The variables were selected based on the work of Ntuli and Kwenda (2014). Ntuli and Kwenda (2014) used these variables because of the effects they have on earnings. The selection of key variables is also influenced by South Africa’s labour market characteristics that encourage individuals to participate in the labour market. In this minor dissertation, most of the variables used were transformed while others were generated because of the nature of the differences in variable names used by NIDS data. This section provides an explanation of these variables before running the estimated regressions.
4.4.1 WAGES
The variable chosen for wages is the monthly net wages from both primary and secondary employment. In this minor dissertation the variable is converted into a natural log of monthly wages. Logarithms are used in the specified model to normalise data. The log of monthly wages is used as the dependent variable and, in this minor dissertation, it will be used interchangeably with the log of monthly earnings.
4.4.2 TRADE UNION MEMBERSHIP
The presence of unions in the economy can change the level and distribution of wages (Farber, 2001). The expectation is that trade union participation will have positive effects on wages for those who participate as union members (Heitmueller, 2006). This implies that union bargaining strength is correlated with higher wages, thus generating union wage premiums. Using monthly wages as the dependent variable has the power to capture the full effects of union participation in the labour market (Freeman & Medoff, 1985). The trade union is a dummy variable in this minor dissertation and a non-union member is the reference category.
4.4.3 AGE AND AGE SQUARED
Theory suggests that the returns due to worker’s effort is delayed, i.e. wages tend to increase with age (Lazear, 1979). For instance, younger individuals who have less experience tend to receive lower wages. However, as they reach middle age, workers are likely to be highly experienced,
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have institutional knowledge, are more reliable, loyal and committed. This affords them to get higher wages (Boston & Davey, 2006). Age will eventually yield negative returns once an employed individual approaches the age of retirement, because older workers are not flexible, are highly likely to resist to change, are expensive to hire and they lack creativity, innovation and ambition (Becker, 1991).
The square of the age variable has been generated from the age variable. This variable was included because it accounts for any non-linear relationship between age and wages. A negative relationship is expected between wages and age squared variable due to diminishing returns to working age.
4.4.4. JOB TENURE AND JOB TENURE SQUARED
Theory states that there should be a positive relationship between job tenure and wages (Duncan & Hoffman, 1981). Workers with more job tenure usually earn more than workers in the same occupation that lack similar job tenure (Mincer, 1974). The job tenure variable is chosen because of the significant impact it has on wages. The job tenure variable was generated by subtracting primary occupation start year with years on each wave of the NIDS data set. After obtaining the job tenure variable, the job tenure squared variable was generated. A negative relationship is expected between wages and the job tenure squared variable, due to diminishing returns to work job tenure. The job tenure squared variable is included to justify any non-linear relationship between wages and job tenure.
4.4.5 RACE AND GENDER
Race and gender remain contributing factors to earnings differentials in South Africa. This is due to historical racial and gender discrimination in the labour market. The reference category for race is African workers and it will be compared with the other racial groups. Historically it was proven that other racial groups received higher wages when compared to African workers (Moll, 1993). The same results are expected in this minor dissertation. The same applies to gender. Men are expected to get higher earnings compared to their female counterparts. Female will thus be used as a reference category for gender variable.
4.4.6 MARRIAGE
The marriage variable is a dummy variable defined as an individual being either married or unmarried. Single will be the base category in this minor dissertation. The unmarried variable
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includes all the individuals who are living with a partner, divorced, widowed or single. Nakosteen and Zimmer (1987) mentioned that married workers are likely to get training related to their work and this enables them to accumulate human capital faster, translating into higher wages. This will result in marriage wage premium.
4.4.7 CHILDREN
The children variable is defined as children who reside with and are solely dependent on an individual worker. The relationship effect of having children is assumed to be negatively related to wages, e.g. having children makes the worker to be less keen to travel long distances in an effort to find better paying jobs. This lower mobility can adversely affect earnings (Cassoni, Allen &
Labadie, 2004). Children are included in this minor dissertation because they capture the dependency on an individual worker.
4.4.8 OLD WOMEN AND MEN
The control variables of old women and men are defined as pension-eligible woman (aged above 59 years) or man (aged above 64 years) in a household. The old women variable was generated by taking the age of all adults who are 60 years and older and multiplying it with the female variable to get the old women variable. The same steps were repeated for generating the old men variable. These variables are included in this minor dissertation, because they capture the dependency on an individual worker.
