Feminization and Informality: The case of Egypt
Reham Rizk1, and Shadwa Zaher2
October 2013
1
2
Lecturer, British University in Egypt
Assistant Lecturer, British University in Egypt
Abstract
The paper seeks to provide gender-based empirical evidence and compares wage differential
between formal and informal wage workers by estimating a multinomial regression model in
Egypt. To this end, the analysis uses a two-step econometric exercise relying on Mincer-type
reduced form wage equation to analyze wage differences between informal and formal sectors
for both male and female separately in different labour market outcomes including
manufacturing and services sector. Therefore, a multinomial logit selection model is used as a
first step to eliminate selection bias. Then the wage equation is estimated for four categories of
wage. This study is distinguished for taking the market structure into account as a more
significant factor in determining gender-wage differential. Hence, using individual-level micro
data from Egyptian labour market surveys for 2006, we first find that worker‟s age and education
has a significant impact on the probability of moving from informal to formal sector. In addition,
we find a significant wage gap between informal and formal workers. Consequently, a clear and
significant productivity differential between formal and informal workers in Egypt was found.
JEL Classification: D13, E26, O17, J02, J16
Key Words: Feminization, Informality, Productivity, employment, gender
1. Introduction
According to the ILO (2002a) “The informal economy absorbs workers who would otherwise be
without work or income, especially in developing countries that have a large and rapidly growing
labour force”. Wahba 2009 defined informality to be the lack of job contract and social security
access. She added that informality in Egypt increased as a share of total employment. De Soto
1989 was the first to analyze the determinants of the informal sector and have forcefully
advocated for the formalization of the informal sector especially in developing countries. He
defined the latter to consist of small informal firms as being led by entrepreneurs who wished to
move to the formal sector but could not (Echevin, Murtin, 2009). We can deduct that the nature
of “informality” among literature differed; the scope of this research is “informal employment”.
It accounts for own account workers and employees employed in informal enterprises;
contributing family workers; employees holding informal jobs; and members of informal
producers‟ (Taymaz, 2009). The conventional segmented markets theory explains labor market
informality to be nothing but a survivalist alternative for those disadvantaged or kicked out of the
formal employment opportunities. In addition to that , debates among literature of the relation
between informal productivity and output has been increasing. For instance, Dabla-Norris and
Gradstein (2005) argued that the informal sector generates between 10 to 20 percent of the
aggregate output in developed countries and more than a third of GDP in developing countries,
reaching in some countries more than 50 percent. With the Egyptian situation fluctuating closely
to these percentages; Jackline Wahba (2009) suggested that empirical evidence showed that
“informalization” has increased significantly in Egypt during the 90s as a result of economic
reforms. Today stylized facts shows in accordance to the World Bank recent statistical report
2012- the informal sector in Egypt constitutes more than 60% of the GDP. The dynamics of this
sector and its relation to some economic variables have been the discussed by most academic
studies. Some have discussed the impact of the informal sector on productivity gaps between
developed and developing economies (Hendy and Zaki, 2012). Others have discussed its impact
on poverty and other economic variables. Nevertheless, none have discussed the effect of
formalization on gender- wage differentials; and employment decisions. Moreover, minimal
attention was given to the relation between the level of employment between formal/informal
sectors‟ and productivity levels among both. These latter two points are the scope of this
research. The importance of this paper lies in offering a policy prescription for governments to
pay attention to the employment decision and productivity gaps between formal/informal sectors;
with special attention to the wage gap between males and females.
To begin with, informality was found to have a negative effect on productivity and hence on
employment; and its one of the main reasons for the productivity gap between developing and
developed countries (ibid, 2012). However, Murtin 2009 argued in his study that the informal
sector may be well split into low productive clusters and high productive ones; these latter
clusters are more similar in characteristics and productivity to the formal sector. Therfore he
concluded that this division between formal and informal sectors‟ is yet ambiguous. As for the
wage differential as a source of decreasing productivity; acompetitve labor market explains that
such wage inequality among both sectors tend to disappear as informal employment may as well
be based of private cost /benefit analysis for both individuals and firms. While on the other hand
the traditionally segmented labor market theory suggested that informal employees are subject to
lower rewards than their formal sector‟s counterparts (tansel,kan,2012). Pratap and Quintin
(2006) contradicted this theoretical view by their recent study that found no difference between
formal and informal sectors in terms of wages in argentina-this is found after controlling for both
individual and employer‟s semi-parametrically characteristics. A recent comprehensive study
concluded that there is a significant wage gap between formal and informal workers (Taymaz,
2009). These owners are basically informally employed with no formal contract (ElMahdi,
2010). As with the litreture, empirical evidence to date also seems to be mixed and inconclusive.
