institutions, governance and technology catch-up in north africa
TRANSCRIPT
Economic Modelling 29 (2012) 2155–2162
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Economic Modelling
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Institutions, governance and technology catch-up in North Africa
Imed DrineUNU-WIDER, World Institute for Development Economics Research, Helsinki, Finland
E-mail address: [email protected].
0264-9993/$ – see front matter © 2012 Elsevier B.V. Alldoi:10.1016/j.econmod.2012.05.038
a b s t r a c t
a r t i c l e i n f oArticle history:Accepted 29 May 2012
JEL classification:C33O47O57K49O1
Keywords:MetafrontierTechnology gapCatching-upEfficiencyStochastic frontierGovernanceNorth Africa
This paper aims to analyse the effects of institution quality on technology catch-up infiveNorth African countries(Algeria, Egypt, Morocco, Sudan and Tunisia) compared to 3 groups of developing and emerging countries (SubSaharan Africa, Asia, and Latin America) over the period 1970–2005. The study adopts a two-stagemethodology.First, we estimate the technology gap using the metafrontier approach. Then we test the relationship betweenthe technology gap and the quality of governance. The empirical results show that institutions (corruption,law and rules and investment climate) are very important in closing the technology gap and speeding up thetechnology catch-up. Other determinants of the technology gap are also identified: foreign direct investment,human capital and trade.
© 2012 Elsevier B.V. All rights reserved.
1. Introduction
During the 1970s, most North African states implementedpost-colonial development programmes, includingmajor infrastructureprojects in education and public health. However,many factors indicatethat the development process applied since the 1970s has failed todeliver its promise. Despite massive investments in infrastructure, edu-cation and health, the North Africa region still suffers frommajor short-comings, with development results that are far below expectations.
The recent crises concerning food and finances highlight the ex-treme fragility of the North African countries and question the sustain-ability of the development processes. The economic and social impactsof these crises on the economies of the region signal the magnitude ofthe challenges faced and the need to reorient its development policies.The social stress and economic instability caused by these challengesgive a good indication of what might be expected in the future.
Many experts highlight the necessity for the North African countriesto transform their commodity-based economies to knowledge-basedeconomies. Indeed, the recent developments in the world economyconfirm that knowledge, innovation and technology are the maindrivers of economic growth. In the more competitive internationalenviornment, improving productivity by bridging the technology gap
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becomes a priority if the economies of the region are to remain viablein the global economy.
However, as a consequence of the excessive reliance on rawmaterialexports for foreign exchange earnings and on foreign markets as asource of industrial products and food items, the economies of theregion have accumulated an important technology lag. While many de-veloping countries are constantly upgrading their technological capabil-ities and becoming increasingly competitive, the North Africancountries still lack the ability to create and adopt new technologies.Moreover, low social capital, security and social tensions, conflicts,and deteriorated business environment (due to excessive public inter-vention and low quality of human capital) are additional bottlenecksthat handicap technology progress in the region.
In 2008 a report by the World Bank1 noted that the capacity of aneconomy to absorb new technology depends on its overall economicstatus and governance. Indeed, the economic and institutional environ-ments as well as the quality of education affect risks for entrepreneurstaking on new technologies. In addition, the experience of many emer-gent economies confirms that good governance can improve legitimacy,engage citizens and spark commitments, and promote innovation.
In the other side, a substantial literature points out that the ability ofany economy to grow and to continue to function in the face of
1 Global Economic Prospects 2008, the World Bank.
2156 I. Drine / Economic Modelling 29 (2012) 2155–2162
unexpected events depends to a large degree on the institutional struc-ture and the operational systems in place, and the resilience of these.Moreover, it is widely accepted that social relations and institutions con-tribute to the effective functioning of the economic system. The role of in-stitutions and good governance in promoting economic growth isconfirmed by many recent studies (see Easterly and Levine, 1997;Knack and Keefer, 1995; Kormendi and Meguire, 1985; Mauro, 1995;Rodrik et al., 2004). For instance, the Global Competitiveness Report,2009–2010 considers important pillars of economic development to bethe institutional environment and solid institutions that play a crucialrole during economic crisis. Using a sample of 140 countries, Rodrik etal. (2004) show that governance is a factor that explains cross-country in-come differences, pointing that institutions matter more than opennessand geography in determining income level.
Most empirical studies on growth focus on factor accumulation andTFP growth, and ignore the role of production efficiency in improvingeconomic growth. Indeed, empirical studies on productivity differencesin general use growth regressions based on the traditional neoclassicalgrowth model (Solow, 1956)2 with extended specifications. A proxyvariable for productivity differences is added directly to the right handside of standard growth regressions, a methodology by Solow (2001)3
criticized because it regroups in the same regression variables thathave very different theoretical status. For studying productivity hesuggests taking these additional variables as right hand side variablesof regressions that have productivity as the dependent variable.
