Inter-industry wage differentials in Pakistan.

Jaffry, Shabbar ; Ghulam, Yaseen ; Shah, Vyoma 等

1. INTRODUCTION

The essential feature of a perfectly competitive labour market is that workers who accept jobs can expect to receive compensation equal to their opportunity cost. Firms pay a wage which is just sufficient enough, to attract workers of the quality they desire and no higher [Krueger and Summers (1988)]. Overall, the markets do not follow the law of one price, contradicting the competitive framework. This is where the problem of wage differentials across different industries needs to be assessed, and has also been the focus of many studies over the years, mainly in the industrialised countries, e.g. USA, European Countries. However, the issue of wage differentials has been addressed by very few studies in the developing countries [Arbache (2001) and Erdil, et al. (2001)]. Wage differentials analysis in developing countries should also have equal importance as in the industrialised countries, in order to gauge the effect of the corporate culture and centralisation/decentralisation on the different industries and labour market of those developing countries.

Numerous wage differential studies have been carried out in the recent years [Krueger and Summers (1988), Lucifora (1993), Rycx (2002)]. Krueger and Summers (1988), who were pioneers in this study area, demonstrated that pay differentials existed in the USA amongst workers with the same working conditions and individual characteristics in different sectors. This study was the start of the growth of literature in this area, around the world. In contrast, obtaining the appropriate data in developing countries is the main challenge, as the data may not be reliable or detailed data in not available.

This paper attempts to fill the gap of the inter-industry wage differentials in developing countries. This paper is the first to examine industry wage differentials in Pakistan using the advanced econometric techniques. It estimates: (i) inter-industry wage differentials (ii) dispersion of industry wage differentials (iii) inter-industry wage differentials by different regions and education level (iv) changes in the trend of wage differentials during a fourteen year period.. The wage differential has been calculated using the methodology used by Rycx (2003). The pseudo-panel approach coined by Deaton (1985) has been used, as the data used in the analysis is not normal panel data. In order to find the wage differentials information from the Labour Force Survey (LFS), which is carried out by the Federal Bureau of Statistics (FBS) Government of Pakistan, data is used for eight different surveys during a fourteen year time period, between 199091 and 2003-04.

The remainder of this paper is organised as follows. Section 2 reviews some empirical literature in this area, Section 3 describes the data, Section 4 explains the methodology and Section 5 gives an overview of the empirical findings. Section 6 gives the conclusion.

2. LITERATURE REVIEW

The existence of unrelenting and systematic wage differentials amongst industrial sectors has been known for many years as demonstrated by the seminal US work by Slichter (1950). Differences in average wages across industries can reflect differences in the composition of their workforces in terms of skills and productivity. However, in more recent years a wide range of studies in different countries have found, that workers with comparable measured characteristics associated with productivity- notable education and experience-earn different wages depending on the industry in which they are employed. Moreover, this pattern of wage differentials across industries has been found to be highly stable over time, so transitory differences in demand across industries cannot be the explanation. Furthermore, the pattern is very similar across industrialised countries, in that the same industries seem to be high-versus low-paying ones having controlled for measured worker characteristics, [e.g. Krueger and Summers (1988)].

This empirical regularity clearly poses a challenge to labour market theory. According to the simplest neo-classical competitive model of wage determination, two individuals with the same productive capabilities should have the same marginal productivity and thus receive the same wage irrespective of the industry in which they are working. It has long been recognised that wage differentials between identical individuals could persist in equilibrium, because higher wages would be needed to compensate workers for less attractive non-wage attributes of particular jobs, such as unpleasant or even hazardous working conditions. Therefore the standard competitive theory of wage setting recognises that there may have to be compensating differentials between jobs with different non-wage attributes that enter into the employee's utility function.

The existence of sectoral effects on workers' wages is well documented in the economic literature [Krueger and Summers (1988); Lucifora (1993); Rycx (2002)]. Krueger and Summers (1988) contributions was particularly prominent, as they used cross-sectional US data with (individual and their job attributes,) and also longitudinal data, which allowed them to analyse individual fixed effects. They found that taking these into account did not reduce measured industry effects on earnings, indeed if anything it increased them. The Analysis of two longitudinal datasets also found substantial industry effects for workers who change jobs, which they saw as evidence against unmeasured labour quality being the main explanation for inter-industry differentials.

Although the exact scale of inter-industry wage differentials is still questionable, [Abowd, et al. (1999), Bjorklund, et al. (2004), Gibbons and Katz (1992), Goux and Maurin (1999)], there is some agreement on the fact that these effects are fairly persistent, closely correlated from one country to another [Helwegc (1992)], and of varying dimensions in the industrialised countries [Hartog, et al. (1997)]. In addition, a number of studies suggest that sectoral effects are significantly weaker in countries having strong corporate traditions? [Edin and Zetterberg (1992); Hartog, et al. (1999); Kahn (1998); Rycx (2003)]. There have been few studies, which have carried out cross-country comparisons of inter-industry wage differentials. Moreover, while various explanations based on efficiency wage mechanisms or rent sharing have been put forward [Benito (2000); Krueger and Summers (1988); Thaler (1989); Walsh (1999)], the existence of industry wage differentials remains a complex and unresolved puzzle.

While the investigation of why similar individuals in similar jobs might be rewarded differently in different industries goes on, other studies have argued from within the strictly competitive framework, that unobserved differences in abilities and jobs in fact account for much of the explanation for inter-industry differential. Goux and Maurin's (1999) study, using longitudinal earnings data for France, infers the importance of unmeasured ability across individuals by focusing on those switching industries. In contrast to Krueger and Summers (1988), they find that inter-industry wage differentials for such workers are very much less than in cross-sectional data. They argue that this difference probably arises because Krueger and Summers(1988), in their longitudinal analysis use a highly aggregated industrial breakdown distinguishing only seven sectors, Goux and Maurin (1999), in contrast, were able to distinguish 99 industries, and demonstrate that aggregating these and repeating their analysis of job switcher did indeed lead to much higher inter-industry differentials.

While Goux and Maurin(1999), discount the importance of "true" inter-industry wage effects, they explore and find substantial differences across firms in France. They find that the average differential in wages paid to the same worker by two different firms is between the range of 20-30 percent, and that most of this is within rather than between industries. Within a given industry, wages rise with the firm size and capital intensity. They thus see modest inter-industry differentials as reflecting cyclical factors, while arguing that inter-firm differences are compatible with efficiency wage models. Larger firms or more capital-intensive ones, find monitoring more costly and are particularly anxious to retain workers with high levels of firm-specific human capital.

There has been limited literature for wage differentials in the context of developing countries. Arbache (2001) has investigated the wage differentials and wage determination in Brazil using the micro-data for 1980s and 1990s, using models with segmentation, which are explained by efficiency wages. The authors also found that unmeasured abilities and efficiency wage models play an important role in wage determination. They have used different wage theories in order to find the wage differential. Erdil, et al. (2001) has compared the inter-industry wage structure for industrialised and developing countries, to find whether the industry wage differentials are consistent and stable independent of time and space. Erdil, et al. (2001) found that the size of inequality in wage differentials is rising and wage differential patterns are similar for both industrialised and developing countries.

3. DATA

This study uses data drawn from the nationally representative Labour Force Survey (LFS) for Pakistan between 1990-91 and 2003-04, which was conducted by Federal Bureau of Statistics Government of Pakistan. The data collection for the LFS is spread over four quarters of the year in order to capture any seasonal variations in activity. The survey covers urban and rural areas of the four provinces of Pakistan as defined by the Population Census. The LFS excludes the Federally Administered Tribal Areas (FATA), military restricted areas, and protected areas of NWFP. These exclusions are not seen as significant since the relevant areas constitute about 3 percent of the total population of Pakistan.

