Is it time for African nation-states to invite foreign intervention in the campaign against terrorism? The case of Boko Haram

As extreme militant Islamists, inspired by ‘Salifist jihadism’ and nurtured by domestic disaffection with non-inclusive political and economic institutions, reek unspeakable havoc in northeastern and northcentral Nigeria, the country once again emerges on the international scene for the wrong reasons. The militant group that brands itself ‘Boko Haram’ has, since its formation in 2002 in Borno State by Mohammed Yusuf, managed to confirm two sad realities in the country: the Nigerian government’s inability to contain it, and the group’s unconstrained capacity to spill the blood of fellow innocent Africans. The extrajudicial killing of Mr. Yusuf in 2009 by Nigerian security forces did not help matters.

U.S Navy Seals in Somalia in the aftermath of Nairobi mall blast in 2013

Answering to the likes of Abubakar Shekau, the de factor leader of the militants once Yusuf was summarily executed, Boko Haram embarked on a killing spree in 2009; to date an estimated five thousand innocent Nigerians, primarily in the northern states of the country, have lost their lives. In the same period the group’s campaign of terror has displaced over 650,000 residents of the region, and close to five hundred women and children have been abducted, including 276 schoolgirls from Chibok in April, 2014. While Boko Haram openly justifies its monstrosities by its ostensible abhorrence of western influence and education in the region (as the name implies) and seeks to establish Sharia laws in northern Nigeria, what informs its existence, however, is readily traceable to the government’s inability to deliver to its citizenry basic social infrastructures and institutions that conduce to modern standard of living. Lacking in all meaningful social indices that measure acceptable standards of economic well-being and self-sufficiency, Boko Haram’s goals and ideology become appealing to the neglected, disenfranchised, and severely marginalized. The youths, poorly educated and wanting in job skills and opportunities to improve their collective lot, see no reasonable substitute to their abysmal and deplorable state of existence. A call to arms provides a false expectation as the means to escape unyielding and devastating levels of unemployment, poverty, and disease amongst residents of heavily impacted areas –Borno, Yobe and Adamawa states. In a very special sense, residents of these states are now twice victimized; once through neglect by their elected representatives, and by the violence visited upon them by Boko Haram.

Abuja blast
The scene of a Boko Haram attack in northern Nigeria, 2014.

So far international response to Boko Haram’s atrocities remains weak and wanting in muscular intervention ordinarily appropriate to the magnitude of inhumanity experienced in northern Nigeria. Nonetheless it took the Nairobi incident of September 21, 2013 where sixty-seven shoppers were massacred by a Somalia-based militants to provoke an international response to what was up till then widely considered an African peculiarity – ethnic strife, tribalism, and religion-inspired brutality. Two weeks after the shooting at the Westgate Mall in Nairobi, a U.S. Navy Seals team attacked a building in Barawe in southern Somalia where Abdulkadir Mohamed Abdulkadir was believed to be taking refuge. Abdulkadir, a Kenyan-born jihadist and a leading member of the Shabab militant sect is known by counter-intelligence agencies to have played major roles in attacks undertaken by the group in Somalia, and co-coordinated activities with an allied terrorist group, al-Hijra. The U.S. assault failed to kill Abdulkadir, but on the same night, a similar U.S led attack in North Africa was successful; Nazih Abdul-Hamed al-Ruqai was captured. Hamed al-Ruqai had been linked to al-Qaeda, and the 1998 Nairobi embassy blasts, and attacks on Israeli targets in 2002 in Mombasa.

As these terrorist groups become bolder and extend their reach across Africa, the question of how to contain, and ultimately neutralize them become immediate and pressing. The obvious toll on civilian lives notwithstanding, the attendant socio-economic consequences of terrorism in Africa compounds the already overwhelming problems that plague the continent. To date, no African government has put forward a concrete plan to effectively combat terrorism either within its territorial competence or beyond its borders. The 1998 agreement between Nigeria, Cameroon, and Niger to form a joint task force to combat destabilizing militancy is yet to produce tangible evidence of effectiveness. A similar 2012 agreement between Nigeria and Cameroon amounts to a pro forma memorandum of understanding that lacks nothing but actual performance. Meanwhile Boko Haram continues its bloody campaign of terror against defenseless civilians.

BokoHaram militants
Boko Haram in Borno State, Nigeria, 2014.

All these raise the question of whether African governments have the means and the requisite commitment to defend its citizens. In the same vein, the question of whether foreign intervention in Africa to rescue the continent from her home-grown terrorists is an admission of failure and ineptitude or a recognition of the continent’s limited experience in counter-terrorism, and the paucity of resources to contain it. The proper perspective, however, is one that recognizes the international scope of these militant groups, and the unmistakable fact that their activities have far reaching consequences beyond the geographical borders of African states. It is within this understanding that foreign intervention holds its appeal. But such foreign intervention would at best be a short-term solution; a lasting solution would require substantive political leadership and effective governments that enjoy the support of those whose affairs they govern.

In Nigeria Boko Haram has so far shown itself to be adept in exploiting fundamental weakness in both public policy and deployment of federal and state resources. In an attempt to prevent future coups, the military was severely degraded and funds that were budgeted to equip a more agile and serviceable military were diverted to fund personal interests. Thus, through graft and bureaucratic corruption, Nigerians inherited an ill-equipped and demoralized military that is now a shell of its former self, and incapable of bringing to order a rag-tag assembly of disaffected youths bent on destabilizing the country. This aside, a lasting solution, however, cannot be gained through the barrels of heavy artillery, for violence only begets violence; a corrective measure that addresses the fundamental problems attendant to bureaucratic corruption such as poor healthcare delivery mechanism, decaying public infrastructure, crippling poverty, and chronic unemployment must be addressed. Otherwise the problems of domestic terrorism would remain a major problem, and our policy makers would have missed the road to peace everlastingly.

African Economic Renaissance: The Role of Social Institutions

The months of March, and May, 2014 were particularly exciting for economists, policy makers and development experts in Nigeria. For in these months, three impactful international conferences, one featuring two Nobel Laureates in Economics, were held in the country. The Economist’s Nigeria Summit 2014 took the lead in March, then came the International Conference on African Development Issues in May, and in the same week, The World Economic Forum on Africa commenced activities. What makes these conferences important is underwritten, not so much by the excitement they generated, but by the promise they held for sustained economic growth in the continent. But did they? Could they have helped unlock the mystery of Africa’s perennially chronic economic underperformance in spite of its vast human and natural resources? Or is it the case that the vectors that have persistently subdued economic development in constituent nation-states of the continent are not purely economic after all?

Professors Eric Maskin and John Ifediora at the African Development Conference

Along with venerable experts on African development - William Easterly, Paul Collier, and Robert Klitgaard – we posit that social institutions matter, and that economic development is path-dependent. The path taken by African states in their quest for sustained development, while different to a large extent from those embarked upon by other resources-rich countries such as Iran, Algeria, Indonesia, and Venezuela, ultimately led in many instances to the same destination: one of missed opportunities, poverty, inadequate infrastructure, marginalized educational systems, a growing pool of unskilled and displaced workers, underdevelopment of crucial sectors, and violent social unrest.

While the path chosen is deliberately purposeful, it is invariably shaped by lived experiences of policy makers, and the political and economic institutions adopted post-independence from colonial rulers. That these institutions matter stems from the fact that they are rules that guide conduct, and sanction what activities maybe engaged to advance society’s welfare in terms of governance, utilization of social resources, property rights, and transaction costs. In almost all resource-endowed countries in Africa that failed to achieve sustained economic development, policy makers relied almost exclusively on oil or other natural resource to drive macroeconomic agenda for growth; and they did so when discovery of natural resources in their territorial competence coincided with periods of nation-building, and experiments with political and economic ideologies. That these countries, in their formative years with rudimentary social institutions, were exposed to, and had to engage foreign companies with superior support from their more economically advanced home economies, played a significant role in shaping political and economic rules adopted by African countries.

Professor Eric Maskin at the Conference

The important roles of social institutions and inclusivity
Economic and political institutions are part of the broader social institutions in any society. These institutions are intimately linked, and synergistic, and form the norms and rules that define a polity. Politics, the process by which a society determines the rules that govern it, is a struggle between groups; the group that prevails acquires the privilege to define the nature of political institutions in the country by way of legislation, and constitutional correctives. These political institutions would either be inclusive, in which case they allow broad distribution of power, and encourage pluralistic participation of constituent groups; or non-inclusive and absolutist by limiting power to the elite with little or no constraint to the exercise of usurped powers. In non-inclusive political institutions as practiced in Nigeria, power, is in practice, unconstrained and narrowly distributed to a few of the elites. Once so constituted, the elites, in order to protect their narrow self-interest and preserve acquired political power, structure economic institutions that are also non-inclusive, and complementary to existing political institutions. Thus, in the normal run of things, non-inclusive political institutions beget non-inclusive economic ones. Such dependency in form and structure applies with equal cogency to inclusive political institutions (as practiced in Western Democracies, and in Japan) that encourage inclusive economic institutions, and promote freedom of choice, an efficient and evenly applied justice system, and protection of property rights.

