Các nhân tố ảnh hưởng tới thu nhập của lao động nhập cư khu vực kinh tế phi chính thức tại Hà Nội
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FACTORS AFFECTING THE INCOME OF MIGRANT WORKERS
IN THE INFORMAL ECONOMY IN HANOI
CÁC NHÂN TỐ ẢNH HƯỞNG TỚI THU NHẬP CỦA LAO ĐỘNG
NHẬP CƯ KHU VỰC KINH TẾ PHI CHÍNH THỨC TẠI HÀ NỘI
Dr. Nguyen Minh Thu
National Economics University
Abstract
The study identified factors affecting the income of migrant workers in Hanoi. To achieve
this goal, this study surveyed 425 migrant workers in 12 urban districts. The analysis of quantile
regression model has identified three factors that have significant impacts on income at all quan-
tile levels, including (i) working experience, (ii) types of job and (iii) gender. In addition, some
factors such as age, working hours, qualification also effect income in some percentiles. On this
basis, the study offers some meaningful solutions to improve income and ensure the lives of mi-
grant workers in Hanoi in the near future.
Keywords: income, informal economy, migrant workers
Tóm tắt:
Nghiên cứu này xác định các yếu tố ảnh hưởng đến thu nhập của lao động nhập cư tại Hà
Nội. Để đạt được mục tiêu đó, nghiên cứu đã tiến hành khảo sát 425 lao động nhập cư tại 12
quận nội thành. Phân tích mô hình hồi quy phân vị đã xác định được ba yếu tố có tác động đáng
kể đến thu nhập lao động nhập cư ở tất cả các mức phân vị, bao gồm (i) kinh nghiệm làm việc,
(ii) loại công việc và (iii) giới tính. Ngoài ra, một số yếu tố như tuổi, số giờ làm việc, trình độ
chuyên môn cũng ảnh hưởng đến thu nhập ở một số mức phân vị nhất định. Trên cơ sở đó, nghiên
cứu đưa ra một số giải pháp nhằm nâng cao thu nhập và đảm bảo cuộc sống của người lao động
nhập cư tại Hà Nội trong thời gian tới.
Từ khoá: khu vực kinh tế phi chính thức, lao động di cư, thu nhập
1. Introduction
The large discrepancy in incomes between workers in rural and urban areas, especially in big
cities and industrial zones like Hanoi, is attracting an influx of workers from other places. On
the other hand, the processes of industrialization, modernization, and urbanization are transform-
ing massive amounts of agricultural land into industrial land and reducing farmland. Furthermore,
climate change is leading to unpredictable and harsher weather, which is resulting in more pre-
carious production of agriculture, less productive farming outcomes, increasing the amount of
non-working hours. As a result, income of agricultural households are being reduced, making
their standards of living drop and leaving their basic needs unmet. These unprecedented phenom-
ena form an increasing push for the migrant wave from rural to urban places.
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Upon reaching the cities, these people get manual jobs not requiring skills, experience and
formal training, but physically demanding and dangerous jobs that urban dwellers do not want
to get like cleaners, builders, street vendors, and porters. Their work is mainly in the informal
economy, with features being unstable, no contracts or only vocal contracts, long working hours,
and no health insurance or government welfare. The average income of informal laborer is 4.4
million VND each month, less than 42% that of formal ones, at around 6.7 million VND/ each
month (GSO, 2017). Such low earnings do not guarantee quality of life to their families or them-
selves. In the same research, the average number of working hours in the informal economy was
49.2 hours a week, which was 2 more hours than their formal counterparts, at 47.2 hours a week.
With long working time and low earnings, they have to struggle with their living in cities, where
the living costs are reaching higher and higher.
As GSO (2016) reported, 79.1% of internal migrants are from rural to urban, which ac-
counts for the largest part in migration. Therefore, the research focuses on analyzing factors af-
fecting the income of rural-urban migrants in search for employment. By investigating the
laborers in Vietnam’s informal economy, this research aims to: (a) Find out the level of each
factor affecting the income of migrant workers in the informal sector in Vietnam and (b) search
for an appropriate policy options for upgrading earnings for migrant workers in the informal
sector in Vietnam in a sustainable way.
