Foreign Technology and Industrial Productivity: Evidence from Egypt

Shimaa A. Elkomy*

Kwok Tong Soo†

* Corresponding author. Department of Economics, Lancaster University Management School, Lancaster LA1

4YX, United Kingdom. Tel: +44(0)77234 07899. Email: s.elkomy@lancaster.ac.uk † Department of Economics, Lancaster University Management School, Lancaster LA1 4YX, United Kingdom.

Tel: +44(0)1524 594418. Email: k.soo@lancaster.ac.uk

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Foreign Technology and Industrial Productivity: Evidence from Egypt

Abstract.

This is the first empirical study that examines the heterogeneous productivity effects of

capital imports and embodied foreign technological advancements in the Egyptian

manufacturing sector over the period 2006 to 2009. The results show that capital imports and

embodied foreign R&D stock both have positive effects on labour productivity, and this is

true especially once we allow for their effects to vary according to industry characteristics.

However, there is no difference in the impact of foreign capital between highly productive

and less-highly productive industries, or between more open and less open industries.

Industries with low technology experience negative productivity effects of foreign capital,

perhaps because, in these industries, foreign investors seek to benefit from low labour costs

rather than invest in productivity-enhancing capital equipment.

Keywords: Capital imports, industrial productivity, foreign R&D spillovers

JEL Classification: D24, L60, O30

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1. INTRODUCTION

Technological development is a key driver of long-run economic growth. The growth

accounting literature finds that the accumulation of physical capital explains less than half of

the growth in income per capita in developed countries in the last century (Grossman and

Helpman, 1991). The developed countries have invested heavily in R&D activities,

innovative production methods, patents and human capital development, resulting in a large

accumulated stock of knowledge and technological progress. The performance of African

countries has been more mixed. Although many African countries have invested in importing

advanced foreign technologies embodied in machinery and equipment, this has not always

led to accelerated economic growth.

However, although capital imports are an important conduit for the transfer of advanced

technologies and technical progress to African countries, the domestic knowledge base and

technical know-how are also critical components of the adoption of foreign technologies.

Government policies in these countries have not taken into account the process of learning by

industrial firms in adopting new technologies (Lall and Wangwe, 1998). The history of the

developed world emphasises the interaction between human capital and technical capabilities

with machinery and equipment to build a country’s knowledge base and know-how.

According to Lall and Pietrobelli (2002), technological development in the manufacturing

sector in African countries is critical for two main reasons. First, industrial productivity

growth is essential for the transformation of the economic structure of low and low-middle

income countries. Second, the technological capability of an industry determines its

international competitiveness and its ability to adopt new innovations and technological

advancement.

This paper examines empirically the heterogeneous impact of foreign technologies and R&D

spillovers embodied in imported capital on the productivity of industries in Egypt, using data

from 2006 to 2009. Like many other African countries, Egypt has invested extensively in

importing foreign technologies. The capital imports of Egypt from the OECD countries

reached on average 40.6 percent of its total industrial imports from 2000 to 2010 (UNCTAD,

2013). In addition, we investigate whether industries with higher productivity levels and

higher shares of trade in industry output have higher knowledge spillovers and improved

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technology from capital imports and foreign R&D. We test for the relevance of existing

technological capabilities by grouping industries into four groups by technology intensity,

and testing for different productivity effects of capital imports and embodied foreign R&D.

Our results should be relevant for drawing policy guidelines and in enhancing our

understanding of the relationship between technological development, capital imports, and

foreign R&D spillovers.

Our paper seeks to examine the effectiveness of economic policies that target the adoption of

foreign technologies in raising labour productivity in Egypt. We attempt to test the argument

of Ajakaiye and Page (2012) that African countries have not succeeded in following the path

to industrialisation, and that their policies have failed to enhance industrial development

through technological advancement. Our paper contributes to the previous empirical literature

on Africa by considering how the industrial structure of Egypt affected its ability to adopt

foreign technologies and benefit from technological advancement embodied in capital

imports.

Existing empirical studies show that, at the macroeconomic level, developing countries with

higher foreign capital imports exhibit significantly higher economic growth rates (Lee, 1995;

Mazumdar, 2001, Caselli and Wilson, 2004). However, these studies assume that foreign

technologies enhance productivity homogeneously across all industries. But industries differ

in terms of their absorptive capacity, international competitiveness and technological

requirements. Therefore, the productivity spillovers of foreign technologies are anticipated

not to be uniform across all industries in the manufacturing sector.

A number of empirical studies have investigated the role of capital imports as a transmission

channel for international technological advancements and R&D activities. Lall and Wangwe

(1998) argue that technological shifts in Africa have been mainly through the importation of

capital goods. The spillovers from foreign technology embodied in capital imports have been

studied by Coe and Helpman (1995), Keller (1998), and Hejazi and Safarian (1999).

However, these studies have focussed on the cross-country productivity effects of foreign

R&D spillovers in the developed world.

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The role of existing productivity and trade openness in enhancing the spillover effects of

foreign capital has been controversial. Liu et al. (2000) and Xu (2000) report that domestic

firms with higher productivity experience significant productivity spillovers from foreign

technologies. However, Jordaan (2005) argues that high productivity industries show limited

technological externalities. Jordaan (2008) and Blalock and Gertler (2009) show that

domestic firms that are laggards in terms of productivity, experience larger productivity

spillovers from foreign technological advancements. While Xu and Sheng (2012) present

evidence that domestic firms with higher international competitiveness are more able to

exhibit productivity gains from foreign technologies, Blomstrom and Sjoholm (1999) find

that domestic firms with less trade openness demonstrate more scope of benefitting from new

advanced foreign technologies. Page (2012) argues that promotion of industrial capabilities

and firm competencies in African countries are principal elements in productivity growth and

industrialisation.

