Clases de troqueles

4.3.1 PUBLIC-PRIVATE WAGE DIFFERENTIALS

The first and basic methodological approach is identical to that used in studies of gender, race or union wage differential. It involves estimation of an earnings regression for public and private sector employees using pooled data and including a dummy variable for a worker‟s sector of employment, written as:

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ln 𝑤_𝑖 = 𝛼 + 𝛽_𝑖𝑋_𝑗,𝑖+ 𝛿𝑠𝑒𝑐𝑡𝑜𝑟𝑑𝑢𝑚𝑚𝑦_𝑖

𝐽 𝑗=1

+ 𝜀_𝑖 (13)

Where X is the vector of the individual, household and job characteristics; sector dummy is a dummy variable holding value 1 if individual is working in private sector and value 0 if individual is working in public sector. The variables included in the equation (13) will be same as included in standard wage equation (1).

Apart from the basic public-private wage differential, it is also interesting to investigate the public-private wage gap at different points of the conditional wage distribution and to do so the study employs the quantile regression models of Koenker and Bassett (1978). Following Buchinsky (1998), the θth (0<θ<1) conditional quantile of the distribution of the (log) wage

w, conditional on a vector of covariates x can be specified as:

𝑄𝜃 𝑤 𝑥 = 𝑥𝛽 𝜃 (14)

The above equation assumes a linear relationship between the population conditional quantile of w, 𝑄_𝜃 𝑤 𝑥 , and the covariates x. For a random sample of (𝑤_𝑖, 𝑥_𝑖) for i=1,...,N, equation (1) becomes:

ln 𝑤_𝑖 = 𝑥_𝑖𝛽 𝜃 + 𝜀_𝜃𝑖, 𝑤𝑖𝑡ℎ 𝑄_𝜃 𝜀𝜃𝑖 𝑥 = 0 (15)

where 𝜀𝜃𝑖 is the error term of the θth conditional quantile. Quantile regression assumes that

𝜀_𝜃𝑖 for the θth conditional quantile‟s error term equals zero.

For a given 𝜃 ∈ 0,1 , 𝛽(𝜃) can be estimated by

𝛽 𝜃 = arg min1

𝑁 𝑤𝑖 − 𝑥𝑖𝛽 (𝜃 − 1(𝑤𝑖 ≤ 𝑥𝑖𝛽

𝑁 𝑖=1

)) (16)

Where 𝛽(𝜃) is estimated separately for each 𝜃 ∈ 0,1 .

A single equation for estimation of a wage differential including dummy variable requires a minor modification of equation (15):

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Where 𝑃_𝑖 is a dummy variable equal to one (1) if the individual i works in private sector and zero (0) otherwise; 𝑥_𝑖is a vector of other variables that are expected to affect wages, such as experience, education and occupation. Quantile regression coefficients can be interpreted as the rates of return to the respective characteristics at the specific quantile of the conditional wage distribution (Buchinsky (1998), Koenker (2005)). Thus, 𝛼 𝜃 measures the private sector wage premium (or penalty if it is negative) at the θth conditional quantile of wages and

𝛽 𝜃 measures the effects of other variables at the point of the conditional wage distribution. If the private sector premium is consistent across the conditional wage distribution then

𝛼 𝜃 does not vary for different thetas (θs). On the other side, if being a public sector employee has no effect on wages, then 𝛼 𝜃 should not be significantly different from zero for any θ.

Equation (17) assumes that the wage determination process for both public and private sector employees is identical. However, the test results shown in the results section suggest that violation of this assumption which also indicates that the variables which determine the wage, could also play an important role in deciding which sector should one chose for employment. The next sub-section discusses the methodology for the employment choice between public and private sector.

4.3.2 PUBLIC-PRIVATE EMPLOYMENT CHOICE

The role of wage differentials in influencing sector choice is highlighted in this sub-section, where the interpretation of sectoral wage differentials is done in terms of expected benefits and the desirability of working in a particular sector. The model presentation ignores the role of expected lifetime income or characteristics such as job security and other non-wage benefits in influencing sector choice due to lack of detailed information.

Formally, workers face a choice between two sectors – the public and the private. A decision of both the worker and the employer influences the selection into a particular sector. A worker has to decide first which sector to seek employment in, and second, an employer to join in that sector. In the presence of job shortages, the cost of seeking a job depends on the probability of not being selected, which in turn is a function of a worker‟s characteristics. Such characteristics determine the cost of seeking employment in a particular sector and also affect the employers‟ hiring decisions. A worker considers the cost and expected benefits before making a sector choice decision.

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Following Van der Gaag and Vijverberg (1988), this study assumes that the expected benefits are equal to the wage differential between the two sectors, and an individual i will join the private sector if the expected benefits exceed the cost, i.e.,

ln 𝑊1𝑖 − ln 𝑊2𝑖 > 𝑋𝑖𝛽 + 𝜀𝑠𝑖 (18)

where 𝑊_1𝑖, 𝑊_2𝑖are the private and public sector wages respectively; 𝑋_𝑖 is a vector of characteristics affiliated with the chances of obtaining a private sector job and includes education, age, regional indicators and a variable indication time of entry into the labour market; and 𝜀_𝑠𝑖 is a 𝑁(0, 𝜍_𝑠2) sector selection equation error term.

The following choice of sector study assumes that there are two wage equations

ln 𝑊_1𝑖 = 𝑍_𝑖𝛾₁+ 𝜀_1𝑖 (19)

ln 𝑊2𝑖 = 𝑍𝑖𝛾2+ 𝜀2𝑖 (20)

where Zi is a vector of wage determining variables; 𝜀1𝑖, 𝜀2𝑖 are random residual terms assumed to be 𝑁 0, 𝜍₁2 , 𝑁 0, 𝜍₂2 . Substituting the wage equations into Equation (18), the private sector selection criterion in terms of a reduced form probit model, where:

𝐼_𝑖∗ _{= 𝐾}

𝑖𝛼 − 𝜀𝑖 21

If 𝐼_𝑖∗ > 0, worker i is in the private sector otherwise not. Here, K includes all exogenous variables in Z and X and 𝜀_𝑖 is the composite error term. The two wage equations and the probit equation (the switching regression) defines the model. Depending on the assumption that (𝜀1, 𝜀2, 𝜀) are 𝑁 0, Σ , maximum likelihood estimates of the model consisting of

equations (19)-(21) are obtained.

Empirical models, such as outlined above are sensitive to the distributional assumption and the specification of both the first step of switching equation and the wage equation. So, to reduce this sensitivity, the study aims to include several variables that influence sector choice but do not influence earnings. To achieve identification of the selection equation, the study needs at least one variable that influences sector choice but may be excluded from the wage equation (i.e. at least one variable in X, which is not in Z). To overcome sensitivity issues, the study includes the number of job holders, moonlight and marital status in switching equation.

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The inclusion of the number of job holders in households may be justified by arguing that if one of the members of the household is in the public sector then other may select the private sector. In the same way if the person is already working in the private sector then as a second job, he would try to get job in public sector or vice versa.