Professors John Ifediora and Thomas Sargent arriving at the conference

The social institutions adopted by a country (political and economic) are deterministic of economic growth patterns. Non-inclusive institutions tend to impede economic development by limiting broad-based distribution of social wealth, and meaningful participation in growth-sustaining economic activities. Since growth is dependent on technological innovation and change, and thus engenders losers in the old system while rewarding innovators, the elites who monopolize political and economic power stand to lose some (if not most) of acquired privileges, would resist such change. It is in this regard that non-inclusive social institutions are detrimental to economic growth. Acemoglu and Robinson (2011) extend this narrative in their book, Why Nations Fail:

Even though mechanization led to enormous increases in total incomes and ultimately became the foundation of modern industrial society, it was bitterly opposed by many. Not because of ignorance or short-sightedness, but rather out of a coherent logic: economic growth and technological change are accompanied by what the great economist, Joseph Schumpeter, called creative destruction. They replace the old with the new; new firms take away resources from established ones, and make existing technologies and machines obsolete. … Fear of creative destruction is often the at the root of the opposition to inclusive economic and political institutions…Growth thus moves forward only if not blocked by the economic and political losers…

The growth-sustaining qualities of inclusive political and economic institutions are their liberating effects on individuals and capital. By giving the governed free choice to pursue activities compelled by self-interest, investment in human capital through education, and acquisition of skills invariably follow. Capital, free to move into areas of higher than normal returns, helps beget technologies that enable both workers and capital to become more productive. This cycle of improvements in skills and technology are the basis of sustained economic growth that is made possible by inclusivity. Nigeria’s failure to achieve broad economic prosperity is (as in many African countries) in more ways than one, attributable to low levels of education, lack of adaptable skills to modern technologies, the inability to emulate advanced economies, and its current state of terroristic conundrum. That this is the case is readily traceable to restrictive social institutions that, by their very nature, do not create adequate incentive to invest in human, and capital development. In the case of Nigeria, beginning in the early 1980s, there was a deliberate effort by the military dictators to marginalize once thriving and effective educational system that produced in the 1960s and 1970s a highly educated workforce. Their effort and intention succeeded; by the late 1980s, and to the present, while Nigeria has more institutions of higher learning than any period in its history, the quality of education rendered is at best sub-standard, and of little value to skill-intensive industries that drive modern production processes. It is in this sense that social institutions matter in a country’s development, and help contextualize Nigerian realities in the decades following the discovery of oil, and subsequent oil booms

The successes of Botswana and Norway in managing to avoid the “Natural Resource Curse” or more popularly the “Dutch Disease Syndrome”, and instead prospered with proper husbandry of their respective natural resources, are good examples of the beneficence of inclusive social institutions. Norway, already an established democracy and wealthy before it discovered oil in 1962 had well functioning social institutions in place that prevented petrolization of its economy. Its oil wealth was not allowed to displace or marginalize other productive sectors of Norway’s economy; instead, tax revenues generated from oil were used to enhance its overall economic welfare. Botswana, rich in diamonds, also avoided the fate of the Dutch Disease through judicious use of derived revenue. This ‘Botswanan’ feat is at once remarkable, but not surprising; remarkable in the sense that it discovered diamonds while in its formative years as a nation-state, but not surprising because its leaders early on made a conscious decision to adopt inclusive political and economic institutions. As a consequence, Botswana is one of the fastest growing economies in the world, and has enjoyed stable political transitions of power without military intervention.

All is not lost in Africa
The classical orthodoxy of economic growth remains relevant, in the sense that domestic manufactures, adequacy of social infrastructure such as roads, reliable electricity supply, and telecommunication are indispensible to economic development. To this list must be added public healthcare, and access to effective education, and the skill level of the workforce. While the Nigerian economy, for instance, continues to grow as measured by its gross domestic product (it surpassed South Africa’s nominal GDP in 2014), its performance in the above cited determinants of growth, with the exception of telecommunication, is not encouraging. The advances made in the telecommunication industry, propelled by the push up north of the continent by South African mobile phone companies, have conferred direct benefits, and positive externalities. The most immediate benefits can be found in enhanced productivity and efficiency in both public and private sectors; mobile phone technology now makes it possible to accomplish in hours what would have taken weeks less than a decade ago.

Dr. Kalu I. Kalu and Professor Maskin at the conference

The advances in telecommunication, and easy access to mobile phones have dramatically improved Nigeria’s financial sector. Banking services now available to Nigerians rival, and sometimes exceed, those in advanced economies. With the exception of the mortgage sub-sector, which is still rudimentary, financial services now account for much of domestic income. But more work remains in this vital sector of Nigerian’s economy because as of 2013, banking services remain confined almost exclusively to individuals and businesses that operate in the formal economy. The informals or those who operate outside the formal economy (farmers, petty retail traders) represent the vast majority of Nigerians who occupy the agricultural and retail sectors. The financial services sector would remarkably advance the country’s growth potential by extending its services to these groups; a goal and task the federal government can facilitate with policies of inclusivity.

Cross section of conference participants

Any serious effort, therefore, to engage development problems in Africa must begin by taking notice of the reality that socio-economic development in the continent may be attained, and sustained only if the processes engaged toward these ends are properly mindful of the cultural and social experiences of Africans. This means looking at things from the point of view of those whose welfare one seeks to improve; for only when the life experiences of the indigenous people are clearly understood would it be possible to work within the context of their cultural and traditional observances to establish accommodative social and economic institutions necessary for sustained development. This approach is what we have termed ‘contextual development’; a process that requires a balanced integration of indigenous cultures, religious beliefs, prevailing social arrangements, and new ideas from developed nations into a unique development strategy that suits a particular nation-state. Contextual development thus requires a good understanding of the needs of the people, and how to design and implement programs that take advantage of the peculiarities of the society, and expectations. It also requires, as an imperative, that one who embarks on development programs in Africa be acquainted with the cultural belief system in the country, the role religion plays, the level of literacy, availability of skilled labor, traditional roles of the sexes, prevailing social arrangements, and most importantly, what development means to the people.

The novelty of this approach to development can be found, not so much in the idea, but in its implementation; for experts in development studies are now very much aware that the old policy of imposing change from without has not produced desired results, but has instead made matters worse despite decades of development assistance to Africa. This strategy necessarily rejects the old development model of one-size-fits-all that assumes social and political institutions as given, and then proceeds to impose pre-packaged solutions that lack relevance to local practices. It is in this very important sense that contextual development strategy is particularly relevant --- that there maybe more than one path to economic development; the path taken by Western countries was accommodative of the lived experiences and circumstances in the West, the African path would have to be accommodative of African realities.

Media Coverage:

  1. The Guardian [1] [2] [3]
  2. THISDAY Newspaper [1]

Horror as Boko Haram Abducts over 185 Children, Women

Godwin Haruna.

Even as the whereabouts of the 219 schoolgirls abducted from the Government Secondary School Chibok, Borno State, Nigeria remains unknown, yet another round of bizarre abductions by the insurgent Boko Haram group has taken place. More than 185 women and children have been reportedly kidnapped in Gumsuri, close to Chibok where the insurgents took away the missing schoolgirls about eight months ago.

According to agency reports, the insurgents kidnapped at least 185 women and children, and killed 32 people in the raid, which happened on Sunday but only filtered out on Thursday due to lack of communication. “They gathered the women and children and took them away in trucks after burning most of the village with petrol bombs,” a local government official was quoted as saying. The militants reportedly arrived in the village from two directions, overwhelming local vigilantes who, in the past, collaborated with hunters and Nigerian soldiers to fend off similar attacks. ‎”They destroyed almost half the village and took away 185 women, girls and boys,” said Gumsuri resident Umar Ari, who trekked for four days to escape to Maiduguri, capital of the state.
Modu Kalli, another resident, said militants burnt houses after pouring gasoline on them and spraying the village with sophisticated weapons and poured canisters of gasoline on houses before setting them on fire. “We lost everything in the attack,” he said. “I escaped with nothing, save the clothes I have on me.” Hundreds Gumsuri residents continue to arrive in Maiduguri, which has been struggling to accommodate thousands of residents fleeing towns and villages overrun by Boko Haram.

In another development, a total of 116 Boko Haram members who attacked Amchide, a border town in Cameroon, have been killed, the Cameroonian army has stated. Residents told reporters that the gunmen attacked Amchide on Wednesday, some arriving in two vehicles and many others on foot. They raided the market area, setting fire to shops and more than 50 houses.

News of the attack on Amchide and the killing of the 116 insurgents came at the time news filtered out that men believed to be the sect’s members invaded Gumsuri village where they unleashed an orgy of violence leaving blood on their trail.

The Nigerian government has battled the insurgents in the last few years without decisively wiping them out because of external support for the terror group. Boko Haram’s exact funding streams remain unclear as the group largely operates outside the banking system. It appears that Boko Haram relies on a combination of local funding sources and lucrative criminal activity, particularly kidnapping for ransom, which apparently is the group’s main source of funding, to the tune of millions of dollars annually. U.S. officials estimate that Boko Haram receives approximately $1 million for the kidnapping and release of each wealthy Nigerians. Additionally, Boko Haram finances itself by bank robberies, protection money from local governors, and alleged foreign donations (such as Britain's Al-Muntada Trust Fund and Saudi Arabia's Islamic World Society). It is suspected that Boko Haram receives funding from local religious sympathisers and individuals opposing the Nigerian government, but hard evidence for this suspicion is lacking thus far even when the Australian negotiator, Dr. Stephen Davis named some suspects. The group receives limited funding from al Qaeda in the Islamic Maghreb, but that support has little impact on Boko Haram’s overall funding. Boko Haram’s financial relationship with other extremist groups appears limited.

