Gender Differences in Employment Among People With Disabilities in China

Abstract

The gender-specific pattern in employment among people with disabilities in China is unclear. This study examines gender differences among people with disabilities in China’s job market using multilevel models. The findings suggest that there are statistically significant gender differences in employment among people with disabilities. It is more likely for men with disabilities to be employed than for women with disabilities. Other individual- and household-level factors including disability type, severity of disability, and household income are also significantly related to the employment of people with disabilities. Effects of these factors on employment are similar among men and women with disabilities, except that household income and being married show stronger relationships with employment among men with disabilities than women. Policy effort should be made not only to advance the employment opportunities of people with disabilities but also to promote gender equality in employment.

Keywords

people with disabilities gender employment China

People with disabilities are those who are unable to perform certain physical or mental functions, which serve as a barrier to their social participation and inclusion. Thus, worldwide attention has focused on the importance of their empowerment (Bullock, Mahon, & Killingsworth, 2010). Employment is thought to be central to the empowerment and well-being of people with disabilities. Employment provides material comfort and financial security, affecting the economic resources available for the purchase of goods and services such as housing, education, and health care. Access to meaningful employment is also an important factor affecting individual identity and self-worth. Without paid work, people with disabilities may be at risk of poverty and social isolation (Statistics New Zealand, 2008).

Employment is of crucial importance for people with disabilities. However, employment opportunities are often not sufficient to meet their needs. Working-age people with disabilities experience lower employment rates than people without disabilities, both in developing and developed countries (World Health Organization & World Bank, 2011). China is not an exception. By 2013, the unemployment rate had reached 10.8% for urban China (China Disabled Persons’ Federation, 2014). Among the employed with disabilities, 78.4% were in agricultural work (including farming, forestry, fishing, and husbandry for their own family or others), an industry which is low paid and insecure (Chen, Lin, Zhang, Song, & Zheng, 2009).

The above figures for China are striking when considered in the context of a continuously booming population of people with disabilities and the Chinese government’s efforts over the past years to secure improvements in the labor market position of people with disabilities. The disabled population in China has increased over the last three decades. Estimates have risen from 52.7 million in 1987 to 84.6 million in 2006. This corresponds to an increase in weighted prevalence of disabilities from 4.9% in the total national population in 1987 to 6.5% in 2006 (Leading Group of the Second China National Sample Survey on Disability & National Bureau of Statistics of the People’s Republic of China, 2008). Under such circumstances, the Chinese government has made the employment of people with disabilities a policy priority and has issued the Regulations on the Employment of People With Disabilities. This regulation has required the percentage of employees with disabilities to exceed 25% in work units with concentrated employment of individuals with disabilities. The regulation also sets a quota of 1.5% disabled employees in other types of work units. Work units that failed to comply had to pay a compensatory fee to the employment security fund for persons with disabilities (Central Government of the People’s Republic of China, 2007). However, it seems that the protective employment policy did not necessarily lead to all of the consequences it had intended.

Though their unemployment has become critical, people with disabilities as well as their employment have occupied a minor role in the literature to date. Extant studies in developed countries have found that persons with disabilities are shown to have lower employment rates than persons without disabilities (Organization for Economic Co-Operation and Development, 2003). Studies have also concluded that people with disabilities are likely to end up in nonstandard forms of employment; they are more likely to be self-employed or working part-time and temporarily (Jones, 2008). While there have been some studies in developed countries, few have been conducted in developing countries. One exception is Mizunoya and Mitra’s (2013) study on 15 developing countries. They found significantly lower employment rates in people with disabilities than people without disabilities in nine out of 15 developing countries, namely Burkina Faso, the Dominican Republic, Mauritius, Bangladesh, Laos, the Philippines, Brazil, Mexico, and Paraguay. In China, there has been a lack of a comprehensive understanding of the employment of people with disabilities. Extant studies have indeed found lower employment rates for people with disabilities (Lai, Liao, & Liu, 2008; Liu, 2013; Lv & Zhao, 2008; Y. Yang, Li, Liu, Lou, & Li, 2008). However, these studies did not control for other factors such as gender, age, and education. They were based on mainly provincial- or city-level data and were thus not nationally representative.

