The survival rate and the Cox model through the hazard function are used in the study to examine the impact of individual worker characteristics on the transition from informal to formal employment in Vietnam with monthly panel data from the Labor and Employment Survey in 2022 GSO. The results show that males are more likely than women to transition from informal to formal employment; increased housework time will reduce mobility; and the level of technical expertise and age of workers are the key factors that explain worker mobility.
Trang 1THE IMPACT OF HOUSEWORK TIME ON EMPLOYMENT TRANSITION FROM THE INFORMAL SECTOR TO THE
FORMAL SECTOR IN VIETNAM
Nghiem Thi Ngoc Bich and Pham Ngoc Toan
Univesity of Labour and Social affairs
Abstract:
The "survival" rate and the Cox model through the hazard function are used in the study to examine the impact of individual worker characteristics on the transition from informal to formal employment in Vietnam with monthly panel data from the Labor and Employment Survey in 2022 GSO The results show that males are more likely than women to transition from informal to formal employment; increased housework time will reduce mobility; and the level of technical expertise and age of workers are the key factors that explain worker mobility
Keywords: Cox model, formal employment, housework time, informal employment, transition
1 Introduction
Informal employment includes jobs without labor contracts and social insurance Workers with informal employment can be in the informal sector (including household enterprises not registered under national law) (ILO, 1993) and the formal sector (ILO, 2004)
Lewis's (1954) model divided the labor market into two areas: the capitalist area - the formal, modern, industrial, or urban and the sufficient– informal, traditional, agricultural, or rural (Fields, 2004) He said that the formal sector creates the conditions for investment and savings, leading to increased labor demand attracting workers from the informal sector characterized by low productivity to the formal sector Thus, the informal labor force in rural areas (usually working in agriculture) will migrate to urban areas, thereby increasing formal employment Lewis also believed that income in the informal sector is the basis for introducing a minimum wage for the formal sector Therefore, workers' wages in the formal sector must be higher than this level and often have a 30% or more gap compared to the informal sector
From a policy perspective, informal employment needs attention for the following reasons: first, informal workers face a lack of support policies on employment and income, and they fall into the gaps in the social security system (Maloney and Arias, 2007) Informal workers are not protected against the risks of old age, illness, unemployment, and disability Second, a large informal economic sector in the economy is a sign of underdevelopment Economies with large informal sectors have higher poverty rates, lower per capita income, and ineffective institutions Third, the informal sector has a negative impact on the economy because informal enterprises have lower productivity than formal enterprises (Ohnsorge and Yu, 2022)
Trang 2Unpaid housework is an important aspect of economic activity and an indispensable factor contributing to the well-being of individuals, families, and society Every day, individuals do housework such as cleaning, cooking, and caring for the elderly, sick, and children These jobs are often not recognized and appreciated for their true value Economically, this work is not considered
a job It isn't easy to measure and has little relevance to market policies However, insufficient attention to housework leads to inaccurate inferences about the level of contribution of each gender and the value of the housework that workers put in to perform, hence limiting the effectiveness of policies in many socio-economic fields, especially gender inequality in the labor market (Stiglitz
et al., 2007)
Women often spend more time on housework than men Based on gendered social norms that view unpaid housework as a woman's privilege, women in different regions, socioeconomic classes, and cultures spend a significant part of their day meeting the expectations of their roles as housewives and mothers Besides, they also participate in labor market activities, thereby creating a "double burden" of work for women Policymakers addressing housework-related issues have important implications for achieving gender equality: they can expand the possibilities and choices of women and men or restrict women to roles traditionally associated with femininity and motherhood (Razavi, 2007)
In Vietnam, the unemployment rate is low, but job quality is an issue society is concerned about Most workers are engaged in informal employment, do not have labor contracts, do not participate
in social insurance, have low income, and do not have job security or benefits such as pensions The formalization of the economy also leads to the development of the labor market; the number