Some security analysts have noted that Boko Haram is less reliant on large funding streams because it generally does not purchase sophisticated weapons and runs very low-cost operations. Many of the weapons at its disposal were stolen from the Nigerian military.

Security concerns in Africa’s most populous country remains a focal point in next year’s general election. The main opposition challenger, General Muhammadu Buhari of the All Progressives Alliance (APC), a former military head of state, has often accused the incumbent President Goodluck Jonathan of incompetence in ending the insurgency that had claimed thousands of lives and destroyed many businesses as well as scuttling the education of millions of children across the three hard-hit North-east states of Borno, Yobe and Adamawa.

Infrastructure and Economic Development in Sub-Saharan Africa

César Calderón* Luis Servén*


The World Bank


JEL Classification: H54, O40, D31, O55

Keywords: Infrastructure, Growth, Income Inequality, Sub-Saharan Africa

* We are indebted to Melvin Ayoglu, Alberto Bihar, Markus Eberhardt, Johannes Fedderke, Delfin Go, Anke Hoeffler, Phil Manners, Ngila Mwase, Martin Ravallion, and participants at the CSAE-Oxford University 2008 conference and the World Bank’s 2008 ABCDE conference in Cape Town for helpful comments and suggestions on earlier drafts. Any remaining errors are our own responsibility. We thank Rei Odawara for assistance, and Cecilia Briceño, David Cieslikowski, Arnaud Desmarchelier, Antonio Estache, Ana Goicoechea, Tsukasa Hattori, and Tito Yepes for their generous help with data issues. The views expressed in this paper are only ours and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.


1.              Introduction

 Decades of economic stagnation and declining living standards have turned Sub- Saharan Africa into the world’s poorest region. In spite of an incipient recovery since the end of the 1990s, with per capita income growth rates outpacing those of rich countries for the first time in many years, leading observers in the development and policy community are advocating a ‘big push’ to help the region escape poverty and regain the lost ground vis-à-vis the rest of the developing world (e.g., Sachs et al 2004, Collier 2006). These calls for action propose a variety of remedial policy agendas, but virtually all of them list infrastructure development among the top priorities.

An adequate supply of infrastructure services has long been viewed as a key ingredient for economic development, both in the academic literature (starting with the work of Aschauer 1989) as well as in the policy debate (e.g., World Bank 1994). Over the last two decades, academic research has devoted considerable effort to theoretical and empirical analyses of the contribution of infrastructure development to growth and productivity. More recently, increasing attention has been paid also to the impact of infrastructure on poverty and inequality (Estache, Foster and Wodon 2002, World Bank 2003, 2006). While the empirical literature on these two topics is far from unanimous, on the whole a consensus has emerged that, under the right conditions, infrastructure development can play a major role in promoting growth and equity – and, through both channels, helping reduce poverty.

In most dimensions of infrastructure performance, Sub-Saharan Africa ranks at the bottom of all developing regions, so the strategic emphasis on infrastructure is hardly surprising. And the literature suggests that some intrinsic features of Africa’s economies may enhance the potential role of infrastructure for the region’s economic development – in particular, the large number of Africa’s landlocked countries, home to a major proportion (about 40 percent) of the region’s overall population, and the remoteness of most of the region’s economies from global market centers. These geographic disadvantages result in high transport costs that hamper intra and inter-regional trade, as variously shown by Limao and Venables (2001), Elbadawi, Mengistae and Zeufack (2006), and Behar and Manners (2008). Reduced openness to trade is the main factor


behind the robust empirical finding that – other things equal – landlocked countries tend to grow more slowly than the rest. However, these geographic disadvantages do not pose an insurmountable obstacle to development -- they can be offset with good transport and communications facilities.1 Africa’s problem is that poor infrastructure adds to its geographic disadvantage.

Aside from external trade, there are many concrete indications that deficient infrastructure hampers Africa’s development in other ways. Reinikka and Svensson (1999) use data from Uganda’s industrial enterprise survey to test the impact of poor infrastructure – as reflected by an inadequate supply of electricity – on firm-level investment, and find that unreliable electricity is a significant investment deterrent. Diao and Yanoma (2003) show that growth in the agricultural sector is constrained by high marketing costs, which largely reflect poor transport (as well as other infrastructure) facilities. Estache and Vagliasindi (2007) argue that an insufficient power generation capacity limits growth in Ghana. Lumbila (2005) finds that deficient infrastructure may hinder the growth impact of FDI in Africa.

This paper offers an empirical assessment of the impact of infrastructure development on growth and inequality, with a focus on Sub-Saharan Africa. The paper uses a comparative cross-regional perspective to place Africa’s experience in the international context. Drawing from an updated data set of infrastructure indicators covering 100 countries and spanning the years 1960-2005, we estimate empirical growth and inequality equations including a standard set of control variables augmented with infrastructure development indicators. The empirical approach extends previous literature in several dimensions: it encompasses different core infrastructure sectors, considers both the quantity and quality of infrastructure, and accounts for their potential endogeneity.

We use the empirical estimates to illustrate the contribution of infrastructure development to growth and equity across Africa. The paper follows recent empirical studies of the contribution of infrastructure to the level and growth of aggregate output and productivity (Sánchez-Robles 1998; Canning 1999; Demetriades and Mamuneas 2000; Röller and Waverman 2001; Esfahani and Ramirez 2003; Calderón and Servén 2003, 2008). It also adds to a still incipient, but rapidly expanding literature on the distributive impact of infrastructure provision and reform (Estache, Foster and Wodon 2002; Calderón and Chong 2004).

The rest of the paper is organized as follows. In Section 2 we offer a brief review of recent literature concerned with the effects of infrastructure development on growth and distribution, with a focus on Sub-Saharan Africa. Section 3 discusses the empirical strategy and the econometric issues that arise when attempting to identify the impact of infrastructure on growth and income distribution. It presents the empirical results and reports illustrative exercises highlighting their implications for Africa. Finally, section 4 offers concluding comments.

1 In other words, geography is only part of the story. Limao and Venables (2001) conclude that poor infrastructure is responsible for a good portion of Africa’s record-high transport costs and its abnormally low intra-regional trade.

2.              Infrastructure and economic development

 There is abundant theoretical work on the contribution of infrastructure to output, productivity and welfare. Much of it is closely related to a literature concerned with the macroeconomic role of productive public expenditure. Arrow and Kurz (1970 were the first to provide a formal analysis of the effects of public capital on output and welfare under alternative financing schemes. In their framework, public capital enters as an input in the economy’s aggregate production function, in the context of a Ramsey model with long-run growth exogenously determined. The endogenous growth version of this basic setup was developed first by Barro (1990), who assumed that the government’s contribution to current production is driven by its flow of productive expenditure, and later extended by Futagami, Morita and Shibata (1993) to include both public and private capital stock accumulation.

This analytical literature has grown enormously in the last fifteen years, exploring a multitude of variants of the basic model, such as alternative tax structures, considering simultaneously public capital and productive current spending flows, adding public capital services in the utility function, or allowing for public infrastructure congestion.2

In turn, empirical research on the impact of infrastructure took off relatively recently, following the seminal work of Aschauer (1989), but it has boomed over the last two decades – literally hundreds of papers have been devoted to assess the effects of infrastructure on growth, productivity, poverty, and other development outcomes, using a variety of data and empirical methodologies. Calderón and Servén (2008) offer a partial account of the literature on the growth and inequality effects of infrastructure; more comprehensive surveys include Estache (2006), Romp and de Haan (2007) and Straub (2007).

The bulk of the empirical literature on the effects of infrastructure has focused on its long-run contribution to the level or growth rate of aggregate income or productivity. The starting point was Aschauer’s (1989) finding that the stock of public infrastructure capital is a significant determinant of aggregate TFP in the U.S. However, his estimate (based on time series data) of the marginal product of infrastructure capital -- as much as 100% per year -- was implausibly high.

The massive ensuing literature on the output impact of infrastructure has employed a variety of data, empirical methods and infrastructure measures. The most popular approaches include the estimation of an aggregate production function (or its dual, the cost function) and empirical growth regressions. Infrastructure is variously measured in terms of physical stocks, spending flows, or capital stocks constructed accumulating the latter.

A majority of this literature finds a positive long-run effect of infrastructure on output, productivity, or their growth rate. More specifically, this is the case with almost all of the studies using physical indicators of infrastructure stocks, but results are more mixed among the growth studies using measures of public capital stocks or infrastructure spending flows (Straub 2007).

