metadata: authors: 0: author: Verner,Dorte
count: Zimbabwe
docna: 0: docna: Are wages and productivity in Zimbabwe
            affected by human capital investment and international trade?
docty: Policy Research Working Paper
subtopic: Human Rights,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,Educational Sciences,Business Cycles and Stabilization Policies,Rural Labor Markets
teratopic: Health, Nutrition and Population
abstracts: cdata!: To analyze what determines wages and
            productivity in Zimbabwe, the author analyzes an
            employer/employee data-set from Zimbabwe's
            manufacturing sector. The author finds that: * Formal
            education, training, and experience positively affect wages
            and productivity positively. * Women are paid roughly 37
            percent less than men although they are not measurably less
            productive. * There is no strong indication of ethnic
            discrimination among employees, but Europeans are being paid
            more in larger firms, although they are marginally less
            productive than workers of African origin. * The wage
            premium for workers who completed secondary school does not
            necessarily reflect greater productivity but may indicate a
            shortage of educated workers. * Workers trained in-house
            earn more although in-house training does not instantly
            affect productivity. Training by outside trainers does
            improve productivity but is not rewarded with higher wages.
            * Apprentices are paid more than non-apprentices. Perhaps an
            apprentice diploma serves as a screening device, when
            hiring. * Temporary workers are more productive than
            permanent workers, perhaps hoping to get a permanent
            contract. * Union members earn less than non-union members
            despite being more productive. Perhaps union members fight
            more to have skills upgraded than for wage increases. *
            Larger exporting firms are marginally less productive and
            pay marginally less than the average firm, but ar more
            productive than smaller firms (and their wages match
            productivity). Workers in larger woods and metals are paid
            less than workers in smaller firms, although they are not
            less productive. * Exporting firms benefit more than
            employees do from trade openness and greater productivity. *
            Foreign-owned firms are more productive than other firms
            (perhaps because of new technology). * Firms that employ
            more expatriates tend to pay more. The more expatriates
            there are in metals firms, the more productive the employees
            are, perhaps because the expatriates bring knowledge about
            new technology to the enterprise. * Employees in the metal
            and textile sectors are paid more than those in the food
            sector, but employees in metals are less productive than
            employees from other sectors.
geo_reg_key: 517195,517191
repnme: repnme: Are wages and productivity in Zimbabwe
            affected by human capital investment and international trade?
display_title: Are wages and productivity in
            Zimbabwe affected by human capital investment and
            international trade
listing_relative_url: /research/1999/04/440868/wages-productivity-zimbabwe-affected-human-capital-investment-international-trade
topicv3: Labor Markets m1326488 855,Manufacturing Sectors m1326521 1281,Education m1327048 470,Jobs m1503931 2670,Discrimination m1505707 3379
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Health, Nutrition and Population
origu: AFT: Human Development 3 (AFTH3)
no_of_pages: 53
author_keys: 0: author_key: 124561
model_extractions: XPOLICY RESEARCH WORKING PAPER 2101

# Summary findings

To analyze what determInes wages and productivity in Zimbabwe, Verner analyzes an employer/employee dataset from Zimbabwe's manufacturing sector. Verner finds that: f Formal education, training, and experience positively affect wages and productivity positively.

- Women are paid roughly 37 percent less than mern although they are not measurably less productive.

There is no strong indication of ethnic discrimination among employees, but Europeans are being paid more in larger firms, although they are marginally less productive than workers of African origin.

The wage premium for workers who completed secondary school does not necessarily reflect greater productivity but may indicate a shortage of educated workers.

_3_ Workers trained in-house earn more although inhouse training does not instantly affect productivity. Training by outside trainers does improve productivity but is not rewarded with higher wages.

- Apprentices are paid more than non-apprentices. Perhaps an apprentice diploma serves as a screening device, when hiring.

Temporary workers are more productive than permanent workers, perhaps hoping to get a permanent contract.

* Union members earn less than non-union members despite being more productive. Perhaps union members fight more to have skills upgraded than for wage increases.

* Larger exporting firms are marginally less productive and pay marginally less than the average firmn, but are more productive than smaller firms (and their wages match productivity). Workers in larger woods and metals firms are paid less than workers in smaller firms, although they are not less productive.

Exporting firms benefit more than employees do from trade openness and greater productivity.

Foreign-owned firms are more productive than other firms (perhaps because of new technology).

* Firms that employ more expatriates tend to pay more. The more expatriates there are in metals firms, the more productive the employees are, perhaps because the expatriates bring knowledge about new technology to the enterprise.

* Employees in the metal and textile sectors are pald more than those in the food sector, but employees in metals are less productive than employees from other sectors.

- This paper a product of Human Development 3, Africa Technical Families -is part of a larger effort in the region to understand how labor markets work in Africa. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Hazel Vargas, room 18-138, telephone 202-473-7871, fax 202-522-2119, Internet adldress hvargas @worldbank.org. Policy Research Working Papers are also posted on the Web at http:// wvwoNXA.worldbank.org/html/dec/Publications/Workpapers/home.html. The author may be contacted at dverner@worldbank. org. April 1999. (49 pages)

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countnes they represent. |_

Produced by the Policy Research Dissemination Center
# Are wages and productivity in Zimbabwe affected by human capital investment and international trade?

Dorte Verner' I would like to thank Helena Ribe, Trina Haque, Arvil van Adams, Gregory Ingram, and Tony Addison for helpful comments, Tyler Biggs for data, and Niels-Hugo Blunch was a great help in finishing this draft.
# **1. Introduction**

This paper studies labor markets in Zimbabwe. Zimbabwe has one of the most highly developed industrial sectors in Africa. It contributes more than 25 percent to GDP. The effectiveness of economic policy in generating economic growth and improving standards of living rests on the capability of creating more employment. This paper analyzes wage and productivity determination. Among the questions addressed are the following:

- Do experience, training and education impact wages and productivity?

- Does discrimination -- related to gender or ethnicity of employees -- exist in the Zimbabwean labor markets?

- Is the labor market competitive?

- Does trade unionization affect wages and productivity?

- Are wages and productivity affected by enterprise characteristics?

- Does international trade change wages and productivity?

The questions are analyzed jointly applying an employer-employee dataset from the manufacturing sector in Zimbabwe. The introduction of productivity and enterprise data adds a new dimension to wage analyses.

Most standard wage regressions do not control for firm characteristics that can affect the wage determination process. The reason is that information on firns and employees are often not available. We control for firm characteristics in this paper. Additionally, this paper goes a step further in that we jointly estimate wage and production functions. This approach allows not only for assessing the marginal impact of demographic and other characteristics on wages but also for comparing the impact of these variables on productivity and wages.

2
The main conclusions from this study are that fonnal education, training,<sup>and experience</sup> impact wages and productivity positively. However, there still exist<sup>some human capital</sup> gaps that need to be accommodated for the labor market to<sup>be characterized as</sup> competitive. Females are being paid less male employees despite<sup>that they are not</sup> measurably less productive. Hereof, there is at least two interpretations:<sup>first, this is poor</sup> discrimination against women; or second, that important data material<sup>has been left out of</sup> the analyses; hence the gender dummy variable pick up the impact<sup>on wages of these</sup> omitted variables. The findings show no strong indication of ethnic<sup>discrimination of</sup> employees in the Zimbabwean labor markets. Trade union members are<sup>found to be paid</sup> less than non-members despite that no gap is found in productivity. That wages are lower for union members can be explained by the fact that unions fight more for skill<sup>upgrading</sup> than for wage increases. Enterprise characteristics are also important.

Furthermore, we find that enterprise characteristics are associated with wages and productivity. Employees in firms engaged in exporting activities are not fully compensated for their high productivity, indicating that firms benefit more than employees do from enhanced productivity following increased openness. Firms with foreign ownership are more productive than other firms and a relatively high number of expatriates employed in a firm are associated with higher wages. Hence, the labor<sup>market</sup> in Zimbabwe is not perfectly competitive but rather segmented. For example,<sup>wages of</sup> workers, with similar skills employed in different sectors, are not equal. Therefore,<sup>we</sup> conclude that there exist structural differences across sectors. The remainder of the paper is organized in eight sections. Section two briefly<sup>describes</sup> trends in employment, output and wages in Zimbabwe. Section three summarizes<sup>recent</sup> labor market studies and section four describes the methodology and data<sup>used in this</sup> study. Section five outlines the wage determination model and section six shows descriptive statistics. Section seven presents findings, and, finally, section eight concludes.