The working sample, based on those who are engaged in wage employment and have positive earnings, comprises a total of 97,122 workers, once missing values and unusable observations are discarded over the time period. This includes variables such as pay, age, gender, education and working characteristics of individual. Estimation covers nine basic industries, which arc: Agriculture and Fishing; Mining and Quarrying; Manufacturing; Electricity, Gas and Water Supply; Construction; Wholesale and Retail Trade, Hotels and Restaurants; Transport, Storage and Communication; Financial Intermediation and Community, Social and Personal Services, which are classified by Pakistan Standard Industrial Classification. The analysis will go on to distinguish 41 sub-sectors within the industries covered.

Table 1 depicts the means and standard deviations of selected variables for overall, as well as for urban and rural areas. There is a clear difference in average characteristics between urban and rural areas. On average, the wages and number of hours worked are higher in urban area, whilst the experience and numbers of job holders in a household are higher in rural areas.

4. METHODOLOGY

The methodology adopted to estimate inter-industry wage differentials is consistent with that of Rycx (2003). A key methodological issue is that the LFSs are only cross-sectional, while ideally, one would like to have a panel of individuals or households that can be traced through time, in order to investigate the changing wage structure and returns to education. In addition, estimation with the cross-section data can be seriously affected by unobserved individual heterogeneity. However, this problem can be circumvented, or at least mitigated, by tracking cohorts as suggested by Deaton (1985), and estimating relationships based on cohort means.

Starting with a simple model, suppose that base panel regression equation could be written as:

[y.sub.it] = [x'.sub.it] [[beta].sub.t] + [[alpha].sub.i] + [[epsilon].sub.it] t = 1,....,T,

where i = index individuals and / = time periods. Unfortunately, in the LFSs, the same individuals are not observed in subsequent surveys. Hence we do not have a genuine panel data available to estimate such an equation. In such circumstances, the approach first developed by Deaton (1985) proceeds as follows. Define a set of C cohorts, based on a district in a province say, such that every individual i is a member of one and only one cohort for each t. Averaging over the cohort members:

[[bar.y].sub.ct] = [[bar.x].sub.ct][[beta].sub.t], + [[epsilon].sub.ct], c = 1,....,C,

where [[bar.y].sub.ct] is the average of the v" for all members of cohort c at time t. this is a so-called 'pseudo-panel'. The 'cohort fixed effects', [[bar.a].sub.ct] will, in fact, vary with t since they comprise different individuals in each cohort c at time t, but can be treated as constant if the number of individuals per cohort is large. Estimation can then proceed with the standard fixed-effects estimator on the cohort means, thus eliminating any unobserved differences between individual cohorts.

Deaton (1985), argues that there is a potential measurement error problem arising from using [[bar.y].sub.ct] as an estimate of the unobservable population cohort mean and an adjustment based on errors-in-variables techniques is therefore needed. However, researchers typically ignore this if the number of observations per cohort is reasonably large. Moreover, Verbeek and Nijman (1992) suggest that when the cohort size is at least 100 individuals, and the time variation in the cohort means is sufficiently large, the bias in the standard fixed-effects estimator will be small enough that the measurement error problem can be safely ignored. Although, this issue will be considered in the analysis, given the size of the LFSs, suitably chosen cohorts should fulfil this size criterion, hence this is the approach used in this paper.

The construction of the pseudo-panel data is undertaken by computing cohort or cell means in each available cross-section, where the cells are defined by the four-digit district codes, age of the individual, provinces and the type of industry in which the individual is working.4 Thus in total, it results in a group between 6000 and 8000 approximately, in each pseudo-panel for each cross-section. Next we present the methodology, which is used in the paper according to the pooled as well as the pseudo panel method in estimation of inter-industry wage differentials.

(a) The Wage Equation

The general framework for analysis of inter-industry wage differentials is given by a standard wage equation. It rests upon the estimation of the following semi-logarithmic wage equation:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [w.sub.i], represents the gross hourly wage of an individual of/ = l,...,n; X represents a vector of individual characteristics of the workers and their job; Y is a set of industry dummy variables; and Z is a vector of firm characteristics; a is the constant, [[beta], [psi], and [delta] are the parameters to be estimated and [[epsilon].sub.i] is the error term.

Inter-industry Wage Differentials Controlling for Individual and Employer Characteristics

In order to obtain "net" inter-industry wage differentials having controlled for other factors, we estimate the wage equation using the sectoral dummies as well as individual and employer characteristics. In this case, the constant no longer refers to the wage of the average worker in the reference sector. Next, the average wage differential of all the sectors compared to the reference is calculated, as the product of the weighted employment share by the estimated sector co-efficient:

[pi]= [K.summation over (k=1)] [[bar.p].sub.k] [[??].sub.k]. ... ... ... ... ... ... (2)

The differentials are then calculated as the sector co-efficient less the average wage:

[d.sub.k] = [[??].sub.k] - [pi], where k = l, ... ,K. ... ... ... ... ... (3)

and for the omitted sector; the differential is the average wage in Equation (2):

[d.sub.K+1] = -[pi] ... ... ... ... ... ... ... (4)

The standard deviation of the inter-industry wage differential adjusted for sampling error and weighted by the sectoral employment shares is computed as follows:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

5. RESULTS

The wage theories that attempt to explain inter industry wage differentials suggest that the skills and tasks of certain jobs might play an important role. Table 1 (see Appendix) shows the mean hourly wage Pakistan over the sample period in basic industries classified by different occupations. The size of wage differences among industries for given occupation is striking. For example, the wage of Legislators, Senior Officials and Managers range from Rs 20.78 per hour in Trade and Hotels to Rs 79.44 per hour in the Financial Institution industry and the wages of professionals range from Rs 23.00 per hour in Agriculture to Rs 49.31 per hour in Construction. For most occupations, the Table I reveals a clear pattern of higher wages in industries which have the overall higher wages compared to the average wage in the economy.

The comparison has not included other industries as in other industries higher wages are more likely to be affected by the level of education. The wage differences included the return to education, which results in high wage. Thus, education plays an important role in deciding the wage level. As Table II (see Appendix) reveals, the Legislators, Senior Officials and Managers, who likely to have minimum education up to the graduate level are earning on and average Rs 28.91 per hour compared to skilled Agricultural and Fishery workers and Elementary Occupations, who are earning on an average only Rs 7.90 and Rs 11.57 per hour, respectively.

The wage differences presented in Tables 1 and 2 are tested in the later analysis of inter-industry wage differentials.

Table 1 below presents the inter-industry wage differentials and their dispersion for one-digit nomenclature for the pooled sample as well as the pseudo panel. The results show that wage differentials exist between workers employed in different sectors, even after controlling for individual characteristics and job characteristics. These differentials are significant both in individual terms (with exception of two sectors) and globally at the 5 percent level of significance. We further note, that the results are more or less same for the pooled and pseudo panel estimation, so the discussion in the paper has only focused on the pseudo-panel approach." Financial intermediaries, Mining and Transport have found to be the best-paid industries. Furthermore, traditional industries like Agriculture, Trade and Restaurants, were found to have the lowest wages.

