Exports, Price Transmission, and Wage Inequality: Evidence

1

Exports, Price Transmission, and Wage Inequality: Evidence Using the Case

of the Impact of AGOA in Ghanaian Manufacturing Firms1

FRANCIS MULANGU2

ABSTRACT

The paper presents evidence of the relationship between exports and wage inequality using manufacturing

firm-level data from Ghana. After contextualizing the Ghanaian manufacture sector a dynamic difference-in-

differences model was used to compare exporting firms to matched non-exporting ones before and after the

African Growth Opportunity Act (AGOA) was enacted. Robustness tests were used to verify if the estimated

export premium is associated with AGOA. After estimating the heterogeneous change in wages associated

with the export premium, the paper finds that exporting per se may not necessarily lead to wage inequality,

but the mechanism by which export premium is transmitted to workers and this mechanism has primarily

benefited unskilled male workers with relatively lower pre-AGOA wages working in smaller firms.

Key words: exports, price transmission, wage inequality, Ghana,

JEL: F1, F6

1 This work was supported by funds from the Swiss Programme for Research on Global Issues for

Development (r4d programme) under the thematic research module "Employment in the context of sustainable development" and the research project “Trade and Labor Market Outcomes in Developing Countries”. The Swiss Programme for Research on Global Issues for Development is being implemented jointly by the Swiss Agency for Development and Cooperation (SDC) and the Swiss National Science Foundation (SNSF). The views expressed here are the authors’ and do not necessarily reflect those of the SDC or those of SNSF. All errors are our responsibility. 2 [email protected], African Center for Economic Transformation (ACET) 50 liberation rd Accra,

Ghana PMB CT 4, (00233) 203100681

mailto:[email protected]

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INTRODUCTION

The evidence has consistently shown that workers in exporting firms earn relatively more than those

in non-exporting firms. For instance, Bernard et al. (2007) calculate the export premia in U.S. manufacturing in

2002 and find that exporting firms’ workers earn 17 per cent higher wages than non-exporters, are 11 per cent

more productive in terms of value-added per worker and have 3 per cent higher total factor productivity.

Several other models also concluded that exports may increase the skill premium globally, including Feenstra

and Hanson (1997). These conclusions have subsequently contributed to the now accepted notion that trade

liberalization leads directly to higher trade volumes and indirectly to wage inequality (Harrigan and Reshef,

2011).

The notion that exports3 create wage inequality between low skill and high skill workers is well

established and accepted as discussed above. However, debates persist regarding whether skilled workers

gain a higher export wage premium or if this wage premium is instead higher for unskilled workers. For

example, Munch and Skaksen (2008) find that wages are higher in Danish firms with high export intensity and

highly educated workers but lower in high-export-intensity Danish firms with workers who have lower levels of

education. However, Schank et al. (2007) estimate separate regressions for blue-collar and white-collar

German manufacturing workers while controlling for a range of individual characteristics, including age,

gender, level of education, and nationality. In contrast to much of the literature, they find a higher export wage

premium for blue-collar workers than for white-collar workers.

To overcome this impasse, a new strand of literature has brought to surface an intuitive, but often

ignored, notion that exporting requires skills. Verhoogen (2008), for instance, shows that exports in Mexico

are associated with a higher skill composition of employment because firms upgrade product quality, a task

that is skill-intensive. More recently, Brambilla, Lederman and Porto (2012) argued that exporting per se may

not necessarily lead to higher skill utilization. What matters is the destination of firms' exports. In particular, for

developing countries, exporting to high-income countries requires skills but exporting per se does not. This is

because firms need to increase product quality, as in Verhoogen (2008), and because firms need to use

skilled labour during the export process, as in Matsuyama (2007).

Contributing to these new developments, the present study takes a further look and argues that price

transmission, the mechanism by which the firm-level export premium is distributed among workers, matters.

While the export destination and whether firms’ product inputs are imported affect the allocation of the export

premium, the mechanism by which the export premium translates into a skill premium also merits

investigation. This mechanism, which can be built around a measure of wage price elasticity, is a function of

factors such as labour market structure, labour search costs, consumers’ response to changing prices, firm

structure, and producer adjustment costs. When exports increase the price firms receive4, the associated

price transmission to workers depends on wage price elasticity, which varies by skill type, gender, and the

sub-sector in which the firm operates. A worker’s wage elasticity with respect to firm price is therefore an

important factor in determining the direction of the wage inequality associated with exports, and the present

study will present evidence of this relationship in Africa.

The notion of price transmission is essential for designing inclusive trade policies. Often this variable

has been considered as exogenous in the trade literature and rarely investigated. For this reason, policy

debates mostly focus on maximizing firms export premium and take the resulting effects on wages as given

without showing great interests in opening the Pandora box that influence the mechanism by which this wage

premium is shared among wage earner types. This paper will argue that price transmission will determine

whether firms can attract and retain skilled workers, which will influence the type of product a firm can export.

In addition to evaluating price transmission, this study provides first-hand evidence of exports, skill

premium and wage inequality in Sub-Saharan African (SSA) countries (Wagner, 2012)3. This is performed by

analysing a set of rare firm-level and worker-level panel (1991-2002) data from Ghanaian manufacturing firms.

Furthermore, the approach employed here distinguishes the present work from previous research. This study

exploits a quasi-natural experiment setting to instrument for exports, thus minimizing many confounders that

may bias the claim of a causal relationship.

3 The underlined assumption here is that trade liberalization leads to higher exports.

4 This was anticipated, as higher prices are the main motivation for exporting

3

This study develops an identification strategy by taking advantage of the natural experiment setting

provided by Ghana’s eligibility for the African Growth Opportunity Act (AGOA) which offers preferential duty

and quota-free access to selected SSA products into US markets5. This strategy is used to evaluate the

impact of exports on firms’ prices, a first order trade effect, after matching exporting and non-exporting firms.