3
# **2. Recent Zimbabwean labor market studies**

Until recently, we had little knowledge of which factors influence employment and wages in the dual labor market in Zimbabwe. Most recently, Mazumdar (1995) and Ann Velenchik (1996a, 1996b) apply a sample of relatively homogenous groups of manufacturing workers in analyzing whether labor markets are competitive, employer size effects, and rent sharing.<sup>2</sup> Furthermore, Mazumdar (1994) estimated the marginal returns to male's education for four African countries. Earnings functions were estimated with variables capturing experience, dummy for permanent and temporary positions, and whether the worker had been an apprentice. The estimated incremental coefficients of the log of earnings for primary, secondary and university education are reported to be 0.5, 0.66 and 1.86. These high returns reflect larger occupational differentials observed in Zimbabwe. The coefficients are around three times higher in Zimbabwe ancl Zambia than in Cameroon and Kenya. However, the estimates may be biased or overestimated if education is correlated with firm characteristics3, indicating that workers in larger firms may be sharing rents from a protected market. Earnings functions with other control variables (industry dummies, ownership firm dummies, size of establishment dummies) indicate that earnings increase with size of establishment. The elasticity of value added with respect to firm size is statistically significant and positive (0.1).

Additionally, Mazumdar (1995) classifies size groups of enterprises. The findings show that differences in earnings are partly explained by enterprise size in Cameroon, Ghana, Kenya, Zambia, and Zimbabwe. The size of enterprise dummies is found to be important

> 2 Other studies related to the labor market in Zimbabwe are on: rural labor markets (Vali (1995) and Adams (1991a, 1991b)); earnings and entries in micro enterprises and self employment (Steel and Webster (1992)); pay and employment reforms (Lindauer and Nunberg (1994)); job security regulations and policies (Fallon and Lucas (1991, 1993)), the macro wage curve (Verner; 1998), labor productivity in manufacturing (Pack (1993)), see Velenchik (1996a, b) and Addison (1996).

- 3 The return to education after introducing other control variables are not reported in the paper.

4
and the coefficients are increasing with size.<sup>4</sup> The workers employed by firms, employing 1500 or more workers receive the highest wages. In Zambia, Ghana, and Cameroon the highest wages were obtained in firms with more than 200 employees and in Kenya it was in the group with 400 or more workers that earned the most. Velenchik (1996a) also found a large premium associated with working for larger firms in Zimbabwe and the premium is found to be larger for white than blue collar workers. Findings suggest that larger firms use higher wages to increase the quality of the applicant pool, to reduce employment turn over, or to strengthen worker loyalty.

# **3. Data and Methodology**

Wages and productivity determination are examined by matched employee-employer manufacturing sector data; the Regional Program on Enterprise Development (RPED) survey data from 1993. The number of firms and employees interviewed are 201 and 1609, respectively. About 10 randomly selected workers from different occupational categories are interviewed in each enterprise. Matched employee-employer datasets have advantages compared to pure employee or employer data sets. An employee dataset may contain information about the sector in which a worker is employed but limited information about the firm. On the other hand, an employer dataset contains information about firms but limited information about individuals employed in the firms, apart from aggregate wage, education, and training costs. An employer-employee dataset allows detailed analyses of hypotheses related to both firms and individuals.

Empirical studies of determinants of wages and earnings inequality have focused primarily on factors affecting labor supply. Examples of long-run labor supply factors

> 4 Earnings per worker could increase with the size of the enterprise because of institutional factors that are correlated with firm size or economic factors. The non-institutional economic factors relating to wage increases are: (i) the inelasticity of labor supply of individual firms, (ii) factors that can be classified under 'efficiency wages'; (iii) profits, economic rent and labor productivity may increase with firm size and wages are set with profit-sharing considerations (see Mazumdar (1995) for details).

_5_
are variables such as education, age, sex, and experience<sup>5</sup> . Variables controlled by the employers, the so-called demand factors, have not often been<sup>used. Without direct</sup> measures of productivity, gender or ethnicity, for<sup>example, cannot establish</sup> discrimination, established correctly. Additionally, it is generally<sup>the case that studies</sup> report positive coefficient estimates on the age of an employee, conditional<sup>on a variety</sup> of covariates. These estimates neither imply that older workers are<sup>more productive than</sup> younger ones, nor that wages rise faster with productivity (see Hellerstein, Neumark,<sup>and</sup> Troshe; 1996). These problems may be overcome by estimnating<sup>individual wage and</sup> productivity equations jointly, and, thus, comparing wages and productivity<sup>for various</sup> groups of workers.

The general-to-specific methodology is used in the regression analysis. First, we formulate a general model that contains all relevant explanatory variables. Second, eliminating statistically insignificant variables one at the time reduces this model<sup>to a</sup> more parsimonious model. In this specification, only statistically significant variables are included.

The statistical models we use are simultaneous single level models.<sup>This may cause</sup> aggregation biases equivalent to problems produced in the learning<sup>achievement</sup> literature. Here workers are the unit of observation and firns are included<sup>in the</sup> individual vector of variables. It is worth noting that in general<sup>this methodology may</sup> cause aggregation biases; it may: (1) overestimate the group effect,<sup>that is, the firm effect,</sup> namely productivity; and (2) underestimate the individual effect<sup>on wages and</sup> productivity. Multilevel estimation takes aggregation biases iinto account.<sup>The firm level</sup> effect impacts average production but also the individual slope of,<sup>for example, gender.</sup>

> 5Groshen (199la) mentions that education, age, occupation, ethnicity, gender and union variables<sup>only</sup> account for 51 percent of the variation in the log of wages, analyzing (US)<sup>data from the Current</sup> Population Survey One Quarter Earnings Sample, 1986. Introducing demand<sup>related factors is likely</sup> to explain parts of the 49 percent of the variation in wages that workers'<sup>characteristics cannot</sup> account for.

6
The gender effect may be different in different firms. Furthermore, the group level effect may not be the same for every single worker. However, the small frmns have too few employees to actually enable us to perform the multilevel analysis. Therefore, we report results obtained by estimating single level models.

The two dependent variables in the analysis --wages and productivity-may not be measured by the same accuracy. The information on wages may be both more accurate and precise than the information on productivity. The problem with the productivity series is, for example, that not all inputs which are used in the production process have be taken into account. If this were the case, one would obtain reasonably high quality estimates of how the various attributes and characteristics impact wages and lower quality estimates of how they affect productivity. This is an important caveat that should be kept in mind. If the productivity series is not measured with the same accuracy the findings from the two equations should be interpreted with caution and, therefore, not on a strictly parallel basis.<sup>6</sup>

# **4. Modeling Wages**

Competing models of wage determination depend on the connection between wages, productivity, and employee-employer characteristics. Standard wage determination analyses (single-equation models) consider employee but rarely employer characteristics in the process of determining wages, and no link is made to productivity. In what follows wages, productivity, and employee-employer characteristics will be analyzed simultaneously.

Regression analyses are used to describe monthly productivity and wages of workers in the manufacturing sector, conditional on individual and firm characteristics. The following equation explains the wage and productivity model:

> 6 I am grateful to Gregory Ingram for this important comment.

7
_y =_ E _I/+ JF,+ E_ The dependent variable (y) is a vector containing two variables: wages (w) and productivity (v). The vector of explanatory variables consists of firm/employer characteristics (F), the demand factors, and employee characteristics (I), the supply factors. When y is reduced to scalar (w), the traditional wage determination model appears. The coefficients P and 6 are parameters revealing marginal impacts of explanatory variables on wages and productivity. The random error term M reflects all other factors.

The production function is estimated in value-added form. The justification is that: (1) inputs may be endogenous; and (2) this specification embeds contrasting production function specifications, e.g., one in which the elasticity of substitution is zero, and, therefore, materials have to be used in fixed proportions and another in which the elasticity of substitution is infinite (see Grilliches and Ringstad; 1971).