Another strand of recent literature has examined the effects of infrastructure on income inequality. The rationale is that infrastructure provision may have a disproportionate effect on the income and welfare of the poor by raising the value of the assets they hold (such as land or human capital), or by lowering the transaction costs (e.g., transport and logistic costs) they incur to access the markets for their inputs and outputs. These effects may occur through a variety of mechanisms documented in the empirical literature; see for example Estache, Foster and Wodon, (2002), Estache (2003), and Calderón and Servén (2008). Of course, for infrastructure development to reduce income inequality, the key ingredient is that it must help expand access by the poor, as argued for example by Estache et al. (2000).3

A related strand of the empirical literature focuses on the poverty effects of specific infrastructure projects using matching techniques that combine samples of beneficiaries with samples drawn from regular household surveys.4 On the whole, the evidence shows that public investment in infrastructure —specifically, in the rehabilitation of rural roads— improves local community and market development. For example, rehabilitation of rural roads raises male agricultural wages and aggregate crop indices in poor villages of Bangladesh (Khandker et al. 2006). Likewise, in Vietnam the

result is an increase in the availability of food, the completion rates of primary school and the wages of agricultural workers (Mu and van de Walle, 2007). In the same vein, other studies find that access to new and improved roads in rural areas enhances opportunities in non-agricultural activities in Peru (Escobal and Ponce, 2002) and in non-farm activities among women in Georgia (Lokshin and Yemtsov, 2005).5

Few empirical studies have tackled directly the inequality impact of infrastructure at the macroeconomic level. Among them are those of López (2004) and Calderón and Servén (2008), both of which use cross-country panel data. López uses telephone density to proxy for infrastructure, while Calderón and Servén employ synthetic indices of infrastructure quantity and quality. In both cases, the finding is that, other things equal, infrastructure development is associated with reduced income inequality. Combined with the finding that infrastructure also appears to raise growth, the implication is that, in the right conditions, infrastructure development can be a powerful tool for poverty reduction.

2 See for example Turnovsky (1997), Glomm and Ravikumar (1997), Baier and Glomm (2001), and Ghosh and Roy (2004).

3 We should also note that there may be two-way causality in this relationship, that is, income inequality may prevent the access of poorer people to infrastructure services. For example, Estache, Manacorda and Valletti (2002) show that income inequality adversely affects access to internet, while Alesina, Baqir and Easterly (1999) argue that more unequal societies devote less effort to the provision of public goods, including infrastructure.

4 This line of research compares the beneficiaries of the infrastructure project under analysis and a control group, using propensity score matching methods to eliminate the bias arising from time-invariant unobservable community characteristics that might affect the project’s outcome.

5 Note that in the Peruvian case, income expansion generated by the rehabilitation of rural roads is faster

than consumption expansion in areas articulated product and factor markets through motorized roads. The excess income is saved since beneficiaries perceived improvements in rural roads as transitory given the record of road maintenance in the area (Escobal and Ponce, 2002).

2.1           Infrastructure and development in Africa

 A strand of recent papers has focused on the development impact of infrastructure in Africa. Ndulu (2006) offers an overview of the big issues, and Ayogu (2007) surveys the empirical literature. Most of the latter deals with the growth and productivity effects of infrastructure development. For example, Estache, Speciale and Veredas (2005) present pooled OLS growth regressions based on an augmented Solow model including a variety of infrastructure indicators, one at a time. Their main conclusion is that roads, power and telecommunications infrastructure – but not water and sanitation -- contribute significantly to long-run growth in Africa. Other studies follow a production function approach. Ayogu (1999) applies it to regional panel data from Nigeria, finding a strong association between infrastructure and output. Kamara (2006) uses data from African countries to calculate various dynamic panel estimates of the effects of infrastructure in an aggregate production function augmented with indicators of the quality of macroeconomic policy. Boopen (2006) likewise presents panel estimates of the output contribution of transport infrastructure using similar data.

South Africa (along with Nigeria) has attracted special attention in this literature, partly reflecting the significantly better quality of its data relative to that of other countries in the region. Perkins, Fedderke and Luiz (2005) use a detailed database on infrastructure investment and capital stocks, spanning as long as a hundred years, to test for the existence of a long-run relation between different infrastructure measures and GDP. Their results suggest a bi-directional relation in most cases. Kularatne (2005) explores the effects of infrastructure investment (as well as social spending on health and education) on GDP. He also finds bi-directional effects, although the impact of infrastructure investment appears to occur indirectly through private investment.

Dinkelman (2008) finds a significant impact of household electrification on employment in South Africa’s rural labor markets.

2.2           Caveats

 Much of the literature reviewed above is subject to some major caveats. There are three main concerns: identification, measurement and heterogeneity. We discuss them next, focusing on the case when the relation of interest is that between infrastructure and output or its growth rate – although similar concerns apply to the relation of infrastructure with other development outcomes.

Consider first the issue of measurement. Infrastructure is a multi-dimensional concept, comprising services that range from transport to clean water. Yet many studies take a single indicator (most often telephone density) to proxy for “infrastructure”.

Omitting other indicators of infrastructure where they are relevant – e.g., in growth empirics – is likely to lead to invalid inferences owing to omitted variable biases. No less problematic is the measurement of infrastructure through spending flows – typically public investment, or its accumulation via perpetual inventory into public capital -- used by much of the literature. Public investment and public capital are likely to be poor proxies for infrastructure accumulation if the private sector plays a significant role in infrastructure provision, as is increasingly the case in many countries. Moreover, even if all infrastructure were owned by the public sector, the link between observed public capital expenditure and the accumulation of infrastructure assets or the provision of infrastructure services may be weak, owing to inefficiencies in public procurement and outright corruption (Pritchett 2000). In fact, these factors are likely behind the generally mixed results obtained by empirical studies using these kinds of infrastructure measures.In turn, the issue of identification is perhaps the most problematic one.

Infrastructure and output (or income) are subject to two-way causality. Richer or faster- growing countries may systematically devote more resources to infrastructure, and empirical assessments of the impact of infrastructure that fail to take this into account are likely to be subject to an upward simultaneity bias. There is no easy solution to this problem. In theory, a full structural model would be able to account for two-way causation. In practice, its empirical implementation poses stringent data requirements.

The closest the literature has come to such a model is perhaps in the use of stripped-down versions of Barro’s (1990) framework (e.g., Canning and Pedroni 2004). An alternative is to use some kind of instrumental variable approach, ideally featuring outside instruments for infrastructure. For example, Calderón and Servén (2003, 2008) employ demographic variables as instruments -- alone or in combination with internal instruments -- in a generalized method of moments (GMM) panel framework. Roller and Waverman (2001) follow a similar approach.

Note also that in a time-series context the issue of simultaneity is arguably more problematic for those studies using investment flows to measure infrastructure services than for those using physical asset stocks. Decision lags and time-to-build suggest that physical assets are likely to be predetermined variables relative to output or productivity, and this may help address identification issues. However, time series data also pose the problem of spurious correlation, which if untreated will result in upward-biased estimates of infrastructure effects on output, particularly in the production-function approach mentioned earlier. Output (or productivity) and infrastructure stocks typically display stochastic trends, and failing to account for them can lead to the spurious finding of a positive and significant association between both variables where in reality there is none. Indeed, this upward bias was largely responsible for Aschauer’s early findings of a very large impact of infrastructure on output using time series data. In a panel context, recent theoretical research shows that spurious regression is much less of an issue provided the cross-section dimension of the data is sufficiently large (Philips and Moon 1999). As for pure time-series models, recent studies often seek to avoid this problem by following cointegration methods to estimate a long-run relation between infrastructure, aggregate output or productivity, and other production inputs. This, however, is typically done in a single-equation context, and therefore it is subject to the same identification problems just discussed, unless the researcher can somehow establish the existence of a single long-run relation among these variables that can be interpreted as ‘the output equation’.

Finally, heterogeneity is a pervasive problem too. The contribution of infrastructure, as summarized by the standard measures described earlier, to output or its growth rate may well vary across countries and time periods, for various reasons. Physical infrastructure stocks, for example, are rarely homogeneous in terms of quality or productivity, which should be reflected in their output or growth impact; yet few empirical studies using physical stock data control for the quality of stocks – which is admittedly hard to do due to the scarcity of infrastructure quality data.7 In this sense spending flows are again especially problematic, as their contribution to the supply of infrastructure assets or services can vary greatly across countries and over time depending on a host of factors ranging from geographic to institutional ones.

Aside from these considerations, technological or other factors may make the impact of otherwise homogeneous infrastructure assets or services differ across locations or time periods. Few empirical studies allow for such heterogeneity, and when they do they typically restrict heterogeneity to tractable forms, with country (or state, province etc) specific effects as the most popular option. Conceptually, heterogeneity could be parameterized by relating it to observable variables – e.g., institutional or governance variables intermediating the translation of spending into assets. In theory, very general forms of parameter heterogeneity – e.g., across countries -- can be easily accommodated empirically, but in practice this demands time-series sample sizes that are often unavailable, as is the case for a number of African countries. A few recent studies (e.g., Bogetic and Fedderke 2006) employ a pooled mean-group approach that allows for unrestricted short-run heterogeneity in the impact of infrastructure, while imposing long- run homogeneity of its effects across countries or industries.

6. Straub (2007) offers a meta-analysis of the output or growth contribution of infrastructure. Less than half of the empirical studies using expenditure-based infrastructure measures find significant positive effects. In contrast, over three-fourths of the studies using physical indicators find a significant positive contribution of infrastructure.