Most previous studies, in both developed and developing countries, have focused on between-group differences, which compare people with disabilities and people without disabilities. These studies overlook within-group gender differences. In fact, gender is a factor that warrants closer scrutiny in China where tremendous differences in gender roles, opportunities, and obligations could condition the dissimilar experiences of men and women with disabilities in all aspects of their life including employment. Understanding gender differences in employment among people with disabilities could help identify reasonable adjustments to employment arrangements and policy, protecting against gender-related disadvantage and providing more equal access to employment. Some literature on people with disabilities in developed countries has addressed gender differences. A study in New Zealand confirmed that participation rates for men with disabilities were greater than those for women with disabilities, across all age groups (Statistics New Zealand, 2008). In the British labor market, the “penalty” for disabilities was found to be greater for women than for men (Jones, Latreille, & Sloane, 2006). Similarly, Randolph and Andresen (2004) have pointed out that women with disabilities face oppression in employment due to gender discrimination. In China, there is some evidence that the employment rate for women is lower than that of men in both urban and rural areas (Wang, 2006; Zhang, 2000).

However, no studies have yet empirically explored the gender gap in employment among people with disabilities in China after controlling for factors such as hukou, area of residence, type of disability, severity of disability, and demographic factors. These factors could be related to the employment of people with disabilities. Within the Chinese hukou system, people are categorized as either “rural” or “urban” (X. Wu & Treiman, 2007). The employment rate of people with disabilities in rural areas is higher than the rate in urban areas (Lai et al., 2008; W. Yang & Dai, 2007). Although the secondary industries and the service sector are more developed in urban areas, people with disabilities in rural areas are commonly classified as “employed” by merely owning their own land (Lai et al., 2008; W. Yang & Dai, 2007). In addition, regional differences affect employment as China’s economic development is unbalanced between its Western, Central, and Eastern regions (Fang, 2006). The employment status of people with disabilities is also dependent on the disability type. Previous studies have found that people with speech-related disabilities have the highest employment rates, whereas people with visual or hearing-related disabilities have the lowest (Lai et al., 2008; Xu, 2010). A study in the United Kingdom found that mental health has a lower employment probability than other types of disabilities (Jones et al., 2006). Also, individual demographic characteristics, such as age, marital status, and years of education, could also be predictors of employment status (Ma & Li, 2006). In addition, age influences employment. A study in New Zealand found that participation was highest between 15 and 44 years of age among people with disabilities (Statistics New Zealand, 2008). Previous studies have also found a positive relationship between education and employment, as disabled people with educational qualifications are significantly more likely to be employed (Jones et al., 2006). Married men with disabilities tend to be employed more than single men, with the reverse applying to women (Jones et al., 2006). Household income also has been frequently included as a household-level determinant of employment. Some researchers have argued that there is a positive association between household income and employment (Y. Wu, 2010; J. Zheng, 2004). Household income was also found to be a better predictor of advantage for employment status than household wealth. This is in the sense that an asset-based index of household wealth is appropriate in urban China but not rural China (Balen et al., 2010).

This article expands upon the literature in the following ways. First, previous studies of gender inequality in employment in China have rarely used nationally representative samples. The current study uses a data set that covered all of China’s 31 provinces. Second, the present study uses a multilevel perspective to examine the gender gap in employment. A concerning issue among existing studies is their lack of distinction between household- and individual-level determinants. This can lead to serious methodological bias. This study aims to use multilevel models to adjust for the nested effects of household- and individual-level determinants. It addresses the following questions: (a) To what extent, if any, are there gender differences in employment among people with disabilities? (b) To what extent do socioeconomic status, disability type, and severity of disability differentially affect the employment of females and males?

