metadata: authors: 0: author: Kinda,Tidiane
1: author: Plane,Patrick
2: author: Veganzones-Varoudakis, Marie-Ange
count: Middle East and North Africa
docna: 0: docna: Firms' productive performance and the
            investment climate in developing economies : an application
            to MENA manufacturing
docty: Policy Research Working Paper
subtopic: Investment and Investment Climate,Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Agricultural Economics,Labor Markets,Business Cycles and Stabilization Policies
teratopic: Macroeconomics and Economic Growth,Social Protections and Labor
abstracts: cdata!: Drawing on the World Bank Investment
            Climate Assessment surveys, this paper investigates the
            relationship between firm-level technical efficiency and the
            investment climate for 22 developing economies and eight
            manufacturing industries. The authors first propose three
            measures of firms' productive performance: labor
            productivity, total factor productivity, and technical
            efficiency. They show that, on average, enterprises in the
            Middle East and North Africa have performed poorly compared
            with other countries in the sample. The exception is
            Morocco, whose various measures of firm-level productivity
            rank close to the ones of the most productive economies. The
            analysis also reveals that the competitiveness of countries
            in the region has been handicapped by high unit labor cost,
            compared with main competitors like China and India. The
            empirical results show then? that the investment climate
            matters for firms' productive performance. This is true
            (depending on the industry) for the quality of various
            infrastructure, the experience and education level of the
            labor force, the cost of and access to financing, as well as
            different dimensions of the government-business relation.
            The analysis reveals that some industries, more exposed to
            international competition, are more sensitive to investment
            climate deficiencies. For some industries, this is also true
            for small and medium domestic enterprises that do not have
            the possibility to influence their investment climate or
            choose their location. These findings bear clear policy
            implications by showing that increasing firms' size and
            improving the investment climate (in particular of small and
            medium firms and industries more exposed to international
            competition) could constitute a powerful means of industrial
            development and competitiveness, in the Middle East and
            North Africa region in particular.
repnme: repnme: Firms' productive performance and the
            investment climate in developing economies : an application
            to MENA manufacturing
display_title: Firms' productive performance
            and the investment climate in developing economies : an
            application to MENA manufacturing
listing_relative_url: /research/2009/03/10362909/firms-productive-performance-investment-climate-developing-economies-application-mena-manufacturing
topicv3: Investment Climate m1326293 405,Manufacturing Sectors m1326521 1281,Agro-Processing m1327137 1966,Production Frontier m1503947 2688,Labor Productivity m1504060 2756
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Protections and Labor
no_of_pages: 53
author_keys: 0: author_key: 926657
1: author_key: 639657
model_extractions: WPS4869 Policy ReseaRch WoRking PaPeR 4869 Firms’ Productive Performance and the Investment Climate in Developing Economies An Application to MENA Manufacturing _Tidiane Kinda Patrick Plane Marie-Ange Véganzonès-Varoudakis_ The World Bank Middle East and North Africa Region March 2009
Policy ReseaRch WoRking PaPeR 4869

# **Abstract**

Drawing on the World Bank Investment Climate Assessment surveys, this paper investigates the relationship between firm-level technical efficiency and the investment climate for 22 developing economies and eight manufacturing industries. The authors first propose three measures of firms’ productive performance: labor productivity, total factor productivity, and technical efficiency. They show that, on average, enterprises in the Middle East and North Africa have performed poorly compared with other countries in the sample. The exception is Morocco, whose various measures of firm-level productivity rank close to the ones of the most productive economies. The analysis also reveals that the competitiveness of countries in the region has been handicapped by high unit labor cost, compared with main competitors like China and India. The empirical results show then? that the investment climate matters for firms’ productive performance. This is true (depending on the industry) for the quality of various infrastructure, the experience and education level of the labor force, the cost of and access to financing, as well as different dimensions of the government-business relation. The analysis reveals that some industries, more exposed to international competition, are more sensitive to investment climate deficiencies. For some industries, this is also true for small and medium domestic enterprises that do not have the possibility to influence their investment climate or choose their location. These findings bear clear policy implications by showing that increasing firms’ size and improving the investment climate (in particular of small and medium firms and industries more exposed to international competition) could constitute a powerful means of industrial development and competitiveness, in the Middle East and North Africa region in particular.

This paper—a product of the Middle East and North Africa Region—is part of a larger effort in the region to use enterprise surveys to identify constraints on productivity and growth in developing countries. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. For information, contact jdethier@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Firms’ Productive Performance and the Investment Climate in Developing Economies: An Application to MENA Manufacturing**

Tidiane KINDA Patrick PLANE Marie-Ange VÉGANZONÈS-VAROUDAKIS _CNRS-CERDI, Université d’Auvergne 65, Boulevard François Mitterrand 63000 Clermont-Ferrand, FRANCE_ _<u>Keywords</u>_ <u>: Manufacturing firms, Productivity, Investment Climate, Developing Countries, MENA.</u> _<u>JEL classification</u>_ <u>: D24, O14, O57.</u> _<u>Corresponding author</u>_ <u>: Marie Ange Véganzonès (veganzones@aol.com)</u>
# **Firms’ Productive Performance and the Investment Climate in Developing Economies: An Application to MENA Manufacturing**

# **1- Introduction**

Recent developments of the economic literature have put the investment climate at the center of economic performance. It is now well documented that the investment climate can significantly affect investment, productivity, and growth<sup>1</sup> . A growing literature suggests, in particular, that successful market-based economies need good governance institutions<sup>2</sup> . Governance exerts a strong influence on the investment climate. On the empirical side, several studies have related economic performances to different measures of governance<sup>3</sup> . The role of security of property rights is one of the best documented and supported by the data<sup>4</sup> . Some authors have also tested the role of corruption<sup>5</sup> and, to a lesser extend, regulation<sup>6</sup> and bureaucratic quality<sup>7</sup> . More recently, the literature has evaluated firm performance and its determinants using enterprise surveys data<sup>8</sup> . This approach, still quite new, aims at strengthening the institutional literature by providing microeconomic foundation.

Investment climate is defined by the World Bank as the policy, institutional and regulatory environment in which firms operate (see World Bank, 2005). Key factors affecting the investment climate are corruption, taxation, regulatory framework, quality of bureaucracy, legal environment, availability and quality of infrastructures, availability and cost of finance, factor markets (labor and capital), technological and innovation support. A good investment climate reduces the cost of doing business and leads to higher and more certain returns on investment. The forward- looking nature of investment underlines the importance of a stable and secure environment. A poor investment climate is also seen as constituting barriers to entry, exit and competitions. The World Bank (2004) reports as well that a better investment climate improves bureaucratic performances and predictability, and contributes to the effective delivery of public goods that are necessary for productive business.

In MENA, various studies point out the deficiencies of the investment climate. This is the case of the World Bank (2004) for governance, as well as of country studies based on enterprises surveys, in particular the World Bank Investment Climate Assessments ( _ICA_ ) of _Egypt_ (2005 and 2006), _Morocco_ (2001 and 2005), and _Algeria_ (2002). _Doing Business_ (World Bank, 2009) also places MENA low on business climate indicators<sup>9</sup> . These deficiencies have been reported as contributing to the poor economic accomplishment of the region<sup>10</sup> . Although MENA countries are, in average, defined as middle income-economies, growth and investment performances in particular have been disappointing for more than three decades<sup>11</sup> . Attractiveness to FDI has also been weak, as well as competitiveness and exports of manufacturing<sup>12</sup> . MENA competitiveness has constantly been affected by poor exchange rate policies and insufficient economic reforms. But other factors, such as the investment climate, can also explain the low productivity and the high production costs at the firm level _._ The World Bank Investment Climate ( _ICA_ ) surveys collect data on inputs and outputs, as well as on various aspects of the investment climate at the firm level. _ICA_ surveys produce both subjective evaluations of obstacles, as well as other more objective information with direct link to cost and productivity on the themes of infrastructure, human capital, technology, governance, and financial constraints. These standardized surveys of large samples of firms from different sectors permit comparative measures of firms’ productive performance. They also provide information to estimate the contribution of investment climate to these 2
performances. In a context of increasing pressure of globalization, _ICA_ surveys can be seen as an instrument for identifying key obstacles to firms’ productivity and competitiveness. They can be used as a support to policy reforms for an increased economic growth.

Drawing on the World Bank firm-surveys, this paper analyzes the relationship between investment climate and firm-level productivity for a large number of developing countries (22 among which 5 MENA economies) and eight manufacturing industries. We first propose different measures of firms’ productive performances, such as Labor Productivity ( _LP_ ), Total Factor Productivity ( _TFP_ ), and Technical Efficiency ( _TE_ ) using a production frontier approach. These indicators are compared to each others as well as across countries, in order to position MENA manufacturing amongst a wide range of firms from other regions. We reveal that enterprises in MENA perform in average poorly, compared to other countries of the sample. The exception is _Morocco_ , whose various measures of firms’ productive performance always rank close to the one of the most productive firms in the sample. An originality of our approach has also been to generate a few composite indicators of investment climate using Principal Component Analysis ( _PCA_ ), which summarizes well the key dimensions of the investment climate. This has allowed as well tackling the problem of multicolinearity when explaining firm productive performances with a wide range of correlated _IC_ variables. We define four dimensions of the investment climate: the Quality of Infrastructure ( _Infra_ ), the Business-Government Relations ( _Gov_ ), the Human Capacity ( _H_ ), and the Financing Constraints ( _Fin_ ). We use, as well, city or region-sector averages to reduce the endogeneity problem underlying the investment climate ( _IC_ ) variables. The analysis finally shows that investment climate matters for firms’ productive performances. This has been done by estimating an efficiency function explaining firm-level productivity for each of our 8 manufacturing industries.

The paper is organized as follows. The second section introduces different concepts of firmlevel productivity and discusses the advantages and limits of the different measures. Section three presents the investment climate ( _ICA_ ) surveys and summarizes their main limitations. The fourth section presents and compares across countries our different estimations of firms’ productive performances by industry. The fifth section introduces the investment climate indicators used in the empirical analysis, and calculates our four broad _IC_ indicators. The sixth section highlights MENA investment climate deficiencies. In the seventh section, we examine whether the various dimensions of the investment climate constraints firms productive performances. The last section concludes

# **2- Measures of Firm-Level Productivity: Methodological Aspects**

Our first challenge has been to measure firms’ productive performance in a relevant way. We propose different approaches and measures. We first consider a non parametric model of productivity, which consists in calculating productive performances without estimating a production function. Non parametric measure of productivity constitutes a simple and already meaningful way of assessing for example Productivity of Labor ( _LP_ ) and Total Factor Productivity ( _TFP_ ). Another way has been to calculate firms’ productive performance from a parametric production frontier. This more sophisticated methodology allows to identify the most efficient firms of the sample and to compare MENA firms’ performances to them.

3
# **2.1- Non Parametric Measures of Productivity and Unit Labor Cost**

Productivity can easily be calculated as the ratio of an output to a specific factor of production (defined as Productivity of Labor when the factor is labor), or to all relevant factors of production (called Total Factor Productivity, _TFP_ ). In this paper, we only refer to productivity levels because of the limited time dimension for the production factors (two to three years at the best) and no time dimension for the _IC_ variables. Our analysis thus focuses on comparisons of firm-level productivity among enterprises, industries and countries<sup>13</sup> .

