WPS8218

Policy Research Working Paper 8218

Product and Factor Market Distortions
The Case of the Manufacturing Sector in Morocco

Jean-Pierre Chauffour
Jose L. Diaz-Sanchez

Macroeconomics and Fiscal Management Global Practice Group
October 2017
Policy Research Working Paper 8218

Abstract
This paper studies the effect of market distortions in the man- increase in TFP would be of 56 percent. The paper also
ufacturing sector in Morocco. Recent microdata are used to finds that industries that are more opened to competition
calculate the extent of resource misallocation associated to (international and domestic) such as machinery and textiles
these distortions and the potential total factor productivity industries present lower levels of market distortions com-
(TFP) gain resulting from their removal. Market distortions pared with more protected industries with relatively little
in the manufacturing sector in Morocco are higher com- competition, such as the food industry. Besides, a higher
pared with developed countries and slightly more important level of TFP can be achieved if more resources are allocated
compared with other developing countries, such as China to “young” and “small” firms. The main results of the paper
and India. These distortions decreased between 2007 and are robust to an alternative estimation that uses a different
2013. Full liberalization would raise TFP by about 84 methodological framework with a less extensive theoretical
percent. If distortions are removed to the level of selected framework. The paper discusses policies to further limit the
developed countries with better resource allocation, the extent of product and factor market distortions in Morocco.

This paper is a product of the Macroeconomics and Fiscal Management Global Practice Group. It is part of a larger effort
by the World Bank to provide open access to its research and make a contribution to development policy discussions
around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors
may be contacted at jdiazsanchez@worldbank.org and jchauffour@worldbank.org.

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development
issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the
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Product and Factor Market Distortions:
The Case of the Manufacturing Sector in Morocco

Jean-Pierre Chauﬀour and Jose L. Diaz-Sanchez∗
The World Bank

JEL Code: O11, 047, 055, L60

Keywords: TFP, Market Distortions, Resource Misallocation,
Productivity, Manufacturing.

∗
Respectively Lead Economist for the Maghreb region and Economist, World Bank. The
views expressed in this article are those of the authors and do not necessarily represent those
of the World Bank. We would like to thank Abdessamad Saidi, Anta Ndoye, Fernando Parro,
Addisu Lashitew, Ragbi Aziz, and participants of a research seminar of the Bank Al-Maghrib
for useful comments and discussions.
1 Introduction
To accelerate its pace of economic convergence, Morocco needs to achieve and
sustain faster economic growth. Dabla-Norris et al. (2013) shows that episodes
of growth take-oﬀ in emerging markets have been mostly associated with a strong
pick-up in total factor productivity (TFP) growth. Yet, in the last two decades,
average TFP growth in Morocco has been much lower than in other emerging mar-
ket economies, especially in East and South Asia. Policies to lift-up TFP would
appear therefore to be critical to accelerate the Moroccan economic convergence
process (World Bank, 2017a).
Productivity increases with the adoption and development of new technologies
(Howitt, 2000; Klenow and Rodriguez-Clare, 2005; and Coe and Helpman, 1995)
and with better institutions and higher social and human capital (World Bank,
2017a and 2017b). In addition, the structural change process of allocating re-
sources from less productive to more productive sectors has also been found to be
one of the main sources of TFP growth (McMillan, Rodrick, and Verduzco-Gallo,
2014). In this paper we focus on another signiﬁcant determinant of aggregate
productivity explored in the literature: resource misallocation across ﬁrms.
Seminal papers in this literature include Restuccia and Rogerson (2008), Hsieh
and Klenow (2009), Bartelsman, Haltiwanger, and Scarpetta (2013), and Asker,
Collards-Wexler, and De Loecker (2014) and suggest that large heterogeneity in
ﬁrm-level productivity within a sector may indicate misallocation of resources
across ﬁrms, with a consequent negative and sizable eﬀect on the aggregate TFP
(see Restuccia and Rogerson, 2017 for a recent survey on this topic). In this paper
we apply the Hsieh and Klenow’s (2009) framework (henceforth HK) to Morocco,
which allows to ﬁrst quantify the level of distortions in the Moroccan manufactur-
ing sector, and second to compute the productivity gains from eliminating these
distortions. In the HK framework, distortions to ﬁrm size (output distortions)
and capital/labor ratio (factor distortions) lead to diﬀerent marginal products of
capital and labor across ﬁrms within industries. Productivity gains then can be
quantiﬁed by equalizing marginal products across ﬁrms.1
Examples of output distortions include regulations restricting market access
and/or ﬁrm size, bribes that have to be paid by ﬁrms in order to operate, pub-
lic output subsidies, trade policies in the form of import protection and export
subsidies (taxes), and distortions generated from exchange rate policies and price
controls. Factor distortions are credit constraints, ﬁnancial policies, tax policies,
and labor regulations that diﬀer across ﬁrms (for instance because of political
connections) and aﬀect the relative prices of capital and labor. Note that output
and factor distortions can simultaneously aﬀect both the size of a ﬁrm and the
relative prices of capital and labor.
The present paper proposes to investigate the eﬀects of market distortions in
the manufacturing sector in Morocco. It calculates the level of resource misallo-
cation and the potential TFP gains associated to liberalization. It uses microdata
1
Note that this is an indirect approach to analyzing misallocation. This approach contrast
with direct approaches, which analyze the impact of speciﬁc frictions (for instance see Buera,
Kaboski, and Shin, 2011; and Moll, 2014, for the case of ﬁnancial frictions).

2
from two World Bank’s Enterprise Surveys (2007 and 2013) for Morocco. Re-
cently, Ragbi and Nihou (2016) undertake a similar study. However, even if they
also apply the HK framework, their results can not be compared directly with
ours.2
The paper ﬁnds that market distortions in the manufacturing sector in Mo-
rocco are higher compared to developed countries and somewhat higher compared
to other developing countries such as China and India. Empirical results provide
evidence in the reduction of market distortions between 2007 and 2013. In ad-
dition, we estimate about 84 percent TFP gains from a full liberalization and
56 percent from removing distortions to the level of selected developed countries
with better resource allocation. Thus, the removal of economic distorsions has the
potential to considerably accelerate the convergence process. The paper also ﬁnds
that industries more opened to international and domestic competition (e.g. ma-
chinery and textiles) present lower levels of output and factor distortions compared
to industries where competition is more limited (e.g. food industry). Finally, the
results show that a higher level of TFP can be obtained if more resources are
allocated to younger and smaller ﬁrms. The paper’s main results are robust to an
alternative estimation that uses a diﬀerent methodological framework with a less
extensive theoretical framework (Bartelsman, Haltiwanger, and Scarpetta, 2013).
The remainder of the paper is organized as follows. Section 2 presents an
overview of market distortions in Morocco. Section 3 gives a brief presentation of
the HK methodology. Section 4 presents the data and describes the construction
of the required variables for the study. The empirical analysis is contained in
Section 5, where we quantify the level of distortions and compute the TFP gains
from resource reallocation. Section 6 discusses policies to further limit the extent
of product and factor market distortions in Morocco. Section 7 provides the ﬁnal
remarks.