of employed workers increases, and the proportion of workers in the informal sector gradually decreases According to ILO (2021), the proportion of workers employed in formal employment increased steadily from 20% to nearly 30% between 2012 and 2018
To answer how the spouse's housework time affects the transition from informal to formal employment in the context of the COVID-19 pandemic and low GDP growth (2.91%) in 2020 By
2022, the economy will have more stability The article will analyze the "survival" rate and the Cox model with labor and employment survey data from the General Statistics Office (GSO)
2 Theoretical basis
Labor market segmentation theory suggests that the labor market is divided into the formal market, with good jobs, such as higher salaries, social insurance, and good working conditions, and the informal market, with jobs of low quality, such as lower wages, and lack of protection from policies Workers are excluded from the formal sector and pushed into the informal sector due to barriers to entry In contrast, the second view holds that workers voluntarily leave the formal sector because they have a comparative advantage in the informal sector and because it brings many benefits beyond wages to workers, for example, flexibility A third view is that the informal sector
Trang 3combines exclusion and regression (Maloney and Arias, 2007) Many empirical studies find evidence of informal sector heterogeneity (Harati, 2013; Nemoto and Zuo, 2017)
Lewis's (1954) model divided the labor market into two areas: the capitalist area - the formal, modern, industrial, or urban and the self-sufficient sector level – also known as informal, traditional, agricultural, or rural (Fields, 2004) He said that the formal sector creates the conditions for investment and savings, leading to increased labor demand, which will attract workers from the informal sector, characterized by low productivity to the formal sector Thus, the informal labor force in rural areas (usually working in agriculture) will migrate to urban areas, thereby increasing formal employment Lewis also believed that income in the informal sector is the basis for introducing a minimum wage for the formal sector Therefore, workers' wages in the formal sector must be higher than this level and often have a 30% or more gap compared to the informal sector
In Vietnam, GSO and ILO (2018), in the report "Informal Labor 2016", proposed a way to identify informal workers applicable specifically to Vietnam Specifically, informal workers are workers with informal jobs; each worker is only identified and determined on one main job (or main job) There, informal employment is defined as employment without social insurance (especially social insurance mandatory) and does not have a labor contract of 3 months or more Thus, informal labor can be found within and outside the informal sector
According to ILO (2021), informal workers are defined as employed workers unofficially This definition includes workers working in the non-economic sector formal1 and working informally
in the formal sector Among them, non-employment Formal includes all employment agreements that do not equip the individual for labor mobilizing legal or social protection through their work, making it easier for them to bear the burden of economic risks They are considered one of the most vulnerable groups to shocks because of little attention and support from the state (ILO, 2021) When assessing the influence of factors determining the transition from informal to formal employment, current studies often use logit regression, probit, multinomial logistic, and a few use
Cox model analysis (Mattijssen et al., 2020; Tansel and Ozdemir, 2019; Gutierrez et al., 2019;
Maciel and Oliveira, 2018; Adriana, 2017; López-Andreu and Verd, 2016) Analyzes of labor market transitions are dynamic and provide limited information on the employment movements of different groups of workers
Variables that affect the probability of workers converting from informal to formal employment are shown as follows:
Age group: Younger age groups often have high rates of informal employment Over time, as workers age, their likelihood of moving from informal to formal work is likely to increase Instead, older workers may decide to move into self-employment once they have accumulated capital and experience The concentration of younger age groups in informal work and older age groups in
Trang 4self-employment causes the informal employment rate to show a U-shaped pattern relative to age (Ulyssea, 2020)
Education: Both human capital theory and signaling theory focus on the impact of education on labor market outcomes Education and experience increase a worker's productivity, and education provides a signal to employers about the worker's future productivity level (Tan, 2014) Therefore, education is expected to be a factor in promoting the transition of employment from informal to formal More educated workers move to better employment statuses and are less likely to fall into informal employment statuses