2. Literature revie
There are many factors affecting the income of labor workforce in the informal sector of
economy. These factors are classified into two groups including a group of objective factors and
a group of subjective factors. The group of subjective factors includes the supply-demand labor
market, government policies, law system,...; the group of subjective factors includes personal
characteristics and occupational characteristics.
Objective factors
Government policies
Accoding to theo Article 90, The 2012 Labor Code, the minimum salary is the lowest pay-
ment paid for the worker doing simplest jobs in normal working conditions and it has to guarantee
a basic living for the worker and their family. The resolution of the 15th Party Central Committee,
the minimum wage passed the agenda of reforming salary policies, in which salary is considered
to be the value of labor power, based on the market regime regulated by the government, with
the purpose being guaranteeing that the worker can live off their salary, and the minimum wage
meets the basic need. In reality, the incomes of workers in the informal sector of economy are
higher than the minimum salary but it does not meet the minimum basic need. Oxfam (2015)
shows that the basic salary of almost all migrants does not meet all the needs of living; the min-
imum wage only guarantees 60% of the living costs for the laborer (Phạm, 2000).
Besides, the policies for poverty alleviation have only focused on the poor living in their
registered place, but do not count the people in relative poverty and multidimensional poverty in
urban area industrial parks, wherein migrants constitute a tremendous majority. Loan policies
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rely on the household registered book (Oxfam, 2015) require a high degree of transparency in fi-
nancial records. Therefore, when they need capital to open or widen their business, invest in
equipment, machines, and improve the production, migrants cannot rely on formal financial or-
ganizations. As a result, the lack of capital is a barrier against their productivity and the increase
of income of migrants. Furthermore, social security and insurance programs do not have any in-
centives for migrants in the informal sector of economy to buy them. When they are sick or ill,
they are not able to afford health centers for the treatment, which makes their illness worse and
prolonged. So their work is discontinued, and their income decreases.
Informal costs
When doing business, producing products or trading in the informal sector of the economy
means many establishment owners, self-account workers, even street vendors have to give some
informal payments to law enforcement agents in order to avoid their “regular visit”. Petty cor-
ruption is already in the mindset of many authorities and offices, and perhaps bribery is an indis-
pensable ritual when someone wants to open their business. Not only do corruption issues deter
economic development, discourage widening, extending and upgrading the production, but it
also takes part of the income of migrants in the informal sector of economy. As the income of
these people is precarious and low, corruption discourages workers from believing in the gov-
ernment to open and develop their business.
Demand and Supply in the labor market
From the perspective of the labor supply, because of the pressure of population growth,
the supply of labor workers outweighs the demand. The phenomenon of the low income from
farming is the push that farmers have to find other sources of income. When the production level
and the productivity is low, laborers move to urban areas and industrial parks. The influx of mi-
grants into urban sectors results in an abundance of the laborers, which makes the value of labor,
especial manual work, drop.
From the perspective of the labor demand, the dramatic growth of industrialization, mod-
ernization and urbanization requires the participation of numerous workers in industrial areas,
foreign processing zones, etc. Besides this, the demand for goods and services in urban areas is
unprecedentedly high. All in all, there are a lot of income opportunities for migrants in urban
areas and industrial areas.
Thus, the supply-demand relationship of the labor market has an influence on the oppor-
tunities of workers, especially workers in the informal economy, partially affects income or ex-
penditure paid by them. The determination of the value, salary or remuneration of the labor
depends on the labor market (Nguyen, 2017).
