Efficiency, inefficiency and the MENA frontier.
Date: 2015-06-26
Institutions: European Central Bank
Subjects: Middle East, North Africa, econometrics, economic analysis, economic development, economic model, frontier, production, regional development

Working Paper Series 

Dimitris Christopoulos 
and Peter McAdam  

Efficiency, Inefficiency and 
the MENA Frontier 

No 1757 / February 2015 

Note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). 
The views expressed are those of the authors and do not necessarily reflect those of the ECB 

 
Acknowledgements  
We thank one anonymous referee of the ECB working paper series. The views expressed are those of the authors alone. 

Dimitris Christopoulos 
Panteion University; e-mail: dk.christopoulos@gmail.com 

Peter McAdam 
European Central Bank; e-mail: peter.mcadam@ecb.europa.eu 

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ISSN 
ISBN 
DOI 
EU catalogue number 

1725-2806 (online) 
978-92-899-1497-0 
10.2866/259398 
QB-AR-14-131-EN-N 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abstract

In a stochastic frontier setting, we examine technical efﬁciency in the Middle
East and North Africa (MENA). Evidence suggests that in addition to economic
indicators, political and social ones play a key role in development and frontier
technical efﬁciency proﬁles. The MENA have been characterized by increasing
economic efﬁciency over time but with marked polarization. The paper anal-
yses and nest many key hypotheses in the literature e.g., the contributions of
religion, of natural resources, demographic pressures, human capital etc. The
originality of our contribution is the use of a large data set (including prin-
cipal components), and extensive robustness checks. The paper should set a
comprehensive benchmark and cross check for related studies of development
technical efﬁciency.

JEL Classiﬁcation: C33, C38, C55, D24, E23, O11
Keywords: Frontier Efﬁciency, MENA, Development, Stochastic Frontier.

ECB Working Paper 1757, February 20151Non Technical Summary

We examine the determinants of technical efﬁciency in the Middle East and North
African nations (the MENA) and link it to economic, political and socio-cultural
indicators. Our framework mirrors two aspects in the production and growth liter-
ature.

First, in exploiting the concept of frontier technology and innovation. Countries
might operate near or behind the frontier with corresponding implications for their
efﬁciency. Improvements over time can occur from efﬁciency improvements within
or outward movements of the frontier.

Second, our framework parallels the emphasis in the literature on the quality of
institutions and cultural features in supporting sustained growth. Within a Stochas-
tic Frontier panel setting, we use a translog production function where production
deviates from its optimum by a random disturbance and a modelled “inefﬁciency
term”. A country is technically efﬁcient if it produces the maximum feasible output
from a given combination of inputs and technology; inefﬁciency is measured as the
distance of each individual observation from the frontier.

We ﬁnd that the MENA have been characterized by increasing economic efﬁ-

ciency over time but with marked polarization.

ECB Working Paper 1757, February 201521

Introduction

In the last thirty years, many economies – e.g., in Asia, Eastern Europe, Latin Amer-
ican – experienced dramatic transformations. Their share of world output and trade
increased markedly. They witnessed seismic social and political changes.

By contrast, the economies of the Middle East and North Africa (MENA)1 have,
by most assessments, stagnated. Why, in comparison to other (initially not dissim-
ilar) economies, did they fall behind? The “Arab Spring” protests from late 2010
onwards pointed to the answers: poor economic prospects, social and political ex-
clusion etc (e.g., Yousef (2004)).

How the MENA emerge after the Spring will matter for the world economy.
But, more generally, it will also provide insights as to how economies, far from the
technical frontier, tackle their problems. What stopped the MENA reaching their
potential? Was it related to factor accumulation; to the quality of institutional fac-
tors; to cultural factors; or all combined? Understanding such issues is the purpose
of this paper.

Such themes are familiar in the growth literature. Our contribution, though, is
different. We examine the precursor for sustained growth, namely efﬁciency within –
and expansion of – the technical frontier among the MENA. As far as we aware, we
are the ﬁrst to address this.2 Indeed, analytical studies on the Arab developmental
model have been surprisingly few (compare the treatment of China and India).3
Yet the MENA region amounts to almost 420 million in (2012) population, and is of
strategic geo-political importance; in short, the region is too big to ignore.

Second, the MENA represent very distinct political economies. “Private” mar-
kets are often beholden to the state for contracts and credit provision, and staffed by
political insiders etc, World Bank (2009). Moreover, with resource abundance parts
of the Arab world have arguably tended towards becoming “rentier” and “extrac-
tive” states.4 Hydrocarbon revenues also partly obviated the need for taxation,
weakening citizens’ stake in governance, see Nabli (2007). Accordingly, the process
of development leading to democracy, and democracy leading to open and con-
testable markets – as per Modernization theory (Lipset (1959)) – was continuously
setback. These aspects necessitate a serious treatment of political, institutional and
cultural factors, as well as economic ones, in the measurement of technical frontier
characteristics.5

The framework used here mirrors two aspects in the production and growth

1This block, as deﬁned by the IMF, comprises Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan,
Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Qatar, Saudi Arabia, Sudan, Syria, Tunisia,
United Arab Emirates, Yemen.

2Although see Herrala and Turk Ariss (2013) who use stochastic frontier analysis to examine the

importance of ﬁnancing constraints in Arab development.

3See for example Stracca (2013) and the references therein.
4See Schwarz (2013) for a discussion of the characteristics of “rentier” states and Acemoglu and
Robinson (2012) for a historical perspective on extractive states. See also the interesting work of
Michalopoulos (2012) for a historical perspective on the inﬂuence of geographic, inequality and
trade on the spread of Islam.

5See Klump (2006) for different approaches to integrating cultural metrics into economic studies.

ECB Working Paper 1757, February 20153literature. First, in exploiting the concept of frontier technology and innovation,
(Acemoglu et al. (2006)). Countries might operate near or behind the frontier with
corresponding implications for their efﬁciency. Improvements over time can oc-
cur from efﬁciency improvements within the frontier or outward movements of the
frontier (through technical progress). Second, our framework parallels the empha-
sis in the literature on the quality of institutions and cultural features in supporting
sustained development.

The paper is organized as follows. The following section provides background
on the Arab developmental model. This shows the early growth and developmental
gains made by the Arab region following colonial independence. But it also shows
that the growth was not sustained, being followed by a deep downturn from the
late 1970s to early 1990s.

Section 3 then discusses the model of technical efﬁciency used. Within a Stochas-
tic Frontier setting, we use a translog production function where production de-
viates from its optimum by a random disturbance and a modelled “inefﬁciency
term”. A country is technically efﬁcient if it produces the maximum feasible output
from a given combination of inputs and technology; inefﬁciency is measured as the
distance of each individual observation from the frontier. Inefﬁciency is modelled
using a variety of economic, political and socio-cultural indicators.

Section 4 describes the data, taken from several different sources, and encom-
passing continuous and categorical series. For example, we use standard indicators
like human capital, openness, ﬁnancial depth in modelling inefﬁciency, but also less
standard one such as political durability, judicial independence, workers’ rights, re-
ligious fractionalization and so on.

Sections 5 and 6 are the empirical sections. Our intention is to provide a rich
characterization of technical efﬁciency among the MENA. We have tried to take a
very broad perspective on the region, given the interconnectedness between many
different factors affecting efﬁciency, and to reduce biases from omitted variables
and channels. We also pursue robustness in several directions: in terms of func-
tional forms, and in terms of indicator selection. We deﬁne those indicators and
their interaction which enhance of impair efﬁciency. In a further step, we then de-
ﬁne the qualitative sign of indicators as reﬂecting strong or weak robustness de-
pending on their regularity. We further decompose technical progress and techni-
cal efﬁciency into their distributional and country-speciﬁc dimensions. Moreover,
we make block decompositions (at aggregate and country level) of inefﬁciency into
those factors associated to standard economic constraints, as well as those relating
to political and socio-cultural factors. Finally, we conclude.

Our main ﬁndings are as follows:

· In addition to purely economic indicators (e.g., factor accumulation, openness
etc.), political and social ones play a key role in MENA efﬁciency proﬁles.
Reforms should therefore attempt to improve all three determinants of the
technical frontier.

· Although TFP growth has been positive, but its growth has been more from

gains in efﬁciency rather than from technical progress.

ECB Working Paper 1757, February 20154· In terms of technical progress, TP, MENA countries are not characterized by
well-separated clusters of technologically backward and advanced countries;
the TP distribution is uni-model and essentially Normally distributed.

· Performance on technical efﬁciency tells a different story: there has been a
limited number of countries that failed to improve or consolidated their per-
formance through time and share a common low steady state and the rest that
signiﬁcantly improved. Thus whilst the MENA have been characterized by
increasing economic efﬁciency, albeit with marked polarization, the efﬁciency
gains witness in the MENA may have saturated.

· Human capital (education) has enhanced efﬁciency in a strong and pervasive

manner.

· Our results conﬁrm the resource-curse interpretation of (some) MENA de-
velopments. Resource rents appear to have loosened efﬁciency incentives.
Moreover, exchange rate volatility (typical of “petrocurrencies”) has retarded
manufacturing growth.

· Financial depth seems not to have enhanced efﬁciency; this may be consistent
with a rent-seeking interpretation and/or that credit has sustained favored
“zombie” ﬁrms at the expense of smaller ones constrained by retained earn-
ings.

· Religious fractionalization and the catch-all “military” government catego-
rization are clear factors which weaken efﬁciency and retard attaining the
technical frontier.

2 The MENA: Some Simple Background

Figure 1a shows shares of world output (PPP-adjusted) for the major trading blocks.
“Developing Asia” and the “Emerging Markets” increased their share of world
output since 1980 from around 25%-to-50% and around 8%-to-30%, respectively.6
The former comparison is striking because Developing Asia’s initial share roughly
matched that of the MENA block and because they shared similarly weak demo-
cratic origins. However, the MENA have however, stayed at around a 5% share.

These developments, moreover, cover a period of great expansion of world
trade, growth and technological diffusion – developments which remarkably seem
to have by-passed the Arab world. This is puzzling because the MENA enjoy many
advantages: proximity to Europe; educated, young labor force; cultural and lin-
guistic similarities; abundant natural resources etc.

Indeed, several decades before the Arab-Spring turbulence, matters looked quite
different. Following colonial independence, many Arab states, buoyed by energy

6Note, the IMF’s deﬁnition of Emerging and Developing Markets overlaps some countries in the
deﬁned MENA region. Accordingly, in calculating shares we stripped the MENA region out of their
deﬁnition, and recalculated accordingly.

ECB Working Paper 1757, February 20155windfalls, engaged in large-scale state planning, nationalization, import substitu-
tion and welfare outreach. This arrangement initially appeared successful. Over
the 1960s and 1970s the MENA (alongside the East Asian “tigers”) were among the
fastest growing in the world, see Amin et al. (2012), World Bank (2004).

Likewise, there was substantial (if uneven) progress on human development7 –
though below that expected given the region’s natural wealth and human resources,
Boutayeb and Serghini (2006). This was the essence of the Arab “Social Contract”:
the toleration of autocracy in return for welfare and growth, World Bank (2004).

But the maxim that growth is easier to start than sustain (Rodrik (2005)) matched
the MENA experience well, World Bank (2004). Unsurprisingly so given the obsta-
cles: restrictive trade regimes; corruption; under diversiﬁed economy; fragmented
capital markets; limited ﬁrm turnover; chronic slack; large low-skill informal mar-
ket; sporadic regional conﬂict etc. (see World Bank (2009), Gourdon (2010), Amin
et al. (2012), Malik and Awadallah (2013), Herrala and Turk Ariss (2013)).

Indeed, the commodity-price falls from the mid-1980s onwards – by exposing
the region’s over-reliance on hydrocarbons – contributed to reversing the earlier
growth gains, cut demand and the (shock-absorbing) ﬂow of remittances.8 It also
strained ﬁscal balances. This was crucial since all social structures and expectations
were predicated on the state providing jobs and security. Pro-education and family-
friendly welfare policies also helped promote a “youth bulge” which, given the
weakened economy, swelled unemployment.9

7For example on education, mortality and poverty, see the United Nations Development Program

data, http://hdr.undp.org/en/statistics.

8Some of the countries in the sample are oil exporters, some not. We control for this, other than
through ﬁxed effects to account for unobserved heterogeneity, also through the addition of the size
of resource rents as an explanatory variable. In addition, though some of the MENA are oil exporters
and some not, through the prevalence of job ﬂows, remittances, and cross-border loans and grants,
the energy sector has a large effect on the entire region.

9Around 60-70% of the region’s population is under 30. Such a youth bulge has often been as-
sociated with social unrest, e.g. Heinsohn (2006). The idea being that youngest sons, if excluded
from economic and social life, compete for social capital through (potentially extreme) religion and
political ideology.

ECB Working Paper 1757, February 20156Figure 1: International Output Shares and Comparative Growth

(a) World PPP GDP Shares (%)

Notes: Shaded Area denotes forecast period. ∗The IMF’s deﬁ-
nition of Emerging and Developing Markets has some overlaps
with countries in the deﬁned MENA region. Accordingly, in cal-
culating trade shares we stripped the MENA region out of their
deﬁnition, and recalculated accordingly.
Source: Derived from IMF WEO Oct 2013.

(b) Real Per Capita GDP Growth Rates: MENA and OECD

Source: IMF, OECD, World Bank. Growth Rates are:

1969-2010

1969-1980
MENA OECD MENA OECD MENA OECD

1980-2010

Mean
Std. Dev.