Using the price impacts, the study derives Deaton (1989) wage compensating variation associated with the

export-triggered firm price change, the export premium, and employs non-parametric regressions to reveal the

heterogeneous price transmission of the export-induced price change to wages. The results provide evidence

of an export wage premium; and this export wage premium is biased by gender and skill level such that

unskilled men have the largest export wage premium, a result that is supported by Schank et al. (2007). The

more general results state that exports have primarily benefited unskilled male workers with initially lower

wages working in small firms.

The policy deliberations in this exercise conclude that in developing countries, trade policies that seek

to reach wage earners must be designed with a clear understanding of the exporting country’s labour market

structure, firm management to workers structures, and the human capital available to perform the exporting

activities. These factors will influence the price transmission mechanism, which will determine the direction of

the wage export premium. In addition, it is important for the ratio of managers to workers to remain at the

optimum to allow for price gains to reach all workers, especially those at the bottom. Finally, as workers are

empowered through minimal management, firms should consider improving human capital by introducing cost

effective quality and production improvement tools that require minimal managerial supervision.

GHANA MANUFACTURED GOODS EXPORTS AND JUSTIFICATION OF IDENTIFICATION STRATEGY

This section discusses the Ghanaian manufacturing structure and export trends of the different

manufacturing sub-sectors before and after Ghana became eligible for the AGOA. The discussion regarding

the Ghanaian manufacture sector in the 1990s was taken from Asante (2002) and Sutton and Kpentey (2012),

who provided an excellent and detailed analysis at the sub-sector level. The dynamic export manufacturing

figures were taken from the United Nations Conference on Trade and Development (UNCTAD) and analyzed

according to the purposes of this study. The objective of this section is to contextualize the study and provide

a suitable foundation and justification for our identification strategy.

Ghana’s Manufacturing Sector in The 1990s

Ghana had a relatively small and underdeveloped manufacturing sector in the 1990s, representing

approximately 10% of GDP. Approximately three quarters of manufacturing output was produced by the

private sector. The sector comprised the following main sub-sectors: petroleum refining; food manufacturing;

textiles, wearing apparel and leather goods; non-ferrous metal products; beverage industries; tobacco and

tobacco products; sawmill and wood products; and chemical products other than petroleum.

The food processing sub-sector accounted for some 9.4% of total manufacturing labor. The main food

products were cocoa food products; dairy products, mainly milk and ice cream; edible oils; wheat flour; and

canned tuna.

Firms in the two principal industries in the drink sub-sector, beer and soft drinks, each accounted for

approximately 4.3% of manufacturing labor. The beer industry had undergone structural change, including

mergers and modernization. There were three major breweries at the time: Ghana Breweries Company, Accra

Brewery Limited, and Guinness Ghana Limited. The soft-drink bottling operations of the Ghana National

Trading Company were divested to the Coca-Cola Bottling Company of Ghana in 1995. Pepsi Cola and D & C

Industries were the other major soft drink producers.

The textile, wearing apparel and leather goods sub-sector accounted for approximately 14% of

manufacturing labor. Prior to 1983, the Ghanaian textile industry had relied on government protection in the

form of low tariffs/duties on raw material imports and the liberal allocation of foreign exchange. The 2005

5 This paper summarized the literature on international trade and firm performance by discussing papers

published since 2006.

4 expiration of the Multi-Fiber Arrangement was a major blow to the Ghanaian textile industry, as it precipitated

the closure of Ghana’s largest spinning and weaving firm, Juapong Textile.

The wood-processing subsector consisted of approximately 250 logging firms and 130 saw milling,

veneering and ply milling companies. It employed 31.6% of the total manufacturing labor force in the mid-

1990s. Further downstream, there were over 20 industrial furniture and woodworking enterprises and some

thousands of wayside wood workers employing up to 10 workers directly or indirectly.

Non-Ferrous Metal Products primarily includes the production of alumina at the smelter run by

VALCO, represented approximately 10% of manufacturing output and employed 1.7% of manufacturing labor.

The smelter processes imported alumina into aluminum; it does not have the capacity to process locally

mined bauxite. Most of the output is used domestically. Furthermore, the iron and steel sub-sector employed

3.2% of the manufacturing labor force. The industry is energy-intensive and relies on hydropower generated

from the Akosombo Dam. Unfortunately, the high energy demand triggered by rapid population growth

competed with the necessary energy required to fuel the smelter and eventually led to its current operation at

20% capacity.

Chemical production covers a wide range of products, such as building materials, toiletries, and

rubber products. The major producers of soap were Unilever Ghana Limited (66% owned by Unilever PLC),

which had acquired the Twifo Oil Palm Plantation, and PZ Ghana Limited. Both firms produce a range of other

toiletries and health care products. Cement was produced in Ghana by Ghacem Ltd, which is privately owned.

It produced standard Portland cement from imported clinker and gypsum. The chemical sub-sector employed

13.6% of total manufacturing labor.

The remaining manufacturing labor force was employed in other sub-sectors, such as paper, non-

metallic, electrical, and the petroleum sub-sector, which was based on the state-owned Tema Oil Refinery

(TOR) that produced refined products from primarily imported crude oil. Ghana began commercial production

of crude oil in late 2010. The country's Jubilee oil field produces high-grade oil. However, TOR was built to

process lower grade crudes and cannot refine oil from the new local fields unless significant investment is

made.