The analysis of wage differentials is performed at different perspectives for Pakistan. One of them is by provinces. Pakistan has four provinces (Punjab, Sind, Balochistan and NWFP). Figure 1 represents the wage differential of each industry by provinces and the last? is the wage dispersion for each province. The highest paid sector is again Financial Intermediaries for all provinces except for Sindh, where Mining is the highest paid sector but less paid than by the NWFP. The lowest paid sector is Trade and Agriculture. For Balochistan, the Social Services sector is paying more compared to all the other provinces, while the lowest paid sector is Trade, which is also the case in Balochistan. By looking at wage dispersion among the provinces, the results suggest that Punjab has the highest wage dispersion i.e. 0.105 log points, while Balochistan has the lowest wage dispersion of 0.067 log points.

[FIGURE 1 OMITTED]

Looking at the wage differentials by the sector of the particular industry that are public or private sectors, findings show that the wage dispersion and differentials are higher in the public sector than in the private sector, except in the Construction and Electricity, Gas and Water industry sectors. This is represented in Figure 2, which also shows the differentials for urban and rural areas of Pakistan. Wage dispersion is almost same in both urban as well as rural areas. However, in the rural area, wages are relatively higher in Mining, Electricity, Gas and Water, Financial and Transport industries compared to the urban area.

[FIGURE 2 OMITTED]

The analysis covers eight different surveys during a 14 year time period, so that each year's differential gives an insight into the trend of wage differential and also the wage dispersion trend over almost a decade. Figure 3 shows the wage differential and wage dispersion for the period between 1990-91 and 2003-04.

[FIGURE 3 OMITTED]

Figure 3 shows that the wage differential has increased almost year on year and wage dispersion has increased from 0.05 to 0.08 over the fourteen years. In the mining industry wage, the differential is almost doubled from 0.15 in the period 1990-91 to 0.42 in the period 2003-04.

To decompose inter-industry wage differentials, these differentials were estimated for various education groups. Figure 4 below, shows that Financial Institutions, Mining and Construction industries are the best paid sectors for the person who is well educated, while Manufacturing and Electricity, Gas and Water are the best paid sectors for a person who has no education or the education is less than the matriculation level . The wage dispersion is higher for the person who has a degree or higher qualifications, as compared to the others with less education. So, a person acquiring the degree or higher education has a more favourable chance to move from one industry to another as compared to those who do not have a degree or higher education. As the dispersion is 0.1090 for them (with degree and higher qualification).

[FIGURE 4 OMITTED]

In order to obtain more detailed results, a two digit industry analysis has also been undertaken. Table 2 represents the wage differentials for two-digit industry sectors. The results show that Financial Institutions, Crude Petroleum and Natural Gas, Fishing, CRM of Pipeline for Transportation are among the best-paid sectors, whilst Retail trade, Personal and Household services, Social and related Community Services and Agriculture are the lowest paid sectors.

Overall, the results show higher wage dispersion for pseudo panel estimates than the pooled estimates, i.e. 0.1349 and 0.1063, respectively. The wage dispersion for the two-digit industry wage differentials is also higher than the one-digit industry wage differentials. For, the two digit wage differential, the wage dispersion is 0.1349 while for the one-digit wage differentials, the wage dispersion is only 0.0927 (according to pseudo-panel estimation).

The estimation of the two-digit wage differentials is carried out by looking at different regions, sectors, education level and area of living, in the same manner as that carried out in one-digit wage differentials. The pseudo-panel estimation results are only reported for these industrial sectors here.6 Table 2A (in the Appendix) shows the results of the wage differential by sector and area of living is shown in Table 2B (see Appendix). For the public sector, the highest paid sectors are CRM of Sports Projects, Financial Institutions, Coal Mining and Real Estate Businesses while for the private sector, CRM of Pipeline for Transportation and CRM of Drainage and Financial Institutions are the highly paid sectors. The wage dispersion is higher in the public sector than in the private sector i.e. 0.1472 and 0.1347, respectively.

Table 2C shows that except for one or two years, during the sample years Crude Petroleum and Natural Gas Production, Fishing, Financial Institutions, Manufacturing of Chemicals, remained in the top ten sectors.. While Agriculture, Personal Household Services, Social Services and Trade sectors have remained in the bottom of the list during the fourteen years sample period.

The wage dispersion over the sample period is shown in Figure 5 below. The figure shows that the wage dispersion has increased during the sample period, but it has decreased from 0.1570 to 0.1233 in the last two survey years. This shows that during the 14 years period, the wage dispersion increased, but from the beginning of 2000 it has started to decrease.

[FIGURE 5 OMITTED]

When analysing wage differentials for different education levels, Table 2D (see appendix) findings suggest that a person with no education, or with education less than the matriculation level, is earning a higher wage in the labour intensive sectors. For example in the CRM of Drainage, CRM of Pipeline for Transportation, Mining, and Fishing sectors compared to a person with an education level below the degree and degree or more than a degree qualification. For this person the highest paid sectors are CRM of Sports Projects, Financial Institutions, Coal Mining, and Building Construction. The wage dispersion is higher for uneducated workers than the person with the education less than the matriculation level, i.e. 0.1436 and 0.0744, respectively, while the wage dispersion is less for the person with a degree or higher qualification as compared to a person without a degree qualification, 0.1777 and 0.1969, respectively.

CONCLUSIONS

This paper has examined the inter-industry wage differentials in Pakistan, and has utilised the data drawn from the Pakistan Labour Force Surveys. This paper is the first to estimate the wage differentials and wage dispersion in Pakistan, with the aid of supplicated econometrics techniques with the focus of (i) inter-industry wage differential (ii) dispersion of industry wage differential iii) inter-industry wage differential by different regions and education levels (iv) changes in trend wage differential during the fourteen years of the sample period.

The paper has utilised the Rycx (2003) methodology for the eight surveys of Pakistan LFS, and has represented two-digit as well as one-digit results. The Empirical findings show that wage differentials exist between workers employed in different sectors, even when controlling for individual and job characteristics. Estimations have been carried out using pooled data as well as pseudo-panel data. In this study, both of the approaches have produced almost similar results. Therefore, only pseudo-panel approach results are reported.

From the regional perspective the average wages are higher in the Punjab province, in the Construction, Electricity, Gas and Water and Transportation and Communication sectors, compared to the other provinces of Pakistan. In the NWFP, the highest wages are paid in the Mining and Finance sectors while Manufacturing is the highest paid sector in the Sindh province.

In terms of public and private sectors, it was found that in the public sector, wages are higher as compared to the private sector, except for Electricity, Gas and Water and Construction sectors. In the urban areas, the wages are higher than in the rural area except in industries like Mining and Electricity, Gas and Water. Our findings also suggest that the hierarchy of sectors in terms of wage differentials is quite similar with the reported in the literature. During the fourteen year sample period, results show that the wage differential for each industry has increased and the Financial Institutions sector being the top amongst all sectors. The wage dispersion has generally increased but has decreased slightly after 2000.

For the two digit industry structure, the results are similar for all the different perspectives. Petroleum, Financial Institutions, Fishing and CRM of Pipeline being the highest paid sectors and Agriculture, Retail Trade and Personal and Household Services are lowest-paid sectors. The analysis by the level of education shows that a person with no education is found to have lower wages than the person with education or with some education, except in the labour industries like Mining and Agriculture where the requirement of education is (not important)? The person with a degree and a higher qualification had an advantage over persons with just a degree qualification, and was found to earn higher wages in Financial Institution, Insurance, Real Estate and Business and in Construction industry than those persons whose education level was below the degree level. The wage dispersion is also lower for the person with a degree and above degree qualifications compared to the person who has less education than the degree level. Overall, the wage dispersion for two-digit industry is higher than the one-digit industry.