2.31
3.70

1.92
1.76

5.63
4.35

2.57
1.99

1.05
2.44

1.62
1.61

010203040506019801985199019952000200520102015MENADeveloping AsiaEmerging/Developing Economies*G7Shares (%)-5.0-2.50.02.55.07.510.012.515.019601965197019751980198519901995200020052010MENAOECDECB Working Paper 1757, February 20157In response to the downturn, many Arab governments engaged in pro-market
policies typically then advocated by the World Bank and IMF (ﬁscal consolidation,
privatization, trade and ﬁnancial liberalization etc.). Even controlling for the scale
of the downturn, the success rate appeared low. This was arguably for two main
reasons. First, that the “private sector” was ill-equipped to raise supply consistent
with the reforms. And second, that these reforms mostly neglected governance
issues10; vested interests and political structures remained. The evolution of GDP
per capita growth MENA (compared to the OECD) since the 1960s is shown in
Figure 1b, showing this early boom then protracted and volatile bust.

The region’s unusually painful and protracted readjustment points to chronic
inefﬁciencies and persistent barriers to growth. Our purpose is to provide a rich
characterisation of these. In the following sections, we outline the methodology
and data involved in this assessment.

3 Empirical Modeling Strategy

A country is technically efﬁcient if it produces the maximum feasible output from
a given combination of inputs and technology, regardless of market demand and
prices. While if a country produces less than is technically feasible given both tech-
nology and inputs, it is inefﬁcient.

Inefﬁciency is measured as the distance of each individual observation from the
frontier. Aigner et al. (1977) and Meeusen and van den Broeck (1977) pioneered
a stochastic version of this model, the stochastic frontier analysis (SFA) method
to estimate potential output and efﬁciency characteristics. This was extended by
Schmidt and Sickles (1984) in the panel context. Greene (2008a,b) provides excellent
discussions of the development of the ﬁeld, and McQuinn (2013) provides a good
recent illustration on international technology spillovers.11

Consider the production function,

Yit = f (Kit, Lit, Hit) evit e−uit
where Y denotes output, K, L, H represent physical capital, labor, human capital
respectively, and subscripts i = 1, 2, ...N and t = 1, 2, ...T respectively index coun-
try and time. uit ∈ (0, ∞) denotes technical efﬁciency and vit captures stochastic
movements in the frontier.

(1)

Given the empirical weakness of Cobb Douglas (e.g., Chirinko (2008), Klump
et al. (2007)) we consider f (·) to be instead described by a translog production func-
tion:

yit = α0i + ∑

αjxjit +

j

1
2

∑
j

∑
m

αjmxjitxmit + ∑

αjtxjitt + αtt +

j

1
2

αttt2 + vit − uit

(2)

10For example, see Walton (2013) on Egypt’s privatization program in the 1990s.
11A related but methodologically distinct method of estimating production frontiers is Data En-
velopment Analysis analysis. A good recent example in the context of the world technology frontier
is by Growiec (2012). Relative to that method, SFA has the advantage of allowing for statistical
inference on the efﬁciency term and on estimated production parameters.

ECB Working Paper 1757, February 20158where y = Log(Y), and {j (cid:54)= m} ∈ [k, l, h] such that for j = k, xjit = Log(Kit) = kit
etc. Variable t is a time trend that proxies disembodied technical progress.12 Pa-
rameters α0i are country-speciﬁc ﬁxed effects speciﬁed in order to distinguish un-
observed heterogeneity from the inefﬁciency component. Many studies, including
Greene (2005), use dummy variables as environmental variables in stochastic fron-
tier analysis.

The translog is a highly ﬂexible functional form: it nests Cobb Douglas; it does
not restrict the elasticity of factor substitution to be constant; nor does it restrict
technical change to be neutral (since “technical progress” pre-multiplies all three
factors). In Appendix D, though, we consider robustness exercises where we use
alternative production forms: namely, the modiﬁed translog and the fourier forms.

The error terms have the usual interpretation: vit is a symmetrically distributed

v ), and uit is a one-sided error truncated at zero uit ∼ N +(µit, σ2

u) where

as vit ∼ N (0, σ2
µit, the mean level of efﬁciency, is given by,

µit = z(cid:48)

itβ

(3)

where zit is a vector of indicators explaining inefﬁciency.

Let us assume that the indicators, z, can be further categorized as economic
indicators (E), indicators relating to the characteristics of Political Institutions (P),
and others reﬂecting Socio-cultural (S) type variables (to be deﬁned below):

uit = β0 + βEE + βPP + βSS + βI I + βtt + wit

(3’)

where wit is an unobservable random variable independently distributed as N +(0, σ2
w)
such that uit ≥ 0. Equation (3’) also nests the restricted form: βP = βS = 0, i.e.,
where political and socio-cultural indicators play no role in explaining inefﬁciency.
Finally, the rate of change of technical efﬁciency is given by βt.

Note we shall include human capital in the inefﬁciency equation since it is likely
that the adoption of best-practice technologies requires skills, see Grifﬁth et al.
(2004). Thus, changes in human capital not only shift the frontier (given its in-
clusion in production function, equation (2)), but also shift economic inefﬁciency
(given its inclusion in inefﬁciency equation (3’)). Moreover, we also ﬁnd slope (or
interactions) effects (contained, amongst other interactions, in block I).

The emphasis on human capital is natural. It is central to modern growth the-
ories, as well as to MENA development. Member countries greatly expanded ed-
ucation services (from a low base in the 1960s). They did so both to modernize
their economy and, it is often argued, effectively compensate citizens for political
exclusion.

12For a discussion of the various forms of technical progress and their implications see Le ´on-
Ledesma et al. (2010). See also Chirinko et al. (2011) and de La Grandville (2009) for a more general
discussion of the factor substitution elasticity.

ECB Working Paper 1757, February 201594 Data

4.1 General Description

We use data from a variety of sources: Center for Systemic Peace, CIRI Human
Rights Data Project,13 Database of Political Institutions,14 Penn World Tables,15 Polity
IV database,16 as well as the United Nations, the World Bank, the CIA (World Fact-
book) and the IMF. Some of these data are continuous numerical series (e.g., GDP,
employee number, FDI), some are categorical (e.g., polity type, strength of workers’
or women’s rights) etc.

In collecting the series, we searched for the furthest backdated and most country-
wide complete data set for the indicators of interest. The tables in Appendix A show
the full series, their deﬁnitions and sources. The data is annual, covers 14 MENA
countries: Bahrain [1980-2008], Egypt [1980-2008], Jordan [1980-2008], Kuwait [1986-
2008], Libya [1986-2008], Mauritania [1980-2008], Morocco [1980-2008], Qatar [1986-
2008], Saudi Arabia [1980-2008], Sudan [1980-2008], Syria[1980-2008], Tunisia [1980-
2008], United Arab Emirates [1986-2008], and Yemen [1989-2008]. Our strategy for
dealing with such a relatively large database is twofold.

First, we sought out different data sources and types to provide a rich analysis
of production and inefﬁciency trends in the MENA. That is to say, indicators which
covered not only economic features but also those relating to Political and Socio-
cultural characteristics.
in Table 1
below), for instance, we use economic indicators alone to model inefﬁciency. This
provides a benchmark since it is most closely aligned with usual practise. After
that, we augment the variable set with indicators from the P and S blocks. This
allows us to judge whether the benchmark parameters are qualitatively robust, and
then assess the statistical impact of the additional indicators.

In our ﬁrst SFA analysis (columns 1: and 2:

Examples of standard economic indicators in the inefﬁciency equation, are ed-
ucation, the degree of openness, sectoral and natural-resource features etc. These
capture endowments in the economy and how activity and resources are efﬁciently
allocated across it. Political and institutional factors include the type of the Gov-
ernment (military/non-military), the size of the public sector, freedom of move-
ment and assembly, judicial independence, regime durability etc. Note, there is
no presumption that political and institutional indicators unanimously hurt efﬁ-
ciency. Public expenditure may contribute positively (e.g., through education, in-
frastructure, nutrition programs), as may even extended regime duration (through
enhanced political stability and order). Moreover, many political indicators such as
women’s rights have in themselves improved over time. Finally, socio-cultural in-
dicators include fractionalization in religious grouping, as well as age distribution,
and demographic pressures etc. Again, these may impact efﬁciency positively or
negatively.

13Cingranelli and Richards (2010).
14Keefer (2010).
15Heston et al. (2012).
16Marshall et al. (2010).

ECB Working Paper 1757, February 201510Naturally, these categorizations are not water-tight. But they constitute an intu-
itive starting point and a useful narrative. Widening the set of admissible indicators
(i.e., to Political and Cultural indicators) in this way is also noteworthy because it
mixes continuous and categorical data. SFA analysis rarely strays beyond the for-
mer data type. But in the MENA case, to do so would miss a wealth of information.
The second aspect of our data strategy is the following. In our initial stochastic
frontier regressions we sample from that large pool of candidate series to uncover
a congruent representation of the production-efﬁciency nexus. To include all se-
ries of interest raises multi-collinearity issues. Accordingly, after the “core” SFA
exercises, we report results where we extract principal components of the E, P, and S
blocks. This relaxes the dimensionality constraint, whilst still preserving our nar-
rative framework. Within the principal components, we can also retrieve the un-
derlying efﬁciency coefﬁcients associated to each indicators, further enhancing our
understanding. Finally, when principal components is applied to categorical vari-
ables, it is important to use, as we do, the polychoric and polyserial (rather than
merely Pearson) correlation matrix.

4.2 A First Look at the Data

Figure 2 shows histograms of representative data: human capital, share of manu-
facturing, openness and trade, government expenditure, regime durability, Chief
Executive as Military Ofﬁcer, the extent of workers’ rights, mobile phone owner-
ship, resource rents, ﬁnancial depth (as measured by credit ﬂows)17, FDI, religious
fractionalizations, and median age. In addition to describing the data, we also dis-
cuss their potential impacts on economic efﬁciency. We group the data discussion
into production data (section 4.2.1); Economic indicators (4.2.2), Political and Social
indicators (respectively, 4.2.3, and 4.2.4)

4.2.1 Production Data

Regarding the production data, variable Y in equation (2) is deﬁned as GDP in
constant 2005$s (chain series). By way of background, though, we note that MENA
output characteristics vary considerably.

In terms of living standards, using GDP per capita (PPP), we have (where [.]
denotes ranking relative to the World) at the top end Qatar [1], UAE [15], Kuwait
[27], Saudi Arabia [46] all the way down to Sudan [182] and Yemen [188]. In terms of
the scale of these economies, Egypt has the largest population (roughly 85 million),
followed by those in the 30–40 million bracket (Algeria, Sudan, Morocco, Saudi
Arabia), then (in the 1-5 million bracket) by the smaller Gulf states (Kuwait, Qatar,
UAE and Bahrain) and Mauritania. For scale in terms of GDP level, Saudi Arabia,
UAE, and Egypt tend to rank the top, Qatar and Kuwait and Morocco are near the
middle, and Yemen, Jordan, Bahrain and Mauritania are the smallest.18

17The efﬁciency of the Arab banking sector is examined in Herrala and Turk Ariss (2013).
18All ﬁgures in this paragraph relate taken from sample year averages from the CIA world Fact-

book.

ECB Working Paper 1757, February 201511Regarding factors of production, the capital stock series was constructed using
the perpetual inventory method from the Penn Investment series.
Initial capital
stocks were constructed for the year 1960: we used the investment share of Real
GDP per capita and population data available in the Penn tables and we assumed a
depreciation rate of 0.095. Labor is the number of employees. The stock of human
capital is from Barro and Lee (2013) and represents the educational attainment of
individuals 25 years or older measured as average years of schooling.19

4.2.2 Efﬁciency Indicators: Economic

For human capital, the average years of schooling was just over 5 years. By contrast,
in 2010 the average years of schooling for the UK, Germany and the US was 9, 12,
and 13 years respectively.

Links between human capital and efﬁciency are intuitive: a high skilled econ-
omy allows the workforce to implement and absorb new technologies (e.g., Cohen
and Levinthal (1989), Grifﬁth et al. (2004)) and catch up with the technological fron-
tier. The extent to which human capital does so depends on:

(a) its quality and appropriateness, and;

(b) any externalities and complementarities induced by skills.

Regarding point (a), despite its expansion in recent decades, the academic qual-
ity of MENA education relative to the rest of the world is an issue (even control-
ling for the level of income and development), see Heyneman (1993). Moreover,
there is often effectively a two-tier system: returns to basic education are very low,
(Pritchett (1999), Makdisi et al. (2006)), but higher following a university education
(Salehi-Isfahani et al. (2009)).

But education is also often thought to play a signalling role: strictly interpreted
that implies that it has no direct effect on improving skills, but helps identifying the
most “suitable” candidates. Accordingly, the tailoring of advanced education to-
wards rote learning and passing entrance exams for tenured state positions (rather
than on market-relevant skills) downplays the expected efﬁciency returns of educa-
tion, Amin et al. (2012). On the other hand, since these economies lag the world
technology frontier, developed-world education may be unsuited to production
conditions, Acemoglu et al. (2006).

The second way human capital may affect efﬁciency comes from demonstra-
tion effects, complementarities and diffusion processes induced by skills. Such ef-
fects can take place through openness and FDI (Foreign Direct Investment), both of
which affect (and are affected by) human capital. Openness and FDI can transfer
technology and more efﬁcient production techniques between countries, helping
to diversify exports, raising productivity and wages, and reinforcing incentives for
acquiring skills (e.g., Benhabib and Spiegel (2005), Grossman and Helpman (1993)).

19Since this data set is available for 5-year periods, we followed common practice and used linear

interpolation to generate complete data records for all years.

ECB Working Paper 1757, February 201512Figure 2: Histogram of Selected Indicators

Notes: Dashed vertical Lines indicates median histogram values (for the continuous variables). Note, these histograms pool all countries and all years.