Ghanaian Manufactured Goods Exports to the USA

When Ghana became eligible for AGOA benefits in 2000, this development came as a surprise, which

was reflected by the subsequent governmental initiatives that followed. Unlike other countries such as

Lesotho that anticipated AGOA by attracting Taiwanese firms, Ghana did not take any ex-ante decision. It was

only later that the government of Ghana began to lay the groundwork to position the country to exploit the

benefits provided by the act. Specific products were identified for preferential access under AGOA, and

structures such as the Export Development Investment Fund (EDIF) were put in place to enhance private

sector export capacity through subsidized loans. Public institutional support included the establishment of an

AGOA Secretariat at the Ministry of Trade and Industry; a Ghanaian AGOA Committee was also established.

A Notable Increase in Manufactured Exports After 2000

After 2000, Ghanaian exports of manufactured goods to the US experienced a notable increase. The

share of Ghana’s US manufacturing exports to Ghana’s manufacturing exports to the rest of the world (ROW)

increased twice as rapidly between 2000 and 2004 than between 1996 and 1999. The share changed from

approximately 7% in 1999 to an average of 17% between 2000 and 2004. However, the growth rate began to

decrease as Ghanaian manufactured goods exports to the ROW began to increase rapidly after 2005.

Figure 1: Ghanaian manufactured goods exports

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Source UNCTAD

Share of light manufactured exports decreased after 2000; while share of medium to heavy

manufacturing exports increased significantly.

To better understand the origin of this growth in manufacturing exports, we decomposed Ghanaian

manufacturing exports on the basis of the extent to which an activity uses skills and technology into the

categories light, medium, and heavy. This categorization was made based on UNCTAD and United Nations

Industrial Development Organization (UNIDO) classifications. Figures 2-4 reveal that there is significant

volatility in manufactured goods exports and exports to the USA are flat for most years.

Figure 2: Ghana light manufactured good exports

Source: UNCTAD

The share of Ghanaian light manufactured exports to the US decreased dramatically after 2000. This

decrease is primarily due to the rapid growth in Ghana’s light manufacturing exports to the ROW, which

increased fivefold between 2000 and 2010. Light manufacturing here include clothes, shoes, furniture, food,

drink processing, etc.

Figure 3: Ghana medium manufactured goods exports

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Source: UNCTAD

The share of medium manufacturing, however, increased more dramatically after 2000, changing

from an annual growth rate of 1.6 between 1996 and 1999 to 9.7 after 2000. Medium manufacturing here

includes metal manufacturing, etc.

Figure 4: Ghana heavy manufactured good exports

Source: UNCTAD

Finally, the share of heavy manufacturing exports to the US also experienced a rapid increase after

2000. Prior to 2000, it had grown at a rate of 1.04%, while the average growth rate after 2000 was 3.4%.

Examples of heavy manufacturing include the chemical and machinery industries.

Increased Textile and Garments Exports After 2002

The AGOA’s apparel provision attracted many African countries, including Ghana. Ghana focused

attention on the textiles/garment industry and obtained export certification visas in 2002. This compelled

Ghana to adopt an effective visa system and related procedures to prevent unlawful transshipment of textile

and apparel articles. This occurred after the AGOA Committee embarked on study tours of other African

countries to learn from their successful experiences, especially in the textiles/garments industries, and sought

to initiate regional collaborative partnerships. The activities of the AGOA committee, among other factors,

contributed to increased textile/garment exports to the U.S. market, which began to increase in 2002 and

peaked in 2006 as illustrated in Figure 5. There are also examples of apparel firms that began operations in

Ghana to cut and sew fabrics and export them to the USA for Wal-Mart. Here there is an important point of

note: the fact that garment/textiles exports started to increase only after 2002 presents a key limitation for our

7 subsequent analyses. Although textiles/garments constitute a major source of overall increase in AGOA-

related exports to the US, subsequent analyses of the impact of increased (AGOA-related) exports on

innovation will not include textiles/garments. This is because our 1992-2002 panel data (on investments in

innovation) does not cover the post-2002 period when garments/textiles exports began to rise.

Figure 5: Ghana textile and apparel exports

Sources: UNCTAD

Based on the above illustrations, it can be concluded that medium and heavy manufactured exports

were responsible for the increase in Ghanaian manufacturing exports to the USA after 2000.

METHODOLOGY

The goal of this exercise is to measure Deaton (1989) wage compensating variation (CV) associated

with an export-induced (here export is instrumented using AGOA eligibility) firm price change. As the price

change in our case will be triggered by exports, here we will call the CV the wage export premium (ep).

Conceptually, this effect can be illustrated using the expenditure function. In equilibrium, household/worker

expenditures must be financed with income. Formally,

(1)

The expenditure function (.)e of household member/worker h is defined as the minimum amount of

expenditures required to achieve a given level of household/worker utility hu . It depends on a vector of prices,

, the level of utility hu , and other household/worker characteristics, hx . On the right-hand side, income

comprises wages w and the sum of the profits earned in different economic activities in which the

household/worker may be involved. Profits include, for instance, the net income from informal enterprises the

worker may do on the side. They depend on prices, technical change and key household/worker

characteristics (summarized by ). Notice that profits are defined as sales net of input purchases, and hence

some of the effects of protection on inputs or intermediate goods can be captured by . In (1),

hT measures

transfers (public or private), savings and other unmeasured factor returns. We lastly assume that exogenous

income .

The first order impact of changes in the price of good can be derived by differentiating equation (1)

(while holding utility constant and adjusting hT ). It follows that that the ep associated with a change in the

th

price is the following:

8

(2)

h

is is the budget share spent on good , is the share of household income from the production of

good , and is the elasticity of the wage earned by the household/worker with respect to the price , the

main variable that determines price transmission.

As we are only interested in wage earners who are not involved in any profit making activities, we will

only estimate the ep associated with a change in wage:

(3)

The sub-sections below discusses the data and the identification strategy employed to estimate

and how the term

is estimated using Hsiao (1986) regression.