The following section presents an overview of the related literature. Section 3 discusses the

estimation methods, while Section 4 discusses the data. The discussion of the empirical

results is presented in section 5 while section 6 provides some conclusions.

2. RELATED LITERATURE

While neoclassical growth theory perceives capital imports as a source of physical capital

accumulation, new growth theory emphasises the importance of capital imports as a channel

for technological advancement, which is perceived as a key determinant of long run growth.

Trade in intermediate goods acts as a channel of technological diffusion and hence as a

source of efficiency improvement, by increasing the variety of intermediate goods and the

degree of specialisation. Many studies explore the role of capital imports and technology

spillovers in enhancing productivity. Brada and Hoffman (1985) argue that the accumulation

of imported capital significantly contributes to technological progress in developing

countries. Grossman and Helpman (1991) show theoretically that the number of new varieties

of intermediate goods and foreign imported technologies are associated with high

productivity in the manufacturing sector, because of higher degrees of specialisation in

production. Similarly, Coe et al (1995) corroborate the finding of technology spillovers

arising from imported capital from developed countries.

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Evenson and Westphal (1995) discuss the theoretical underpinnings of the technological

advancement that is stimulated by the number of firms adopting new technical knowledge.

The costs of adopting new foreign technologies affect the decision of firms to adopt these

technologies. Firms in developing countries tend to adopt classical and old vintage

technologies to avoid loss of productivity in the initial period of learning by doing. Baily and

Gersbach (1995) argue that which generation the technology is in (advanced technology or

old vintage technology) is adopted in a developing country is conditional on the level of

internationalisation and the ability of domestic labour to effectively deal with the new

production methods and technologies. Temple and Voth (1998) and Hendricks (2000) build

on the work of De Long and Summers (1993) and suggest considerable productivity returns

from equipment investment in the presence of human capital to facilitate the adoption of

superior technology. The main argument is that domestic human capital is required for the

optimal adoption of foreign technologies and their adaptation to make them appropriate for

domestic factor endowments and industrial circumstances.

Numerous empirical studies test for the effect of foreign technologies embodied in capital

imports on productivity using cross-country data. Lee (1995) finds that capital imports

exhibit relatively higher productivity effects than domestic capital goods in developing

countries. Similarly, Mazumdar (2001) verifies that output growth in developing countries is

affected positively by the technology spillovers of capital imports. Caselli and Wilson (2004)

show that capital imports contribute significantly to differences in TFP across countries.

The cross-country literature based on innovation-driven growth theory discusses the

relevance of foreign R&D embodied in capital imports in enhancing domestic productivity.

Coe and Helpman (1995), Keller (1998), Hejazi and Safarian (1999), and Cecchini and Lai-

Tong (2008) argue that productivity growth is attributable to the transmission of technology

and knowledge from R&D investments which is embodied in foreign imports. Coe and

Helpman (1995) and Hejazi and Safarian (1999) present evidence that the growth in

productivity of domestic economies is significantly affected by the technology transfer and

knowledge spillovers of foreign R&D investments, and that they exhibit larger productivity

effects than domestic R&D. These papers also discuss the importance of international trade as

a channel of dissemination of international R&D. Lichtenberg and de la Potterie (1998) show

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that foreign R&D spillovers from imported capital exhibit stronger productivity effects than

foreign R&D spillovers from total imports. Cecchini and Lai-Tong (2008) show the

significant productivity effects of foreign R&D capital stock embodied in the capital imports

and FDI of six Mediterranean countries.

The microeconomic studies that examine cross-industry productivity spillovers of capital

imports present less rosy conclusions about the technology effects of the latter. This

framework allows a deeper investigation and considers the dynamics of the interaction

between capital imports and industrial productivity growth. Karake (1988) empirically

verifies the positive and significant impact of foreign technologies embodied in capital

imports on manufacturing output growth in Egypt. Similar results are obtained by Iscan

(1998). On the other hand, Keller and Yeaple (2009) report that productivity spillovers

accruing to US manufacturing firms from imports are insignificant.

Keller (2000) and Halpern et al (2006) classify the productivity effects of capital imports into

efficiency gains and technology spillovers. Keller (2000) identifies the significant role of

imported machinery in transferring advanced foreign technologies, while Halpern et al (2006)

show that the productivity gains from capital imports in the manufacturing sector in Hungary

are mainly due to the efficiency spillovers of expanding the variety of intermediate inputs.

Chamarbagwala et al (2000) show that capital imports exhibit larger technology effects

across 27 industries only in countries at a higher stage of development. The empirical

evidence shows that the stage of development, signifying the presence of skilled and educated

labour, determines the industrial productivity gains from capital imports.

In the same vein, Hasan (2002) shows that in India, capital imports in the technologically

intensive industries of chemicals, pharmaceuticals, electronics, and machinery, exhibit

significant positive productivity effects. Less technologically orientated industries, however,

experience larger productivity effects from the addition of domestic capital. Similarly,

Chuang and Hsu (2004) find that only Chinese industries operating close to the technological

frontier and near to best practice are able to demonstrate significant productivity spillovers

from capital imports. Given the inconclusive outcomes of the empirical literature, the present

paper investigates the productivity spillovers of capital imports and technology spillovers of

embodied foreign R&D capital.