Method

Data

The data for this study come from China’s Second National Sampling Survey on Disabilities (hereafter referred to as the 2006 Disability Survey), conducted by the Chinese National Bureau of Statistics from April 1 through May 31, 2006. The sampled population was people with disabilities, and their household members were also surveyed. In the survey, 2,526,145 individuals in 771,797 households were drawn from 31 provinces (including autonomous regions and municipalities). The response rate was 83.46%. The survey used multistage, stratified random cluster sampling to derive nationally representative samples. Within each province (including autonomous regions and municipalities), counties (xian and qu) were stratified based on subordinate administrative areas, local geographical characteristics, and local gross domestic product. This allowed for anticipated variability. Towns (zhen), villages (cun), or communities (shequ) within each selected county were successively chosen using a three-stage sampling strategy, conducted with probabilities proportional to cluster size. The survey used three questionnaires: a household questionnaire, a questionnaire for people with disabilities, and a community questionnaire. There were 52 items, collecting information on both households and individuals, with an emphasis on living conditions, the cause of disabilities, and the needs of people with disabilities. As the study focused on the employment of people with disabilities instead of the general population, this sample included only those respondents 18 years or older who were potential labor force participants. The final sample size was 84,051. For more information on the 2006 Disability Survey, refer to the Archives of China’s Second National Sample Survey on Disabilities (Leading Group of the Second China National Sample Survey on Disability, 2009).

It is worthwhile to point out that disabilities in this study referred to physician-diagnosed disabilities. In the 2006 Disability Survey, diagnoses were made from medical examinations performed by a designated physician. Follow-up diagnostic assessments specified the severity of the disabilities based on four degrees, from least severe to most severe (X. Zheng et al., 2011). For instance, severity of visual disability was measured by best-corrected visual acuity as follows: Less than 0.02 indicated the most severe level of disability (=4), between 0.02 and 0.05 indicated very severe (=3), between 0.05 and 0.1 indicated somewhat severe (=2), and between 0.1 and 0.3 indicated the least severe (=1).

Analytical Strategy and Variables

We first describe the outcome variable (employment status) and explanatory variables, both at the individual level and at the household level. Using multilevel models, we then examine bivariate and multivariate associations between the outcome variable and explanatory variables. With the help of a group of multivariate random intercept models, we reveal the gender difference of employment status among people with disabilities. To facilitate interpretation, we present the gender-specific pattern in the predicted probabilities of being employed for all age groups. Similarly, we report the gender-specific pattern in the marginal effect of age on employment.

The dependent variable is employment status. It measures whether the respondents had a paid job, for at least 1 hr, during the week of the survey (01/25/2006–01/31/2006). Respondents who had worked for payment are coded with 1, and those who had not worked for payment are coded as 0. One hour of paid employment per week is a low bar of employment, but it is congruent with the form of employment in which people with disabilities are concentrated. Previous studies have found that people with disabilities are significantly more likely to be in temporary or part-time employment (Jones, 2008; Schur, 2003). These forms of employment are flexible, enabling people with disabilities who may not undertake standard types of employment to work part-time (Schur, 2003). Distinction between full- and part-time jobs is limited by questionnaire design. Employment status is only classified into employed and unemployed, without considering underemployment.

The individual independent variables include gender, years of education, age (and age squared), marital status, hukou, region, disability type, and severity of disability. Gender is a binary variable: female (=0) and male (=1). Age is calculated by subtracting birth year from 2006. Marital status is a binary variable: married (=1) and unmarried (=0). The variable of years of education is a continuous variable measuring how many years of education the respondents had received. Hukou is categorized as rural (=0) and urban (=1). Area of residency is a categorical variable. We grouped 28 provinces into three regions: West (=1), Central (=2), and East (=3) according to the Seventh Five-Year Plan of the Chinese government. The disability type includes visual disability (=1), auditory disability (=2), speaking and language disability (=3), physical disability (=4), intellectual disability (=5), and mental disability (=6). For people with multiple disabilities, the disability type with the highest severity level was recorded. The severity of disability is an ordinal variable ranging from 1 (the least severe) to 4 (the most severe). The household-level explanatory variables include the logarithm of household income and the proportion of people with disabilities in the household. The logarithm of household income is calculated based on household income over the past year. We also include proportion of people with disabilities within a household, a variable indicating the economic burden of a household. Table 1 shows the basic descriptive statistics for the variables used in the analysis.