Carneiro and Henly (2001) considered the determinants of earnings and selection of workers into
both formal and informal employment. He used 1997 Brazilian household surveys; they used lee
(1978)‟s three step procedures of simultaneous modeling of participation decision and earnings.
The results implied that age, tenure, education and gender are significant determinants of the
wage differentials among both sectors. Moreover, Gang and Van Soest studied wage differentials
among both sectors using quarterly panel data from Mexico. They found that such diferential
differs with respect to the significance of some determinants for one sector over the other. For
instance, they found that returns to education are effective for both sectors, yet more significant
for the formal sector over its informal counterpart (ibid, 2012). On a gender based analysis; such
informal employment dualism (in which the informal sector is composed of two segments; one
displays higher levels of earnings while the other is not) proved to be more severe for women
employees than men (cho &cho, 2010). Also Blau and khan (1996) measured the gender
earnings gap for 10 OECD countries; they found that market structure plays a very important
role in the analysis of the gender earnings gap.. Finally, The wage gap between male and female
workers is less obvious in formal sector compared to informal sector (Hendy and Zaki, 2012). If
we are to impose formalization to the informal sector, one has to take into account the effect of
regulations and strict law enforcement on both productivity and output. Several studies described
this relationship, for instance Almeida and Carneiro (2006) explained in their study that
enforcement of certain laws with the process of formalization may actually reduce informal
employment; however, it may also decrease average wages, productivity and investments by
reducing firm‟s access to unregulated workers. Moreover, regulatory burden and public policy
effect differs according to the firm size. The study of Mckinzie and Sakho (2007) concluded that
owners of large firms who stayed in the informal employment are of high entrepreneurial
abilities than those of the formal sector. Therefore, formalizing their enterprises may lead to a
negative effect of losing their productivity and entrepreneurial skills. On the other hand some
studies revealed that the higher the complications of tax administration procedures, the lower the
probability of registration with the tax department; and the lower the productivity levels (Magdi
2012). The present study aims to identify the complex inter-linkages between enforcing
formality policies, female wages‟ and employment opportunities. Our motivation is twofold:
economic and empirical interests. On the one hand, Egypt, witnessing both macro and
microeconomic changes during the last decade, has undertaken numerous policies that affected
labor market. On the other hand, regarding the empirical motivation, Egypt as most of
developing countries suffers from an important lack of empirical studies. This framework uses in
the meantime a discrete choice model of labor market outcomes using the Egyptian Labor
Market and Panel Survey (ELMPS), 2006 . Therefore, the objective of this paper is to
compliment the exiting litreture by examining the earnings of males to females in both formal
and informal sectors, with a special relevance to how will this earnings gap affects productivity
among both sectors. The paper will be organized as follows; an introduction of the nature of
informality and the different theoretical and empirical arguments discussing wage and gender
differentials between formal and informal sectors. This section is followed by stylized facts in
the Egyptian economy. Section three to five then describes the econometric model, data and
empirical results and then section six concludes.
2. Stylized Facts
In Egypt, CAPMAS defined the informal sector as “the unorganized private sector which
includes: 1) retail trading activities (four employees or less); 2) manufacturing industries and
repair services (nine employees or less) or business entities that are not covered by law (Magdi,
2012). It is worth mentioning here that women entrepreneurs working informally in rural areas
are higher when compared to men in the same field. Nassar 2013 in her study “socio-economic
conditions of work in greater Cairo: Formal/informal comparison and gender differentials”
showed that 33.2 percentage of females in Egypt works in the informal sector; compared to 53.9
percent of males that work in the same sector. More than a third of the informally self-employed
females are under the poverty line in comparison to 9.5% of the formal sector. A reason may be
given to the working conditions and educational attainment given to informal females compared
to formal ones. The same study showed that 83% of women in informal business work the whole
week compared to only 40.5% for their counterpart women. This again may be due to lower
incomes offered to women in the informal employment vis a vis formal one. Moreover, there is a
large pool of theoretical studies primarily based on heterogeneous workers or matching models
that proves that more productive workers are mainly selected in the formal sector than informal
one. Moreover, In Egypt, children between the ages of 6-15 are estimated to be between 2 and
2.5 million. They are mostly working as street vendors, domestic workers, agricultural labourers,
factory workers, laundry workers and helpers for mechanics (ECWR, 2008, U.S. Department of
Labor‟s Bureau of International Labor Affairs), with the vast majority of them (83%) working in
rural areas. The gender pattern of private sector employment growth is quite striking. The rate of
growth of women employment declined by 15.8 percemt compared to a minimal decline for men
to reach 7.43 percent in the same sector. This is surveyed over the period 1990-1998. This shows
a huge increase in the informal sector by 53.4% ( as a shelter for these women) (ibid, 2013).
