Perceptions of Good Jobs

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BACKGROUND PAPER FOR THE WORLD DEVELOPMENT REPORT 2013

Perceptions of Good Jobs

Huafeng Zhang Ingunn Bjørkhaug Anne Hatløy

Tewodros Kebede

Analytical Report

Jianyang, China Sichuan

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Fafo-report 2012:19 ISBN: 978-82-7422-880-1

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Development Report 2013 team, the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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Chapter 1 Introduction

Jobs have taken a center stage in the policy debate due to recent world developments ranging from consequences of the financial crises to that of the Arab uprising that is partly fueled by youth unemployment as well as political discontentment. The World Development Report 2013 focuses mainly on jobs and their connections with important dimensions of economic and social development. To this end, jobs can be seen as having transformational roles in three dimensions:

living standards, productivity and social cohesion. In essence, focusing on what a good job for development is from these perspectives will provide insights help address diverse job agendas.

The notion of a good job may seem normative but can also be anchored in basic economic arguments. Instead of having a list of criteria such as structure of earning, health benefits, and pension plans to characterize a job as good job, the WDR 2013 calls for focusing on the overall features of a job as seen from its value in terms of increasing living standards, productivity growth, and increasing social cohesion. However, it may be difficult to identify a single type of job that is considered as good job in all dimensions. A job that is considered good in one aspect, such as increasing income, may not necessarily be considered as prestigious job. Instead of trying to focus on a single job, one may be interested to look into various facets of a given job and conduct an assessment from different perspectives

This study has been conducted in order to better understand and explain how jobs are perceived in a number of selected countries. The report is primarily targeted to provide inputs towards the WDR 2013 and is part of a series of studies conducted on perception of good jobs in four countries: Colombia, China, Egypt and Sierra Leone. The main emphasis has been to explore the nature of jobs that affect living standards and enhance social cohesion. The report addresses the following research questions:

- What are the most important factors affecting labor force participation?

- What are the relations between jobs and household wealth?

- Can perceptions and stereotyping of jobs be regarded as constraints for job creation?

- What are the linkages between job benefits and job stability?

- What are the determinants of job satisfactions?

- Jobs can be evaluated using a human empowerment perspective by focusing on three features of job: cognitive, creative and autonomous activities. What is the relationship between job status and human empowerment?

- Does inclusion in the labor force contribute to increased social trust and confidence in institutions?

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This study is conducted in the Jianyang in Sichuan Province, China. Jianyang is located in the center area of Sichuan Province, which is one of the 23 provinces in Western China¹. Jianyang consists of 26 towns, 29 townships and 797 villages, with a population of around 1.5 million people.

Within past few decades, China has experienced rapid economic growth. The economic growth in China has also been accompanied widening disparities between urban and rural areas, between Western and Eastern China. The economic reforms since 1970s have introduced large amount of international companies, private and individual companies. The state-owned enterprises have also experienced reconstructions since 1990s. The internal rural-urban labor migration was also one of the important aspects in Chinese urban labor market. The tremendous changes in Chinese labor market have also changed the perceptions and attitudes of people towards their work.

Sichuan province is one of the South-Western provinces, which is developed better than the North-Western provinces, but lags behind most of the Eastern provinces. Among all the 31 provinces and regions in mainland of China, the per capita disposable income in Sichuan was ranked at 23 in 2011. Sichuan is one of the main agricultural provinces. Jianyang is one of the largest counties in China, and one of the 173 counties in Sichuan Province. Jianyang is also one of the main counties that provides food and meat production in Sichuan. Jianyang has developed rapidly in the recent years, and in 2010 was ranked for the first time at top 20 in comprehensive economic evaluation among the counties in Sichuan.

This report is organized as follows. This chapter provides background to the study area. In chapter two a description of the methodology used in this study is presented. Chapter three presents description of the data collected for this study, including socio-economic background and characteristics of labor force in the study area, Jianyang. Chapter four outlines the results and discusses main findings. Summary of the main findings is found in the last chapter.

1 China has 23 provinces, 5 autonomous regions, 2 special administrative regions and 4 municipalities which are administratively at provinces level. Sichuan is one of the Western Provinces.

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Chapter 2 Methods

2.1 Good jobs survey

The main objective of the survey was to obtain data on perceptions of jobs in addition to obtaining information on basic labor force indicators, economy and social trust.

The main design characteristics of the sample used in this study were as follows:

1. The target population of the study was all households living in Jianyang in Sichuan Province, China

2. The sample frame was based on the 2010 census in China and was provided by Jianyang statistics bureau.

3. The survey population was classified into two main reporting domains: Rural and Urban 4. Due to the low response rate, a second sample was drawn to increase the valid sample

size. First sample allocated 90 clusters, and 20 extra clusters were sampled in second sample.