The wage differences presented in Table 1 and 2 (see Appendix) are confirmed by inter-industry wage differentials presented above. One explanation suggests that wage premiums are paid in an effort to ameliorate work place problems, such as shirking, by increasing the cost of job loss to the employee. Jobs for which the configuration of duties and tasks are especially costly to monitor should for this reason, be paid higher premiums than those that are not as expensive. This can be seen in Mining industry, the table shows that technicians and associate professionals are earning roughly 37 percent more than the average wage of the technician. Job conditions are also the important source of wage variations as it depends on the degree of workers' exposure to risky or hazardous conditions on the job. In comparison of overall wages in industry Agriculture, Mining and Trade and Hotels, the result suggest that Mining industry found to pay more to its workers in all different occupations involved in that industry compared to other two industry because of risky nature of this industry.

Thus, the wage differential can be explained by the level of skill required in the particular industry, job conditions and the education plays a vital role in deciding the wage premium across different industries. High skilled worker are likely to earn more compared to semi-skilled or skilled worker. Nature of different industries requires different level of skill for e.g. Financial Industry required more highly skilled worker compared to Agriculture and Trade and Hotel industry. Different occupation share in industry shows that in the Agriculture industry almost 88 percent workers are low skilled compared to 84 percent highly skilled worker in Financial industry, which could explain the wage gap between Agriculture and Financial industry.

In conclusion, results show that the magnitude of industry wage differentials vary substantially over the years and amongst different regions. This analysis suggests that a broad labour policy will not be sufficient to tackle the high wage dispersion and wage differentials in Pakistan. Our findings indicate that policies need to be tailored to the very specific context of the labour market in Pakistan.

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Comments

This paper estimates the inter-industry wage differentials in Pakistan. The authors very rightly point out that there is paucity of research on this topic and it is important to understand the dynamics of labour markets in Pakistan to make informed policies for labour, skill development etc. The paper starts by saying that it is important to analyse inert-industry wage differential in order to assess the effectiveness of corporate culture and decentralization on different industries and labour market but the paper sheds no light on this problem as the authors do not follow it up.

The review of literature is nicely done, however it misses a couple of studies on wage differentials in Pakistan [Nasir (2000) and Hyder and Reilly (2005)]. The review proposes many testable hypotheses regarding wage differentials (though authors do not put forward their own): (1) Workers with comparable characteristics (education, experience) earn different wages in different sectors; (2) sectoral effects are weaker in countries with strong corporate culture; (3) Efficiency wage mechanism-firms pay higher wages to attract and retain workers and to deter them from shirking; (4) intra-industry wage differentials i.e., within a given industry wages rise with firm size and capital intensity. The empirical part however does not test any one of these hypotheses except for the basic hypothesis about inter-industry wage differentials.

The main finding of the study is that wage differentials exist between workers employed in different industries, even after controlling for individual and job characteristics. The paper, however, does report some interesting results. The public sector wages are found to be higher than the private sector wages. Another result indicates that construction is the best paid industry for the educated while manufacturing industry is best paid for uneducated. These results seem counter-intuitive and it would be nice to have the authors through some light on this peculiar phenomenon.

Lubna Hasan

Pakistan Institute of Development Economics,

Islamabad.

REFERENCES

Hyder, A. and B. Rielly (2005) The Public and Private Sector Pay Gap in Pakistan: A Quantile Regression Analysis. The Pakistan Development Review 44:3, 271-306.

Nasir, Z. M. (2000) Earnings Differential Between Public and Private Sectors in Pakistan. The Pakistan Development Review 39:2, 111-130.

Shabbar Jaffry <[email protected]> is Reader in Eeonomics and Director of the Postgraduate Programme in Economics, Yaseen Ghulam <[email protected]> is Senior Lecturer, and Vyoma Shah <[email protected]> is a PhD Student at Portsmouth Business School, University of Portsmouth, UK.

(1) In addition to these variables we have used education levels, regions, occupations, industries, marital status and quarters dummies. We have also used dummies for different employment status, gender and area.

(2) The real hourly wage is calculated as weekly income/number of hours worked per week and then deflated with GPI (General Price Index) for that particular year.

(3) Experience has been computed as: agc-6-ycars of education.

(4) We choose to use the four-digit district codes, age, provinces and industry type to allow for unobserved differences between these similar individuals such as differences in the quality of their education, their skills and attitudes etc to be controlled via fixed effects.

(5) Results obtained from pooled estimation are available from the author on request.

(6) One digit pooled estimation results are available on request.

Appendix

Table 1
Mean Hourly Wages of Occupations in Basic Industries

 Industry

Occupation Agricultural Mining Manufacturing

Legislators, Senior 26.1219 33.3581 41.7393
 Officials and Manages
Professional 23.0009 32.7199 41.7047
Technicians and Associate 12.6344 28.1743 16.2670
 Professionals
Clerk 21.5746 23.7133 20.7932
Service Workers 9.9355 7.6128 16.1869
Skilled Agricultural and 6.9696 19.0416
 Fishery Workers
Crag and Related Trade 17.6576 15.0342 12.5220
 Workers
Plant and Machine 12.2202 12.5402 12.3830
 Operators
Elementary Occupations 9.9647 12.5775 12.4122

 Industry

 Electricity Trade and
Occupation Gas and Water Construction Hotels

Legislators, Senior 36.5462 33.5668 20.7856
 Officials and Manages
Professional 47.6373 49.3120 30.9649
Technicians and Associate 18.1658 15.1839 14.4296
 Professionals
Clerk 26.2785 22.5994 8.1760
Service Workers 19.0936 10.1105 13.0371
Skilled Agricultural and 14.9358 9.2092 11.6460
 Fishery Workers
Crag and Related Trade 29.6086 20.0304 12.7897
 Workers
Plant and Machine 15.7401 12.7178 13.7168
 Operators
Elementary Occupations 16.3891 10.8017 11.4083

 Industry

 Financial Social
Occupation Transportation Institution Services

Legislators, Senior 42.2613 79.4410 21.5245
 Officials and Manages
Professional 45.4365 41.3551 36.8811
Technicians and Associate 15.8097 34.6113 22.3397
 Professionals
Clerk 26.3882 33.9685 26.0441
Service Workers 12.2079 19.8938 12.4464
Skilled Agricultural and 12.1555 7.0685 13.0684
 Fishery Workers
Crag and Related Trade 17.0052 20.4212 13.4236
 Workers
Plant and Machine 16.5810 23.4406 13.6101
 Operators
Elementary Occupations 10.5101 17.9216 14.7913

Occupation Average Wage

Legislators, Senior 28.91267
 Officials and Manages
Professional 38.25718
Technicians and Associate 20.63367
 Professionals
Clerk 20.72142
Service Workers 12.94138
Skilled Agricultural and 7.897704
 Fishery Workers
Crag and Related Trade 14.22085
 Workers
Plant and Machine 14.57857
 Operators
Elementary Occupations 11.57454

Table 2
Occupational Share in Basic Industry

 Industry

Occupation Agricultural Mining Manufacturing

Legislators, Senior 1% 5% 3%
 Officials and Manages
Professional 1% 2% 2%
Technicians and Associate 3% 8% 4%
 Professionals
Clerk 1% 6% 3%
Service Workers 1% 2% 3%
Skilled Agricultural and 35% 0% 0%
 Fishery Workers
Craft and Related Trade 0% 28% 55%
 Workers
Plant and Machine 4% 5% 12%
 Operators
Elementary Occupations 53% 44% 17%