3.              Empirical assessment

 We turn to the empirical assessment of the contribution of infrastructure to growth and equity. Our empirical strategy involves estimation of simple equations relating growth and inequality to a set of standard controls, augmented by measures of the quantity and quality of infrastructure. For this purpose, we construct a large macroeconomic panel data set spanning the period 1960-2005 and comprising a total of 136 countries (see Table A1 for the detailed list of countries, and Table A2 for the list of variables and data sources). To avoid potential distortions introduced by very small economies, in which infrastructure poses some special issues – owing for example to indivisibilities -- we limit our coverage to countries with total population over one million. Also, to remove cyclical factors and focus on longer-term effects, we work with non-overlapping 5-year averages. Data is not available for all countries in all time periods, and hence the panel is unbalanced. To keep a meaningful time-series dimension, we restrict our regression sample to countries with at least three consecutive 5-year observations.


7 Neglecting quality may lead to seriously misleading inferences. Hulten (1996) finds that differences in the effective use of infrastructure resources explain one-quarter of the growth differential between Africa and East Asia, and more than 40 percent of the growth differential between low- and high-growth countries.Among the few studies that attempt to control for infrastructure quality, Esfahani and Ramirez (2003) report significant growth effects of infrastructure in a large panel data set in which the contribution of infrastructure is affected by institutional factors. Calderón and Servén (2008) find significant growth effects of a synthetic indicator of infrastructure quality in an empirical framework including both quantity and quality effects.

3.1           Methodological issues

Our methodological approach allows us to address some of the problems commonly encountered in empirical evaluations of the development impact of infrastructure. The first one is measurement. In contrast with the abundant literature that measures infrastructure in terms of an investment flow or stock (“public capital”) or a single physical asset (such as telephone density), we consider different types of core infrastructure assets. Second, our estimation procedure deals with potential endogeneity and/or reverse causality running from growth and inequality to infrastructure development. Third, we also take account of heterogeneity along various dimensions. On the one hand, our estimations control not only for the quantity of infrastructure, but also for its quality. On the other hand, we allow for some degree of heterogeneity in the relationship between infrastructure and growth, by including unobservable country- specific effects in our empirical specification. We also perform some robustness experiments letting the coefficients of the empirical equation vary with selected country characteristics.

3.1.1  Measuring the quantity and quality of infrastructure

While infrastructure is a multi-dimensional concept, empirical studies typically take a single-sector approach. For instance, Easterly (2001) and Loayza, Fajnzylber and Calderón (2005) use indicators of telephone density to appraise the effects of infrastructure on growth. One reason behind the single-sector approach is the difficulty of properly capturing the multiple dimensions of infrastructure in a simple way. Another reason is the high correlation often found among indicators of different types of infrastructure assets. For example, in our sample the correlation between standard measures of telephone density and power generation capacity (measured respectively by a country’s total number of telephone lines, and its total power generation capacity, in both cases relative to the number of workers) exceeds 0.90, which makes it hard to disentangle in a regression framework the separate roles of the two types of assets.

To overcome this problem, while still keeping account of the multi- dimensionality of infrastructure, we use principal component analysis to build synthetic indices summarizing information on the quantity of different types of infrastructure assets as well as the quality of services in different infrastructure sectors.8 These synthetic indices combine information on three core infrastructure sectors -- telecommunications, power, and roads -- and help address the problem of high collinearity among their individual indicators.9 We denote the synthetic quantity and quality indices that result from this procedure IK and IQ, respectively. The indices can be expressed as linear combinations of the underlying sector-specific indicators, and hence their use in a regression context is equivalent to imposing linear restrictions on the coefficients of the individual infrastructure indicators. These restrictions can be tested using standard Wald tests, as we shall do below.

Proceeding in this manner, we define the synthetic infrastructure quantity index IK1 as the first principal component of three variables: total telephone lines (fixed and mobile) per 1000 workers (Z1/L), electric power generating capacity expressed in MW per 1000 workers (Z2/L), and the length of the road network in km. per sq. km. of arable land (Z3/A). Each of these variables is expressed in logs and standardized by subtracting its mean and dividing by its standard deviation.

7 Neglecting quality may lead to seriously misleading inferences. Hulten (1996) finds that differences in the effective use of infrastructure resources explain one-quarter of the growth differential between Africa and East Asia, and more than 40 percent of the growth differential between low- and high-growth countries.Among the few studies that attempt to control for infrastructure quality, Esfahani and Ramirez (2003) report significant growth effects of infrastructure in a large panel data set in which the contribution of infrastructure is affected by institutional factors. Calderón and Servén (2008) find significant growth effects of a synthetic indicator of infrastructure quality in an empirical framework including both quantity and quality effects.

8 Alesina and Perotti (1996) used principal component analysis to create a measure of political instability, while Sanchez-Robles (1998) employed it to build an aggregate index of infrastructure stocks.

9 We should caution that the sector-specific indicators of infrastructure quantity and quality employedbelow, while standard in the literature, are subject to caveats regarding their homogeneity and international comparability. For example, the quality and condition of a ‘paved road’ can vary substantially across countries – even within the same country. More homogeneous measures of infrastructure performance would be clearly preferable, but unfortunately they do not exist, at least with any significant coverage across countries and time periods.The index accounts for almost 80 percent of the overall variance of the three underlying indicators and, as Table 1 shows, it is also highly correlated with each one of them.

As a robustness check, we compute an alternative index of infrastructure quantity, IK2, which uses main telephone lines instead of the combined main lines and mobile phones employed in the first index; this is in accordance with much of the empirical literature, which uses main lines to measure telephone density. However, the correlation between the two synthetic quantity indices is over 0.99 (see Table 2); this is unsurprising given the similarly high correlation between the two indicators of telephone density underlying the respective synthetic indicators.

Measuring infrastructure quality is less straightforward. The country and/or time- series coverage of some of the objective quality indicators that should be most informative (such as the frequency of power outages or phone faults) is severely limited. In turn, some subjective indicators of perceived infrastructure quality offer broad cross- country coverage, but lack a time-series dimension (see Calderón and Servén 2008). We opt for using the available objective indicators that allow broadest sample coverage.

Specifically, we construct a synthetic index of infrastructure quality IQ, defined as the first principal component of three indictors of quality of service in telecommunications, electricity and roads, respectively. The indicators are: waiting time (in years) for the installation of main telephone lines (Q1), the percentage of transmission and distribution losses in the production of electricity (Q2) and the share of paved roads in total roads (Q3). The first of these three variables is admittedly not a direct indicator of the quality of telecommunications networks, but is robustly positively correlated with the conceptually preferable measure (the number of telephone faults per 100 main lines) whose availability is severely limited in our sample; see Calderón and Servén (2008). All three variables are rescaled to lie between zero and one in such a way that higher values indicate better quality of infrastructure services.

Using the weights obtained from the principal components procedure, the synthetic index of infrastructure quality can be expressed as:

IQ = 0.608*Q1 + 0.559*Q2 + 0.564*Q3

The index captures approximately 60 percent of the total variation of the three underlying indicators, and shows a high correlation with each of them, as reported in Table 1.

Like with the quantity index, as a robustness check we compute an alternative index of infrastructure quality, IQ2. We do so by dropping from the list of variables the indicator of waiting time (in years) for the installation of main telephone lines (Q1) – which is related to quality of service only indirectly. As shown in Table 1, the remaining two variables carry approximately equal weights in the synthetic index that result from this procedure. Moreover, the correlation between the two synthetic quality indices (shown in Table 2) exceeds 0.93.

Table 2 also shows that the indicators of quantity and quality of infrastructure share a good deal of common information —i.e., the full-sample correlation between IK and IQ ranges from 0.63 to 0.73, depending on the specific indices considered. Closer inspection reveals that the same applies to individual infrastructure sectors: the respective stocks and their quality measures are also positively correlated —i.e. 0.59 for telecommunication, 0.46 for electricity, and 0.54 for roads.

The synthetic indices can be used to provide a summary perspective on Africa’s infrastructure performance vis-à-vis other world regions and developing country groups. This is done in Figure 1. The choice of comparator groups in the figure deserves some comment. Given the preponderance of low-income countries across Africa, the best comparator region is probably South Asia, which is likewise dominated by low-income economies. For the same reason, we use as another comparator the group of non-African low-income economies. For illustration, the figure also shows the infrastructure performance of the East Asian tigers (which could be appropriate comparators for Africa’s upper middle income economies), along with that of industrial countries. The top panel of the figure offers a comparative perspective on infrastructure quantity, using the synthetic quantity index IK1, while the bottom panel refers to quality as measured by IQ1 (the alternative indices IK2 and IQ2 give a very similar picture). In both cases, the graphs

depict the situation in the early 1990s as well as that in the early 2000s.10 The

performance of each region is measured relative to the overall sample mean (equal to zero by construction).

10 The qualitative conclusions are be unchanged if we instead compare the early 1980s with the most recent period. The country samples are smaller, however.

The figure conveys three messages. First, Sub-Saharan Africa consistently lags behind the comparator regions, in terms of both quantity and quality of infrastructure. Second, over the last fifteen years progress in Africa has been slower than in other developing regions, and as a result Africa has fallen further behind in both dimensions. Third, in the case of infrastructure quality, the region’s performance has worsened also in absolute terms, not just relative to that of other regions.