However, the same study confirmed that for the service, trade and hotels sector has witnessed the
highest rate of growth in female employment over the same period. Similarliy for the electricity,
gas, water and mining sectors with a minimal percentage that were ignored bu the referred study.
3. Econometric Model
As discussed in the introduction, this paper aims to examine the impact of informality on labour
market decision and wages for male and female separately. To do so, we start by estimating
multinomial probit regression .This modeling technique links between the probability of
choosing between one or more choices and the attributes of the choices. In the empirical work of
the present study, we choose, in the first stage, a random utility in order to compare the different
utility levels associated with each labor market decision and then choose the highest utility
among them. The individual (male or female) chooses exposed to eight labour market outcomes
and the base outcome is unemployment 3 . The next four outcomes are wage employment in
formal/informal manufacturing and services sectors4. The next three are wage employment in
formal/informal self –employers, waged employee, and unpaid family worker. The last three
outcomes are not classified into sectors because of lack of sufficient number of observations. The
utility of alternative j for individual i can then be represented as follows,
z ij' ,denotes a vector of individual characteristics and attributes of the choices such as gender,
years of schooling, age and Registered (formal) – availability of social security. β are the model
parameters to be estimated.
is used to scale the vector
implication of labour market choice outcomes, and
The likelihood function under
, by
and it does not change the
.
has the same value like the likelihood function under
and,  ij represents the disturbance term that is characterized
in this model to be multivariate normal distribution and correlated across choices:
).
We are using a multinomial probit model to understand the determinants of labor market
decision for men and women living in urban areas. The estimated coefficients from the
3
4
This outcome includes unemployed people during past three months.
Agriculture sector is dropped from the model because the formal and informal category contain very few.
multinomial probit model are difficult to interpret quantitatively. Thus, we calculated the
marginal effects of each variable on labour market outcomes. This gives the individual
probabilities of being in one status rather than the other. Therefore, our model generates
probabilistic distribution over the different alternatives.
Then, we estimate a Mincer-type reduced form equation to analyze wage differences between
informal and formal sectors. Since that the wage rate is determined only for employed workers,
thus selection process needs to be taken into account in estimating the wage equation.
=
where
for
,j
labour market outcomes
is the utility of being at labour market j for individual i, W is the (log) wage rate for
wage workers (Tymaz, 2009). The following variables are used as repressors „in the multinomial
probit model.
The age of the person has a significant impact on labour market decision. We added a number of
dummies the capture the non-linear effect of the age variable. The omitted variable is less than
15 years old.
The status in the household is captured by “child dummy” that takes the value of 1 if aged less
than 30 for daughter/ son and 0 otherwise. The omitted variable is the parent category that
includes all other people that are not included in the child category.
The educational level effects are captured by five dummies: primary5, secondary, tertiary6. The
omitted variable is the illiterate category.
There are two dummy variables for marital status: single7 and divorced8. The omitted variable is
the married category9.
The effects of household size are captured by parent household size and child household
interactions. The household size is measured by the (log) number of persons in the household. It
is interacted with child and parent dummy to show that household size effects on parent and
children are different.
5
Preparatory and primary school degree are merged together
Tertiary category includes vocational, above intermediate, university and postgraduate level.
7
Less than minimum and never married is grouped into single category
8
Divorced and windowed is grouped into divorced category
9
Married and contracted married is grouped into married category
6
We use also a dummy variable “register “ for any of the household member that benefits from
social security and zero otherwise. Finally, we use a dummy for a person in a household whose
head is unemployed and zero otherwise.
Finally, two variables are added to the wage equation to control their effects on wage .They are
:(i) firm size and is captured by six dummy variables (small 5-9, 10-24 employees; medium size
25-49 employees; large 50-99, more than 100 employees). The omitted variable is the microfirms which are less than 5 employees. Finally, Full –time dummy variable is used to capture
employment status. The omitted variable is part-time category.
4. Data
Data used in this study are obtained from the Egyptian Labor Market and Panel Survey (ELMPS1998 & 2006). The latter is a nationally- representative household survey that consists of a total
of 8349 households distributed as follows: A total of 3 684 households followed since the ELMS
1998, 2176 new households that split from these households and a refresher sample consisting of
2498 households was also included to ensure that the data continue to be nationallyrepresentative after the split of some household that were present in 1998. Both surveys'
questionnaires (see Barssoum, 2007) are composed of three major sections: (1) a household
questionnaire administered to the head of household or the head‟s spouse that contains
information on basic demographic characteristics of household members, movement of
household members in and out of the household since 1998, ownership of durable goods and
assets, and housing conditions, (2) an individual questionnaire administered to the individual him
or herself containing information on parental background, detailed education histories, activity
status, job search and unemployment, detailed employment characteristics, a module on
women‟s work, migration histories, job histories, time use, earnings and fertility. Also, a new
critical module has been added to the questionnaire in order to allow a more profound study of
marriage dynamics in Egypt. This module contains detailed information on costs of marriage and
costs of divorce and is only available in the 2006 data. (3) a household enterprise and income
module that elicits information on all agricultural and non-agricultural enterprises operated by
the household as well as all income sources, including remittances and transfers.