In the empirical analysis, we first discuss Labor Productivity ( _LP_ ), which gives a first idea of the productive performance of the firms. Productivity of Labor has the advantage not to be affected by the error in measuring the capital stock. However, the technology is partially described and calculation of productivity suffers from the omission of this variable. Productivity of Labor can be complemented by calculations of Unit Labor Cost, defined as the ratio of firm average wage to firm’s labor productivity. This indicator allows comparisons of the cost of labor across countries in competition in the world markets. Firms’ productivity of labor can also be biased by the choice of the exchange rate when converting production into US$. This is less the case of _TFP_ because the same rate applies to the output ( _Y_ ) at the numerator, and to the intermediate consumption ( _ICons_ ) and capital stock ( _K_ ) in the denominator.

Non parametric Total Factor Productivity ( _TFP_ ) constitutes another simple (and also more complete) way to evaluate firms’ productive performances. Under the hypothesis of constant returns to scale, (i.e., perfect competition for goods but also for factors that are remunerated at their marginal productivity) weights of Intermediate Consumptions ( _ICons_ ) and of Labor (Wages, _W_ ) are estimated as the ratio of the cost of these factors to the Total Cost of Production including profit ( _Y_ ). The contribution of Capital ( _K_ ) is then calculated as the complement to one. The advantage of this approach, based on the Solow residuals, is that it does not require the inputs to be exogenous or the inputs elasticity to be constant. The disadvantage is that two hypotheses have to hold: (a) constant returns to scale; and (b) competitive input markets. Another limitation can be seen in the fact that productivity, being calculated as the residual of the production function, is considered as a random variable, which makes it difficult to justify that some exogenous factors can explain productive differences. The equation is as follows:

# **2.2- Parametric Production Functions and Production Frontiers**

In the parametric approach, _TFP_ is calculated as the residual of an estimated production function, thus relaxing the hypotheses of constant returns to scale (but not automatically of productivity as a random variable). Various hypotheses can be made regarding the technology of production. The Cobb Douglas and the Translogarithmic production functions are the most commonly used. Although both present good mathematic properties, the elasticities of the production to the inputs are easy to read and to interpret with the Cobb Douglass technology.

4
In the case of a parametric production function, production is derived from the optimization problem of the firms, which in perfect competition maximize current and expected profits by equating production prices to their marginal costs. This hypothesis does not permit any waste of resources or organizational weaknesses. The production frontier approach, however, allows for non optimal behaviors of the firms. Enterprises can be positioned in regard to the most efficient firms that define an empirical production frontier. Firm-level Technical Efficiency ( _TE_ ) can then be defined as the firms’ productivity gap (or efficiency gap) to the “ _best practice”,_ the empirical practice of the firms which are located on the production frontier.

The deterministic parametric production frontier approach can be implemented in a rather simple way, under the restrictive assumption that the production does not suffer from the classical disturbances. The higher positive residual of the regression is used as a correction term, to defining the most efficient observation. The other observations are positioned comparatively to this most efficient observation. Correction is applied to the intercept of the regression for all the observations, except this one. The residual of estimation (<sup>_u_</sup> _i_<sup>) is a</sup> random variable, uncorrelated and independent of the right-hand side variables.<sup>_u_</sup> _i_<sup>can be</sup> transformed as an indicator of efficiency of value 1 (or 100% when expressed in percent) when<sup>_u_</sup> _i_<sup>= 0. For the firms of the sample for which the residual is not zero,</sup><sup>_u_</sup> _i_<sup>measures the</sup> potential performance gain that these enterprises can achieve. The deterministic parametric production frontier is specified as follows: With

- _X:_ Production factors

- _Β :_ Parameters of the equation -<sup>_u_</sup> _i_<sup>:  Technical Efficiency (</sup><sup>_TE_)</sup> - _i_ : Firm index

In the stochastic model, the likelihood estimation method is typically applied to estimate a “composite” error term which is split into two uncorrelated elements. The first term _(v)_ , which is a random variable, represents the external shocks to the firm. These shocks, independent and identically distributed, follow a normal distribution, with zero average and σ2 standard deviation. The second term represents the Technical Efficiency _(-u_ ). We will suppose that _u_ follows a truncated normal distribution. In this specification, firms’ productive performances are not assimilated to a random variable and can then be explained by exogenous factors. The interest of this approach can also be seen in the fact that _TEs_ have a relative form and can be compared across countries and regions. Although there is a wide range of choices as regard the statistical distribution of the efficiency term ( _u_ ), the ranking of firms according to the efficiency term is generally not sensible to the choice of the specific distribution (Coelli, Prasada Rao and Battese, 1998). Equation is as follows: With _- Y:_ Production _- X:_ Production factors 5
- _Β :_ Parameters of the equation

- _v_ : External shocks

- _u_ : Technical Efficiency ( _TE_ ) - _i_ : Firm index

## **_2._ 3** **_-_ Explaining Technical Efficiency**

A complementary approach, when having calculated Technical Efficiency ( _TE_ ), is to explain the reasons for firms’ diverse performances. Firms’ inefficiency can be explained by “exogenous” factors which affect either the technology of production, or the firm’s ability to transform inputs into outputs. In the literature, these factors have been estimated in two different ways. A simple method consists in estimating the stochastic production frontier, and in regressing the residual of the estimation (the Technical Efficiency, _TE_ ) on a vector of explanatory factors (z). This method is called the “Two Steps” procedure. Different estimation procedures can be used. The simplest way is to run an OLS regression. Another possibility is to apply a Tobit model, in order to address the question of the distribution of the efficiency. The “Two Steps” procedure presents, however, some shortcoming in separating the Technical Efficiency ( _TE_ ) from the production frontier. When some production frontier inputs (x) are explained by factors affecting efficiency, there is an issue of simultaneity<sup>14</sup> . Because the Technical Efficiency term ( _TE_ ) is correlated with the production frontier inputs (x), the likelihood estimation of the stochastic production frontier is biased, due to the omission of important explanatory variables.

In fact, a relatively new branch of the literature proposes to estimate the production frontier and the factors explaining inefficiency at the same time. This is the “One Step” procedure. In this case, the parameters of the equation (here _β_ and _δ)_ are simultaneously estimated by the likelihood estimation method. The stochastic version of the model can be written as follows: With

- _Y_ : Production - _X_ : Production factors - _Z_ : Factors explaining Technical Efficiency

_- v_ : External shocks _- u_ : Technical Efficiency - _β_ / _δ:_ Parameters of the equation - _i_ : Firm index

# **3- The ICA Firm Surveys: Data Limitations**

The World Bank Investment Climate ( _ICA_ ) surveys collect data on inputs and outputs, as well as on a large variety of quantitative and qualitative (perception-based) indicators of the investment climate. In building the database, we have tried to incorporate as much information as possible. We have integrated in our sample 23 countries which participate in the five main regions of the developing world: Sub-Saharan Africa ( _AFR_ ), East Asia ( _EAS_ ), South Asia ( _SAS_ ), Latin America and the Caribbean ( _LAC_ ), Middle East and North Africa ( _MENA_ , see list of countries in _Annex_ 1)<sup>15</sup> . In this sample, MENA is represented by 5 countries: _Algeria_ (2002), _Saudi Arabia_ (2005), _Lebanon_ (2006), _Morocco_ (2000, 2004) and 6
_Egypt_ (2004, 2006)<sup>16</sup> . _Syria_ (2003) and _Oman_ (2003), which were initially part of the sample, had to be removed because of a very low rate of answer to the questionnaire. By broadening the initial sample to a large number of countries from different regions, we have intended to compare MENA performances to the ones of emerging economies which appears as major competitors in the world market: _China_ and _India_ in particular.

To estimate firm-level productivity, we initially considered a population of almost 20,000 firms, coming from 13 manufacturing industries. This initial sample had to be reduced due to various limitations. Calculation of productive performances requires information on at least 5 variables: (1) production, (2) intermediate consumption, (3) labor, (4) wages, and (5) capital stock. For several enterprises, part of this information appeared difficult to get. For others, answers showed flagrant inconsistencies. Enterprises were eliminated in particular when the calculation of productive performances revealed to be questionable or not in line with the income per capita in the country<sup>17</sup> . Some industries as well had to be merged, due to insufficient observations. In fine, 12 414 enterprises (3073 for the MENA region) regrouped in eight industries were retained when estimating the production frontiers (see _Annex 2_ )<sup>18</sup> .

As for inputs and output, investment climate variables are subject to measuring errors. In the surveys, some firms did not report the full range of investment climate measures. Other firms reported numbers that were not credible. This is also due to the fact that most of investment climate factors are qualitative variables of perception, thus allowing answers to vary depending on the firms, the regions or the countries. Our choice has been to keep as many firms as possible, providing sufficient information on a wide range of investment climate variables. Once outliers and incomplete observations were removed, 5002 observations were left, among which 1483 for the MENA region, what represent 34% of MENA initial population and 30% of the total number of enterprises with _IC_ variables (see _Annex 2_ ).<sup>19</sup> .

Another question relates to the endogeneity of the _IC_ variables, due to the qualitative nature of investment climate factors. This is particularly true for perception variables (such as obstacles to operation) for which firms are asked to position their answer on a given scale<sup>20</sup> . The perception of the scale might be different across firms, industries, regions and countries. Besides, when answering the questions on their investment climate, firms may be influenced by the perception they have of their own productivity and may attribute their inefficiencies to external factors. High-performing firms, as well, may be proactive in reducing their investment climate constraints, for example by working with the authorities to limit inspections or secure more reliable power supply. They also can choose a location with better infrastructure and production conditions, what relates to the endogeneity of implantation.

In the empirical part, we assume these endogeneneities and use appropriate estimation techniques to evaluate the impact of the investment climate on the firms’ productive performances. We measure in particular investment climate variables as city or region-sector averages of firm-level observations<sup>21</sup> . This also helps to mitigate the effects of missing observations for some firms. Actually, if we take each investment climate indicator at the firm level, we end up with a smaller sample of observations in which all indicators are available. Furthermore, to address the issue of endogeneity of firms’ implantation, we restrict the sample to the enterprises that are less likely to choose their location. We define a category of domestically owned firms employing less than 150 workers by excluding from the sample the foreign, as well as large domestically owned firms, Exchange rate constitutes another source of uncertainty which may lead to over or under evaluate firm’s productive performances. This rate is used to convert production and production factors into US dollars. Several exchange rates can be chosen to calculate and 7
compare firm-level productivity across countries. In this study, we considered the current market rate in US dollars which has the interest to be the rate that firms use for their economic calculations<sup>22</sup> .

# **4- Estimating Firm-Level Productivity: MENA Performance Gap**

In this section, we present our three measures of firm-level productivity: Productivity of Labor ( _LP_ ), Total Factor Productivity ( _TFP_ ) and Technical Efficiency ( _TE_ ). The data have been pooled across the 22 countries of our sample<sup>23</sup> . Firm-level productive performances are calculated for each of the eight industries. Differences and similarities across countries have been analyzed. A pattern of generally low productive performances is observed in the MENA region, with however some countries showing better results.