In the neoclassical view, exogenous technological progress, mea-sured by the Solow residual, drives long-run productivity growth. Ac-cordingly, TFP is interpreted as technological progress as production isassumed to happen along the production frontier. However, in theshort run because of many sources of inefficiencies, production doesnot take place along the production frontier and a rise in TFP can indi-cate an improvement in technical efficiency. A number of other expla-nations, including human capital (Lucas, 1988; Romer, 1990a, 1990b),international trade (Romer, 1990a; Grossman and Helpman, 1991), in-novation (Aghion and Howitt, 1992), etc., are provided by the literatureto argue that productivity can continue to grow even without exoge-nous technical progress.
Indeed, the new growth theory provides an internal explanationfor long-run productivity growth and explains technical progress asa result of specific actions. The productivity term, therefore, standsfor technological components as well as non-technological compo-nents which obviously can vary across countries.4 While technicalprogress represents a shift of the production function, technical effi-ciency changes represents other productivity changes, as learningby doing and diffusion of new technologies.
Compared to developed countries where technological progressprovides the main source of TFP growth, TFP growth in developingcountries depends mainly on their capacity to acquire and absorb newtechnologies. Indeed, technology adoption is an important channelthrough which productivity growth is achieved. For instance, Cominand Hobjin (2010) estimate a model using data on the diffusion of 15technologies in 166 countries and show that differences in the
2 The Solow residual represents the change in output which cannot be explained byinput growth and is identified as technological progress.
3 According to Solow (2001) TFP growth can be explained not only by technicalchange but also by improvements in technical efficiency. Consequently, TFP is to beconsidered as left-side variable where technological as well as non-technological fac-tors affect the TFP. Therefore, the best way to test the impact of non-technological fac-tors on growth is through their impact on TFP. In our analysis we focus on TFP ratherthan on the growth rate.
4 “Most cross-country differences in per capita output are due to differences in total fac-tor productivity (TFP), rather than to differences in the levels of factor inputs. These cross-country TFP disparities can be divided into two parts: those due to differences in the rangeof technologies used and those due to non-technological factors that affect the efficiencywith which all technologies and production factors are operated”, Comin and Hobjin(2010), Pp. 3.
absorption of new technologies explain at least one quarter ofcross-country differences in income per capita.
Rather than studying the determinant of TFP, we estimate economicefficiency. Production efficiency is an allocative component of TFP in ad-dition to a technical one according to Fare et al. (1994). This definitionmakes insightful contributions to exiting literature on growth theory.In addition, we estimate the technology progress to measure the tech-nological capability of the region compared to that of the successfulemerging economies. The main contribution of the paper is the focuson the competitiveness of the North African countries by studying theregion's technology catch-up and its main determinants.
The paper studies link between the technological capability and in-stitution quality in North Africa. To measure the region's technologicalcapability we use the metafrontier approach, which has the advantagethat it is able to separate technological change from efficiency change.Another advantage of this approach is that it enables an annualmeasurefor the technology gap for each country each year. This measure will beused to investigate the determinants of the technological catching-upprocess.More precisely, we analyse the impacts of the economic and in-stitutional environment on the technology progress.
The paper is structured as follow. Section 2 provides a general over-viewof knowledge and institutional deficiencies inNorthAfrica. Section3 explains the methodology used in this paper. Section 4 presents anddiscusses the empirical results. Section 5 concludes and gives themain policy recommendations.
2. Knowledge and institutional deficiencies
Weak governance and knowledge deficiencies in North Africa hand-icap human progress and impede the development of successful entre-preneurs. Indeed, North Africa experiences a wide knowledgedeficiency as a result of low public investment in ‘research and develop-ment’ and failure to promote human resource development and tech-nology transfers. The 2009 United Nations Human DevelopmentReport pointed out that only 0.2% of the Middle East and North Africaregion's GDP is at present spent on scientific research, compared toDenmark, France, Switzerland and the United States where thecorresponding figure is between 2 and 3.6%. According to UnitedNations Development Programme and Arab Fund for Economic andSocial Development (2009) the annual expenditure on scientific re-search per capita in the region is less than $10 compared to acorresponding figure of $575 and $1304 for Ireland and Finland,respectively.
Compared to most performing developing regions, East Asia andLatin America, North Africa scores lowerwith respect to the four indica-tors (Tables 1a and 1b). In addition, the region scores lower than thefour regionswith respect to innovative system indicator. The importantdeficit in the innovative system that contributes to the technology gap ismainly explained by the scarcity of entrepreneurship and innovativehuman resources. Indeed, because of poor expenditures on R&D andweek institutional support, the business environment in the region innot effective enough to promote entrepreneurship and innovation. In-vestment in R&D is extremely low (less than 1/7 of the world average)and access to information and sources of knowledge is still limited (only6.5% of the population uses internet).