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3. METHODS

The main objective of this paper is to investigate whether capital imports have an impact on

productivity. Following Aslanoglu (2000), Liu et al. (2000) and Driffield and Love (2007),

we estimate the following logarithmic regression equation:

ln 𝐿𝑃𝑖𝑡 = 𝛿0 + 𝛿1 ln𝑀𝐿𝑖𝑡 + 𝛿2 ln𝐾𝐿𝑖𝑡 + 𝛿3 ln𝑊𝐿𝑖𝑡 + 𝛿4 ln𝑊𝐶𝐿𝑖𝑡

+ 𝛿5 ln𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛿6 ln𝐶𝐼𝑖𝑡 + 𝜇𝑖 + 𝜈𝑡 + 𝜀𝑖𝑡 (1)

LP is labour productivity, namely the ratio of gross value added to the total number of

employees in industry 𝑖. CI is capital imports per employee, and is measured as the annual

flow of investments in purchasing foreign machinery and equipment per employee. Hence 𝛿6

is our main coefficient of interest; if imported capital has a positive and significant impact on

labour productivity, this supports the hypothesis that domestic industries are able to

efficiently assimilate superior imported technologies.

The skill-intensity of labour is measured by two proxy variables: wages (WL), calculated as

total remuneration per unit of labour, and the white collar labour ratio (WCL), measured as

the ratio of white collar labour to total employment. White collar labour includes

entrepreneurs, managers, technicians and specialists, and administrators and secretaries.

These two proxy variables control for the industry’s ability to adopt advanced foreign

technologies (Buckley et al., 2002; Sinani and Meyer, 2004, and Rosell-Martinez and

Sanchez-Sellero, 2012). White collar labour signifies labour with certain educational levels

and technical abilities, while the average wage rate reflects the average skill level of labour

(Globerman, 1979 and Balasubramanyam et al., 1999).

ML is the total materials per unit of labour. KL is the capital-labour ratio, measured as the

share of fixed capital assets to labour. Firmsize is the average revenue per firm in each

industry which reflects some of the market characteristics and structure (Liu et al., 2000).

Large firm size is expected to generate productivity gains due to economies of scale and

lower average costs. Since all variables are in logs, the coefficient estimates denote

elasticities while 𝜇𝑖 are the industry-specific effects and 𝜈𝑡 are time-specific effects, and 𝜀𝑖𝑡 is

the random error term.

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Many empirical studies discuss the role of R&D embodied in foreign capital stock in

generating productivity spillovers. Coe and Helpman (1995), Keller (1998), Lichtenberg and

de la Potterie (1998), Hejazi and Safarian (1999), and Cecchini and Lai-Tong (2008) find that

the stock of foreign R&D has larger productivity effects on the domestic economy than the

stock of domestic R&D. Following existing literature, we test for the impact of foreign R&D

stock embodied in imported capital on domestic labour productivity. We therefore include the

foreign R&D stock channelled through capital imports:

ln 𝐿𝑃𝑖𝑡 = 𝛿0 + 𝛿1 ln𝑀𝐿𝑖𝑡 + 𝛿2 ln𝐾𝐿𝑖𝑡 + 𝛿3 ln𝑊𝐿𝑖𝑡 + 𝛿4 ln𝑊𝐶𝐿𝑖𝑡

+ 𝛿5 ln𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛿6 ln 𝑆𝑅𝐷𝑖𝑡 + 𝜇𝑖 + 𝜈𝑡 + 𝜀𝑖𝑡 (2)

Equation (2) replaces CI with SRD, which is the foreign R&D stock embodied in capital

imports, to test for the presence of productivity spillovers of foreign R&D which depend on

imports of foreign capital. This is the indirect productivity gain from new foreign machinery

and equipment which results from the transfer of new technologies, new intermediate

products, and the expansion of the variety of inputs. Following Lichtenberg and de la Potterie

(1998), Xu and Wang (1999) and Cecchini and Lai-Tong (2008), SRD in industry 𝑖 in year 𝑡

is calculated as

𝑆𝑅𝐷𝑖𝑡 = 𝐶𝑀𝑖𝑡 𝑆𝑖𝑡𝑑

𝑌𝑖𝑡 (3)

𝐶𝑀𝑖𝑡 refers to capital imports (note this is not the same as 𝐶𝐼𝑖𝑡 as defined in equation (1),

which is capital imports per employee), 𝑆𝑖𝑡𝑑 is the total R&D stock in all OECD countries in

industry 𝑖 in year 𝑡, and 𝑌𝑖𝑡 is the total output of that industry in all OECD countries1.

Analogously to Lichtenberg and de la Potterie (1998), the stock of foreign R&D in each

industry is equal to the amount of capital imports multiplied by the R&D/output ratio of the

OECD in each industry. The Foreign R&D capital stock is computed from annual R&D

investments for each industry using the permanent inventory method:

𝑆𝑖𝑡𝑑 = 𝐼𝑖𝑡1−[(1−𝜆) (1+𝑟𝑡)⁄ ] (4)

Where 𝐼𝑖𝑡 is the R&D investment in industry 𝑖 in year 𝑡, 𝑟𝑡 is the annual growth rate of R&D

annual investments, and 𝜆 is the depreciation rate which is assumed to be 10 percent per year

(Cecchini and Lai-Tong, 2008). Imports of capital machinery and equipment from OECD

countries constitutes on average 76 percent of Egypt’s total capital imports from 2000 to

1 Countries included are Australia, Austria, Belgium, Canada, Czech Republic, Estonia, Finland, France,

Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Korea, Mexico, Netherlands, Norway, Poland,

Portugal, Slovenia, Spain, United Kingdom and United States.