Table 1.

Descriptive Statistics for Variables Used in the Analysis.

Variable	Whole sample		Subsample
Variable	Whole sample		Male		Female
Employed (=1)	0.39	(0.49)	0.43	(0.49)	0.32	(0.46)
Age	56.56	(16.71)	57.03	(17.37)	53.27	(17.08)
Years of education	7.03	(2.93)	7.16	(2.95)	6.76	(2.87)
Married (=1)	0.73	(0.44)	0.74	(0.45)	0.72	(0.45)
Urban (=1)	0.34	(0.48)	0.32	(0.47)	0.37	(0.48)
Region: Central (=2)	0.31	(0.46)	0.31	(0.46)	0.30	(0.46)
Region: East (=3)	0.39	(0.49)	0.38	(0.49)	0.41	(0.49)
Type of disability: Hearing (=2)	0.27	(0.44)	0.29	(0.45)	0.22	(0.41)
Type of disability: Speaking and language (=3)	0.03	(0.16)	0.03	(0.16)	0.03	(0.17)
Type of disability: Physical (=4)	0.42	(0.49)	0.43	(0.49)	0.39	(0.49)
Type of disability: Intellectual (=5)	0.05	(0.21)	0.05	(0.21)	0.05	(0.22)
Type of disability: Mental (=6)	0.12	(0.32)	0.09	(0.29)	0.15	(0.36)
Severity of disability: Somewhat severe (=2)	0.27	(0.44)	0.29	(0.45)	0.24	(0.44)
Severity of disability: Very severe (=3)	0.11	(0.31)	0.11	(0.32)	0.12	(0.33)
Severity of disability: Most severe (=4)	0.12	(0.32)	0.11	(0.32)	0.12	(0.37)
Log of household income	8.96	(1.01)	8.89	(1.03)	9.09	(0.94)
Proportion of people with disability	0.43	(0.24)	0.44	(0.25)	0.42	(0.24)

Note. The number of observations is 84,051 (55,287 males & 28,764 females). Values are reported as M (SD).

Multilevel Modeling

Multilevel modeling was used in the analysis for two reasons. First, as discussed above, it can distinguish the determinants that influence the employment of people with disabilities at different levels—both at the individual level and at the household level. This has the potential to shape employment status. Second, the data are structured hierarchically: The 84,051 individuals were nested within 78,622 households. Multilevel modeling accommodates possible error dependence within households and corrects for the bias in the estimates of the coefficients resulting from clustering. It also produces robust standard errors and thus reliable significance tests (Raudenbush & Bryk, 2002; Zhou, 2015). However, the common cross-sectional regression methods violate the assumption of independent error. This violation could lead to downward biases in standard errors of the estimated parameters and an overstatement of the statistical significance of the predictors (X. Yang & Guo, 1999). The likelihood of employment for individuals within a household could be correlated. For instance, members of a family are genetically related to each other and usually have been raised within the same family (Frisell, Öberg, Kuja-Halkola, & Sjölander, 2012; Rabe-Hesketh & Skrondal, 2012). The presence of an earned source of income by another household member could also influence the market participation decisions of people with disabilities. Despite the wide application of research designs in which twins, full siblings, and half-siblings are used as comparison groups in matched cohort and matched case-control studies, these designs are problematic, due to the within-family correlation. Therefore, they should have received more scrutiny from a statistical and methodological standpoint (Frisell et al., 2012). Thus, we specified two-level hierarchical models to estimate the gender differences in the likelihood of employment among people with disabilities. A random intercept model turns out to fit the data better than a random coefficient model—with the interaction item of gender and household income—and allowing for the heterogeneous effects of gender among households. Hence, we discarded the random coefficient model in favor of the random intercept model. It is worthwhile to point out that we tried a three-level model including community level, but it did not fit the data better than the two-level model.