Moreover, over the last two decades braindrain in the region has in-creased andmillions of highly skilled people have immigrated to devel-oped countries, causing a massive economic and social loss in additionto significant development opportunities beingmissed. According to es-timations by the Arab Labor Organization, losses in Middle East andNorth Africa have increased 20‐fold, currently totaling 200 billion dol-lars versus 11 billion dollars in the 1970s. The region accounts for 31%of the braindrain from developing to developed countries (contributingapproximately 50% of the doctors, 23% of the engineers and 5% of thescientists who migrate from the third world). According to many
Table 1aKnowledge economy index1.
Economic incentives andinstitutional regime
Innovationsystem
Education and humanresources
Information and communicationtechnology
Knowledge economyindex
MENA (including North Africa) 3.8 4.4 3.4 5.1 4.2Sub Saharan Africa 2.8 5.3 1.5 2.6 3East Asia and the Pacific 5.7 8.8 5.3 7 6.7South Asia 2.7 7.2 1.9 1.8 3.4Latin America 4.7 6.5 4.3 5.3 5.2
Source: Knowledge Assessment Methodology Data Base, World Bank (2008).1 The knowledge economy index is the simple average of the normalized performance scores of a country; for more details refer to the World Bank. The methodology used by the
World Bank is based on the supposition that the knowledge economy comprises four pillars: economic incentive and institutional regime, education and human resources, theinnovation system, and ICT. Higher value indicates better performance.
Table 1bPerformance scores compared with the developing countries.
Economic incentives andinstitutional regime
Innovation system Education and humanresources
Information and communicationtechnology
Knowledge economyindex
Ranking among 135 countriesAlgeria 109 91 94 99 96Egypt 91 71 80 93 83Jordan 55 55 57 73 62Morocco 87 88 109 78 92Sudan 131 122 120 96 120Tunisia 65 69 88 65 72
Source: Knowledge Assessment Methodology Data Base, World Bank (2008).
Table 2Institutional development in North Africa.
Country Year Percentile rank1 Governance score
(0–100) (−2.5 to +2.5)
Political stabilityAlgeria 2009 12.7 −1.2Egypt 2009 24.5 −0.63Libya 2009 68.9 0.62Mauritania 2009 13.7 −1.17Morocco 2009 30.2 −0.43Sudan 2009 1.4 −2.65Tunisia 2009 53.3 0.23
Government effectivenessAlgeria 2009 34.8 −0.59Egypt 2009 44.3 −0.3Libya 2009 11.9 −1.12Mauritania 2009 21 −0.9Morocco 2009 51.4 −0.11Sudan 2009 7.1 −1.32Tunisia 2009 65.2 0.41
Control of corruptionAlgeria 2009 37.6 −0.49Egypt 2009 41 −0.41Libya 2009 13.8 −1.1Mauritania 2009 30.5 −0.66Morocco 2009 51.4 −0.23Sudan 2009 6.2 −1.24Tunisia 2009 57.6 0.02
Source: Kaufmann et al. (2010), The Worldwide Governance Indicators: Methodologyand Analytical Issues.
1 Percentile ranks indicate the percentage of countries worldwide that rate belowthe selected country. Higher values thus indicate better governance rate.
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experts, this critical situation is also explained by the region's restric-tions on freedom as well as its bureaucracy and corruption.
Weak governance and corruption are additional bottleneckshandicapping the human and institutional development of the region.Familialism is still important in the region, and favours and kin reciproc-ities often govern people's interaction (Abou-Laban and Abou-Laban,1987). As is shown in Table 2, most North African countries suffer frombad governance and political instability. Indeed, with a few exceptions,the region is below the world average in terms of government effective-ness and political stability. Moreover, most countries rank poorly interms of corruption; Corruption Index consistently ranks these nationsbelow the world median.
As is shown in Table 3, in terms of human development, many ofthe region's nations have achieved higher levels than other develop-ing countries, as indicated by the relatively higher life expectancyfor females and literacy rates. However, the recent gains in humandevelopment are fragile, insufficient (especially for Morocco andMauritania), and are not built on long-run government policies(Arab NGO Network for Development and Christian Aid, 2009).
3. Empirical methodology
The first step of our analysis consists of determining the stochasticproduction frontier for each group of countries, as they are assumedto be endowed with different production technologies. We use thestochastic production frontier method with variable inefficiency asproposed by Battes and Coelli (1995). This method is used increasinglyin the literature for analyzing productive inefficiency at themacroeconomic level (Adkins et al. (2002), Méon and Weill (2004),Mastronarco (2008)). The method consists of using the best practicesof the countries under consideration to construct a production frontierthat makes it possible to calculate, the potential output (for a givencountry) at a given date for a technology and a given quantities of in-puts. Productive inefficiency is defined as the ratio of actual output topotential output. If it equals one, then the country is making a perfect
use of its factors of production relative to other countries within thegiven group.