2 Market Distortions in Morocco
The free movement of capital and labor to the most productive manufacturing
ﬁrms is restrained by market distortions, including distortions in Morocco’s in-
ternational exchange system. In this section we present an overview of the main
market imperfections that currently aﬀect the optimal allocation of resources in
both the tradable and nontradable sectors of the Moroccan economy. To be consis-
tent with the grouping of market distortions in the paper’s theoretical framework
section (Section 3), we describe here separately output and factor distortions. Out-
put distortions aﬀect the quantity of production leaving the input mix unaﬀected.
These distortions are more related to limited competition, deteriorated business
environment, and reduced international trade. Factor distortions aﬀect the use of
capital relative to labor and are mostly associated to high credit constraints and
2
In contrast to Ragbi and Nihou (2016), our empirical exercise takes into account a recent
revision of the HK framework (see Hsieh and Klenow, 2013). Although most of their analysis
is not aﬀected by this change, the level of distortions and the TFP gains associated to a full
liberalization cannot be compared with ours or with recent papers in this literature.

3
labor rigidities.

2.1 Competition, Business Environment, and International
Trade
There are many factors limiting competition in the Moroccan economy. According
to a study by the Royal Institute of Strategic Studies (IRES), “by reducing compe-
tition, protection allows for the emergence of monopolistic rents. Businesses that
beneﬁt from this situation have used price increases to collect super proﬁts rather
than investing in competitiveness through quality and new techniques” (IRES,
2014). Like in most countries in the Middle East and North Africa (MENA) re-
gion (Rijkers, Freund, and Nucifora 2014, Diwan, Keefer, and Schiﬀbauer, 2016,
Diwan and Hadar, 2016), Morocco’s capitalism is dominated by personal connec-
tions and privileges, which are an integral part of a complex network of economic
interests that hinder the emergence of new players (World Bank, 2009, 2015b).
The eﬀect is to maintain monopolies, impede the performance of the economy and
weaken the growth of productivity and job creation. A recent study estimates that
almost 13 percent of a representative sample of Moroccan manufacturing compa-
nies are held by individuals connected with circles of economic inﬂuence (Saadi
2016). These businesses operate in various economic sectors and are apparently
more eﬀective than their unconnected counterparts owing, in part, to the privi-
leges that they enjoy in terms of commercial protection, application of the rules
and regulations and access to ﬁnancing. Beyond considerations of equity, the ex-
istence of connected enterprises can partially explain the slow overall structural
transformation of the economy to the extent that these ﬁrms operate in sectors
with low new business entry and exit rates and greater concentration.
While some sectors have liberalized (mobile telephony, for example), this
progress cannot mask the lack of real competition between economic agents in
many other sectors.3 The regulatory framework governing competition has been
updated, but its implementation has been suspended since 2014 pending the ac-
tual appointment of the members to the new Competition Council. Despite the
many reforms aimed at refocusing the government on its general interest missions,
public enterprises and establishments (EEPs) continue to play a major role in the
national economy, creating de facto entry barriers to key sectors (agriculture and
marine ﬁsheries; energy, mines, water and the environment; infrastructure and
transportation; housing, urban planning and local and regional development). In-
deed, because of their status, modes of governance, public ﬁnancial support and
often monopolistic position they can constitute an obstacle to the opening up of
many sectors.4
3
The density of new businesses (a proxy for competition strength) in comparison with the
total population is higher than in other countries in the region but low in comparison with many
emerging market economies (Klapper and Love, 2010).
4
At the end of 2015, the EEP sector represented about 8 percent of gross domestic prod-
uct (GDP) and almost 25 percent of the country’s total investment through 212 public estab-
lishments, 44 companies in which the government had direct equity holdings and some 442
subsidiaries and public shareholdings.

4
Over the past 15 years, Morocco has made signiﬁcant changes to its business
environment and its public policies to modernize the economy and encourage eﬃ-
ciency. The eﬀorts to improve the business climate have been recognized in many
international rankings, such as the World Bank Doing Business, and owing in par-
ticular to business start-up reforms (World Bank, 2015a).5 However, the business
climate is still seen by most players as too unpredictable and bureaucratic and
does not inspire the conﬁdence that economic agents need to invest in the medium
or long term. The importance of constraints such as burdensome, slow, complex
and opaque administrative formalities and procedures is regularly conﬁrmed in
surveys of businesses. The constraints most often mentioned by the formal sec-
tor are corruption, competition from the informal sector, low workforce education
levels and diﬃculties in accessing ﬁnancing (World Bank, 2013a). For very small
enterprises and small and medium enterprises (VSEs and SMEs), these barriers
can be insurmountable (World Bank, 2014a).
The incidence of entrenched corruption aﬀecting businesses in Morocco is high
(EBRD, EIB, and World Bank 2016). The frequency and incidence of requests
for beneﬁts or informal payments faced by Moroccan businesses in their relations
with government departments and public services, in their applications for licenses
and construction permits, or in import operations or tax payments appear to
be signiﬁcantly higher than the MENA region average, which itself compares
unfavorably to other middle-income countries that are Morocco’s competitors.
Furthermore, while corruption involves the visible costs related to illicit payments
and a poor allocation of resources, its highest cost is invisible and involves missed
opportunities: the businesses that are not created, the investments that are not
made, the loans that are not granted, and, ultimately, the jobs that are not
created.
The Moroccan economy has gradually opened up to international trade. Tariﬀ
and nontariﬀ barriers have been lowered and international trade procedures have
been simpliﬁed. Modern trade infrastructure has been developed for all types
of transport (such as the Tanger-Med port) and the country has been fully up-
dating its customs regulations and its transportation and trade logistics. This
has facilitated the emergence of “new” industries in sectors such as automobile,
aeronautics, and electronics that occupy a more central position in the global pro-
duction space and are thus a potential source of higher revenues than traditional
exports.
Despite these eﬀorts, Morocco’s share in international trade has tended to
decline since the early 1980s, while most of its competitors have seen their shares
increase. Morocco trade performance is slowed by high production factor costs
owing to protected service sectors and rigid domestic prices, by a ﬁxed exchange
rate regime and by a skills and qualiﬁcations mismatch. To date, Morocco has
been able to penetrate only a small portion of potential markets.
One of the explanations for Morocco’s diﬃculties in beneﬁting more from the
development of international trade lies in the delays and costs related to mer-
5
Morocco’s Doing Business ranking improved from the 129th position to the 68th position
during 2010-2016.

