Years of education 0.0335 11.5270 *** 0.0368 9.3790 *** 0.0366 6.3690 *** 0.0165 2.5800 ***Experience 0.0181 7.2450 *** 0.0143 4.1780 *** 0.0210 4.3780 *** 0.0174 3.1970 ***Experience Squared -0.0372 -10.2970 *** -0.0266 -5.2780 *** -0.0432 -6.6580 *** -0.0366 -4.4060 ***Years in operation 0.0059 5.0340 *** 0.0006 0.3330 0.0125 5.5340 *** 0.0042 1.8940 *Log (hours) 0.3955 16.6470 *** 0.3865 12.3940 *** 0.3978 9.1810 *** 0.4443 6.2040 ***Salaried workers 0.1530 11.9120 *** 0.1982 9.5190 *** 0.2518 7.3980 *** 0.1309 8.1060 ***Non-paid workers 0.0106 0.5610 0.0723 2.0660 ** 0.0174 0.6610 -0.0038 -0.0900Capital (10-3) 0.0009 2.9180 *** 0.0006 1.4470 0.0022 4.1130 *** 0.0004 1.0500Fixed premises (dummy) 0.0483 1.7480 * 0.0864 2.2970 ** -0.0313 -0.5810 0.0103 0.1850Registered with SHCP 0.1996 7.8660 *** 0.1904 5.5910 *** 0.2358 4.7350 *** 0.1825 3.2260 ***Involuntary entry into ME (dummy) -0.1966 -7.7840 *** -0.2005 -5.9480 *** -0.1444 -3.0210 *** -0.1951 -3.4950 ***Married (dummy) 0.1507 7.0170 *** 0.1921 6.8880 *** 0.1211 2.5290 ** 0.0779 1.8800 *

Adjusted R2 0.3415 0.3439 0.3746 0.3351Number of Observations 6,444 3,205 1,842 1,397***/**/* statistically significant at 99%, 95%, and 90%, respectively

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Table 9 Urban Microenterprises: Females Regression of Log (Earnings)

FemalesAll Sectors Services Commerce Manufacture

Explanatory variables Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statisticConstant 3.5325 17.7610 *** 2.7052 8.2240 *** 4.3802 16.5840 *** 2.9892 6.2070 ***Years of education 0.0403 5.6470 *** 0.0545 5.0640 *** 0.0378 3.6580 *** -0.0046 -0.2440Experience 0.0128 2.3910 ** 0.0109 1.1760 0.0092 1.2580 0.0156 1.2260Experience Squared -0.0282 -4.0170 *** -0.0156 -1.2570 -0.0256 -2.7020 *** -0.0361 -1.9820 **Years in operation 0.0051 1.9450 * 0.0059 1.2460 0.0082 2.1930 ** 0.0008 0.1590Log (hours) 0.4144 12.1730 *** 0.6116 10.1280 *** 0.2429 5.7020 *** 0.5391 6.4910 ***Salaried workers 0.3624 10.6730 *** 0.2422 5.2650 *** 0.5208 6.9120 *** 0.2499 7.6570 ***Non-paid workers 0.0935 2.9730 *** 0.0424 0.6890 0.1008 2.5510 ** 0.1924 2.7530 ***Capital (10-3) 0.0008 0.7940 0.0001 0.0390 0.0008 1.8880 * 0.0170 7.7650 ***Fixed premises (dummy) 0.0410 0.6450 -0.0417 -0.4000 0.0762 0.9180 0.2000 1.0830Registered with SHCP 0.2850 4.7440 *** 0.1431 1.3270 0.3595 4.5190 *** -0.0229 -0.1150Involuntary entry into ME (dummy) -0.0986 -1.3060 -0.2185 -1.9390 * -0.0963 -0.8620 0.1633 1.1030Married (dummy) -0.0274 -0.6750 -0.0181 -0.2720 -0.0360 -0.6400 0.0751 0.7550

Adjusted R2 0.3405 0.3747 0.2934 0.4964Number of Observations 2,016 696 1,030 290***/**/* statistically significant at 99%, 95%, and 90%, respectively