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2010 (UNCTAD, 2013). This suggests that foreign R&D stocks embodied in capital imports

from the OECD may be an important channel for technology spillovers.

Our use of panel data enables us to compare the results both with and without controlling for

unobserved heterogeneity by industry through the use of industry fixed effects. In reporting

our results we proceed sequentially, first without including any fixed effects, then including

year fixed effects to control for unobserved time-specific effects, and finally including both

year and industry fixed effects to control for time-specific and industry-specific effects. A

Hausman test rejects the null hypothesis of no correlation between the regressors and the

fixed effects, suggesting that the fixed effects model is appropriate. All regressions reported

below include standard errors which are clustered by industry; this controls for

heteroskedasticity and correlation of the error term within industry.

There is a potential endogeneity problem in estimating equations (1) and (2). While labour

productivity may depend on imported capital as measured by either CI or SRD, it may also be

that how much capital is imported, depends on how productive labour is. Such endogeneity,

if it exists, would bias our results. Hence, in addition to conventional OLS and fixed effects

estimates, we also performed an instrumental variables regression to try and overcome the

endogeneity bias. Following Wang (2010), we use a one-year lag, the square of the one-year

lag, and the two-year lag of capital imports as instruments. The F-statistic of the excluded

instruments in the first stage regression shows that the instruments are highly correlated with

the instrumented variables. The Sargan test for overidentification does not reject the null

hypothesis of no correlation between the instruments and the error term, suggesting that the

instruments are valid. We also performed a Hausman test to compare the results of the

instrumental variables regression with the OLS regression, and cannot reject the null of no

significant difference between the two coefficient vectors. As a result, we do not report the

instrumental variables results below; however, the results are provided in the Appendix.

4. DATA

Our analysis of the productivity effects of capital imports and technology spillovers of

embodied foreign R&D stock makes use of a panel of 128 four-digit ISIC industries

comprising the whole manufacturing sector in Egypt from 2006 to 2009. The data source for

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the Egyptian data is the Annual Census of Industrial Production in Private Establishments

issued by the Central Agency for Public Mobilization and Statistics. The census includes data

on the number of firms, classification of employment by job, total remuneration and wages,

value added, costs of factors of production, domestic and foreign capital formation and fixed

assets. Around 9,500 establishments are covered, on average, in each year. The foreign R&D

measure is obtained from the OECD’s Analytical Business Enterprise Research and

Development (ANBERD) and Structural Analysis (STAN) databases. The R&D measure is

available for 30 two-digit ISIC industries; we have assumed that all four-digit industries

within each two-digit industry have the same level of R&D. The measure of R&D intensity is

the ratio of R&D expenditure to total value added per industry in current PPP prices. All

nominal values are converted into real terms using the wholesale price index from the World

Development Indicators (World Bank, 2012); producer price index data by industry is not

available.

[ TABLE 1 HERE ]

In terms of industrial structure, extraction of crude petroleum and natural gas contributes 84

percent of the total value added in the manufacturing sector. Pharmaceuticals is the second

largest industry in terms of value added, followed by the manufacture of coke and refined

petroleum products, basic metals, and non-metallic mineral products. This supports the

argument of Page (2012) who contends that the absence of diversification in the

manufacturing sector and the lack of sophistication of industries are important impediments

to sustainable economic growth in African countries. Table 1 shows the labour productivity,

capital intensity, imported capital share and value added share of the 30 industries. The

extraction of crude petroleum and natural gas is the most productive industry, followed by the

manufacture of printing and media products, coke and refined petroleum products, and non-

metallic mineral products. Hence the most productive industries are the extractive industries.

Table 1 also shows a positive relationship between labour productivity and the capital-labour

ratio (the correlation is 0.82; see also Table 3). However, the industries that have relatively

high shares of capital imports are not necessarily characterised by high capital-labour ratios

or high productivity. Remediation activities, paper, computers, electronic and optical

products, and basic metals exhibit the highest capital import shares. Nevertheless, the

extraction of crude petroleum and natural gas, basic metals, chemicals and paper industries

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constitute 67 percent of the total foreign imported capital in the manufacturing sector. This

indicates the large relative size of the crude petroleum industry, and the high capital import

shares of the other industries.

Table 2 presents the descriptive statistics of the variables used in the analysis. For most

variables, the mean is much larger than the median, which suggests that the variables are

right-skewed, and hence estimating the model in logs as we do may yield better statistical

properties. Mining support service activities showed no spending on total materials and zero

revenues, since this industry is monopolised by one public firm, whereas our data is for

private firms. In this industry and also in water collection, treatment and supply, there is no

investment in foreign machinery and equipment.

[ TABLE 2 HERE ]

Table 3 reports the correlation coefficients between the variables used in the analysis. The

dependent variable, labour productivity, is positively correlated with all the explanatory

variables, and is especially highly correlated with the capital-labour ratio and capital imports.

In the regression analysis reported in the next section, we will explore whether these positive

correlations hold up in multivariate analysis. Most of the explanatory variables are only

weakly correlated with each other, which reduces the likelihood of multicollinearity being a

problem for our analysis. Of those explanatory variables with higher correlation with each

other, larger firms are associated with more materials per worker and a higher wage rate.

Capital imports are positively associated with the capital intensity of an industry, while

capital imports and the R&D stock embodied in imported capital are positively related to

each other.