5. The selection of clusters was based on probability proportionate to size (PPS)

6. In first sample, 44 primary sampling units (PSUs) (clusters) were selected in urban area, where 18 households were sampled in each cluster; while 46 clusters were allocated in rural areas, where 15 households were sampled in each.

7. In second sample, 10 clusters were allocated in both urban and rural area, where 20 households selected in each cluster. With this design, the final sample size in the survey was 110 clusters making up a total sample size of 1910 households.²

2.2 Questionnaire

To understand the populations own perception of jobs calls for detailed information about what „good‟ and „bad‟ job characteristics are and to understand the constraints of accessing a good job. In addition, barriers to labor market entry and potential solutions for these barriers are important indicators that could have policy relevance. An associated concept of job stereotypes could also help highlight what is regarded as a good job and is obtained through vignettes depicting various types of jobs, and a questionnaire was designed for this study.

Part I of the questionnaire was administered at the household level. The respondent for this part of the questionnaire was the household head or any other eligible knowledgeable person who could provide information for the household as well as other household members. The household member is called for responding to questions on household level information such

2 In 2 rural clusters, large amount of households had migrated to urban area, while the cluster did not have many households. Therefore, during field work decisions were made to select all the households in these two clusters.

Additional 28 households were included into the sample in the second sample.

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as demographics, education, labor force participation to household members (age 14 and above), household economic conditions and assets.

Part II of the questionnaire was administered to a randomly selected individual (RSI) in the selected household. The RSI was selected among all household members aged 18 or above.

The RSI responded to a number of questions about the person‟s current job status and its associated features; the person‟s own perceptions of jobs; issues regarding social trust, confidence on various institutions and participations on social organizations. These question items are intended to be answered only by the randomly selected individual. The structure of the questionnaire is depicted in Figure 1 below.

Figure 1 Overview of structure of instruments

2.3 Empirical methods

Descriptive statistical methods including frequencies, means, graphs and tables are used to describe the various indicators used in this study. In addition to the descriptive statistics, the study employs regression models to explore relationship with various factors affecting the dependent variable under investigation. Generally, the perception indicators used in this study are measured using five Likert items: 1=Not at all satisfied; 2=somewhat unsatisfied; 3=neither;

4=somewhat satisfied; 5=very satisfied. These values make up an ordinal set and ordinary least square (OLS) regression will not be suitable to explain the various job satisfaction indicators used in the study as it can give estimates which imply predictions of the values outside the feasible range. Hence, ordered logit regressions are used to explain factors affecting job satisfaction.

Another type of indicator used in the study takes binary values (0 or 1). Specifically, labor force participation is recorded as whether individuals are in or out of the labor force. OLS will not be suitable for dummy variable indicators either as it predicts values beyond 0 and 1. So, logistic regression is used to explore factors influencing labor force participation.

All Household Members (PART I)

 Demographics

 Household composition

 Education

 Labor force participation

 Housing

 Economic conditions

 Assets

Selection of RSI

Information on RSI’s (PART II)

 Job status and features

 Perceptions on job

 Social trust and participation

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Multiple Correspondence Analysis (MCA) was used in this report to analyze the pattern of relationships of several categorical variables. It is an extension of correspondence analysis (CA), which was developed by Hirschfeld (1935) and Jean-Paul Benzécri (1973). MCA is part of a family of descriptive methods, such as Principal Components Analysis, that use mathematical procedure to reveal pattern in complex data sets. It is very useful in mapping both variables and individuals so as to construct complex visual maps whose structuring can be interpreted. The increasing use of visualizations in presentations makes this method more popular in illustrating complex relationships between variables. The method is particularly suitable when responses to question items are recorded in categorical scale. In this report, we employed Multiple Correspondence Analysis in analyzing perception of different of jobs, social trust and confidence in institutions. Technical description of the method can be obtained in Greenacre and Blasius (2006).

This report also constructs a wealth index from a set of assets owned by the household. This wealth index is a linear asset-based index, constructed following procedures as in the Demographic and Health Surveys (Rustein and Kiersten 2004). It considers households‟ access to durable consumer goods and other asset indicators related with housing, water, sanitary facilities and other amenities owned by households. To construct this asset index, we use the following set of mixed asset-based and health-related variables for determining wealth tertiles:

- Household ownership of consumer durables (mobile phone, sofa set, chair, table, bed, mattress, mat, sewing machine, gas cooker, stove, water heater, water filter, electric fan, vacuum cleaner, microwave, fridge, freezer, air conditioner, washing machine, TV, radio, bicycle motorbike, cars, DVD player, satellite connection, internet access, personal computer, photo camera, video camera)

- Characteristics of households‟ dwelling (number of rooms, floor material, electricity, toilet facility, water sources)

- Households‟ ownership of dwelling

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Chapter 3 Data

The survey was originally planned to cover 1482 households in both rural and urban areas of Jianyang. Due to the expected low response rate after the field work started, an additional sample of 428 households was complemented.