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Table 10 Descriptive Statistics among Urban Microenterprises by Gender and Economic Sector

MalesAll Sectors Services Trade Manufacturing

Explanatory variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.Years of education 7.2588 4.7775 7.795 5.0401 7.0679 4.5978 6.2806 4.183Experience 29.3974 16.106 27.9735 15.7985 31.5494 17.3729 29.8268 14.6768Experience Squared (10-2) 11.2357 10.797 10.3203 10.2181 12.9702 12.2248 11.0489 9.7666Years in operation 9.1079 9.461 9.3056 9.4962 8.0081 8.7877 10.1043 10.0813Log (hours) 5.2196 0.4882 5.162 0.518 5.2883 0.5201 5.2613 0.3346Salaried workers 0.4291 1.1413 0.3382 0.8033 0.285 0.7818 0.8275 1.8761Non-paid workers 0.1979 0.5672 0.1167 0.4241 0.3898 0.7624 0.131 0.487Capital (10-3) 21.1908 67.0519 20.8982 65.3439 19.9352 54.098 23.5176 84.1623Fixed premises (dummy) 0.3547 0.4554 0.3637 0.4338 0.4017 0.4904 0.272 0.4452Registered with SHCP 0.4143 0.4926 0.4212 0.4938 0.4908 0.5001 0.2978 0.4574Involuntary entry into ME (dummy) 0.1659 0.372 0.1579 0.3647 0.2079 0.4059 0.1288 0.3352Married (dummy) 0.7606 0.4268 0.7395 0.439 0.7856 0.4105 0.7759 0.4171Number of Observations 6,344 3,205 1,842 1,397

FemalesAll Sectors Services Trade Manufacturing

Explanatory variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.Years of education 6.3948 4.4126 7.6954 5.3364 5.6592 3.6843 5.8862 3.5613Experience 30.4206 15.8625 26.7313 15.6283 32.9816 15.6871 30.1793 15.3161Experience Squared (10-2) 11.7691 10.875 9.5846 9.8083 13.3363 11.3904 11.4457 10.5326Years in operation 6.6579 8.1681 6.2629 7.7567 6.3854 7.8849 8.5736 9.7362Log (hours) 4.9073 0.8023 4.7289 0.8304 5.1391 0.7284 4.5125 0.7185Salaried workers 0.1711 0.6808 0.2615 0.73 0.0893 0.4317 0.2448 1.1124Non-paid workers 0.3155 0.6606 0.2457 0.5839 0.4019 0.7215 0.1759 0.5581Capital (10-3) 11.9412 47.8631 16.2897 59.6566 10.9625 44.2429 4.9812 18.6279Fixed premises (dummy) 0.3802 0.4849 0.3585 0.478 0.467 0.4992 0.1241 0.3303Registered with SHCP 0.4013 0.4903 0.3966 0.4895 0.4796 0.4998 0.1345 0.3418Involuntary entry into ME (dummy) 0.0645 0.2457 0.0733 0.2608 0.0621 0.2415 0.0517 0.2219Married (dummy) 0.5084 0.5001 0.4885 0.5002 0.5359 0.499 0.4586 0.4991Number of Observations 2,016 696 1,030 290Source: Encuesta Nacional de Micronegocios en Areas Urbanas, 1992

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Table 11 Descriptive Statistics among Rural Microenterprises by Gender and Economic SectorMales