[ TABLE 3 HERE ]

5. ESTIMATION RESULTS

Table 4 presents the results of estimating equations (1) and (2) for various specifications. All

standard errors reported are clustered by industry. Columns (1) and (4) show the results

without year or industry fixed effects, columns (2) and (5) include year fixed effects, and

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columns (3) and (6) include both year and industry fixed effects. The results in columns (1)

and (2) show that capital imports have a positive and significant effect on domestic industrial

productivity. This implies that capital imports result in significant productivity gains and

efficiency spillovers that exceed the effect on physical capital accumulation. However, when

controlling for both year and industry fixed effects in column (3), capital imports become

insignificant. This implies that a large part of the positive effects obtained in columns (1) and

(2) reflects industry-specific productivity determinants (Lee, 1995). Our other measure of

capital imports, SRD, which is the foreign R&D stock embodied in capital imports, is never a

significant determinant of labour productivity in columns (4) to (6).

[ TABLE 4 HERE ]

The capital-labour ratio has a robustly significant positive effect on labour productivity across

all specifications. The coefficient estimate on KL is the largest of all the coefficient estimates.

Our results show that a one percent increase in the KL ratio results in an average increase of

approximately 0.6 percent in labour productivity. Hence physical capital accumulation is an

important contributor to industrial productivity and industries characterised by relatively high

productivity are those with the highest capital-labour ratios. The materials-labour ratio ML

has a consistently negative coefficient; a higher materials to labour ratio reduces labour

productivity, although this result is only weakly significant in the specifications without

industry fixed effects.

The average wage bill WL has a positive and significant effect on labour productivity. Our

results show that a one percent increase in the average wage bill increases labour productivity

by an average of 0.15 to 0.17 percent, and suggests that more highly skilled labour (as

reflected by higher wages) is more productive. The ratio of white collar to total employment

WCL has a consistently positive but insignificant effect on labour productivity. This suggests

that it is not the share of white collar workers that enhances labour productivity. The

correlation between WL and WCL in Table 2 is only 0.33, which does not suggest that these

two variables are highly correlated with each other. However, introducing industry fixed

effects in columns (3) and (6) reduces the size of the coefficient on WCL, suggesting that the

role of white collar workers is industry-specific. We also find significant labour productivity

gains from larger average firm size. This supports the idea that economies of scale lead to

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increased productivity. However, controlling for industry fixed effects in columns (3) and (6)

reduces the significance of firm size, once again suggesting that this result is partially driven

by industrial characteristics.

Hence, the results in Table 4 show that the technological advancement embodied in capital

imports does not have a significant effect on overall labour productivity across Egyptian

industries, once unobserved industry-specific characteristics have been controlled for. This

suggests that there is a lack of technical knowledge and technological capabilities in the

domestic economy needed to adapt the imported technology to domestic production

circumstances as discussed by Blomstrom et al. (1992), Pack (1993), Chamarbagwala et al.

(2000) and Halpern et al. (2006). These studies show that the knowledge spillovers from

capital imports in developing countries are insignificant because of the weak technological

capabilities of these countries.

Tables 5 and 6 extend the analysis to examine the different productivity effects of capital

imports and embodied foreign technology by the absorptive capacity, international

competitiveness and the technology orientation of domestic industries. Table 5 reports the

results for capital imports, while Table 6 reports the results for embodied foreign R&D

stocks. In both tables, all results include both industry and year fixed effects. In column (1) of

both tables, we examine whether industries with higher labour productivity have different

effects of capital imports on labour productivity. We divide industries into those above and

below the median labour productivity; this corresponds to 67,770 LE per worker. We interact

this indicator with CI and SRD. This is similar to the idea in other studies such as Kokko

(1994), Xu (2000) and Bijsterbosch and Kolasa (2010), that the absorptive capacity of

domestic industries may be reflected in their labour productivity. We find in column (1) of

Tables 5 and 6 that all industries experience productivity gains from capital imports and

embodied foreign R&D, since the coefficients on CI and SRD are positive and significant.

However, the interaction terms are not significant, suggesting that industries with high labour

productivities do not have an additional effect of capital imports and embodied foreign R&D

relative to industries with low labour productivities.

[ TABLE 5 HERE ]

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[ TABLE 6 HERE ]

Column (2) of Tables 5 and 6 tests for the productivity effects of CI and SRD based on the

trade openness of industries. Industries are classified as being open to trade if they are above

the median in terms of the share of exports and imports as a percentage of value added; this

corresponds to 40 percent of industry value added. As in column (1), we also interact this

indicator variable with CI and SRD to see if more open industries have larger effects of

capital imports and embodied foreign R&D stocks. We find a positive and significant effect

of CI on labour productivity in Table 5, but not for SRD in Table 6. In both tables, the

indicator variable for openness is significantly negatively related to labour productivity;

controlling for all the other factors, greater openness in an industry actually reduces labour

productivity. The interaction terms are insignificant in both cases, suggesting that there is no

additional productivity gain in industries with high levels of openness, relative to those with

low levels of openness.

As a final robustness check on our results, we divide the industries into four technological

groups: low technology, medium-low technology, medium-high technology and high

technology, following the OECD definition of technological intensity of industries

(Hatzichronoglou, 1997 and Boothby et al., 2010). The classification of industries into these

four categories is based on the 30 industry two-digit ISIC industrial classification, and is

shown in Table 7. Note that most industries belong to the low and medium-low technology

groups (12 and 11 industries, respectively). High technology industries are taken as the

reference group, and as in the rest of Tables 5 and 6, we interact the indicator variables for

technological groups with CI and SRD to identify whether industries in different

technological groups have different effects of capital imports and embodied foreign R&D

stock on labour productivity. Note that, because the classification of industries is time-

invariant, the indicators for technological groups drop out of the regression.