In our empirical analysis, we control for individual region, since the employment dynamics is
varied in rural areas compared with urban areas. Furthermore, majority of workers in rural areas
are informal. Thus we focus our analysis in urban areas only.
The wage rate is positively correlated with worker productivity in competitive market. In this
context, any difference in wage rate between formal and informal workers can be attributed due
to differences in productivity. The worker productivity differs by human capital accumulation
and thus wages rate changes. In imperfect competitive labour market, the wage rate is
determined by the bargaining power between firm and worker. In this case, we have two effects;
(i) the wage becomes positively correlated with worker productivity because the workers will
share a part of productivity. (ii) The wage could become unrelated to worker productivity if it is
determined only by the bargaining power. In such case, the bargaining power of the workers in
formal sector is stronger than informal sector. Thus results should be interpreted with caution.
5. Empirical Results
The marginal effects on labour market participation are presented in table (1). It is apparent that
the probability of having high unemployment rate among female is more compared to male at
younger ages. However, at older ages the probability of unemployment decreases for females
and increases significantly among men. This is because women are more engaged in home
production at younger ages and that lead to being late in entering labour force. While men are
more concerned to enter labour market at early stages to be either able to satisfy his family‟
needs or have a family in the near future. Parent household size variable has a significant
negative effect on the probability of non-employment for men and positive effect on probability
of women non-employment. This supports the above idea that men are more likely needed for
workplace employment and the value of household production are more value for women as
household size increases. The probability of unemployment decreases significantly among men
and women by increasing education level.
As male and female get younger, they are more likely to decrease employment in informal sector
and transfer to formal sector. This is appears obviously as the probability of male employment
decreases by 17% and 6% in informal manufacture and services respectively; the probability of
female employment decreases by 3%
and 15 % in informal manufacture and services
respectively. However, the female and male employment increases in formal services sector by
20% and 45% respectively. This supports the U- shape relation between age and informality.
Moreover, more educated people are less likely to be employed in the informal sector. The
probability of male employment by tertiary education decreases by 22% for both informal
manufacture and services sector. Also, the probability of female employment by tertiary level
decreases by 2% and 45% for informal manufacture and services sector respectively. Thus, that
leads to increases in the probability of employment in informal manufacture by 2% services by
43% among male workers and 70 % in services sector among females. In the same time, the
probability of female employment in manufacture sector declines by 5%, this is due higher level
of taxes and social security payments and complexity of legal quality.
It is observed that if there is a formally employed person (registered) in the household, other
members of the household will benefit from social security benefits and this may discourage
other household member to get a formal job. The probability of male employment declines in
self-employed and unpaid worker in a family by 3% and 17% respectively.
While the
probability of female employment declines in self-employed and unpaid worker in a family by
5% and 7% respectively.
Finally, if the household head who earn money for the household is unemployed, the incentives
for other household members may change. For example the probability of employment for both
self-employed and unpaid worker decline by 0.2% and 0.3% among males and 0.1% and 0.1%
among females respectively. To illustrate the wage differentials between informal and formal
sectors, we carry out Mincerian wage equation. Since wage is determined only for waged
employee, we used multinomial probit sample selection model to avoid selection bias in
estimating wage equation. The wage equation is estimated by taking into account a group of
selection variables. The selection variables are formal and informal manufacture and services
sector for males and females separately. In addition to, self employed and unemployed categories
for males and females are added to selection variables. It is observed from the age variable that is
used to estimate the age- earnings profile, as age increases, the wage increases for formal and
informal sector. However, earnings of formal sector are greater than informal in the service
sector as well. Moreover, the wages of female in informal manufacturing sector earn more than
the male. The firm size 10 variable used to capture the effect of firm size on earnings. As firm gets
larger, it tends to pay higher wages. Firm size has a positive effect on wages but insignificant.
Full –time employees is used to capture the effect of employment status on wages. The findings
of table (2) indicate that there are significant wage gap between formal and informal workers
after controlling both individual and selection characteristics. These differences in wage are most
likely because of the share of formal employees is greater formal sector compared to informal
sector. Moreover, education and firm size increases the share of formal employees compared to
informal.