Of course, these cross-regional comparisons conceal a great deal of heterogeneity within Africa in terms of infrastructure performance. A closer look at the country-specific data reveals a sharp contrast between the performance of some low-income economies (such as Niger or Togo), which lag well behind the rest of low-income developing countries, and that of the region’s richer economies (South Africa, Botswana, Mauritius), which are roughly on par with countries of similar income levels in other developing regions. The appendix documents these differences across sub-regions of Africa, and offers extensive details on various dimensions of infrastructure performance, including that of universality of access to infrastructure services– an area in which Africa also lags significantly behind other regions.

3.1.2  Econometric methodology

Our empirical strategy is based on estimation of simple equations relating growth and inequality to a set of standard controls, augmented by the synthetic measures of the quantity and quality of infrastructure, in a panel data setting. This poses some well- known problems: (a) the presence of unobserved country-specific effects and common time effects, and (b) potential endogeneity of the regressors. To address these issues, we employ the generalized method of moments (GMM) developed by Arellano and Bond (1991) and Arellano and Bover (1995) for dynamic panel data models. Specifically, we deal with common factors through the inclusion of period-specific dummies, and unobserved country effects are handled by differencing. To control for endogeneity we rely on instrumental variables.

This approach permits relaxing the assumption of strong exogeneity of the explanatory variables by allowing them to be correlated with current and previous realizations of the time-varying error term. In this context, we use a mixture of internal instruments in the spirit of Arellano and Bond (1991) -- that is, suitable lags of the explanatory variables – along with external instruments for our variables of interest, infrastructure quantity and quality, The reason for this two-track approach is double. First, infrastructure might not be weakly exogenous – e.g., anticipated future productivity shocks might encourage infrastructure investment today. This would make lags of the infrastructure indicators —both quality and quantity – invalid as instruments. Second, our infrastructure indicators might contain measurement error, particularly in the case of infrastructure quality, as discussed earlier. To address these problems, we employ demographic variables as outside instruments. Specifically, we use current and lagged values of urban population and population density of each country as instruments for the quantity and quality of infrastructure. The role of these and other demographic variables as determinants of infrastructure demand has been stressed by a number of studies; see e.g. Canning (1999). Further, while demographic factors drive the demand for infrastructure (in terms of both quantity and quality), there is no reason to expect them to exhibit any systematic relation with measurement errors in the latter.

Of course, for the demographic variables to provide valid instruments, they must also not belong in the growth regression —i.e., they must satisfy the exclusion restrictions. In light of existing literature, we see this requirement as fairly uncontroversial. Although some analytical arguments can be found in the literature for a role of population density as a determinant of long-run growth, the potential growth effect that they highlight is mediated by other variables already included in our empirical specifications. For example, Herbst (2000) argues that Africa’s land abundance may have helped reduce inter-country conflict by lowering population density, thus contributing to forge stronger national institutions and thereby facilitating economic development in the longer term. We view this as an argument (additional to others made in the literature) for a growth role of institutional quality, which we include in our regressions as a standard control. On a different tack, from a very long run perspective, it has also been argued that low population density may retard technological innovation and thereby economic development (Klasen and Nestmann.2006). While some indirect effect of this kind may well be possible, the literature on innovation and technological upgrading points instead to factors such as education and competition as the more direct drivers of innovation. To account for the latter, in our empirical growth specifications we include measures of openness and human capital among the standard controls.

We rely on the system GMM estimator (Arellano and Bover 1995), which combines the equation of interest expressed in first differences – using lagged levels of the regressors as internal instruments – and in levels – using lagged differences as instruments. Consistency of the GMM estimator depends on the validity of the internal and external instruments, which can be checked through two specification tests (Arellano and Bond, 1991; Arellano and Bover, 1995): (i) Tests of over-identifying restrictions (Hansen and difference-Sargan tests) that evaluate the validity of the full set of instruments, as well as selected subsets, by testing the null hypothesis that they are uncorrelated with the estimated residuals. Failure to reject the null hypothesis gives support to the model. (ii) Tests of serial correlation of the residuals – specifically, of the null hypothesis that the residual of the regression in differences shows no second-order serial correlation (first-order serial correlation of the differenced error term is expected even if the original error term (in levels) is uncorrelated, unless the latter follows a random walk). Second-order serial correlation of the differenced residual would indicate that the original error term is itself serially correlated. This would render the proposed internal instruments invalid (and would call for higher-order lags to be used as instruments). Again, failure to reject the null lends support to the model.

The standard errors of the efficient two-step GMM estimator are significantly downward biased in small samples. The bias arises from the fact that the approximation to the asymptotic standard errors does not take into account the extra small-sample variation due to the use of estimated parameters in constructing the efficient weighting matrix. Windmeijer (2005) proposes a correction that accounts for this fact. The correction term vanishes with increasing sample size and provides a more accurate approximation in finite samples when all moment conditions are linear. Thus, in our estimations we use Windmeijer’s correction, as implemented in STATA by Roodman (2006).

3.2     Growth, infrastructure stocks and the quality of infrastructure services

As a preliminary step, the top half of Table 2 shows that the aggregate indices of infrastructure quantity and quality are strongly and positively associated with long-run growth. Specifically, we find a positive correlation (0.34) between average annual growth in real GDP per capita over 1960-2005 and the average of each of the two synthetic indices of infrastructure quantity over the same period. We find an even stronger positive correlation between average growth per capita and the synthetic indices of aggregate quality of infrastructure services (0.42 for IQ1 and 0.35 for IQ2). Across infrastructure sectors (not shown in the table), growth is positively correlated with telecommunication stocks (0.24 for total phone lines, and 0.21 for main phone lines), electricity generating capacity (0.15) and the length of the road network (0.22). In addition, growth is positively associated with the quality of telecommunications (0.23), quality of electricity supply (0.14) and road quality (0.23).

Table 3 reports the GMM estimates of the parameters of the growth regression augmented by the synthetic indices of infrastructure performance. As already noted, the two alternative indices of infrastructure quantity, as well as those of quality, are very highly correlated, and hence to save space we only report results using IK1 and IQ1. The set of standard control variables included in the regressions comprises measures of human capital (secondary enrollment, from Barro and Lee 2001), financial depth (from Beck, Demirguc-Kunt and Levine 2000), trade openness, institutional quality, lack of price stability, government burden and terms of trade shocks – in addition to the lagged level of output per worker, to capture conditional convergence. The standard errors reflect Windmeijer’s (2005) small-sample correction.

Column 1 of Table 3 reports parameter estimates including only the synthetic quantity indicator IK1in the regression, and thus neglecting infrastructure quality. Among the standard controls, the estimates show evidence of conditional convergence in real output per capita. They also suggest that human capital accumulation and lower inflation significantly encourage economic growth. The coefficients of the remaining controls carry the expected signs, but none is statistically significant.

In turn, the infrastructure quantity index carries a positive and significant coefficient, suggesting that infrastructure contributes to economic growth. Further, the specification tests shown at the bottom of the table (Hansen and difference-Sargan tests, as well as the second-order serial correlation test) show little evidence against the validity of the moment conditions underlying the empirical specification.

Column 2 adds to the regression the synthetic indicator of infrastructure quality. It also carries a positive and strongly significant coefficient. The estimated coefficient of the quantity indicator declines somewhat in size, but remains positive and significant as well. Thus, both infrastructure quantity and quality contribute to growth. On the whole, there is a gain in precision, and in addition to the significant regressors in column 1, two other standard control variables – government burden and terms of trade shocks – now carry significant coefficients, whose signs are in accordance with expectations. The specification tests continue to lend support to the model specification.

Column 3 reports the result of adding to the specification the squared values of the infrastructure quantity and quality indices, thus allowing for a quadratic effect of infrastructure development on growth. This provides a simple test for non-constant returns to infrastructure development. However, the estimates offer little evidence of nonlinearities, neither individually nor jointly – a Wald test of the joint significance of the two quadratic terms in column 3 yields a p-value of 0.98 -- while the linear effects of infrastructure quantity and quality remain virtually unchanged and strongly significant.

The coefficients of the standard controls also show little change, as do the specification tests.One potential concern with the estimates in columns 1-3 is that they implicitly assume that the effect of infrastructure development on growth is homogeneous across countries. If in reality the effect is heterogeneous, the estimates would be inconsistent. More specifically, a number of recent papers (e.g., Sachs et al 2004, Collier 2006) have argued that infrastructure development is likely to have a bigger growth impact in African countries. In column 4 we test this view by interacting the aggregate indices of infrastructure quantity and quality with a dummy for Sub-Saharan African countries, and adding them to the regression. The estimates of these Africa-specific effects, over and above the average effects, are positive – in line with the literature mentioned above – but very imprecise. Indeed, a Wald test cannot reject the null hypothesis that they are jointly insignificant (the p-value equals 0.40).

We performed other tests of heterogeneity by interacting the infrastructure indicators with a dummy for landlocked countries (which are numerous in Africa) and, alternatively, doing the same only with the road density and quality measures. These tests failed to yield significant evidence that the growth impact of infrastructure development is different for landlocked countries.