The Level 1 sampling model, link function, and structural model are as follows:

y_{i j} | φ_{i j} ~ Bernoulli (m_{i j}, φ_{i j})

η_{i j} = logit (\frac{φ_{i j}}{1 - φ_{i j}})

\begin{array}{l} η_{i j} = β_{0 j} + β_{1} {(gender)}_{i j} + β_{2} {(age)}_{i j} + β_{3} {(age)}_{i j}^{2} + \\ β_{4} {(marital)}_{i j} + β_{5} {(education year)}_{i j} + β_{6} {(hukou)}_{i j} + \\ β_{7} {(median)}_{i j} + β_{8} {(east)}_{i j} + β_{9} {(hearing)}_{i j} + \\ β_{10} {(speaking)}_{i j} + β_{11} {(physical)}_{i j} + β_{12} {(intellecutal)}_{i j} + \\ β_{13} {(mental)}_{i j} + β_{14} {(disability level)}_{i j} + ϵ_{i j} \end{array}

The Level 2 models are as follows:

\begin{array}{l} β_{0 j} = γ_{00} + γ_{01} {(family income)}_{j} + \\ γ_{02} {(disability proportion)}_{j} + u_{0 j} \end{array}

Plugging the Level 2 models into the Level 1 structural model, we obtain

\begin{array}{l} η_{i j} = γ_{00} + γ_{01} {(family income)}_{j} + \\ γ_{02} {(disability proportion)}_{j} + β_{1} {(gender)}_{i j} + \\ β_{2} {(age)}_{i j} + β_{3} {(age)}_{i j}^{2} + β_{4} {(marital)}_{i j} + \\ β_{5} {(education year)}_{i j} + β_{6} {(hukou)}_{i j} + \\ β_{7} {(median)}_{i j} + β_{8} {(east)}_{i j} + β_{9} {(hearing)}_{i j} + \\ β_{10} {(speaking)}_{i j} + β_{11} {(physical)}_{i j} + \\ β_{12} {(intellecutal)}_{i j} + β_{13} {(mental)}_{i j} + \\ β_{14} {(disability level)}_{i j} + u_{0 j} + ϵ_{i j} \end{array}

Results

Table 2 presents the estimation from the multilevel models, including a null model (Model 1), a random intercept model with individual-level explanatory variables (Model 2), and a random intercept model with both individual- and household-level explanatory variables (Model 3). After comparing these models based on their likelihood ratio test (p < .001) and evidence from the literature, the random intercept model with both individual- and household-level explanatory variables (Model 3) was selected.

Table 2.

Multilevel Models of Gender Difference in Employment for People With Disabilities in China (N = 84,051).

Variables	OR (Exp(B)) [95% CI]
Variables	Model 1		Model 2		Model 3
Fixed effect
Gender (male)	3.22	[3.09, 3.35]*	3.82	[3.53, 4.14]*	3.97	[3.66, 4.30]*
Age			1.30	[1.28, 1.32]*	1.30	[1.28, 1.32]*
Age squared			1.00	[1.00, 1.00]*	1.00	[1.00, 1.00]*
Hukou (urban)			0.08	[0.07, 0.09]*	0.06	[0.06, 0.07]*
Years of education			1.00	[0.99, 1.01]	0.99	[0.98, 1.00]
Marital status (married)			2.92	[2.70, 3.15]*	3.17	[2.92, 3.44]*
Region
Central			0.84	[0.79, 0.89]*	0.82	[0.77, 0.87]*
East			0.62	[0.59, 0.66]*	0.56	[0.52, 0.60]*
Disability type
Hearing			2.02	[1.85, 2.21]*	1.98	[1.81, 2.17]*
Speaking and language			3.03	[2.56, 3.59]*	2.81	[2.37, 3.33]*
Physical			0.35	[0.32, 0.39]*	0.36	[0.33, 0.39]*
Intellectual			0.46	[0.40, 0.53]*	0.45	[0.39, 0.52]*
Mental			0.26	[0.23, 0.29]*	0.26	[0.23, 0.29]*
Severity of disability			0.51	[0.49, 0.53]*	0.52	[0.50, 0.54]*
Log of household income					1.30	[1.25, 1.34]*
Proportion of people with disability					3.25	[2.84, 3.71]*
Intercept	0.16	[0.15, 0.17]*	0.06	[0.05, 0.08]*	0.00	[0.00, 0.01]*
Random effect
Variance (household)	2.72*		1.58*		1.13*