6. Conclusion and Policy recommendations
Among the most important challenges facing governments in developing countries and hence the
Egyptian government; is the lack of effective development strategies which can generate new
employment and income opportunities. As mentioned above the informal employment today
plays an important role in determining such development strategies due to its growing size and
effect on growth levels. The main hypothesis of this study was to examine the impact of the
informal economy on labour market decision and wages for male and females. The paper‟s main
findings can be summarized in the following:
1. There is a significant increase in workers employment in the formal sector compared to
the informal sector which explains the productivity between two sectors.
2. There is a significant wage gap between formal and informal sectors. These findings are
robust with respect to self-selection variables.
3. Male earnings are higher than female earnings in both manufacturing and service sectors
in informal sector and the gap is much higher for formal sector.
It is apparent from the most of the findings coincides with the theoretical and empirical litreture
discussed above; except with the second result that contradicts the hypothesis of the alternative
theory that there are no earning differences between formal/informal sectors‟. This could be
reasoned that the theory assumes homogenous structure for the informal sector which is not the
empirically accurate.
There are several policy prescription that the government need to follow to decrease this earning
gap between the two sectors and most importantly, decrease this huge eage differential among
10
This variable contains a lot of missing variables, most of reported coefficients are insignificant.
each. First the government should decrease the vulnerability of unemployment among the
informal sector by decreasing its tendency to undertake continuous structural changes among
incomes and employment. Second, women working in the informal sector –as proved empirically
in this study- are basically uneducated which undermines their chances of accessing better wage
employment in the formal sector. Therefore the government should adopt a focused illiteracy
programs for working women in the informal sector to increase their competitive skills and allow
them better opportunities. Finally, the lack of governmental institutional support for women in
having and raising children as we mentioned above that unemployment increases for younger
women than for older ones –when compared to men in that context- makes it difficult for women
whose careers have been interrupted to return to their previous formal sector jobs. Therefore
government‟s should ensure the existence of a better institutional support for these women to
counter their settlement in the informal market. Last but not least, incentive should be given to
women to work on a full time basis as part time basis jobs are significantly associated with the
earnings gap among the two sectors.
7. References
Bourguignon, F., Fournier, M., and Gurgand, M. (2007). Selection Bias Correction Based on the
Multinominal Logit Model:Monte Carlo Comparisons. Journal of Economic Surveys, 21, 174205.
Dabla-Norris, E., Gradstein, M., and Inchauste, G. (2005). What causes firms to hide output?
The determinnats of informality. IMF Working Paper No.05/160.
Echevin, D. and Murtin, F. (2009). “Identification of the productivity gap between formal and
informal sectors in three western African Countries”, prepared for the CSAE conference, Oxford.
El Hamidi, F. (2011). How do women entrepreneurs perform? Empirical evidence from Egypt.
Economic Research Forum, Working Paper No.621.
Gonenc, R., Libfritz, W., and Yilmaz, G. (2007). Enhancing Turkey's Growth Prospects by
Improving Formal Sector Business Condition. OECD Economics Department Working Papers,
No.542.
Hendy, R., and Zaki, C. (2012). On informality and Productivity of Micro and Small Enterprises:
Evidence from MENA Countries. Economic Research Forum, Working Paper 719.
Khotkina, Z. A. (2007). Employment in the Informal sector. Economic Research Forum,
Working Paper No.745.
Langsten, R. and Salem, R. (2008). “Two Approaches to Measuring Women's Work in
Developing Countries: A Comparison of Survey Data from Egypt”, Population and Development
Review, 34 (2), 283-305.
Nader, Y. F. (2007). “ Microcredit and the socio-economic well-being of women and their
families in Cairo”, The Journal of Socio-Economics, 37, 644–656.
Taymaz, E. (2009). Informality and productivity: productivity differentials bewteen formal and
informal firms in Turkey. ERC Working papers in Economics, 09/01 March.
Wahba, J. (2009). Informality in Egypt: a stepping stone or a dead end? Economic Research
Forum, Working Paper 456.
Tansel, Aysit; Elif Oznuz Kan, “The formal/informal employment earnings gap: evidence from
turkey”, discussion paper series no. 6556, 2012.
Wamuthenya ,Wambui R., “gender differences in the determinants of formal and informal sector
employment in Urban areas of Kenya across time”, 18th IAFFE conference discussion paper in
Boston, 2009.