As a robustness check, we repeated all the estimations in Table 3 replacing the synthetic indices IK1 and IQ1 with the alternative indices IK2 and IQ2. The results (not shown to save space) were virtually unchanged, which is unsurprising in view of the high correlation between the two sets of indices.

The final robustness check concerns the use of synthetic infrastructure indices in the regressions, rather than the underlying sector-specific variables. As noted earlier, this is equivalent to a regression imposing linear restrictions on the parameters of the latter, forcing them to enter in the empirical equation in the proportions dictated by the principal components. Specifically, use of the synthetic quantity index amounts to imposing two linear restrictions on the three underlying sector-wise quantity measures, while use of the quality index likewise amounts to placing two restrictions on the three underlying sector- wise quality measures. These restrictions can be tested through standard Wald tests.

Taking this approach with the specification including (linear) effects of both quantity and quality of infrastructure, reported in column 2, yields a p-value of .57 when all four restrictions are jointly tested.15 This implies that the use of the synthetic indices of infrastructure quantity and quality does not due undue violence to the data and hence lends support to our approach.

We can employ these results to give an idea of the economic significance of the effects of infrastructure development on growth. Using the estimates in the second column of Table 3, we calculate the contribution of infrastructure development —as proxied by the aggregate indices of infrastructure quantity and quality, IK1 and IQ1 — to growth over the last 15 years of the sample. That is, for each country in the sample we compare the average values of IK1 and IQ1 over 2001-5 with those observed in 1991-5, and multiply the observed change by the corresponding regression coefficient. This calculation is illustrative rather than conclusive, because -- among other simplifying assumptions – it is based on the implicit hypothesis that changes in infrastructure development do not lead to changes in any of the other growth determinants.

The calculation shows that, on average, growth in the world sample increased by 1.6 percent in 2001-5 relative to 1991-5 due to infrastructure development (see Figure 2). This total comprises 1.1 percent due to accumulation of infrastructure stocks, and 0.5 percent due to improved infrastructure quality. The largest contribution of infrastructure development to growth was achieved in South Asia, where it reached 2.7 percent per annum. Of this total, enlarged stocks increased growth by 1.6 percent per year, and enhanced infrastructure quality raised growth rates by 1.1 percent per year in 2001-5 relative to 1991-5. Finally, infrastructure development made, on average, a smaller contribution to growth in Sub-Saharan Africa than in other regions – just 0.7 percent per annum. While the expansion in infrastructure stocks raised the growth rate by 1.2 percent per annum, the deterioration of the quality of infrastructure services in the region contributed to reduce the growth rate by 0.5 percent per annum.

3.3          Infrastructure and income distribution

We turn to the regressions exploring the empirical relationship between infrastructure development and income inequality. Our dependent variable is the Gini coefficient, for which the main source is the database constructed by Deininger and Squire (1996) complemented by the WIDER-UNU database on income inequality and poverty. The selection of explanatory variables follows the existing empirical literature on the determinants of income inequality (Li, Squire and Zou, 1998; Milanovic, 2000; Lundberg and Squire, 2003). Among the regressors we include the (log) level of GDP per capita and its square, to allow for nonlinear effects in the spirit of the conventional Kuznets curve.16 In addition, we continue to include our education proxy, the measure of financial depth, macroeconomic instability (proxied by the CPI inflation rate), and trade openness. As before, infrastructure quantity and quality are measured by the synthetic indices IK1 and IQ1 derived from principal components analysis described earlier.

As a preliminary step, Table 2 shows that across countries the Gini coefficient of income inequality is strongly negatively correlated with the synthetic indices of infrastructure quantity and quality: their correlations with inequality range from -.47 to -.56.17 The literature on the linkages between infrastructure and income distribution argues that infrastructure development can reduce inequality if it enhances the access of the poor to telecommunication services, electricity, roads and railways, safe water and sanitation.18 The first column in the bottom half of Table 2 confirms this view: across countries,19 the percentage of population with access to the services of each of these infrastructure sectors is negatively associated with the degree of income inequality, as measured by the Gini coefficient of income distribution. For our purposes, one key question is to what extent the synthetic indices of infrastructure quantity and quality capture trends in access.20 To assess this issue, we correlate the access figures with the synthetic indices of infrastructure quantity and quality. The bottom half of Table 2 shows that access to the various infrastructure services is positively and very significantly associated with the synthetic indices of infrastructure quantity and quality. The only exception is the correlation between electricity access and infrastructure quality, which is positive but not significant. On the whole, therefore, these facts suggest that the empirical results below regarding the income distribution impact of infrastructure development (as measured by the synthetic quantity and quality indices) do capture the distributional effects of changes in access to infrastructure services.

Estimation results from the inequality regressions are presented in Table 4. Sample size is somewhat smaller than in the previous table because of the more limited availability of income distribution data. As before, column 1 reports estimations excluding infrastructure quality. Among the standard controls, we note first that the sign pattern of the level and square of output per capita conforms to the ‘Kuznets curve’ hypothesis – i.e., the linear term is positive and the quadratic term is negative. Second, education is negatively associated with income inequality. Finally, trade openness – as proxied by the ratio of exports and imports to GDP – tends to make the distribution of income more unequal, as found by Barro (2000).

The infrastructure quantity index IK1 has a significant negative effect on inequality in column 1. This is consistent with the view that infrastructure development enhances the ability of poor individuals and/or residents of backward areas to access additional productive opportunities. The diagnostic tests (Hansen and difference-Sargan tests of joint validity of instruments, and the second-order serial correlation test) lend support to the specification of the model and the choice of instruments.

Column 2 adds the infrastructure quality indicator IQ1. Its coefficient is negative and significant, while that of the quantity indicator declines roughly by half but remains negative and significantly different from zero. Among the other regressors, the quadratic income term becomes insignificant. The rest of the coefficients do not show major changes, nor do the specification tests.

As with the growth regressions, in column 3 of Table 4 we allow for nonlinear effects of infrastructure development on inequality. As the table shows, there is very little evidence of non-linearities in the effects of infrastructure quantity or quality. A Wald test cannot reject the null hypothesis that they are jointly zero (p-value = 0.21). In turn, column 4 looks for a differential effect of infrastructure quantity and/or quality on inequality in Africa vis-à-vis other regions. As in the case of income growth, we fail to find much evidence of any such difference, and a Wald test fails to reject the null that Sub-Saharan Africa behaves just like the rest of the sample in this regard (p-value = 24).

All these empirical exercises make use of the synthetic indices IK1 and IQ1. As a robustness check, we repeated the estimations using the alternative indices IK2 and IQ2. The results (not shown to save space) were virtually unchanged, except for a modest indication of a negative quadratic effect of infrastructure quantity IK2 on inequality, which fell just short of statistical significance.

Finally, we can follow the same procedure as with the growth regressions to assess the validity of the parameter restrictions implicitly imposed by the use of the synthetic indicators of quantity and quality rather than the individual sectors’ indicators. A Wald test of these restrictions, as implicitly embedded in column 2 of Table 4, yields a p-value of 0.30, thus lending support to the use of the principal components.21

To illustrate the economic significance of the empirical results, we focus again on the regression results in the second column of table 4. Consider first the contribution of infrastructure development to changes in income inequality over the last 15 years. As before, we compare the levels of infrastructure development over 2001-5 with those over1991-5 for the average country in Sub-Saharan Africa, as well as for that of other regions across the world. Using the coefficient estimates, Figure 3 shows that average inequality –as proxied by the Gini coefficient– in the world declined by 3 basis points in 2001-5 relative to 1991-5 due to infrastructure development (2 basis points due to accumulation of infrastructure stocks and 1 basis point due to improved infrastructure quality). Like in the case of growth, the largest contribution of infrastructure development to inequality reduction was achieved by South Asia (6 basis points) where enlarged stocks yielded a reduction in the Gini coefficient by 4 basis points and enhanced infrastructure quality added another 2 basis points. Infrastructure development in Sub- Saharan Africa made –on average– a comparatively smaller contribution to inequality reduction –approximately 2 basis points. While larger infrastructure stocks reduced the Gini coefficient by 3 basis points, the worsening quality of infrastructure services in the region raised the Gini coefficient by 1 basis point

3.4          Counterfactual exercises

We can also use our econometric estimates to illustrate the growth consequences of alternative infrastructure development scenarios. To do this, we calculate the growth increase that each country in Sub-Saharan Africa would experience in two counterfactual scenarios of infrastructure development. The first one involves catching up with countries in other regions. The second scenario is one of ‘keeping up’ with them.

Because our regression sample includes African countries of different per capita income levels-- 30 low-income countries and 3 upper-middle income countries22 – we use different benchmarks for each of these two income groups. For the low-income countries in Sub-Saharan Africa, we use as benchmark the average of 12 low-income countries from other regions for which we have data. For the upper-middle income African countries in our sample (Botswana, Gabon and South Africa), we use the average of 23 countries from other regions in the same income category. The details of the two scenarios are as follows.

Scenario A: in 2001-2005, we raise the level of infrastructure development of each Sub-Saharan African country so as to reduce in half its infrastructure gap relative to the average country in other regions in the relevant per-capita income group. To give a more concrete idea of the benchmarks considered, the low-income countries outside Africa closest to the average level of infrastructure development are Pakistan (quantity) and India (quality). In turn, the average for upper-middle income countries is given by Chile (quantity) and Hungary (quality).