4.4.9 EDUCATION
Better educated individuals (those with tertiary qualifications) usually receive higher wages, an indication that an increase in level of education, assuming all else being equal, will produce an increase in wages (Barro, 2001). The level of education for workers is grouped into 4 categories namely: no schooling; primary education; secondary education and tertiary education. The base category is no schooling. The expectation is that there will be positive returns to other categories of education, compared to no schooling.
4.4.10 LOCATION - PROVINCE AND GEOGRAPHY
The inclusion of location as a control variable is that it takes into account the differentials in the costs of living between provinces.
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4.4.11 PROVINCE
The province variable is defined as the province where an individual resides and works. The distribution of earnings varies across provinces in South Africa (Statistics South Africa, 2015). Workers in Gauteng province usually have higher wages than workers in the other provinces, which suggests negative returns to those living in other provinces (Hofmeyr & Lucas, 2001). Higher wages are one of the reasons why workers are attracted to large cities (De la Roca & Puga, 2012). Gauteng province is regarded as the base category in this minor dissertation.
4.4.12 GEOGRAPHY
The geography variable is explained as the urban, traditional or rural dwelling within each province. Individuals who reside and work in urban areas receive higher earnings than their counterparts in rural and traditional areas (De la Roca & Puga, 2012). Urban areas are more developed with greater opportunities than traditional and rural areas. Hence, workers in the urban areas are expected to get higher earnings than those in the rural and traditional areas (De la Roca & Puga, 2012). Traditional areas is used as a reference category. Positive returns are expected for individuals who reside in rural and urban areas in relation to traditional areas.
4.4.13 SECTOR AND OCCUPATION TYPE
Earnings vary across sectors and occupations (Statistics South Africa, 2015). Seven occupations and nine sectors were chosen as samples for this minor dissertation. The elementary dummy variable is chosen as the base category for occupation type. The Agriculture, Hunting, Forestry and Fishing dummy variable is chosen as a reference category for the sector type. The base categories of both sector and occupation types are chosen mainly because they are the less paying sectors and occupations.
4.4.14 FIRM SIZE
The firm size variable is included in this minor dissertation, because it is believed that workers employed at larger firms generally receive higher wages. This is also consistent with competitive labour markets and the human capital model (Oi & Todd, 1999). This variable is included in this minor dissertation to see if large firms are positively correlated with higher wages. Table 2 presents
45 the description of all the variables selected in this minor dissertation, some basic descriptive statistics of individual characteristics by union status and period.
TABLE 2: Description of selected variables
VARIABLE DESCRIPTION
Log Monthly Wages (Dependent
Variable) The natural log of an individual’s monthly wages
Trade union membership Dummy variable: 1 if individual is a member of a trade union , 0 otherwise
Age Age in completed years
Age² Age in completed years squared
Gender Dummy variable: 1 if an individual is male, 0 if an individual is female.