Note. OR = odds ratio; CI = confidence interval.

p < .05. **p < .001.

A null model without any covariates was used first. A maximum likelihood test indicated that the multilevel model fitted better than the common binary logistic regression (p < .001). In Model 2, a random intercept model with individual-level covariates was used. In Model 3, household-level covariates were added, and both covariates were statistically significant predictors of employment. In addition, the proportion of the variance in employment between households was 25.6% (intraclass correlation). The odds of employment for men were 3.97 times higher than for women. The control variables—age (age and age squared), hukou, marital status, region, disability type, severity of disability, logarithm of household income, and proportion of people with disabilities within a household—were all statistically significant predictors of employment as well. The effect of age on employment was curvilinear. The odds of employment began increasing at 18 years of age and then began decreasing around 35 years of age. The odds of being employed for urban residents were only 6% of the odds of being employed for rural residents. Compared with people with visual disabilities, the odds of employment for people with auditory and speech/language disabilities are 1.98 and 2.81 times higher, respectively, whereas the odds of employment for people with physical, intellectual, and mental disabilities are 64%, 55%, and 74% of the odds for people with visual disabilities, respectively. Severity of disability is negatively related to possibility of being employed. As the severity of disability increases, the odds of employment decreases. The logarithm of household income and proportion of people with disabilities are both positively related to employment. With a one-unit increase in the logarithm of family income, the odds of employment increase by 0.30. For every one-unit increase in proportion of people with disabilities within the household, we expect a 2.25-fold increase in the odds of employment.

This study compares gender differences in the predicted average levels of employment (see Figure 1) and the marginal effect of age (see Figure 2) among people with disabilities. Figures 1 and 2 are visualizations of the curvilinear relationship between age and employment for people with disabilities. Figure 1 indicates that the gender gap in the probability of employment persisted over the life course, and disabled women were less likely to work than disabled men. The gap was wide in early adulthood, reaching its maximum in the mid-30s and narrowing in the later stages of life (especially after 65 years). The probability of employment peaked at around 35 years for both disabled men and disabled women.

Figure 1.

Predicted probabilities of employment for people with disabilities by age and gender in China.

Figure 2.

Marginal effect of employment for people with disability by age and gender in China.

Figure 2 shows the marginal effect of age on the predicted probability of employment for people with disabilities. Marginal effects of age on employment status are a measure of the instantaneous effect that a small amount of change in age has on the predicted possibility of being employed, when other covariates are controlled. For disabled men, the marginal effect of age on employment decreased before 40 years and then increased with a peak at 60 years old, when the marginal effect was approximately 0.030. For disabled women, the marginal effect decreased at 30 years, with a sharp decline after 50 years. The gap between men and women was widest at 40 years. The marginal effect of age for men was smaller than that for women from 18 to 50 years (after which it was bigger for men).