The employee characteristics explaining wages and productivity can be classified under four headings: (1) experience, schooling, and training; (2) other individual indicators; (3) sector of employment; and (4) institutions. Firm characteristics are also organized under four headings: (1) openness; (2) location; (3) age; and, (4) size.

Worker characteristics': First, a trained work force provides flexibility in adapting to economic changes. Further, human capital is important to enhance productivity and longterm economic growth (see Barro (1991); Mankiw, Romer and Weil (1992)). A more educated work force increases worker productivity, innovative behavior and facilitates the adoption and use of new technologies. More skilled employees can easier adjust to

> 7 They have been justified in great detail on theoretical grounds (e.g. Wvelch (1969), Mincer (1974), and Levey and Murnane (1992)).

8
changes in the economic and technological environment than less skilled workers. This analysis may indicate areas of scarcity in training and education. Second, other individual characteristics, for example, gender and ethnicity, are included. The gender and ethnicity variables reveal information on the female-male wage gap and wage differentials associated with different nationalities. Third, the sector of employment is included. The competitive model predicts the sector of employment having no impact on earnings differences of otherwise similar workers. Fourth, institutions such as trade unions are included, as they are important in determining wages.

The enterprise characteristics: First, the openness variables are: engagement in Research and Development (R&D) activities, export, number of expatriates, and licensing of technology. Firms engaged in export activities are particularly vulnerable to external shocks. Second, the location may be important as, for example, the cost of living in cities are generally higher than in towns, hence workers require compensation. Third, enterprise age may impact wages as more established firms can afford to pay higher wages than newly established firms do. On the other hand, the former may have lower productivity as older firms may not have the recent technology available. Fourth, the impact of firm size has been studied (see Velenchik; 1 996a, b and Mazumdar; 1995).

We estimate the wage and value added equations jointly. The multivariate regression, being a joint estimator, estimates the between-equation covariances, so we are able to test parameter restrictions across equations. Since it is assumed that employees with different characteristics are perfect substitutes in the production process, but with potential different productivity simultaneous estimation allows for comparison of relative productivities and wages of employees distinguished by various characteristics. This includes evidence related to gender and ethnic discrimination in wages, the causes of increasing renumeration over the life cycle, etc. The case in which equality of the parameter estimates of a particular variable in the two equations cannot be statistically rejected is interpreted as evidence compatible with competitive spot labor markets (see 9
Hellerstein, Neumark and Troshe (1996)). The instances where the parameter estimates can be rejected to be equal points towards non-competitive labor markets or discrimination.

# **5. Descriptive statistics and background information**

In the following sub-sections we present background information on key variables in the joint wage and productivity determination. We consider different elements contributing to human capital accumulation such as formal education and training. Furthermore, we shed light on ethnicity.

## **5.1 Formal education and training**

Table 1 gives the distribution of workers' highest education in the manufacturing sector. A small share (1.6 percent) of workers in the sample did not complete any level of formal education. This compares to primary and secondary education, completed by 41 percent and 54 percent of all workers, respectively.<sup>8</sup> The data indicate no large discrepancy in the level of education between female and male employees. Of females 35 percent had completed primary school and 57 percent secondary school, compared to 42 percent and 43 percent for males. Furthermore, workers of European origin have higher education than African workers. The percentage of Europeans with secondary education is 17 percentage points higher than Africans.

Human capital can be increased by workers training on-the-job or off-the-job. Training increases the flexibility of the work force to acquire new skills for new jobs as the structure of economies and occupations change. Skilled workers enhance the quality and

> 8 The gross enrollment rates in primary, secondary, and tertiary school were 106 percent, 38 percent, and 5 percent, respectively, in 1993.

10
efficiency of product and output development and they also train co-workers with lesser skills. In industrialized countries, governments have increasingly emphasized the importance of employer-led training. Training provides workers with the skills necessary for improving competitiveness, adaptability and GDP-growth. Furthermore, skill acquisition may reduce wage inequalities.

**Table 1**

|**Highest Level of Com**|**pleted Educati**|**on**(%)|
|---|---|---|
|All|Women|Men|
|No education<br>1.6|1.6|_1.5_|
|Primary<br>40.9|35.0|42.3|
|Secondary<br>53.5|57.0|52.5|
|Vocational<br>_1.5_|1.7|1.5|
|University<br>_1.5_|2.3|1.3|

Data Source: RPED data.

The incidence of training off-the-job in Zimbabwe is low as compared to industrialized and semi-industrialized countries. Data indicate that a small number of firms in Zimbabwe train workers formally. Around 17 percent of the employees receive formal training in-house or by outside providers. The low number may indicate that manufacturing enterprises in our sample underinvest in formal training. Biggs _et al._ (1995) compare Zimbabwe to Japan and find that only about one-fifth of the number of people trained in Japan are trained in Zimbabwe, and around half of the number in WestGermany. Tan and Batra (1995) find that in Taiwan 38 percent of large firms and 4 percent of small firms train employees in-house.

In Zimbabwe, external training is carried out in technical schools and training courses, and sometimes abroad. The metal sector trains its employees more than any other manufacturing subsector. More than 40 percent of the firms supply in-house training to their employees. The metal sector also engages more than other sectors in external training. This may be explained by the technological complexity in the metal sector. The 11
woodworking sector trains the least; 35 percent of the firms train externally and 23 percent provide internal training.

## **5.2 Ethnic groups**

This paper considers three ethnic groups. The 1982 census reports that 97.5 percent of the Zimbabweans were African. The second largest ethnic group, Europeans, accounts for 2 percent of the total population. The RPED data sample consists of 95 percent Africans, 3 percent Europeans, and 2 percent of Asian origin. Hence, the sample reflects the actual composition of the population.

Prior to 1980 skilled jobs were rmore or less reserved for Europeans or other white people and education was intended to benefit this group in particular. Hence, Africans are less well equipped in terms of education than Europeans to engage in formal labor market activities. Therefore, the immigration of Europeans commencing in the middle of the 1970s led to a skill shortage in Zimbabwe. Africans were not only discriminated against in terms of education but also in apprenticeships controlled by trade unions'.

# **6. Findings**

This section presents evidence amassed from the data. Appendix A encloses a list of variables and describes how each variable is constructed. The findings of the regression analysis are presented in Appendix B.

## **6.1 Regression findings**

This sub-section reports findings of the joint estimation of wages and productivity. The tables present a subsection of the findings of presented in the tables in Appendix B.

> 9The number of apprentices has not increased since Independence, rather it has decreased by 25 percent: there were 2000 in 1981 and 1500 in 1993 (Kanyenze; 1995).

12
The regressands are the log of monthly wages and value added in manufacturing. The regressors are mentioned above. The regression models reported are all parsimonious specifications obtained from model reduction.

# **6.1.1 Formal education and occupation differentials**

Do education and occupational affect wages and productivity? Employees are distinguished by occupation and divided into six groups: (1) manager; (2) administrative or clerk; (3) commercial or salesperson; (4) supervisor or foreman; (5) technician; and (6) the remaining. We find that each occupational title, controlling for other individual and firm characteristics, is statistically significant and positive (the reference group being workers in maintenance, production, support, and trainees. The introduction of individual occupational controls in the wage equation shrinks the relationship between wages and education, particularly in the case of highly educated workers.

The estimated coefficients on the education variables in the wage equation reveal the impact of human capital on wages (Table 2). Completed secondary, vocational, technical and university education impact wages positively and the coefficients are statistically significantly different from zero (completed primary education and no education completed being the reference category). This finding is as expected; the more education a worker has completed the higher the wage the worker receives, conditional on a variety of individual and enterprise covariates including occupation. The size of the parameter estimates shows that the wage premium increases rapidly with the completed level of education. An employee who has completed secondary education receives a 17 percent wage premium compared to that of an employee having completed primary or no education. The wage gap in favor of university graduates is 65 percent.<sup>10</sup> The impact on monthly wages of vocational training at the highest level of education is 23 percent.

> **10** The premia are calculated as follows: for coefficient estimates in the range -0.25 to 0.25 the absolute value is used. Coefficients larger than this are used in the formula: l00*(exp{abs[parameter estimate] - I }) to obtain the premia.