The survey has a response rate of 51percent resulting in final sample size of 966 households as shown in Table 1. The main reasons of such low response rate include unclear or invalid addresses registered during census, reconstruction in urban area, and rural-urban labor migration in rural area. Regarding the randomly selected individual survey, the response rate is 46 percent resulting in a final sample size of 871 RSIs including 4 partly completed interviewed RSIs.

Table 1 Interview status for households and randomly selected individual

Interview status Households (%) Randomly selected individual (%)

Interview completed 51 46

Interview partly completed 0 0.2

No contact 11 3

Refusal 3 0.3

Convinced for interview after refusal 0.3

No usable information 3 2

No building structure 2

Vacant 26

Building under reconstruction 0.1

Unclear status 5

Incomplete household interview - 49

Total 100 100

Sample Size 1910 1910

The study did not conduct any substitution of households or RSIs that could not be contacted during the survey period. This is mainly needed so as not to introduce a bias that may be due to systematic absence of households and RSIs. In situations where people who do not work are more likely to be at home, substitutions would increase a sampling bias through oversampling of RSIs that are unemployed or outside the labor force. During the survey implementation, repeated visits have been made to interview selected households and individuals and hence reduce non- response rates of the survey.

3.1 Demographic and dwelling characteristics

The sampling of the households in this survey was based on the registered households in 2010 census. However, due to the rural-urban labor migration, large amount of rural population migrated to work in urban area and sent money back to their households in the rural area. The labor migration can be temporary, seasonal or permanent, but households still considered them as their household members. During the survey for RSI questions, these labor migrants were to large extent under-sampled, as they were not available in their original home, and not in the sampling

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frame in their current place. Only the household members, who were reported as being at home the day before the interview, were eligible for RSI interviews.

Figure 2 shows that age and gender structure of population in urban and rural area were found to be similar, while the age and gender structure of the population who actually lived in rural and urban area were quite different. In rural area, there was very apparent out-migration of young people who are between 15 to 40 years old; while the elderly and the children were left behind. In urban area, many of these young people were not included, that is, the proportion of the young people living in the urban area might be much higher as shown in the figure.

Figure 2 Age and gender structure of population in urban and rural area

Most urban households (77 percent) lived in apartment building, while most rural households lived in single-story houses (33 percent) or two-or three story houses (60 percent). The majority of the people who lived in rural area owned their houses (97 percent), while around two-thirds of the urban households owned the houses or apartments, 23 percent rented the house, and the rest of the urban people occupied the house for free or due to other reasons. The mean number of rooms in the houses people were living in was 3.6 in urban areas, and 5 in the rural areas. All the

8.0% 6.0% 4.0% 2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84

85+ All urban population

Female Male

8.0% 6.0% 4.0% 2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84

85+ All rural population

Female Male

8.0% 6.0% 4.0% 2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+

Urban population at home

Female Male

8.0% 6.0% 4.0% 2.0% 0.0% 2.0% 4.0% 6.0% 8.0%

0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84

85+ Rural population at home

Female Male

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households in urban area and over 99 percent of the rural households were connected to electricity. On the other hand, the urban and rural households access to water sources and toilet facilities varied much. While 89 percent of the households in urban area had piped water into building, 84 percent rural households mainly used water from open or covered well, and only 13 percent rural households had piped water into the buildings. Similarly, most urban households used flushed toilet to piped sewer systems (85 percent), and only one-tenth of the rural households did the same. The remaining urban households used flushed toilet connected to septic tank or other places, or the ventilated improved pit latrine. Almost one-fourth of the rural households used flushed toilet connected to other places, one-forth rural households used ventilated improved pit latrine, another one-forth rural households used pit latrine with or without slab, and 14 percent rural households used composting toilet, bucket toilet or other kind of toilet.

3.2 Economic situations

Households‟ income was not asked in this survey, as good quality of income data is rather difficult to collect in such small surveys. Instead, the set of consumer goods owned by households, together with other asset indicators were asked by the survey. They were used to construct a wealth index to identify households‟ economic situations. When ranking the households‟ wealth index, household size was taken into account, that is, the ranking of the wealth index was based on the population. And among all the households, they were grouped into three groups, with each group containing one third of the population according to the constructed wealth index.