 Industry

 Electricity Trade and
Occupation Gas and Water Construction Hotels

Legislators, Senior 7% 1% 5%
 Officials and Manages
Professional 7% 1% 1%
Technicians and Associate 20% 1% 4%
 Professionals
Clerk 12% 1% 22%
Service Workers 6% 1% 53%
Skilled Agricultural and 2% 0% 0%
 Fishery Workers
Craft and Related Trade 12% 17% 1%
 Workers
Plant and Machine 21% 1% 0%
 Operators
Elementary Occupations 14% 78% 14%

 Industry

 Financial Social
Occupation Transportation Institution Services

Legislators, Senior 3% 26% 16%
 Officials and Manages
Professional 2% 19% 11%
Technicians and Associate 12% 24% 23%
 Professionals
Clerk 4% 15% 6%
Service Workers 8% 7% 17%
Skilled Agricultural and 0% 0% 1%
 Fishery Workers
Craft and Related Trade 6% 1% 8%
 Workers
Plant and Machine 33% 2% 4%
 Operators
Elementary Occupations 34% 6% 14%

Occupation Average Wage

Legislators, Senior 28.91267
 Officials and Manages
Professional 38.25718
Technicians and Associate 20.63367
 Professionals
Clerk 20.72143
Service Workers 12.94138
Skilled Agricultural and 7.897704
 Fishery Workers
Craft and Related Trade 14.22085
 Workers
Plant and Machine 14.57857
 Operators
Elementary Occupations 11.57454

Table 2A
Industry Wage Differential for Different Provinces

Industry Punjab Sindh

CRM of sports projects 0.2836 0.7471
Crude petroleum and natural gas production 0.2734 0.6786
CRM of docks and communication project 0.3026 0.6118
CRM of pipe line for transportation 0.9271 0.5546
Fishing 0.3668 0.5444
Financial Institutions 0.6680 0.5120
Insurance 0.2778 0.4265
Basic metal industries 0.2702 0.3890
CRM of sewerage water mains and strom 0.2845 0.3775
 water drains
Coal Mining 0.1613 0.3580
Other manufacturing industries 0.2252 0.3204
Mfg of paper and paper products 0.1418 0.3133
Mfg of chemicals and chemical, petroleum, 0.3685 0.3123
 coal, rubber and plastic products
Manufacture of fabricated metal products, 0.1881 0.3017
 machinery and equipment
International and Other Extra-territorial 0.6872 0.2537
 Bodies
Transport and storage 0.2317 0.2528
Mfg of textile, wearing apparel and 0.1114 0.3295
 leather industries
Mfg of wood and wood products 0.1969 0.2235
Mfg of non-metalic mineral products 0.2485 0.2192
Forestry and logging 0.0574 0.2162
Electricity, gas and steam 0.3465 0.2072
Real estate and business 0.2325 0.2033
Communication 0.3116 0.1955
CRM of streets, roads, highways 0.3320 0.1668
 and bridges
Sanitary and similar services 0.0769 0.1607
Restaurants and Hotels 0.1578 0.1593
Building construction 0.2605 0.1517
CRM of irrigation, flood control, 0.2097 0.1416
 drainage, reclamation and
 hydro-electric project
Wholesale Trade 0.2143 0.1408
Mfg of food, beverages and tobacco 0.1844 0.1338
Public administration and defense services 0.2935 0.1236
Other Mining 0.2920 0.1149
Recreational and cultural services 0.0429 0.1039
Water work and supplies 0.1880 0.0975
Personal and household services -0.1069 0.0651
Activities not adequately defined 0.2979 0.0412
Crude Metal or Mining -0.1247 0.0412
Retail Trade 0.0212 -0.0141
Construction projects 0.3498 -0.0463
Social and related community services 0.0126 -0.1277
Agriculture, livestock and hunting -0.1444 -0.1299
WASD 0.1663 0.1550

Industry NWFP Balochistan

CRM of sports projects -0.2884 0.1343
Crude petroleum and natural gas production 0.5114 0.6658
CRM of docks and communication project -0.0178 0.2962
CRM of pipe line for transportation -0.1573 0.2309
Fishing 0.1616 0.4583
Financial Institutions 0.5949 0.5626
Insurance 0.4288 -0.3443
Basic metal industries -0.0434 0.3003
CRM of sewerage water mains and strom 0.8728 0.1808
 water drains
Coal Mining 0.2844 0.2426
Other manufacturing industries 0.1526 0.1345
Mfg of paper and paper products 0.1204 0.3249
Mfg of chemicals and chemical, petroleum, 0.0569 0.1984
 coal, rubber and plastic products
Manufacture of fabricated metal products, 0.0767 -0.0089
 machinery and equipment
International and Other Extra-territorial 0.1671 0.4869
 Bodies
Transport and storage 0.2001 0.1832
Mfg of textile, wearing apparel and 0.0187 -0.0492
 leather industries
Mfg of wood and wood products 0.2162 0.1008
Mfg of non-metalic mineral products 0.0464 0.0568
Forestry and logging 0.1884 0.2085
Electricity, gas and steam 0.2446 0.2457
Real estate and business 0.3320 0.2097
Communication 0.2896 0.2922
CRM of streets, roads, highways 0.1983 0.1866
 and bridges
Sanitary and similar services -0.0213 0.3087
Restaurants and Hotels 0.2305 -0.1092
Building construction 0.1682 -0.0104
CRM of irrigation, flood control, 0.5081 -0.0281
 drainage, reclamation and
 hydro-electric project
Wholesale Trade -0.0044 0.1382
Mfg of food, beverages and tobacco 0.1310 0.0379
Public administration and defense services 0.2680 0.2394
Other Mining 0.5038 -0.0214
Recreational and cultural services 0.3645 0.3643
Water work and supplies 0.1687 0.2515
Personal and household services -0.0919 -0.0175
Activities not adequately defined -0.1036 0.1343
Crude Metal or Mining 0.1868 0.4958
Retail Trade -0.0485 -0.1539
Construction projects 0.7645 0.1343
Social and related community services 0.0396 0.1002
Agriculture, livestock and hunting -0.0848 -0.0324
WASD 0.1183 0.1013

Table 2B
Industry Wage Differential for Area of Living and Sector

 public private
Industry sector sector

Agriculture, livestock and hunting -0.1158 -0.1055
Forestry and lugging 0.1222 0.1654
Fishing 0.2829 0.4952
Coal Mining 0.5470 0.2556
Crude petroleum and natural gas production 0.4813 0.5086
Crude Metal or Mining 0.0323 0.3346
Other Mining 0.3304 0.3793
Mfg of food, beverages and tobacco 0.1572 0.1333
Mfg of textile, wearing apparel and 0.1105 0.1260
 leather industries
Mfg of wood and wood products 0.2647 0.1937
Mfg of paper and paper products 0.3343 0.1520
Mfg of chemicals and chemical, petroleum, 0.4544 0.2471
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.2192 0.1939
Basic metal industries 0.4662 0.2211
Manufacture of fabricated metal products, 0.2651 0.1618
 machine and equipment
Other manufacturing industries 0.2037 0.2430
Electricity gas and steam 0.2619 0.2800
Water work and supplies 0.1486 0.1648
Building construction 0.1460 0.1772
CRM of streets, roads, highways and bridges 0.2026 0.2659
CRM of irrigation, flood control, drainage, 0.2465 0.1134
 reclamation and hydro-electric project
CRM of docks and communication project 0.1456 0.3847
CRM of sports projects 0.7820 -0.0358
CKM of sewerage, water mains and storm 0.2610 0.5473
 water drains
CRM of pipe line for transportation 0.2207 0.6379
Construction projects 0.4307 0.1386
Wholesale Trade 0.2379 0.1365
Retail Trade -0.0413 -0.0267
Restaurants and Hotels 0.0724 0.1245
Transport and storage 0.2502 0.2259
Communication 0.2937 0.2004
Financial Institutions 0.6384 0.5255
Insurance 0.4023 0.2718
Real estate and business 0.5314 0.1608
Public administration and defense services 0.1995 0.2629
Sanitary and similar services 0.1856 0.0199
Social and related community services -0.0140 -0.0792
Recreational and cultural services 0.2308 0.0823
Personal and household services -0.0676 -0.0458
International and other Extra-territorial 0.1550 0.5363
 Bodies
Activities not adequately defined -0.0143 0.1281
WASD 0.1472 0.1347