5
chandise export and import logistics. According to the Doing Business indicator
that measures distance to the frontier in terms of the accumulation of delays and
costs (excluding customs duties) associated with three categories of procedures
(documentary compliance, border compliance and domestic transport), Morocco
scores better than other countries in the MENA region, but not as well as its
main competitors, particularly Central and Eastern Europe. Morocco was ranked
86th out of a total of 160 countries in 2016 on the Logistic Performance Index,
falling 24 places from its average ranking during the period 2007-14 (World Bank
2016b). Morocco ranks well for the quality of its infrastructure but loses in this
classiﬁcation owing to weaknesses and dysfunctions in foreign trade services. In
fact, Morocco is ranked 124th in the provision of customs services (it was ranked
73rd from 2007 to 2014) and is ranked 122nd in terms of the ability to track and
trace consignments.
Other sources of market distortions are tariﬀ and nontariﬀ trade barriers. At 25
percent, maximum most-favored nation (MFN) tariﬀs on goods are still relatively
high, particularly in comparison with the zero duty applied to preferential trade
partners under the free trade agreements signed with the EU, the US and other
countries. High duties on intermediate products tend to be an obstacle to the
productivity of ﬁrms and the growth of Moroccan exports. This results from the
fact that generally speaking a tax on imports ends up becoming a tax on exports,
and this eﬀect is becoming more important since the diﬀerence in export unit
values between ﬁrms that both import and export and ﬁrms that export only has
been increasing over time (World Bank, 2017a). Nontariﬀ measures has a similar
eﬀect on resource misallocation.

2.2 Credit Constraints and Labor Rigidities
The Moroccan ﬁnancial system operates relatively eﬀectively in mobilizing na-
tional savings but has more diﬃculty in allocating it to the most productive
activities. There are many reasons for this, including objective prudential con-
straints related to the risk of concentration and maturity transformation and more
subjective behaviors such as the reluctance and averseness of commercial banks to
risk-taking. Another reason could be the still relatively low level of competition
prevailing in the Moroccan banking sector (see Benazzi and Rouiessi, 2017). Ac-
cording to the Doing Business indicator on getting credit, Morocco is ranked 101st
in the world and is relatively far from the frontier, particularly in comparison with
other emerging market economies such as Romania, Cambodia or Kenya. Fur-
thermore, even though the government recently undertook reforms to promote the
diversiﬁcation of ﬁnancial products and mechanisms for the allocation of capital,
these reforms have thus far had few concrete results and there is still a signiﬁcant
gap between the advanced development of the banking system and the develop-
ment of the equity market. The Casablanca Stock Exchange (CSE) suﬀers from
a lack of new issuers, particularly key large institutional issuers. Family conglom-
erates and medium-sized enterprises, which could develop more rapidly, generally
seem reluctant to share information concerning their capital and governance.
The limited convertibility of the dirham represents another source of capital

6
misallocation. Full convertibility of the dirham is prevented by capital controls
still in place for domestic agents for the purchase of foreign exchange, which im-
pede the development of national ﬁrms. Furthermore, the ﬁxed exchange rate
regime encourages, in the case of an overvaluation, economic agents to invest
in non-export oriented ﬁrms, generating negative market distortions on export-
oriented ﬁrms (which are often more productive than the average), and contribut-
ing to the “anti-export bias” (World Bank, 2006).
On the labor front, more than half of the working age Moroccans (15-64 years
of age) does not participate in the economic activity of the country, which makes
it one of the countries with the lowest employment rates in the MENA region,
and across the globe. As a result, in 2016, some 2.7 million youth aged 15-29
(or almost one in three young people) were neither in education, employment
or training (NEET). Moreover, most (90 percent) of the private jobs are in the
informal sector. Social mobility and the ability to move from a less productive job
to a more productive job are limited and take time, particularly for young people
and women.
Morocco has adopted labor legislation that is based on the conventions and
recommendations of the International Labor Organization. The negotiation, adop-
tion and implementation of this legislation on the basis of international conven-
tions have, however, resulted in cumbersome and restrictive labor market reg-
ulations. According to the 2017 Doing Business indicator “Hiring and ﬁring”,
the Moroccan labor market is particularly heavily regulated. Moroccan law pro-
hibits ﬁxed-term contracts for permanent tasks and limits their duration to 12
months, after which they cannot be renewed. Although the Moroccan law allows
a certain amount of ﬂexibility in terms of the hours worked, overtime and premi-
ums for night work are expensive compared to international practices. Moroccan
legislation on leave (annual leave, statutory leave, maternity/paternity leave) is
more generous than that in other emerging market countries that are Morocco’s
competitors. The labor regulations governing private sector terminations are re-
strictive as only businesses with more than 10 employees may terminate jobs for
economic, technical or structural reasons. Reducing staﬀ for economic reasons is
subject to a prior agreement with the regional authorities. As a result, the num-
ber of employees terminated by medium-sized businesses is very low and almost a
quarter of the businesses contacted for the Investment Climate Assessment (ICA)
survey in 2008 indicated that they would have reduced their staﬀ if redundancy
conditions were less restrictive.
The minimum wage in Morocco is high compared with the average national
income or the average productivity of workers. In 2015, the minimum wage in
urban areas was equal to approximately 100 percent of the national per capita
income or more than 50 percent of the average wage in the formal private sector,
rates that were extremely high not just in the region but also in comparison with
international standards, including the OECD countries. Such high legal minimum
wages deter the creation of jobs in the formal sector, particularly for young job-
seekers with limited qualiﬁcations. Overall, the high cost of labor in the formal
sector contributes to a lower demand for employees on the part of employers and

7
explains the low labor market participation rate and structural underemployment.