13
||Prod|**Educati**<br>uctivity|**on and**|**Table 2**<br>**Occupat**|<br>**ion Differ**<br>Wages|**entials**|||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itl|Coef.|Std. Err.|t|P>Itj|
|jobman|-0.194|0.141|-1.382|0.167|1.112|0.069|16.229|0.000|
|jobamd|0.060|0.110|0.548|0.584|0.687|0.053|12.858|0.000|
|jobcom|-0.092|0.156|-0.588|0.557|0.390|0.076|5.138|0.000|
|jobsup|0.040|0.104|0.384|0.701|0.499|0.051|9.847|0.000|
|jobtech|0.105|0.215|0.487|0.626|0.882|0.105|8.410|0.000|
|duni|-0.088|0.261|-0.340|0.734|0.501|0.127|3.944|0.000|
|dvoc|0.125|0.244|0.512|0.609|0.232|0.119|1.949|0.052|
|dsec|0.126|0.070|1.786|0.074|0.172|0.034|5.023|0.000|

The estimated wages of a manager, an administrative or a clerk, a commercial or salesperson, a supervisor or a technician are all significantly different from the wages of the reference group (workers in maintenance, production, support staff, and trainees). The wage premia are 204 percent, 99 percent, 47 percent, 65 percent and 42 percent, respectively.

After analyzing the impact of education and occupation on wages we now turn to their impact on productivity. The findings reveal no occupational productivity differential (see Table 2, second column). The estimated productivity of managers, administratives or clerks, commercials or sales people, supervisors or technicians is surprisingly<sup>not</sup> statistically significant from that of the reference group. The same holds for workers that have completed vocational and higher education. However, there exists a positive and marginally significant production differential by completed secondary education. The workers with completed secondary education are 13 percent more productive than their co-workers with no or completed primary education.

Are the above mentioned estimated impacts different in the wage and productivity equations? We find that the productivity differentials by education fall behind wage differentials by education. Furthermore, significantly so for completed secondary 14
education as the test of equality of the wage and productivity differentials rejects equality (F = 4.91, Prob > F = 0.03). Hence, the positive wage gap between workers with completed secondary education and non or little completed education cannot be completely justified by higher productivity. These findings indicate shortage of educated employees. Also productivity differentials by occupation fall behind wage differentials by occupation. This is revealed by the rejection of all tests of equality between the two estimates (F(jobman) = 82.87, Prob > F = 0.00; F(obamd) = 31.39, Prob > F = 0.00; F(jobcom) = 9.18, Prob > F = 0.001, F( jobsup) = 18.71, Prob > F = 0.00; F(iobtech)<sup>=</sup> 2.52, Prob > F = 0.00)).

Another regression model, where completed secondary education is substituted with completed primary education, shows that the return to primary education is below that of secondary education. The coefficient estimates of the other human capital variables are in line with the previously reported.

# **6.1.2 Differentials by training**

Does training impact wages and productivity? Regressions indicate that training is associated with higher firn-level productivity. The productivity effect of skilled worker training is estimated to be high in other low-income countries, for example, 1.43 for Indonesia and 0.39 for Columbia (see Tan and Batra (1996)). This indicates that return to training in Zimbabwe is in the lower end comparing among low-income countries.

The hypothesis that in-house training has no impact on wages is rejected. Formal structured training in-house is associated with a wage-premium. Employees trained inside the firm earn 64 percent more than workers who have not been trained in-house do. However, the productivity enhancing effect of in-house training is not statistically significantly higher compared to the workers who did not receive training (see Table 3). This may indicate that in-house training is not productive. However, it seems more likely that it takes time before effects are revealed as increased productivity.

15
||Prod|uctivity|<br>**T**|**Table 3**<br>**rainin**|**g**<br>Wages||||
|---|---|---|---|---|---|---|---|---|
||<br>Coef.|<br>Std. Err.|<br>t|P>ItI|Coef.|Std. Err.|<br>t|P>jtj|
|dtrain|-0.136|0.230|-0.593|0.554|0.420|0.112|3.747|0.000|
|dtraiou|0.332|0.103|3.208|0.001|0.075|0.050|1.490|0.137|
|metatrai|-0.024|0.076|-0.313|0.754|-0.120|0.037|-3.245|0.001|
|texttrai|0.118|0.299|0.395|0.693|-0.484|0.146|-3.324|0.001|
|woodtrai|-0.083|0.469|-0.178|0.859|-0.454|0.229|-1.987|0.047|
|nexptrao|-0.115|0.094|-1.218|0.223|-0.168|0.046|-3.664|0.000|

Training obtained outside an enterprise is revealed in productivity. The employees receiving training from external suppliers are statistically significantly more productive than non-trained co-workers, but trained employees are not compensated equally. This is revealed by the wage differential not being significantly different from zero. Another explanation is that the impact of training is not instantaneously reflected in wages due to a delay from the time the productivity increase from the training is realized until this productivity increase is rewarded as higher wages.

Trade unions are often associated with training. This seems also to hold in Zimbabwe. Trade union bargaining at the firm level may increase social welfare by counterbalancing a firm's monopsonistic power in wage determination."1 Long term contracts are socially optimal when workers are being trained because workers' incentive to quit and leave with the skills obtained is reduced. Alternatively, firms and local unions wage bargaining may ensure that post-training wage is set sufficiently high to deler inefficient quits, and thus to ensure that the number of trainees the firm takes on is near the social optimal number. Hence, we would expect to see higher post-training wages in our findings. In fact, we did so for training supplied by outside providers.

This is shown theoretically in Brooth and Chatteri (1997).

16
Does Zimbabwe underinvest in training? The answer obtained from the regression analysis appears to be 'yes'. Evidence of underinvestment in training is suggested by very high returns to in-the-house training reflecting its relative scarcity. The reasons for no or too little training being provided may be imperfect capital markets, limited access to information, and other market failures.

Another implication of the figures is that policies to encourage increased enterprise training will lead to larger productivity gains for the economy. Firm level gains from training and exporting (see below) are shared with employers in the form of higher pay. These activities generate skills and knowledge that are employee specific in the sense that the employee may quit and take the accumulated human capital with her. This makes trained employers more valuable to the firm than other workers from other firms. To the extent that knowledge obtained from training is a partly firm specific, employer and employees have an incentive to share the costs and benefits of investing in training. In Zimbabwe, employer-investments in training are associated with a wage premium and employers in firms engaged in exporting (see Table 3) obtain an additional premium.

# **Manufacturing subsectors**

In the following we consider different subsectors in manufacturing. The analysis shows that we can reject the hypothesis that employees trained within the firm have no impact on wages in the metal and textile sectors. Trained employees receive lower wages in these sectors than in the food sector. This is in addition to what is already captured by the sector variable (see below). However, workers in the metal and textile sectors are not statistically significantly less productive. These findings indicate that structural differences exist across sub-sectors.

Employees trained by outside providers that work in enterprises with a high number of expatriates receive lower wages. However, they are not less productive than the employees who did not undergo training outside the firm are. An explanation for this could be that the higher the number of expatriates working in a firm, the more open it is 17
to international markets, and, therefore, wages are affected by external factors. Hence, training is necessary to keep up with international developments and it is an investment that does not (instantly) show up in wages, but does in productivity.