## **4.1- Firm-level Labor Productivity and the Unit Labor Cost**

Firm-level Productivity of Labor ( _LP_ ) is estimated as the ratio of the firms’ Value Added ( _Y_ ) to the Number of Permanent Workers. The Value Added is calculated as the difference between Total Sales ( _S_ i, j) and Total Purchase of Raw Material -- excluding fuel ( _IC_ i, j)<sup>24</sup> . We make the hypothesis that firms are price takers, thus purchasing raw material at world price, what looks like an acceptable assumption for the manufacturing industry which is exposed to international competition. In this case, prices in dollar of production and intermediary consumptions are comparable across countries. Equation is as follows: With

- Y :  Value Added. i, j - L :  Number of Permanent Workers i, j - i / j:  Enterprise and country index respectively.

Tables 2 and 3 display (by country and by industry) the averages Labor Productivity ( _LP_ ) and Unit Labor Cost. Unit Labor Cost has been computed by dividing the “Total Wages” ( _W_ ) by the “Value Added” ( _Y_ ). For each country, average productivity (Unit Labor Cost) is expressed in percent of the level of the country with the most performing firms (or the country with the lowest Unit Labor Cost). Calculations in level are given in Tables 3.1 and 3.2 in _Annex_ 3<sup>25</sup> . The analysis reveals a relatively stable ranking of countries. _South African_ and _Brazilian_ firms perform -- in average and in most industries -- the best. This result is consistent with the relatively high incomes in the two countries (2710 and 2780 dollars per capita respectively, see World Bank, 2005). _Morocco (2004)’s_ firms also participate in the best performances of the sample, especially in _Metal & Machinery Products, Chemical & Pharmaceutical Products_ , _Leather_ and _Agro-Processing._ As far as other MENA countries are concerned, the ranking remains also rather stable. _Egyptian_ and _Lebanese’s_ firms are systematically among the least performing in all industries (although _Morocco_ and _Egypt_ have the same GDP per capita, at around 1300 US dollars in 2003). In _Algeria_ , firm-level Productivity of Labor ( _LP_ ) ranks an intermediate position, close to _India_ in _Agro-Processing_ and _Chemical & Pharmaceutical Products,_ but behind in _Textile_ and _Metal & Machinery Products_ (firms’ performances are always lower than in China). _Moroccan’s_ firms thus remain the most performing ones in MENA, with levels of Productivity of Labor ( _LP_ ) far ahead from the two Asiatic giants, and close to the most productive firms/countries of the sample<sup>26</sup> .

8
This relative efficiency of some MENA countries, however, is not sufficient to understand the capacity of these countries to promote industrial and export activities. Remuneration of labor is an important factor which should be in line with productivity. By combining information on Productivity of Labor ( _LP_ ) and the cost of labor, the relative Unit Labor Cost gives an idea of the competitiveness. Table 3 presents some information on the subject. It is worth noticing that the Unit Labor Cost in MENA is one of the highest of our sample of countries. This is particularly true in _Algeria_ and _Egypt_ – countries where firm-level Productivity of Labor ( _LP_ ) is among the lowest – but also in _Morocco_ and to some extend _Lebanon_ . In MENA, the Unit Labor Cost is of the same magnitude than in the most performing countries of the sample, sometimes even higher (see the case of _Brazil_ ), and by far much superior than in the majority of Asian economies ( _India_ , _China_ , _Sri Lanka_ , _Bangladesh_ and _Thailand_ ). In _China_ and _India,_ salaries (around 100 US dollars per month for unskilled workers) are far lower than in _Morocco_ (more than the double). In the labor intensive sectors of _Textile_ and _Garments_ , cost of labor is 2 to 2 and a half time higher in _Egypt_ and _Morocco_ than in _India_ . This situation should be seriously addressed, if MENA wants to compete in the world market. If not, MENA will continue to suffer from the faster technological innovation in Asia, where wages remain low.

**Table 1. Firm-Level Labor Productivity** _<u>(Country average, in % of the country with the most productive firms)</u>_

|**Country***|**Textile**|**Leather**|**Garment**|**Agro**<br>**Processing**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**South Africa (**2003)|52||**100**|**100**|94|97|87|**100**|
|**Brazil(**2003)|**100**|**100**|50|50|66|**100**|38||
|**_Morocco (_**_2004)_|_54_|_80_|_54_|_79_|**_100_**|_91 _||_66_|
|**_Morocco (_**_2000)_|_56_|_94 _|_55_|_85_|_48_|_63_||_57_|
|**_Saudi Arabia (_**_2005)_||||_77_|_92 _||**_100_**||
|**Ecuador (**2003)|58|91|80|48|50|54|42|66|
|**ElSalvador (**2003)|71|59|55|35|28|51||46|
|**China (**2002)|52|69|45||31||||
|**Thailand (**2004)|62||62|45|40||31|43|
|**Guatemala (**2003)|43||64|31|26|36|33|48|
|**India (**2002)|35|66|53|21|22|17|||
|**Honduras (**2003)|56||50|29|23|39|21|26|
|**India (**2000)|39||48||28|24|||
|**Pakistan (**2002)|40|35|49|22||17|||
|**Tanzania (**2003)||||35|||20||
|**Philippines (**2003)|32||32|14|||||
|**_Algeria (_**_2002)_|_27_|||_21_|_19_|_19_||_31_|
|**Bangladesh (**2002)|18|53|16|9||11|||
|**Nicaragua **2003|13|38|26|17|13|17|16|21|
|**Sri Lanka (**2004)|13||27|9|17|||28|
|**Zambia (**2002)|16|||13|24|18|||
|**Ethiopia (**2002)|11|20|20|10|||10||
|**_Egypt (_**_2006)_|_14_|_15_|_14_|_12_|_16_|_11_|_10_|_13_|
|**_Egypt (_**_2004)_|_15_|_20_|_14_|_9_|_11_|_11_|_11_|_11_|
|**_Lebanon (_**_2006)_|_11_||_17_|_8_|||_7_||

_Note_ : * Ranking of countries goes from the ones with the most productive firms to the ones with the least productive firms. _Source_ . Authors’ calculations 9
# **Table 2. Firm-Level Unit Labor Cost**

_<u>(Country average, % of the country with the highest unit cost)</u>_

|**Country***|**Textile**|**Leather**|**Garment**|**Agro**<br>**Processing**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**ElSalvador (**2003)|52|**100**|**100**|85|**100**|63||87|
|**Nicaragua (**2003)|**100**|72|80|87|88|**100**|92|79|
|**Guatemala (**2003)|64||83|**100**|79|87|89|74|
|**_Algeria (_**_2002)_|_73_|||_89_|_89_|_96_||**_100_**|
|**Philippines **2003)|66||92|83|||||
|**South Africa (**2003)|86||97|74|80|88|69|64|
|**_Morocco _**_2004)_|_81 _|_79_|_91 _|_75_|_75_|_76_||_60_|
|**Honduras (**2003)|36||78|88|76|63|96|86|
|**_Egypt (_**_2004)_|_51_|_66_|_77_|_77_|_55_|_86_|**_100_**|_57_|
|**_Egypt (_**_2006)_|_60_|_86_|_76_|_71 _|_46_|_80_|_92 _|_51 _|
|**_Saudi Arabia (_**_2005)_||||_89_|_59_||_55_||
|**_Lebanon (_**_2006)_|_55_||_53_|_61_|||_92 _||
|**_Morocco (_**_2000)_|_62 _|_62 _|_84 _|_60_|_58_|_66_||_62 _|
|**Zambia (**2002)|46|||75|48|88|||
|**Brazil(**2003)|48|54|72|68|56|49|65||
|**Sri Lanka (**2004)|86||64|71|39|||32|
|**Bangladesh (**2002)|49|34|60|69||55|||
|**Ethiopia (**2002)|71|25|45|56|||55||
|**Ecuador (**2003)|48|59|52|50|42|32|62|53|
|**Thailand (**2004)|42||56|49|35||52|34|
|**China (**2002)|39|41|54||38||||
|**Pakistan (**2002)|31|41|33|47||51|||
|**India (**2000)|36||38||37|46|||
|**India (**2002)|32|27|35|42|35|44|||
|**Tanzania (**2003)||||33|||31||

_Note_ : * Ranking of countries goes from the ones with the most expensive labor to the ones with the least expensive one. _Source_ . Authors’ calculations

# **4.2-Firm-Level Total Factor Productivity**

In this section, firm-level Total Factor Productivity ( _TFP_ ) is calculated from a non parametric production function. Production factors include Labor ( _L_ ) and Capital ( _K_ ). Same hypotheses and definitions as before apply to input and output variables. Equation is as follows: PTF i, j = Log( _Y_ i, j) – α Log ( _K_ i, j) – β Log ( _L_ i, j) (7) With

- Y :  Value Added i, j

- - L :  Number of Permanent Workers i, j

- K i, j:  Gross Value of Property, Plant and Equipment

10
- β: Ratio of Total Wages ( _W_ ) to Total Production Cost ( _Y_ ). - α = 1- β - i / j:  Enterprise and country index, respectively

**Table 3. Firm-Level Total Factor Productivity** _<u>(Country average, in % of country with the most productive firms)</u>_

|**Country***|**Textile**|**Leather**|**Garment**|**Agro**<br>**Processing**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal &**<br>**Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**South Africa**(2003)|88||**100**|**100**|91|82|**100**|**100**|
|**Brazil(**2003)|**100**|**100**|87|**100**|**100**|**100**|91||
|**_Morocco (_**_2000)_|_80_|_81_|_79_|_79_|_70 _|_90 _||_71 _|
|**Thailand (**2004)|70||90|75|73||78|82|
|**_Morocco (_**_2004)_|_73 _|_64_|_77_|_77_|_70 _|_79_||_80_|
|**_Saudi Arabia_**_(2005)_||||_70 _|_68_||_81_||
|**Ecuador (**2003)|69|74|76|73|75|72|78|64|
|**ElSalvador (**2003)|76|70|66|64|61|69||76|
|**Philippines (**2003)|64||77|65|||||
|**_Algeria (_**_2002)_|_65_|||_44_|_59_|_66_||_76 _|
|**Honduras (**2003)|61||72|55|57|84|50|54|
|**Guatemala (**2003)|65||67|54|62|56|54|73|
|**India (**2000)|67||63||58|58|||
|**China (**2002)|59|58|56||45||||
|**Zambia (**2002)|58|||52|55|52|||
|**Pakistan (**2002)|55|58|56|54||48|||
|**India (**2002)|59|61|49|54|51|50|||
|**Tanzania (**2003)||||55|||53||
|**Sri Lanka (**2004)|41||51|61|51|||56|
|**Bangladesh (**2002)|51|46|57|50||44|||
|**Nicaragua (**2003)|49|51|45|47|42|50|44|52|
|**Ethiopia (**2002)|51|34|46|49|||36||
|**_Lebanon (_**_2006)_|_35_||_39_|_40_|||_37_||
|**_Egypt (_**_2004)_|_41_|_36_|_35_|_39_|_34_|_33_|_36_|_43_|
|**_Egypt (_**_2006)_|_37_|_30_|_33_|_41_|_34_|_34_|_31_|_38_|

_Note_ : * Ranking of countries goes from the ones with the most productive firms to the ones with the least productive firms. _Source:_ Authors’ calculations Table 3 presents the firm-level relative _TFP_ by industry, under the reasonable assumption that a sector-based technology leads to a more homogeneous production function. As for Productivity of Labor, results are presented in percent of the average _TFP_ of the most performing country (detailed calculations are given in _Annex_ 4). Conclusions are quite similar than for Productivity of Labor.