Table 3 presents the models for male and female subsamples. Both models in Table 3 examine the outcome variable separately for men and women with disabilities, following the same structure of Model 3 in Table 2. Explanatory variables, including age (age and age squared), hukou, years of education, marital status, region, disability type, severity of disability, logarithm of household income, and proportion of people with disabilities, are significantly associated with being employed for both subsamples. The logarithm of household income and marital status had a significant, positive relationship with the odds of employment; however, the intensity of the effect was stronger for men than for women. The odds of employment were 3.89 times higher with a per unit increase in the logarithm of household income for men but only 1.52 times higher for women. Among males with disabilities, the odds of employment for those married were 3.06 times higher than for the unmarried, whereas among females, the odds for married were 1.98 times higher. For other covariates, the effects were similar in the female and male subsamples.

Table 3.

Comparison of Gender Effect on Employment for People With Disabilities in China Using Multilevel Model.

Variables	OR (Exp(B)) [95% CI]
Variables	Model 4 (Female)		Model 5 (Male)
Fixed effect
Age	1.27	[1.23, 1.31]**	1.28	[1.26, 1.31]**
Age squared	1.00	[1.00, 1.00]**	1.00	[1.00, 1.00]**
Hukou (urban)	0.09	[0.07, 0.13]**	0.08	[0.07, 0.10]**
Years of education	1.03	[1.01, 1.05]**	0.97	[0.96, 0.98]**
Marital status	1.98	[1.72, 2.27]**	3.06	[2.76, 3.41]**
Region
Central	0.68	[0.61, 0.75]**	0.93	[0.87, 1.00]*
East	0.50	[0.44, 0.57]**	0.65	[0.61, 0.70]**
Disability type
Hearing	1.67	[1.45, 1.93]**	1.87	[1.69, 2.07]**
Speaking and language	2.64	[2.03, 3.45]**	2.39	[1.97, 2.90]**
Physical	0.40	[0.34, 0.47]**	0.40	[0.36, 0.44]**
Intellectual	0.51	[0.42, 0.63]**	0.47	[0.40, 0.54]**
Mental	0.35	[0.29, 0.42]**	0.26	[0.22, 0.30]**
Severity of disability	1.10	[1.05, 1.16]**	1.36	[1.30, 1.41]**
Log of household income	1.52	[1.25, 1.86]**	3.89	[3.31, 4.56]**
Proportion of people with disability	0.60	[0.55, 0.65]**	0.54	[0.51, 0.57]**
Intercept	0.04	[0.02, 0.07]**	0.01	[0.01, 0.02]**
Random effect
Variance (household)	1.11**		1.09**
Number of observations	28,764		55,287

Note. OR = odds ratio. CI = confidence interval.

p < .05. **p < .001.

Discussion

This study has examined the gender-specific pattern in employment among people with disabilities in China. Among people with disabilities, males showed higher odds of being employed. Other explanatory variables including marital status, hukou, region, age, disability type, severity of disability, household income, and the proportion of people with disabilities in the household were all significant predictors of employment. However, the effect of these factors on employment did not show much difference between the male and female subsamples, except for household income and marital status.

Men with disabilities were more likely to be employed than women, and the gender gap persisted over the life course. This finding is consistent with gender inequality found in the general population (Wang, 2006; Wei, 2011). The likelihood of employment peaked around age 35 for both men and women with disabilities. It was lower than the peak age in the general population, which was 43 (Li & Ma, 2006). Gender is a source of employment disadvantage facing women with disabilities. Disabled women are less likely to be employed than disabled men, partly because they are thought to be less productive in the labor market (Barnes & Mercer, 2005). In addition, government policy does not set clear guidance on the ratio of women to men, which could potentially legitimize employers’ prioritizing of disabled men over their female counterparts. Furthermore, household-level factors influence disabled men and women differently. One possible explanation is that traditional Chinese family culture upholds women to be the primary caregivers for children and the elderly, and to be responsible for household chores (Bao & Xu, 2007). This could affect women’s decision to enter the workforce. The exclusion of disabled women deprives them of job opportunities and employment resources (Wang, 2006; Zeng & Wei, 2004), and could lead to a self-fulfilling prophecy. If women with disabilities see that the market fails to reward them on the basis of productivity, or that participation in the job market could lead to stressful work–family conflicts, they may conclude that it is not worthwhile to apply for jobs nor to devote time or money to improving their human capital. The interaction arising from their rejection to do so and the lack of social support may buttress employers’ discrimination. This could lead to failure to identify institutional discriminatory practices, thereby contributing to the exacerbation of disabled women’s disadvantage.