8. Appendix
Table (1): Determinants of employment decesion, urban regions 2006
(Multinomial probit regression model, marginal effects)
Unemp
unemp
Informal worker
Manuf
services
0.005018
0.063308
5
0.058281
**
0.111791
7***
0.116506
8***
0.135220
5***
0.152891
***
0.157611
4***
0.163041
3***
0.175864
9***
0.063308
5**
0.063308
5***
0.063308
5***
0.063308
5***
0.063308
5***
0.063308
5***
0.063308
5***
0.063308
5***
formal
worker
Manuf
services
waged employe
e
Self
employe
e
unpaid
worker
Male
Child
Age
groups
20-24
25-29
30-34
35-39
40-44
0.065216
5**
0.027636
0.026622
*
0.045738
7**
0.012022
8
0.003302
1
45-49
0.022497
5
50-54
0.084977
2**
55+
0.508934
4***
0.09964
97
0.07359
93**
0.021222
1
0.01011
57
0.03972
11
0.19451
63**
0.00054
44
0.022584
8
0.28771
77***
0.028478
8
0.03186
22
0.34257
54***
0.03124
93
0.38057
65***
0.01597
08
0.00353
89
0.00109
29
0.02140
87
0.43201
74***
0.00604
82
0.015340
6
0.03651
4
0.42627
51***
0.02867
91
0.03941
63
0.42563
79***
0.01115
82
0.02104
26
0.45623
54***
0.02085
79
0.000092
8
0.009501
5
0.000947
4
0.16797
62**
0.033618
5
0.015475
2
0.094821
4***
0.023129
2*
0.012508
0.030079
6*
0.016568
1
0.021388
8
0.028586
3
0.001656
8
0.021805
3
Educatio
nal level
Primary
Secondar
y
Tertiary
Single
0.006444
8
0.028241
5**
0.038954
7***
0.035317
***
0.063565
9***
0.151148
3***
0.226699
2***
0.012611
8
0.076137
9***
0.153506
5***
0.228055
2***
(0.07085
02)***
(0.125413
1)***
0.017139
1
0.030044
4
0.004817
4
0.004907
6
Divorced
0.089813
8**
Parent
family
size
Child
family
size
registered
(0.026923
8)**
0.05288*
*
0.038452
5***
..
unemploy
ed HH
..
0.051973
9
0.06420
64**
0.07549
74**
0.00675
5
0.007282
0.014037
0.07074
12***
0.23391
37***
0.022459
9**
0.02398
96
0.43076
48***
0.00476
38
0.03500
89
0.03173
11
0.07800
28
(0.05173
1)***
0.05222
77*
0.02120
41**
0.008816
1
0.018158
8
0.017696
1*
0.049044
6***
0.012388
-0.00516 0.01739
15
0.00905
56
0.01522
03
(0.06294
67)**
0.21189
26***
0.02760
8
..
(0.10962
63)**
..
..
..
..
..
0.04137
71**
0.060310
5
0.007550
4
0.130859
1
0.00062
56
0.12393
44
0.050293
0.006694
4
(0.019927
9)**
(0.027515
8)**
0.004964
2
0.02221
34
0.022871
4
(0.079908
4)*
0.01420
61
0.014035
7
0.001373
4
0.009103
2
0.013846
8
0.018788
2
(0.039122
)***
0.002803
1
0.081734
9***
0.02603
53
0.024828
8
0.001206
5
0.02048
32
0.02859
32
0.00571
61**
0.11863
73
0.00694
18
0.002652
9
0.001654
4
0.002273
3
0.003063
2
0.004053
3
0.004668
6
..
(0.172770
6)***
(0.038574
)***
Female
Child
Age
groups
20-24
25-29
0.062449
2
30-34
0.08297
0.01121
31
0.00570
77
35-39
40-44
45-49
50-54
55+
Educatio
nal level
Primary
Secondar
y
Tertiary
Single
Divorced
Parent
family
size
Child
family
size
registered
unemploy
ed HH
0.072189
5
(0.152593
2)**
(0.141288
5)**
0.051357
9
(0.39679
9)***
(0.027857
8)**
(0.038893
3)***
(0.040603
5)***
(0.033193
8)***
(0.033994
2)***
(0.105670
3)**
(0.178233
9)***
(0.199272
2)***
(0.172338
4)***
(0.152145
9)***
0.04130
28
0.09222
53
0.00446
01
0.00869
11
0.20843
61*
0.00426
91
0.00219
91
0.23767
66***
0.00400
81
0.01281
63
0.19271
59**
0.01401
38
0.20015
39**
0.01595
78
0.00077
81
0.094718
1
0.006694
4***
0.019927
9***
0.027515
8***
0.027857
8*
0.038893
3
0.040603
5
0.033193
8
0.033994
2
..
0.132861
8***
0.358678
9***
0.458020
2***
0.060890
3
0.02628
97
0.12885
85*
0.00528
88**
0.05998
49**
0.05750
43*
0.07224
55*
0.49940
67***
0.01515
28**
0.70820
42***
0.03996
43**
0.00625
82*
0.055656
4
*0.03714
32
0.00615
1
0.00995
04
..