## **6.1.3 Differentials by experience**

Labor market experience is now considered. The worker's age is included in the<sup>wage</sup> equation both in level and squared allowing for non-linearities. The findings show<sup>that</sup> age both in level and squared is statistically significantly different from<sup>zero and of the</sup> expected sign (see Table 4). The estimated life-cycle wage<sup>profile has the quadratic</sup> shape known also from industrialized countries. The positive effect<sup>of experience on</sup> wages is increasing in the younger age and continues at older age but<sup>at a decreasing rate.</sup> The productivity profile follows the pattern of wages and the wage<sup>gap and productivity</sup> gap are equal in size.

|||||**Table 4**<br>**Exper**|<br>**ience**||||
|---|---|---|---|---|---|---|---|---|
||Prod|uctivity|||Wages||||
||Coef.|Std. Err.|t|P>Itj|Coef|Std. Err.|t|P>jtl|
|age|0.056|0.016|3.535|0.000|0.056|0.008|7.313|0.000|
|agesq|-0.001|0.000|-3.000|0.003|-0.001|0.000|-5.979|0.000|

# **Other individual characteristics**

## **6.1.4 Differentials by apprenticeship and contracts**

Does the type of contract and undertaking apprenticeship affect wages and productivity? It is of interest for both the employer and the employee to form a long-run employment relationship, thereby helping to build and retain firmn-specific skills. Table 5 shows the estimated wage differential associated with permanent employment. It is estimated to 12 percent and is statistically significant. Hence, workers with permanent contracts obtain 18
higher wages than workers with temporary contracts. Therefore, the policy implemented after Independence to protect employees seems not to have affected wages directly as they are higher than those of unprotected workers are. This finding is coherent with what Fallon and Lucas (1991) found estimating wage equations using time series data. However, the labor legislation may have affected the number of permanent jobs created negatively because firms have invested more in physical capital than in long-term engagement to employment. There does not seem to be a trade offbetween enhanced job security and wages. Rather the contrary, job security endows insider workers with bargaining power enhancing the possibility of achieving higher wages.

**Table 5 Apprenticeship and Contracts**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itj|Coef.|Std. Err.|<br>t|P>JtI|
|dpermem|-0.245|0.098|-2.500|0.013|0.118|0.048|2.468|0.014|
|apprmeta|0.027|0.056|0.478|0.633|0.107|0.027|3.939||
|dappr|-0.064|0.121|-0.531|0.596|0.282|0.059|4.780|0.000|

The productivity differential associated with holding a permanent contract is negative and statistically significant. Is the impact from the type of contract of the same size on wages and productivity? We reject the hypothesis of equality of productivity and wages of permanently employed, with a p-value of effectively zero, (F=13.17, Prob > F = 0.00). This finding contradicts the intuitive explanation presented in Fallon and Lucas (1991) that temporary workers lack the incentive to work hard because they are not covered by Zimbabwe's permission clause. Temporary workers are more productive than permanent indicating that the former may be working hard in an attempt to obtain a permanent contract.

In the sub-sectors we find that the wage premium obtained by permanently employed workers is statistically insignificantly different from zero in woods, textiles, and metals, 19
the sector of comparison being food. Hence, for permanent employees higher wages and no higher productivity are a pattern, which seems equal across the entire manufacturing sector. _Summa summarum,_ the findings indicate that workers with a permanent contract not only benefit in terms of increased job security but they also get higher wages, despite being less productive.

In Zimbabwe limited apprentice programs are available. M0oreover, apprentices are paid a low wage, while employers of apprentices are not compensated. Do employees who have been an apprentice obtain higher wages than others do? The point estimate of the wage differential is 0.28 and the wage gap 33 percent; a difference that is significantly different from zero, controlling for individual and firm characteristics. For the valueadded specification, the estimate of the productivity differential is negative, but statistically insignificant. Hence, the wage premium is out of line with productivity. An apprentice diploma may serve as a screening device in staff selection as diplomas often do in developed countries.

Turning to the four sectors; apprentices in the metal sector obtain a significantly higher wage premium than apprentices in other sub-sectors. Furthermore, an apprentice working in metals impact productivity positively, but is statistically insignificant. This indicates that productivity is not higher for apprentices in metals than in other sectors.

# **6.1.5 Ethnicity differentials**

Does ethnic discrimination in wages exist in Zimbabwe accounting for productivity? Under perfect competition in the capital and labor markets, equivalent employees in equivalent jobs are compensated equally, that is, there is no discrimination. Findings in the regression analysis indicate that the origin of an employee is not very important per se, controlling for the level of human capital and other characteristics. Wages of Africans, Asians, and Europeans are not statistically different.

20
**Table 6 Ethnicity**

||Prod|uctivity|||Wages<br>||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|t|P>jtJ|Coef.|Std. Err.|t|P>ItI|
|europea|1.839|0.729|2.523|0.012|-0.116|0.355|-0.326|0.744|
|<br>empleuro|-0.253|0.137|-1.841|0.066|0.152|0.067|2.273|0.023|
|<br>textasia|<br>1.725|0.536|3.216|0.001|1.045|0.261|4.000|0.000|
|woodasia|0.812|0.665|1.222|0.222|0.753|0.324|2.327|0.020|
|emplasia|-0.179|0.080|-2.224|0.026|-0.059|0.039|-1.503|0.133|

The tests of the null hypotheses that workers of European or Asian ethnic origins are generally not paid more than African workers is not rejected. However, the findings from the production functions show that on average Europeans are more productive than Asians and Africans. Europeans employed in larger firms are paid (statistically significantly) more than Africans but are also less productive. The overall effect, however, is that Europeans are more productive than any other ethnic group.

The wage gap between Asians and Africans increases with firm size and Asians are paid marginally -- although not significantly -- less than Africans. Moreover, the Asians are statistically significantly less productive in larger firms than in smaller compared to employees of other ethnic background.

In the textile and wood sectors Asians are paid higher wages, and, furthermore, they<sup>are</sup> significantly more productive in the former sector than in other sectors. However, the test for parameter equality indicates that wages and productivity are in line in textiles, the<sup>p-</sup> value is high, indicating that the coefficients are not different.

# **6.1.6 Gender differentials**

Are men and women equally productive and paid the same? The regression analyses reveal that female employees are paid statistically significantly less than their male<sup>co-</sup> workers, controlling for individual and other characteristics. It seems dubious whether 21
this finding is due alone to women being less productive than men are. In fact,<sup>the point</sup> estimate of the productivity differential is not statistically different<sup>from zero. This</sup> implies that, on average, women's lower wages do not reflect lower<sup>productivity than</sup> men's do. One interpretation of this finding is that men and women filling<sup>the same jobs</sup> in enterprises, and despite being equally productive, women are paid less,<sup>consistent with</sup> the standard wage discrimination hypothesis. The productivity gap is insignificant<sup>and</sup> the wage gap is 13 percent, thus the entire wage gap might be attibuted to discrimination.<sup>12</sup>

**Table** 7 **Gender**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>ItJ|Coef.|Std. Err.|<br>t|P>JtJ|
|dfemale|-0.019|0.082|-0.232|0.817|-0.127|0.040|-3.182|0.001|

The lower wages that Zimbabwean women earn do not seem to originate from being employed in the textile sector. The findings show that women working in textiles do<sup>not</sup> earn less than their male colleagues, controlling for the textile sector. Furthermore,<sup>when</sup> interaction variables for completed education and being a female were included,<sup>they</sup> turned out to be statistically insignificant and that indicates that returns to women's completed education is not lower than men's return.'<sup>3</sup> Furthermnore, the gender<sup>gap is not</sup> different across manufacturing sub-sectors.

12 However, there exist other possible explanations for this finding. WVomen might be employed in lowerpaying jobs or occupations although these jobs are not less productive.<sup>Then the level of detail of</sup> occupation would have to be finer than the one that we use. This could arise<sup>because of so-called taste</sup> discrimination (see Becker 1971). Another explanation for women's<sup>wages being lower than their</sup> productivity may occur if enterprises deviate in the degree to which they<sup>have implemented labor-saving</sup> technological change. Normally, such changes are not fully accountecd for in the book value of capital and if technological change eliminates more jobs held by females than males the parameter<sup>estimates could be</sup> biased.

> **3** There is too few observations on females to perform a sensitivity<sup>analysis of women's wage</sup> determination.

22
**6.1.7 Differences by trade unions**

**Table 8 Trade Unions**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>ItJ|Coef|Std. Err.|<br>t|P>Itt|
|dunion|0.141|<br>0.063|2.222|0.026|-0.172|0.031|-5.585|0.000|

Does unionization make a difference to wages? The answer is yes, as the union premium in the Zimbabwean manufacturing sector is statistically significant and negative. Union members earn less than non-union members do when controlling for firm and individual characteristics in the wage regression. The estimated wage differential associated with being a union member is approxirnately -17 percent. This provides evidence that trade unions reduce wage differentials, and, therefore, positively affect the income distribution. This finding is surprising because findings from wage analyses in developed countries usually show that union members earn more than their non-union member colleagues. However, Rama (1997) finds also that union members are paid less in CFA countries applying a cross-section dataset of African countries. Additionally, union members are statistically significantly more productive than non-members; the productivity gap is 14 percent. The test of the hypothesis that the wage and productivity gap is of equal size is highly rejected (F=23.48, Prob > F = 0.00).