0204060800102030405060708090Natural Resource Rents (% of GDP), Resrent04812162024280102030405060708090100Domestic Credit to Private Sector (% of GDP),  DCPS050100150200-15-10-50510152025303540Foreign Direct Investment (% GDP), FDI0510152025012345678910Human Capital, H020406080010203040506070Imports from Arab World (% total imports), Im_AW01020304050405060708090100Imports from High Income Countries (% total imports), Im_HI0510152025300.02.55.07.510.012.515.017.520.022.525.0Manufacturing Valued Added (% GDP), Manu0102030400102030405060708090100Regime Durability (years), Durable0204060800102030405060708090100Government Consumption (% of GDP), GY050100150200Chief Executive a Military Officer, Military050100150200250Workers’ Rights, Worker0102030401214161820222426283032Median Age (years), Mage050100150200250300020406080100120140160Mobile Subscriptions per 100 people, Mobile0204060801001200.00.10.20.30.40.50.60.70.80.91.0Religious Fractionalization, ReligFrac0204060800255075100125150175200225250Merchandise Trade (% GDP), Open01012ECB Working Paper 1757, February 201513Alternatively, trade and investment openness may increase economic volatility,
e.g., through international shocks, displacing home industries and skill structures.
They may also lead to lower levels of skill accumulation if countries import skill-
intensive goods rather than producing them domestically. Efﬁciency gains from
such sources may therefore be contingent on the pre-existence of skilled labor, see
Wijeweera et al. (2010).

Moreover, around two-thirds of MENA FDI goes to resource-rich, labor-scarce
countries (e.g., Saudi Arabia and Qatar attracted respectively around over 45% and
10%, in 2010). Most of this is horizontal FDI and associated to the energy sector.
The rest is largely found in non-tradeables (telecommunications, tourism, construc-
tion).20 FDI in Manufacturing, in particular, tends to be low (at best around 10% of
all FDI)21 and FDI in high-tech services in the MENA region is essentially zero (see
World Bank (2009) and Gourdon (2010)).

On merchandize trade, judged on tariff and non-tariff barriers (as well as infras-
tructure bottlenecks), MENA trade regimes are among the most globally protected
and fragmented, Bhattacharya and Wolde (2010), Kee et al. (2009). There is thus
relatively limited regional trade (intra-MENA trade has for the last three decades
typically been below 10% of total exports). What intra-MENA trade there is ap-
pears to be highly regionally clustered. Exports, moreover, are dominated by fuels
and minerals. Weak trade links have been compounded by chronic over-valuation
and volatility of real exchange rates, Nabli (2007), the similarity of inter-MENA
factor and resource endowments, the dominance of fuels themselves (which have
inhibited diversiﬁcation), as well as political and rent-seeking factors (see Malik
and Awadallah (2013)).

Another important aspect for efﬁciency among Economic factors is the sectoral com-
position of the economy. The median value added of Manufacturing is around 12%
(and bi-modal in distribution). Otherwise, natural resource rents amount to around
20% of GDP, with positive skew (indicating members with substantial resource
rents as a proportion of output).

Natural resources are thus a key component in the MENA (directly or indirectly
through remittances). However, countries with a high ratio of natural resources ex-
ports to GDP tend to growth slowly in the medium run compared to their resource-
scarce counterparts, see Gylfason (2001).22 Resource rich economies may lose sight
of the need for efﬁcient use of resources, may under accumulate human capital and
delay reductions in fertility, Gylfason (2001), Galor and Mountford (2008). These
disadvantages are in addition to the usual concern that resource wealth encourages
rent seeking. Finally, and somewhat in contrast to the MENA situation, Imbs and
Wacziarg (2003) shows that advanced and technically efﬁcient economies are more

20Source: UNCTAD (2011).
21The comparative advantage of MENA manufacturing tends to be in unskilled labor (e.g, cloth-
ing). Moreover, the signiﬁcant wage premia in the public sector works against the development of
labor-intensive manufacturing (in labor-abundant MENA countries).

22Although in the MENA region, high resource rents helped fund the expansion in education,
health and welfare which is deemed to have positively affected efﬁciency. This had spillovers to non
oil-producing countries via remittances, job ﬂows and cross-border loans and grants.

ECB Working Paper 1757, February 201514likely to be characterized by less economic specialization as they become richer (see
also World Bank (2009)).

In contrast to resource rents case, we might expect large efﬁciency gains from
Manufacturing. This reﬂects its tradeable nature, its capital and skill intensity, its
ease of technology transfer. Moreover Rodrik (2013) identiﬁes industrialization and
manufactured exports as the most reliable drivers for rapid and sustained growth
(embodying, quite uniquely, unconditional convergence). One factor potentially re-
tarding the development of manufacturing is (1) its generally very small size in the
MENA, and (2) exchange rate volatility typical of petro-currencies (perhaps itself
also linked to policy preferences for cheap, imported staples). Services and Agri-
culture, by contrast, are often characterized by low productivity, low skill intensity,
sheltered competition and are constrained by home markets.23

4.2.3 Efﬁciency Indicators: Political

Whether the chief executive ofﬁcer is a current military ofﬁcer (=1 if a military rank
applies, 0 otherwise) is a catch all for the inﬂuence of the military in government.
Judging by the histogram, outcome are equally split in the MENA region. The effect
on efﬁciency though may be ambiguous.

Military-dominated governments may divert scarce resources away from pro-
ductive civilian use. Sporadic regional conﬂict in the MENA region undermines
macroeconomic stability. Alternatively, in so far as Military-led government em-
phasise internal stability and containing ethnic rivalries etc., they may promote a
more stable business climate than would otherwise prevail.

Workers’ Rights indicates the extent to which workers enjoy internationally recog-
nized rights, including a prohibition on forced labor; a minimum age for child labor;
and acceptable conditions of work with respect to minimum wages, hours of work,
and occupational safety and health. A score of 0 [1] and {2} indicates that workers’
rights were severely restricted [somewhat restricted] and {fully protected} during
the year in question. The ﬁrst two cases categories dominate the distribution.

Again, the effect of workers’ rights on efﬁciency is unclear. Negative conse-
quences might be that they entrench insider power and slow reallocation within
the economy. Positive effects might arise if employment stability promotes worker
loyalty and productivity and, more generally, improved nutrition and health (rela-
tive to, say, the informal sector).

Finally, regime durability (Durable) refers to the number of years since the most
recent regime change (deﬁned by a three-point change in the “POLITY”24 score
over a period of three years or less) or the end of transition period deﬁned by the
lack of stable political institutions. Like the military indicator, its efﬁciency effect is
not clear cut.

23Although given the scarcity of water resources in the MENA region, agriculture is not a domi-

nant economic activity in most countries.

24This variable is described in Appendix A.

ECB Working Paper 1757, February 2015154.2.4 Efﬁciency Indicators: Socio-Cultural

A deﬁning characteristic of the MENA is their low median age. Median age can mat-
ter for economic efﬁciency; east Asia’s economic performance is often associated
with its “demographic dividend”. But this seems not to have carried over to the
MENA (e.g., Amin et al. (2012), Chap. 3). Job creation, although high by interna-
tional standards in recent decades, was surpassed by labor force growth.25 High
levels of youth unemployment mean faster depreciation of skills, weakened incen-
tives to acquire skills, and many ﬁrst jobs starting in the informal economy.

Information plays an important role for efﬁciency. In this framework information
and communication technologies such as the cultural adoption of Mobile technolo-
gies (phones, internet access, text messaging, pagers) etc. are expected to improve
countries’ efﬁciency performance and promote growth, e.g., Jensen (2007).

Finally, consider Religious fractionalization. This is computed as Fracj = 1 − ∑N
i=1 s2
ij
where sij
is the share of group i in country j; the higher the index the greater the
fractionalization. Religious fractionalization may create efﬁciency bottlenecks in
the form of biases in credit allocation and ﬁnancial depth, home bias, limits on mar-
ket size, low social trust (although it may enhance intra-group cohesion) etc. Any
such negative effects are likely, though, to be contingent on the state of economic
development, the quality of institutions, the level of religious tolerance, Alesina
et al. (2003).26

Moreover, most MENA members have a dominant religious group, usually Sunni
Islam. The remaining religions include Shia and other Islamic sects, Christian and
Coptic (in Egypt), some Jewish and migrants’ religions (e.g., Hindu) etc.27 The dis-
tribution of religious fractionalization appears bi-model with a median around 0.13
which suggests relatively small religious fractionalization against some countries
which have somewhat larger fractionalization.

5 Estimation Results

In our exercises, Equations (2) and (3) can be estimated in one single step by a
maximum likelihood estimator, following Battese and Coelli (1995). We employ an
unbalanced sample, with the maximum dimensions of the sample being 1980 to

25Although overall fertility rates have declined since 1980 to around 2.8 children/woman. Note,
the MENA tend to have a low labor participation rate (just over 50%), driven mainly by low female
participation.

26We restrict our analysis to the Religion variable only since for two countries (i.e., UAE and
Yemen) the Ethnic and Language diversiﬁcation variables (often also used in this context) are miss-
ing for the years 2007- 2008 and 1991-2006 respectively.

27Note some interesting cases: in Syria although Sunnis dominate the population, the minority
Alawite Shia (just over 10%) dominate government and military. Also in Bahrain, 60-70% are Shia
Islam whilst King Hamad bin Isa bin Salman Al Khalifa is a Sunni.

ECB Working Paper 1757, February 2015162008, see Table 1.28

In keeping with our motivation, we ﬁrst estimate a (B)aseline model of produc-
tion and inefﬁciency equations which emphasises economic indicators, with and
without interactions: respectively, models MB and MB
I . We then (A)ugment that
baseline model with the addition of political and socio-cultural indicators, again
with and without interactions: MA and MA
I .
Most of the translog production parameters have no direct interpretation. Ac-
cordingly, we derive the following more informative statistics (see Appendix B):29:

1. Input elasticities, Ey,j = ∂Y

∂J · J
Y ;

2. Technical Progress, TP = ∂y/∂t;

3. Total Factor Productivity growth, TFP = TP + (−∂µ/∂t).

Due to the use of a translog, metrics [1.] to [3.] are time and country speciﬁc (we
evaluate them at the mean and median).30 The second section of table 1 shows the
inefﬁciency parameter estimates, followed by the Technical Efﬁciency Index.

Table 2 examines various production restrictions and diagnostics:

1. Production is separable in its inputs;

2. Technical Progress is neutral;

3. Validity of country ﬁxed effects;31

4. Incremental signiﬁcance of the E, P, S and I blocks;

5. The signiﬁcance of the parameter, γ = σ2

u/σ2 which indicates the extent to
which deviations from the frontier are due to noise, γ → 0, or technical inefﬁ-
ciency, γ → 1;

6. As well as the Silverman bootstrapped p-value for the null of Uni-modality in

the TE and TP series (see Tables 1, 2 and Table 5) and;32

7. The Bayesian Information Criteria (BIC) and the observation number.33

28For Egypt, Mauritania, Morocco, Saudi Arabia, Sudan, Syria, Tunisia, Jordan and Bahrain we
have 1980-2008. For Kuwait, Libya, Qatar, and the UAE we have 1986-2008. And 1989-2008 for
Yemen.

29The full set of parameter results are available in Appendix E
30We report both, reﬂecting the possibility of skewness and/or multi-modality.
31Note, for brevity the ﬁxed are not reported in the tables but are available on request.
32Appendix C deﬁnes the test and the particular bootstrap method we used.
33The lower the BIC the better the ﬁtting is the model; this result is valid under the assumption

that the models under comparison have the same number of observations.

ECB Working Paper 1757, February 201517Table 1: Technology Frontiers: Estimates

MB

0.180∗∗∗
0.489∗∗∗
0.509∗∗∗

−0.024∗∗∗
−0.023∗∗∗
0.026∗∗∗
0.028∗∗∗

2.369∗∗∗
0.230∗∗∗
0.069∗∗∗
0.001
−0.128∗∗∗
0.091∗∗∗
−0.113∗∗∗
−0.028∗∗∗
−0.334∗∗∗
0.046
0.065∗∗∗
−0.050∗∗∗

Production Equation
Ey,k
Ey,l
Ey,h

TP
TP median
TFP
TFP median

Inefﬁciency Equation
β0
h
resrent
GY
Open
FDI
ManuY
MAW
MH I
XH I
dcps
βt
Assn
MedAge
Worker
ReligFrac
Durable
Military
Mobile
Resrent × h
Open × h
FDI × h
ManuY × ∆e
MedAge × h
Worker × MedAge

MB
I

0.196∗∗∗
0.569∗∗∗
0.222∗∗∗

−0.008∗∗∗
−0.012∗∗∗
0.036∗∗∗
0.032∗∗∗

3.004∗∗∗
−0.542∗∗∗
0.171∗∗∗
0.038
−0.387∗∗∗
0.012∗∗∗
−0.109∗∗∗
−0.033∗∗∗
−0.322∗∗∗
0.055∗
0.075∗∗∗
−0.044∗∗∗

−0.075∗∗∗
0.228∗∗∗
−0.005∗∗
0.0001

MA

MA
I

0.081
0.568∗∗∗
0.164∗∗∗

−0.008∗∗∗
−0.020∗∗∗
0.020∗∗∗
0.008∗∗∗

0.329
−0.187∗∗∗
0.026∗∗∗
0.145∗∗∗
−0.089∗∗∗
0.004∗∗∗
−0.119∗∗∗
−0.047∗∗∗
−0.076
0.012
0.596∗∗∗
−0.028∗∗∗
−0.009
0.139
−0.007

0.637∗∗∗
−0.003∗∗∗
0.056∗∗
0.001

0.022
0.468∗∗∗
0.216∗∗∗

−0.016∗∗∗
−0.013∗∗∗
0.018∗∗∗
0.012∗∗∗

3.842∗∗∗
−1.146∗∗∗
0.143∗∗∗
0.148∗∗∗
−0.292∗∗∗
0.011∗∗∗
−0.079∗∗∗
−0.046∗∗∗
−0.024
0.006
0.288∗∗∗
−0.034∗∗∗
−0.013
−0.725∗∗∗
−0.554∗∗∗
0.165∗∗∗
−0.003∗∗∗
0.172∗∗∗
0.005
−0.091∗∗∗
0.165∗∗∗
−0.006∗∗∗
0.0001∗∗∗
0.003∗∗∗
0.183∗∗

TE
TE median

0.787
0.821

0.789
0.823

0.723
0.748

0.748
0.859

Notes: Baseline: MB; Baseline with interactions: MB
A; Augmented
with Interactions: MA
I . ***, ** and * respectively indicate the 1%, 5%, and 10% level
of signiﬁcance. Numbers in squared brackets denote probability values. Ey,j is the
elasticity of output with respect to factor inputs. TP is the technical progress growth
rate. TFP is the total factor productivity growth rate. TE is technical efﬁciency. See
Appendix B for derivations. Values are means unless otherwise stated. Fixed effect
estimates, α0i are suppressed for reasons of space but are available on request.