Industry Urban Rural

Agriculture, livestock and hunting -0.0831 -0.0755
Forestry and lugging 0.0956 0.1673
Fishing 0.3882 0.5862
Coal Mining 0.2515 0.3823
Crude petroleum and natural gas production 0.4433 0.4997
Crude Metal or Mining 0.1031 -0.0640
Other Mining 0.1059 0.5167
Mfg of food, beverages and tobacco 0.0876 0.1115
Mfg of textile, wearing apparel and 0.0954 0.0561
 leather industries
Mfg of wood and wood products 0.1488 0.1670
Mfg of paper and paper products 0.1631 -0.0200
Mfg of chemicals and chemical, petroleum, 0.2375 0.1905
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.1371 0.1066
Basic metal industries 0.2055 0.0912
Manufacture of fabricated metal products, 0.1163 0.0813
 machine and equipment
Other manufacturing industries 0.2132 0.1565
Electricity gas and steam 0.1859 0.2851
Water work and supplies 0.0981 0.0961
Building construction 0.1989 0.1452
CRM of streets, roads, highways and bridges 0.2249 0.2160
CRM of irrigation, flood control, drainage, 0.1808 0.0943
 reclamation and hydro-electric project
CRM of docks and communication project 0.2557 0.2577
CRM of sports projects 0.6310 -0.2384
CKM of sewerage, water mains and storm 0.4250 -0.0650
 water drains
CRM of pipe line for transportation 0.5825 0.3818
Construction projects 0.3168 0.0189
Wholesale Trade 0.1261 0.1428
Retail Trade -0.0667 -0.0991
Restaurants and Hotels 0.1089 0.0394
Transport and storage 0.1856 0.1952
Communication 0.2079 0.2463
Financial Institutions 0.5588 0.6728
Insurance 0.3426 0.1740
Real estate and business 0.1738 0.2100
Public administration and defense services 0.1408 0.2293
Sanitary and similar services 0.0893 -0.0549
Social and related community services -0.0260 0.0802
Recreational and cultural services 0.1016 0.1743
Personal and household services -0.0514 0.0951
International and other Extra-territorial 0.4932 0.5819
 Bodies
Activities not adequately defined -0.0300 0.1832
WASD 0.1164 0.1202

Table 2C
Industry Wage Differential for Year 1990-91 to 1996-97

Industry Year 9091 Year 9192

Agriculture, livestock and hunting -0.0915 -0.1105
Forestry and lugging -0.1897 0.1240
Fishing 0.6402 0.4193
Coal Mining -0.1169 0.2393
Crude petroleum and natural gas production 0.9430 0.3213
Crude Metal or Mining 0.2802 0.3205
Other Mining 0.2627 0.2997
Mfg of food, beverages and tobacco 0.1430 0.1443
Mfg of textile, wearing apparel and 0.1499 0.1227
 leather industries
Mfg of wood and wood products 0.2619 0.1572
Mfg of paper and paper products 0.1739 0.0959
Mfg of chemicals and chemical, petroleum, 0.2774 0.0420
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.2387 0.1490
Basic metal industries 0.1580 0.2718
Manufacture of fabricated metal products, 0.0669 0.3129
 machinery and equipment
Other manufacturing industries 0.0575 0.2495
Electricity, gas and steam 0.3181 0.2386
Water work and supplies -0.1106 0.0655
Building construction 0.1637 0.1033
CRM of streets, roads, highways and bridges 0.2432 0.2323
CRM of irrigation, flood control, drainage, -0.0606 0.1671
 reclamation and hydro-electric project
CRM of docks and communication project 0.1856 0.5684
CRM of sports projects 0.1708 -0.1522
CRM of sewerage, water mains and storm
 water drains
CRM of pipe line for transportation 0.0603 -0.1605
Construction projects
Wholesale Trade 0.0081 0.0632
Retail Trade -0.0233 0.0090
Restaurants and Hotels 0.0683 0.1786
Transport and storage 0.1993 0.2091
Communication 0.2419 0.1474
Financial Institution 0.4856 0.4435
Insurance 0.3475 0.1117
Real estate and business 0.1573 0.1070
Public administration and defense services 0.1583 0.2369
Sanitary and similar services 0.4183 0.0010
Social and related community services -0.0942 -0.0121
Recreational and cultural services 0.0003 0.3946
Personal and household services -0.0449 -0.0050
International and other Extra-territorial 0.2798 0.3361
 Bodies
Activities not adequately defined 0.1087 0.1173
WASD 0.1183 0.1213

Industry Year 9394 Year 9697

Agriculture, livestock and hunting -0.1297 -0.0846
Forestry and lugging 0.2934 0.1318
Fishing 0.3876 0.4693
Coal Mining 0.2104 0.1851
Crude petroleum and natural gas production 0.2069 0.5736
Crude Metal or Mining -0.0143
Other Mining 0.0449 0.4045
Mfg of food, beverages and tobacco 0.1393 0.0849
Mfg of textile, wearing apparel and 0.1753 0.0988
 leather industries
Mfg of wood and wood products 0.0441 0.1412
Mfg of paper and paper products 0.1962 0.2182
Mfg of chemicals and chemical, petroleum, 0.2590 0.3122
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.1812 0.2495
Basic metal industries 0.2088 0.1789
Manufacture of fabricated metal products, 0.1628 0.1792
 machinery and equipment
Other manufacturing industries 0.1035 0.2313
Electricity, gas and steam 0.2632 0.1741
Water work and supplies 0.1996 0.0209
Building construction 0.1739 0.1091
CRM of streets, roads, highways and bridges 0.1684 0.1512
CRM of irrigation, flood control, drainage, 0.2488 0.0677
 reclamation and hydro-electric project
CRM of docks and communication project -0.0936 0.5057
CRM of sports projects -0.0341
CRM of sewerage, water mains and storm 0.0592 0.2998
 water drains
CRM of pipe line for transportation 0.0472 0.8313
Construction projects 0.2923
Wholesale Trade 0.1511 0.1652
Retail Trade 0.0089 -0.0900
Restaurants and Hotels 0.1708 0.1053
Transport and storage 0.2874 0.2758
Communication 0.2460 0.1456
Financial Institution 0.4539 0.4356
Insurance 0.1309 0.4691
Real estate and business 0.1620 0.2113
Public administration and defense services 0.2542 0.1278
Sanitary and similar services 0.1661 0.1262
Social and related community services 0.0527 -0.0559
Recreational and cultural services -0.0926 0.1330
Personal and household services 0.0398 -0.0444
International and other Extra-territorial 0.2433 0.0457
 Bodies
Activities not adequately defined 0.1192 -0.0580
WASD 0.1420 0.1181