Data

The study employed an extensive firm and worker data set from the Regional Project on Enterprise

Development & Ghana Manufacturing Enterprise Survey (EDGMES) supported by the World Bank and made

available by the Centre for the Study of African Economics at the University of Oxford. These data tracked

more than 200 manufacturing firms and an average of more than 1000 workers per round between 1991 and

2002 from four of the major cities in Ghana, namely, Accra, Kumasi, Cape Coast, and Takoradi. The firms

constituted a panel intended to be broadly representative of the size distribution of firms across the major

sectors of Ghana’s manufacturing industry. These sectors include food processing, textiles and garments,

wood products and furniture, metal products and machinery. A further discussion of the data and their

descriptive statistics is provided in the appendix.

Model Specification and Matching Method

Ghana was among the first countries to become eligible for the AGOA in 2000. We therefore estimate

the impact of AGOA on firm price by comparing the price figures before and after 2000, taking firms that

export outside of Africa as the treatment group and compare them with firms that do not export at all. We used

“export outside of Africa” because the EDGMES data do not provide specifics regarding the destination of

firms’ exports. In addition, between 2000 and 2004, Ghanaian manufacturing exports to the USA constituted

approximately 25 per cent of total manufacturing exports outside of Africa, a figure that increased since 2000

from an average of approximately 10.6 per cent between 1996 and 1999. Therefore, the identification strategy

will not be particularly affected, as Ghanaian manufacture exports outside Africa also grew by 34 per cent

between 2000 and 2004, but when the data on manufacturing exports to the US are removed, the growth rate

changes to minus 43 per cent, implying that the growth in Ghanaian manufactured goods exports to the USA

is responsible for the difference.

We use a difference-in-differences (DiD) approach to evaluate the difference in prices between firms

that export outside of Africa and those that do not before and after Ghana was awarded the right to export

duty free to the USA through AGOA. As the data used in this analysis are non-experimental data, a matching

approach is recommended to address the non-random nature of the data (Dehejia and Wahba, 2002). The

matching method we implemented used a Mahalanobis metric matching to adjust for pre-AGOA observable

differences between firms that export outside of Africa and those that do not.5

We considered the years 1998 and 1999 as the base years and the years 2000, 2001, and 2002 as

the follow-up years, such that the average of the two base years will be compared with the individual follow-up

years. Note that the treatment variable is dynamic to further account for firms that may have begun to

export after AGOA eligibility. That is, if firm i exported outside of Africa in 2001 and did not in 2002 for

whatever reason, then and

. Conceptually, we estimated a propensity score matching

dynamic (DiD) such that:

(4)

9

where t є {1998, 1999, 2000, 2001, 2002}

: Producer price in year t

: = 1 if a firm exports outside of Africa and 0 otherwise

: Year dummy such that =1 if year=1999 and 0 otherwise




: Vector of firm characteristics

Equation (4) is estimated using a 2SLS fixed effect model to accommodate the dynamic nature of the

treatment variable while controlling for time invariant variables, and addressing the endogeneity of the export

variable.

Endogeneity of Exporting Variable

There are various potential sources of selection bias that may obscure the causal relationship, as

exporting outside of Africa is likely to be non-random. A firm may decide to export based on its access to a

port, the sector in which it operates, access to resources, and, in the case of sophisticated markets, the

number of skilled workers. Correcting for this selection bias will depend on the availability of observable

covariates that are correlated with firms’ export decisions. We use a firm’s location, sub-sector, number of

skilled workers, output, raw material costs, and capital as instruments for exporting outside of Africa and

estimated equation four using a two stage approach.

Table 1: Determinants of exporting outside of Africa

VARIABLES COEFFICIENTS P-VALUE

Real value of output 2.68E-11***

0.004

Real total cost of inputs -8.66E-11***

0.000

Number of skilled workers 0.0029395***

0.000

Real value of capital -6.30E-13 0.143

Location FE Yes

Sub-sector FE Yes

Constant -0.03 0.560 * p < 0.10,

** p < 0.05,

*** p < 0.01

The results of the first stage are presented in table 1 above and indicate that real output and the

number of skilled workers are positively correlated with exporting outside of Africa and real input costs are

negatively correlated with exporting outside of Africa.

Estimates of the Impact of Exporting on Prices and Robustness Check

The second stage of this empirical analysis consists of estimating equation (4) as expressed above

while accounting for the instrument variables used in table 1 using a 2SLS estimator. The results indicate that

the estimated value of is positive and statistically significant. In addition, this impact variable grew by 22

per cent of the years considered with an average impact (over the three years of interest) of slightly below one

10 per cent. That is, exporting, instrumented by AGOA, had a statistically significant, positive impact on producer

prices.

Table 2: Impact of exporting on firm prices

VARIABLE COEFFICIENT P-VALUE

Ex -0.25152** 0.045

*Ex 0.078434* 0.071

*Ex 0.10006** 0.039

*Ex 0.117615** 0.039

Number of worker 0.00016* 0.071

Weekly hours of work -0.00154 0.140

Replacement value of capital -2.23E-13* 0.052

Location FE Yes

Sub-sector FE Yes

Constant 7.074***

0.000 * p < 0.10,

** p < 0.05,

*** p < 0.01

We also estimated a placebo regression assuming that the AGOA eligibility occurred in 1999 as a

robustness check and found that the placebo variable *Ex was not statistically significant (table 3). This

indicates that if we had assumed that Ghana became eligible for AGOA in 1999, we would have found that

AGOA had no statistically significant impact on producer prices.