It is assumed that each country i, belonging to group k=1, 2, 3, 4makes use of its factors of production according to group specific
Output
Inputs
S. Frontier
for Group 1
S. Frontier
for Group 3
S. Frontier
for Group 4
S. Frontier
for Group 2
Metafrontier
Fig. 1. Stochastic metafrontier curve.
Table 3Human development in North Africa.
Life expectancy at birth, females(2000–2005)
Literacy rate, all adults(2007)
Algeria 72.2 75.4Egypt 72 66.4Libya 75.7 86.8Mauritania 64 55.8Morocco 71.8 55.6Sudan 57.8 60.9Tunisia 75.1 77.7
Source: Earth Trends, World Resources Institute.
2158 I. Drine / Economic Modelling 29 (2012) 2155–2162
technology. The stochastic group-k frontier model to be estimatedwill be defined as:
Yit kð Þ ¼ f Xit kð Þ;β kð Þ� �
exp Vit kð Þ−Uit kð Þ� �
;
where; i ¼ 1;……:;Nk and t ¼ 1;……; Tð1Þ
where
Yit(k) is the output for the country i in the group k in period t.Xit(k) is the vector of inputs used by the country i in the group k in
period.β(k) is the vector of unknown parameters associated with the
group k.
The termVit is an error term identically and independently distributedaccording the normal process N(0,σkv
2 ). The term Uit is a non-negativeerror term independent from Vit, which represents the technical ineffi-ciency. Uit is distributed independently as a zero-truncated normalprocess.
The technical efficiency of the i-th country with respect to thegroup-k frontier is defined as5:
TEki ¼Yit
f Xit kð Þ;β kð Þ� �
exp Vit kð Þ� � ¼ exp−Uit kð Þ ð2Þ
The technical efficiencies (TEi) which allow us to examine the perfor-mance of the i-th country relative to the individual group frontier cannotbe used to measure technological capability as countries from differentregions operate under different production technologies. Battes et al.(2004) propose an approach where productivity in a given region orgroup can be estimated relative to a metafrontier. Compared to the re-gional frontier which represents the state of technology in the region,the metafrontier represents the state of technology at the global level(sample of countries). The metafrontier function is therefore a frontierfunction that envelops all frontiers of individual countries or groups(see Fig. 1). The main advantage of the metafrontier approach is that itallows distinguishing between efficiency and technological change.
The metafrontier production function model for the county i isexpressed by:
Y�it ¼ f Xit ;β
�� �exp V�
it−U�it
� � ð3Þ
where Yit* is the metafrontier output that dominates all group fron-tiers and β* denotes the vector of parameters for the metafrontier
5 The technical efficiency of country i from group k is defined as the ratio of actualoutput in country i to potential output of country i using the k-group technology giventhe observed input mix for country i in period t.
function and results from solving the following linear program(Battes et al., 2004)6:
Minβ�∑Ii¼1∑T
t¼1 lnYit−lnY�itj j
Subject to ln f Xit ;β�ð Þð Þ≥ ln f Xit kð Þ;β kð Þ
� �� �
for all k ¼ 1;2;3;4
8><>:
ð4Þ
Now, we can have an estimate for the technical efficiency with re-spect to the metafrontier (TEi*), as:
TE�i ¼Yit
f Xit ;β�ð Þ exp Vitð Þ ¼ exp−U�
it ð5Þ
The technology gap for country i (TGRi) is defined as the ratio be-tween the potential production can be achieved using the technologyof the group to which country i belongs, and the potential productionin the hypothetical case where country i has access to the better tech-nology over all “i” s in all groups of countries (the gap between thegroup frontier and the metafrontier).
TGRi ¼f Xit kð Þ;β kð Þ� �
f Xit;β�ð Þ ð6Þ
The ratio is equal to one when the country's technology frontier co-incides with the metatechnology frontier. Countries closer to themetafrontier have higher technology gap ratios and are considered asmore advanced technologically. An increase over time in the technologygap ratio indicates a technological catch-up for which we want to testthe main determinants.
The technical efficiency relative to the metafrontier is defined as:
TE�it ¼ TEit � TGRit ð7Þ
where
TEit technical efficiency relative to the stochastic frontier for agiven group,
TGRit technology gap ratio for that group.