Table 2D
Industry Wage Differential for Year 1997-98 to 2003-04

Industry Year 9798 Year 9900

Agriculture, livestock and hunting -0.0852 -0.1437
Forestry and logging 0.0695 0.3096
Fishing 0.4518 0.4830
Coal Mining 0.3990 0.0943
Crude petroleum and natural gas production 0.4571 -0.9639
Crude Metal or Mining 0.5248
Other Mining 0.5249 -0.0383
Mfg of food, beverages and tobacco 0.1507 0.1514
Mfg of textile, wearing apparel and leather 0.1483 0.1565
 industries
Mfg of wood and wood products 0.1808 0.2453
Mfg of paper and paper products 0.0436 0.0984
Mfg of chemicals and chemical, petroleum, 0.3482 0.3749
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.2982 0.1323
Basic metal industries 0.4408 0.3120
Manufacture of fabricated metal products, 0.1419 0.3426
 machinery and equipment
Other manufacturing industries 0.2667 0.3081
Electricity gas and steam 0.1754 0.3037
Water work and supplies 0.1391 0.2193
Building construction 0.1306 0.1183
CRM of streets, roads, highways and bridges 0.3152 0.2881
CRM of irrigation, flood control, drainage, 0.2951 0.0439
 reclamation and hydro-electric project
CRM of ducks and communication project 0.2175
CRM of sports projects 1.6543
CRM of sewerage, water mains and storm -0.1326 0.9728
 water drains
CRM of pipe line for transportation 0.6742
Construction projects 0.0187
Wholesale Trade 0.2502 0.3075
Retail Trade -0.0029 0.0442
Restaurants and Hotels 0.1119 0.1642
Transport and storage 0.1526 0.1963
Communication 0.2233 0.2902
Financial Institutions 0.5761 0.7153
Insurance 0.5503 0.2543
Real estate and business 0.1713 0.3961
Public administration and defense services 0.1464 0.2391
Sanitary and similar services -0.0250 0.2284
Social and related community services -0.0234 0.1337
Recreational and cultural services 0.5822 -0.1953
Personal and household services -0.1504 -0.0501
International and other Extra-territorial 1.2532 0.6190
 Bodies
Activities not adequately defined 0.1606 -0.1292
WASD 0.1255 0.1570

Industry Year 0102 Year 0304

Agriculture, livestock and hunting -0.1147 -0.0577
Forestry and logging 0.0893 0.2294
Fishing 0.5665 0.3564
Coal Mining 0.2458 0.4359
Crude petroleum and natural gas production 0.3554 0.8110
Crude Metal or Mining 0.3731
Other Mining 0.2280
Mfg of food, beverages and tobacco 0.1247 0.1570
Mfg of textile, wearing apparel and leather 0.1312 0.0963
 industries
Mfg of wood and wood products 0.1717 0.2739
Mfg of paper and paper products 0.1031 0.2227
Mfg of chemicals and chemical, petroleum, 0.2743 0.2455
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.2410 0.1360
Basic metal industries 0.1934 0.3339
Manufacture of fabricated metal products, 0.3722 0.0439
 machinery and equipment
Other manufacturing industries 0.1988 0.2494
Electricity gas and steam 0.2383 0.2318
Water work and supplies 0.1014 0.0291
Building construction 0.2406 0.2294
CRM of streets, roads, highways and bridges 0.2442 0.0932
CRM of irrigation, flood control, drainage, -0.0934 0.1406
 reclamation and hydro-electric project
CRM of ducks and communication project 0.5194
CRM of sports projects
CRM of sewerage, water mains and storm
 water drains
CRM of pipe line for transportation
Construction projects
Wholesale Trade 0.0868 0.1246
Retail Trade -0.0552 -0.1181
Restaurants and Hotels 0.1764 -0.0042
Transport and storage 0.2319 -1.1964
Communication 0.2382 0.2700
Financial Institutions 0.5658 0.6355
Insurance -0.0133 0.3532
Real estate and business 0.4605 -0.0945
Public administration and defense services 0.1803 0.1911
Sanitary and similar services -0.0064 -0.0951
Social and related community services -0.1189 -0.1783
Recreational and cultural services 0.0684 -0.2146
Personal and household services -0.0363 -0.0482
International and other Extra-territorial 0.9748 0.4788
 Bodies
Activities not adequately defined 0.2041
WASD 0.1447 0.1233

Table 2E
Industry Wage Differential for Different Education Levels

 No Formal Middle but
Industry Education Below Metric

Agriculture, livestock and hunting -0.1096 0.0098
Forestry and lugging 0.2163 0.2052
Fishing 0.5721 0.2198
Coal Mining 0.1931 0.1180
Crude petroleum and natural gas production 0.4985 0.7537
Crude Metal or Mining -0.4233
Other Mining 0.5218 0.1601
Mfg of food, beverages and tobacco 0.1378 0.1098
Mfg of textile, wearing apparel and leather 0.0290 0.0519
 industries
Mfg of wood and wood products 0.1664 0.1420
Mfg of paper and paper products 0.2135 0.0320
Mfg of chemicals and chemical, petroleum, 0.2684 0.1039
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.2166 0.0691
Basic metal industries 0.3436 0.0799
Manufacture of fabricated metal products, 0.2234 0.0999
 machinery and equipment
Other manufacturing industries 0.1802 0.0712
Electricity, gas and steam 0.3531 0.2346
Water work and supplies 0.3133 -0.0029
Building construction 0.1410 -0.0505
CRM of streets, roads, highways and bridges 0.2494 0.2842
CRM of irrigation, flood control, drainage, 0.1088 -0.0859
 reclamation and hydro-electric project
CRM of ducks and communication project 0.3327
CRM of sports projects -0.1253 0.1435
CRM of sewerage, water main, and storm 0.7316
 water drains
CRM of pipe line for transportation 0.6612 0.8609
Construction projects 0.2903 -0.2526
Wholesale Trade 0.1221 0.1832
Retail Trade -0.0112 -0.1231
Restaurants and Hotels 0.1609 -0.0783
Transport and storage 0.2929 0.1303
Communication 0.1539 0.1761
Financial Institutions 0.6892 0.3560
Insurance 0.3218 -0.0715
Real estate and business 0.3001 -0.0892
Public administration and defense services 0.3343 0.1639
Sanitary and similar services 0.1925 -0.1301
Social and related community services -0.0563 -0.0371
Recreational and cultural services 0.1307 -0.0491
Personal and household services -0.0807 -0.1325
International and other Extra-territorial 0.3992 0.3969
 Bodies
Activities not adequately defined 0.2466 -0.2526
WASD 0.1438 0.1174

 Inter but
Industry Below Degree Degree

Agriculture, livestock and hunting -0.1639 -0.1150
Forestry and lugging -0.0842 -0.2112
Fishing 0.3359 -0.0304
Coal Mining 1.7218 0.2378
Crude petroleum and natural gas production 0.4417 -0.1711
Crude Metal or Mining 0.1624 -0.0078
Other Mining -0.6637
Mfg of food, beverages and tobacco -0.0785 -0.2554
Mfg of textile, wearing apparel and leather 0.1201 0.1290
 industries
Mfg of wood and wood products -0.0391 -0.0896
Mfg of paper and paper products 0.2541 -0.0017
Mfg of chemicals and chemical, petroleum, 0.2974 0.0821
 coal, rubber and plastic products
Mfg of non-metalic mineral products 0.1256 -0.3608
Basic metal industries 0.3207 0.0254
Manufacture of fabricated metal products, 0.0745 -0.0155
 machinery and equipment
Other manufacturing industries 0.3997 0.2753
Electricity, gas and steam 0.1057 -0.1557
Water work and supplies 0.1118 -0.3523
Building construction 0.6439 0.9474
CRM of streets, roads, highways and bridges 0.0053 -0.3115
CRM of irrigation, flood control, drainage, 0.5212 -0.5553
 reclamation and hydro-electric project
CRM of ducks and communication project
CRM of sports projects 0.9758
CRM of sewerage, water main, and storm -0.2154
 water drains
CRM of pipe line for transportation 0.2619
Construction projects 0.4838
Wholesale Trade 0.0081 0.0208
Retail Trade 0.0960 0.1408
Restaurants and Hotels 0.3548 0.3112
Transport and storage 0.1996 -0.0229
Communication 0.2675 -0.2797
Financial Institutions 0.5515 0.1586
Insurance 0.1936 0.0681
Real estate and business 0.0458 0.0659
Public administration and defense services 0.0475 -0.0774
Sanitary and similar services 0.4005 -0.0643
Social and related community services -0.1683 -0.2576
Recreational and cultural services 0.2460 0.1162
Personal and household services -0.0429 -0.1120
International and other Extra-territorial 0.2823 0.4474
 Bodies
Activities not adequately defined -0.0583 -0.1952
WASD 0.1969 0.1777