3 Theoretical Framework
In this section we provide a sketch of the Hsieh and Klenow (2009) model, incor-
porating their correction appendix (Hsieh and Klenow, 2013).6 This is a standard
model of monopolistic competition with heterogeneous ﬁrms, a Melitz-type model
without international trade. The key diﬀerence is that instead of Melitz (2003)
ﬁrms are not only heterogeneous with respect to their productivity but also face
idiosyncratic distortions to their output and factor prices.
The HK framework is composed of a closed economy with two inputs, labor and
capital. There are S diﬀerentiated intermediate goods industries and one single
homogeneous ﬁnal goods industry. Inputs are only used in the intermediate goods
industries. The ﬁnal goods industry is merely an assembly sector that combines
the S diﬀerent sets of diﬀerentiated intermediate goods to produce a homogeneous
ﬁnal output. Intermediate ﬁrms compete on monopolistic competitive markets to
sell their diﬀerentiated outputs to ﬁnal producers. Final producers compete in
a perfectly competitive market to sell their products to a representative ﬁnal
consumer.
Final producers of the ﬁnal good Y combines the output Ys of S manufacturing
industries using a Cobb-Douglas production technology:
S S
Y = Y s θs , where θs = 1 (1)
s=1 s=1

Industry output Ys is itself a CES aggregate of Ms diﬀerentiated products:
σ
Ms σ −1
σ −1
Ys = Y si σ (2)
i=1

Each diﬀerentiated ﬁrm uses a Cobb-Douglas technology function:
αs 1−αs
Ysi = Asi Ksi Lsi (3)

It is important to note that capital and labor shares are allowed to diﬀer across
industries (but not across ﬁrms within an industry).
Two types of distortions are introduced: output distortions that aﬀect the
quantity of production while leaving the input mix unaﬀected (denoted by τY ),
and factor distortions that aﬀect the use of capital relative to labor (denoted by
τK ).7 The proﬁt equation below of ﬁrm i operating in sector s make explicit the
market distortions:
6
See their work for a more detailed exposition.
7
HK shows that the overall market distortion in this framework is equivalent to a modiﬁed
model where instead of output and factor distortions there are only capital and labor distortions.

8
πsi = (1 − τYsi )Psi Ysi − ωLsi − (1 + τKsi )RKsi (4)

where Psi is the output price ﬁxed by each ﬁrm, R is the rental price of capital
and ω is the wage common to all ﬁrms in a given sector.
Proﬁt maximization in a monopolistic competition setting yields the standard
condition that the ﬁrm’s output price is a ﬁxed markup over its marginal cost:
αs 1−αs
σ R ω (1 + τKsi )αs
Psi = (5)
σ−1 αs 1 − αs Asi (1 − τYsi )

Equation (6) and (7) shows the expression of τYsi and τKsi from the ﬁrst-order
conditions:
αs ωLsi
1 + τKsi = (6)
1 − αs RKsi

σ ωLsi
1 + τYsi = (7)
σ − 1 (1 − αs )Psi Ysi

Factor distortions (τKsi ) appear when the ratio of labor compensation to the
capital stock diﬀers relative to what one would expect from the degree of output
elasticity with respect to capital and labor. τKsi is positive in a ﬁrm paying higher
capital costs, and it is negative if the labor cost is relatively high.8 Similarly,
output distortion (τYsi ) is measured when labor’s share is diﬀerent compared with
what one would expect given the degree of industry elasticity of output with
respect to labor. The output distortion gives then an indication of how suboptimal
is the ﬁrm size (in terms of labor).
Also from the ﬁrst-order conditions, Ysi is given by:

Aσ σ
si (1 − τYsi )
Ysi = (8)
(1 + τKsi )αsσ

Thus the production and allocation of resources across ﬁrms depends not only
on ﬁrm TFP levels, but also on the output and capital distortions they face. In
addition, if resource allocation is driven by distortions rather than ﬁrm TFP, this
will result in diﬀerences in the marginal revenue products of labor and capital
across ﬁrms.
The marginal revenue products of labor and capital are respectively:
σ − 1 Psi Ysi 1
M P RLsi (1 − αs ) =ω (9)
σ Lsi 1 − τYsi
8
HK shows that it is straightforward to write the model with the capital and labor market
distortion separately and to obtain the same results. However the output distortion will no
longer be identiﬁed.

9
σ − 1 Psi Ysi 1 + τKsi
M P RKsi αs =R (10)
σ Ksi 1 − τYsi

Distortions in this framework can be viewed as taxes. Thus, in equations (9)
and (10) the after-tax marginal revenue products of capital and labor are equalized
across ﬁrms. The before-tax marginal revenue products must be higher in ﬁrms
that face disincentives, and can be lower in ﬁrms that beneﬁt from subsidies.
Now, HK shows that industry productivity (T F Ps ) is a function of output
and factor distortions. HK uses the distinction between revenue productivity
and physical productivity initially developed in Foster et al. (2008) and denoted
respectively by T F P R and T F P Q.9 The use of a ﬁrm-speciﬁc deﬂator yields
TFPQ, whereas using an industry deﬂator gives TFPR:
Ysi
T F P Qsi Asi = αs 1−αs (11)
Ksi Lsi

Psi Ysi
T F P Rsi Psi Asi = αs 1−αs (12)
Ksi Lsi

In this framework, TFPR does not vary across ﬁrms within an industry unless
plants face output and/or factor distortions.
In the absence of distortions, more factors of production should be allocated
to ﬁrms with higher TFPQ to the point where all ﬁrms have the same TFPR (this
comes from a property of this model that higher output results in a lower price).
From the marginal revenue products of labor and capital we obtain:

αs 1−αs (1 + τKsi )αs
T F P Rsi ∝ (M RP Ksi ) (M RP Lsi ) ∝ (13)
1 − τYsi

Thus, we show ﬁrst that TFPR is a function of output and factor distortions.
Second, high ﬁrm TFPR is a sign that the ﬁrm confronts barriers that raise the
ﬁrm’s marginal products of capital and labor, rendering the ﬁrm smaller than
optimal.
Having deﬁned TFPR and TFPQ ( A) we can obtain the expression of
industry total productivity (T F Ps ):10
1
Ms σ −1 σ −1
T F P Rs
T F Ps = Asi (14)
i=1
T F P Rsi
9
TFPQ reﬂects the quality and variety of a plants products, not just its physical productivity
(see HK for the demonstration).
10
HK shows that a Lucas span-of-control model (diminishing returns in production rather
than utility) and monopolistic competition are isomorphic for aggregate TFP if the number of
ﬁrms is proportional to labor input.

10
where T F P Rs is the average TFPR of the sector s. Thus, if T F P Rsi were
equalized across ﬁrms, T F Ps would be distortion-free and equal to:
1
Ms σ −1
−1
As = Aσ
si (15)
i=1