Technical efficiency relative to the metatechnology can, therefore,be decomposed into technical efficiency measured with reference togroup ‘k’ technology, and technology gap ratio between the group tech-nology and metatechnology. Improving economic performance forcountry i can be achieved through better use of inputs, with respect to
6 All production points of all groups are enveloped by the metafrontier. Therefore, allproduction points of the group stochastic frontiers may lie on or below themetafrontier i.e. the metafrontier dominates all the country's frontiers.
Table 4Estimates for parameters of the stochastic frontier models1.
Variable North Africa Asia Africa Latin America Pooled frontier Metafrontier
Constant −1.253(1.77)
−0.007(0.313)
1.255(1.19)
1.538***(0.029)
1.097***(0.11)
3.196
Trend −0.246***(0.009)
−0.043***(0.003)
−0.100***(0.004)
−0.043***(0.002)
−0.075***(0.004)
−0.067
Physical capital 0.494***(0.035)
1.088***(0.045
0.943***(0.012)
0.852***(0.002)
0.882***(0.021)
0.869
Labour 0.841***(0.076)
0.045***(0.003)
0.143*(0.08)
0.537***(0.04)
0.161***(0.035)
0.167
Human capital 0.244***(0.009)
−0.013***(0.002)
0.022***(0.005)
−0.385***(0.004)
−0.042(0.101)
−0.179
σ2 0.143***(0.02)
0.309**(0.162)
0.94**(0.55)
0.69***(0.312)
0.34***(0.083)
γ 0.97***(0.004)
0.85***(0.028)
0.99***(0.008)
0.99***(0.001)
0.99***(0.012)
Log-L 380.85 288.89 111.5 575.06 137.35
1 Standard errors are given in parentheses. *,**,*** represent the respective significance at 1%, 5% and 10%.
2159I. Drine / Economic Modelling 29 (2012) 2155–2162
the group technology, or/andupgradingproduction technology (closingthe technology gap).
4. Empirical results
We consider four groups of countries.7 The first group includes fiveNorth African countries: Algeria, Egypt, Morocco, Sudan, and Tunisia.The second group includes seven Asian countries: Bangladesh, India,Indonesia, Malaysia, Pakistan, Sri Lanka and Thailand. The third group in-cludes 10 Latin American countries: Brazil, Columbia, Costa Rica,Dominique Rep., Equateur, Guatemala, Mexico, Panama, Paraguay andUruguay. The fourth group includes 5 African countries: Cameroon,Kenya, Malawi, Mali and Senegal.
The stochastic frontier model is estimated assuming the Cobb–Douglass function form:
Yit ¼ AitKαkit L
βkit H
γkit exp Vit−Uitð Þ; where i ¼ 1;……:;N and t
¼ 1;……:; T ð8Þ
Yit is the output (GDP)Kit is the stock of physical capital,Hit is the stock of human capital,Lit is the economically active populationAit the total factor production, expressed as : exp(δk+tkt),
where, δk is a scaling parameter, and tk the technical progressratio.
The tem Vit is an error term identically and independently distributedaccording the normal process N(0, σkv
2 ). The term Uit is a non-negativeerror term independent from Vit, which represents productiveinefficiency. It is distributed independently as a zero-truncated normalprocess.
The analysis uses data from a balanced panel observed for the period1970–2005. The output variable is defined as GDP at constant price. Wefollow Bosworth and Collins (2003) and we define the human capitalindicators as Hit=hitLit where hit=(1.07)sit, s is the average number ofyears of schooling (Barro and Lee, 2000). Similarly to Bosworth andCollins, we use the perpetual inventory method to calculate the stockof physical stock series; the depreciation rates (di) are assumed to be4%. Data for GDP, investment, and labour are extracted from the
7 Because the lack of data on human capital and the initial level of physical capital,only limited number of countries in each group could be included. We assume thatcountries which are geographically close are more likely to share the same technologyof production. In addition, institutional and economic structures are likely to be quietsimilar.
World Bank data base. The initial stock of capital is from Nehru andDhareshwar (1993).
4.1. A metafrontier estimates
The estimation of the frontier model is presented in Table 4 andthe Limdep 8 package was used to estimate the frontier model. Ingeneral, the t-values show that almost all coefficients are significantat the 1% level.
The generalized likelihood-ratio test for the null hypotheses that theregional frontiers do not differ is LR8=2438. This has a p-value of 0.00,so we reject the null hypothesis that regional frontiers are identical. Asthe stochastic frontiers across regions differ, it is not possible to use thepooled stochastic frontier and we have to estimate a metafrontier. Weuse Shazam codes provided by Battes et al. (2004) for estimating ofthe metafrontier parameters and the technology gap ratio.9
Table 4 provides average technical efficiency and technology gapscores for North Africa over the period 1970–2005. The average tech-nical score is about 0.96, indicating that effective output is about 96%of its potential, which suggests the possibility of only modest im-provements using the current technology.
