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.
THE 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 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) Unpaid 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 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, not have labor contracts, not participate in social insurance, have low income, and 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) 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 combines 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 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 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 self-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 It 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 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|xj) = h0(t)exp(xjβx) (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: (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 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 By gender Dummy variable by gender sex1 Male sex2 Female Housework time (continuous variable) ti_hw Total time spent doing housework in week sex_ti_hw Interaction between variable ti_hw and gender Age group Dummy variable by age group Age group1 From 15-34 Age group2 From 35-54 Age group3 55 and higher Technical qualification (dummy variable) Technical expertise1 No degree certificate Technical expertise2 Primary Technical expertise3 Intermediate Technical expertise4 Colleges Technical expertise5 University or higher Dependency ratio in the household (continuous variable) Mean Std Dev Min Max 0,482 0,518 0,500 0,500 0 1 13,227 11,251 164 8,348 12,109 164 0,344 0,474 0,183 0,475 0,499 0,386 0 1 0,755 0,048 0,046 0,037 0,114 0,430 0,213 0,210 0,188 0,318 0 0 1 1 Dependency ratio Occupational group occup_92 occup_93 occup_94 occup_95 occup_96 occup_97 occup_98 occup_99 occup_910 Type of Ownership ownership ownership ownership Area urbanrural1 urban-rural By region region1 region region3 region4 region5 region6 The ratio between the number of children and people over 60 years old to the total number of people in the household Dummy variable by occupational group Senior leader High-level technical expertise Intermediate-level technical expertise Office Assistant Service staff Technical labor Manual labor Assembler and operator Simple labor 32,54 25,14 100 0,011 0,083 0,033 0,017 0,187 0,074 0,123 0,103 0,369 0,106 0,276 0,178 0,130 0,390 0,261 0,329 0,304 0,482 0 0 0 0 0 1 1 1 1 State Non - State FDI 0,103 0,848 0,049 0,303 0,359 0,217 0 1 Urban Rural 0,396 0,604 0,489 0,489 0 1 Red River Delta North Midlands and Mountains Central Coast Central Highlands Southeast Mekong River Delta 0,181 0,236 0,202 0,088 0,117 0,176 0,385 0,425 0,401 0,283 0,322 0,381 0 0 0 1 1 1 Source: Calculation from labor and employment survey data in 2022 Table 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 (%) Employment Unemployment No economic activity Have job 94.66 50.37 10.93 Unemployment 0.88 31.81 2.00 No economic activity 4.46 17.82 87.07 Total 100 100 100 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 Informal employment 97.77 8.79 Formal employment 2.23 91.21 Total 100 100 Source: Calculation from the quarterly Labor and Employment Survey 2022 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 1.00 Figure 1: Possibility to move from informal to formal employment by gender, technical qualification, age group, and economic ownership 0.00 0.25 0.25 0.50 0.50 0.75 0.75 1.00 Kaplan-Meier failure estimates thời gian 0.00 0 10 CMKT = CMKT = CMKT = 15 analysis time gender = male gender = female 15 CMKT = CMKT = 0.25 0.50 0.75 1.00 Kaplan-Meier failure estimates 0.00 0.00 0.25 0.50 0.75 1.00 Kaplan-Meier failure estimates 10 10 15 analysis time agegr = 15-34 agegr = 55+ 10 15 analysis time agegr = 35-54 shuu = State shuu = FDI shuu = None state Source: Calculation from labor and employment survey data in 2022 According to technical expertise level: The results of Figure (top right) show that the technical qualification group (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 higher 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 (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 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 0.00 0.25 0.50 0.75 1.00 Kaplan-Meier failure estimates 10 15 analysis time urban = urban urban = rural 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 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 chi2(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 _t Sex Male Housework time ti_hw sex_ti_hw Age group 35-54 55+ Technical and technical qualifications Primary Intermediate Colleges University or higher Dependency rate Dependency rate Occupation group Leaders Managers High-level technical expertise Mid-level technical expertise Office assistant staff Service staff Skilled Labor Manual labor Assembler and Operator Type of Ownership Non - State FDI Area Urban Economic zone North Midlands and Mountains Central Coast Central Highlands Southeast Mekong River Delta Haz Ratio Std Err z P>z 1.086 0.015 6.120 0.000 0.995 1.001 0.001 0.001 -7.230 1.210 0.000 0.226 0.778 0.409 0.007 0.008 -29.230 -43.830 0.000 0.000 0.808 1.673 1.636 1.621 0.017 0.030 0.030 0.032 -10.270 28.940 26.590 24.820 0.000 0.000 0.000 0.000 1.000 0.000 -1.230 0.220 8.127 8.891 8.425 10.208 2.482 0.717 3.880 10.528 0.275 0.242 0.220 0.271 0.053 0.034 0.083 0.216 61.840 80.370 81.530 87.660 42.390 -7.100 63.500 114.960 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.334 1.049 0.004 0.017 -89.310 3.000 0.000 0.003 1.181 0.010 19.100 0.000 0.748 0.863 0.679 1.053 0.848 0.009 0.011 0.014 0.013 0.012 -23.190 -11.900 -19.300 4.210 -12.110 0.000 0.000 0.000 0.000 0.000 Source: Estimated COX model from the 2022 quarterly labor and employment survey The results from Table 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 10 employing female workers In addition, the trend is that women prefer to flexible jobs without time constraints, so they are willing to accept freelance jobs without labor contracts and in the informal sector Housework time: The employee's housework includes house cleaning, household repairs, cooking, washing dishes, taking care of children, taking care of older people, going to the market, and taking and picking up children from school, etc The results show that as the time spent doing housework increases, the likelihood of switching from informal to formal employment will decrease (the coefficient of the variable ti_hw is less than one and statistically significant), if an employee has time to