A first conclusion concerns the ranking of the most performing countries. As previously, _South Africa_ and _Brazil_ present, in most industries, the most productive firms. These countries are again followed by _Morocco_ , which firms’ productive performances are quite good in most industries. When compared to _Brazil_ , _Moroccan_ firms show a _TFP_ gap of 10 to 30 percent depending on the sector, what is less than the revenue gap between the two countries (47 %, and 38.5% in _PPP_ respectively). As far as other MENA countries are concerned, ranking is also quite similar than for Productivity of Labor ( _LP_ ). As previously, _Egypt_ and _Lebanon_ rank at the bottom of the sample (with a limited number of enterprises for the latter country), while 11
_Algeria_ stays in an intermediate position. _TFP_ calculations thus confirm the productivity gap assessed through Productivity of Labor<sup>27</sup> .

# **4.3- Firm-Level Technical Efficiency**

Firm-level Technical Efficiency ( _TE_ ) is based on the likelihood estimation procedure. As seen in section 2.2., this method allows splitting the error term into two independents factors: the error term _(v),_ which follows a normal distribution, and the Technical Efficiency _(u_ ), which obeys a truncated normal distribution. The technology of production explains the Value Added ( _Y_ ) by the Capital ( _K_ ) and the Labor ( _L_ ). Same hypotheses and definitions as before apply to input and output variables. Equation is as follows: With:

- Y :  Value Added i, j

- L :  Number of Permanent Workers i, j

- K i, j:  Gross Value of Property, Plant and Equipment

- _dum_ j: Country-dummy variables

- _α, β:_ parameters of the equation

- _vi_ , j:  Error term

- _u_ i, j: Technical Efficiency ( _TE_ ).

- i / j:  Enterprise and country index respectively.

Production frontiers have been estimated by industry. As mentioned before, this leads to more homogeneous production frontiers and makes it easier to attribute the residual to differences in efficiency. Differences in coefficients of capital and labor have justified this choice; against an alternative assumption consisting in estimating the same production frontier for all sectors, with specific sector-based dummies (see Table 4).

**Table 4: Estimations of the Stochastic Production Frontiers**

||||**_D_**<br>|**_ependant Va_**<br>|**_riable: Valu_**<br>|**_e Added_**<br>|||
|---|---|---|---|---|---|---|---|---|
|**_Independent_**<br>**_Variables_**|**Textile**|**Garment**|**Leather**|**Agro**<br>**Processing**|**Metal&**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Non Metal**<br>**& Plastic**<br>**Materials**|**Wood**<br>**&**<br>**Furniture**|
|**Log (labor)**|0.659<br>(30.53)***|0.811<br>(42.69)***|0.826<br>(20.20)***|0.695<br>(31.22)***|0.877<br>(33.21)***|0.673<br>(22.21)***|0.886<br>(22.35)***|0.941<br>(29.18)***|
|**Log (capital)**|0.354<br>(24.87)***|0.260<br>(20.96)***|0.277<br>(11.00)***|0.404<br>(28.62)***|0.289<br>(18.52)***|0.444<br>(22.89)***|0.281<br>(13.54)***|0.228<br>(12.79)***|
|**Intercept**|2.007<br>(18.94)***|1.350<br>(9.22)***|1.419<br>(9.81)***|1.863<br>(13.99)***|1.716<br>(15.61)***|2.065<br>(15.39)***|1.419<br>(9.73)***|1.644<br>(11.51)***|
|**σ2u**|0.33|0.22|0.80|0.73|1.12|0.39|1.30|0.79|
|**σ2**|0.99|0.92|1.40|1.47|1.76|1.13|1.86|1.19|
|**σ2u/ σ2**|0.33***|0.24***|0.57***|0.50***|0.64***|0.35***|0.70***|0.66***|
||(6.17)|(3.00)|(6.33)|(8.17)|(12.80)|(5.00)|(10.00)|(13.20)|
|**Observations**|2011|2800|634|2190|1622|1274|907|1033|

_Note_ : * Significance level 10 %; ** 5 %; *** 1 %. Z statistics are into brackets. Regressions include country-dummy variables. _Source_ : Authors’ calculations 12
Table 4 presents the estimation results of the production frontiers. In most industries, the sum of the coefficients relative to labor and capital inputs is close to one. It is a little bit higher for some sectors than can be suspected to face investment indivisibilities. In comparison with other sectors, _Textile_ is probably the most exposed to the competition and the production technology does not reject this hypothesis. For all industries, the coefficients are strongly statistically significant at the 99% level of confidence.

**Table 5. Firm-Level Technical Efficiency**

_<u>(Country average, in % of country with the most productive firms)</u>_

|**Country***|**Textile**|**Leather**|**Garment**|**Agro**<br>**Processing**|**Metal&**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**South-Africa **2003|85||**100**|**100**|**100**|89|**100**|**100**|
|**Brazil**2003|**100**|**100**|87|80|98|**100**|62||
|**_Morocco _**_2004_|_58_|_70 _|_81_|_70 _|**_100_**|_72 _||_92 _|
|**_Saudi-Arabia _**_2005_||||_72 _|_76 _||_81_||
|**_Morocco _**_2000_|67|76|80|71|68|83||70|
|**Thailand **2004|64||93|67|65||47|66|
|**Ecuador **2003|57|86|61|61|63|60|57|63|
|**ElSalvador **2003|40|62|65|58|55|63||66|
|**Guatemala **2003|51||77|45|57|45|48|67|
|**Honduras **2003|58||66|42|48|60|37|48|
|**India **2000|47||66||45|34|||
|**India **2002|42|56|66|41|46|32|||
|**Pakistan **2002|43|49|61|40||31|||
|**China **2002|46|45|51||35||||
|**Philippines **2003|36||53|39|||||
|**_Algeria _**_2002_|_33_|||_35_|_39_|_38_||_54_|
|**Nicaragua **2003|22|55|41|34|38|30|31|49|
|**Tanzania **2003||||43|||32||
|**Zambia **2002|29|||30|41|21|||
|**Sri Lanka **2004|17||37|26|33|||39|
|**Bangladesh **2002|24|41|32|28||19|||
|**Ethiopia **2002|20|30|36|22|||23||
|**_Egypt _**_2004_|_21_|_30_|_21_|_17_|_22_|_17_|_19_|_32_|
|**_Egypt _**_2006_|_17_|_15_|_22_|_22_|_25_|_14_|_19_|_24_|
|**_Lebanon _**_2006_|_21_||_23_|_16_|||_13_||

_Note_ : * Ranking of countries goes from the ones with the most productive firms to the ones with the least productive firms. _Source_ . Authors’ calculations Table 4 also specifies the percentage of the residual explained by the Technical Efficiency ( _TE_ ). It can be seen that, in all industries, the efficiency term accounts for a significant part of the total residuals and is statistically significant at 99%. This result justifies the production frontier approach, against the production function approach. In this model, _TE_ explains from 24% of the error term in _Garment_ to 70% in _Non Metallic & Plastic Materials_ . _TEs_ are distributed in an interval of 0 to 1 (1 is the value of the sector’s most efficient firms; see _Annex_ 5). In Table 5, _TEs_ are in percent of the average _TE_ of the most performing country. In average, our results for Technical Efficiency ( _TE_ ) are close to the ones obtained for the non parametric _TFP_ under the hypotheses of constant returns to scale. The ranking of countries, in particular, remains unchanged. As previously, _Brazilian_ and _South African’s_ firms show the best performances in all industries, along with _Moroccan’s_ firms. Only in _Garment_ and _Leather_ , _Moroccan’s_ firms are surpassed by _Thailand_ and _Ecuador_ respectively. Ranking of MENA countries, as well, is unchanged.

13
## **4.5- Firm-Level Productivity Measures: A High Correlation**

_Annex_ 6 displays, by industry, the Spearman coefficients of correlation of our three measures of firm-level productivity. All coefficients are highly significant and show a high degree of correlation between the different measures. This is the case in all industries, but more specifically in _Wood & Furniture, Non Metallic & Plastic Materials,_ and _Metal & Machinery Products_ (after _Agro-Processing, Chemicals & Pharmaceutical Products, Leather,_ and _Textile)_ . This result justifies our choice of Technical Efficiency ( _TE_ ) as a measure of firmlevel productivity to be explained by the countries/ industries investment climate (see section 6). It will also make our findings more general, because they can be extrapolated to the different indicators of firm-level performance.

# **5- Assessing the Investment Climate of the Manufacturing Industries**

Another step in our analysis has been to differentiate and categorize the different dimensions of the investment climate. The World Bank Investment Climate ( _ICA_ ) surveys provide information on a large number of investment climate ( _IC_ ) variables -- in addition to general information on firms’ status, productivity, sales and supplies. These _IC_ variables are classified into 6 broad categories: ( **_a_** ) _Infrastructures and Services_ , ( **_b_** ) _Finance_ , ( **_c_** ) _BusinessGovernment Relations_ , ( **_d_** ) _Conflict Resolution/Legal Environment_ , ( **_e_** ) _Crime_ , ( **_f_** ) _Capacity, Innovation, Learning_ , ( **_g_** ) _Labor Relations_ .

In the surveys, there are multiple indicators that cover a similar theme. Within the same theme, the correlation between indicators can be high. One solution consists to limit the number of indicators. This can however lead to a biased estimation, due to the omission of important explanatory variables. Also, it is not sure that the _IC_ variables retained are good proxy of investment climate _._ A solution to overcome these problems consists in generating a few composite indicators. Because we intend to determine which investment climate variables are more detrimental to firm performances, we tried to take into consideration an as large as possible set of _IC_ variables which are not typically used in the literature. Since these variables are likely to be correlated, we applied Principal Component Analysis ( _PCA_ ) to produce a limited number of composite indicators<sup>28</sup> .

Based on the _ICA_ surveys, we defined the investment climate by four broad categories: “Quality of Infrastructure” ( _Infra_ ), “Business-Government Relations” ( _Gov_ ), “Human Capacity” ( _H_ ), and “Financing Constraints” ( _Fin_ ). As seen in section 3, our choice of indicators has been restricted by important data limitations. This is also why we have not been able to cover all aspects initially developed in the surveys. Indicators have been selected on the bases of being available for the countries of our sample, as well as capturing the different key dimensions of the investment climate. Besides, we have tried to complete as much as possible the qualitative (perception-based) _IC_ indicators by quantitative information, in order to get a better picture of the investment climate in each industry/country.

The Quality of Infrastructure indicator ( _Infra_ ) has been defined by six variables: Obstacle for the operation of the enterprise<sup>29</sup> caused by deficiencies in ( **_a_** ) Telecommunications, ( **_b_** ) Electricity, and ( **_c_** ) Transport; ( **_d_** ) Does the Firm Own or Share a Generator, ( **_e_** ) if yes, which Percentage of Electricity Comes from that Source; Does the Enterprise have access to ( **_f_** ) E- mail or ( **_g_** ) Internet in its Interaction with Clients and Suppliers. Infrastructure deficiencies constitute an important constraint to private sector development in developing countries (see 14
World Bank, 1994). In the literature, deficiency in infrastructure is seen as a burden for enterprises operations and investment. Infrastructures are considered, as well, as a complementary factor to other production inputs. In particular, infrastructure stimulates private productivity by raising profitability of investment<sup>30</sup> . Furthermore, infrastructure increases firms’ productive performances by generating externalities across firms, industries and regions<sup>31</sup> .