Table 3: Placebo regression of the impact of exporting on firm prices

VARIABLES COEFFICIENTS P-VALUE

Ex -0.2193** 0.038

Placebo ( *Ex) -0.0199 0.204

*Ex 0.0915* 0.040

*Ex 0.1197** 0.017

*Ex 0.1308** 0.02

Number of worker 0.0002* 0.067

Weekly hours of work -0.0016 0.171

Replacement value of capital -1.91E-13* 0.082

Location FE Yes

Sub-sector FE Yes

Constant 7.0845***

0.000 * p < .10,

** p < 0.05,

*** p < 0.01

Now that we know that exports affected on producer prices, we need to analyse how this price

change was transmitted to wage earners. We wish to determine the extent to which the gains from exports are

passed on to workers and whether the distribution of the wage premium is dependent on worker

characteristics.

Estimate Wage Price Elasticity

11

We estimated the wage price elasticity using the specification presented in equation (5). Specifically,

we estimated it by gender, sector, skill, and location. We used the full data set (1991-2002) to estimate the

elasticity variables. Conceptually, we estimated the following equation:

(5)

: Wage rate of worker m in firm n in year t.

: Vector of worker characteristics

: Error term

The coefficient is our coefficient of interest, as it will be used to estimate the elasticity variable .

However, we recognize that this elasticity variable is not uniform across workers. We take a further step by

deriving the differences in elasticities by a worker’s competence, sub-sector, and firm location, as these

variables are important in determining price transmission. These differences are captured by the coefficients

of the interaction terms in equation (5), namely and .

Table 4: Elasticities of wage to price change

VARIABLES ELASTICITIES ROBUST S. E.

Price 0.0608678***

0.0184849

-0.021317***

0.0048895

Age 1.111316***

0.0741412

-0.4188391***

0.0316592

Gender (male=1) -0.0015583 0.0051225

Education 0.0249369 0.0113425

0.054014***

0.0054997

Competence (skill=1) 0.0328628***

0.002717

Number of Unskilled 0.0121617***

0.001113

Tenure 0.0060192***

0.001333

Firm age 0.0218557***

0.006099

Sector

Drink 0.0010319** 0.0004095

Food 0.0054609** 0.002699

Chemical 0.0032847***

0.0007683

Machines -0.0008496 0.0011887

Metal 0.0031114 0.0047413

Furniture -0.0125117** 0.0061555

Wood 0.00736***

0.0020632

Textile 0.0006914 0.0007051

Garment -0.0066017***

0.0017332

Price*Gender 0.0131164***

0.0028348

Price*Drink -0.0006535** 0.0003077

Price*Food -0.0012151 0.0011312

Price*Chemical -0.0020465***

0.000543

Price*Metal 0.0018475 0.0021263

12

Price*Machine 0.0003812 0.0005488

Price*Furniture 0.0005555 0.0027412

Price*Wood -0.0041469***

0.0009896

Price*Textile -0.0006887** 0.000307

Price*Garment 0.0018722** 0.0009235

Price*Competence -0.0049998***

0.0016655

Firm total wage bill 0.0004833***

0.000036

Location FE Yes

Year FE Yes * p < .10,

** p < 0.05,

*** p < 0.01

The results state that the variable in equation three is non-linear, positive, and statistically

significant. The nature of the price transmission is such that a one per cent change in firm price will result in a

0.02 per cent change in the wage rate. However, the nature of this elasticity variable depends on sub-sector,

worker gender, worker competence, and firm location, as illustrated by the statistical significance of the

relevant interaction variables in table 4.

The wage price elasticity is larger for males and lower for skilled workers. This implies that male

unskilled workers will benefit the most from a change in a firm’s prices, while skilled female workers will

benefit the least. At the sub-sector level, while wage earners in the garment industry have a higher wage price

elasticity, those in the drink, chemical, wood, and textile sub-sectors have relatively lower wage price

elasticities than the benchmark. The location of the firm did not appear to influence the process by which price

is transmitted to wage earners. Considering firms located in Takoradi as the benchmark, the coefficients of the

location-price interaction terms were not statistically significant.

DISCUSSION

The impact of AGOA on wage is estimated here in two stages. We first measure the change in firm

prices before and after 2000, the year AGOA was enacted between firms that export outside of Africa and

those that do not export. Using firms that export outside of Africa turns out to be appropriate here for two

reasons. First, Ghana’s manufacturing exports to the USA constituted approximately 25 per cent of its total

manufacturing exports outside of Africa. Second, most of the variations of Ghana’s exports outside of Africa

between 2000 and 2004 were due to exports in the USA. The results of the interaction terms between

exporting and year variables (2000, 2001, and 2002 against 1998 and 1999 which were used as base years)

as illustrated in table 2 suggest that AGOA increased the price firms receive. A Placebo regression was later

undertaken to test the robustness of the results. The regression verified the results if AGOA was enacted in

1999 and found that the results are not significant. Since our objective is to measure the impact of this price

change on wage within firms, we first estimated the elasticities of wage to firm price change. By combining the

price change with the wage elasticity, we derived heterogeneous wage changes ep associated with AGOA as

derived in equation (3). Next we estimate a series of non-parametric regressions to study the distributional

effects of exports across firm type and worker type in the next sub-section.

Distributional Effects of Exports

Generally, the export wage premium decreases as firm size increases. This negative relationship

appears valid when comparing the export wage premium with the number of skilled workers, number of

unskilled workers, and firm age. A potential explanation is the small number of large and old firms. However,

the relationship between the export wage premium and the capital replacement value follows an inverse U

shape. Medium sized firms, in terms of capital replacement value, benefit from exports to a greater extent.