4 Data Description and Measurement
This paper uses ﬁrm-level ﬁnancial and economic data taken from the World
Bank’s Enterprise Survey 2007 and 2013 (henceforth WBES2007 and WBES2013)
for Morocco. To be sure, a more extensive survey (optimally a census) would have
been preferable, the WBES however appears as a second-best option given the
restricted access to the public of a recent government census of registered ﬁrms.
Nevertheless, other papers in the literature have embraced this second-best option
and it has been shown that WBES could give very similar results compared to
census data (Inklaar, Lashitew, and Timmer, 2015).
Manufacturing data from the WBES consists of formal manufacturing ﬁrms
with at least 5 employees. Since the smallest formal ﬁrms and all informal man-
ufacturing ﬁrms are not part of the data, our measure of misallocation might
underestimate overall resource misallocation in manufacturing (this key issue will
be examined later in the paper). Sampling for the WBES is conducted using strat-
iﬁed sampling procedures to ensure representativeness. Only the most important
industries are selected for sampling. Since the relative importance of manufactur-
ing sectors changed between the two surveys, the selected industries are not the
same in the two surveys as well. In the second stage, the sample size is chosen to
ensure a representative sample for the proportion of ﬁrms and the average sales in
the industry. Further stratiﬁcation is made on ﬁrm size and geographical location
to select the ﬁrms that are covered by the survey.
We regroup the selected manufacturing industries into 7 larger industries for
each survey (see Annex). This regrouping represents a compromise between the
required detailed manufacturing in the HK framework and limits to data avail-
ability (Inklaar, Lashitew, and Timmer, 2015, follows the same approach).
Our empirical approach requires ﬁrm-level data for production factors and
value-added. Then, we only keep in our database ﬁrms with complete data on total
production, cost of intermediate inputs, capital stock, and labor inputs. Value
added is measured as the diﬀerence between sales and the cost of intermediate
inputs. Cost of intermediate inputs is calculated by adding up three major cost
categories: energy consumption (fuel, electricity and other energy costs), cost of
raw materials, and overhead and other expenses. The capital stock is measured
from the book value of assets, summed across the two categories “machinery,
vehicles and equipment” and “land and buildings”. Following HK, we use labor
cost rather than employment as a measure of labor inputs. The objective is to
account for diﬀerences in both hours worked and human capital. In addition, we

11
also remove loss-making ﬁrms with negative valued and ﬁnally exclude industries
with less than 4 observations.
The two databases were reduced after the “cleaning” process and this was
mainly due to a lack of capital stock data for a large group of ﬁrms. For instance,
“Machinery” was dropped from both surveys, although this industry only rep-
resents about 5 percent of the manufacturing sector’s value added (UNIDO). A
critical point is to assess whether the ﬁnal sample is not too diﬀerent from the
original (representative) sample. Inklaar, Lashitew, and Timmer (2015) proposes
a rule of thumb to address this issue: if the remaining database has less than 40
observations or if it represents less than 40 percent of the original sample it should
preferably not be used for empirical analysis. WBES2007 respects both criteria.
The ﬁnal database has 312 observations which represents more than 50 percent
of the initial sample. Concerning WBES2013, the ﬁrst criterion is fulﬁlled (61 ob-
servations remained), however the second one is not (the ﬁnal sample represents
around 30 percent of the initial sample). We aknowledge that representativeness
could have been importantly aﬀected for the WBES2013 and these results should
be taken with caution. For this reason, as a robustness exercise, we apply an
alternative methodology to compute resource misallocation (Bartelsman, Halti-
wanger, and Scarpetta, 2013) that uses less theoretical restrictions and does not
require a capital stock measure, allowing then to use a higher share of the initial
sample for the empirical exercise.

5 Computing Distortions and Potential Gain from
Reallocation
To be able to compute the degree of resource misallocation and the potential gain
from resource reallocation a calibration exercise is required. Following HK and
as is common in this literature, the rental price of capital (R) is set to 0.10 (the
idea is to account for a 5% real interest rate and for a 5% depreciation rate). HK
demonstrates that the level of R aﬀects only the level of factor distortion but not
the TFP gains from resource reallocation. The elasticity of substitution between
ﬁrm value-added (σ ) is set to 3. HK shows that the gains from liberalization
are increasing in σ so the choice of 3 is a conservative one (in the literature the
estimates for σ go from 3 to 10, see for instance Broda and Weinstein, 2006, and
Hendel and Nevo, 2006).
As HK, α is set to be 1 minus the labor share in the corresponding industry
in the United States. Since we cannot separately identify the average capital
distortion and the capital production elasticity in each industry we need to pick
these shares from a relatively undistorted economy.
One more variable is needed to be able to compute the TFP gains associated
to liberalization: Asi , deﬁned in equation (11). The problem here is that we do
not observe real output for each ﬁrm (Ysi ) but rather its nominal output (Psi Ysi ).
HK notes however that ﬁrms with high real output must have a lower price to
explain why buyers would demand the higher output. Then, Ysi can be simply

12
inferred from the observed Psi Ysi by assuming an elasticity of demand of −1/σ .
Thus, we raise Psi Ysi to the power σ/(σ − 1) to arrive at Ysi . This shortcut is one
of the reasons that has made this approach so popular. Henceforth, even without
data on ﬁrm output prices, it is possible to compute both revenue and physical
productivity for each ﬁrm.
The ﬁnal step is to calculate “eﬃcient” output using equation (15) so we can
compare it with actual output levels. For each industry, we calculate the ratio of
actual TFP (equation 14) to this eﬃcient level of TFP. Finally we aggregate this
ratio across sectors using the Cobb-Douglas aggregator from equation (1):
θ
S Ms σ −1 σ −1
Y Asi T F P Rs
= (16)
Yef f icient s=1 i=1
As T F P Rsi
Before computing the degree of distortions and the TFP gain from resource
reallocation we follow HK and remove the top and bottom percentiles of TFPR
and TFPQ across industries. At this stage we calculate the industry shares (θs =
Psi Ysi /Y ) that will be used for the aggregation.

5.1 Results for Resource Misallocation Levels
Table 1 provides a measure of dispersion (standard deviation) for TFPR and
compare it with other developed and developing countries. In the HK frame-
work dispersion of TFPR represents the level of market distortions. First, results
show that dispersion of TFPR in Morocco decreased between 2007 and 2013.
Then, this decrease represents an improvement in resource allocation, although
as we already noted, this result should be taken with caution since results us-
ing WBES2013 could not be fully representative of the Moroccan manufacturing
sector in that year. In a subsequent sub-section we will show however that when
using an alternative methodology to compute distortions, this result hold. Second,
Table 1 shows that dispersion of TFPR in Morocco (whether we use WBES2007
or WBES2007) is not only higher compared to developed countries (US, France,
South Korea) but it is also more important than the two largest developing coun-
tries (India and China). An additional set of country comparators could have
been regional peers. Unfortunately no analogous studies exist in the literature for
other countries in the region.11
Table 2 shows the dispersion of TFPR by industry. Results are obtained us-
ing the WBES2007 since results from this survey are more robust compared to
WBES2013. We observe a large heterogeneity across sectors ranging from 0.60
to 1.13. “Textiles” and “Machinery” are the industries with the lowest TFPR
dispersion and “Food” and “Chemicals” are the ones with the highest dispersion
levels.12 This diﬀerence across industries can be explained ﬁrst by sector-speciﬁc
market distortions. Diﬀerent degree of competition and concentration as well as
11
In the robustness sub-section we show that market distortions in Morocco are also higher
than the average of a large group of developing economies.
12
Remark that in WBES2007 for Morocco “Confectionery” is treated as a separate industry
and not included as usual in the “Food” industry.