Gender: There are differences between male and female workers in formal and informal employment Human capital theory will explain this trend by the difference in human capital between men and women Another explanation for women's participation in informal work: as workers weigh the costs and benefits of different forms of employment, women may decide to enter the informal sector because they value the flexibility it offers and because it allows them to balance their life and work responsibilities (Maloney and Arias, 2007) Gender differences in informal employment may result from discrimination by employers in formal companies against women (Jokela, 2019; Young, 2010)
Housework time: According to Becker (1985), each individual has limited time in a day, and each individual's energy is limited, so an individual who spends a lot of time on housework will have little time and energy left for work in the market Additionally, heavy participation in housework activities signals low work commitment and productivity to employers and can lead to low wages and unstable employment (Boye, 2019) In addition, women have to shoulder unfair family obligations, so they participate a lot in informal work The informal economy can be a way for women to combine paid work with family responsibilities, given the flexibility and autonomy of this sector (ILO, 2014)
Sectors and occupations: According to ILO (2014), the first step towards designing effective policies to enable the transition to formality is to identify the heterogeneity of the informal economy, the types of workers, different dynamics, and drivers leading to the growth of the informal economy Workers with basic characteristics (gender, age), employment status, sector (service, agriculture, industry), business type and size, and location (urban or rural areas) will have different shifting abilities
Region: Workers in urban areas are more likely to move from informal to formal employment than rural workers It may happen that when individuals in rural areas lose their jobs, they are more likely to join the self-employed group Besides, economic activities in rural areas are mainly agricultural; the proportion of non-agricultural enterprises is lower than in urban areas, so the ability to create jobs in rural enterprises is lower
Trang 5It can be seen that most of the studies on the transition from informal to formal employment are conducted in developing countries and from middle-income countries or cross-country studies Few studies have been conducted on the transition process from informal to formal employment in low-income countries (LIC) In LIC countries, the infrastructure, institutions, level of enforcement, and functioning of credit markets may be fundamentally different from those in more developed countries Therefore, this article will supplement information on the shift of jobs from informal to formal in LIC country, in the case of Vietnam This country has been heavily affected by the COVID-19 pandemic The article uses numbers The monthly labor and employment survey data was collected in 2022 by the General Statistics Office (GSO) to capture the short-term transition for 12 months It can be said that this is the first study in Vietnam to carry out this analysis based
on monthly recurring data, which allows us to have information on short-term labor mobility through “survival” analysis and the Cox model
3 Research methods
3.1 Methods
In this article, informal employment includes all employment arrangements in which the employee does not have a labor contract, does not participate in social insurance, and is not legally or socially protected through their work, making them more susceptible to economic risks This definition includes workers working in the informal sector and working informally outside the informal sector (ILO, 2013a) The informal sector consists of everyone working in unregistered businesses, whether self-employed, family workers or business owners
The article uses a quantitative approach through Kaplan-Meier life table analysis and an econometric time model - Cox's hazard ratio model (1972) to estimate the probability of job transitions from the informal sector to the formal sector The Cox model predicts when workers will decide to change their employment form under the condition that the independent variable changes over time The hazard coefficient in the model is the ability of workers who have not yet switched to switch jobs immediately The model assumes that the hazard rate for worker j, h(t|xj),
is a function of the independent variables xj and is written as:
h(t|x j ) = h 0 (t)exp(x j β x ) (1)
The risk function consists of two separate parts The first part is h0(t), called baseline hazards This value is calculated by setting x=0 so that the baseline risk level for the jth person corresponds to the risk ratio with xj=0 The Cox model is semi-parametric because it is estimated without determining the underlying risk The second part of the model is called relative risk, which acts as