# **Enterprise characteristics**

Are employer or enterprise characteristics associated with individual wages and productivity. Findings not presented in this version indicate that omitting enterprise-level control variables from the analysis would bias the coefficient estimates in an individual23
level wage equation, as leaving out significant variables will cause omitted variable biases."<sup>4</sup>

# **6.1.8 Differentials by sectors**

Does the sector in which an employee is employed imake a difference to wages? Eniployees in the metal and textile sectors earn more and less, respectively, than their colleagues in the food sector, controlling for other firm and individual characteristics in the regression. The sectoral wage premium is 7 percent for metals and minus 30 percent for textiles, compared to the food and wood sectors.

**Table 9**

**Sectors**

||Product<br>Coef. S|ivity<br>td. Err.|T<br>P|>Itj<br>|Wages<br>Coef.<br>S|td. Err.|t<br>P|>jt)|
|---|---|---|---|---|---|---|---|---|
|metal<br>-|0.232<br>|0.077 -3|.026 0|.003<br>|0.069|0.037<br>|1.840 0|.066|
|<br> <br>textile<br>-|<br> <br>0.921|<br>0.375<br>-2|<br>.454 0|<br>.014<br>-|0.358|0.183 -|1.957 0|.051|
|<br> <br>textiexp<br>-|<br>0.015|<br>0.004 -4|.139 0|.000<br>-|0.006|0.002<br>-|3.226 0|.001|
|<br> <br>metatrai<br>-|<br>0.024|<br>0.076 -0|<br>.313 0|<br> <br>.754<br>-|<br>0.120|<br>0.037<br>-|3.245 0|.001|
|<br> <br>texttrai<br>|<br>0.118<br>|<br>0.299<br>0|<br>.395 0|<br> <br>.693<br>-|<br>0.484|0.146<br>-|3.324 0|.001|
|<br> <br>woodtrai<br>-|<br> <br>0.083<br>|<br> <br>0.469 -|<br>.178 0|<br> <br>.859<br>-|<br>0.454|0.229 -|1.987 0|.047|
|<br> <br>textasia<br>|<br> <br>1.725<br>|<br>0.536<br>3|<br>.216 0|<br> <br>.001<br>|<br>1.045|<br>0.261<br>4|<br>.000 0|<br>.000|
|<br> <br>woodasia<br>0|<br> <br>.812<br>|<br> <br>0.665<br>1|<br>.222 0|<br> <br>222<br>|<br>0.753|<br>0.324<br>2|.327 0|.020|
|<br> <br>apprmeta<br>0|<br> <br>.027<br>|<br> <br>0.056<br>0|<br>.478 0|<br> <br>.633<br>|<br>0.107|<br> <br>0.027<br>3|<br>.939 0|<br>.000|
|<br> <br>nexpmeta<br>|<br> <br>0.152<br>|<br> <br>0.039<br>3|<br>.874 0|<br> <br>.000<br>|<br>0.078|0.019<br>4|.061 0|.000|
|<br> <br>emplmeta<br>|<br> <br>0.042<br>|<br> <br>0.016<br>2|<br>.565 0|<br>.010<br>-|0.009|0.008 -|1.083 0|.279|
|<br> <br>folimeta<br>|<br> <br>0.328<br>|<br> <br>0.058<br>5|<br>.674 0|<br> <br>000<br>|<br>0.110|0.028<br>3|.897 0|.000|
|<br> <br>nexptext<br>0|<br> <br>.100<br>|<br> <br>0.092<br>1|<br>.092 0|<br> <br>.275<br>|<br>0.074|<br> <br>0.045<br>|<br>1.642 0|<br>.101|
|<br> <br>empltext<br>|<br> <br>0.240<br>|<br> <br>_0.055_<br>4|<br>.397 0|<br> <br>.000<br>|<br>0.045|<br> <br>0.027<br>|<br>1.706 0|<br>.088|
|<br> <br>fountext<br>-|<br> <br>0016|<br> <br>0003 -|<br>038 0|<br> <br>000<br>|<br>0.003|<br>0.002<br>2|.071 0|039|
|<br> <br>nexpwood -|.<br>0.496|.<br>0.286<br>-|.<br>1.733 0|<br> <br>.083<br>|<br>0.405|<br> <br>0.139<br>2|<br>.909 0|<br>.004|
|<br>emplwood -|<br>0.122|<br> <br>0.026<br>-|<br>4.783 0|<br>.000<br>-|0.032|0.012<br>-|2.545 0|.011|

4The introduction of enterprise-level covariates reduces the estimated relationship between wages, and, for example, completed university education consistent with the controls being correlated with employee quality. Nevertheless, a significant positive relationship is still present.

24
However, in both sectors the employees are statistically less productive than in the food and wood sectors. The employees in metals and textiles are, respectively, 21 percent and 60 percent less productive. The p-value testing equality shows that productivity is equal to wages in the textile sector, but not in the metal sector (F(metal)= 14.78, Prob > F = 0.00; F(textile)= 2.16, Prob > F = 0.14). These findings indicate that on average wages are too high in the metal sector, which may be caused by rents. However, wages in the textile sector are in line with productivity. Therefore, there is no direct evidence of economic rents due to, for example, monopoly power in textiles.

Firms operating in the textile sector which are engaged in export activity pay a marginally lower wage premia than the ones not engaged in these activities. Furthermore, on average, the employees are statistically significantly less productive than in other subsectors. The coefficient estimates are very small but equality of gaps is rejected (F=6.33, Prob > F = 0.01).

The number of expatriates employed in an enterprise proves important in determining wages in the metal, textile and wood sectors, all parameters are statistically different from zero and positive. Furthermore, the number of expatriates is statistically significant in the determination of value added in metals (F = 3.46, Prob > 0.06). However, in the other two sectors they are insignificantly different from zero. Hence, employees with more expatriates around are only marginally more productive in metals, controlling for a number of covariates.

The age of an enterprise is only significant in determination of wages in the textile sector, but the coefficient estimate is low. The same holds for productivity. In the other sectors the number of years since the firm was founded has no significant impact on wages or productivity, controlling for other enterprise and employee characteristics.

The enterprises in the metal sector with foreign licenses pay statistically significantly higher wages than other firms. The wage premium associated with being employed in 25
metals in a firm holding foreign licenses is 11 percent. Furthermore, these enterprises have employees that are statistically significantly more productive (39 percent) than in other sectors. The hypothesis testing that the coefficient estimates in the wage and production function are equal is highly rejected, indicating that employees are more productive than wages reveal (F=1 3.70, Prob > F = 0.00).

As mentioned above, the hypothesis that training within the firm has no impact on wages in the metal, wood and textile sectors can be rejected, foods being the reference group. Trained employees get a lower wage in these sectors than in the food sector, but they are not statistically significantly less productive.

Does firm size impact wages and productivity. In metals productivity is statistically significantly higher in larger firms than smaller firms are. However, wages are not significantly higher. In the textile sector larger firrns are lboth more productive and pay higher wages. Furthermore, the productivity gap is larger than the wage gap in textiles (F = 12.2, Prob > F = 0.00). Additionally, in woods the wage and productivity gaps are both significant and negative. This indicates that employees in woods are less productive and are paid lower wages than in foods. The test statistics reveal that the negative gaps are of equal size, indicating wages are in line with productivity in the wood sector (F = 12.06, Prob > F = 0.0).

# **6.1.9 Differentials by location**

Does the location of the enterprise -- Harare, Bulawayo or else where -- impact on wages and productivity? We estimate the location premium to be statistically significant and positive (see Table 10). Workers employed in an enterprise placed in Harare receive a 34 percent wage premium and workers in Bulawayo receive 19 percent more than their colleagues working outside these locations. These wage-premia may be interpreted as compensation to city-based employees due to the generally higher price-level. Moreover, 26
employees in Harare and Bulawayo are by far more productive than colleagues elsewhere.