Subjective factors
Personal characteristics of migrants in the informal sector of economy
Gender
The labor market of Vietnam is still in transformation, except for some positions of man-
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agement or female-dominated employment, the payment for women is still low compared to men
in all types of occupations or positions. The average income of men is 1 million VND higher
than their female counterparts in the informal sector of economy in Hanoi (Nguyen, 2017). In
other economies, Bhatti (2013) suggests that men get a 10.44% higher amount in their income in
comparison with women in the labor market of Pakistan, while Lee and Lee (2006) point out that
Korea has a much bigger gap, with men getting 36.69% more in their earnings compared to fe-
males. In the area of Southeast Asian Nations, females in Manila, Philippines get 516 pesos less
than their male counterparts, while in smaller cities of the Philippines, the gap is much severer,
at 775 pesos (Hagen Koo & Peter, 2005). Therefore, gender is one of the main factors influencing
labor market as a whole, and workers in the informal sector of economy.
Age
Previous research often points out that young workers get higher income than their older
counterparts, because the youths have health on their side. Besides this, workers age 25-45 are
usually the head of the family, thereby the financial pressure for them is higher than others. It is
both the motive and the push to make them earn more money. Sukti and his partners (2015) con-
cluded age was one of three prime factors affecting the income gap between the informal sector
and the formal sector in Thailand. When also using age to analyze the income gap between the
informal and formal sectors, the result showed that 50% of the income disparity stemmed from
individual characteristics, including age of the laborer. In Vietnam, Nguyen (2017) suggested
that workers under 35 years old in the informal sector get higher income than those who are above
35 years old, but this discrepancy is not big.
Highest level of education
In almost all research, education is considered to be one of the main factors affecting the
income of workers. When holding other factors affecting income constant, one rural-urban mi-
grant in the Red River delta with a university earns 39% more than their counterparts who just
finished primary school, and the figure for urban-urban migrants is higher, at 71% (Jean-Diere
et al., 2012). In other economies, Lee and Lee (2006) show that the number of years going to
school has a positive effect on income, with a one-year increase resulting a 4% increase in the
income. Pakistan’s labor market saw the 4.8% increase (Bhatti, 2013), and the variable affected
most on the income of migrant laborers of Philippines (Hagen & Peter, 2005); and the increase
in income was 6.32% in the model for worker in service sector of Vietnam (Tong, 2015).
Experience
The number of years of working experience has a positive effect on income: an one-year
increase of working experience results in a 3.02% increase in the income in the Korean Labor
market (Lee and Lee, 2006). The figure for Pakistan is 3.05% (Bhatti, 2013) and the proportion
of the increase is 6.43% within the service sector in Vietnam (Tong, 2015). To resume, the re-
search above assumed that working experience has a positive effect on income. In addition, when
conducting research on workers in the informal sector of the Red River delta, Jean-Diere (2012)
saw that the working experience before migration does not influence the income of workers from
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the rural areas, while it has a statistically significant positive effect on the income of migrant
workers from urban areas. This result shows the disadvantage of migrants from the rural areas
when the joined the labor market in the urban areas.
Occupational characteristics
Working time
The income of Pakistanis rises by 0.19% when they work one more hour (Bhati, 2013).
Similarly, in the service sector of Vietnam the figure is 0.36% (Tong, 2015). The hired workers
in Hanoi who work above 8 hours earn 4.56 million per month, and make 4.09 million VND with
8 hours working per day on average, and those who work under 8 hours earn 3.47 million VND
each month (Nguyen, 2017). This research all shows that working time influences income with
a positive effect.
Working conditions and environment
Working conditions and environments are diverse in the informal sector of economy. Peo-
ple working indoors are production workers, salespeople, domestic servants, etc. They are often
hired by other people or sometimes self-employed, and many of them get fixed payment. Others
work outdoors, or their workplace is mobile like builders, motorbike taxi drivers, or street ven-
dors, thereby their productivity somehow depends on weather conditions. The harsh environment
affects the working production and thereby affects the remuneration of the laborer (Nguyen,
2017). Therefore, when analyzing factors affecting the income of workers in the informal sector
of economy, we cannot ignore the condition of working place.