The “Business-Government Relations” indicator ( _Gov_ ) includes three to six variables (depending on the industries): Obstacle for the operation of the enterprise caused by ( **_a_** ) Tax Rate, ( **_b_** ) Tax Administration, ( **_c_** ) Customs and Trade Regulations, ( **_d_** ) Labor Regulation, ( **_e_** ) Business Licensing and Operating Permits, and ( **_f_** ) Corruption. This indicator illustrates the capacity of the government to provide an investment-friendly environment and reliable conditions to the private sector. Corruption is seen as having an adverse effect on firms’ productive performances. This fact is well documented and often described as one of the major constraints facing enterprises in the developing world (see the World Bank, 2005). Corruption increases costs, as well as uncertainties about the timing and effects of the application of government regulations (see Tanzi and Davooli, 1997). Taxation and regulations have also a first order implication on firms’ costs and productivity. Although government regulations and taxation are reasonable and warranted in order to protect the general public and to generate revenues to finance the delivery of public services and infrastructures, over-regulation and over-taxation deter productive performances by raising business start-up and firms’ operating costs.

The “Human Capacity” indicator ( _H_ ) is represented by three to four variables: Obstacle for the operation of the enterprise caused by deficient ( **_a_** ) Skill and Education of Available Workers; ( **_b_** ) Education level<sup>32</sup> and ( **_c_** ) Years of Experience of the Top Manager; ( **_d_** ) Training of the Firm’s Employees. Human capital constitutes an essential factor of firms’ productive performances. Human capital stimulates capital formation by raising firms’ profitability. Human capital is also at the origin of positive externalities<sup>33</sup> . Because skilled workers are better in dealing with changes, a skilled work force is essential for firms to adopt new and more productive technologies (see Acemoglu and Shimer, 1999). Besides, new technologies generally require significant organizational changes, which are better handled by a skilled workforce (see Bresnahan, Brynjolfsson and Hitt, 2002). Human capital gives also the opportunity to the enterprises to expand or enter new markets.

The “Financing Constraints” indicator ( _Fin_ ) consists of three variables: Obstacle for the operation of the enterprise caused by: ( **_a_** ) Cost, and ( **_b_** ) Access to Financing; ( **_c_** ) Access to an Overdraft Facility or a Line of Credit. Access to (and cost of) financing represent major determinant(s) of firms’ productive performances. Access to financing allows firms to finance more investment projects, what leads to an increased productivity through higher capitalistic intensity and technical progress embodied in the new equipments. Besides, financial development has a positive effect on productivity as a result of better selection of investment projects and higher technological specialization through diversification of risk. A developed financial system creates more profitable investment opportunities by mobilizing and allocating resources to the projects that will generate the most surplus (see Levine, 1997, for a synthesis).

All four aggregated indicators have been generated at the branch level, thus defining in each country the specific investment climate of each industry. This has implied to produce 32 aggregated indicators (four indicators multiplied by eight industries) by applying Principal Component Analysis ( _PCA_ ) to the initial indicators<sup>34</sup> . The analysis usually treats investment 15
climate as exogenous determinant of firms’ performance. As seen in section 3.3, however, this is not always the case. In order to address this issue, we have measured _IC_ variables as city or region-sector averages of firm-level observations. This has helped, as well, to increase the number of observations by integrating in the sample firms for which information is insufficient. This has been done for “Infrastructure” and “Business-Government Relations”. For “Human Capacity” and “Financing Constraints”, however, the initial indicators having been interpreted as specific to each firm, information has been kept at the firm level (except for the variable “Skill and Education of Available Workers”) . The initial _IC_ indicators are presented in _Annex 7_ , along with some information on firms’ characteristics. The figures highlight well, in average, MENA deficient investment climate, as well as the specificities of the industrial sector in the region.

# **6- Investment Climate: How Do MENA Economies Perform?**

Chart 1 in _Annex_ 8 confirms what we know of MENA investment climate. When MENA is compared to the non MENA countries of the sample, the region always ranks below. This is true for all four dimensions of the investment climate. MENA investment climate is in average of poorer quality than in East Asia ( _EAP)_ , Africa ( _AF)_ , Latin America ( _LA)_ and South Asia ( _SA_ ) -- except for the quality of infrastructures which appear as less a constraint than in this last region (see Chart 2 in _Annex_ 8). These findings, which are in line with the literature (see World Bank, 2005), can clearly be related to the disappointing firm productive performances assessed previously.

A more detailed analysis reveals, however, differences across countries and indicators. It is again _Morocco_ who seems to suffer the least from _IC_ limitations, except from financing constraints. Quality of infrastructures, business-government relations and human capacity inadequacies do not appear very much higher than in _South Africa_ , the country where firms’ productive performances are in average the highest (see Chart 3 in _Annex_ 8). On the opposite, firms in _Lebanon_ appear to face strong inadequacies in infrastructures and businessgovernment relations. _Egypt_ and _Saudi Arabia_ are in an intermediate position, with however relatively high deficiencies in the business-government relation, in particular in _Egypt_ . These results are also in line with our findings on firms’ productive performances (see section 3).

As far as the different dimensions of the _IC_ are concerned, a disaggregated approach shows which specific aspects are of more concern in the region. Limitations in all three components of the financing constraint demonstrate MENA deficit and cost of funding. This is also true for most dimensions of human capacity and of government-business relations (in particular the tax rate and administration, the labor regulations, and the licenses and operating permits, see Table 1 in _Annex_ 7). MENA deficient financial system, as well as firms difficulties (SMEs in particular) in finding affordable credit, are important aspects often emphasized in the literature, at the same time as the limitations of various dimensions of the business environment and the lack of training and expertise of the labor force<sup>35</sup> . As far as the quality of infrastructures is concerned, our results are more mitigated than usually highlighted in the literature. If MENA firms seems, in average, to face more constraints in electricity delivery (more enterprises rely on a generator), as well as in internet connection, quality of telecommunications and transports does not appear as very strong obstacles to operation (see Table 1 in _Annex_ 7). Although this finding looks somehow in contradiction with the conclusions of several studies, differences may be due to our small number of MENA countries, as well as to the presence of Morocco whose quality of infrastructures is not perceived as a strong limitation<sup>36</sup> . These results are confirmed at the country level, with 16
_Morocco_ experiencing more deficiencies in the various dimensions of the financing environment, _Egypt_ and _Saudi Arabia_ in different aspects of the government-business relation and _Lebanon_ in all components of the quality of infrastructure and government-business relation (see Charts 4 to 9 in _Annex_ 8).

Finally, MENA enterprises are characterized by a smaller size and a lower export orientation than in the rest of the sample (see Table 1 in _Annex_ 7). _Morocco_ , however, show a high export rate, in particular in the _Textile_ , _Leather_ and _Garment_ industries, as well as _Lebanon_ , in _Wood and Furniture_ . _Morocco_ presents as well an above average foreign participation in the capital of the firms (see Chart 10 in _Annex_ 8).

# **7- Is the Investment Climate Explaining Firm-Level Productivity?**

In this section, we estimate several variants of a model of Technical Efficiency ( _TE_ ) which explains the production frontier and the factors contributing to the efficiency at the same time (following the one step procedure, see section 2). Investment climate is first defined by a few indicators of infrastructures, human capacity, government-business relation and financing constraints. To overcome multicolinearity, we introduce then our four _IC_ composite indicators: _Infra_ , _H_ , _Fin_ and _Gov_ . After having controlled for endogeneity of _IC_ variables, we finally address the question of endogeneity of implantation, by restricting our sample to the domestic firms of less than 150 workers. We show that our results are unambiguous and robust to the different specifications and samples of firms.

## **7.1- Common Model with Individual Indicators of Investment Climate**

Our empirical model considers a same representation for all industries. This model is estimated at the branch level, thus allowing the coefficients to vary across branches. We explain firms’ production frontiers and Technical Efficiencies ( _TEs_ ) at the same time by regressing the logarithm of the production factors (capital and labor), as well as various plants characteristics and investment climate variables, on the logarithm of the firms’ value added. At this first stage of investigation, we have used initial _IC_ variables before aggregation. The model is as follows: With:

|_y i,j_|Value Added<sup>37</sup>|
|---|---|
|_l_ _i,j_:|Number of Permanent Workers|
|_k_i,j:|Gross Value of Property, Plant and Equipment|
|_Sizei,j_:|Size of the firm|
|_Foreigni,j_:|Foreign capital_(% of firm’s capital_)|
|_Exporti,j_:|Export_(% of firm’s sales_)|
|_RegElecti,j_:|Electricity delivery (_obstacle for the enterprise, regional average_)|
|_RegWebi,j_:|Utilization of Internet (_regional average_)|
|_Credi,j_:|Overdraft facility or credit line|

17
_AccessFi,j_ : Access to financing ( _obstacle for the enterprise, regional average_ ) _EduMi,j_ : Level of education of the top manager ( _number of years_ ) _ExpMi,j_ : Experience of the top manager ( _number of years_ ) _Trainingi,j_ : Training of workers _RegLregi,j_ : Labor regulation (obstacle for the enterprise, regional average) _RegCorrupi,j_ : Corruption (obstacle for the enterprise, regional average) c _i:_ Country-Dummy variables c: Intercept _vi,j_ : Error terms _i / j:_ Enterprise and country index respectively The choice of the _IC_ variables has been based on their availability for as many firms/ industries/ countries as possible, as well as on capturing the different key dimensions of the investment climate. Our variables cover properly the four axes of the investment climate defined in the previous section. To address the problem linked to the endogeneity of the _IC_ variables when estimating the _TE_ frontier models, we have considered the city region-sector averages of Electricity delivery ( _RegElect_ ), Access to Internet ( _RegWeb_ ), Labor regulation ( _RegLreg_ ), and Corruption ( _RegCorrup_ ). The number of explanatory variables, however, has been limited by the multicolinearity between several _IC_ variables when estimating the _TE_ frontier models.

Other individual variables have consisted in: the percentage of sales exported by the firms ( _Export_ ), the percentage of foreign ownership of firms’ capital ( _Foreigni,j_ ), as well as the firm size ( _Sizej_ ). Export is a factor of productivity by confronting firms to international competition. Foreign ownership, as well, increases productivity if foreign investors bring new technologies and management techniques. As for the size, we intend to test the hypotheses of scales economies and increasing returns to scale in big enterprises<sup>38</sup> . It is worth noting that the expected sign for these variables is negative, due to the fact that the one step procedure explains firm-level inefficiency. The same precautions must be taken when interpreting the sign of the coefficients of the other variables. Country-dummy variables have also been introduced when estimating the production frontiers. There are good reasons to think that production may vary across countries for motives other than production factors. The country dummies can pick up the effect of countries specific factors, such as endowment in natural resources, national-level institutions, macro or political instability, trade policy, etc... Country-dummy variables are intentionally not included in the second part of the equation, when explaining Technical Efficiencies ( _TEs_ ), since they could reduce the impact of some _IC_ variables.