a function of the explanatory variables The model proves that the basic risk is the same in all cases
j Thus, when the basic Hazard coefficient does not change, workers' ability to immediately change jobs will be affected by the change in the relative Hazard coefficient
Model (1) can also be rewritten as follows:
Trang 6(2) Where β is the blocking coefficient to be estimated
The Cox hazards model implemented in the study with the data described above, with the time variable being the month, will report the results according to model (1) The correlation coefficient
in the model will correspond to the exp(βx) values or the relative hazard coefficient
3.2 Data
The data used in this study are taken from the Labor and Employment Survey (LFS) data 2022 conducted monthly by the GSO The sample number is about over 800 thousand observations This data repeats between survey months, thus helping to create panel data through the variables province, district, commune, household number, and member code Thus, each working worker can be in a state of formal or informal employment; segmented data allows for determining the transition from informal to main employment
Table 1 below describes the variables used in the model from the sample survey Male laborers employ 48.2%; Average housework time is 13.2 hours/week; Workers in the 15-to-34-year-old group use 34.4%; The 35-to-54-year-old group use 47.4%; Group aged 55 and older uses 18.3% The group of workers without a certificate is 75.5%, and the rate with a certificate is 24.5% The dependency ratio in the worker's household (equal to the number of people under 15 years old and those over 60 years old in the total number of people in the household) is 32.54% Workers are mainly working in simple occupations, employing 36.9% Question 84% of workers work in the non-state sector; about 60.4% of employment is in rural areas
Table 1: Description of variables from the employee survey sample
Variable name Variable explanation Mean Std Dev Min Max
By gender Dummy variable by gender
sex1 Male 0,482 0,500 0 1 sex2 Female 0,518 0,500 0 1
Housework time (continuous variable)
ti_hw Total time spent doing housework in 1
week
13,227 11,251 0 164 sex_ti_hw Interaction between variable ti_hw and
gender
8,348 12,109 0 164
Age group Dummy variable by age group
Age group1 From 15-34 0,344 0,475 0 1 Age group2 From 35-54 0,474 0,499 0 1 Age group3 55 and higher 0,183 0,386 0 1
Technical qualification (dummy variable)
Technical expertise1 No degree certificate 0,755 0,430 0 1 Technical expertise2 Primary 0,048 0,213 0 1 Technical expertise3 Intermediate 0,046 0,210 0 1 Technical expertise4 Colleges 0,037 0,188 0 1 Technical expertise5 University or higher 0,114 0,318 0 1
Dependency ratio in the household (continuous variable)
Trang 7Dependency ratio The ratio between the number of
children and people over 60 years old
to the total number of people in the
household
32,54 25,14 0 100
Occupational
group
Dummy variable by occupational
group
occup_92 Senior leader 0,011 0,106 0 0 occup_93 High-level technical expertise 0,083 0,276 0 1 occup_94 Intermediate-level technical expertise 0,033 0,178 0 1 occup_95 Office Assistant 0,017 0,130 0 1 occup_96 Service staff 0,187 0,390 0 1 occup_97 Technical labor 0,074 0,261 0 1 occup_98 Manual labor 0,123 0,329 0 1 occup_99 Assembler and operator 0,103 0,304 0 1 occup_910 Simple labor 0,369 0,482 0 1
Type of Ownership
ownership 1 State 0,103 0,303 0 1 ownership 2 Non - State 0,848 0,359 0 1 ownership 3 FDI 0,049 0,217 0 1
Area
urbanrural1 Urban 0,396 0,489 0 1 urban-rural 2 Rural 0,604 0,489 0 1
By region
region1 Red River Delta 0,181 0,385 0 1 region 2 North Midlands and Mountains 0,236 0,425 0 1 region3 Central Coast 0,202 0,401 0 1 region4 Central Highlands 0,088 0,283 0 1 region5 Southeast 0,117 0,322 0 1 region6 Mekong River Delta 0,176 0,381 0 1
Source: Calculation from labor and employment survey data in 2022
Table 2 shows that in 2022, 94.66% of employed people kept their jobs, 0.88% became unemployed, and 4.46% of unemployed people participated in the labor market Among the unemployed, 31.81% remain unemployed, 17.82% are not participating in economic activities, and 50.37% have found a job
Table 2: Change in economic participation status (%)
Have job Unemployment No economic activity Total
Employment 94.66 0.88 4.46 100 Unemployment 50.37 31.81 17.82 100
No economic activity 10.93 2.00 87.07 100
Source: Calculation from labor and employment survey data in 2022
Those who got jobs in 2022 also had a transition from informal to formal employment and vice versa but still accounted for a small proportion Only 2.23% of people working informally switched
to formal employment; on the contrary, about 8.8% of people with formal employment switched
to informal employment (see Table 3)
Table 3: Transition of economic participation status (%)
Informal employment Formal employment Total
Informal employment 97.77 2.23 100
Formal employment 8.79 91.21 100
Trang 8Source: Calculation from the quarterly Labor and Employment Survey 2022