I ; Augmented: M

ECB Working Paper 1757, February 201518There are many complementarities between the various model results, indicative
of the underlying robustness. Almost all parameters are signiﬁcant, qualitatively
robust34 and, in the inefﬁciency equation, appear to have plausibly-signed coefﬁ-
cients.

Although all models are nested, we cannot discriminate between them since the
ﬁrst two and last two have different sample sizes. But, within those two groups
I outperforms MA.
and using the BIC statistic, model MB
Thus, the addition of the interaction variables is supported by the data. The ﬁnal
model MB
I is attractive from our standpoint since it is both congruent with the data
(all blocks are signiﬁcant) and the most general. It is our preferred case.35 In the fol-
lowing sections, we shall discuss the production and inefﬁciency estimation results
in a sequential manner (respectively, in sections 5.1 and then in 5.2).

I outperforms MB, and MA

5.1 Production

Regarding factor elasticities, the labor elasticity is estimated at around 0.47 − 0.57,
whilst the (physical) capital elasticity is estimated less precisely: 0.02 − 0.20. These
ﬁgures though are close to Saliola and Seker (2011) who report labor and capital
elasticities for 51 counties (including 6 MENA members) of 0.4 and 0.1, respectively;
for some countries such as Egypt they report capital elasticities of an even lower
value.36 The low and sometimes insigniﬁcant capital elasticity may reﬂect low cap-
ital intensity in production or that the capital stock is essentially unproductive.37
The human-capital elasticity tends to be estimated at around 0.2. Our results thus
support Henry et al. (2009) and other studies who ﬁnd signiﬁcant human capital
elasticities (albeit in a different sample context).

Regarding the diagnostic tests (Table 2), the restrictions of a unitary substitution
elasticity, of neutral technology, and of no underlying country heterogeneity are all
strongly rejected. The production function chosen therefore seems an adequate rep-
resentation of the data. Parameter γ tends to be estimated above 0.9 suggesting that
large parts of the total variation in output from the frontier is attributable to techni-
cal efﬁciency. Kneller and Stevens (2003) reports similar values of the γ coefﬁcient
using country-level data sets. Moreover, block exclusion of the E, P, S and Interac-
tion indicators is statistically inadmissible, justifying their inclusion.38

34All overlapping parameters are qualitatively the same (except βh in MB which is positive and

signiﬁcant).

35A Likelihood ratio (LR) test, equal to twice the log of the ratio of the likelihoods and distributed
b ) (where m∗
as χ2(mb − m∗
I and MB, respectively)
b , mb denote the number of parameters in model MB
further conﬁrmed this. For models MB
I vs. MA the LR test equals 28.26 and 75.4
respectively while the 5% critical values for 4 and 6 degrees of freedom are 7.78, 12.59 respectively.
Accordingly we select model MB

I vs. MB and MA

I over MB and MA

36See also Bond (2002).
37For a similar conclusion on some African states, see Devarajan et al. (2001)
38Note, we made several speciﬁcation searches: for several inefﬁciency indicators we included
quadratic and higher powers to examine non linearity and threshold effects, plus a wider variety of
interactions. However, these were rarely statistically signiﬁcant and did not produce a better ﬁt.

I overMA.

ECB Working Paper 1757, February 201519Table 2: Technology Frontiers: Tests and Diagnostics

Production Equation
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i
TP Unimodal

Inefﬁciency Equation
γ
σ2
βE = 0
βP = 0
βS = 0
βI = 0
TE Unimodal

MB

MB
I

MA

MA
I

[0.003]
[0.007]
[0.010]
[0.574]

[0.001]
[0.002]
[0.008]
[0.614]

[0.015]
[0.001]
[0.003]
[0.997]

[0.001]
[0.001]
[0.007]
[0.860]

0.989∗∗∗ 0.990∗∗∗ 0.741∗∗∗ 0.929∗∗∗
0.011∗∗∗ 0.008∗∗∗ 0.004∗∗∗ 0.006∗∗∗
[0.003]

[0.001]

[0.020]
[0.002]
[0.010]
[0.014]
[0.222]

[0.002]
[0.002]
[0.001]
[0.001]
[0.005]

[0.435]

[0.021]
[0.212]

BIC
Obs.

−318.657 −321.277 −280.266 −300.831

316

316

302

302

Notes: See notes to Table 1.

5.1.1 Total Factor Productivity (TFP) and Technical Progress (TP)

TFP growth indicates the extent to which the frontier grows over time (keeping
inefﬁciency constant). The MENA average annual TFP growth is around 2% to
3%. However, there is an interesting compositional story behind the TFP growth
numbers. Technical progress TP has diminished TFP growth (−1% to −2%) while
the rate of efﬁciency change −βt is positive, signiﬁcant and greater in absolute size
than the TP value. This suggests that it is developments in efﬁciency that has been the
most important factor in the improvement in the TFP growth in Arab world (this is
a theme we will take up in section 5.5.1).

The use of average full-sample numbers such as these, however, masks two key

aspects:

(1) How technical progress rates have evolved over time.
(2) How technical progress rates rank by country.

We discuss these below, focusing mostly on preferred model MA
I .

Technical Progress Over Time. Figure 3 draws an estimated Epanechnikov ker-
nel density for TP in ﬁve-year windows.39 Also shown (in Table 3) are the higher
moments of the TP distribution, and the probability-values from the Jarque-Bera
(JB) Normality test. We also, to repeat, employ the Silverman (1981) test to test

39The histograms have been suppressed for brevity but are available on request.

ECB Working Paper 1757, February 201520the null of uni-modality in the distribution of TP. The test results are depicted as
bootstrapped probability values. In effect, this modality test allows us to examine
convergence or divergence in technology characteristics among the MENA.40

The TP distribution appears Normally distributed and uni-modal. In intuitive
terms, this indicates that Arab countries share a common technology which remains
effectively unchanged over time. This is interesting since it suggests that there is no
(statistically signiﬁcant) technological leaders among the MENA. There may be dif-
ferences between countries in terms of TFP growth but it is not related to technical
progress. Instead, as hinted above, it must be related to differing degrees of techni-
cal efﬁciency. Some countries are clearly hampered in reaching their most efﬁcient
production by the factors we identiﬁed, relating to institutional and cultural factors
as well as economic ones.

Figure 3: Technical Progress Distributions

Note: Dashed vertical Lines indicates median histogram values.

Table 3: Technical Progress: Distributional Characteristics

Median
Std. Dev
Skewness
Kurtosis
Normality
TP Uni-modality

1981-1985 1986-1990 1991-1995 1996-2001 2002-2008
−0.019
0.042
0.173
1.802
[0.168]
[0.228]

−0.038
0.042
0.070
2.035
[0.337]
[0.287]

−0.061
0.046
−0.033
2.411
[0.669]
[0.257]

0.006
0.042
0.047
1.792
[0.186]
[0.299]

0.019
0.030
0.598
2.968
[0.194]
[0.562]

1981-2008
−0.012
0.051
−0.240
2.560
[0.110]
[0.860]

40The ﬁnal, full sample, bootstrap Silverman p-value in Table 3 corresponding to that reported
in Table 2. Henderson et al. (2008) followed a similar approach to test the existence of a common
steady state using a sample of 118 countries from the the Penn world data.

0246810-.20-.15-.10-.05.00.05.10.151981-19850246810-.20-.15-.10-.05.00.05.10.151986-19900246810-.20-.15-.10-.05.00.05.10.151991-199502468-.20-.15-.10-.05.00.05.10.151996-0104812-.20-.15-.10-.05.00.05.10.152002-0802468-.20-.15-.10-.05.00.05.10.15Full Sample: 1981-2008ECB Working Paper 1757, February 2015215.2 Inefﬁciency Equation

The inefﬁciency equation represented by (3) is in terms of distance to the technical
frontier. Thus a negative coefﬁcient indicates a variable that contributes towards a
catching up of that frontier (i.e., implies a decrease in inefﬁciency).

In the following sections, we review variables which, respectively, worsen and
enhance efﬁciency. Thereafter we analyze the interaction effects. Finally, we exam-
ine the behavior of the series of Technical Inefﬁciency itself.

5.3 Indicators which worsen Efﬁciency

Looking across Table 1, indicators which worsen inefﬁciency are (excluding inter-
actions),

· Resource-dependency;

· Government expenditure;

· FDI;

· Financial Depth;

· Religious Fractionalization, and;

· Military governments.

We already discussed the possible pro and con efﬁciency effects of resource depen-
dency, FDI41, religious fractionalization and military governments. We therefore
need not repeat them, except to conﬁrm that they worsen efﬁciency. Consider the
two remaining terms.

Government expenditures comprise purchases of goods and services, subsidies,
employees’ compensation, and most expenditures on defense and security. Such
expenditures have not enhanced efﬁciency.42 In the case of subsidies, their intention
is clearly social cohesion (essential in the Arab world). Regarding defense expen-
ditures, these have tended to involved arguably wasteful duplication of resources
across the region, Malik and Awadallah (2013).43 Plus given that much of the mil-
itary hardware is imported, technology spillovers to other sectors appear to have
been limited.

Severe ﬁnancial frictions are known to characterize the MENA region with, for
example, only 10% of MENA ﬁrms using bank ﬁnance (World Bank Business En-
vironment Survey)44. Bank lending tends to have been skewed to large, well-
connected enterprises in low turnover markets (see Herrala and Turk Ariss (2013),

41Gente et al. (2014) develop a framework for analyzing conditions under which FDI may or may

not be growth enhancing. See also de La Grandville (2012).

42The effect is positive in all cases in Table 1 but only signiﬁcant in the ﬁnal two columns.
43By way of illustration, the average (over 1998-2012) of military expenditures as a fraction of

output were OECD (2.5%) as against 6.6% in the Arab region (Source: SIPRI Database).
44This is based on 1999-2000 survey data, see http://go.worldbank.org/RV060VBJU0

ECB Working Paper 1757, February 201522Rocha et al. (2011), World Bank (2009)). Otherwise, ﬁrms are mostly small family
businesses with limited access to external ﬁnance; and domestic equity and debt
markets are underdeveloped. Financial infrastructures in general are weak with
high agency and monitoring costs, weak judicial systems etc. Unsurprisingly there-
fore ﬁnancial depth has not enhanced efﬁciency (given its inefﬁcient and skewed
allocation).

5.4 Indicators which enhance Efﬁciency

Factors enhancing efﬁciency include,

· human capital;

· median age;

· openness;45

· manufacturing share;

· workers’ rights and;46

· regime duration;

We have already discussed human capital, manufacturing share, and openness. The
previous arguments as to their efﬁciency effects need not therefore be repeated.
The other variables which enhance efﬁciency are median age, workers’ rights and
regime duration. The ﬁrst two will be discussed in the next section.

The Military indicator, recall, worsened efﬁciency. But, perhaps surprisingly,
regime durability improves it. Certainly, a key feature of the Arab world is/was the
remarkable longevity of its leaders.47 Stable autocratic governments therefore seem
to represent a double-edged sword. Their military characteristic may, for example,
by crowding out civilian activities worsen efﬁciency but their durability might, by
putting emphasis on internal stability and the containing of ethnic rivalries stabilize
the business climate. Moreover, durability may positively enhance policy makers’
time preferences and their commitment to large investment projects.48

45Exports to High Income countries are either insigniﬁcant (the full case) or only signiﬁcant at

10%. And in both cases, the effect is to deepen inefﬁciency.

46The rights to freedom of assembly and association (Assn) imparts a positive effect but is only

signiﬁcant at the 12% level.

47Muammar al-Gaddaﬁ ruled Libya for over 40 years (1969-2011), Ali Abdullah Saleh was Pres-
ident of North then uniﬁed Yemen for over 30 years (1978-2012), Hosni Mubarak served a similar
term as Egyptian President (1981-2011) – and before him, Nasser (18 years) and Sadat (11 years) –
the al-Assad family have ruled Syria since 1971, and the House of Saud, the Al Thani family (Qatar)
and al-Khalifa (Bahrain) represent long-standing ruling dynasties.

48Such an interpretation is consistent with the seminal work of Olson (2000) on autocrats distin-

guishing “roving” and “stationary” bandits.

ECB Working Paper 1757, February 2015235.5 Interaction Terms

The interacted variables in the inefﬁciency equation (from the ﬁnal column) are
human capital, median age and the growth of the effective exchange rate:

µ =

Resrent

. . . βR
+
βO
−
βF
+
β M
−
βW
−
β MY
−

Open

FDI

MedAge

Worker

ManuY

+

+

+

+

+

+

(Resrent × h)

(Open × h)

(FDI × h)

(MedAge × h)

βR,h
−
βO,h
+
βF,h
−
β M,h
+
βW,M
+
β MY,∆e
+

(Worker × MedAge)

(ManuY × ∆e) + . . .