As is shown in Table 5, after a continuous decrease during the1970s, the average technical efficiency scores have showed a steadyincrease since the last 1980s. However, the high technical efficiencyscores from the regional model mask an important gap. Indeed, overthe period 1970–2005, the average technical efficiency of the regionrelative to the metatechnology is only about 0.19. This suggests thatNorth African countries are highly inefficient relative to themetafrontier. Among the five North African countries, Egypt has onaverage the better performance with a technology gap ratio of 0.51on average (Table 1 in the appendix).
As is shown by Fig. 2, the slight increase in technical efficiency dur-ing the last decade has not compensated for the widening technologygap relative to the best performing developing countries (Asia andLatin America). The technology used in North Africa lags behind theglobal technology and the gap continues to widen over time. Whilemany Asean and Latin Anerican countries are constantly upgradingtheir technological capabilities and are becoming increasingly compet-itive, North African countries still lack the ability to create and adoptnew technologies. The increasingly widening gap relative to globaltechnology in the last decade suggests that the capacity of the region
8 The LR Statistic is defined by λ=−2 ln [L(H0)− ln L(H1)]. Where L(H0) is the valueof the likelihood functions for the stochastic frontier estimated by pooling the data forall groups, and L(H1) is the sum of the values of the likelihood functions for the region-al stochastic production functions estimated separately.
9 Technology gap scores by country are presented in the appendix.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
North AfricaAsiaSSALatin America
Fig. 2. Technology gap ratio variations for the four groups.
2160 I. Drine / Economic Modelling 29 (2012) 2155–2162
to absorb new technology is very limited. This confirms once again thatthe region iswithout a real engine for economic growth and that its eco-nomic performance is reliant on the international conditions.
The main conclusion from the technical efficiency analysis is thatNorth African countries cannot expect to see an improvement in eco-nomic performance unless they are successful in improving in theirability to acquire and adopt new technologies. This confirms thatthe development processes in the region are distorted and essentialreforms are needed if these countries are to remain viable in the glob-al economy. So what are the main requirements for the region inorder to be able to catch up and to fill the technology gap?
4.2. Economic and institutional reforms and technology catch-up inNorth Africa
A substantial literature shows that good institutions are essential forhigher economic performance and for helping to foster innovation anddiffusion of new technologies (Easterly and Levine, 1997; Knack andKeefer, 1995; Kormendi and Meguire, 1985; Mauro, 1995; Rodrik etal., 2004). Zachariadis (2004) concludes that efficiency has been signif-icant in determining the growth trajectories of many developed econo-mies. Méon andWeill (2004) test the relationship between governanceand technical efficiency for a sample of 62 countries and find that rule oflaw affects economic efficiency. Adkins et al. (2002) test the relation-ship between efficiency and economic freedom and find that the lackof economic freedom results in lower efficiency.
It is also argued that openness to foreign markets10 and high skilledpeople11 facilitates the absorption of new technology and helps to fastertechnological catch-up. For instance, Coe et al. (1997) find that for de-veloping countries foreign R&D spillovers increase with the intensityof trade and quality of the labour force. Iyer et al. (2006) confirmthese findings and show that developing countries can increase theirtechnological catch‐up by opening up to trade and foreign direct invest-ment. The authors consider that countrieswith thewidest technologicalgaps can catch upmore rapidly through higher FDI inflows, trade liber-alization and mobility of human capital.
Moreover, it is well argued that financial development promoteseconomic growth (Guiso et al., 2004; Rajan and Zingales, 1998) andallows more efficient allocation of production factors across economic
10 See Romer, 1990a, 1990b, Grossman and Helpman, 1991, and Aghion and Howitt(1992).11 See Romer, 1993, 1994, and Lucas, 1988.
activities (Bertrand et al., 2007; King and Levine, 1993a, b; Rajan andZingales, 2003). However, less attention has been paid to the role offinancial development in fostering technology diffusion amongcountries.
The following step is to determine if economic and institutionalreforms can contribute to enhance efficiency and facilitate the ab-sorption of new technologies. We propose to regress the technologygap of the region relative to the metatechnology on human capital, fi-nancial development, trade openness, foreign direct investment, andthe quality of governance. A panel of five North African countries isused to test the relationship between the technology gap and its de-terminants. Annual data a used are covering the period 1984–2004.