Thus, studies related to the income of rural-to-urban migrant workers have shown objective
and subjective factors affecting labor income in the informal economy. However, these studies
only conduct descriptive statistics or simple analytical methods such as simple or multiple re-
gression, have not done in-depth quantitative analysis. This is the gap for the research to propose
a model and method to study factors affecting the income of migrant workers in the informal
economy.
3. Methodology
Proposed model
The study was conducted in Hanoi with the same legal environment, so it limited the scope
of assessing the impact of subjective factors on income. Based on previous studies, the proposed
model includes 2 main factor groups affecting the income of migrant workers in the informal
economy in Hanoi as following:
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Figure 1: Proposed research model
Data collection method
The topic uses two methods of collecting primary and secondary data, specifically as
follows:
First, overview documents from a number of sources such as public books, international
and national magazines (e.g. journal of economics and development), newspapers, previous re-
search, essays, posts on reliable websites (WIEGO, ILO, GSO, etc.) to get hold of information
about living conditions, income of non-informal migrant workers, factors affecting income and
the level of their influence on income.
Second, conduct a sample survey in the form of direct interviews based on prepared ques-
tionnaires. In addition, conducting in-depth interviews to find out more about the needs and issues
that have not been mentioned in the questionnaire. This also helps to check the data collected
from the questionnaires.
The questionnaire includes three main section. Section A collects personal information,
having 6 questions about gender, year of birth, marital status, highest level of schooling (calcu-
lated until high school), and professional qualifications. Section B gathers information about em-
ployment, including 17 questions regarding to the employment of the migrant worker in the
informal economy before and after migration, and also the occupational characteristics of the
main jobs the worker was holding (status of employment, working experience, working time,
etc.). Section C includes some questions on income of workers in the informal sector.
Hanoi now totally has 30 administrative units as districts, including 12 urban districts, 17
rural districts, 1 town. The influx of working migrants flows mainly into urban districts in search
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for job opportunities and better earnings. Therefore, the study focused on surveying in 12 urban
districts of Hanoi: Ba Dinh, Bac Tu Liem, Cau Giay, Dong Da, Ha Dong, Hai Ba Trung, Hoan
Kiem, Hoang Mai, Long Bien, Nam Tu Liem, Tay Ho and Thanh Xuan. Because of limitations
of time and finance, in each district we selected one ward to do interviews. The surveyed ward
was the area wherein informal migrant workers crowded. Migrant workers often looked for jobs
around markets, wholesales markets, bus stations, construction sites, food streets, etc. (Hoang
Thien Trang, 2017).
Besides, in order to get reliable results, the sample size needs to be larger than, or at least
equal to, the minimum level permitted. In our survey, the size of sampling is determined by Iarossi
formula (2006):
Nz2 σ2
n =
Nε2 + z2 σ2
In which: N - The size of population
z - Standardized score
σ2 - Population Variance
ε - Sampling error size
In Hanoi, there are 1 million migrant workers working in the informal sector (Vietnam
women’s union, 2016), and the growth of this city population is above 200,000 people per year,
in which 70% are driven by migration, equivalent to 140,000 people each year. Because of the
data limitation about the size of migrant workers in the informal economy for the surveyed period,
we estimate based on data from 2016 the number of informal migrant workers in the year 2019,
and it is 1.5 million. A bit of research to determine impacts of resources on incomes in rural areas
in Thanh Hoa has the size of sampling error ε of 2.5%, standardized score z with 95% confidence
level being 1.96, and a population standard deviation of 0.0144-0.0255% (Chu Thi Kim Loan &
Nguyen Kim Huong, 2015). Therefore, the minimum sample size of the survey is 138 observa-
tions.
Data analysis method
Descriptive statistics is used to describe basic characteristics and features of data gathered.
The main purpose of descriptive statistics is to provide a brief summary of the samples, often
represented through tables, diagrams, charts, histograms, etc. which illustrate general results of
the survey with parameters about mode, mean, median, etc. This method allows the user to show,
capture basic features, characteristics of the researched object at scientific, clear, logical, eye-
catching, and convenient ways that facilitate understanding, observation, and comparison.