4 Results and discussion
4.1 Results from the Kaplan-Meier life table analysis method
By gender: The results below show that the probability of switching from informal to formal
employment is different between male and female workers; the probability of switching jobs for male workers is higher than for female workers, and the probability of switching jobs is higher for male workers than for female workers—employment conversion from informal to formal increases gradually over time
Figure 1: Possibility to move from informal to formal employment by gender, technical
qualification, age group, and economic ownership
Source: Calculation from labor and employment survey data in 2022
According to technical expertise level: The results of Figure 1 (top right) show that the technical
qualification group 5 (group with university level or higher) is at the top, implying that workers with university level or higher can switch from informal employment to formal employment is
analysis time gender = male gender = female
Kaplan-Meier failure estimates
thời gian CMKT = 1 CMKT = 2 CMKT = 3 CMKT = 4 CMKT = 5
analysis time agegr = 15-34 agegr = 35-54 agegr = 55+
Kaplan-Meier failure estimates
analysis time shuu = State shuu = None state shuu = FDI
Kaplan-Meier failure estimates
Trang 9higher than other groups After six months, workers with university degrees or higher have a 50% probability of converting informal employment to formal employment Still, groups with college degrees need about eight months, and groups with lower levels of education need about 12 months, but this has not yet increased to this level
By age group: Figure 1 (bottom left) shows that the older age groups, the lower the likelihood of
switching from informal to formal employment The age group 15-34 is determined to have the highest ability to switch from informal to formal employment, with the lowest being the group aged 55 and over
According to 6 economic regions and urban and rural areas, the results also show a difference
between urban and rural areas; specifically, workers in urban areas are more likely to shift jobs from informal to formal than in rural areas…
Figure 2: Possibility to switch from informal to formal employment in rural and urban
Source: Calculation from labor and employment survey data in 2022
4.2 Results from the COX model
This section analyzes the influence of independent variables on the probability of workers transitioning from informal to formal employment The level of influence is determined by the Hazard coefficient minus 1 If the coefficient is greater than 1, the corresponding independent variable has a positive relationship with the probability of changing jobs from informal to formal Thus, the coefficients of the independent variables indicate the percentage change of each variable
in the probability of switching to formal employment The estimated model has an LR value
analysis time urban = urban urban = rural
Kaplan-Meier failure estimates
Trang 10chi2(26) = 127918.3, and Prob>chi2 = 0.000; most of the estimated variables are statistically significant, so the model is suitable for inclusion in the analysis
Table 4: COX model estimation results
Sex
Male 1.086 0.015 6.120 0.000
Housework time
ti_hw 0.995 0.001 -7.230 0.000 sex_ti_hw 1.001 0.001 1.210 0.226
Age group
35-54 0.778 0.007 -29.230 0.000 55+ 0.409 0.008 -43.830 0.000
Technical and technical
qualifications
Primary 0.808 0.017 -10.270 0.000 Intermediate 1.673 0.030 28.940 0.000 Colleges 1.636 0.030 26.590 0.000 University or higher 1.621 0.032 24.820 0.000
Dependency rate
Dependency rate 1.000 0.000 -1.230 0.220
Occupation group
Leaders Managers 8.127 0.275 61.840 0.000 High-level technical expertise 8.891 0.242 80.370 0.000 Mid-level technical expertise 8.425 0.220 81.530 0.000 Office assistant staff 10.208 0.271 87.660 0.000 Service staff 2.482 0.053 42.390 0.000 Skilled Labor 0.717 0.034 -7.100 0.000 Manual labor 3.880 0.083 63.500 0.000 Assembler and Operator 10.528 0.216 114.960 0.000
Type of Ownership
Non - State 0.334 0.004 -89.310 0.000 FDI 1.049 0.017 3.000 0.003
Area
Urban 1.181 0.010 19.100 0.000
Economic zone
North Midlands and Mountains 0.748 0.009 -23.190 0.000
Central Coast 0.863 0.011 -11.900 0.000 Central Highlands 0.679 0.014 -19.300 0.000 Southeast 1.053 0.013 4.210 0.000 Mekong River Delta 0.848 0.012 -12.110 0.000
Source: Estimated COX model from the 2022 quarterly labor and employment survey
The results from Table 4 show the influence of independent variables on the probability of shifting employment from informal to formal as follows:
By gender: There is a difference between male and female workers in the ability to transition from
informal employment to formal employment The estimated marginal coefficient is 8.6%, reflecting that male workers have a higher ability to transition from informal to formal than female workers at 8.6% Although female workers have achieved high levels of education in recent years, female workers still encounter barriers from household chores and even social prejudices in