housework, they will tend to choose flexible work, not bound by a labor contract, thus reducing the possibility of them switching jobs from one job to another, informal to formal However, this effect does not differ between men and women (the coefficient of the interaction variable sex_ti_hw is not statistically significant); this reflects the impact of housework time on the ability of men and women to convert is the same Age group: The comparison reference group is 55 years and older Workers aged 15-19 have a 22.2% higher probability of transitioning from informal to formal employment than the 35-54year-old group and 59.1% higher than the 55-year-old and older group This shows that the older the age group of workers, the more difficult it is to transition from informal to formal employment These results are consistent with studies in developing countries (Tansel and Acar, 2017; Tansel and Ozdemir, 2019) By level of technical expertise: The results show that the probability of converting jobs from informal to formal compared to the group without a certificate: i) the group of workers with elementary education is lower than the group without having a certificate (reference group) is 19.2%; ii) the group of workers at intermediate level has a higher mobility of 67.3%%; iii) at college level, the probability of transfer is 63.6% higher; and iv) in the group with university degrees or higher is 62.1% The higher technical expertise a worker has, the easier it is to transition from informal to formal employment This result in Vietnam is consistent with Danquah and colleagues' (2019) and Tansel and Ozdemir's (2019) research Dependency ratio: the ratio of older people and children in the worker's family does not seem to affect the transition from informal to formal employment (coefficient p=0.220) By occupational group: Most of the estimated coefficients are greater than one and statistically significant (except for the dummy variable for the Technical Workers group), similar to the group of simple workers (the reference group) then the remaining occupations are easier to transition from informal to formal Occupation groups requiring high- and middle-level technical expertise, technical workers, assemblers, and operators are occupations in which workers have a high ability to move 11 By type of ownership: The coefficient of the FDI variable greater than one and the non-state dummy variable less than are statistically significant, showing no difference in mobility between labor groups in the government sector (reference) and working groups at FDI enterprises The probability of transitioning from informal to formal employment is lower in the non-state sector than in the FDI and state sectors Region: Workers in urban areas have a probability of shifting employment from informal to formal employment, about 18% higher than those in rural areas Rural workers face barriers because the demand for labor in businesses in rural areas is often low, and workers here often freelance work By economic region: The estimated coefficients are all statistically significant and have a value less than (except for the Southeast region), so compared to the Red River Delta (reference), the remaining regions have a lower probability of transition The Central Highlands is the region with the lowest probability of transition Conclusion and recommendations Reducing informal employment is an important part of national development A formalization strategy requires an integrated and comprehensive approach The conclusions below add perspective to policies to promote formal employment in Vietnam The results of non-parametric analysis using the Kaplan-Meier method and the Cox model show that the transition from informal employment to formal employment depends on the characteristics of the worker and the time the worker works in the market Educational attainment is a characteristic that appears to have a major influence on the transition from informal to formal Workers trained in the market have better opportunities to transition from informal to formal employment In general, if workers are trained, and the higher the level of training, it will promote the ability to convert informal employment to formal employment and reduce the rate of informal employment in Vietnam This shows that improving technical qualifications is an important solution for the employment formalization process The time spent doing housework by workers also hinders the transition from informal to formal employment Besides, the older workers are, the more difficult it will be to switch; Workers with simple occupations are more difficult to transfer; Workers working in the FDI can easily transition from informal to formal employment Thus, to shift employment from informal to formal, it is necessary: Economic restructuring: That is, through macroeconomic development, shift labor towards reducing labor in agriculture and increasing labor in the industrial and service sectors Incentive mechanisms and enforcement measures for businesses must accompany economic restructuring It 12 is necessary to focus on formalizing businesses through facilitating the business registration process, simplifying registration procedures, and specifying tax incentive policies Considering the role of education in promoting formalization and stemming the flow out of formal employment, there is a need to best enable lifelong learning and skills development for less educated workers The government needs to update skills training programs so that workers can better meet market needs and align with international practices Workers also need to proactively equip themselves with knowledge and skills to meet the labor market requirements It argues that policies that support work-family balance, such as flexible work arrangements, can help reduce the gender labor market gap by encouraging greater involvement of partners in household work The report highlights the importance of addressing the unequal division of household labor as a critical factor contributing to the gender labor market gap It recommends implementing family-friendly policies and shows how they can lead to more equitable sharing of household responsibilities and ultimately contribute to narrowing the gender labor market gap References: Adriana, P.V.N (2017), ‘Analysis of formal-informal transitions in the Ecuadorian labour market’, CEPAL Review N° 123, CEPAL Becker, G (1985), ‘Human capital, effort, and the sexual division of labour’, Journal of Labour Economics, 3(1), S33-S58 Boye, K (2019), ‘Care more, earn less? 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