The economic disparity between urban and rural was quite apparent. Table 2 shows households‟

economic situation in urban and rural areas, and high rural-urban economic disparities in Jianyang. As many as 90 percent of the urban households were ranked as the richest; while 44 percent of the rural households were ranked as the poorest and 19 percent rural households were among the richest.

Table 2 Household wealth asset index by location

Urban Rural Total

Poor third 2 44 36

Middle third 8 36 31

Rich third 90 19 33

Total 100 100 100

Sample size 475 493 968

In addition to the use of objective measure of wealth based on household assets, households were asked to provide a subjective assessment of their economic situations that are classified into three different categories: live well; neither rich nor poor and poor. The households‟ subjective assessments on the economic situation indicated that 19 percent of households considered themselves as living well, while 30 percent considered themselves as being poor (Table 3).

Although wealth index indicates that urban households have much better economic situation than

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rural households, there are still 13 percent urban households consider themselves as poor, and 31 percent think they are living well. In rural area, 34 percent households report themselves as poor.

While wealth index indicates people‟s objective deprivation, people‟s subjective assessment illustrates very well how people rank themselves among the people in the areas they live.

Table 3 Household economic situation

Urban Rural Total Subjective assessment

on household economic situation

We live well 31 16 19

We are neither rich nor poor 56 50 51

We are poor 13 34 30

Satisfaction with current financial situation

Fully satisfied 4 2 3

Rather satisfied 18 13 14

Neither 39 31 32

Less than satisfied 27 30 29

Not at all satisfied 12 25 22

Financial situation during last year

Save money 20 13 14

Just get by 52 53 53

Spent some savings 13 7 8

Spent savings and borrowed money 13 18 17

Only borrowed money 3 9 8

Sample size 478 494 972

In addition to these indicators households were also asked how satisfied that they were regarding their current financial situation. Although only 13 percent urban households and 30 percent rural households assessed themselves as poor, many more households were not satisfied with their current financial situation. As shown in Table 3, only 22 percent households in urban area, and 15 percent households in rural area were satisfied with their current financial situation; while as many as 39 percent urban household, and 55 percent rural households were not satisfied.

In the year before the survey, 20 percent of urban households and 13 percent rural households in Jianyang managed to save money; while half of the households just got by. The remaining one- third households had to spend savings or borrow money in the past year.

3.3 Labor force participation

For the purpose of this study we have used the ILO definition for unemployment. ILO defines the unemployed as „the person who during the past seven days was without work, was currently available for work and was seeking for work‟. Only around 14 percent of individuals of age 18 and above did not participate in the labor force (Table 4). More people were economically active in rural area (90 percent) than in urban area (72 percent), while 16 percent women and 10 percent men aged 18 and above were out of labor market. The unemployment rate was quite low in Jianyang, as of the 86 percent economically active individuals only 1.2 percent were unemployed. The unemployment rate was quite similar in urban and rural areas, also similar among women and men.

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Table 4 Employment status (Age 18 and above)

Location Gender

Total

Urban Rural Men Women

Employment status

Employed 72.3 89.2 89.0 83.4 86.3

Unemployed 1.1 1.1 1.1 1.0 1.1

Out of labor force 26.6 9.7 9.9 15.6 12.7

Sample size 1283 1572 1439 1416 2855

Unemployment rate (ILO definition)

Employed 98.4 98.8 98.8 98.8 98.8

Unemployed 1.6 1.2 1.2 1.2 1.2

Sample size 939 1414 1236 1117 2353

3.4 Characteristics of randomly selected individuals

Altogether 874 RSIs were interviewed, among whom 446 were in urban area and 428 in rural area; while 417 were men and 457 were women.

Table 5 shows the age and gender distribution, marital status, educational level, employment status of the interviewed RSIs in the survey. The table also shows the wealth index distribution of RSI‟s households, and whether the interviewed RSI had sickness of prolonged nature. Only 10 percent of the interviewed RSIs were aged 18 to 34, 38 percent were 35 to 49 years old, one-third were 50 to 64 years old, and 20 percent were 65 or older. The age distribution of the RSIs was quite different between urban and rural areas. The urban RSIs were younger than the rural RSIs, as 21 percent urban RSIs and 7 percent rural RSIs were aged 18 to 34; while 36 percent urban RSIs compared to 57 percent rural RSIs were 50 years of age and above. The rural-urban labor migration has caused such differences, and those who have migrated were mainly young population. The gender distribution of RSIs shows that more women were selected for RSI interviews than men. The age and gender distributions of RSI were similar in both urban and rural areas and as many as 86 percent of the RSIs were married or cohabitant, while only 14 percent were single and never married.