(4)

From this, we see the key role played by human capital; whilst resrent and FDI
worsen inefﬁciency in isolation, when interacted with h they improve efﬁciency
(i.e., βR,h, βF,h < 0). In other words, that part of resource rent and FDI activity that
is skill intensive boosts efﬁciency. By contrast, the previous benevolent effects of
openness on efﬁciency reverses when interacted with h (although the net effect is
good for efﬁciency, see later table 4).

Likewise, for median-age interactions that βW,M, β M,h > 0 is striking since both
of their individual (non-interacted) effects improves efﬁciency. The positive prod-
uct can perhaps best be interpreted as the “youth bulge” phenomenon: in the Arab
World well educated youth often experience high entry barriers into formal em-
ployment (see World Bank (2004)) and are associated to social unrest. This deprives
the economy of high-potential employees and strengthens insiders’ power. Like-
wise, whilst workers’ rights positively impact efﬁciency,49 as applied to high-skill
outsiders it could be used as a barrier to entry (to new labor cohorts).

Finally, Table 4 shows the total effect in terms of elasticities. The elasticity of
inefﬁciency with respect to human capital is negative, as is median age, the share of
manufacturing, as well as in fact openness. However the net effect of resource rents
and FDI remain signiﬁcantly positive (i.e., such as to worsen inefﬁciency).

49The exact channels are unclear but could, for example be related to strengthening trust and

promoting longer-term planning, generating incentives for skills, promoting nutrition etc.

ECB Working Paper 1757, February 201524Table 4: Key Elasticities

Eµ,FDI
Eµ,H
Eµ,MedAge
Eµ,ManuY
Eµ,Open
Eµ,Resrent

Elasticities
0.002∗∗∗
−0.695∗∗∗
−0.322∗∗∗
−0.078∗∗∗
−0.069∗∗∗
0.021∗∗∗

5.5.1 Technical Efﬁciency

Technical Inefﬁciency compares the inefﬁciency under the control of ﬁrms inside the
economy to purely stochastic factors. Given the estimated equations, we calculate
the composite error εit = vit − uit. Technical inefﬁciency is then computed using the
conditional expectation E {uit | εit}, see Jondrow et al. (1982) ((B.1)) .

Looking back at Table 1 we see that average technical efﬁciency is around 0.75.
This implies that the average MENA TE could be increased by 25% if inputs were
used at their most efﬁcient point. Such a level of technical efﬁciency is comparable
to other country-group studies.50 As with Technical Progress, moreover, we can
decompose Technical Efﬁciency into a time and country-speciﬁc dimension, with
the same supporting metrics.

Technical Efﬁciency over Time. Figure 4 and Table 5 reveal the general rejection
of uni-modality in the distribution of technical efﬁciency. Over the full sample, this
is strongly rejected and only marginally accepted (i.e., barely above 10%) in the
early 1980s and at the end of the sample. The distribution is therefore not only gen-
erally bi-modal but is also characterized by visually well-separated peaks. There
has also been, as we demonstrate below, much country ﬂux in efﬁciency rankings.
Finally, the ﬁgure also reveals the remarkable transformation that has taken place
over time in median technical efﬁciency: rising from around 0.5 to almost unity.

50Henry et al. (2009) report an average efﬁciency index of 0.73 for a sample of 57 developing

countries over 1970-1998.

ECB Working Paper 1757, February 201525Figure 4: Technical Efﬁciency Distributions

Note: Dashed vertical Lines indicates median histogram values. Smoothness and bandwidth con-
sideration imply Kernel densities do not necessarily truncated at unity.

Table 5: Technical Efﬁciency: Distributional Characteristics

Std. Dev
Skewness
Kurtosis
Normality
TE Uni-modality

1981-1985 1986-1990 1991-1995 1996-2001 2002-2008
0.224
−0.439
1.535
[0.035]
[0.010]

0.235
−0.061
1.919
[0.258]
[0.113]

0.180
−1.030
2.352
[0.005]
[0.001]

0.153
−1.743
4.682
[0.000]
[0.146]

0.240
0.123
1.695
[0.132]
[0.060]

1981-2008
0.252
−0.702
2.210
[0.000]
[0.005]

Technical Efﬁciency by Country. Over the full sample, the TE distribution thus
appears bi-modal and negatively skewed (a fat tail to the left). And so, unlike the
uni-modal Normally distributed TP series, these features suggest that there has
been polarization across countries in terms of technical efﬁciency with respect to
the frontier.

Accordingly, the panels in Figure 5 further categorize countries into those with
High (0.8 ≤ TE ≤ 1), Medium (0.6 ≤ TE < 0.8) and Low average technical efﬁciency
(TE < 0.6). We also further categorize into countries which have exhibited inter-
band transition (shown in the right panel in dashed vertical lines).51

To illustrate: Qatar, Libya, Saudi Arabia, UAE and Jordan show zero transition
from the High region; Kuwait, Sudan, and Yemen are clustered at the other extreme.

51Note, this country ranking appears relatively robust. Table E.8 calculates the Spearman rank
correlation coefﬁcient of the country set across several methods and ﬁnds the correlation in the
range of around 0.7-1.0.

0.00.40.81.21.60.00.20.40.60.81.01981-850.000.250.500.751.001.251.500.00.10.20.30.40.50.60.70.80.91.01.11.21986-900.00.51.01.52.02.50.00.10.20.30.40.50.60.70.80.91.01.11.21991-95012340.00.10.20.30.40.50.60.70.80.91.01.11.21996-0102468100.40.50.60.70.80.91.02002-080.00.40.81.21.62.02.40.00.10.20.30.40.50.60.70.80.91.01.11.2Full Sample: 1981-2008ECB Working Paper 1757, February 201526Figure 5: Polarization and Shifts in Technical Efﬁciency

Notes: Dashed vertical lines in the rhs panel indicate max-min ranges for countries which have
moved between categories. Note the horizontal axes have no interpretation; they merely admit
sufﬁcient space to separate out the country names.

However six states (Egypt, Mauritania, Morocco, Syria, Tunisia, Bahrain) have risen
over time, often from initially very low efﬁciency levels.

Summary and Comparison of TP and TE So far we have looked at the distribu-
tional characteristics of both TE and TP over the full sample and over sub samples.
We conclude:

· In terms of technical progress, MENA countries are not characterized by well-
separated clusters of technologically backward and advanced countries. This
is because the TP distribution is uni-model and essentially Normal.

· Performance on technical efﬁciency tells a different story: there has been a
limited number of countries that failed to improve or consolidated their per-
formance through time and share a common low steady state and the rest that
signiﬁcantly improved their performance.

6 Robustness

In addition to these results, we performed robustness with respect to functional
form assuming the same indicators as in the ﬁnal column of Table 1. We chose
Fourier and Modiﬁed translog as alternative (and more general) speciﬁcations. Full
results are in Appendix D.

However, we also check robustness with respect to additional variables. To in-
clude all the series of interest that are listed in Appendix A raises issues of dimen-

0.00.20.40.60.81.00.00.20.40.60.81.0Technical Efficiency, TEHighLowMediumEgyptLibyaMauritaniaMoroccoSyriaTunisiaBahrainKuwaitSudanYemenQatarSaudi ArabiaUAEJordanTransition TE CountriesNon-Transition TE Countries||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||ECB Working Paper 1757, February 201527sionality and collinearity. Accordingly, as discussed below, we estimate stochastic
frontier systems where we extract principal components from the E, P, and S blocks.

6.1 Principal Components Analysis

With principal component (PC) analysis we are able to transform the original vari-
ables z = [z1, z2, ..., zk](cid:48)
into a new set z = [z1, z2, ..., zk](cid:48)
which are linear combina-
tions of the original z’s and are mutually orthogonal (see Jolliffe (2004) for a thor-
ough treatment). They are constructed by calculating the eigenvectors of the corre-
lation matrix of the original variables. By ranking the new orthogonal variables by
importance, we can summarize the data with fewer components, say k − m.

The inefﬁciency equation corresponding to (3) is then,

where β∗
bance vector.

k−m = (cid:2)β∗

1, β∗

2, · · · β∗

k−m

it(k−m)β∗

µit = z(cid:48)
it
(cid:3) is the reduced vector coefﬁcient and ω∗

k−m + ω∗

(5)

it is a distur-

When PCA is applied to categorical variables, note, it assigns larger weights to
the most skewed variables, creating a biased correlation matrix, see Kolenikov and
Angeles (2009), Holgado-Tello et al. (2010). In such cases, it makes more sense to
use polychoric or polyserial correlations. We use the following rule. If a series con-
tains more than 10 categories it is considered to be continuous. And any correlation
between continuous variables is calculated using the standard Pearson correlation
coefﬁcient (e.g., as in the GY-FDI bivariate correlation). If there are fewer than 10
categories, we implement a polychoric correlation (e.g., as in the Military-Injud cor-
relation). If there is a mix of data types, we chose polyserial/biserial correlation
(e.g., as in the Military-Durable correlation).

There are several practises for reducing the number of principal components
from k to k − m. We retained those principal components with eigenvalues at or
above unity, Draper and Smith (1981). We then paired down the number of PCs in
the estimation system by using the BIC; if the exclusion of one additional PC did
not increase the BIC statistic, the procedure is terminated and the model with the
lowest BIC is retained as the best-ﬁtting model.

Once the ﬁnal model is obtained in terms of the selected zit, we retrieve the

coefﬁcients of each group-variable according to (see Myers (1986)):

β pc = Λ(cid:48)

k−m (cid:101)β∗

k−m

(6)

k−m is a k × (k − m) matrix of eigenvectors and (cid:101)β∗

where Λ(cid:48)
k−m is the vector of es-
timated coefﬁcients. Table 6 shows the β pc’s, and Table E.7 shows the full SFA
estimates. Our aims in running PCA are three fold:

(1) To assess whether the parameters in Table 1 are robust to the inclusion of

additional indicators.

(2) To assess the signiﬁcance and sign of the additional indicators contained in

the PCs.

ECB Working Paper 1757, February 201528(3) To assess the overall contribution of the Economic, Political and Socio-Cultural

Indicators to technical efﬁciency, by country.

Points (1) and (3) will be respectively covered in sections 6.2 and 6.1.1.

Table 6: Retrieved Coefﬁcient Values: principal components

S
AgdeO
AgdeY
MedAge −0.026
Mobile
ReligFrac
Urban
Worker

P

0.068∗∗∗ Assn
0.088∗∗∗ Disap
Domov

E
0.034∗∗∗ H

−0.012
0.003

Dcbs
Dcps

−0.122∗∗∗ Durable −0.004∗∗∗ ∆e

0.057∗∗∗ Formov −0.011∗
Injud

FDI
−0.012∗∗ GY

−0.089∗∗∗
−0.089∗∗∗ Military

I

−0.025∗∗∗ FDI × H
−0.026
Open × H
−0.034∗∗∗ ManuY × ∆e

0.018∗∗∗ Worker × MedAge

Resrent × h

−0.011
−0.047

−0.0027∗
−0.0115∗∗
0.0016∗∗∗
0.0017
−0.029∗∗

Tort
Wopol

−0.019
0.022

0.057∗∗∗

0.015∗∗∗ MAW
MH I
−0.017
ManuY −0.011∗
Open
0.088
Resrent −0.013
βt
XH I

−0.030∗∗∗
−0.054∗∗∗

On point (2) we see, for example that an increase in urbanization (commonly
regarded as promoting scale economies and demonstration effects) is efﬁciency en-
hancing.52 By contrast, the two age dependency terms (old and young) worsen inefﬁ-
ciency.53

Variables associated with the protection of basic rights – Women’s Rights, Tor-
ture and Disappearances – are intuitively signed (i.e., improvements on these indices
promotes efﬁciency). But they are not signiﬁcant. The efﬁciency-enhancing effects
of improvements in external freedom of movement and in judicial independence,
though, are signiﬁcant.

6.1.1 Principal Component Contributions

Now we come to Point (3) above: overall contributions. In tables 1 and 6, we can see
the individual impacts on (in)efﬁciency: e.g., human capital (an E variable) reduces
economic inefﬁciency, Religious fractionalization (a S variable) raises it. In the prin-
cipal components context, however, we can also examine the marginal contribution
to efﬁciency of the entire block variables in themselves.

To calculate the contribution of Political, Social and Economic blocks to technical
efﬁciency we modify the method of Coelli et al. (1999) to the principal components

52We also tried estimating with population density as a substitute for urbanization and found

similar results.

53This is plausible: a population skewed towards retirees faces shortfalls in their labor force and
may bias public funds towards pension/health expenditures (potentially at the cost of productive
investment). Likewise, one skewed towards the very young, downward biases efﬁciency for the
reasons already discussed.

ECB Working Paper 1757, February 201529case. The contribution of each block on technical efﬁciency is computed as the dif-
ference between gross efﬁciency (full model, MA
I ) and efﬁciency net of the contri-
bution of the relevant blocks. The latter can be computed – to take the example of
Political block – by replacing equation (3) by,

µ∗ = z

(cid:48)
∗β∗

(7)

where,

µ∗ = min [β0∗ + βE∗ E + βS∗ S + βI ∗I + βt∗ t − βP∗ P]
and then recalculating the efﬁciency predictions. Thus the marginal contribution of
the Political block to efﬁciency relative to the full model is given by,

(8)

C P = 100 ·

E {−uit | εit} − E (cid:8)−u∗

it | ε∗

it

E {−uit | εit}

(cid:9)

(9)

where u∗ is the one-sided error and composite error associated with the mean ef-
ﬁciency process, equation (8), and ε∗ is the associated composite error. The results
are reported in Table 7.

To illustrate: (1) the average contribution of Polity block for Bahrain to efﬁciency
is 1.5%. Thus, if that country had a gross efﬁciency score of 0.90, efﬁciency would
be 0.89 were it not for the effect of polity block; (2) the average contribution of Polity
block for Jordan to efﬁciency is -7.1%. Thus if the country also had a gross efﬁciency
score of 0.90, efﬁciency would be 0.97 were it not for the effect of polity block has
on efﬁciency levels. Thus a negative multiplier denotes that the block constitutes a
constraint in attaining high(er) efﬁciency.