When comparing the export wage premium across worker characteristics, we find a negative

relationship between the export wage premium and the initial wage rate. Workers who had lower wages

before AGOA benefited the most from exporting and those that had higher wages benefited the least. This

13 relationship also appears valid across genders. Although unskilled workers on average benefited more from

exporting than skilled workers, the distributional impact among unskilled workers is such that workers who had

lower wages before AGOA benefited the least and those that had higher wages benefited the most. However,

among skilled workers, a similar distributional difference is not apparent.

Exports appear to have primarily benefited unskilled male workers with initially lower wages working

in small firms. Conversely, skilled female workers with initially higher wages working in large firms benefited

the least. The distributional results suggest the presence of a heterogeneous export premium by firm and

worker types. This is why many have concluded that exporting leads to wage inequality. However, we argue

that this claim may not be entirely correct. In fact, when comparing the export premium by pre-AGOA wage

rate, the difference in export wage premia by initial wage level provides suggestive evidence of a catching up

process. Although there are differences in the gains from exporting by worker type, the difference is favorably

skewed toward workers who had lower pre-trade wages.

14

Figure 6: Non-parametric estimates of the wage export premium by firm size (a=across number of workers,

b=number of skilled workers, c=number of unskilled workers, d= replacement value of capital, e=firm age)

a.

.00

2.0

02

5.0

03

.00

35

wa

ge

imp

act

-.5 0 .5 1 1.5 2Total Number of Workers

kernel = gaussian, degree = 0, bandwidth = .14

Local polynomial smooth

.00

25

.00

26

.00

27

.00

28

.00

29

wa

ge

imp

act

0 1 2 3 4 5Log Skilled Workers



.00

24

.00

26

.00

28

.00

3.0

03

2.0

03

4

wa

ge

imp

act

0 2 4 6 8Log Unskilled Workers



.00

24

.00

25

.00

26

.00

27

.00

28

.00

29

wa

ge

imp

act

10 15 20 25Log real replacement value of plant and machinery (deflator 1991 Cedis, millions



.00

23

.00

24

.00

25

.00

26

.00

27

.00

28

wa

ge

imp

act

0 20 40 60 80Firm age

kernel = gaussian, degree = 0, bandwidth = 2.22


b.

d. c.

e.

15 Figure 7: Non-parametric estimates of the wage export premium by initial (2000) wage rate (a= full data,

b=male workers, c= female workers, d= skilled workers, e=unskilled workers)

a.

.00

18

.00

2.0

02

2.0

02

4.0

02

6.0

02

8

wa

ge

imp

act

0 2 4 6 8 10Log of Real hourly earnings before tax (1991 Cedis)



.00

26

.00

27

.00

28

.00

29

.00

3.0

03

1

wa

ge

imp

act

2 4 6 8 10Log of Real hourly earnings before tax (1991 Cedis)



.00

12

.00

14

.00

16

.00

18

.00

2

wa

ge

imp

act




.00

1.0

01

5.0

02

.00

25

.00

3

wa

ge

imp

act




.00

2.0

02

5.0

03

.00

35

wa

ge

imp

act




b.

c. d.

e.

16

Determinants of Wage Export Premium

We took a further step to evaluate the determinants of ep. We wish to determine whether there are

other variables (firm level) that can explain the distributional impact of ep to deduce important policy

recommendations. We considered variables such as worker unionization, the ratio of managers to the total

number of workers, production efficiency, and whether firms import their inputs, whether a firm is state owned,

and the weighted average of firms’ human capital.

Table 5: Determinants of the wage export premium

VARIABLES COEFFICIENTS S.E.

Import -7.1E-05 5.77E-05

Union -0.00021***

6.86E-05

Efficiency -7.6E-05 0.00016

Number of manager -1.5E-05** 7.13E-06

State 0.000744***

0.000228

Human capital 2.61E-05** 1.05E-05

Sub-sector FE Yes

Constant 0.002676***

0.000128 * p < .10,

** p < 0.05,

*** p < 0.01

The results above indicate that the union and number of manager variables are negatively correlated

with ep. In a competitive labour market, unions will undermine workers’ export wage premia as their presence

is generally seen as a source of labour market inefficiencies. The more managers a firm has, the lesser its ep,

as the trade premium will primarily be absorbed by managers. However, firms that are state owned and those

with higher levels of human capital are positively correlated with ep. Unlike private firms, state owned firms

are less aggressive in maximizing profits. In Ghana, public servants are the beneficiaries of a series of

benefits that in most cases undermine a firm’s profit maximization motive. This is why ep is positively

correlated with state owned firms.

CONCLUSION AND POLICY RECOMMENDATIONS

The present study contributes to the export and wage inequality debate by arguing that price

transmission, which is determined by both macro-economic policies and internal firm structures, matters. The

argument suggests that in addition to export destination and input importation, the process by which price

gains due to exporting are transmitted to workers matters, and this is captured by the wage price elasticity. In

the case of Ghanaian manufacturing firms, the wage price elasticity of skilled workers is lower than that of

unskilled ones, such that an export triggered price increase will relatively benefit unskilled workers, supporting

the results presented by Schank et al. (2007) in the case of Ghanaian manufacturing firms.

The results suggest that firms should be encouraged to rearrange their hierarchies in a manner that

keeps the ratio of managers to the total number of workers to a reasonable minimum to facilitate the

transmission of trade benefits and allow it to reach workers lower in the hierarchy. However, excessively little

supervision can affect productivity and quality. In addition, it is important to empower the workers by

introducing cost effective, “management free” quality and production improvement tools. These human capital

enhancing tools will increase the benefits workers gain from trade.

The firms that mostly benefited from AGOA are composed of blue collar workers who are involved in

transient activities that generate a small export premium. Should the AGOA be extended beyond 2015,

several issues will need to be addressed to improve African countries’ participation rate in the scheme.