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Table 5 RSI characteristics

Urban Rural Total

Age 18-34 21 7 10

35-49 44 37 38

50-64 21 35 32

65+ 15 22 20

Gender Male 48 48 48

Female 52 52 52

Marital status Single 10 6 7

Married/Cohabitant 84 86 86

Widow/Divorced/Separated 6 8 7

Education No school or no stage completed 14 45 38

Elementary or intermediate completed 46 50 49 Secondary (high school) or higher completed 40 5 13

Employment status Wage work 35 13 18

Farm work 2 60 47

Self-employed/ family businesses 25 3 8

Unemployed 2 1 1

Out of labor force 37 23 26

Wealth index Poor 2 41 32

Middle 7 40 33

Rich 91 19 35

Physical or psychological illness of prolonged nature

Yes 24 45 40

No 76 55 60

Total 100 100 100

Sample size 447 428 875

The educational level completed by the interviewed RSI varied quite much in the urban and rural areas. As Table 5 shows, 40 percent of the urban RSIs completed secondary high school or higher education, while it was only 5 percent in rural area. As the population in urban area only comprised of 23 percent of the population in Jianyan County, the education level of the whole population was not high. Only 13 percent of the RSIs completed secondary high school or higher education, and 38 percent of the RSIs have never been in school or did not complete elementary school. In rural area, almost half (45 percent) of the population had never attended school or did not complete any level; while in urban area, it was 14 percent. As to the employment status, 18 percent of the RSIs were wage earners, and almost half were farmers, and 8 percent were self- employed or worked in family business. Slightly more one-fourth of the interviewed RSIs were out of labor force, while only one percent was unemployed. As discussed before, the temporarily migrated members in the interviewed households were not among eligible for RSI interview, therefore, relatively more interviewed RSIs were out of labor force than that was the case in the whole population.

Table 5 describes the characteristics of the RSIs and what type of work they were mainly involved in the previous 12 months. However, as Table 6 displays, many people were engaged in more than one activity. One-third of the individuals who specified their main activity to be wage work during the last 12 months also conducted additional work. On the other hand, only 10

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percent agricultural workers have other additional work activities. However, among those who were defined as out of the labor force, 46 percent conducted some type of activity in the past 12 months.

Table 6 Main employment status of RSI by reported activities

Main employment status in the last 12

months All work activities conducted in the

last 12 months

Wage work

Agricultural work¹

Self employed

Un- employed²

Out of labor

force³ Total

No work -¹ - - 100 54 15

Wage work 66 - - 0 4 13

Agricultural work - 89 - 0 37 52

Self-employed - - 77 0 3 7

Wage and agricultural work 28 8 - 0 2 9

Wage and self-employed 2 - 6 0 0 1

Self-employed and agricultural work - 2 10 0 0 2

Wage, agricultural work and self-

employed 3 0 7 0 0 1

Total 100 100 100 100 100 100

Sample size 210 271 110 10 273 874

1 Not possible

2 Not working last 12 months, searching for work last seven days

3Not working last 12 months, not searching for work last seven days

Chapter 4 Results

In this chapter, we will present the main results. Section 4.1 presents determinants of labor force participation among the population age 18 and above. The relationship between wealth and jobs are explored in section 4.2. Section 4.3 deals with job stereotype and how this differs by different social categories. Section 4.4 presents job benefits to wage workers. Job satisfactions assessed from different perspectives are presented in section 4.5. The role of job in empowering people is presented in section 4.6. The last section presents how having a job influences social trust and confidence in institutions.

4.1 Determinants of labor force participation

The previous section discussed characteristics of people in and outside the labor force. In this section, we will investigate the features of individuals that are relevant for labor force participation. We conducted a logistic regression for the population in age group 18-65 and the results are shown in Table 7 below.

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Table 7 Logistic regression of labor force participation

Variables Estimate Std. Error P-value³ Odds ratio

Female, compared to Male -0.760 0.153 .000** 0.468

Age 0.372 0.035 .000** 1.451

Age squared -0.428 0.042 .000** 0.652

Elementary completed¹ 0.062 0.235 .792 1.064

Intermediate completed¹ 0.131 0.231 .570 1.140

Secondary or higher level completed¹ 0.577 0.290 .047** 1.781

Slightly difficult health condition² 0.067 0.315 .832 1.069

Difficult health condition² -3.053 0.703 .000** 0.047

Household size 0.309 0.129 .017** 1.362

Dependency ratio -0.205 0.094 .030** 0.815

Wealth index -0.071 0.121 .559 0.932

Urban compared to Rural -1.250 0.238 .000** 0.287

Constant -3.794 0.764 .000** 0.022

-2 Log likelihood 1342.38

1 Compared to No Education; ²Compared to No health problems; ³Significant at 5% level are starred.

Household population 18-65 years old, n=2405

Women are less likely to participate in the labor market

The logistic regression model shows that women are less likely to participate in the labor market as compared to men exhibited by the negative and significant relationship with likelihood of labor force participation. This is in line with the descriptive observation made earlier in that more women are out of labor force.