0.17995
59**
0.06105
53
0.00668
78
0.00246
37
..
..
..
0.00313
52
0.009122
1
0.061874
8
0.149608
5***
0.085681
3**
0.003986
4
0.067008
6
0.194242
7***
..
0.010062
5
0.010508
6
..
..
0.00614
5
0.00234
32
0.00724
31
0.13089
5***
0.000805
5
0.000070
4
0.000816
8
0.019088
7
0.004305
2
0.002396
9*
0.006010
5*
0.019626
7**
0.002348
0.003654
6
0.004198
7
0.003191
3
0.003130
9
0.003527
1
0.004611
7
0.002891
9
0.009142
3
0.020337
6
0.003910
2
0.001533
2
0.000963
4
0.001379
8
0.007585
7
0.059376
7***
0.001524
7
0.000342
7
0.071518
3***
0.001610
6
Table (2): Determinants of wages 2006
(Multinomial probit participation decision corrected wage equation estimates )
Male employees
Female employees
Manufacturing
Services
Manufacturing
Services
Inform
al
Formal
Inform
al
Formal
Inform
al
Formal
Inform
al
Forma
l
2.1568
-0.5325
2.8669
0.6102
2.7236
0.0537
1.0201
0.5166
Age groups
20-24
25-29
30-34
35-39
40-44
0.6699* 1.6252
**
1.0938* 3.1484
*
1.0096* 0.5242
*
1.5477
3.9934
2.7136
4.8619
3.0638
2.6917
0.1559
-0.4959
-3.2195
0.5273
0.6423* 1.5583
**
1.0487* 3.0187
**
1.0088* 0.5237
*
1.5464* 3.9901
3.1827
6.6088
3.791
3.5797
-0.4583
1.6485
1.0099
0.9783
0.6622* 1.6065
**
1.0812* 3.1121
**
1.0253* 0.5323* 1.5717* 4.0552
**
*
*
4.0705
5.2529
4.6805
0.2594
-1.196
0.8325
2.3901
0.1579
0.7016* 1.7021
**
1.1455* 3.2972
**
1.0298* 0.5346* 1.5787* 4.0733
**
0
0.8789
0
0.7168
0
0.1197
0.1302
0.9691
3.6254
1.0083
5.3937
-0.647
3.6943
1.2829
2.4385
0.4827
0.7064* 1.7137
**
1.1533* 3.3197
**
1.0352* 0.5374* 1.5869* 4.0943
**
*
0
0
0.0004
0.5563
0.8455
0.0171
0.1246
0.9062
45-49
50-54
55+
3.3261
0.4068
5.281
0.6522
3.7185
1.3128
2.3991
3.5449
0.712** 1.7274
*
1.1625* 3.3463
**
1.0551* 0.5478* 1.6175* 4.1733
**
*
0
0.8138
0
0.8455
0.0004
0.0167
0.1382
0.3958
3.1695
0.5633
5.0398
2.0957
3.5902
1.089
2.4791
11.455
5
0.7379* 1.7902
**
1.2048* 3.4679
**
1.0712* 0.5561* 1.6422* 4.237*
**
*
*
0
0.7531
0
0.5457
0.0008
0.0504
0.1313
0.0069
3.1754
2.9235
4.5559
2.7022
3.5582
1.0054
2.4709
7.132
0.683** 1.6569* 1.1151* 3.2098
*
**
1.0382* 0.539** 1.5915* 4.1063
**
*
0
0.0778
0
0.3999
0.0006
0.0623
0.1207
0.0826
-0.4014
-0.4884
-0.5053
-1.6587
3.6837
0.4698
-4.3704
0.9003
0.5428
1.3168
0.8862
2.5509
1.2308* 0.639
*
1.8868* 4.8682
*
0.4596
0.7107
0.5686
0.5156
0.0028
0.4623
0.0207
0.8533
-0.7883
-0.1149
-0.7654
-0.6654
-0.355
-0.8053
-3.2834
6.3984
0.6244
1.5149
1.0195
2.9347
1.0213
0.5302* 1.5655* 4.0393
*
*
0.2069
0.9395
0.4528
0.8206
0.7282
0.129
0.0361
0.1134
-0.2123
-1.3704
0.0592
-1.9945
-1.8791
0.0891
-2.5949
3.4562
0.7327
1.7776
1.1963
3.4435
1.1575
0.6009
1.7744
4.5782
0.7721
0.4408
0.9605
0.5625
0.1047
0.8822
0.1438
0.4504
4.8891
0.4379
0.2942
0.1499
-1.4515
1.9785
-3.8227
2.1878
Firm size
(5-9)
(10-24)
(25-49)