13
Table 1. Dispersion of TFPR

Country Morocco US France South Korea China India
Data Year 2013 2007 1997 2005 2012 2005 1994
S.D ln(TFPR) 0.77 1.01 0.49 0.48 0.55 0.63 0.67

N ote: S.D. = standard deviation. Industry groups are weighted by their value-added shares.
The database year is indicated next to each country name. Values for the U.S., China, and India
comes from Hsieh and Klenow (2009), values for France are from Bellone and Mallen-Pisano
(2013), and numbers for South Korea comes from Oh (2016).

protection vis-`a-vis international competitors could play a sizable role. Indeed,
industries more opened to competition (international and domestic) as “Machin-
ery” and ”Textiles“ present lower levels of market distortions compared to more
protected industries with relatively little competition such as “Food”.13 Second,
the composition of ﬁrms for each sector can also explain the cross-sector diﬀer-
ence in market distortions. Thus, the share of ﬁrms that are exported-oriented,
small, politically connected, public, and old, could play a key role to explain these
results. For instance, the high level of market distortions in “Food” could be
explained by its relative high share of small ﬁrms (which have on average higher
credit constraints) and/or by the industry high percentage of politically connected
ﬁrms (Saadi, 2016), which could generate negative distortions to the remaining
ﬁrms. In addition, the high level of resource misallocation in “Chemicals” could
be explained by the strong state ownership in the phosphate industry.

Table 2. Dispersion of TFPR by Industries

Industry Food Confectionery Textiles Chemicals Machinery Other
S.D ln(TFPR) 1.06 0.85 0.62 1.13 0.60 0.87

N ote: S.D. = standard deviation. Results obtained using the WBES2007 database. Results for
“Machinery” obtained using the WBES2004 database.

To further investigate the role of ﬁrm characteristics and resource misallocation
we run weighted least squares (WLS) regressions of TFPR (relative to industry
means) on a range of dummies accounting separately for diﬀerent ﬁrm features.
This simple methodology has been used in this context by Hsieh and Klenow
(2009) and Busson et al. (2013) and results should not be interpreted in terms of
causation. Table 3 below present the estimation results.
Regression (1) shows that “old ﬁrms” present 21 percent lower TFPR. High
entry barriers and low competition would allow these businesses to continue to
operate despite low proﬁtability. Thus, more resources should be allocated to
“young ﬁrms” to increase global TFP. In regression (2) results indicate that small
companies have 18.9 percent higher TFPR than medium size ﬁrms and TFPR
for large ﬁrms is not signiﬁcantly diﬀerent than the one for medium size ﬁrms.
13
In 2013, for example, the Competition Council identiﬁed a tacit pricing agreement between
market participants in the dairy products sector and reported a high level of concentration.

14
Table 3. WLS Regressions of ln(T F P R)/T F P R) on Diﬀerent Dummies

(1) (2) (3)
Old -0.210*
(created ≤1985) (0.115)
Small 0.189*
(5 to 19 workers) (0.105)
Large -0.007
(≥100 workers) (0.130)
Exported oriented 0.077
(≥ 60% exported) (0.140)

N ote: The dependent variable is the deviation of log TFPR from the industry mean. In (1) the
independent variable is a dummy for export-oriented ﬁrms. In (2) the independent variable is a
dummy for old ﬁrms. In (3) dummies for small and large ﬁrms are the independent variables and
the omitted group is medium size ﬁrms. Regressions are weighted least squares with industry
value-added shares as weights. Entries are the dummy coeﬃcients, with standard errors in
parentheses. * indicates signiﬁcance at the 10 percent conﬁdence level.

Consequently, providing extra resources to small enterprises would produce higher
returns than providing resources to larger ones. Credit constraints, which typi-
cally have more incidence on small ﬁrms, can partly explain why small companies
cannot acquire more resources to grow. Export-oriented ﬁrms exhibit 7.7 percent
higher TFPR (regression 3). Keeping in mind the large standard errors associated
to the coeﬃcient, this result suggests that more resources should be allowed to
export-oriented business. At ﬁrst sight this seems a counter-intuitive result since
it is normally expected that these ﬁrms face large positive distortions resulting
from better access to credit or preferential treatment in export processing zones.
Yet, this “positive” distortions can be more than compensated by other “nega-
tive” distortions linked to the overall “anti-export” biais of economic policy (see
Section 2 and 6).

5.2 TFP gain from Reallocation
Before analyzing results in this sub-section, it is important to highlight that this
exercise makes no allowance for measurement error or model misspeciﬁcation.
Such errors could lead us to inﬂate eﬃciency gains from resource reallocation.
Table 4 provides TFP gains (in %) for Morocco and other developed and de-
veloping countries from equalizing TFPR across ﬁrms in each industry group.
Results are obtained using WBES2013 instead of the richer data in WBES2007
because the computation of TFP gain needs the most recent measures of physical
productivity.
Full liberalization would boost aggregate manufacturing TFP in Morocco by
83.8 percent. Morocco’s TFP gains from liberalization is well above the one in
the US. This is not surprising since, as shown in Table 1, the level of market
distortions in Morocco are much higher than in the US.
Morocco’s beneﬁts from liberalization are also very much in line with those

15
Table 4. TFP Gains from Equalizing TFPR within Industries

Country Morocco US China India Mean of 52
MIC and LIC
Data Year 2013 1997 2005 1994 2002-11
TFP gain in % 83.8 (55.6*) 42.9 86.6 127.5 113.4

N ote: Entries are 100 ∗ (Yef f icient /Y − 1). Numbers for Morocco are authors calculations.
Numbers for the mean of 52 MIC and LIC are also authors calculations using results from
Inklaar, Lashitew, and Timmer (2015). Values for the U.S., China, and India come from Hsieh
and Klenow (2009), values for France are from Bellone and Mallen-Pisano (2013), and numbers
for South Korea come from Oh (2016). *TFP gain (in %) from removing market distortions
to the level of the less three distorted economies studied in this paper (US, France, and South
Korea).