Equation ( _9_ ) has been estimated on unbalanced panels, going from 380 observations (in _Leather_ ) to 1601 observations (in _Garment_ ) depending on the industry. A Cobb-Douglass production function has been chosen to estimate the production frontiers. We have also maintained our previous assumption as regard the specification of the technology, as well as of the Technical Efficiency ( _TE_ ). Although the sample size modifies when incorporating the regressors explaining the firm distance to the frontier, the coefficients of the technology are marginally (but downward) affected. These modifications display the potential impact of the interactions and the limitation that we would face when estimating the Technical Efficiency ( _TE_ ) determinants through the two stage method, as previously discussed<sup>39</sup> . Sector-based estimates are presented in Table 6.

.

18
# **Table 6. Estimation Results: Common Model with Individual** **_IC_ Variables**

_(Dependant Variable: Value Added)_

|**_Independent_**<br>**_Variables_**|**Textile**|**Leather**|**Garment**|**Agro**<br>**Industry**|**Metal&**<br>**Machinery**<br>**Products**|**Chemic**<br>**& Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**_ln(l ) _**|0.657<br>_(16.14)***_|0.789<br>_(28.82)***_|0.735<br>_(7.12)***_|0.560<br>_(13.32)***_|0.871<br>_(21.75)***_|0.540<br>_(11.09)***_|0.883<br>_(18.78)***_|0.860<br>_(10.18)***_|
|**_ln(k)_**|0.321<br>_(14.61)***_|0.255<br>_(14.93)***_|0.242<br>_(7.18)***_|0.395<br>_(24.64)***_|0.268<br>_(13.21)***_|0.444<br>_(20.01)***_|0.235<br>_(11.28)***_|0.249<br>_(8.81)***_|
|**_Intercept_**|0.720<br>_(1.55)_|1.597<br>_(4.21)***_|1.993<br>_(2.25)**_|3.780<br>_(5.79)***_|1.654<br>_(4.88)***_|2.985<br>_(6.08)***_|0.157<br>_(0.55)_|1.251<br>_(2.22)**_|
|**_Size_**|0.018|-0.105|-0.092|-0.195|0.600|-0.193|-0.316|0.014|
||_(0.11)_|_(0.21)_|_(0.48)_|_(2.57)**_|_(0.96)_|_(1.92)*_|_(1.29)_|_(0.07)_|
|**_Foreign_**|-0.242|-0.384|-0.011|-0.005|-0.397|-0.005|-0.000|-0.007|
||_(0.53)_|_(0.43)_|_(1.30)_|_(3.36)***_|_(1.16)_|_(1.88)*_|_(0.01)_|_(1.07)_|
|**_Export_**|-0.006|-0.183|-0.007|-0.001|-0.107|-0.005|-0.019|-0.009|
||_(1.06)_|_(1.43)_|_(2.87)***_|_(1.06)_|_(0.97)_|_(1.64)_|_(1.22)_|_(1.32)_|
|**_RegElect_**|0.077|0.323|0.228|0.042|1.006|0.053|-0.025|0.068|
||_(0.54)_|_(0.60)_|_(1.94)*_|_(0.83)_|_(1.92)*_|_(0.86)_|_(0.16)_|_(0.60)_|
|**_RegWeb_**|-2.641|2.138|0.329|-0.426|0.768|-0.757|-1.542|-0.847|
||_(2.43)**_|_(1.26)_|_(0.94)_|_(2.07)**_|_(0.50)_|_(3.39)***_|_(1.77)*_|_(1.57)_|
|**_Cred_**|-1.011|-2.421|-0.403|-0.144|-1.842|-0.085|-0.304|-0.554|
||_(2.08)**_|_(2.42)**_|_(2.74)***_|_(2.38)**_|_(2.07)**_|_(1.02)_|_(1.25)_|_(2.26)**_|
|**_AccessF_**|0.006<br>(0.11)|0.118<br>(0.65)|0.059<br>(1.41)|0.044<br>(2.34)**|-0.022<br>(0.11)|0.068<br>(2.43)**|0.126<br>(1.74)*|-0.051<br>(1.22)|
|**_Training_**|-0.135|0.234|-0.142|-0.217|0.428|-0.123|-0.400|-0.103|
||_(0.43)_|_(0.33)_|_(0.93)_|_(3.23)***_|_(0.56)_|_(1.22)_|_(1.34)_|_(0.59)_|
|**_EduM_**|-0.148|-0.282|-0.076|-0.064|-0.673|-0.073|-0.096|-0.158|
||(2.02)**|(1.53)|(2.08)**|(3.03)***|(2.61)***|(1.96)*|(1.46)|(2.84)***|
|**_ExpM_**|-0.037|0.045|-0.000|-0.003|0.014|-0.002|-0.006|-0.000|
||_(2.26)**_|_(1.50)_|_(0.05)_|_(0.90)_|_(0.48)_|_(0.38)_|_(0.56)_|_(0.04)_|
|**_RegLregul_**|0.024|-0.827|-0.069|0.007|0.362|0.020|-0.112|-0.006|
||_(0.13)_|_(1.52)_|_(0.50)_|_(0.10)_|_(0.70)_|_(0.20)_|_(0.53)_|_(0.05)_|
|**_RegCorrup_**|0.081|0.074|0.168|-0.054|-0.272|-0.008|0.073|0.124|
||_(0.51)_|_(0.17)_|_(1.53)_|_(0.96)_|_(0.59)_|_(0.11)_|_(0.52)_|_(1.40)_|
|**_Constant_**|1.460|-2.422|1.493|3.388|-2.612|2.358|1.279|1.568|
||_(2.87)***_|_(1.25)_|_(2.00)**_|_(5.45)***_|_(1.34)_|_(4.94)***_|_(1.91)*_|_(2.66)***_|
|**Observations**|942|380|1601|1494|838|695|774|480|
|**sigma_u**|0.75|1.69|0.77|0.90|1.46|0.75|1.10|0.64|
|<br>**sigma_v**|0.86|0.81|0.54|0.43|0.76|0.46|0.57|0.67|
|**Wald chi2**|1351.45|2787.67|241.01|1306.40|2484.52|1060.30|1321.23|300.67|
|**Prob> chi2**<br>_Notes_<br>:<br>|0.00<br>The one step p|0.00<br>rocedure exp|0.00<br>lains firm-lev|0.00<br>el inefficienc|0.00<br>y. Variables_Siz_|0.00<br>_e_,_Foreign_an|0.00<br>d_Export_are|0.00<br>expected with|

_<u>Notes</u>_ <u>:</u> The one step procedure explains firm-level inefficiency. Variables _Size_ , _Foreign_ and _Export_ are expected with a negative coefficient. All regressions contain country-dummy variables when estimating the production function. * Significant at 10%; ** significant at 5%; *** significant at 1%. Absolute value of z statistics are in parentheses. _<u>Source</u>_ <u>. Authors’ estimations.</u> A first set of conclusions concerns the production frontiers. Our regressions confirm the choice to estimate a production frontier by industry. Elasticites of capital and labor reveal to be different from one industry to another. Impact of capital is strong in _Chemicals & Pharmaceutical Products_ , _Agro-Processing_ and, to a lower extend, _Textile_ . On the opposite, elasticity of labor is higher in _Metal & Machinery, Non Metal & Plastic Materials, Wood & Furniture, Leather_ and _Garment_ . These industries look like being more intensive in labor, although two of them: _Metal & Machinery_ and _Non Metal & Plastic Materials_ , are usually considered as applying more capitalistic technologies in developed economies. This result is confirmed by the computation of the ratio of the two elasticities (capital/ labor). All coefficients are highly significant (at 1% level), what stresses the robustness of our results 19
Another result shows that we are close to the constant returns to scales, legitimating the hypothesis underlying the non parametric _TFP_ measures (see section 2.1). Our estimations also highlight that some differences in production frontiers can be explained by countries specific conditions. This hypothesis is supported by the data, as country-dummies are well significant at this stage of estimations.

More interesting, our estimations verify that differences in the investment climate participate in firms’ Technical Efficiencies ( _TE_ ) discrepancies. This is true for all aspects of the investment climate, except for the “Government-Business” relations. Our results confirm that a good quality of infrastructure (proxied by the quality of the electric network and the availability of internet access), a satisfactory access to financing, as well as the availability of expertise at the firm level (such as education and experience of the manager, and training of the employees) are important factors for enterprises productive performances. This outcome, which is consistent with the theory, makes a real contribution to the empirical literature by validating, for a large sample of industrial firms in developing countries, the role of a substantial set of _IC_ variables on firms’ productive performances.

This finding appears, however, quite different from one industry to another. First, as expected, it looks like that estimation has suffered from the colinearity of several _IC_ variables. In fact, although each broad category of _IC_ variables (except Government-Business Relation) ends up being significant in almost all industries, it is very rare to find two significant _IC_ variables in the same category<sup>40</sup> . Impact of _IC_ variables can also vary. Access to credit seems more detrimental in _Leather, Metal & Machinery Products_ and _Textile_ (the estimated coefficient of this variable in higher than in the other industries). Access to the internet looks more critical in _Textile_ and _Wood & Furniture_ . As for Human Capacity, the education of the top manager should be more a high priority in _Metal & Machinery Products_ , _Textile_ and _Non Metal & Plastic Materials._ Interestingly, _Textile_ and _Metal & Machinery Products_ look more sensitive to _IC_ deficiencies. Beside, firms’ performances depend on more dimensions of the _IC_ in these two sectors. This finding may be explained by the fact that these industries are more exposed to international competition and need a supportive investment climate to be able to compete efficiently.

As for Business-Government Relations, neither labor regulations ( _RegLreg_ ), nor corruption ( _RegCorrup_ ) emerge as an obstacle to firms productive performance, although this outcome has to be considered with caution because of the probably high correlation between explanatory variables. Difficulties have also occurred in validating the impact of other individual variables. Firms’ size ( _Size_ ) and foreign ownership of capital ( _Foreign_ ) justify scales economies and externalities linked to participation of foreign capital in just two sectors ( _Agro-Processing,_ and _Chemical & Pharmaceutical Products_ ). Export orientation ( _Export_ ) appears as a determinant of productivity in only one industry: _Garment._ This result meets, however, with what we know about this sector, where external competitive markets are a stimulating source for a high productivity level. Identically, regressions results are poor in two sectors: _Leather_ and _Wood & Furniture_<sup>_41_</sup> . All these difficulties, when individual factors are considered, explain why we have then focused our analysis on a few _IC_ composite indicators. These indicators are tested econometrically in the next section.

# **7.2- Common Model with Composite Indicators of Investment Climate**

In this specification, the _IC_ individual variables have been replaced by our four composite indicators: Quality of Infrastructure ( _Infra_ ), Business-Government Relations ( _Gov_ ), Human Capacity ( _H_ ), and Financing Constraints ( _Fin_ ). This model allows introducing much more _IC_ 20
variables than previously<sup>42</sup> . Like in the first empirical model, we have considered a same representation for all industries. The model is still estimated at the branch level and explains the logarithm of the firms’ value added and Technical Efficiency ( _TE_ ) in one step. Other control variables are unchanged. The model is as follows: ln( _y i,j_ ) = c _i_ + ά1 ln( _l i,j_ ) + ά2 ln( _k_ i,j) + β _Sizei,j_ + γ _Foreigni,j_ + δ _Exporti,j_ Estimation results reinforce our previous findings (see Table 7). Production frontiers are robust to the introduction of different _IC_ variables, with little changes in returns to scales or in the elasticities of production factors across industries. Countries specific conditions are also validated by the data.