Besides gender, other factors have also been found to be significantly related to employment. Compared with people with visual disabilities, people with auditory disabilities and people with speaking and language disabilities were more likely to be employed. However, people with physical, intellectual, or mental disabilities were less likely to be employed. As the severity of the disability increased, the likelihood of employment decreased. People with disabilities probably face disadvantage arising from the assumption that they are incapable of meeting the demands of work discipline in the labor market (Barnes & Mercer, 2005). In addition, their inability to perform some social functions is usually attributed to their own physical condition, instead of social barriers intolerant of people with disabilities (W. Yang & Dai, 2007). Such a culture could render employers unwilling to provide the necessary accommodation for people with disabilities in the workplace and leave people with disabilities in an unsupportive environment.

Household income was positively associated with employment, and its effect on the likelihood of employment was moderated by gender, with a stronger association for men than women with disabilities. In addition, an increase in the proportion of people with disabilities was associated with an increased likelihood of employment. People with disabilities in rural areas and Western China are more likely to be employed than their counterparts. This might be partially explained by the type of employment undertaken by people with disabilities. Previous evidence suggests that people with disabilities are concentrated in nonstandard forms of employment such as independent contracting, part-time, and temporary employment (Jones, 2008). In less developed areas, for instance rural and Western China, most employment is in the informal sector, where entry is easier than in the formal sector. This makes it easier for people with disabilities to participate in the labor market, either as self-employed or part-time workers. These jobs usually have lower wages and fewer benefits though. However, for people with disabilities, earning some money is better than earning nothing.

The conclusions of this study should be understood within their limitations. First, this study was limited by the questionnaire design, which only classified employment status into employed and unemployed (without considering underemployment). Previous studies have noted that people with disabilities who are employed have remained concentrated in nonunion and low wage jobs (Smith & Hutchison, 2004). However, we are not able to identify the types of employment and wages of people with disabilities from the current data. Future research could focus on underemployment among persons with disabilities. Second, there are other household-level factors that we are unable to include into the models due to the questionnaire design. This study did not fully capture household socioeconomic status. Due to questionnaire design, only household income was measured, not occupational prestige or education. Third, it is possible that the job market for people with disabilities nowadays looks different from what the 2006 Disability Survey captured. However, data from the 2006 Disability Survey are the most up-to-date, nationally representative data set on people with disabilities to which researchers have access.

Employment and social interactions in the workplace are central to a person’s life in modern society (Butterworth, Hagner, Helm, & Whelley, 2000). Our study found that women with disabilities are more disadvantaged in the job market than men. Political effort should be made not only to promote the employment of people with disabilities as a whole but also to advance gender equality in employment. Many countries, including China, have specific measures, for example quotas, aimed at increasing employment opportunities for people with disabilities. However, there is no special attention paid to the ratio of women with disabilities to that of men. This policy gray area could leave room for, and legitimize, employers prioritizing men with disabilities over their female counterparts.

Furthermore, our conclusion that people with disabilities in rural areas and in Western China tend to be employed more than their counterparts in urban and Eastern China might be explained by the finding that people with disabilities are likely to be self-employed or in temporary/part-time work. Lower wages related to this sort of work might discourage some people with disabilities from working. The government, as well as nongovernmental organizations, should work toward creating a more socially just tolerant and sustainable environment, where both men and women with disabilities can gain the confidence to find a job, escape poverty, and fully participate in society.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research received funds from the Program of the National Social Science Foundation of China (14BRK025).

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