[ TABLE 7 HERE ]

The results of these specifications are reported in column (3) of Tables 5 and 6. Both CI and

SRD have positive and significant effects on labour productivity in the high technology

industries (the excluded category), and very similar effects for medium-high and low-medium

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technology industries, since the coefficients on the interaction terms are not significant. It is

in the low technology industries that there is a reduced effect on labour productivity of both

CI and SRD. In both cases, the net effect is negative; having foreign capital inflows actually

reduces labour productivity compared to if there are no foreign capital inflows, in low

technology industries. Overall our results are similar to those of Hasan (2002) who shows

that only capital imports in technologically intensive industries exhibit significant positive

productivity effects in the Indian manufacturing sector. Our negative results for low

technology industries suggests that in these industries, foreign capital invests in Egypt to

benefit from low labour costs, rather than investing in productivity-enhancing capital

equipment.

Finally, it is instructive to compare our results in Tables 5 and 6 with those of Table 4. Since

Tables 5 and 6 always include time and industry fixed effects, the appropriate comparison is

with columns (3) and (6) of Table 4, in which neither CI nor SRD has a significant impact on

labour productivity. Our results in Tables 5 and 6 show that, once we allow for the effects of

capital imports to vary depending on industry characteristics, both CI and SRD do in fact

have positive and significant effects on labour productivity, even though these effects do not

differ significantly based on these differential characteristics.

6. CONCLUSIONS

This paper is one of the first empirical studies that attempts to uncover the heterogeneous

technology spillovers and efficiency gains from capital imports and embodied foreign R&D

stocks across all industries of the manufacturing sector in Egypt. Our results show that there

is an overall positive effect of capital imports on labour productivity, especially once we

allow for the effects to vary according to industry characteristics. However, there is no

difference in impact between low productivity and high productivity industries, and no

difference in impact between industries which are more open or less open to international

trade. In low technology industries, capital imports have a negative effect on labour

productivity, in contrast to the positive effect in other industries. We speculate that this may

be caused by the fact that, in such industries, foreign investors seek to benefit from low

labour costs rather than invest in productivity-enhancing capital equipment.

16

Our results have important implications for policy making in developing countries. Economic

theory suggests that less developed countries benefit from importation of foreign

technologies without incurring the high costs of innovation. Our results suggest that this may

indeed be the case. However, we also find that the technology spillovers of this technology

transfer are not spontaneous or homogeneous across industries; rather, they are conditional on

domestic industrial capabilities. These industrial capabilities could be developed through two

main channels: technical assistance from foreign suppliers, and the development of domestic

labour through the training of more technicians and engineers.

In this light, two developments may highlight the difficulties faced by developing countries in

importing foreign technology. First is the fact that many developing countries legally restrict

the presence of foreign labour. For instance, in Egypt, the law states that no more than 20

percent of the number of employees in any firm may be foreign. As a result, the transmission

of know-how from foreign partners to domestic ones may be limited. Second, foreign

partners are usually wary about the protection of their intellectual property as embodied in

high-technology goods. Here, the development of communications technology may enable

foreign partners to retain control over operations in developing countries; however, at the

same time the same communications technologies may aid those who seek to illegitimately

exploit the technologies brought by the importation of foreign capital.

17

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Table 1. Productivity, Capital Intensity and Foreign Imported Capital

ISIC code Industrial sector name LP KL CI share VA share 06 Extraction of petroleum and natural gas 58.91 12.93 25.36 84.20 18 Printing and media products 13.84 171.63 0.05 0.22 19 Coke and refined petroleum products 10.34 25.81 5.72 2.74 23 Other non-metallic mineral products 5.59 55.35 7.09 1.58 11 Beverages 3.78 33.21 16.42 0.55 13 Textiles 3.76 33.83 18.64 0.48 28 Machinery and equipment n.e.c. 3.71 33.13 16.52 0.10 20 Chemical products 2.68 11.54 32.78 0.97 33 Repair of machinery and equipment 2.19 0.27 1.25 0.01 09 Mining support service activities 1.68 0.22 0.00 0.01 25 Fabricated metal products 1.58 18.08 8.73 0.27 19 Basic metals 1.37 2.00 36.49 2.74 21 Pharmaceuticals 1.31 1.41 16.23 2.76 16 Wood and cork products 1.10 14.74 9.35 0.03 17 Paper products 1.05 6.17 40.31 0.60 22 Rubber and plastics products 1.02 3.36 22.10 0.48 10 Food products 1.01 4.72 8.37 0.67 12 Tobacco products 0.83 0.09 18.55 0.35 27 Electrical equipment 0.81 5.39 16.22 0.42 36 Water collection, treatment and supply 0.75 0.05 0.00 0.00 26 Computer and electronic products 0.70 0.64 36.54 0.11 29 Motor vehicles 0.60 0.68 16.38 0.52 32 Other manufacturing 0.53 1.80 12.71 0.06 08 Other mining and quarrying 0.51 1.00 14.46 0.05 25 Other transport equipment 0.48 1.24 29.87 0.27 15 Leather and related products 0.29 0.53 17.01 0.05 31 Furniture 0.20 0.36 10.46 0.25 14 Wearing apparel 0.16 0.21 20.83 0.99 01 Crop and animal production 0.15 2.41 3.48 0.02 39 Remediation activities 0.08 0.07 98.50 0.00

Notes: The reported figures are the mean of the real values of the variables in the four digit industry from 2006 to 2009. LP is measured as the real value added per unit of labour and the reported figures are in 00,000LE. KL is the real fixed assets per labour and the reported values are in 00,000 LE. CI share is the percentage share of foreign capital investment of the total investments in machinery and equipment. VA share is the percentage of each industry in total manufacturing value added.