Renewal should consider the program’s major shortcomings to make AGOA more inclusive, accessible and

permanent. Preferences should to be strengthened, enhanced and improved to ensure that trade and

investment may be channelled towards medium manufacturing sectors with export potential in a durable

manner. However, this would require making provisions such as the third party provision more permanent, as

this would encourage investors to make long-term commitments.

17

APPENDIX

The study employed an extensive firm and worker data set from the Regional Project on Enterprise

Development & Ghana Manufacturing Enterprise Survey (EDGMES) supported by the World Bank and made

available by the Centre for the Study of African Economics at the University of Oxford. These data tracked

more than 200 manufacturing firms and an average of more than 1000 workers per round between 1991 and

2002 from four of the major cities in Ghana, namely, Accra, Kumasi, Cape Coast, and Takoradi. The firms

constituted a panel intended to be broadly representative of the size distribution of firms across the major

sectors of Ghana’s manufacturing industry. These sectors include food processing, textiles and garments,

wood products and furniture, metal products and machinery. Table 1 below provides an overview of the

summary statistics.

Table A1: Summary statistics

VARIABLES MEAN MINIMUM MAXIMUM

Firm Level variables

Number of workers 86.07 1 1800

Weekly working hours 45.65 11 98

Replacement value of capital 7.04E+09 0 4.00E+11

Producer price index 1161.22 394.57 2811.27

Exporting firm 0.25 0 1

Exporting outside of Africa 0.14 0 1

Firm age 19.5 0 76

Number of unskilled workers 76 0 1593

Number of skilled workers 16 1 253

Real value of output 8.71e+08 0 1.18e+11

Real total cost of inputs 3.60e+08 0 4.33e+10

Real value of capital 7.04e+09 0 4.00e+11

Imports 0.53 0 1

Union 0.50 0 1

Efficiency 0.46 0.003 0.82

Management as percentage of

all workers 3.27 0 50

State owned 0.04 0 1

Human capital 10.32 0 23

Employee Level variables

Log real per hour wage 4.64 -3.34 9.63

Age 33.52 14 82

Gender (male=1) 0.80 0 1

Education 10.80 0 27

18

Competence (Skilled =1) 0.31 0 1

Tenure (years) 7.04 0 52

Cross-cutting variables

Location Dummies

Accra 0.59 0 1

Cape Coast 0.04 0 1

Kumasi 0.31 0 1

Takoradi 0.06 0 1

Sub-sector Dummies

Drink processing 0.02 0 1

Food processing 0.13 0 1

Small-Scale Resource

Intensive 0.01 0 1

Chemical 0.05 0 1

Machines 0.03 0 1

Metal 0.19 0 1

Furniture 0.18 0 1

Wood 0.08 0 1

Textile 0.04 0 1

Garment 0.19 0 1

Others 0.08 0 1

Number of firms in survey 297

Number of worker in survey 1075 578 1524

Source: EDGMES

The average number of workers per firm (including permanent, temporary, full-time and part time) is

86.1 with a maximum of 1800. Between 1991 and 2002, this variable has increased by an average rate of 3.6

per cent annually, indicating a modest growth in the manufacturing sector. The peak year was 1998 with an

average of 101.6 and the lowest was 1993 with an average of 49.4.

The average weekly hours worked per firm is 45 with a maximum of 98 hours and a minimum of 11

hours. In terms of location, 58.6 per cent of the firms are located in Accra, 4.2 per cent in Cape Coast, 30.9

per cent in Kumasi, and 6.3 per cent in Takoradi. When comparing the weekly hours of work across locations

we find little variation. Firms located in Cape Coast have the most hours worked at 51, and those in Takoradi

have the fewest hours worked at 44. The replacement value of capital captures the replacement value of both

land and equipment. It is estimated to be approximately 7 billion Ghana Cedi with a maximum of 400 billion

Ghana Cedi. The sector with the largest capital value is drink manufacturing followed by the textile sector. The

small-scale, resource intensive sub-sector has the lowest capital value.

The producer price index averages to approximately 1161 with a maximum of 2811 and a minimum of

394. On average, this index is highest for firms located in Cape Coast and lowest for those in Takoradi, with

19 very little variation across locations. However, the differences across sectors are rather large, with the

garment sector having the highest prices and the metal sector the lowest.

The summary statistics suggest that 25 per cent of firms are involved in exports. However, this value

varies substantially by location. Of the firms located in Takoradi, 51 per cent are involved in export activities,

while only 24 per cent of those located in Kumasi are. This difference is obvious given the proximity of

Takoradi to the port. Furthermore, 60 per cent of the exporting firms export outside of Africa to more

sophisticated markets; and this is equivalent to approximately 14 per cent of all manufacturing firms.

Manufacturing firms in Ghana appear to remain in business for long periods. The average age of firms

is 19.5 years with a maximum of 76 and a minimum of 0. The oldest firms are located in Cape Coast, while

the youngest are Kumasi. This is understandable, as Cape Coast is considered the oldest city in the country

and Kumasi recently became the economic capital of Ghana.

The average number of unskilled workers per firm is 76 with a minimum of 0 and a maximum of 1593.

Generally, unskilled workers are more attractive to firms located in Takoradi and least attractive to those in

Kumasi. This is because Takoradi hosts a number of low-skill manufacturing firms due to its proximity to the

port, while Kumasi attracts businessmen involved in activities that require a minimum skill level.

The real value of firms’ output and input costs average to approximately 871 million and 360 mill ion

Ghana Cedis, respectively. For both of these variables, the textile firms experience the highest output and

input values and firms in the small-scale, resource intensive sub-sector the lowest. In terms of location, firms

located in Takoradi have the highest output values and input costs and those located in Cape Coast have the

lowest.