The general model to be estimated is defined as follows12:
TGRit ¼ αi þ β1trendþ β2Educationit þ β3FinDevitþ β4Opennessit þ β5FDIit þ γInstit ð5Þ
where
trend is time trend.Education is an indicator of human capital as defined previously.FinDev is an indicator of financial development and is approximat-
ed by the ratio credit to the private sector over GDP.Openness is an indicator of trade liberalization and is defined as the
ratio of total exports and imports over GDP.FDI is defined as the total flows of FDI over GDP.Inst is an indicator of the quality of the institutional environ-
ment in the region and is captured by five variables13: indi-cators of democratic accountability, of corruption,14 ofbureaucracy, of investment profile, and of law and order.15
The results of our econometric analysis are presented in Table 6 andsuggest that, first, human capital, trade openness and foreign direct in-vestment contribute positively and significantly to bridge the technolo-gy gap in the region. Second, except for investment profile, all theinstitutional variables contribute significantly to technology gap.
12 First, we test a model where only macroeconomic variables are included, next thesecond and third models include both macroeconomic and institutional variables.13 The data for the institutional variables are available only for 1985–2004.14 This variable proxies the degree to which government agents use their politicalpower for private gain. Data are extracted from International Country Risk (ICR) Guide(2005).15 The data are extracted from the International Country Risk (ICR) Guide (2005).
Table 5Average technical efficiency scores and technology gap for North Africa.
Technical efficiency( TEit)
Technology gap ratio(TGRit)
Technical efficiency relativeto the metatechnology(TEit=TEitTGRit*)
1970 0.9615 0.1273 0.12241971 0.9613 0.1367 0.13141972 0.9613 0.1467 0.14111973 0.9612 0.1583 0.15211974 0.9611 0.1685 0.16201975 0.9610 0.1745 0.16771976 0.9609 0.1797 0.17261977 0.9608 0.1871 0.17981978 0.9606 0.1946 0.18691979 0.9604 0.2044 0.19631980 0.9602 0.2173 0.20871981 0.9600 0.2200 0.21121982 0.9600 0.2283 0.21911983 0.9598 0.2394 0.22971984 0.9597 0.2488 0.23881985 0.9596 0.2657 0.25501986 0.9596 0.2547 0.24441987 0.9597 0.2472 0.23721988 0.9597 0.2413 0.23161989 0.9598 0.2364 0.22691990 0.9599 0.2240 0.21501991 0.9601 0.2060 0.19771992 0.9602 0.1958 0.18801993 0.9603 0.1882 0.18071994 0.9592 0.0870 0.08341995 0.9594 0.2108 0.20221996 0.9597 0.1993 0.19121997 0.9600 0.1879 0.18041998 0.9603 0.1772 0.17021999 0.9605 0.1725 0.16572000 0.9608 0.1625 0.15622001 0.9611 0.1525 0.14662002 0.9614 0.1450 0.13942003 0.9616 0.1384 0.13312004 0.9619 0.1312 0.12622005 0.9621 0.1242 0.1195
2161I. Drine / Economic Modelling 29 (2012) 2155–2162
Moreover, the sign of the coefficient is as expected: a higher level of cor-ruption and of bureaucracy impacts negatively technology gap ratio,and better enforcement rules and democratic accountability help theregion to achieve higher production frontier.
Table 6Estimates for the determinants of the technology gap ratio1.
Model 1 Model 2 Model 3
Trend 0.015(0.012)
−0.032(0 .011)
−0.035**(0.011)
Education 0.852*(0.468)
0.645(0.377)
0.762**(0.370)
Credit/GDP −0.0003(0.0002)
0.095(0.069)
Trade liberalization 0.001*(0.0009)
0.008(0.001)
0.002**(0.001)
FDI/GDP 0.011**(0.005)
0.010(0.008)
0.011*(0.006)
Investment profile 0.004(0 .005)
−0.003(0.005)
Corruption −0.064*(0.034)
−0.073**(0.018)
Law and order 0.028***(0.008)
0.026***(0.007)
Bureaucracy −0.038*(0.015)
−0.028*(0.01)
Democratic accountability 0.024***(0.009)
0.019**(0.068)
n observations 100 100 100
1 Standard errors are given in parentheses. *,**,*** represent the respective signifi-cance at 1%, 5% and 10%.
Therefore, the low scores in terms of institution quality recorded inthe regionmay be considered as themain cause of the large andwiden-ing technology gap. Thismeans that political commitment fromgovern-ments in the region to address insecurity and uncertainty in economicrelationships is still insufficient. In addition, despite different strategiesto attract foreign direct investment, the investment climate and the pol-icies of countries toward free capital flows still seem ineffective to pro-mote technology transfer in the region. The ability and the facilities tostart and operate a business seem to be limited by the malfunctioningof the administrative and fiscal systems.
The positive, significant coefficient confirms that international tradeimproves total factor productivity in the region through the transfer oftechnology and/or the use of higher quality intermediate input(Rivera-Batiz and Romer, 1991). Moreover, results show that FDI isnot merely the means of opening new markets for multinational com-panies but also a way for achieving higher productivity through tech-nology transfers. On the other hand, higher quality of the educationsystem contributes significantly to reducing the technology gapthrough better ability to create and adapt new technologies and favortechnology transfer.