observed in China, despite diﬀering considerably in economic size and structure.
Finally, we observe that Morocco’s potential TFP gain are below the mean of 52
middle and low income countries (MIC and LIC). This does not mean necessar-
ily that the level of distortions in Morocco is lower than the average of these 52
economies and than in India. Indeed, India for instance, has less market distor-
tions than Morocco and still has higher potential TFP gain. This is due to the
key role that the level of physical productivity plays in computing eﬃciency gains
in this framework.
A full-ﬂedged liberalization would imply radical policy changes. However, it
remains purely theoretical. From a practical point of view, it would be more
suitable to compute the eﬃciency gains obtained if resource misallocation in Mo-
roccan manufacturing reaches the level of less distorted economies. To perform
this simple counter-factual exercise we choose the three countries in Table 4 with
the lowest dispersion in TFPR (US, France, and South Korea). Subsequently we
replace the distortion level of Morocco with the average of these three countries
and we proceed to compute again the TFP gains associated to this new level of
resource misallocation. We obtain a TFP gain in the Moroccan manufacturing
sector of 55.6 percent from removing distortions to the level of countries with the
best allocation of resources.
An ambitious liberalization program has then the potential to have a substan-
tial impact in aggregate productivity. A full liberalization would imply that the
manufacturing sector would almost double in size after the reform process. Fur-
thermore, the strong increase in eﬃciency in this sector would lead potentially to
a transfer of resources from other sectors of the economy (agricultural and ser-
vices) to manufacturing. However, this broader eﬀect would be damped by the
extent to which distortions in the other sectors would be reduced as well causing
an additional resource reallocation across sectors.
Finally, in this empirical exercise the eﬀect of the informal sector on market
distortions in manufacturing is captured only indirectly. Indeed, lower labor cost
in the informal sector would lead to less than optimal labor demand from for-

16
mal ﬁrms.14 Firm data accounting for the informal sector would allow to assess
resource misallocation in this sector, which represents around 6 percent of the
manufacturing sector as a whole (Haut Commissariat au Plan ). Intuitively, in-
formal ﬁrms face more distortions than in the formal sector. Notably, these ﬁrms
have incentives to remain small in order to avoid detection. In addition, informal
ﬁrms face higher credit constraints.15 Busso et al. (2012) modify the HK frame-
work to include the informal sector and apply the model to Mexico. They ﬁnd
an astonishing 200 percent increase in TFP from a full liberalization compared
to 138 percent found in Inklaar, Lashitew, and Timmer (2015) applying the HK
procedure to Mexico without the informal sector.16

5.3 Robustness: Alternative Measure of Market Distor-
tions and New TFP Gain Calculation
As a robustness exercise we compute market distortions and associated produc-
tivity gains using Bartelsman, Haltiwanger, and Scarpetta’s 2013 methodology,
which as previously mentioned, relies on a much less extensive theoretical frame-
work than HK. The idea here is that low levels of market distortions would better
ensure that more resources will be allocated to more productive ﬁrms, allowing
them to grow in size. The approach uses a decomposition of labor productivity
for a given industry initially presented in Olley and Pakes (1996):
Y
≡ω= θi ωi = ω + (θi − θ)(ωi − ω ) (17)
L i i

Where Y and L represent respectively real output and labor for a given sec-
tor. The symbol (ω ) denotes the weighted average of ﬁrm labor productivity with
employment shares used as weights (θi =Li / i Li ). This can be written as the
unweighted average labor productivity (indicated by the upper bar) plus the co-
variance between ﬁrm size (in terms of employment) and ﬁrm labor productivity.
We observe ﬁrst that the more this covariance is higher the more the level of pro-
ductivity increases. Second, a positive covariance indicates that more productive
ﬁrms are also larger. (Bartelsman, Haltiwanger, and Scarpetta, 2013) develop a
model where greater distortions to resource allocation lead to lower covariances.
In contrast to the previous empirical analysis, this methodology does not re-
quire data on capital at the ﬁrm level. This allows us to be able to use a larger
share of each survey (eliminating the previous issue of representativeness). Thus,
WBES2007 and WBES2013 contain 325 (about 55% of the initial sample) and
149 (around 70% of the initial sample) observations respectively.
14
The WBES2013 ﬁnds that ﬁrms consider the informal sector as one of the top obstacles
they face.
15
Although this does not enjoy a full consensus in the empirical literature, see for instance
Kaplan et al. (2011).
16
More precisely, 138 is a number obtained by the authors using results from Inklaar, Lashitew,
and Timmer (2015).

17
We compute the covariance of productivity (in logs) and ﬁrm size by industry
and we aggregate these covariance terms using employment shares. This aggre-
gate covariance value represents the level of resource misallocation. We obtain a
covariance term of 0.06 and 0.01 using the WBES2013 and WBES2007 respec-
tively. This result implies a decrease in market distortions. Then, we obtain the
same result than the one found using the HK methodology.
Even though the covariance term increased between the two surveys, the value
(0.06) is much lower (implying higher market distortions) than the US, Germany,
and the Netherlands (respectively 0.51, 0.28, and 0.30; Bartelsman, Haltiwanger,
and Scarpetta, 2013) and slightly lower compared to the mean covariance of 52
MIC and LIC (0.12; Inklaar, Lashitew, and Timmer, 2015). These two ﬁndings
are in line with the results obtained using the HK approach.
With respect to the TFP gain from reallocation, only a back-of-the-envelope
calculation is possible. Bartelsman, Haltiwanger, and Scarpetta (2013) ﬁnds the
aggregated covariance term for the US to be 0.51. The authors show that in an
accounting sense, this implies that productivity in the average US manufacturing
industry is 51 percent higher than it would be if the size of ﬁrms were determined
randomly within industries. Analogously, the most recent covariance term value of
0.06 implies that productivity in the average Moroccan manufacturing industry
is only 6 percent higher than it would be if the size of ﬁrms were randomly
determined within industries. Although this is only an approximation, we could
argue that if in the Morocco’s manufacturing sector the level of market distortions
were reduced to the level found in the US, this sector will increase productivity by
45 percent (51-6). This value (45 percent) is very close to the value found using
HK from eliminating distortions up to the level of three relatively undistorted
economies (55.6 percent).

6 Policy Discussion
Eﬀective market institutions that limit the extent of product and factor market
distortions could be gauged on the basis of at least three objectives: improvement
in competition and business climate, low structural unemployment and high eco-
nomic participation, and strong international economic and ﬁnancial integration.
In all these areas, there is scope for removing market distortions and gradually
improving Morocco’s manufacturing TFP (World Bank, 2017a), which will help
to reach the goals of the Industrial Acceleration Plan (2014-2020).

(i) Improved Competition and Business Climate

Morocco could strengthen its market institutions to promote and guarantee
market openness as well as free competition. Morocco could act in the following
two strategic areas to improve resource allocation: strengthen competition and
tackle rent seeking; and improve the business environment.
First, steps need to be taken to increase the competition regulatory authorities’
autonomy and clarify their powers, and reduce visible rents (for example, land,

18
approvals, licenses and administrative authorizations) and invisible rents (regu-
latory loopholes). Access to urban and industrial property should be improved
by a transparent, facilitating regulation. The role of the diﬀerent public players
operating in the tradable and non-tradable sectors needs to be reviewed to make
sure they behave in keeping with the principles of the new business framework.
The authorities should put an end to discretionary practices and ensure that the
laws and regulations apply equally to all businesses.
Second, business climate reforms call for close, eﬃcient coordination among
the diﬀerent public and private players concerned. The strategic management
and coordination of the programs and activities of the line ministries also pose a
complex challenge, in particular in view of the proliferation of sectoral programs
and public and private stakeholders. The success of the public initiatives will in
fact hinge in large measure on the government’s capacity to coordinate, monitor
implementation, and assess public policies at various levels: the government, cen-
tral administrations, local administrations, autonomous agencies, private sector
operators, and active civil society representatives.