**Table 10 Location**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|t|P>Itf|Coef.|Std. Err.|<br>t|P>ItI|
|Harare|0.794|0.104|7.612|0.000|0.293|0.051|5.761|0.000|
|Bulawayo|0.441|0.111|3.992|0.000|0.191|0.054|3.541|0.000|

We reject equality of productivity and wages of workers employed in both Harare and Bulawayo (F(Harare)-22.2, Prob > F = 0.00 and F(Bulawayo)= 4.94, Prob > F = 0.03). These findings indicate that workers in Harare and Bulawayo are far more productive than elsewhere but they are not being fully compensated herefore.'<sup>5</sup>

# **6.2.10 Differentials by openness**

Does increased internalization benefit Zimbabwean manufacturing workers? Increased openness to trade itself exacerbates the influence of new technologies, because trade liberalization induces more imitation, makes reverse engineering easier, and makes complementary imports cheaper. Increased openness also increases competition, thereby increasing the incentives to imitate. The percentage share of exports in enterprise production here measures openness. Additionally, we inciude three other proxies for openness in the analysis: (1) foreign licenses; (2) the number of expatriates; and (3) spending on research and development.

The findings in Table 11 indicate that employees in exporting firms are paid statistically significantly more (0.6 percent) than workers in firms not exporting. An increase in the

> 15 One observation related to these findings, is that there are only a few firms outside these two locations (13 percent). Another point is that some of the higher productivity is reflected in non-wage benefits, not included in wages, hence, total earnings may be in line with productivity.

27
export share leads to an increase in wages, controlling for individual and firm characteristics. A 10-percentage-points increase in the export share increases wages by 0.06 percentage points. Furthermore, if the employee has been trained, the impact on wages is 0.12. Additionally, the finding that trained and educated workers employed in exporting firms obtains a significant wage-premium provides evidence of a skill-biased technological change, suggesting that a more open economic environment benefit workers. This is due to, for example, that they are more likely to be trained and, thus, to obtain higher wages. Employees working in larger firms receive less than workers in smaller firms do when the export share increases; the effect on wages is 0.11 percentage points.

**Table 11 Openness**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|<br>t|P>Itj|Coef.|Std. Err.|<br>t|P>jtI|
|exportsh|0.021|0.006|3.414|0.001|0.006|0.003|1.943|0.052|
|forownsp|0.540|0.095|5.670|0.000|0.070|0.046|1.501|0.134|
|numexpat|-0.115|0.070|-1.656|0.098|0.064|0.034|1.875|0.061|
|trainexp|0.009|0.006|1.591|0.112|0.006|0.003|2.079|0.038|
|textiexp|-0.015|0.004|-4.139|0.000|-0.006|0.002|-3.226|0.001|
|forowexp|-0.019|0.004|-4.943|0.000|0.000|0.002|0.063|0.950|
|emplexp|-0.003|0.001|-2.672|0.008|-0.001|0.001|-1.986|0.047|
|nexptrao|-0.115|0.094|-1.218|0.223|-0.168|0.046|-3.664|0.000|
|nexpmeta|0.152|0.039|3.874|0.000|0.078|0.019|4.061|0.000|
|folimeta|0.328|0.058|5.674|0.000|0.110|0.028|3.897|0.000|
|nexptext|0.100|0.092|1.092|0.275|0.074|0.045|1.642|0.101|
|nexpwood|-0.496|0.286|-1.733|0.083|0.405|0.139|2.909|0.004|

Furthermore, the size of export share impacts productivity positively and it is statistically significant. Hence, firms with higher export shares are more productive due to, e.g., resources are used more efficiently. The impact on productivity is 0.21 percentage points of a 10-percentage-point increase in the export share of production. In addition, if employees are being trained the impact increase to 0.3 percentage points.

28
The test of equal coefficient estimates in the wage and productivity equations indicates that they are not of equal size (F=5.85, Prob > F = 0.02). Hence, a higher degree of internationalization improves productivity more than wages.

Is the impact of openness equal across manufacturing sectors? The answer to this question is 'no'. An increase in export share has no significant impact on wages in the wood and metal sectors, revealed by the interaction variables between the sector and export share in output being statistically insignificantly different from zero. However, exports affect wages in the textile sector negatively, compared to the other sub-sectors. The size of the negative wage premium in textiles shows that it completely offsets the overall export premium. However, the impact on productivity in textiles is statistically significant and positive but lower than in the other sub-sectors. In textiles, the effect of a 10-percentage-point increase in export shares on productivity is 0.06 compared to 0.21 on the average in manufacturing.

The variable emplexp reveals that iarger firms engaged in export activities are marginally less productive and pay marginally lower wages than the average firm is. However, larger exporting firns are still more productive than other firms are. The test for equality of the parameter estimates in the two equations is only marginally non-rejected (F=2.79, Prob > F = 0.09). This indicates that wages are more in line with productivity in larger sized exporting firms than in average sized firms.

The second variable related to openness is the ownership structure of a firm. A firn with some foreign ownership is more likely to be attached to international markets than purely Zimbabwean firms. In fact, the foreign ownership variable proves to have an impact on productivity, controlling for both enterprise and individual characteristics. Enterprises owned or partly owned by foreigners are statistically significantly more productive than firms owned by locals, but they do not pay statistically significantly higher wages.

29
Foreign or partly foreign owned firms engaging in export activities do not pay an additional wage premium. Findings for the productivity equation show that the level of productivity gained from exports and foreign ownership are not additive, as the coefficient estimate on forowexp is negative and statistically significant. This finding indicates that there exist productivity gains from internaLtionalization and, further, that proxies used to capture this all contribute differently.

The third variable that proxy openness is ownership of foreign licenses. Firms that purchased foreign licenses do not on average pay significantly higher wages than other firms nor are they on average more productive. However, ownership structure impacts statistically significantly wages in metals, and this sector has also higher productivity. The hypothesis that the coefficients are of equal magnitude is rejected (F=13.70, Prob > F = 0.00). This finding indicates that firms possessing foreign licenses are more productive than others in the metal sector are, and these firms do not compensate employees equally in terms of wages. There is no impact on wages or productivity in the other sectors.

The fourth variable related to openness is expatriates. Wages are statistically significantly higher the more expatriates employed in ;a firm. One more employed expatriate is associated with a 6 percent increase in wages, _ceteris paribus._ Furthermore, there exists an additional premium, which is statistically significant and positive in all sub-sectors, foods being the reference sector. The premium ranges from 41 percent in the wood sector to 7 percent in textiles. The production function estimates show that only firms with expatriates in metals are more productive than workers in the other = sectors, and the productivity is only marginally higher than wages (F= 3.46, Prob > F 0.06).

# **6.1.11 Differentials by firm size**

Do larger firms pay their employees more than smaller firms do, and, if so, are wages in line with productivity? Yes, larger firms -- measured by the number of full-time and parttime workers in the firm -- do pay higher wages than smaller firms (see Table 12). This 30
finding is in line with Velenchik (1996a, 1996b)."6 Not only do larger firms pay more than smaller firms do in Zimbabwe but they are also more productive as revealed by<sup>the</sup> production function estimates. The impact of the firm size on wages and productivity is both statistically significant and positive but the impact on the latter is far larger than on the former (F = 1022.6, Prob > F = 0.00). A 10-percentage-point increase in firm<sup>size</sup> increases wages by 1.86 percentage points.

**Table 12 Firm Size**

||Prod|uctivity|||Wages||||
|---|---|---|---|---|---|---|---|---|
||Coef.|Std. Err.|t|P>jtI|Coef.|Std. Err.|t|P>ItI|
|lempl|1.366|0.036|37.775|0.000|0.186|0.018|10.577|0.000|
|emplexp|-0.003|0.001|-2.672|0.008|-0.001|0.001|-1.986|0.047|
|empleuro|-0.253|0.137|-1.841|0.066|0.152|0.067|2.273|0.023|
|emplasia|-0.179|0.080|-2.224|0.026|-0.059|0.039|-1.503|0.133|
|emplwood|-0.122|0.026|-4.783|0.000|-0.032|0.012|-2.545|0.011|
|emplmeta|0.042|0.016|2.565|0.010|-0.009|0.008|-1.083|0.279|

In the above sections the importance of the firm size was already mentioned. We can summarize the findings as follows. Larger firms engaged in export activities pay lower wages but are a little less productive. Europeans are paid more than employees of African origin in larger firms, but are also less productive than these ethnic groups, ceteris paribus. Employees working in woods and metals in larger firms are paid<sup>less</sup> than in smaller finns, but they are not less productive.