According to these ﬁndings:

(i) For almost all countries the contributions of the Polity block is such as to re-
duce efﬁciency.54 For example, in Mauritania, Qatar, Sudan, and Yemen the
effect is of the order of a 10-15% loss in efﬁciency from the inﬂuence of Political
factors.

(ii) Outcomes are in absolute terms more dramatic among Social variables (S).
In Bahrain, Jordan, Kuwait, Sudan and Tunisia they change the efﬁciency ef-
fect by around 20% in absolute value. There is a 50-50 split between posi-
tive and negative contributions. Thus, unlike Political factors, social factors
(demographics, urbanization, workers’ rights) can be both supportive or un-
supportive.

(iii) The economic block (E) is more mixed between negative and positive marginal
In most countries, taking out the economic variables would

contributions.
make major changes in technical efﬁciency.

54Three countries (Bahrain, Egypt, Saudi Arabia) do register positive contributions but these are

very close to zero in value.

ECB Working Paper 1757, February 201530The picture across countries is often a rather nuanced one. For Kuwait, for exam-
ple, (S)ocial factors place a big constraint on efﬁciency (−25.5), driven largely by
its high religious fractionalization. However, Bahrain, which also has high fraction-
alization (though below Kuwait’s), has a high penetration of mobile technologies
and high median age, which implies that social factors play a net enhancing role
in efﬁciency (+19.1). On (P)olitical factors, Yemen, for example, suffers from low
durable regimes, and high restrictions on external and domestic freedom of move-
ment. On Economic factors, Qatar, even though it deﬁnes the technical frontier for
the MENA, still has many constraining economic factors (when viewed through the
lens of the PCs): principally its high resource rents. If it had fewer such rents and a
higher manufacturing sector, for example, it would (all else constant) enjoy higher
overall technical efﬁciency from its E(conomic) inputs.

Note, some variables in Table 6 are insigniﬁcant, and Assn has a counter-intuitive
sign. So there are some noise factors, and caution should be exercised in too literal
an interpretation. However, Table 7 does conﬁrm that Political, and Social factors
alongside Economic ones do matter for the attainment of efﬁciency and frontier
performance. And that they can differ considerably across countries, being either
supportive or un-supportive. Moreover, in using the PC retrieved coefﬁcients and
the original indicators, one can trace out the determinants of country-speciﬁc block
constraints for the attainment of technical efﬁciency.

Table 7: Contributions of Political and Socio-Cultural Factors In Efﬁciency

C E
C S
C P
1.5
15.5
19.1
Bahrain
1.0 −0.6 −16.5
Egypt
−7.1
15.2
22.5
Jordan
−0.6 −25.5
8.9
Kuwait
−5.3
4.0
1.3
Libya
Mauritania −15.0 −7.4 −19.2
−1.2
12.8
Morocco
12.4
2.7 −19.7
−14.4
Qatar
9.3
1.8
4.6
Saudi Arabia
−9.6 −22.4 −13.6
Sudan
−1.1 −11.5 −15.0
Syria
15.5 −7.5
−0.1
Tunisia
−2.1 −10.1
UAE
8.9
−8.3 −4.5 −9.2
Yemen

Finally, Table 8 shows the correlations of the ﬁrst principal component of each
of the blocks.55 All PCs are positively correlated, in the range 0.2-0.6. This conﬁrms
that economic and political reform and even social factors are complementary in
raising economic efﬁciency. Put another way, economic reforms (e.g., those enacted

55The ﬁrst principal component has the largest possible variance (that is, accounts for the maxi-

mum of the data variability).

ECB Working Paper 1757, February 201531in the 1980s) may have limited success unless accompanied by institutional reforms.
Interestingly, though, the highest correlation lies with cultural factors (e.g., demo-
graphic characteristics, religious fractionalization, urbanization, workplace rights).
Two caveats should be borne in mind. First, as always, correlation does not im-
ply causation. Thus, we do not know whether political factors should be “ﬁxed”
prior to, or along side, economic reforms. Second, the splitting of variables into
blocks is, to repeat, far from watertight. For example, we considered human capital
to be an economic variable, but we could equally rationalize it as a political one –
part of the Arab Social Contract that compensated oppressed citizens. The corre-
lations between the principal components reﬂects those links, without necessarily
being informative about causality between them.

Table 8: Correlations of First principal components

P S

E
E
1
P 0.19
1
S 0.55 0.35

1

6.2 Robustness Comparisons
Now we can pool our various results: those of Table 1, two variants of model MA
I
under different production speciﬁcations (Fourier and Modiﬁed Translog), see Ta-
ble E.9, plus the PCA (Table 6). This variety allows us to assess model robustness
which we here deﬁne as the robustness of coefﬁcients signs across methods.56

In that respect, we deﬁne variables as “strongly” sign-robust as ones having
a common and signiﬁcant sign across all methods. Variables are “weakly” sign
robustness if at least one of the coefﬁcient signs is distinct and/or insigniﬁcant.57
Otherwise, there is no robustness. According to this classiﬁcation, we derive Table
9 (derived from Table E.9):

56Note, we do not try to assign model weights. That would not be straightforward since they
have different sample sizes and thus non-comparable likelihoods. Although we did earlier note an
ordering of the B and A models in favor of interactions.

57Some variables cannot be used to assess robustness since they only appear in one method (e.g.,

Injud)

ECB Working Paper 1757, February 201532Table 9: Robustness in Sign

Enhance Efﬁciency

Strongly Robust
βt
H × FDI
H × Resrent
ManuY
Worker

Weaken Efﬁciency

Military
ReligFrac

Weakly Robust

Durable
H
MAW
Open

Dcps
FDI
ManuY × ∆e
Resrent
Worker × MedAge

From this we see the efﬁciency importance of human capital, both in itself but
also as an enabling factor in FDI and resource rents, which otherwise retard efﬁ-
ciency. Trade and manufacturing share also robustly enhance efﬁciency. The pro-
tection of workers’ rights (perhaps for efﬁciency wage and nutrition reasons) also
enhances efﬁciency in a strongly robust manner.

The presence of a military-led government and religious fractionalization worsen
efﬁciency in a strongly robust sense. Finally, ﬁnancial depth (long a speciﬁc concern
in the Arab world, Herrala and Turk Ariss (2013)), as proxied by domestic credit,
has also not enhanced technical efﬁciency.

7 Conclusions

Why has the Arab world fallen behind? Which factors shaped this outcome? To
begin to address those questions, we estimated the MENA technical frontier and es-
tablished its determinants. We divided efﬁciency-related variables into economic,
political and socio-cultural ones. We estimated the frontier in multiple ways: us-
ing different production functions and exploiting a large data set using principal
components. Our results paint a remarkably consistent and robust picture. In some
dimensions we conﬁrm received wisdom, in others we modify or overturn it.

The MENA have been characterized by increasing economic efﬁciency, albeit
with marked polarization: some countries consistently at the top or bottom of ef-
ﬁciency ranges, around half having improved over time. Such increased average
efﬁciency contributed positively to TFP growth. But technical progress – another
element in TFP growth – has been regressive, with the MENA consigned to a low
average technological base. The ﬂip side of this is that the MENA may have ex-
hausted efﬁciency gains, and so moving forward there must be signiﬁcant and im-
proved progress on economic fundamentals.

Human capital has enhanced efﬁciency; more educated workers are better able
to implement advanced technologies. Thus the MENA’s pro-education emphasis,
although behind Western proﬁciency levels, has yielded (perhaps unexpectedly)

ECB Working Paper 1757, February 201533Indeed, when FDI and merchant trade are skill-
strong and pervasive returns.
intensive, they become efﬁciency enhancers, otherwise not. Trade, manufacturing
share and the protection of workers’ rights also are identiﬁed as robustly enhancing
efﬁciency.

We conﬁrm the resource-curse interpretation of MENA developments. Resource
rents may loosen efﬁciency incentives. This is intuitive in so far as much of the
extraction work may be done by foreign ﬁrms with limited spillover of techni-
cal expertise to the non-resource economy. Moreover, exchange rate volatility and
likely overvaluation (characteristic of petro-currencies) has retarded manufacturing
growth. Other related features may also hinder efﬁciency: heightened rent seeking;
under-diversiﬁed product range; and governance issues. On the other hand, such
revenues helped fund the education expansion that underpinned MENA develop-
ment.

Financial depth seems not to have enhanced efﬁciency; this may be consistent
with the rent-seeking view and/or that credit has sustained favored “zombie” ﬁrms
at the expense of smaller ones constrained by retained earnings. Finally, we identi-
ﬁed religious fractionalization and the catch-all “military” government categoriza-
tion as being strongly robust determinants of weakened efﬁciency.

In providing such a comprehensive characterization of the MENA efﬁciency
proﬁles, we have attempted to set a benchmark and cross check for related stud-
ies in the literature. And to contribute more generally to ongoing discussions of
how regional efﬁciency and development may progress.

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ECB Working Paper 1757, February 201538A Data Sources and Deﬁnitions

Variables
AGDEo

AGDEy

ASSN

DCBS

DCPS

DISAP

Table A: Data Description (I)

Full Description
Old Age dependency ratio. The ratio of older dependents, people older than 64 to
the working-age population (those aged 15-64). Data are shown as the proportion
of dependents per 100 working-age population. Source: World Bank. [S]
Young Age dependency ratio. The ratio of population younger than 15 years of age
to the working-age population (those aged 15-64). Data are shown as the proportion
of dependents per 100 working-age population. Source: World Bank. [S]
A score of 0 indicates that citizens’ rights to freedom of assembly or association
were severely restricted or denied completely to all citizens; 1 indicates that these
rights were limited for all citizens or severely restricted or denied for select groups;
2 indicates that these rights were virtually unrestricted and freely enjoyed by prac-
tically all citizens in a given year. Source: CIRI Human Rights Data Project. [P]
Domestic credit provided by the banking sector includes all credit to various sec-
tors on a gross basis, with the exception of credit to the central government. The
banking sector includes monetary authorities and deposit money banks, and sav-
ings and mortgage loan institutions and building and loan associations. Source:
World Bank.
Domestic credit to private sector: ﬁnancial resources provided to the private sec-
tor (e.g., loans, purchases of non-equity securities, trade credits etc) that establish
a claim for repayment. For some countries these claims include credit to public
enterprises. Source: World Bank.
Disappearances are cases in which people have disappeared, political motivation
appears likely, and the victims have not been found. Knowledge of the where-
abouts of the disappeared is, by deﬁnition, not public knowledge. However, while
there is typically no way of knowing where victims are, it is typically known by
whom they were taken and under what circumstances. A score of 0 indicates that
disappearances have occurred frequently in a given year; 1 indicates that disap-
pearances occasionally occurred; 2 indicates that disappearances did not occur in a
given year. Source: CIRI Human Rights Report. [P]

DOMMOV Freedom of Domestic Movement. This variable indicates citizens’ freedom to travel
within their own country. A score of 0 indicates that this freedom was severely
restricted, 1 indicates the freedom was somewhat restricted, and 2 indicates unre-
stricted freedom of foreign movement. Source: CIRI Human Rights Report. [P]

Note: Textual descriptions of the variables are generally taken from their description in the orig-
inal sources, or edited versions of that text. We generally use the variable names consistent with
those given in the corresponding data set. [S]=Sociocultural, [P]=Political indicator. Non labelled
indicators are [E]conomic ones.

ECB Working Paper 1757, February 201539Table A: Data Description (II)

E
FDI

Full Description

Variables
DURABLE Regime Durability: The number of years since the most recent regime change (de-
ﬁned by a three-point change in the POLITY score over a period of three years or
less) or the end of transition period deﬁned by the lack of stable political institu-
tions (denoted by a standardized authority score).
In calculating the DURABLE
value, the ﬁrst year during which a new (post-change) Polity is established is coded
as the baseline “year zero” (value = 0) and each subsequent year adds one to the
value of the DURABLE variable consecutively until a new regime change or tran-
sition period occurs. Values are entered for all years beginning with the date of
independence if that event occurred after 1800. Source: Marshall et al. (2010). [P]
Trade-Weighted Real Exchange Rate. Source: IMF.
Foreign direct investment are the net inﬂows of investment to acquire a lasting man-
agement interest (10 percent or more of voting stock) in an enterprise operating in
an economy other than that of the investor. It is the sum of equity capital, reinvest-
ment of earnings, other long-term capital, and short-term capital as shown in the
balance of payments. This series shows net inﬂows (new investment inﬂows less
disinvestment) in the reporting economy from foreign investors, and is divided by
GDP. Source: World Bank.
Indicates citizens’ freedom to leave and return to their country. 0= indicates that
this freedom was severely restricted, 1=freedom was somewhat restricted, 2= unre-
stricted freedom of foreign movement Source: CIRI Human Rights Report. [P]
General government ﬁnal consumption expenditure (calculated as % of GDP). This
includes all government current expenditures for purchases of goods and services
(including compensation of employees). It also includes most expenditures on na-
tional defense and security, but excludes government military expenditures that are
part of government capital formation. Source: World Bank. [E]
Human Capital (educational attainment of individuals 25 years or older measured
as average years of schooling). Because these data are available for 5-year periods,
we follow standard practise and linearly interpolated between periods. Source:
Barro and Lee (2013).
Independence of the Judiciary. This variable indicates the extent to which the ju-
diciary is independent of control from other sources, such as another branch of the
government or the military. 0=“not independent”, 1=“partially independent”, 2=
“generally independent”. Source: CIRI Human Rights Report. [P]
Physical Capital. Estimates of the physical capital stock are generated using the
perpetual inventory method. Source: Penn World Tables.
Number of Employees. Source: Derived from Penn World Tables.