One of the most interesting outcomes, nevertheless, concerns the investment climate which four dimensions are now significant with the expected sign. As we actually explain firm-level inefficiency, a positive coefficient is expected for three out of our four indicators. This is the case of _RegInfra_ , _RegGov_ and _Fin_ , which are interpreted as obstacle for the operation of the firms. On the opposite, _H_ being constituted of variables which are supposed to improve Technical Efficiency, a negative coefficient is expected for this variable (see section 5 for the definition of the axes of the composite indicators). Beside, our model validates the impact of a much more substantial number of _IC_ variables incorporated in the aggregated indicators. This result has to be stressed because it is the first time (to our knowledge) that the empirical literature brings evidence of the role of such a significant set of _IC_ variables for such a large and diversified sample of industrial firms. It is also of first important for MENA, knowing the deficiencies of different dimensions of the investment climate which improvement could constitute a powerful mean of boosting firms’ efficiency and of catching up with more efficient and competitive countries. Improving the financial environment in _Morocco_ , the government-business relation in _Egypt_ , _Saudi Arabia_ and _Lebanon_ , and the quality of infrastructure in _Lebanon_ in particular would go in this direction.

Findings by industry bring, as well, quite interesting comments. Human Capacity ( _H_ ), Infrastructure ( _Infra_ ), and Financing Constraints ( _Fin_ ) appear to be the most robust investment climate factors for firm-level productivity. All three broad indicators explain quite well productivity discrepancies in most industries while Business-Government Relations ( _Gov_ ) constitutes a less constant dimension. Our empirical analysis also reveals that some industries: _Textile_ (for _H_ , _Infra_ and _Fin_ ), _Metal & Machinery Products_ (for _H_ and _Gov_ ) and _Wood & Furniture_ (for _H_ and _Fin_ ) appear more sensitive and vulnerable than others in front of a deficit of their investment climate (the estimated coefficients of the _IC_ variables are higher for these industries). This comment may be extended to _Non Metal & Plastic Materials_ and _Garment_ for, respectively, Human Capacity ( _H_ ) and Government-Business Relation ( _Gov_ ).

These findings confirm in a different way some conclusions of the previous model. As mentioned before, this result may be due to the fact that most of these industries face international competition. This fragility justifies that a particular attention be paid when taking decisions that may affect these sectors’ investment climate. This also means that the pay off of an improvement of the investment climate would be more substantial in these industries, which could play a leading role in the industrial capacity and export development of the countries. This conclusion is all the more important for the MENA economies, knowing the high specialization of some of them ( _Morocco_ and _Egypt_ in particular) in the _Textile_ and _Garment_ industries (see Table 2 _Annex 7_ ). Improving the investment climate in these two 21
sectors would greatly help to resist to the strong international competitions and reinforce the export orientation of the two countries.

**Table 7. Estimation Results: Common Model with Aggregated** **_IC_ Variables** _<u>(Dependant Variable: Value Added)</u>_

|**_Independent_**<br>**_Variables_**|**Textile**|**Leather**|**Garment**|**Agro**<br>**Industry**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**&Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**_ln(l )_**|0.637<br>_(16.01)***_|0.778<br>_(27.90)***_|0.879<br>_(15.19)***_|0.551<br>_(12.54)***_|0.885<br>_(25.26)***_|0.578<br>_(11.84)***_|0.836<br>_(17.87)***_|0.923<br>_(15.50)***_|
|**_ln(k)_**|0.337<br>_(15.06)***_|0.252<br>_(16.57)***_|0.196<br>_(7.40)***_|0.397<br>_(24.54)***_|0.258<br>_(13.11)***_|0.447<br>_(20.05)***_|0.248<br>_(11.91)***_|0.254<br>_(9.31)***_|
|**_Intercept_**|1.081<br>_(2.01)**_|2.149<br>_(5.93)***_|1.326<br>_(4.62)***_|4.302<br>_(5.77)***_|1.883<br>_(5.90)***_|2.868<br>_(4.26)***_|1.738<br>_(4.54)***_|1.223<br>_(2.78)***_|
|**_Size_**|-0.809|-0.333|-0.037|-0.212|-0.159|-0.198|-0.490|0.273|
||_(1.54)_|_(1.77)*_|_(0.33)_|_(2.75)***_|_(0.22)_|_(1.99)**_|_(2.22)**_|_(1.10)_|
|**_Foreign_**|-0.426<br>_(0.90)_|-0.006<br>_(0.76)_|-0.014<br>_(0.50)_|-0.005<br>_(3.48)***_|-0.541<br>_(1.05)_|-0.006<br>_(1.72)*_|0.004<br>_(0.54)_|-0.019<br>_(1.28)_|
|**_Export_**|-0.016<br>_(0.81)_|-0.020<br>_(1.95)*_|-0.078<br>_(1.81)*_|-0.001<br>_(1.14)_|-0.114<br>_(1.04)_|-0.008<br>_(1.49)_|-0.017<br>_(1.53)_|-0.186<br>_(1.08)_|
|**_RegInfra_**|0.762<br>_(2.52)**_|-0.079<br>_(0.66)_|-0.057<br>_(0.95)_|0.014<br>_(0.27)_|0.833<br>_(1.83)*_|0.204<br>_(2.35)**_|0.262<br>_(1.71)*_|0.318<br>_(2.32)**_|
|**_H_**|-0.716<br>_(1.76)*_|-0.138<br>_(0.79)_|-0.116<br>_(1.08)_|-0.253<br>_(5.03)***_|-1.174<br>_(1.52)_|-0.147<br>_(1.71)*_|-0.488<br>_(2.33)**_|-0.768<br>_(2.24)**_|
|**_,_**<sup>**_RegGov_**</sup>|-0.259<br>_(1.21)_|-0.072<br>_(0.72)_|0.185<br>_(2.48)**_|-0.047<br>_(1.48)_|0.706<br>_(1.70)*_|-0.068<br>_(1.39)_|-0.060<br>_(0.54)_|0.136<br>_(0.86)_|
|**_Fin_**|0.778<br>_(2.40)**_|0.219<br>_(1.68)*_|0.035<br>_(0.50)_|0.124<br>_(3.86)***_|0.257<br>_(0.54)_|0.148<br>_(2.67)***_|0.330<br>_(2.36)**_|-0.208<br>_(1.26)_|
|**Constant**|-0.961<br>_(0.95)_|0.162<br>_(0.19)_|0.506<br>_(1.84)*_|3.243<br>_(4.82)***_|-6.121<br>_(2.83)***_|1.508<br>_(2.32)**_|0.703<br>_(1.04)_|-0.522<br>_(0.71)_|
|**Obs**|929|433|1555|1481|826|741|750|461|
|**sigma_u**|1.31|1.11|0.25|0.91|1.98|0.70|1.10|0.56|
|**sigma_v**|0.86|0.60|0.73|0.37|0.65|0.56|0.53|0.75|
|**Wald chi2**|1579.56|2375.90|925.66|1343.79|3117.04|1010.55|1490.81|893.91|
|**Prob> chi2**<br>_Notes_<br>:The one|0.00<br>step procedure|0.00<br>explains firm-|0.00<br>level ineffici|0.00<br>ency. The ex|0.00<br>ected sign of t|0.00<br>he_IC_ aggreg|0.00<br>ated variables|0.00<br>is positive for|

_<u>Notes</u>_ <u>: The one step procedure explains firm-level inefficiency. The expected sign of the</u> _IC_ aggregated variables is positive for _RegInfra_ , _RegGov_ and _Fin,_ and negative for H (see definition of variables in section 5). Variables _Size_ , _Foreign_ and _Export_ are also expected with a negative coefficient. All regressions contain country-dummy variables when estimating the production function. * Significant at 10%; ** significant at 5%; *** significant at 1%. Absolute value of z statistics are in parentheses. _<u>Source</u>_ <u>. Authors’ estimations</u> By using our _IC_ aggregate indicators, however, we don’t always better explain productivity. This is somehow the case of _Metal & Machinery Products_ and _Agro-Processing_ , but essentially of _Garment_ for which a very few aspects of the investment climate seem to help firms to perform better<sup>43</sup> . No improvement is seen, either, in _Leather_ , which is again poorly explained by the model. This fact is, however, largely compensated by the tremendous gain of information through the large set of _IC_ variables now explaining firm-level productive performances, as well as by the validation of another variable of interest: the GovernmentBusiness Relation ( _Gov_ )<sup>44</sup> . We will also show in the next section that Garment is better explained by the data, when dealing with small and medium domestic firms.

Another addition of the model consists in validating the role of more plants characteristics in explaining firm-level Technical Efficiency ( _TE_ ). This is true for the variable _Size_ , which justify scales economies in four industries instead of two previously: _Wood & Furniture_ and _Leather_ in addition to _Agro-Processing_ and _Chemicals & Pharmaceutical Products_ . This constitutes an interesting result that would justify a policy of concentration of small enterprises, which importance in developing countries is well documented. Concentration 22
could be seen as a powerful means of boosting efficiency and competitiveness of the industrial sector, thus contributing to industrial development and economic growth. Besides, export orientation ( _Export_ ) explains externalities linked to export activities in _Leather,_ in addition to _Garment_ (with a stronger coefficient for _Garment_ ), what confirms the exposure to international competition of these two industries. Increase the export capacity of some industries appears, though, as another mean to stimulate firm’s efficiency and to promote a diversified economic growth, where industry is subject to play a major role.

# **7.3- Technical Efficiency and Endogeneity of Firms’ Implantation**

Another test of robustness has consisted in addressing the question of a possible endogeneity in firms’ location. City or region-sector averages _IC_ indicators would not be exogenous regressors if, for example, more efficient firms tend to establish in locations where the investment climate is better. In order to evaluate this bias, we have re-run our previous model on a set of firms which are less likely to choose their location. This had led to eliminate foreign firms and large domestically owned firms. Following Dollar et _al_ . (2005), we define our new sample as the domestically owned firms employing less than 150 workers<sup>45</sup> . Results of this new set of estimations still confirm our previous findings (see Table 8).

A first conclusion concerns the investment climate, which impact on firms’ performances is still validated by the data. This is true for all four dimensions of the investment climate. This result confirms that small and medium domestic firms are sensitive as well to changes in the different dimensions of the investment climate.

A detailed analysis also reveals that the influence of the investment climate can be different for this category of firms. This is the case in _Textile_ , _Garment_ and _Non Metal & Plastic Materials_ , where impact of _IC_ variables is stronger than for the whole sample (see section 6.2). In _Textile_ , this is true for all three significant dimensions of the investment climate ( _Infra, H_ and _Fin)._ In _Garment_ , Financing Constraints _(Fin)_ and Infrastructure ( _Infra)_ appear now as constraints for small firms’ productive performances, in addition to a stronger impact of Business-Government Relations ( _Gov_ ). Besides, small firms in _Non Metal & Plastic Material_ are more sensitive to limitations in Infrastructure ( _Infra)_ and Human Capacity _(H)_ . This outcome is likely to show that, in the three industries, big and foreign firms can resist more to a degradation of the investment climate. This finding also tends to confirm that big enterprises have the possibility to influence positively their investment climate, or to establish in locations where the investment climate is more favorable.