Table 1 Means and Standard Deviations of Selected Variables (1)

 Overall

Characteristic Mean Std.Dev.

Real Hourly Wage (in PKR) (2) 2.73 0.76
Prior Potential Experience (1) 21.23 13.38
Number of Hours Worked in a Year 2532.72 613.49
Number of Job Holders in a 2.18 1.34
 Household
Number of Observation 97122 97122

 Urban

Characteristic Mean Std.Dev.

Real Hourly Wage (in PKR) (2) 2.85 0.77
Prior Potential Experience (1) 20.62 13.24
Number of Hours Worked in a Year 2535.78 600.91
Number of Job Holders in a 2.17 1.30
 Household
Number of Observation 58550 SR550

 Rural

Characteristic Mean Std.Dev.

Real Hourly Wage (in PKR) (2) 2.54 0.699
Prior Potential Experience (1) 22.15 13.53
Number of Hours Worked in a Year 2528.06 632.07
Number of Job Holders in a 2.19 1.40
 Household
Number of Observation 38572 38572

Table 1 Single Digit Industry Wage Differential in Pakistan

 Pooled Pseudo
Industry Estimation Estimation

Mining 0.2790 0.2927
Manufacturing 0.0957 0.1121
Electricity, Gas and Water 0.1117 0.1317
Construction 0.1511 0.1609
Trade and Restaurants -0.0436 -0.0357
Transport 0.1497 0.1607
Financial Intermediaries 0.4176 0.4315
Social Services -0.0030 * -0.0106 *
Agriculture -0.0592 -0.0666
Weighted Adjusted Standard Deviation 0.0855 0.0927
[R.sup.2] 0.4719 0.4822
F-statistic 884.66 346.57
No. of Observation 97102 60580

* Shows that the wage differential is statistically insignificant.

Table 2
Two-digit Wage Differentials for Pseudo Panel and Pooled Estimation

 Pseudo Results

Industry Wage Diff Tstat

CRM of pipe line for transportation 0.5783 3.0125
Financial Institutions 0.5679 23.4669
Crude petroleum and natural gas production 0.4908 5.1787
Fishing 0.4809 12.8634
International and Other Extra-territorial 0.4723 4.9593
 Bodies
CRM of sports projects 0.4384 1.2962
CRM of sewerage, water mains and storm 0.4306 3.2254
 water drains
Other Mining 0.3831 6.5996
CRM of docks and communication project 0.3566 5.9544
Insurance 0.3406 5.6065
Mfg of chemicals and chemical, petroleum, 0.2824 11.7091
 coal, rubber and plastic products
Basic metal industries 0.2769 9.0124
Coal Mining 0.2718 5.5097
Electricity, gas and steam 0.2509 12.1414
Communication 0.2496 11.4626
CRM of streets, roads, highways and 0.2324 9.3948
 bridges
Other manufacturing industries 0.2313 7.7754
Transport and storage 0.2237 15.8011
Real estate and business 0.2176 4.7117
Public administration and defence services 0.2139 14.3924
Mfg of wood and wood products 0.2025 7.3571
Mfg of non-metallic mineral products 0.1981 8.8373
Construction projects 0.1895 1.3391
Manufacture of fabricated metal products, 0.1887 5.3141
 machinery and equipment
Mfg of paper and paper products 0.1745 5.6258
Building construction 0.1684 12.2476
Forestry and logging 0.1561 3.9820
Wholesale Trade 0.1475 5.4840
CRM of irrigation, flood control, 0.1441 0.2895
 drainage, reclamation and
 hydro-electric project
Water work and supplies 0.1373 5.0199
Mfg of food, beverages and tobacco 0.1373 7.7775
Mfg of textile, wearing apparel and 0.1363 8.6287
 leather industries
Crude Metal or Mining 0.1265 0.7171
Restaurants and Hotels 0.1236 4.9837
Recreational and cultural services 0.1212 1.7488
Activities not adequately defined 0.1118 2.2333
Sanitary and similar services 0.1033 2.0950
Retail trade 0.1007 -1.9090
Social and related community services -0.0319 -1.7456
Personal and household services -0.0322 -3.4658
Agriculture, livestock and hunting -0.0559 -13.7539
WASD 0.1349

 Pooled Estimation

Industry Wage Diff Tstat

CRM of pipe line for transportation 0.5207 2.7652
Financial Institutions 0.5510 29.6057
Crude petroleum and natural gas production 0.4600 4.8657
Fishing 0.5017 13.5331
International and Other Extra-territorial 0.4870 5.6342
 Bodies
CRM of sports projects 0.4243 1.3123
CRM of sewerage, water mains and storm 0.3611 3.0280
 water drains
Other Mining 0.2896 5.3176
CRM of docks and communication project 0.2269 2.6520
Insurance 0.3272 5.9143
Mfg of chemicals and chemical, petroleum, 0.2501 11.1953
 coal, rubber and plastic products
Basic metal industries 0.1546 5.7004
Coal Mining 0.2760 6.9681
Electricity, gas and steam 0.1783 12.4290
Communication 0.2046 11.5588
CRM of streets, roads, highways and 0.1982 7.7247
 bridges
Other manufacturing industries 0.1540 6.3214
Transport and storage 0.1681 18.7410
Real estate and business 0.1785 4.2280
Public administration and defence services 0.1531 16.4301
Mfg of wood and wood products 0.0943 4.5149
Mfg of non-metallic mineral products 0.0866 4.5621
Construction projects 0.1174 0.8177
Manufacture of fabricated metal products, 0.0548 1.8191
 machinery and equipment
Mfg of paper and paper products 0.0560 1.5319
Building construction 0.1588 19.0002
Forestry and logging 0.1500 4.0300
Wholesale Trade 0.1120 5.0838
CRM of irrigation, flood control, 0.1373 2.1218
 drainage, reclamation and
 hydro-electric project
Water work and supplies 0.0623 2.8258
Mfg of food, beverages and tobacco 0.0677 4.0052
Mfg of textile, wearing apparel and 0.1070 10.7220
 leather industries
Crude Metal or Mining -0.0126 -0.0585
Restaurants and Hotels 0.0664 3.0805
Recreational and cultural services 0.1065 1.7964
Activities not adequately defined -0.0079 -0.1220
Sanitary and similar services 0.0408 0.9858
Retail trade -0.0661 5.4366
Social and related community services 0.0088 0.9771
Personal and household services -0.0601 -5.3640
Agriculture, livestock and hunting -0.0740 -12.5543
WASD 0.1063