Finally, we used different indicators to measure financial develop-ment in the region and we find no significant coefficient. This resultshows that the financial sector, which is supposed to support technolo-gy catch-up by providing financial resources, seems to have no role inimproving the capacity of the local economy to absorb new technolo-gies. This confirms the financial system's inability to allocate capita effi-ciently. More effort is therefore needed to make local financial sectorscomply with international standards for capital.
5. Conclusion
North Africa finds itself at a crossroad, with new opportunities butalso faced challenges with real social and economic debacles. Devel-opment programmes implemented by the different states have failedto deliver significant results, and essential reforms are needed if theseare to remain viable in the global economy.
In this paper we use the metafrontier approach to study the linksbetween the economic and institutional context in North Africancountries and their technological performance. Our results suggest,first, that the capacity of the region to acquire and adopt new technol-ogies is very limited. Second, governance and institution deficienciesare shown to largely explain the low economic performance of the re-gion during the 1990s and the early 2000s. This paper shows that im-provement in governance and institutions is a key issue for theregion's catch-up process. In addition, the paper proves that the dis-appointing quality of governance in the region is the cause of ineffi-ciency. But institutional variables are not the only cause of lowefficiency performances; the deficit in structural reforms constitutesanother major explanatory factor (financial development). This hasbeen the case over the whole period 1970–200, indicating that re-forms are an important issue which the governments in North Africamust address if the region is to catch up with the more successful de-veloping economies. But reforms have to be supported by good gov-ernance and institutions that would help the region to achieve ahigher development horizon. Improvements to macroeconomic man-agement as well as institutional reforms are essential to enhance effi-ciency and facilitate the absorption of new technologies.
An effective reform approach inNorth African countries can be basedon the following actions: building market institutions, developingpolitical institutions, ameliorating the investment climate, as well asstrengthening the rule of law and combating corruption. The NorthAfrican countries have also to encourage the private sector to improveopportunities for youth employment and develop the financial sectorwhich has been shown to be very important in increasing the economicefficiency.
2162 I. Drine / Economic Modelling 29 (2012) 2155–2162
Appendix A
Table 1Technology gap ratios for North African countries.
Algeria Egypt Morocco Sudan Tunisia Average
1970 0.093 0.223 0.155 0.020 0.146 0.1271971 0.094 0.233 0.168 0.021 0.167 0.1371972 0.096 0.245 0.182 0.022 0.188 0.1471973 0.097 0.257 0.199 0.023 0.216 0.1581974 0.097 0.263 0.216 0.022 0.244 0.1691975 0.098 0.259 0.219 0.021 0.275 0.1741976 0.099 0.294 0.217 0.021 0.268 0.1801977 0.100 0.334 0.214 0.022 0.266 0.1871978 0.100 0.374 0.213 0.022 0.263 0.1951979 0.100 0.423 0.215 0.023 0.262 0.2041980 0.101 0.485 0.219 0.022 0.259 0.2171981 0.102 0.542 0.208 0.020 0.229 0.2201982 0.100 0.624 0.196 0.017 0.204 0.2281983 0.102 0.715 0.185 0.015 0.180 0.2391984 0.104 0.788 0.179 0.013 0.160 0.2491985 0.106 0.898 0.170 0.011 0.145 0.2661986 0.105 0.858 0.162 0.010 0.138 0.2551987 0.106 0.834 0.157 0.009 0.131 0.2471988 0.107 0.814 0.152 0.009 0.125 0.2411989 0.108 0.801 0.147 0.008 0.118 0.2361990 0.110 0.747 0.142 0.008 0.112 0.2241991 0.106 0.679 0.133 0.007 0.105 0.2061992 0.102 0.648 0.124 0.007 0.098 0.1961993 0.098 0.628 0.117 0.007 0.092 0.1881994 0.072 0.247 0.061 0.006 0.049 0.0871995 0.175 0.599 0.147 0.015 0.118 0.2111996 0.168 0.564 0.141 0.014 0.111 0.1991997 0.161 0.529 0.134 0.012 0.104 0.1881998 0.154 0.496 0.127 0.010 0.098 0.1771999 0.148 0.492 0.121 0.009 0.093 0.1732000 0.142 0.465 0.110 0.008 0.087 0.1632001 0.136 0.436 0.101 0.007 0.082 0.1532002 0.130 0.417 0.094 0.006 0.077 0.1452003 0.124 0.400 0.090 0.005 0.072 0.1382004 0.118 0.381 0.084 0.005 0.068 0.1312005 0.112 0.362 0.079 0.004 0.064 0.124Average 0.113 0.510 0.155 0.013 0.150
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