This outcome can be considered as of first importance, knowing the potential of job creation of small and medium enterprises. Actually, it is well documented that small businesses generally deal with poor investment climate. They have, for example, a more difficult and more expensive access to the financial system. They have not the power, as well, to lobby policy makers to get better regulations. They also attract less qualified people who prefer higher salaries in bigger enterprises. They have less the capacity to compensate deficient infrastructure, buying a generator or paying for expensive internet connections (in addition to the fact that they don’t choose their location (see World Bank, 2005). This makes of this category of firms a great potential for an improved performances of the industrial sector. This is particularly true for our MENA economies, which are characterized by a relatively small size of firms (see Table 1 _Annex_ 7).

23
It is also interesting to note that, when focusing on small and medium domestic firms, we find that more _IC_ variables explain firm-level performances. This is due to the fact that big enterprises are less sensitive to _IC_ limitations and bias downward the estimated coefficients when dealing with the whole sample. Restricting the sample to small and medium firms better highlights the impact of _IC_ and firms characteristics on firm-level performances and competitiveness, thus drawing more substantial policy implications. This is well illustrated by the case of _Garment_ , for which very few _IC_ variables were previously significant.

**Table 8. Estimation Results: Common Model with Aggregated** **_IC_ Variables and Sample Differentiation (domestic firms with less than 150 employees)** _<u>(Dependant Variable: Value Added)</u>_

|**_Independent_**<br>**_Variables_**|**Textile**|**Leather**|**Garment**|**Agro**<br>**Industry**|**Metal &**<br>**Machinery**<br>**Products**|**Chemic**<br>**&Pharm**<br>**Products**|**Wood**<br>**&**<br>**Furniture**|**Non Metal**<br>**& Plastic**<br>**Materials**|
|---|---|---|---|---|---|---|---|---|
|**_ln(l )_**|0.547<br>_(9.01)***_|0.882<br>_(23.30)***_|0.975<br>_(15.3)***_|0.460<br>_(5.92)***_|0.834<br>_(15.85)***_|0.549<br>_(6.74)***_|0.779<br>_(11.39)***_|0.981<br>_(14.07)***_|
|**_ln(k)_**|0.319<br>_(12.38)***_|0.252<br>_(16.25)***_|0.177<br>_(6.06)***_|0.384<br>_(18.61)***_|0.251<br>_(10.87)***_|0.390<br>_(13.89)***_|0.223<br>_(10.01)***_|0.252<br>_(8.74)***_|
|**_Intercept_**|2.153<br>_(4.18)***_|1.732<br>_(5.00)***_|-0.309<br>_(0.93)_|2.105<br>_(2.33)**_|1.903<br>_(4.86)***_|2.426<br>_(3.11)***_|2.238<br>_(3.16)***_|1.024<br>_(2.42)**_|
|**_Size_**|-2.897<br>_(1.91)*_|0.045<br>_(0.27)_|0.186<br>_(0.84)_|-0.357<br>_(2.88)***_|-2.331<br>_(1.76)*_|-0.345<br>_(2.44)**_|-0.412<br>_(2.51)**_|0.678<br>_(1.37)_|
|**_Export_**|-0.417<br>_(0.98)_|-0.010<br>_(1.85)*_|-0.003<br>_(0.81)_|-0.005<br>_(1.79)*_|-0.475<br>_(0.99)_|-0.016<br>_(1.49)_|-0.013<br>_(1.62)_|-0.316<br>_(0.95)_|
|**_RegInfra_**|1.170<br>_(2.13)**_|-0.127<br>_(1.09)_|0.763<br>_(2.97)***_|0.007<br>_(0.09)_|0.869<br>_(2.01)**_|0.161<br>_(1.83)*_|0.157<br>_(1.60)_|0.472<br>_(1.72)*_|
|**_H_**|-1.352<br>_(2.04)**_|-0.133<br>_(0.86)_|-0.276<br>_(0.77)_|-0.201<br>_(2.82)***_|-1.103<br>_(1.35)_|-0.108<br>_(1.23)_|-0.263<br>_(2.50)**_|-1.444<br>_(2.09)**_|
|**_RegGov_**|-0.171<br>_(0.51)_|-0.105<br>_(1.17)_|1.552<br>_(2.88)***_|-0.045<br>_(1.05)_|0.424<br>_(0.94)_|-0.067<br>_(1.48)_|-0.063<br>_(0.81)_|0.154<br>_(0.54)_|
|**_,_**<sup>**_Fin_**</sup>|1.170<br>_(2.05)**_|0.222<br>_(1.97)**_|0.665<br>_(3.33)***_|0.093<br>_(1.96)**_|0.496<br>_(0.99)_|0.146<br>_(2.60)***_|0.178<br>_(2.42)**_|-0.520<br>_(1.57)_|
|**_Constant_**|-0.254<br>_(0.15)_|-0.348<br>_(0.44)_|-3.389<br>_(2.40)**_|1.894<br>_(2.58)***_|-1.468<br>_(0.73)_|1.509<br>_(3.33)***_|1.389<br>_(3.36)***_|-2.307<br>_(1.40)_|
|**Observations**|730|359|1093|1123|639|607|650|395|
|**sigma_u**|1.42|1.02|0.28|0.73|1.41|0.43|0.80|0.91|
|**sigma_v**|0.90|0.45|0.73|0.77|0.71|0.80|0.51|0.69|
|**Wald chi2**|663.77|1615.42|763.09|787.50|1175.83|479.31|576.56|796.86|
|**Prob> chi2**<br>_Notes_<br>:The o|0.00<br>ne step proce|0.00<br>dure explain|0.00<br>s firm-level|0.00<br>inefficiency|0.00<br>. The expecte|0.00<br>d sign of th|0.00<br>e_IC_ aggrega|0.00<br>ted variables i|

_<u>Notes</u>_ <u>: The one step procedure explains firm-level inefficiency. The expected sign of the</u> _IC_ aggregated variables is positive for _RegInfra_ , _RegGov_ and _Fin,_ and negative for H (see definition of variables in section 5). Variables _Size_ , _Foreign_ and _Export_ are also expected with a negative coefficient. All regressions contain country-dummy variables when estimating the production function. * Significant at 10%; ** significant at 5%; *** significant at 1%. Absolute value of z statistics are in parentheses. _<u>Source</u>_ <u>. Authors’ estimations</u> In addition, our estimations confirm once more that it is the small and medium firms more exposed to international competition that suffer the most of the deficiencies of their _IC_ . This is particularly true for _Textile_ and _Garment_ , but also to some extent for _Non Metal & Plastic Material_ . This finding still verifies that an improvement of the _IC_ would particularly benefit to this category of firms, which competitiveness and export capacity could be significantly boosted.

Finally, another result tends to confirm the importance of the size as a factor of productivity and efficiency. Actually, small and medium domestic firms appear to gain more from concentration than big and foreign ones (what looks like a reasonable outcome). This is the case in _Textile_ and _Metal & Machinery Products_ where the variable _Size_ is now significant, as 24
well as in _Agro-Processing_ and _Chemicals & Pharmaceutical Products_ where the coefficient of this variable shows a significant increase compared to previous estimations. This constitutes an interesting result that would again justify a policy of concentration of small enterprises<sup>46</sup> .

# **8- Conclusions**

In this paper, we have empirically verified that investment climate ( _IC_ ) matters for firms’ productive performance. This finding is true for several aspects of the investment climate, in particular the quality of various infrastructures, the experience and education of the labor force, the cost and access to financing, as well as different dimensions of the governmentbusiness relation. This outcome (which is consistent with the theory) makes a real contribution to the empirical literature by validating, for a large sample of industrial firms in developing countries, the role of a substantial set of _IC_ variables.

Policy implications of our findings are comprehensible by showing what determinants of productivity cause producers to be more efficient, and where should reform be targeted to have the greatest impact on productivity. In most industries, it is the dimension of infrastructures, human capacity and/or financing that more often account for firms’ productive performance. Building on these three dimensions of the investment climate would have a large pay-off for the efficiency and competitiveness of the manufacturing industry as a whole. This factor should be kept in mind when dealing with the reform agenda of many developing countries, the MENA region in particular, in which poor investment climate hinders economic development and catch-up with more efficient and competitive countries.

A more in-depth analysis also reveals interesting differences across industries. Actually, although most industries appear sensitive to different dimensions of the investment climate, firms in _Textile_ and _Metal & Machinery Products_ look like to suffer more of investment climate limitations. This comment may be broadened, to some extent, to _Non Metal & Plastic Materials_ and _Garment._ This may be due to the fact that these sectors face international competition and need a supportive investment climate to compete efficiently. This fragility justifies that a particular attention be paid when taking decisions that may affect these sectors. This also means that the pay off of an improvement of the investment climate in terms of productive performances and competitiveness would be more substantial in these industries, which could play a leading role for the industrial capacity and export development of the countries.. **This result constitutes an important means of appreciation of the positive impact of investment climate improvement since MENA** manufacturing **suffers from a deficient integration into the world economy,** as well as from a high competition in the world market.

Another interesting finding can be seen in the fact that impact of investment climate varies for small and medium (under 150 workers) domestic firms. This is the case in _Textile_ , _Garment_ and _Non Metal & Plastic Materials_ , where investment climate constraints emerge stronger than for the whole sample. This result is likely to show that, in these industries, big and foreign firms have the possibility to influence positively their business environment, and/or establish in locations where the investment climate is better. This finding also implies that improvement of the investment climate of small and medium enterprises in these industries would generate substantial productivity gains and largely boost competitiveness of this category of firms. This outcome has to be considered as of first importance, knowing the significance of small enterprises in developing countries, MENA in particular, as well as their substantial potential of job creation. Interestingly, another result tends to confirm the 25
importance of the size as factor of productivity and efficiency. Actually, small domestic firms appear to gain more from concentration than big and foreign ones. This is the case in _Textile_ and _Metal & Machinery Products,_ in addition to _Agro-Processing_ and _Chemicals & Pharmaceutical Products._ This constitutes an interesting result that would justify a policy of concentration of small enterprises as a powerful means of efficiency and competitiveness of the industrial sector, thus contributing to industrial development and economic growth Actually, like other developing countries, MENA is increasingly concerned about improving competitiveness and productivity as the region face the intensifying pressure of globalization. This is particularly true in MENA _Textile_ , _Garment_ and _Leather_ industries, in which export specialization can be high in some countries. Among the region, the World Bank firm-surveys provide a standard instrument for identifying key obstacles to productivity, and prioritize policy reforms. This instrument can be used to boost competitiveness and diversify MENA economies. This factor should be taken into consideration if MENA wants to face the increasing international competition of countries such as _China_ and _India,_ which have successfully diversify their economy and benefit, in addition, from low labor costs. **Targeting reforms on small and medium enterprises, as well as on those industries and investment climate variables which are the most inadequate and which favor the most productivity would constitute an important element of MENA strategy of growth and employment for the future.** 26