Higher education increases labor force participation

Secondary or higher education is positively related to probability of participating in the labor force. The relatively insignificant but positive relationship of higher level education shows the relative importance of having a higher education to participate in the labor market.

Health matters

Self-reported chronic health status of individuals is a key indicator that determines labor force participation. The reported chronic health status is classified into three categories: individuals who has no reported health problem; individuals with slightly difficult health situation in that they have reported health problem but are less hindered to go out on their own; individuals who have health problems that makes it difficult to move around by themselves. Individuals with slightly difficult health conditions have similar labor participation, while those with serious health problems are much less likely to participate in the labor force.

Wealth does not matter

Wealth of individuals as measured by using a wealth index constructed from the list of assets is found not to be related to the likelihood of labor force participation.

Household size, Dependency ratio

Households with more household members had higher labor participation, while the households with higher dependency ratio, individuals are less likely to participate in the labor force.

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The labor force participation is significantly higher in rural area.

4.2 Jobs and household wealth

Higher employment contribute to household economy in the rural areas

Table 8 shows the ranking of households‟ wealth index in all the interviewed households in Jianyang by the number of employed members in the household. As discussed in chapter 3.3 we see that the labor force participation is high and most people who were able to work would be engaged in some type of work.

In the urban households people without any employed persons were the least poor, but apart from this, there were no significant differences between the households when one or more persons were employed.

In rural areas, households with no members employed or one household member who worked were significantly poorer than other households, and few household were in the rich third tertile of the wealth index. People with three or more employed persons were relatively better off.

Table 8 Household wealth index by the number of employed persons in the household Number of members employed Wealth index tertile (%)

Total (%) Sample size Poor third Mid third Rich third

Urban

No employed member 38 42 19 100 65

One employed member 34 38 28 100 78

Two employed members 26 38 36 100 188

Three or more employed members 36 28 36 100 115

Total 32 36 32 100 446

Rural

One employed member 61 24 15 100 46

Two employed members 45 29 25 100 139

Three or more employed members 27 37 36 100 230

Total 38 33 30 100 427

Employment type related more with households’ economic situation

The households‟ wealth was related to the type of work conducted by the household members, as described in Table 9. In both the urban and the rural areas, households involved in farming activities had a low wealth index: among the households in the urban areas who were engaged in farming activities together with other types of employment, as many as 58 percent were in the poorest wealth index, and only 17 percent were among the rich tertile of the wealth index. When the household were self-employed together with other types of employment, the household were more likely to be among the well offs. This was in particular evident in the rural areas, where 63 percent of the household who were self employed and engaged with other types of employment were among the better offs.

In the urban area the households with wage employment seemed to be relatively better off than the other types of employment statuses, as fewer people (23 percent) are poor.

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One of the differences that have an impact on the households‟ economic situation in the rural areas is whether or not they are able to conduct any income related activities in addition to their farming activities. In China, most rural families have access to land distributed by the government, but if the household do not have any extra labor force that can conduct income generating activities outside the farm they are likely to be worse off than farmers who send family members to urban areas to work. In Table 9 we see that households that only live of farming is poorer than families who are engaged in farming and wage employment.

Table 9 Household wealth index by employment status Location

Wealth index

tertile Total Sample size Poor Mid Rich

Urban

No employment 38 42 19 100 67

Only wage employment 23 39 39 100 214

Only self-employment 38 33 29 100 58

Self-employment and wage employment 32 29 38 100 79 Farming together with other employment 58 25 17 100 57

Total 33 35 32 100 475

Rural

Only wage employment 41 30 28 100 53

Only farming 54 27 19 100 96

Farming and wage employment 34 37 29 100 277

Self-employment together with other

employment 16 21 63 100 54

Total 38 32 30 100 493

4.3 Perception about job types

Randomly selected individuals were asked to choose from eight different types of professions and rank them according to their preference, irrespective of their present work activity. This is in order to understand job stereotyping that will shed light on features of good jobs by ranking them across three different dimensions: preference for them, most important to society and easiest to pursue.

The most preferred job is illustrated in Figure 3. There are very few gender differences between male and females with regard to what job they preferred, however, the difference between urban and rural areas is significantly different.

The most preferred job in the urban areas was to be a government employee among the men (29 percent), and a shop owner among the women (29 percent). However, both men and women preferred these two professions more than the other types of work in the urban areas. To be a teacher or a doctor was also preferred by a number of both the men and the women. A lower number of people preferred to be farmers and very few preferred to be carpenters, taxi drivers or hairdressers.