FORMOV

INJUD

GY

H

K

L

ECB Working Paper 1757, February 201540Table A: Data Description (III)

Variables
MAW

MH I

Full Description
Merchandize imports from economies in the Arab World are the sum of Merchan-
dize imports by the reporting economy from economies in the Arab World. Data
are expressed as a percentage of total Merchandize imports by the economy. Data
are computed only if at least half of the economies in the partner country group had
non-missing data. Source: World Bank.
Merchandize imports from high-income economies are the sum of Merchandize
imports by the reporting economy from high-income economies according to the
World Bank classiﬁcation of economies. Data are expressed as a percentage of total
Merchandize imports by the economy. Data are computed only if at least half of
the economies in the partner country group had non-missing data. Source: World
Bank.

MEDAGE Median Age. The data is every ﬁve years and was linearly interpolated. Source:

MANUY

CIA World FactBook. [S]
Manufacturing value added to total value added. Manufacturing refers to indus-
tries belonging to ISIC divisions 15-37. Value added is the net output of a sector
after adding up all outputs and subtracting intermediate inputs.
It is calculated
without making deductions for depreciation of fabricated assets or depletion and
degradation of natural resources. The origin of value added is determined by the
International Standard Industrial Classiﬁcation (ISIC), revision 3. Source: UNData
http://data.un.org/Browse.aspx?d=SNA.

MOBILE

MILITARY This indicator = 1 if the source includes a rank in their title, 0 otherwise. If chief
executives were described as ofﬁcers with no indication of formal retirement when
they assumed ofﬁce, they are always listed as ofﬁcers for the duration of their term.
If chief executives were formally retired military ofﬁcers upon taking ofﬁce, then
this variable scores 0. Source: Keefer (2010). [P]
Mobile cellular telephone subscriptions are subscriptions to a public mobile tele-
phone service using cellular technology, which provide access to the public
switched telephone network. Post-paid and pre-paid subscriptions are included.
Source: World Bank. [S]
Merchandize trade as a share of GDP is the sum of Merchandize exports and im-
ports divided by the value of GDP, all in current U.S. dollars. Source: World Bank.
Revised Polity2 Score. Subtracts “AUTOC” from “DEMOC” indices. Ranges from
+10 (strongly democratic) to -10 (strongly autocratic). See Polity IV documentation
for further details (see www.systemicpeace.org/inscr/p4manualv2010.pdf) Source:
Marshall et al. (2010). [P]

POLITY

OPEN

ECB Working Paper 1757, February 201541Table A: Data Description (IV)

Variables
POPDEN

Full Description
Population density is midyear population divided by land area in square kilome-
ters. Population is based on the de facto deﬁnition of population, which counts
all residents regardless of legal status or citizenship–except for refugees not per-
manently settled in the country of asylum, who are generally considered part of
the population of their country of origin. Land area is a country’s total area, ex-
cluding area under inland water bodies, national claims to continental shelf, and
In most cases the deﬁnition of inland water bodies
exclusive economic zones.
includes major rivers and lakes. Source: World Bank. [S]

RELIGFRAC Following the literature fractionalization is computed as: Fracj = 1 − ∑N

RESRENT

TORT

URBAN

WOPOL

i=1 s2
ij
where sij is the share of group i in country j. The higher the index the greater the
fractionalization. Source: Alesina et al. (2003) and Encyclopedia Britannica Book
of the Year 2010. [S]
Total natural resources rents are the sum of oil rents, natural gas rents, coal rents
(hard and soft), mineral rents, and forest rents. Source: World Bank.
Torture refers to the purposeful inﬂicting of extreme pain, whether mental or
physical, by government ofﬁcials or by private individuals at the instigation of
government ofﬁcials. Torture includes the use of physical and other force by po-
lice and prison guards that is cruel, inhuman, or degrading. This also includes
deaths in custody due to negligence by government ofﬁcials. A score of 0 indi-
cates that torture was practiced frequently in a given year; 1 indicates that torture
was practiced occasionally; and 2 indicates that torture did not occur in a given
year. Source: CIRI Human Rights Report. [P]
Population in the largest city (% of urban population). Population in largest city
is the percentage of a country’s urban population living in that country’s largest
metropolitan area. Source: World Bank. [S]
Women’s Political Rights. These include: the right to vote; to run for political
ofﬁce; to hold elected and appointed government positions; to join political par-
ties; to petition government ofﬁcials. A score of 0 indicates that women’s political
rights were not guaranteed by law during a given year. A score of 1 indicates
that women’s political rights were guaranteed in law, but severely prohibited in
practice. A score of 2 indicates that women’s political rights were guaranteed in
law, but were still moderately prohibited in practice. Finally, a score of 3 indicates
that women’s political rights were guaranteed in both law and practice. Source:
CIRI Human Rights Data Project. [P]

ECB Working Paper 1757, February 201542Table A: Data Description (V)

Full Description

Variables
WORKER Workers’ Protection. Indicates the extent to which workers enjoy internationally rec-
ognized rights at work, including a prohibition on the use of any form of forced or
compulsory labor; a minimum age for the employment of children; and acceptable
conditions of work with respect to minimum wages, hours of work, and occupa-
tional safety and health. A score of 0 indicates that workers’ rights were severely
restricted; 1 indicates that workers’ rights were somewhat restricted; and 2 indicates
that workers’ rights were fully protected during the year in question. Source: CIRI
Human Rights Data Project. [S]
Merchandize exports to high-income economies are the sum of Merchandize exports
from the reporting economy to high-income economies according to the World Bank
classiﬁcation of economies. Data are expressed as a percentage of total Merchandize
exports by the economy. Data are computed only if at least half of the economies in
the partner country group had non-missing data. Source: World Bank.
GDP in constant 2005$s (chain series). Source: Penn World Tables.

XH I

Y

ECB Working Paper 1757, February 201543B Production and Technical Metrics

Technical Inefﬁciency, TE, compares the inefﬁciency under the control of the econ-
omy to stochastic factors beyond its control. Given the estimated production func-
tion, we can calculate the residuals εit = vit − uit for each observation. Technical
inefﬁciency euit can then be computed using the standard Bayes conditional prob-
ability formula (Jondrow et al. (1982)) as the expected value of uit conditional on
εit:

TIit = E {uit | εit} =

σλ
1 + λ2

(B.1)




φ

(cid:16)

(cid:17)

(cid:101)Zit
(cid:16)



1 − Φ






(cid:17) − (cid:101)Zit

(cid:101)Zit
, σ = (cid:112)

where (omitting subscripts for convenience) λ = σu
λσ −
σv
σ , E (u) = µ = z(cid:48)β and φ (·) and Φ (·) are the respective density and cumulative
ελ
density function of the standard Normal. Technical efﬁciency is then solved as,

u + σ2
σ2

v , (cid:101)Z = µ

We also report parameter,

TEit = e−TIit

γ =

σ2
u
σ2

∈ (0, 1)

(B.2)

This indicates the extent to which deviations from the frontier are due to noise,
γ → 0, or technical inefﬁciency, γ → 1. Differentiating production function (2)
with respect to time keeping inefﬁciency constant, we obtain the rate of Technical
Progress:

TPit =

∂y
∂t

= αt+∑

αjtxjit+αttt

j

(B.3)

This is time-varying and country-speciﬁc. The growth of Total Factor Productivity
is given by,

TFP = TPit −

∂uit
∂t

(B.4)

where | ∂µ

∂t | is the rate of change of Technical Efﬁciency

Regarding the production and inefﬁciency elasticities, these were derived as
usual by the coefﬁcient in the log case, and by differentiate the inefﬁciency equation
with respect to the variables of interest in the non-logged (e.g., level, ratio) case.

Finally, we assess the validity of a number of interesting production restrictions.
First, that production is separable in its inputs. In terms of (2), this Cobb-Douglas
restriction amounts to:

Second, the test of neutral technical progress amounts to,

αjm = αjt = αtt = 0

αjt = 0

∑
j

(B.5)

(B.6)

ECB Working Paper 1757, February 201544Third, given our classiﬁcation of data into Economic, Political and Sociocultural
indicators (and interactions), we can test their incremental block signiﬁcance in in-
efﬁciency as,

βE = 0; βP = 0; βS = 0; βI = 0

(B.7)

C The Silverman test and Bootstrap

Let Zi, i = 1, 2, .....m denote a sample Z of size m from a distribution with unknown
density f . A non parametric estimate of this density ˜f (z) is as follows,

˜f (z, ¯h) = m−1 ¯h−1

K (cid:2)(1/ ¯h)(zi − z)(cid:3)

m
∑
i=1

(C.1)

where K is a kernel normal function while ¯h > 0 is the bandwidth. A test statistic
(Silverman (1981)) can then be written as,

crit = inf (cid:8) ¯h : ˜f (z, ¯h) has at most q modes(cid:9)
˜hq

(C.2)

which is used to test the null hypothesis that is f has q modes against the alternative
of greater than q modes. A bootstrap procedure is employed to compute the ˜hq
statistic, given by,

crit

yi = [1 + ( ˜hq

crit)2/σ2)−0.5(Zi + ( ˜hq

crit)2ei)]

(C.3)

where Zi is sampled uniformly, with replacement, from the data z1, .....zm, σ2 is the
sample variance of the data, and ei is a normal random variable. In this way yi is
randomly drawn from a smooth conditional distribution. The conditional kernel
density for a bootstrap sample Y = {y1, .....ym} is given by,

˜f∗(z, ¯h) = m−1( ˜hq

crit)−1

m
∑
i=1

K (cid:2)(1/ ˜hq

crit)(yi − z)(cid:3)

(C.4)

Acceptance or rejection of the null hypothesis can be based on the following expres-
sion,

crit ≥ ˜hq
(C.5)
where ˜hq∗
crit is associated with the conditional kernel density ˜f∗(z, ¯h) using the boot-
strapped sample Y = [y1, .....ym]. Finally, the Hall and York (2001) method was
applied to Silverman’s test to obtain the correct critical values.

˜P = P[ ˜hq∗

crit]

D Robustness

To check the robustness of our model to alternative functional forms, we also use
the Modiﬁed Translog and Fourier production functions.

ECB Working Paper 1757, February 201545D.1 Modiﬁed Translog

The modiﬁed translog production function (MT) suggested by Griliches and Ringstad
(1971) is given by:

(yit − lit) = α0i + αk(kit − lit) + αllit + αh(hit − lit)
+ αklkitlit + αkhkithit + αlhlithit
+ αkkk2
+ αktkitt + αltlitt + αhthitt + αtt + αttt2 + vit − uit

it + αhhh2
it

it + αlll2

(D.1)

D.2 Fourier Production Function

The Fourier production function (F) is a ﬂexible functional form that combines
trigonometric and polynomial terms considered to achieve a close approximation
to the true frontier, e.g., Mitchell and Onvural (1996), Berger and Mester (1997). In
particular the trigonometric terms are mutually orthogonal in the interval [0, 2π] so
that each additional term can make the approximating function closer to the true
DGP. This form is given by,

yit = α0i + ∑

αjzjit +

j

1
2

∑
j

∑
m

αjmzjitzmjt + ∑

αjtzjitt + αtt +

j

1
2

αttt2

+ ρtCos(xt) + ρ∗

t Sin(xt) +

(cid:104)

J
∑
j=1

J
∑
κ>j

ρjκCos(xjit − xiκt) − ρ∗

jκSin(xiκy − xiκt)

(cid:105)

+

(cid:104)

J
∑
j=1

J
∑
κ>j

ψjκCos(xijt − xiκt − xt) − ψ∗

jκSin(xiκy − xiκt − xt)

(cid:105)

+ vit − uit

(D.2)

The variables x are re-scaled values of the original variables, such that each re-
scaled variable is in the interval [0, 2π], and where J = 3 reﬂecting the three factors
of production (we follow Gallant (1982) in constructing the re-scaled variables).58

(cid:48)

a(ln ωi + ln ςi), i = 1, 2, ...N where ωi = − min[ln κi] + 1/105, ς is a vector of
a = [hz1, hz2, ..., hzN] is a vector of multi indices. The common scaling factor
max{di} , where di = ln ωi + ln ςi is chosen to restrict x ∈ [0, 2π] in order to reduce approximation

58Let us set xi = δh
inputs and trend while h
δ =
problems near endpoints.

6

(cid:48)

ECB Working Paper 1757, February 201546E Additional Tables

Table E.1: Model MB

Inefﬁciency Equation

2.369∗∗∗
0.230∗∗∗
0.069∗∗∗
0.001
−0.128∗∗∗
0.091∗∗∗
−0.113∗∗∗
−0.028∗∗∗
−0.334∗∗∗
0.046
0.065∗∗∗
−0.050∗∗∗

Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Ey,k
Ey,l
Ey,h

TP
TP median

−2.989∗∗∗ β0
−2.867∗∗∗ h
−0.937

0.270∗∗∗ GY

resrent

−0.308

Open
0.266∗∗∗ FDI
0.113∗∗∗ ManuY

−0.164∗ MAW
0.423∗∗∗ MH I
XH I
0.003
−0.022∗∗∗ dcps

βt

0.008
0.051
0.004∗∗∗

0.180∗∗∗
0.489∗∗∗
0.509∗∗∗

−0.024∗∗∗
−0.023∗∗∗

0.026∗∗∗ TE
0.028∗∗∗ TE median

TFP
TFP median
Diagnostics and Tests
γ
Cobb Douglas
σ2
Neutral Technical Change
βE = 0
α0i = 0 ∀i
TP unimodality
TE unimodality
Notes: See notes to Table 1. BIC = −318.657, Obs = 316.