(50-99)
100+
Don't know
0.7934* 1.9249
**
1.2954
3.7288
1.3559
0.7039* 2.0785* 5.3628
*
*
0
0.82
0.8203
0.9679
0.2845
0.005
0.0661
0.6834
-0.0484
1.7581
-0.3948
2.7402
0.4617
0.118
-2.8302
0.2024
0.5954
1.4444
0.972
2.798
1.1895
0.6175
1.8234* 4.7047
0.9352
0.2236
0.6846
0.3275
0.698
0.8485
0.1208
0.9657
0.0063
-2.0268
-0.4532
-5.9922
-0.9479
-0.4562
-3.9291
1.4447
0.4433
1.0755* 0.7238
*
2.0835* 0.846
*
0.4392
1.2968* 3.346
*
0.9888
0.0596
0.5313
0.0041
0.2627
0.2991
0.0025
0.666
-0.5685
-0.4121
0.209
-2.478
2.5323
-0.1482
0.4062
1.3938
0.4185
1.0153
0.6833
1.9669
0.9077* 0.4712
*
1.3914
3.59
0.1744
0.6849
0.7597
0.2078
0.0053
0.7531
0.7704
0.6979
-0.7928
-0.5225
0.5858
-1.8651
0.8064
0.3685
0.6558
3.0782
0.3923* 0.9518
*
0.6406
1.8439
0.7944
0.4124
1.2178
3.1422
0.0434
0.5831
0.3606
0.3118
0.3102
0.3718
0.5903
0.3274
-0.6662
0.998
0.8038
2.7377
1.2401
0.6993
0.2353
2.0325
0.4033* 0.9785
*
0.6585
1.8956* 0.8026* 0.4167* 1.2304
3.1746
0.0987
0.3078
0.2223
0.1488
0.1225
0.0935
0.8483
0.5221
1.178
0.7955
-0.1601
-0.0903
-1.6427
0.2047
2.168
1.6657
0.7055
2.0308
0.5858* 0.3041
0.898*
2.3169
Educational level
Primary
Secondary
Tertiary
Full-time
dummy
0.4321* 1.0483
*
0.0064
*
0.448
0.8205
0.9645
0.0051
0.5011
0.0159
0.4723
1.0213
-0.2058
0.4998
0.6678
-1.3458
0.3523
2.5679
0.8082
0.991
2.4043
1.618
4.6575
2.0086
1.0428
3.0791
7.9445
0.3028
0.9318
0.7574
0.886
0.5029
0.7355
0.4044
0.919
21.4808 -0.091
0.7649
-1.2563
17.5857 0.5032
1.2221
0.9397
0.8896* 2.1582
**
1.4524
4.1807
2.112** 1.0965
*
3.2376
8.3536
0
0.9664
0.5985
0.7638
0
0.6463
0.7059
0.9105
-0.1971
20.7492 -1.6696
-0.9251
0.2224
13.6576 0.5707
0.8687
2.1075
1.4183* 4.0826
**
2.0332
1.0555
3.1168* 8.0418
**
0.4205
0.9255
0
0.6826
0.6492
0.8332
0
-0.4705
28.16
-0.3137
-0.4797
-0.1608
19.4012 0.5337
0.3846
0.8608
2.0884* 1.4055
**
4.0457
2.03
1.0539* 3.1119
**
8.0293
0.5847
0
0.8234
0.9056
0.9369
0
0.8639
0.9618
-0.3034
-0.0655
-0.4191
27.6661 0.1607
-0.1691
0.4637
18.224
0.8307
2.0154
1.3563
3.9042* 1.9347
**
1.0044
2.9658
7.6521
**
0.7149
0.9741
0.7573
0
0.9338
0.8663
0.8758
0.0174
-0.1096
0.3493
0.2742
0.6824
0.6114
0.3817
0.6155
0.0459
Selection variables
unemployed
Informal ,
manuf
Informal,serv 0.6999
ices
Formal,
Manuf
Formal,servi
ces
Selfemployeed
0.9434
0.9004
2.1845
1.4701
4.2318
1.1736
0.6093
1.799
4.6417
0.9032
0.873
0.8521
0.8719
0.6024
0.5311
0.7323
0.9921
-3.4978
0.1024
-4.9142
3.7353
-2.389
-1.1569
-1.5848
4.7547
1.1438
2.7749
1.8675* 5.3755
*
2.4788
1.2869
3.7999
9.8043
0.0022
0.9706
0.0085
0.4872
0.3353
0.3688
0.6767
0.6278
N
3596
3596
3596
3596
1574
1574
1574
1574
R2 squared
0.4726
0.2823
0.3318
0.1563
0.286
0.6748
0.194
0.174
F-stat
135.206 59.9099 75.3676 28.7544 27.2496 137.009 16.771
2
6
Constant
14.802
3