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Table 2. Descriptive Statistics

Variables Mean Median St.Deviation Minimum Maximum Labour productivity 4.03 1.03 10.80 0.08 58.91 Capital-labour ratio 14.76 2.20 32.67 0.05 171.6 Materials per labour 2.44 0.70 8.50 0.00 47.19 Wages per labour 0.16 0.11 0.16 0.03 0.72 White collar ratio (%) 0.29 0.26 0.12 0.12 0.65 Firm size 1900.5 206.9 6597.5 0.00 34587 Foreign imported capital 0.07 0.01 0.29 0.00 1.58 Foreign R&D ratio 0.002 0.0004 0.003 0 0.018

Notes: The reported figures are the mean of the real values of the variables in the four digit industry from 2006 to 2009. White collar ratio is a percentage; firm size is measured in 00,000LE per firm; all other variables are measured in 00,000LE per labour.

Table 3. Correlation Matrix

Ln LP Ln KL Ln ML Ln WL Ln WCL Ln Firmsize

Ln CI Ln SRD

Ln LP 1.00 Ln KL 0.82 1.00 Ln ML 0.16 0.04 1.00 Ln WL 0.28 0.03 0.28 1.00 Ln WCL 0.28 0.16 0.14 0.33 1.00 Ln Firmsize 0.26 -0.06 0.60 0.51 0.29 1.00 Ln CI 0.63 0.69 0.07 0.03 0.21 0.00 1.00 Ln SRD 0.35 0.28 0.05 0.21 0.14 0.18 0.62 1.00 Notes: Variable names are as defined in Section 3.

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Table 4. Productivity Effects of capital imports and SRD in Manufacturing Sector from 2006

to 2009.

Dependent variable: Natural log of labour productivity (1) (2) (3) (4) (5) (6) Ln KL 0.542*** 0.541*** 0.579*** 0.577*** 0.576*** 0.605*** (0.030) (0.031) (0.061) (0.023) (0.024) (0.061) Ln ML -0.100* -0.101* -0.053 -0.095* -0.096* -0.052 (0.057) (0.057) (0.081) (0.057) (0.057) (0.083) Ln WL 0.177*** 0.173*** 0.148** 0.169*** 0.166*** 0.148* (0.058) (0.058) (0.074) (0.058) (0.058) (0.076) Ln WCL 0.054 0.058 0.027 0.071 0.075 0.038 (0.098) (0.098) (0.121) (0.099) (0.099) (0.122) Ln Firmsize 0.218*** 0.219*** 0.155* 0.212*** 0.213*** 0.152* (0.038) (0.038) (0.090) (0.038) (0.039) (0.091) Ln CI 0.047** 0.048** 0.034 (0.023) (0.024) (0.021) Ln SRD 0.015 0.015 0.006 (0.019) (0.019) (0.022) R2 0.797 0.797 0.554 0.794 0.795 0.548 Log Likelihood -326.6 -326.3 -126.7 -329.210 -328.8 -128.9 Root Mean Sq. Error 0.601 0.603 0.348 0.605 0.607 0.350 Industry effects No No Yes No No Yes Time effects No Yes Yes No Yes Yes No. of obs. 363 363 363 363 363 363

Notes: Standard errors clustered by industry in parentheses. * p<0.10 ** p<0.05 *** p<0.01.

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Table 5. Industrial characteristics and the productivity effects of capital imports.

Dep var: Natural log of labour productivity

(1) (2) (3)

Ln KL 0.443*** 0.537*** 0.590***

(0.058) (0.059) (0.070)

Ln ML -0.013 -0.010 -0.054

(0.078) (0.073) (0.080)

Ln WL 0.131** 0.137* 0.144*

(0.053) (0.078) (0.077)

Ln WCL 0.008 0.037 0.004

(0.092) (0.110) (0.120)

Ln Firmsize 0.075 0.175** 0.162*

(0.082) (0.079) (0.091)

Ln CI 0.042* 0.060** 0.075*

(0.022) (0.028) (0.042)

LPhigh 0.668*** (0.031) Ln CI×LPhigh -0.006

(0.031) OPNhigh -0.476***

(0.148) Ln CI×OPNhigh

-0.037

(0.028)

Ln CI×TEClow

-0.102*

(0.058)

Ln CI×TECmedium-low

-0.051

(0.064)

Ln CI×TECmedium-high

-0.015

(0.069)

R2 0.646 0.583 0.564 Log Likelihood -84.987 -114.452 -122.781 Root Mean Square Error 0.311 0.337 0.346 No. of obs. 363 363 363

Notes: Standard errors clustered by industry in parentheses. * p<0.10 ** p<0.05 *** p<0.01. All results include unreported industry and year fixed effects. LPhigh is a dummy variable which is equal to 1 for industries above the median labour productivity. OPNhigh is a dummy variable which is equal to 1 for industries above the median level of openness to international trade. TEClow, TECmedium-low and TECmedium-high are dummy variables for industries with low, medium-low and medium-high technology levels, respectively; the excluded category is high-technology industries.