All Sectors Services Commerce ManufactureExplanatory variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.ConstantYears of education 5.71 3.90 6.95 4.59 5.97 3.92 4.70 3.16Experience 30.84 15.98 26.95 16.39 31.92 15.08 32.10 16.40Experience Squared 1205.54 1116.23 990.69 1041.81 1243.98 1084.32 1296.43 1182.71Years in operation 10.83 12.49 8.36 10.67 7.35 7.80 15.80 15.44Workers (including owner) 2.04 1.53 1.47 0.78 2.22 1.71 2.20 1.61Capital (10-3) 12.08 45.62 10.95 15.95 18.31 70.69 6.53 15.05Fixed premises (dummy) 0.40 0.48 0.40 0.44 0.55 0.50 0.24 0.43Registered with SHCP 0.43 0.50 0.45 0.50 0.52 0.50 0.31 0.47Married (dummy) 0.70 0.46 0.66 0.48 0.83 0.38 0.59 0.49Indigenous (dummy) 0.15 0.35 0.08 0.27 0.18 0.39 0.15 0.35Population in locality 8.35 5.64 9.91 6.59 7.94 4.89 7.83 5.61

Number of Observations 268 62 103 103Females

All Sectors Services Commerce ManufactureExplanatory variables Mean S.D. Mean S.D. Mean S.D. Mean S.D.ConstantYears of education 4.77 4.00 5.26 4.26 4.02 3.78 5.69 3.85Experience 31.13 16.05 28.35 16.45 35.23 14.41 26.23 17.03Experience Squared 1225.07 1052.56 1069.85 1051.73 1446.59 1040.64 969.60 1004.73Years in operation 6.68 9.07 7.16 8.67 4.58 6.80 10.73 12.50Workers (including owner) 1.50 1.00 1.41 1.14 1.63 1.01 1.34 0.68Capital (10-3) 5.55 21.07 9.07 34.89 4.72 8.10 1.62 4.97Fixed premises (dummy) 0.29 0.46 0.26 0.44 0.41 0.49 0.09 0.28Registered with SHCP 0.36 0.48 0.34 0.48 0.49 0.50 0.09 0.28Married (dummy) 0.43 0.50 0.33 0.47 0.48 0.50 0.46 0.51Indigenous (dummy) 0.16 0.37 0.12 0.33 0.15 0.36 0.26 0.44Population in locality 8.40 5.53 9.00 5.99 8.85 5.49 6.35 4.39

Number of Observations 174 58 81 35Source: Encuesta Regional de Servicios Financieros a Unidades de Producción Rural, 1994

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Table 12 Rural Microenterprises: Regression of Log (Earnings) Males

Males

All Sectors Services Commerce Manufacture

Explanatory variables Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic

Constant 5.8494 11.5920 *** 3.7669 3.1240 *** 6.4893 7.8410 *** 6.7071 17.7770 ***

Years of education -0.0154 -0.5170 0.0714 1.3600 -0.0338 -0.6510 -0.0917 -2.0730 **

Experience -0.0047 -0.1880 0.0168 0.2970 -0.0253 -0.6440 -0.0024 -0.0970

Experience Squared -0.0001 -0.2350 -0.0004 -0.4570 0.0002 0.3030 0.0000 -0.0980

Years in operation 0.0132 1.9130 * 0.0403 2.7310 *** 0.0325 1.5440 -0.0020 -0.2050

Workers (including owner) 0.1943 3.1210 *** 0.1821 1.2420 0.2441 2.9490 *** 0.1916 2.0830 **

Capital (10-3) 0.0026 2.4900 ** -0.0006 -0.0720 0.0023 1.5770 0.0059 0.6510

Fixed premises (dummy) 0.2043 0.9700 0.1586 0.5220 0.3935 1.2890 -0.2150 -0.5600

Registered with SHCP 0.5983 3.1830 *** 1.0327 3.1090 *** 0.5185 1.9610 * 0.3998 1.3550

Married (dummy) 0.1908 1.0900 1.0065 2.7940 *** -0.1325 -0.4730 -0.1960 -0.8640

Indigenous (dummy) -0.4204 -1.5990 1.0169 1.8980 * -0.0004 -0.0010 -1.2634 -3.0330 ***

Population in locality -0.0137 -1.0820 -0.0105 -0.4980 -0.0290 -0.9830 -0.0076 -0.3420

Adjusted R2 0.1678 0.2813 0.2290 0.2382

Number of Observations 268 62 103 103

***/**/* statistically significant at 99%, 95%, and 90%, respectively

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