[0.003]
[0.007]
[0.010]
[0.574]

0.787
0.821

[0.989]
[0.011]
[0.003]
[0.435]

ECB Working Paper 1757, February 201547Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Eyk
Eyl
Eyh

TP
TP median

Table E.2: Model MB
I

Inefﬁciency Equation

3.004∗∗∗
−0.542∗∗∗
0.171∗∗∗
−0.075∗∗∗
0.038
−0.387∗∗∗
0.228∗∗∗
0.012∗∗∗
−0.005∗∗
−0.109∗∗∗
0.0001
−0.033∗∗∗
−0.322∗∗∗
0.055∗
0.075∗∗∗
−0.044∗∗∗

−2.802∗∗∗ β0
−2.871∗∗∗ h
−1.481

0.253∗∗∗

−0.252

resrent
resrent × h
GY

0.273∗∗∗ Open
0.105∗∗∗ Open × h

−0.127

FDI

0.440∗∗∗ FDI × h
ManuY
0.002

−0.019∗∗∗ ManuY × ∆e

MAW
0.006
MH I
0.068
0.004∗∗∗ XH I
dcps

0.196∗∗∗ βt
0.569∗∗∗
0.222∗∗∗

−0.008∗∗∗
−0.012∗∗∗

0.036∗∗∗ TE
0.032∗∗∗ TE median

TFP
TFP median
Diagnostics and Tests
γ
Cobb Douglas
σ2
Neutral Technical Change
βE = 0
α0i = 0 ∀i
TP unimodality
TE unimodality
Notes: See notes to Table 1. BIC = −321.274, Obs = 316.

[0.001]
[0.002]
[0.008]
[0.614]

0.789
0.823

0.990∗∗∗
0.008∗∗∗
[0.001]
[0.212]

ECB Working Paper 1757, February 201548Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Eyk
Eyl
Eyh

TP
TP median

Table E.3: Model MA

Inefﬁciency Equation
β0
h
resrent

−0.629
0.588
0.518
0.093∗∗ GY

0.329
−0.187∗∗∗
0.026∗∗∗
0.145∗∗∗
−0.089∗∗∗
0.004∗∗∗
−0.119∗∗∗
−0.047∗∗∗
−0.076
0.012
0.596∗∗∗
−0.028∗∗∗
−0.009
0.139
−0.007

0.637∗∗∗
−0.003∗∗∗
0.056∗∗
0.001

Open
FDI
ManuY

−0.001
−0.010
−0.017
−0.149∗∗ MAW
MH I
0.124
0.013∗∗∗ XH I
dcps
0.003
βt
0.004

−0.197∗∗∗ Assn

0.003∗∗∗ MedAge
Worker
ReligFrac

0.081
0.569∗∗∗ Durable
0.163∗∗∗ Military

Mobile

−0.008∗∗∗
−0.020∗∗∗

0.020∗∗∗ TE
0.008∗∗∗ TE median

TFP
TFP median
Diagnostics and Tests
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i

[0.115]
[0.001]
[0.003]

γ
σ2
βE = 0
βP = 0
βS = 0
TE unimodality
TP unimodality
Notes: See notes to Table 1. BIC = −280.266. Obs = 302.

[0.997]

0.723
0.748

0.741∗∗∗
0.004∗∗∗
[0.020]
[0.002]
[0.010]
[0.222]

ECB Working Paper 1757, February 201549Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Eyk
Eyl
Eyh

TP
TP median

TFP
TFP median

Table E.4: Model MA
I

Inefﬁciency Equation
β0
h
resrent
resrent × h
GY

−0.631
−0.286
−0.836∗∗∗
0.135∗∗∗
0.338
0.158∗∗ Open
−0.084∗∗ Open × h
−0.188∗∗

FDI

0.321∗∗∗ FDI × h
0.023∗∗∗ ManuY

−0.008∗∗ ManuY × ∆e
MAW
−0.007
−0.214∗∗∗ MH I
0.005∗∗∗ XH I
dcps
βt

0.022
0.468∗∗∗ Assn
0.216∗∗∗ MedAge

MedAge × h

−0.016∗∗∗ Worker
−0.013∗∗∗ Worker × MedAge

ReligFrac

0.018∗∗∗ Durable
0.021∗∗∗ Military

Mobile

TE
TE median

3.842∗∗∗
−1.146∗∗∗
0.143∗∗∗
−0.091∗∗∗
0.148∗∗∗
−0.292∗∗∗
0.165∗∗∗
0.011∗∗∗
−0.006∗∗∗
−0.079∗∗∗
0.0001∗∗∗
−0.046∗∗∗
−0.024
0.006
0.288∗∗∗
−0.034∗∗∗
−0.013
−0.725∗∗∗
0.003∗∗∗
−0.554∗∗∗
0.183∗∗
0.165∗∗∗
−0.003∗∗∗
0.172∗∗∗
0.005

0.748
0.859

0.929∗∗∗
0.006∗∗∗
[0.002]
[0.002]
[0.001]
[0.005]

Diagnostics and Tests
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i

γ
σ2
βE = 0
βP = 0
βS = 0
TP unimodality
TE unimodality
Notes: See notes to Table 1. BIC = −300.831. Obs = 302.

[0.001]
[0.001]
[0.007]

[0.860]

ECB Working Paper 1757, February 201550Table E.5: Stochastic Frontier using Modiﬁed Translog Production Function

Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Ey,k
Ey,l
Ey,h

TP
TP median

TFP
TFP median

−0.631
−0.286
−0.836∗∗∗
0.135∗∗∗
0.338
0.158∗∗
−0.084∗∗
−0.188∗∗

Inefﬁciency Equation
β0
h
resrent
resrent × h
GY
open
open × h
FDI

0.321∗∗∗ FDI × h
0.023∗∗∗ ManuY

−0.008∗∗ ManuY × ∆e
MAW
−0.007
−0.214∗∗∗ MH I
0.005∗∗∗ XH I
dcps
βt

0.022
0.468∗∗∗ Assn
0.216∗∗∗ MedAge

MedAge × h

−0.016∗∗∗ Worp
−0.013∗∗∗ Worp × MedAge

ReligFrac

0.018∗∗∗ Durable
0.021∗∗∗ Military

Mobile

TE
TE median

3.842∗∗∗
−1.146∗∗∗
0.143∗∗∗
−0.091∗∗∗
0.148∗∗∗
−0.292∗∗∗
0.165∗∗∗
0.011∗∗∗
−0.006∗∗∗
−0.079∗∗∗
0.0001∗∗∗
−0.046∗∗∗
−0.024
0.006
0.288∗∗∗
−0.034∗∗∗
−0.013
−0.725∗∗∗
0.003∗∗∗
−0.554∗∗∗
0.183∗∗
0.165∗∗∗
−0.003∗∗∗
0.172∗∗∗
0.005

0.748
0.859

0.929∗∗∗
0.006∗∗∗
[0.002]
[0.002]
[0.001]
[0.005]

Diagnostics and Tests
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i

γ
σ2
βE = 0
βP = 0
βS = 0
TP Uni-modality
TE Uni-modality
Notes: See notes to Table 1. BIC = −241.202. Obs = 302.

[0.001]
[0.001]
[0.007]

[0.069]

ECB Working Paper 1757, February 201551Table E.6: Stochastic Frontier Analysis using Fourier Production Function

Production Function
k
l
h
kk
ll
hh
kl
kh
lh
kt
lt
ht
t
tt

ρT
ρ∗
T
ρkl
ρkh
ρlh
ρ∗
kl
ρ∗
kh
ρ∗
lh

Ey,k
Ey,l
Ey,h

Inefﬁciency Equation

−1.067∗∗∗ β0
−1.712∗∗∗ h
−0.323

resrent
resrent × h

0.159∗∗∗
0.285∗∗∗ GY
open
0.281
open × h
−0.028
−0.027∗∗∗ FDI

0.402∗∗∗ FDI × h
0.018∗∗∗ ManuY

−0.017∗∗∗ ManuY × ∆e
MAW
−0.003
−0.121∗∗∗ MH I
0.004∗∗∗ XH I
dcps
βt

0.013

−0.061∗∗∗ Assn
−0.030
−0.042
−0.075∗∗∗ Worker
−0.093∗∗∗ Worker × MedAge

MedAge
MedAge × h

0.037

ReligFrac

−0.100∗∗ Durable
Military

0.052∗∗∗ Mobile
0.524∗∗∗
0.608∗∗∗

−1.774∗∗
0.773
0.020
−0.022

0.229∗∗∗
−0.050∗∗∗
−0.002

0.012∗∗∗

−0.003
−0.153∗∗
0.0001∗
−0.042∗∗∗
−0.063
0.017
0.448∗∗∗
−0.043∗∗∗
0.034
0.771∗∗∗
−0.007∗∗∗
−0.446∗∗∗
0.143∗
1.803∗∗∗

−0.0004

0.079∗∗∗
0.0003

TP
TP median

−0.030∗∗∗
−0.028∗∗∗

TFP
TFP median
Diagnostics and Tests
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i

TP unimodality

0.014∗∗∗ TE
0.016∗∗∗ TE median

[0.001]
[0.023]
[0.001]

[0.354]

γ
σ2
βE = 0
βP = 0
βS = 0
TE unimodality

0.777
0.938

0.885∗∗∗
0.006∗∗∗
[0.001]
[0.012]
[0.001]
[0.152]

Notes: See notes to Table 1. BIC = −294.124. Obs = 302.

ECB Working Paper 1757, February 201552Table E.7: Stochastic Frontier Analysis using principal components

Production Function
k
l
h
kk
hh
ll
kl
kh
lh
kt
lt
ht
t
tt

Ey,k
Ey,l
Ey,h

TP
TP median

Inefﬁciency Equation
β0
0.191
0.766∗
P1
P2
0.087
0.034∗
P3
−0.285
P4
S1
0.005
−0.060∗
S2
−0.061
S3
S4
0.09
0.007∗∗∗ E1
E2
0.0008
0.015∗
E3
−0.131∗∗∗ E4
0.003∗∗∗ E5
βt
FDI × h
open × h

0.053
0.412∗∗∗
0.090∗∗∗ ManuY × ∆e

Worker × MedAge

−0.0003∗∗∗ resrent × h

0.002∗∗∗

0.765∗∗∗
−0.029∗∗
0.041∗∗∗
0.034∗∗
0.006
0.161∗∗∗
0.105∗∗∗
0.056
0.046
−0.018

0.082∗∗∗

−0.004

0.052∗∗∗
0.015∗∗∗
−0.030∗∗∗
−0.003∗
−0.012∗∗
0.002∗∗∗
0.007
−0.025∗∗

0.030∗∗∗ TE

−0.039∗∗∗ TE median

TFP
TFP median
Diagnostics and Tests
Cobb Douglas
Neutral Technical Change
α0i = 0 ∀i
TP Uni-modality
TE Uni-modality
Notes: See notes to Table 1. BIC = −330.240. Obs = 316.

[0.007]
[0.042]
[0.001]
[0.909]

γ
σ2

0.798
0.848

0.992∗∗∗
0.007∗∗∗

[0.271]

ECB Working Paper 1757, February 201553Table E.8: Spearman Rank Correlation Between TE and TP w.r.t MA
I

TE

TP

0.68 0.77

1

1

0.79 0.85

0.70 0.78

0.88 0.98

MB
I

MA
I

MPC
I

MMT
I

MF
I

ECB Working Paper 1757, February 201554Table E.9: Robustness in Sign

MB MB
I

MA MA
I

MPC
I

MMT
I

MF
I

E
(cid:104)H(cid:105)
(cid:104)Resrent(cid:105)
GY
(cid:104)Open(cid:105)
(cid:104)FDI(cid:105)
(cid:104)(cid:104)ManuY(cid:105)(cid:105)
(cid:104)MAW (cid:105)
MH I
XH I
∆e
Dcbs
(cid:104)Dcps(cid:105)
(cid:104)(cid:104)βt(cid:105)(cid:105)
P
Assn
(cid:104)Durable(cid:105)
(cid:104)(cid:104)Military(cid:105)(cid:105)
Wopol
Disap
Tort
Formov
Dommov
Injud
S
Agdeo
Agdey
MedAge
Mobile
Urban
(cid:104)(cid:104)ReligFrac(cid:105)(cid:105)
(cid:104)Worker(cid:105)
I
(cid:104)(cid:104)Resrent × H(cid:105)(cid:105)
Open × H
(cid:104)(cid:104)FDI × H(cid:105)(cid:105)
(cid:104)Worker × MedAge(cid:105)
(cid:104)ManuY × ∆e(cid:105)
MedAge × H

+
+
(+)
–
+
–
–
–
(+)

–
+
(+)
–
+
–
–
–
+

+
–

+
–

–
+
–

(+)

–
+
+
–
+
–
–
(–)
(+)

+
–

(–)
–
+

(–)
(+)

+
(–)

–
+
+
–
+
–
–
(–)
(+)

+
–

(–)
–
+

–
(+)

+
–

–
+
–
+
+
+

–
(–)
(–)
(+)
(–)
–
+
(–)
–
+
(–)
–
–

+
–
+
(+)
(–)
(–)
–
(+)
–

+
+

+
–
–
+
–

–
+
+
–
+
–
–
+
+

+
–

–
+
+

–
+
+
–
+
–
–
–
+

+
–

+
–
+

–
(+)

+
–

+
(+)

+
–

–
–
–
(+)
+

–
–
+
–
–
–
+
+
+
+
–
–
I ; Augmented: M

Notes: Baseline: MB; Baseline with interactions: MB
A;
Augmented with Interactions: MA
I . All of these results taken from (Table
(Tables 6 and E.7); Modiﬁed Translog: MMT
1). principal components: MPC
I
(Table E.5); Fourier: MF
I (Table D.2). Variables within “(cid:104)(cid:104)(cid:105)(cid:105)” and “(cid:104)(cid:105)” de-
notes “strong” and “weak” sign robustness, respectively. A blank entry
means not applicable. Variable signs within “()” indicate that the signed
coefﬁcient is not signiﬁcance at 10%.

I

ECB Working Paper 1757, February 201555