The employees in firms engaged in R&D are not paid statistically significantly more<sup>or</sup> less than workers in firms not participating in these activities. It should be noted<sup>that</sup> manufacturing workers is a privileged group of workers because they have a<sup>stable</sup> income and often receive extra benefits. It is one of the groups with the highest incomes

> **16** That earnings increase with establishment size has been found elsewhere (see Groshen<sup>(1991).</sup>

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in the economy. However, the above mentioned findings show that more openness to trade does not necessarily decrease overall wage inequality, as this group will not loose its privileges due to increased international competition, except for in textiles.

# 7. **Conclusion**

This paper applies individual-level data on employees matched with firm-level data to estimate wage and production functions of employees with different demographic and other characteristics.

Most standard wage regressions do not control for firm characteristics that can affect the wage determination process. The reason is that information on firms and employees are often not available. We control for firm characteristics in this paper. Additionally, we go a step further in that we jointly estimate wage and production functions. This approach allows not only for assessing the marginal impact of demographic and other characteristics on wages but also for comparing the impact of these variables on productivity and wages.

For certain groups of workers, wage differentials match productivity differentials, while for others they do not. First, female employees are generally paid less than male employees and the negative wage premium they receive do not reflect a corresponding negative productivity premium. This may suggest gender discrimination. The wage differential between men and women is estimated at around 37 percent. However, for the case of the textile sector, no discrimination seems to be present.

Second, we find that employees' experience as measured by age is reflected equally in wages and productivity differentials over the life-cycle, both wages and productivity increase, but at a decreasing rate. No clear differences exist in returns to education due to gender. No direct link between firm-specific experience is established. Rather, the link is indirect, as firm-specific experience is implicitly captured by the age-variable.

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Third, we find that the wage premium increases with the level of education<sup>and with</sup> occupational title. Productivity differences are revealed for secondary education<sup>and they</sup> fall behind wage differentials by education. Hence, the positive wage gap<sup>between</sup> workers with completed secondary education and workers with no or little completed education cannot be completely justified by higher productivity, but may rather<sup>indicate</sup> shortage of educated workers. Further, no overall occupational productivity<sup>differential</sup> is established, whereas productivity differentials fall behind wage differentials<sup>when</sup> exarnining wages and productivity of the occupational groups individually.

Fourth, the findings reveal the existence of an asymmetry in training: formal in-house training is found to be associated with higher wages, while there does not seem to be any (instantaneous) effect on productivity from formal in-house training. The opposite seems to be true for training by outside providers: here there is a productivity enhancing effect, while this is not awarded as higher wages, however.

Furthermore, an additional wage and productivity premium exist for enterprises engaged in exporting activities, indicating that new technology has an enhancing effect on<sup>both.</sup> Fifth, long-term contracts help firms to retain and build firm specific skills. We<sup>find no</sup> trade-off between enhanced job security and wages, but rather that job security endows insiders with increased bargaining power. Eventually, that may lead employers to invest relatively more in physical capital than in long-term engagement to employment. The trend in manufacturing employment has been slowly increasing since 1980, which<sup>further</sup> strengthens this finding. However, the productivity differential associated with<sup>a</sup> permanent contract is negative, indicating that temporary workers do not<sup>lack the</sup> incentive to work hard as might otherwise be expected. Temporary workers<sup>are more</sup> productive than permanent workers, indicating that the former may work hard<sup>in an</sup> attempt to obtain a permanent contract.

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Sixth, the findings indicate that apprentices obtain a positive wage premium but they are not more productive than their colleagues who have not been an apprentice are. This indicates that an apprentice diploma may serve as a screening device when hiring. Furthermore, the wage premium related to apprenticeship iis higher in metals than in any other sub-sector.

Seventh, the ethnic origin -- African, European or Asian -- of an employee is not very important in the determination of wages. However, Europeans are more productive than others are. Europeans employed in larger firms are paid more than others controlling for education, occupation and other individual and firm characteristics. Eighth, union members earn less than non-union members despite being more productive.

Enterprise characteristics are associated with individual wages and productivity: First, workers employed in an enterprise located in Harare and Bulawayo are both more productive and paid higher wages. The productivity differential is significantly larger than the wage differential.

Second, employees of firms engaged in exporting activities are paid higher wages, which can be explained by higher productivity in these firms. However, employees are not fully compensated for their high productivity, indicating that firms benefit more than employees do from the enhanced productivity following from opening up the economy to world markets.

Third, larger exporting firms are marginally less productive and pay marginally lower wages than the average firm, but they are still more procluctive than smaller firms and wages match productivity.

Fourth, firms with foreign ownership are more productive than other firms. One reason for this could be that foreign ownership brings new technology to the firm. However, wages are not higher in firms that are foreign or partly foreign owned.

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Fifth, a relatively high number of expatriates employed in a firm are found to be associated with higher wages. This holds for all sectors, though an additional premium exists within woods, metals and textiles.

Sixth, larger firms are more productive and pay higher wages; the former is significantly larger than the latter. Seventh, employees employed in the metal or textile sectors are paid higher wages than their colleagues are in the food sector. Employees in metals are less productive than employees from other sectors. Furthermore, their productivity is lower than their wages. The more expatriates in firms in metals, the more productive the employees are; this may be caused by expatriates bringing knowledge about new technology to the enterprise, spilling over to other employees. Enterprises in metals that purchase foreign licenses are both more productive and pay higher wages than enterprises that do not purchase licenses, the productivity differential is larger than the wage differential. Larger firms in metals are found to be more productive than smaller firms; wages are not significantly higher. Additionally, firms engaged in exporting in the textile sector pay lower wages than other sectors, but employees are also less productive, though less so than reflected in wages. International competition may have caused lower wages as we control for most other firm and individual characteristics.

This finding can be interpreted in the following way. Zimbabwe is a relatively small country or economy and only one or a few firms produce each good, which may imply enterprises gaining monopoly power. Hence, the price paid by consumers is higher than it would be in a competitive market where competition is either arising from other national enterprises or from foreign enterprises. Part of the price is economic rent. If the rent part of the product price is shared between the employer and the employees wages will be higher than in a competitive product market. Increased openness indicates increased competition and, therefore, reduced economic rents. This impacts negatively on wages. The overall effect of increased international trade on welfare depends on the size of the reduced consumer prices, money wages and goods included in workers' 35
baskets of consumption. One possible outcome of increased competition is<sup>that workers</sup> are better off because the economic rent part of goods prices is reduced more than the part of wages to which rents contribute. Older firms in textiles pay higher wages but<sup>are less</sup> productive than firms established more recently.

In this study, also the importance of the size of the firm has been analyzed. We can shortly summarize the findings as follows. Larger firms in textiles are both more productive and pay higher wages and the productivity gap is found to be larger than the wage gap. In woods larger firns are associated with negative wage and productivity gaps, indicating that employees in woods are less productive than in foods. Trained employees receive lower wages in metals and textiles than in other sectors, but they are not less productive. Larger firms engaged in export activities pay lower wages and are a little less productive than firms not or less engaged in these activities. Furthermore, Europeans are being paid more than employees of African origin in larger firms, but are also less productive than these ethnic groups, ceteris paribus. Employees working in larger firms in woods and metals are paid less than employees are in smaller firms, but they are not less productive. The labor market in Zimbabwe is not perifectly competitive but rather segmented. For example, the wages of workers with similar skills employed in different sectors are not equal. As a result, we conclude that there exist structural differences across sectors.

The analysis in this paper provides evidence supporting the role of increased openness to international markets; it benefits productivity and to a large extent also wages.<sup>The same</sup> holds for human capital obtained through training or education. Economic<sup>policies</sup> aiming at increasing productivity in Zimbabwean enterprises and, hence, long-run economic growth should emphasize training and education of employees and<sup>promote</sup> openness of the economy towards the international markets via acquisition of knowledge and technology from abroad.

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