Multiple regression was employed to provide estimates of the conditional mean of the re-
sponse variable given certain values of the predictor variables and to determine the overall fit
(variance explained) of the model and the relative contribution of each of the predictors to the
total variance being examined. In this research, the dependent variable is income; the independent
ones are gender, schooling, age, etc.
In addition, quantile regression, an extension of linear regression, is also used. It was first
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introduced by Koenker & Bassett in the year 1978 (Koenker & Bassett, 1978). Instead of esti-
mating the model with average effects using the OLS linear model, the quantile regression pro-
duces different effects along the distribution (quantiles) of the dependent variable. Median
regression is more robust to outliers than the OLS regression.
4. results and discussion
Sample description statistics
To achieve the study purpose, the minimum sample size of this survey is 138 observations.
In reality, the bigger the sample size is, the more significant and reliable the statistical testing
and estimation are. Thus, the study was carried out with a larger extent corresponding to a sample
size of 425 observations. Some information about respondents as following.
Table 1. Generalinformation about the respondents
Number of respondents
Percentage (%)
100.00
58.59
Gender
425
249
176
425
102
127
79
Male
Female
41.41
Age
100.00
24.00
15 to 24
25 to 34
29.88
35 to 44
18.59
45 to 54
77
18.12
55+
40
9.41
Level of schooling
Primary school graduate or below
Secondary school graduate or below
High school graduate or below
Working hours
8 hours or below
8 to 12 hours
425
47
100.00
11.06
213
165
425
98
50.12
38.82
100.00
23.06
236
91
55.53
Above 12 hours
21.41
Source: Synthesized from the result of the survey
Regresion analysis
Based on the proposed model, perform regression analysis with the dependent variable is
monthly income of migrant workers in the informal economy (million VND/month). There are
15 independent variables, including:
AGE: age of the migrant workers (years)
EXP: years of experience in the current job (years)
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HOURS: number of working hours per month (hours)
SEX: dummy variable representing gender (1: male, 0: female)
MAR1: dummy variable representing married workers (Yes = 1, No = 0)
MAR2: dummy variable representing separated/divorced/widowed workers (Yes = 1,
No = 0)
SCHOOL: years of schooling (years)
QUALIFI1: dummy variable representing people who attending technical training or vo-
cational centers (Yes = 1, No = 0)
QUALIFI2: dummy variable representing people who holding primary/intermediate cer-
tificate (Yes = 1, No = 0)
QUALIFI3: dummy variable representing people who holding college or university degree
(Yes = 1, No = 0)
JOB1: dummy variable representing wage worker (Yes = 1, No = 0)
JOB2: dummy variable representing street trade (Yes = 1, No = 0)
JOB3: dummy variable representing freelance worker (Yes = 1, No = 0)
EAT_STAY: dummy variable representing wage worker who got support for meals (Yes=
1, No = 0)
The existence of heteroscedasticity is a major concern in the application of regression analy-
sis, including the analysis of variance, as it can invalidate statistical tests of significance tests of
significance that assume the modelling errors are uncorrelated and uniform-hence that their vari-
ances do not vary with the effects being modeled. So, the study test the heteroscedasticity before
conducting the regression analysis.
To test heteroscedasticity, the research used the White test, the null and alternative hypothe-
ses are:
H : The variances of the errors are equal
0
H : The variances of the errors are not equal
1
Table 2: The result of testing heteroscedasticity
Source
Heteroskedasticity
Skewness
chi2
198.26
30.29
1.71
df
108
15
P-value
0.0000
0.0109
0.1911
0.0000
Kurtosis
1
Total
230.25
124
Source: Synthesized from the results of the model, using STATA 13
The table 3 showed that total P-value < 0,05, so we rejected H and accepted H1 hypothesis,
0
thereby the model had the phenomenon heteroscedasticity. To fix the phenomenon heteroscedas-
ticity, we used the robust standard error method.