Married Dummy variable: 1 if individual is married, 0 if an individual is single
Children
Dummy variable for the presence of children aged below 15 years in the household: 1 if children below 15, 0 otherwise
Elderly men Dummy variable for co-residence with men aged 65 and older Elderly women Dummy variable for co-residence with women aged 60 and older
African* Dummy variable : 1 if an individual is African, 0 otherwise
Coloured Dummy variable : 1 if an individual is Coloured, 0 otherwise
Asian/Indian Dummy variable : 1 if an individual is Indian/Asian, 0 otherwise
White Dummy variable : 1 if an individual is White, 0 otherwise
No schooling* Dummy variable: 1 if individual has 0 years of schooling, 0 otherwise Primary
Dummy variable: 1 if individual‘s schooling is in the range grade 1 to 7, 0 otherwise
Secondary
Dummy variable: 1 if individual‘s schooling is in the range grade 8 to 12, 0 otherwise
Tertiary
Dummy variable: 1 if individual‘s schooling is NTC 1, NTC 2, NTC 3, Diploma, Bachelor’s Degree, Honours Degree, Masters Degree, Doctorate, 0 otherwise
Elementary occupations*
Dummy variable: 1 if an individual works in an elementary occupation, 0 otherwise
Professionals Dummy variable: 1 if an individual is a professional, 0 otherwise
Clerical support workers Dummy variable: 1 if an individual works as a clerical supporter, 0 otherwise Service and sales workers Dummy variable: 1 if an individual works in service and sales, 0 otherwise Craft and related trades workers
Dummy variable: 1 if an individual works in Craft and related trades, 0 otherwise
Plant and machine operators and assembly
Dummy variable: 1 if an individual works as a plant and machine operator , 0 otherwise
Agriculture, Hunting, Forestry and
Fish Dummy variable: 1 if an individual works in the agriculture sector, 0 otherwise
Private households Dummy variable: 1 if an individual works in private households, 0 otherwise Mining and Quarrying Dummy variable: 1 if an individual works in the mining sector, 0 otherwise Manufacturing
Dummy variable: 1 if an individual works in the manufacturing sector, 0 otherwise
Electricity, gas and water supply and Transport
Dummy variable: 1 if an individual works in the electricity, transport or water sector, 0 otherwise
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Construction Dummy variable: 1 if an individual works in the construction sector, 0 otherwise Wholesale and Retail trade; repair etc
Dummy variable: 1 if an individual works in the wholesale or retail sector, 0 otherwise
Financial intermediation, insurance,
real Dummy variable: 1 if an individual works in financial sector, 0 otherwise
Community, social and personal services
Dummy variable: 1 if an individual works in the communications and social sector, 0 otherwise
Gauteng* Dummy variable: 1 if individual resides in Gauteng province, 0 otherwise
Western Cape Dummy variable: 1 if individual resides in Western Cape province, 0 otherwise Eastern Cape Dummy variable: 1 if individual resides in Eastern Cape province, 0 otherwise Northern Cape Dummy variable: 1 if individual resides in Northern Cape province, 0 otherwise
Free State Dummy variable: 1 if individual resides in Free State province, 0 otherwise
KwaZulu-Natal
Dummy variable: 1 if individual resides in Kwa-Zulu Natal province, 0 otherwise
North West Dummy variable: 1 if individual resides in North West province, 0 otherwise
Mpumalanga Dummy variable: 1 if individual resides in Mpumalanga province, 0 otherwise
Limpopo Dummy variable: 1 if individual resides in Limpopo, 0 otherwise
Traditional* Dummy variable: 1 if individual resides in a traditional area, 0 otherwise
Urban Dummy variable: 1 if individual resides in an urban area, 0 otherwise
Farm Dummy variable: 1 if individual resides in a rural area, 0 otherwise
Firm size Number of individuals employed in a firm
Job tenure Number of years that an individual has spent working
Job tenure² Number of years that an individual has spent working squared
Source: Ntuli and Kwenda (2014)
* Represents base categories for: Race, Education, Occupation; Sector; Provinces and Geography
Table 2 summarises the variables that enter the different models explained above. All the specified models will use a log of monthly wages as the dependent variable and all the listed variables in table 2 as independent variables.
Endogeneity can be caused by omitted variables, measurement errors and simultaneity which could in turn result in biased estimates (Gujarati & Porter, 2009). In cases where the explanatory variable is endogenous, Schmidheiny (2014) states that the pooled OLS estimators will be biased and inconsistent. As a result, either instrumental variables estimation, structural equations models or fixed effects panel models must be used to resolve the problem of OLS bias due to endogeneity (McManus, 2011). This study controls for the endogeneity by using a selection model known as Churdle model.
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4.5 Conclusion
This chapter has shown that the panel data used in this minor dissertation has a time frame of 7 years. The data used was sourced from the NIDS and the NIDS data is a national panel study that is conducted by SALDRU. The aim of the NIDS is to understand and also to investigate possible reasons why some individuals are making progress while others are not (Yu, 2012).
With regard to the estimation technique, it was indicated that panel data estimation techniques such as pooled OLS, union/non-union model and the selection model will be used to estimate the union wage premium. Oaxaca-Blinder decomposition is used primarily to measure whether there is discrimination in the labour market, i.e. to see if the wage advantage of union members is indeed a union wage premium or whether there are other work characteristic differences that can also explain the union wage premium.
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