25

Table 6. Industrial characteristics and the productivity effects of embodied foreign R&D stock.

Dep var: Natural log of labour productivity (1) (2) (3) Ln KL 0.463*** 0.579*** 0.627*** (0.063) (0.060) (0.064) Ln ML -0.009 -0.003 -0.050 (0.080) (0.077) (0.080) Ln WL 0.129** 0.141* 0.150** (0.054) (0.078) (0.075) Ln WCL 0.021 0.038 -0.016 (0.095) (0.113) (0.119) Ln Firmsize 0.059 0.155* 0.134 (0.085) (0.083) (0.088) Ln SRD 0.041* 0.005 0.077** (0.021) (0.030) (0.038) LPhigh 0.548*** (0.133) Ln SRD×LPhigh -0.041

(0.031) OPNhigh -0.242*

(0.144) Ln SRD×OPNhigh

0.018

(0.031) Ln SRD×TEClow

-0.142***

(0.052) Ln SRD×TECmedium-low

-0.078

(0.061) Ln SRD×TECmedium-high

-0.088

(0.063) R2 0.642 0.573 0.563 Log Likelihood -86.948 -118.632 -122.994 Root Mean Square Error 0.313 0.341 0.346 No. of obs. 363 363 363

Notes: Standard errors clustered by industry in parentheses. * p<0.10 ** p<0.05 *** p<0.01. All results include unreported industry and year fixed effects. LPhigh is a dummy variable which is equal to 1 for industries above the median labour productivity. OPNhigh is a dummy variable which is equal to 1 for industries above the median level of openness to international trade. TEClow, TECmedium-low and TECmedium-high are dummy variables for industries with low, medium-low and medium-high technology levels, respectively; the excluded category is high-technology industries.

26

Table 7. Technological Classification of the Two-digit Industries

Low Technology Medium-Low Technology ISIC code

Industrial sector name ISIC code

Industrial sector name

01 10 11 12 13 14 15 16 17 18 31 39

Crop and animal production Food products Beverages Tobacco products Textiles Wearing apparel Leather and related products Wood and cork products Paper products Printing and recorded media Furniture Remediation activities

06 08 09 19 22 23 24 25 32 33 36

Extraction of petroleum and natural gas Other mining and quarrying Mining support service activities Coke and refined petroleum products Rubber and plastics products Other non-metallic mineral products Basic metals Fabricated metal products Other manufacturing Repair of machinery and equipment Water collection, treatment and supply

Medium-High Technology High Technology ISIC code

Industrial sector name ISIC code

Industrial sector name

20 28 29

Chemical products Machinery and equipment n.e.c. Motor vehicles

21 26 27 30

Pharmaceuticals Computer and electronic products Electrical equipment Other transport equipment

27

APPENDIX Table A1. 2SLS Estimation of Productivity Effects of capital imports and SRD in Manufacturing Sector.

Dependent variable: Natural log of labour productivity (1) (2) (3) (4) (5) (6) Ln KL 0.581*** 0.581*** 0.468*** 0.582*** 0.587*** 0.499*** (0.176) (0.176) (0.172) (0.032) (0.033) (0.163) Ln ML -0.199** -0.199** -0.059 -0.192** -0.192** -0.081 (0.081) (0.081) (0.119) (0.081) (0.082) (0.113) Ln WL 0.112 0.112 0.186** 0.077 0.082 0.194** (0.082) (0.082) (0.095) (0.082) (0.082) (0.098) Ln WCL 0.292** 0.292** 0.224 0.271** 0.276** 0.204 (0.146) (0.146) (0.179) (0.121) (0.123) (0.188) Ln Firmsize 0.257*** 0.257*** -0.088 0.222*** 0.228*** -0.076 (0.051) (0.051) (0.103) (0.049) (0.052) (0.102) Ln CI 0.039 0.039 0.015 (0.173) (0.173) (0.069) Ln SRD 0.110** 0.095* -0.048 (0.054) (0.053) (0.065) R2 0.834 0.834 0.352 0.835 0.837 0.338 Log Likelihood -104.460 -104.460 9.727 -104.110 -103.438 8.317 Root Mean Sq. Error 0.513 0.513 0.317 0.512 0.509 0.321 Industry effects No No Yes No No Yes Time effects No Yes Yes No Yes Yes Sargan test 1.210 1.220 4.137 2.752 2.771 8.265 Sargan test p-value 0.546 0.543 0.115 0.252 0.250 0.016 F test excluded instruments 6.14 6.09 10.40 28.84 28.65 14.57 F test p-value 0.00 0.00 0.00 0.00 0.00 0.00 Hausman test 9.94 9.66 6.00 12.77 12.54 5.95 Hausman test p-value 0.127 0.139 0.539 0.046 0.051 0.545 No. of obs. 139 139 128 139 139 128

Notes: Standard errors clustered by industry in parentheses. * p<0.10 ** p<0.05 *** p<0.01. ln CI and ln SRD are assumed to be endogenous and are instrumented with the one year lag, two year lag, and squared one year lag of the respective variables. The Sargan test is the test of overidentification, and is distributed as a chi-squared under the null that the estimated regression is over-identified. The F test of excluded instruments is the test of the joint significance of the excluded instruments in the first stage regression. The Hausman test is the test for whether the vector of coefficients is the same between non-instrumented and instrumented regressions, and is distributed as a chi-squared under the null that there is no significant difference between the coefficients.

28