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Table 3: Robust multiple regression result
Overall assessment about the model
R2
R2-adjusted
F statistic
P-value
N
0.5381
0.5058
9.31
0.000
425
The value of each variable
Variable name
CONSTANT
AGE
Coefficient
7.134
Standardized deviation
t-statistic
5.25
P-value
0.000
1.360
0.018
0.029
0.003
0.377
0.362
0.720
0.398
0.426
0.575
0.704
1.047
0.901
1.018
0.558
-0.022
0.087
-1.22
3.04
0.223
EXP
0.003***
0.014**
0.000***
0.105
HOURS
SEX
0.008
2.46
2.399
6.37
MAR1
0.588
1.62
MAR2
0.081
0.11
0.911
SCHOOL
QUALIFI1
QUALIFI2
QUALIFI3
JOB1
0.515
1.29
0.197
0.592
1.39
0.165
0.613
1.07
0.287
1.082
1.54
0.125
-3.485
-4.997
-4.554
-1.888
-3.33
-5.55
-4.47
-3.38
0.001***
0.000***
0.000***
0.001
JOB2
JOB3
EAT_STAY
****, **, *** significance at 10%, 5%, 1%
Synthesized from the results of the model using STATA 13
Quantile regression analysis
The result of the multiple regression with the OLS method showed that the model had the
phenomenon heteroscedasticity, proving that the level of effect on the income of independent
variables was different on each segment of the income. Therefore, quantile regression would
show this difference clearly. In order to assess how the effect of independent variables changed
at quantiles of the income, the research conducted quantile regression analysis at percentiles 10%,
25%, 50%, 75% và 90%. Table 3 was the result of quantile regression, distributed on the two
sides of the percentile 50%.
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Table 4: Quantile regression coefficients at different quantiles
Coefficients of the quantile regression
10%
-0,024
25%
-0,030*
0,052**
0,002
50%
-0,036**
0,061**
0,003*
0,708
75%
-0,031
90%
-0,018
INCOME
AGE
EXP
0,069***
0,005**
0,372
0,102***
0,006**
0,732
0,126***
0,006
HOURS
MAR1
0,297
0,330
MAR2
0,700
0,396
1,029
0,178
-0,921
SCHOOL
QUALIFI1
QUALIFI2
QUALIFI3
JOB1
0,652
0,537
0,517
0,434
0,603
0,239
0,319
0,152
0,066
0,481
0,873
0,784
0,709
1,010
1,072
1,667***
-0,712
0,855*
-2,100**
-2,861***
-2,642**
1,413***
-1,429**
0,769
0,518
0,143
-2,615***
-3,852***
-2,636**
1,693***
-1,288**
-5,491***
-6,608***
-5,195***
2,202***
-0,736
-7,420**
-8,643***
-8,101***
2,773***
-1,366*
JOB2
-1,542*
-1,350
JOB3
SEX
1,660***
-1,498**
EAT_STAY
*, **, *** significant at 10%, 5%, 1%
Compared with multiple regression, the regression results at different quantiles are different.
The results show that there are only 3 factors that affect income of the informal sector workers
at all quantile levels, including EXP, JOB2 and SEX.
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Figure 2: Regression coefficients of years of working experience
and gender of migrant workers
Source: Synthesized from the result of the quantile regression using Stata 13
At all the percentiles, years of experience has a positive effect on income. Especially, the
higher the income is, the more experience impacts on income, and vice versa. In the high-income
group, years of working experience made the higher increase in income than the low-income
group. The increase was huge at the 65th percentile and 85th percentile.
In addition, it is clear that gender disparities occur at all income percentiles. From the 35th
percentile, the income of males outweighed the income of females with the gap widening at
higher percentile levels. There is real gender income inequality and it gets more severe in the
high-income group.
In contrast, some factors such as MAR1, MAR2, SCHOOL, QUALIFI1 and QUALIFI2
were not statistically significant at all quantile levels. This is also the same results as the conven-
tional multiple regression model.
For the remaining factors, the influence of factors on income is different at different quan-
tile. For example, the age of the migrant did not have effect on income at 10th, 75th and 90th per-
centiles. But in the range from the 25th percentile to the 50th percentile, i.e. income of the migrants
is in the lower middle group, age really has a negative effect on their income.