On average, firms have approximately 16 skilled workers. Those with the highest number of skilled

workers are located in Takoradi, and those with the lowest number are located in Kumasi. However, the

average number of unskilled workers is 76, such that firms located in Takorai have the highest average

number of unskilled workers and those in Cape Coast have the lowest. In terms of sub-sector differences, the

textile sub-sector has the most skilled and unskilled workers, while the small-scale, resource intensive sub-

sector has the fewest.

The average real value of capital is 7 billion Ghana Cedis, and the textile sub-sector has the highest

value and the small-scale, resource intensive sub-sector the lowest. Furthermore, firms located in Takoradi

have the highest capital values and those in Kumasi the lowest. Nearly half of the firms import and have

workers who are registered with a union. The firms that import the most are in the drink sub-sector, and those

that import the least are in the wood sub-sector. With respect to unions, the chemical sub-sector has largest

number of firms registered with a union. The small-scale, resource intensive sub-sector has the fewest

unionized firms.

Most of the firms in this sample operate below the optimal efficiency level, with an average efficiency

level of 0.46, a maximum of 0.82 and a minimum of 0.003. This stochastic efficiency variable was estimated

following the framework of Aigner, Lovell and Schimdt (1977). The most efficient firms operate in the food sub-

sector and the least efficient ones in the wood sub-sector. This ratio is the highest in the food sub-sector and

the lowest in the small-scale, resource intensive sub-sector. Approximately 4 per cent of the surveyed firms

are state owned. While 21 per cent of these firms are in the drink sub-sector, 2 per cent are in the metal sub-

sector. Furthermore, most the state owned firms are located in Takoradi, while Accra has the fewest.

We estimated human capital by estimating the weighted firm average of education and obtain an

average value of 10.32 and a maximum of 23. The sub-sector with the highest human capital value is the

chemical sub-sector, and that with the least is the small-scale, resource intensive sub-sector. The average

ratio of managers to the total number of workers per firm is 3.27 with a maximum of 50 and a minimum of 0.

This simply means that for every manager, there are 3.27 non-managerial workers in a firm. The natural

logarithm of the real, pre-tax, per hour wage is 4.6 with a maximum of 9.6. The sectors that pay the highest

wages are the drink and chemical sectors, and the least attractive in terms of remuneration is the small-scale,

resource intensive sector. Between 1991 and 2002, real wages have grown by an average rate of 1.3 per

cent.

20

The average worker’s age is 33 with a maximum of 82 and a minimum of 14. Age differences across

sector and location are not statistically significant. Overall, 80 per cent of workers are male, and only the

small-scale, resource intensive sector is dominated by female workers, who represent 92 per cent of all

workers in this sector. The average worker has 10.8 years of education with a maximum of 27 and a minimum

of 0. Surprisingly, years of education do not statistically significantly difference across gender. On average, 31

per cent of workers are considered skilled, and skilled workers have four more years of education than

unskilled workers. The average tenure of a worker is seven years with a maximum of 52 and a minimum of 0.

Worker turnover appears to be highest among unskilled workers, who on average remain in a job for three

fewer years than skilled workers, and workers in the garment industry on average remain in a position for two

fewer years than in other sectors.

REFERENCES

Aigner, D., C. Lovell, and P. Schmidt. (1977) “Formulation and estimation of stochastic frontier

production models”, Journal of Econometrics 6, 21-37.

Asante, Yaw (2002), African imperatives in the new world trade order: A case study of the

manufacturing sector in Ghana. Paper Presented at an AERC Workshop in Kampala,

Uganda November 13 – 16 2002.

Bernard, A.B., J.B. Jensen, S.J. Redding and P.K. Schott. (2007), “Firms in international trade”,

Journal of Economic Perspectives, 21(3), 105-130.

Brambilla, I., D. Lederman, and G. Porto (2012) “Exports, export destinations and skills”, American

Economic Review, 102(7), 3406-38.

Deaton, A. (1989) “Rice prices and income distribution in Thailand: a non-parametric analysis.

Economic Journal 99 (Conference volume):1-37.

Dehejia, R., and S. Wahba. (2002) “Propensity score matching methods for non-experimental causal

studies”, Review of Economic Studies, 84(1), 151-161.

Feenstra, R. and G. Hanson. (1997) “Foreign direct investment and relative wages: Evidence from

Mexico's maquiladoras”, Journal of International Economics 42(3-4): 371-393.

Harrigan, J and A. Reshef. (2011) “Skill biased heterogeneous firms, trade liberalization, and the skill

premium”, Working Paper 17604, National Bureau of Economic Research

Hsiao, C. (1986), Analysis of Panel Data, Econometric Society monographs No. 11, New York:

Cambridge University Press.

Matsuyama, K. (2007) “Beyond icebergs: towards a theory of biased globalization”, The Review of

Economic Studies, 74(1), 237--253.

Munch, J, J. Skaksen. (2008) “Human capital and wages in exporting firms”, Journal of International

Economics, 75(2), 363-372.

Schank, T, Schnabel, C, J Wagner. (2007) “Do exporters really pay higher wages? First evidence

from German linked employer–employee data”, Journal of International Economics, 72(1), 52-

74.

Sutton, J. and B. Kpentey (2012) Am Enterprise Map of Ghana. International Growth Center, London

UK.

Verhoogen, E. (2008) “Trade, quality upgrading, and wage inequality in the Mexican manufacturing

sector” Quarterly Journal of Economics, 123(2), 489-530.

21

Wagner, J. (2012) “International trade and firm performance: a survey of empirical studies since

2006”, Review of World Economics, 148(2), 235–267.

Documents

Exports, Price Transmission, and Wage Inequality: Evidence