In the rural areas, the most preferred job was to be a farmer, both among the men (32 percent) and the women (34 percent). However, to be a shop owner, teacher and a government employee

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was also preferred across both genders. Only a small number wanted to be carpenters, taxi drivers or hairdressers in the rural areas.

Figure 3 Preferred job by location and gender³

Figure 4 shows a map of people‟s three most preferred jobs, by a number of characteristics among the population, using multivariable correspondence analysis. The characteristics which are related have a short separating distance, while characteristics that are not linked are spread apart.

The plot shows that each of the work categories falls in its own quadrant. Quadrant I can be labeled „urban, rich and educated‟, this group was among the wealthy population, who were either not working or self employed. Quadrant II can be labeled „non-educated females‟ as the characteristics of this group were that they were aged females aged 50 years and above and with little or no education. Quadrant III can be labeled „rural male farmers‟. They were living in the rural areas and poor. Quadrant IV can be labeled „the wage workers‟ and they had completed

3 n=754 adults 18-65 years of age in Jianyang, China: February 2012. The tables show the percentage of jobs that was ranked number one the most preferred job for themselves.

0% 10% 20% 30% 40% 50%

Hairdresser Taxi driver

Carpenter Doctor Teacher Government employee Shop owner Farmer

URBAN - Male

0% 10% 20% 30% 40% 50%

Hairdresser Taxi driver Carpenter Doctor Teacher Government employee Shop owner Farmer

URBAN - Female

0% 10% 20% 30% 40% 50%

Carpenter Doctor Teacher Government employee Shop owner Farmer

RURAL - Male

0% 10% 20% 30% 40% 50%

Hairdresser Taxi driver Carpenter Doctor Teacher Government employee Shop owner Farmer

RURAL - Female

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17 some lower education and were employed.

The findings in Figure 4 show that the „urban, rich and educated‟ preferred jobs that required education, such as to be a doctor, a teacher or a government employer. „The wage workers‟

showed a wider preference in what jobs they wanted, ranging from government employees and doctors to being hairdressers or shop owners. Among the „non-educated females‟ and „the rural male farmers‟, there were weaker preferences to what type of job they preferred.

Figure 4 Job preferences in relation to location, gender, job status, age education and socio-economic status. Multivariate correspondence analysis

In both urban and rural areas both men and women thought farming was the easiest job to pursue (see Figure 5). This is as expected in the rural areas, as most people there have access to land and to farming activities. Few people considered the seven other work alternatives to be easy to pursue in the rural areas. However, the population in the urban areas also seemed to consider farming to be the job that was most accessible for themselves. Next to being a farmer, both men and women in the urban considered it easiest to pursue to be a shop owner.

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18

Contrary to the preference of being a government employee illustrated in Figure 3, few people considered this to a job that would be easy to pursue. In the urban areas only nine percent of the men and five percent of the women regarded it easiest to pursue a job as a government employee.

Figure 5 Job type easiest to pursue by location and gender⁴

The findings in Figure 5 complement the findings in Table 10, which shows the mean value of what job they found most easy to pursue. The job that required no formal education was perceived as easy to get, and the jobs that required higher degree of education were difficult to pursue. Independent of age, gender and education, people found it most easy to pursue a job as a farmer, and most difficult to become teachers, doctors and to work as a government employee.

4 n=739 adults 18-65 years of age in Jianyang, China: February 2012. The tables show the percentage of jobs that was ranked number one as the easiest job to pursue for themselves.

0% 20% 40% 60% 80%

Doctor Hairdresser Taxi driver Government employee Teacher Carpenter Shop owner Farmer

URBAN - Male

0% 20% 40% 60% 80%

URBAN - Female

0% 20% 40% 60% 80%

RURAL - Male

0% 20% 40% 60% 80%

RURAL - Female

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19

Table 10 Mean value of preferred job by location, gender and education¹

Farmer

Shop

owner Carpenter Hair- dresser

Taxi

driver Teacher Doctor

Government employee

Area Urban 2.9 3.6 4.4 4.3 4.4 5.0 5.7 5.7

Rural 1.8 4.2 4.0 4.6 4.8 5.3 5.5 5.9

Gender Male 2.1 4.3 3.9 4.7 4.6 5.3 5.5 5.7

Female 2.0 3.9 4.3 4.3 4.7 5.4 5.6 6.0

Highest education completed

No school or no stage 1.7 4.4 3.6 4.5 4.9 5.6 5.5 5.9

Primary or Junior secondary 2.0 4.0 4.2 4.5 4.6 5.3 5.5 5.9

Senior secondary or higher 3.1 3.7 4.8 4.4 4.4 4.4 5.7 5.5

Total 2.1 4.1 4.1 4.5 4.7 5.2 5.5 5.9

1 Ranking of the easiest job to pursue for themselves, ranging from one to eight, where one is the easiest and eight is the most difficult to pursue

The response to what type of job that is most important for the society is similar across location and gender (Figure 6). Teachers, farmers and government employees range highest, followed by doctors. Shop owners, carpenters, taxi drivers and hairdressers were ranked low along the dimension of what they positioned as important for the society.