(ii) A More Eﬃcient and Inclusive Labor Market

Morocco’s poor labor outcomes are partly due to malfunctioning labor market
institutions. The Moroccan Labor Code lays down cumbersome, restrictive reg-
ulations unsuited to the country’s needs for structural change –especially to the
ongoing job reallocation process required for eﬃciency purposes. Compared with
many competitor countries, labor market regulations are particularly restrictive
in terms of the use of ﬁxed-term contracts, ﬁring and working hours ﬂexibility.
The minimum wage is very high compared with the national average and median
income, and collective agreements and seniority pay could end up pushing wage
levels higher than staﬀ productivity. Lastly, high social security contributions also
help drive up the cost of labor and discourage formal employment, particularly
for young people. As a result, only a minority of workers are covered by a social
security system. All in all, the labor market rules fuel the vicious cycle of infor-
mality that accounts for 9 in 10 private jobs. The active labor market policies
and employment agencies cannot provide the expected outcomes.
Given the demographic and social challenges, it would be in Morocco’s best
interest to expand the current set of programs designed to place employment at
the center of public policy by reshaping its labor market institutions on more
sound, inclusive foundations. Estimates suggest that overhauling the labor code
would signiﬁcantly raise economic participation and employment, especially for-
mal employment for young people and women, and reduce unemployment without
jeopardizing wages (Angel-Urdinola, Barry, and Guennouni 2016). Like other re-
forms conducted in a number of countries, this reform should be guided by the
principles of ﬂexibility of employment, income security for workers and eﬀective
active labor market policies.

(iii) Increased Integration into the International Economy

19
The poor penetration of Moroccan exports, both goods and services, high-
lights the country’s sizeable competitiveness problems. These problems concern
not only the high costs of inputs, due to protection of the service sectors and
domestic price rigidity, but also product quality and the quality of trade-related
infrastructure and logistics. Despite the remarkable achievement of the Tanger
Med Port and real progress with customs management, Morocco is handicapped
by longer lead times and higher costs for its export and import logistics than
its main competitors. These problems are exacerbated by a ﬁxed exchange rate
system and capital controls that distort the economy in favor of the nontradable
goods sector, but which form a substantial obstacle to Morocco’s price compet-
itiveness, product diversiﬁcation and regional and global integration. Given the
current state of market incentives, the lack of diversiﬁcation into more sophis-
ticated export products is also partly due to exporter risk aversion. Moroccan
export ﬁrms are often old, small and less capable of supplying the global value
chains than local subsidiaries of multinationals.
It has been clearly established theoretically and empirically that the institu-
tions governing a country’s foreign trade serve to capitalize on the beneﬁts of spe-
cialization and the international division of labor for greater economic eﬃciency,
faster structural change and higher incomes. Morocco will raise its productivity
(among others, through better resource allocation) if its foreign trade sector is
able to develop and contribute more to growth. Greater integration of Morocco
into the global economy would entail an end to the “anti-export bias” that contin-
ues to be endemic to the institutions and policies governing Morocco’s exchange
system, including a more ﬂexible exchange rate regime, the liberalization of capi-
tal controls, lower tariﬀ and non-tariﬀ barriers to trade, better trade facilitation
and an improved investment regime (World Bank, 2006 and 2017a). The prospect
of an ambitious Deep and Comprehensive Free Trade Agreement (DCFTA) with
the European Union and its embedded potential for modernizing Morocco’s rules
and regulations constitutes a strategic objective with a strong transformational
potential for the economy.

7 Concluding Remarks
We studied in this paper the eﬀect of output and factor distortions in the manufac-
turing sector in Morocco. We found that market distortions are higher in Morocco
compared to developed countries and slightly more important compared to other
developing countries such as China and India. We also provided evidence on the
reduction of market distortions between 2007 and 2013 (although these economic
distortions remain relatively high). We estimated about 84 percent TFP gains
from a full liberalization and 56 percent from removing distortions to the level
of selected developed countries with better resource allocation. The paper found
that industries more opened to competition present lower levels of market distor-
tions compared to more protected industries with relatively little competition. In
addition, we showed that a higher level of TFP in the manufacturing sector can be
reached if more resources are allocated to “young” and “small” ﬁrms. Finally, we

20
found that the main results of the paper are robust to an alternative estimation
that is also used in the literature.
A natural extension of this work would be to study the eﬀect of resource mis-
allocation in the service sector. Ragbi and Nihou (2016) compute the level of
distortions in the service sector using the HK methodology. They ﬁnd lower dis-
tortion levels in the service sector compared to the manufacturing sector. This
is especially true for the construction sector (a result somehow surprising). A
more in depth analysis of the drivers of market distortions in this sector (espe-
cially compared to manufacturing) and a computation of TFP gain associated to
liberalization would be interesting to investigate.

21
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25
ANNEX
WBES2013: Industry Groups and Capital and Labor Shares

Industry Groups Capital Share Labor Share
Food, beverages and tobacco 0.489 0.511
Textiles, textile products and wearing apparel 0.234 0.766
Wood, paper, printing and publishing – –
Chemicals, rubber and plastics 0.381 0.619
Basic and fabricated metal products 0.290 0.710
Machinery – –
Miscellaneous manufacturing 0.406 0.594

Source: Bureau of Labor Statistics (average 2002-2010).
N ote: To compute the capital and labor shares for each of the industry groups we compute a
weighted average of the shares from the 2-digit industries that make up each of these industry
groups. “Wood, paper, printing and publishing” and “Machinery” are dropped because of data
availability.

WBES2007: Industry Groups and Capital and Labor Shares

Industry Groups Capital Share Labor Share
Food 0.489 0.511
Confectionery 0.489 0.511
Textiles 0.306 0.694
Chemicals 0.281 0.719
Machinery – –
Electronics – –
Miscellaneous manufacturing 0.406 0.594

Source: Bureau of Labor Statistics (average 2002-2010).
N ote: WBES2007 gives directly these aggregated industries. To compute the capital and labor
shares for each of the industry groups we compute a weighted average of the shares from the
2-digit industries that make up each of these industry groups. “Machinery” and “Electronics”
are dropped because of data availability.