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Figure 2: The regression coefficients of the age of the migrant
and the average number of working hours per month
Source: Synthesized from the result of the quantile regression using Stata 13
The coefficient regression of working hours in the low income group (10th percentile) and
the upper middle income group (50th and 70th percentiles) are statistically significant. However,
because these coefficients are quite small, the change in working time have a negligible effect
on income.
In addition, the study found significant differences in income among different types of job
(wage worker, freelance worker, street trader) and between workers holding university degree or
above and those not holding any technical/professional qualification in most of the percentiles.
Conclusion and Policy Implications
The results show two main factors affecting the income of migrant workers at all per-
centiles, including years of experience and gender. It is consistent with previous studies, which
show the main factors in the research model. However, this study focuses on different percentiles
which tend to be more severe in the high-income group.
In addition, age is also one of the factors to be considered in the research models. It is sim-
ilar to the work of Nguyen (2017) and Skuti et al. (2015). Compared to previous studies, thanks
to the percentile regression method, the paper clearly shows the impact of working time on em-
ployee’s income and its significance for different group of migrant workers.
Combining with the open-ended question about difficulties of workers in the process of
working and earning money, the study has made some conclusions about the income of migran
workers: (1) the average income of migrant is quite low in comparison with ones in formal econ-
omy; (2) there is an imbalance in income distribution and it gets more severe in the high-income
group; (3) the influence of some factors such as marital status, years of schooling and intermediate
56
INTERNATIONAL CONFERENCE FOR YOUNG RESEARCHERS IN ECONOMICS & BUSINESS 2020
ICYREB 2020
or lower qualifications on the research sample is not significant; (4) their jobs are not stable and
precarious. Moreover, they met barriers in approaching loan programs in both migrant-sending
communities and migrant-receiving communities, because they do not meet the conditions.
In conclusion, workers in the informal sector of the economy suffered tremendous setbacks
while making earnings. Support from external parties is essential as workers themselves are not
aware of their right to benefits and the situation in the demand-supply labor market.
On that basis, the study proposed some recommendations in order to raise incomes and en-
sure the lives of migrant workers in urban districts as follows: (i) encourage and improve the ed-
ucation and qualification for migrant workers to equip more knowledge and skills; (ii) support
and subsidize workers who need loans which encourage them to promote production or doing
business; (iii) Provide information on workers’ rights, promote health insurance and social in-
surance programs to ensure social security for them.
REFERENCES
1. Chu Thi Kim Loan, Nguyen Van Huong (2015), The impact of resources on farm house-
hold income in Thanh Hoa province: A case study in Tho Xuan and Ha Trung, Journal of Science
and Development
2. General Statistics Office (2017), The 2016 informal employment in Vietnam.
3. General Statistics Office & United Nations Population Fund (2016), The 2015 national
internal migrant survey.
4. Hagen Koo & Peter C. Smith (2005), Migration, the urban informal sector, and earnings
in the Philippines, The Sociological Quarterly, Vol.24, No.2, pp.219-232.
5. Oxfam Vietnam (2015), Brief report: Legal and practical barriers for migrant workers
in the access to social protection, Labor Rights Program of Oxfam in Vietnam.
6. Pham Quy Tho (2000), Effects of rural-urban migration and employment of migration
in the period of industrialization and modernization.
7. Purwaka Hari Prihanto & Adi Bhakti (2017). Profile of informal sector workers and fac-
tors affecting informal sector employment, Vol.5, No.2.
8. R.Koenker, G.Bassett (1978), Regression quantiles, Journal of the Econometric Society,
46 (1978)
9. Sukti Dasguta, Ruttiya Bhula-or and Tiraphap Fakthong (2015), Earnings differentials
between formal and informal employment in Thailand.
10. Tong Quoc Bao (2015), Analysis of factors affecting income in the service sector in
Vietnam, Journal of Science.
57
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