Figure 6 Most important job for the society by location and gender⁵

5 n=759 adults 18-65 years of age in Jianyang, China: February 2012. The tables show the percentage of jobs that was ranked number one as most important job for the society.

0% 10% 20% 30% 40% 50%

Carpenter Shop owner Doctor Government employee Farmer Teacher

URBAN - Male

0% 10% 20% 30% 40% 50%

Hairdresser Taxi driver Carpenter Shop owner Doctor Government employee Farmer Teacher

URBAN - Female

0% 10% 20% 30% 40% 50%

Carpenter Shop owner Doctor Government employee Farmer Teacher

RURAL - Male

0% 10% 20% 30% 40% 50%

Hairdresser Taxi driver Carpenter Shop owner Doctor Government employee Farmer Teacher

RURAL - Female

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20 4.4 Job benefits for wage workers

In this section, we will describe job associated benefits for wage employees. This will help assess the labor market situation for wage employees in addition to providing a basis for assessing job satisfaction in the subsequent sections.

Overall, having a long term contract for a wage worker gave more benefits than for people who had oral or written contract for a short time period. The highest frequency (72 percent) of benefits received by long term contract was maternity leave, and the official policy in China is four months paid maternity leave. However, among the people with short term contracts, only 18 percent received maternity leave as a benefit. Health insurance was a benefit given to 71 percent of the people with long term contract, compared to 19 percent among the people with short term contract. In addition to maternity leave and health insurance, pension after retirement, paid sick leave, bonuses, learning opportunities and school fees to the children was a common benefit to many of the people with long term contracts. Job benefits is strongly related to the type of job contract a person has as illustrated Table 11; the table display how the situation for employees with long term wage contract offers more benefits compared to employees with short term contracts (less than one year).

Table 11 Benefits from employer by contract status

Job related benefits Short term contract (%)¹ Long term contract (%)²

Maternity leave 18 72

Health insurance from employer 19 71

Learning opportunities 24 70

Pension after retirement 12 68

Paid sick leave 25 65

Bonuses 26 60

School fees 17 60

Unemployment benefits 4 39

Housing allowances 15 36

Free meals 45 34

Transportation allowances 10 22

Stock shares 0 8

Sample size 120 88

1 Oral or written contracts for less than one year

2 Long term contract=contract for one year or more

The contrast between benefits for employees with long and short term contracts becomes sharp when the number of benefits provided is taken into account. To this end, long-term contract employees had large number of benefits as compared to short term contract employees (Figure 7).

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21

Figure 7 Distribution of benefits among wage workers

All randomly selected respondents who were wage workers were asked about their access to different benefits and their evaluation on the different work benefits, such as health insurance, pension insurance, transportation allowances, and housing allowances. More than half of the respondent were not willing to pay, in order to get a long-term contract or paid sick leave; while one-third were not willing to pay for health, pension insurance, housing and transportation allowances. Around one-fifth of the respondents were willing to pay for different benefits, but could not tell how much they would like to pay.

The work benefits had actually quite high correlation with people‟s satisfaction in the work. All the interviewed wage earners were classified into one of the four groups, according to their accesses to different work benefits. The work benefits here refer to the list of benefits in Table 11. Among all the interviewed wage earners, only six percent did not have any work benefits or only had one or two benefits, one-forth had three kinds of work benefits, and one-third had 4 to 5 or 6 to 8 work benefits, respectively. Only 8 percent wage workers had more than 8 kinds of work benefits.

In Figure 8 these four groups of people and their satisfaction with job stability, form of contract, training and skill development, social status from job, potential for future personal development and level of income are listed. Two answers “very satisfied” and “somewhat satisfied” were grouped to be presented as “satisfied” for all the questions. Not surprisingly, those who had most work benefits were most satisfied on all the aspects of the work, and they were in particular satisfied with their job stability, the form of contract they had and the social status the job offered them. Overall, they were the people who were most satisfied across all groups. People who had 6 to 8 benefits in their work was less satisfied with their job than the ones who enjoyed even more

0%

5%

10%

15%

20%

25%

30%

0 1 2 3 4 5 6 7 8 9 10 11 12

Percentage

Number of benefits

Short term contract (n=120) Long term contract (n=88)

Perceptions of Good Jobs