Scenario B: over the period 1991-1995 to 2001-2005, we make infrastructure development in Sub-Saharan African countries grow at the same average pace observed in the group of countries of comparable per-capita income in other regions – thus leaving Africa’s (relative) infrastructure gap unchanged at its 1991-95 level. Among low-income countries, Indonesia comes closest to the average infrastructure growth (in terms of both quantity and quality) outside Africa. Among upper-middle-income economies, the rough benchmark is Brazil in infrastructure quantity and Malaysia in infrastructure quality.

It is important to stress that these counterfactual scenarios involve no presumption about the desirability, on welfare grounds, of the assumed infrastructure expansion. More fundamentally, these exercises focus only on the growth benefits of catching up, and ignore the costs that it might involve – for example, in terms of public resources that could be diverted from other uses in order to support enhanced infrastructure development. As we shall see below, such costs are quite significant, and hence these illustrative exercises have to be viewed with caution.

To save space, we report the results of each scenario organizing Africa’s low- income countries (LICs) by geographic subregion: West Africa (ECOWAS), East Africa (EAC), Southern Africa (SADC), and a residual group that we label Central Africa.23 On the other hand, we group together the three upper-middle-income countries (UMCs) in the sample (Botswana, Gabon and South Africa).

Consider the first scenario of reducing in half the gap between Africa’s levels of infrastructure development and the average in the comparable income category. Figure 4 shows that Central African LICs would gain, on average, 2.2 percentage points of growth—roughly equally attributable to the increased infrastructure quantity and the improved quality of infrastructure services (1.16 and 1.06 percent, respectively). Low- income countries in East and West Africa also would raise their growth by 1.6 percentage points, and those in South Africa by just over 1 percent—with most of the increase coming from the larger infrastructure stocks in all three cases. Finally the growth increase for upper-middle income countries in Sub-Saharan Africa would also reach around 1.6 percent per annum, but the bulk of the increase would be due to improvements in the quality of infrastructure services.

The second scenario assumes catch-up with the rate of change, rather than the level, of infrastructure development – in each African country, infrastructure growth (in quality and quantity) would have proceeded at the same pace as in the average country in the comparable income group outside Africa. In other words, Africa’s level of infrastructure development is assumed to keep up (rather than catch up) with that of the relevant benchmark group. The bottom half of Figure 4 shows that in this scenario West Africa LICs reap the biggest gain: their growth would rise by 1.7 percent per annum (of which most would come from faster growth in infrastructure quality, 1.3 percent). At the other end, growth increases in East Africa LICs and Central Africa LICs are just below 1 percent per annum (and quality improvements would account for two-thirds of this total). Finally, the growth rate of upper-middle income African countries would rise by 1.2 percent, and the increase would be fully attributable to higher quality of infrastructure services.

We can use these counterfactual scenarios to illustrate also the equity potential of infrastructure development. This is shown in Figure 5. The top half of the figure shows that the biggest redistributive payoffs under the first scenario (catch-up) are attained by Central Africa LICs, with a reduction in the Gini coefficient by 4.7 basis points, of which over half (0.026) is attributable to enlarged infrastructure stocks. At the other end, the equity gain among South Africa LICs would be much more modest -- a decline in the Gini coefficient by 0.026, again mostly attributable to faster accumulation of infrastructure assets (0.017). In turn, East- and West Africa LICs, as well as upper- middle-income African countries, would achieve a decline in the Gini coefficient of just over 0.03. In low-income countries this would be mainly due to the expanded quantity of infrastructure, while in middle-income countries most of the effect would be due to the improved quality of infrastructure services.

Finally, the bottom half of Figure 5 shows the change in the Gini coefficient that would have occurred if infrastructure development in each African country had kept up with the average outside Africa. In this case, West Africa LICs and South Africa LICs reap the largest reductions in income inequality, with declines in the Gini coefficient of 0.034 and 0.031, respectively. For the latter group, the inequality decline is almost equally attributable to larger infrastructure stocks and higher quality, while for the former  it is mainly due to the improved quality of infrastructure services. In turn, the upper- middle-income countries of Sub-Saharan Africa under this scenario experience a decrease in the Gini coefficient of 0.023, due in full to the improved quality of infrastructure services.

So far we have focused on the benefits of infrastructure development – but what about the costs? The faster pace of infrastructure development assumed in the counterfactual scenarios must surely involve extra costs too. Higher investment would be needed to acquire the additional infrastructure assets, and higher current expenditures would likewise be necessary to operate and maintain them. Assessing the magnitude of these extra expenditures is not an easy task, owing to the very limited availability of data on investment and, especially, O&M costs of infrastructure, as well as their unavoidable heterogeneity across countries. However, an illustration of the required investment can be provided using the unit capital costs reported by Yepes et al (2008) to compute a rough estimate of the additional investment needs that would be posed by infrastructure expansion.

For brevity, we focus on the first counterfactual scenario described above. It is important to stress that the lack of suitable data forces us to restrict our attention to the costs of acquiring additional infrastructure assets (roads, power generation capacity, telephone lines), ignoring the costs of maintaining them, as well as the costs of upgrading their quality as assumed in the counterfactual simulation.

Figure 6 reports the results of these calculations, expressed as percentage of GDP.24 The figure shows that the infrastructure investment effort implicit in the catch-up scenario is quite considerable – as much as 15 percent of GDP in the low-income countries of East and Central Africa. The effort is more modest, but still substantial, among Southern Africa’s low-income countries, as well as upper middle-income economies, where it ranges between 7 and 8 percent of GDP. Although international data on infrastructure investment are quite scarce, the available information suggests that these numbers exceed by a wide margin those observed across the developing world, perhaps with the exception of some rapidly-growing East Asian countries.25

Furthermore, given the fairly limited involvement of the private sector in infrastructure across the region (with South Africa as the main exception) one can conjecture that the bulk of this additional spending would correspond to the public sector, for which it would pose a heavy burden indeed. In fact, in a number of countries in Sub- Saharan Africa total government revenue is well below 20 percent of GDP. This means that in those countries a fast-pace infrastructure catch-up would be financially infeasible, and this even ignoring its associated O&M costs. Instead, the acceleration of infrastructure development would have to be spread over a number of years. Most importantly, its benefits would have to be compared with those of other pressing demands on scarce government resources, a task beyond the scope of this paper.

** The country composition of these sub-regions is as follows: West Africa (ECOWAS) comprises Benin, Burkina Faso, Cote d'Ivoire, Gabon, Gambia, Ghana, Niger, Nigeria, Senegal, and Togo, East Africa (EAC) comprises Burundi, Kenya, Somalia, Tanzania, and Uganda; Southern Africa (SADC) includes Angola, Botswana, Mauritius, Mozambique, Namibia, South Africa, Swaziland, Zambia and Zimbabwe; and Central Africa consists of Cameroon, Central African Republic, Congo, Dem. Rep., Congo, Rep., Ethiopia and Sudan.

4.               Concluding remarks

Poor infrastructure is commonly viewed as one of the key obstacles to economic development in Sub-Saharan Africa. In this paper we have provided an empirical evaluation of the potential contribution of improved infrastructure to growth and equity in the region. Our assessment is based on the estimation of infrastructure-augmented growth and income inequality regressions using a large data set comprising 100 countries over the period 1960-2005. The empirical approach encompasses different core infrastructure sectors, considers both the quantity and quality of infrastructure services, and employs instrumental variable techniques to account for the potential endogeneity of infrastructure and non-infrastructure determinants of growth and inequality.

We find robust evidence that infrastructure development – as measured by an increased volume of infrastructure stocks and an improved quality of infrastructure services – has a positive impact on long-run growth and a negative impact on income inequality. The evidence also suggests that these impacts are not different in Sub-Saharan Africa vis-à-vis other regions. A variety of specification tests and robustness checks lend support to our empirical experiments. Since most African countries are lagging in terms of infrastructure quantity, quality, and universality of access, the tentative conclusion is that infrastructure development offers a double potential to speed up poverty reduction in Sub-Saharan Africa: it is associated with both higher growth and lower inequality.

Illustrative exercises show that our results are significant not only statistically, but also economically. Simple decompositions of observed growth and inequality changes suggest that over the last fifteen years infrastructure development made a contribution to growth and equity in virtually all world regions. Outside Africa, such contribution was particularly large in East and South Asia, and smallest in Western Europe, where infrastructure was already highly developed by the early 1990s. Infrastructure also helped in Sub-Saharan Africa, but to a much more modest extent than in Asia, and the primary reason seems to be the region’s lack of progress on the quality of infrastructure services over the sample period. Finally, counterfactual simulations also illustrate the substantial gains in terms of growth and equity that most Sub-Saharan African countries could reap if their levels of infrastructure development were to catch up, or even just keep up, with those of comparator country groups.

Speeding up infrastructure development also entails costs, however. Illustrative calculations show that just cutting in half the infrastructure quantity gap between African countries and those of comparable income levels in other regions could require as much as 15 percent of GDP in additional investment, plus potentially large (but hard to quantify) amounts in additional O&M expenditures – with most of the burden likely falling on the public sector. Barring a massive increase in aid flows, the sheer magnitude of these figures likely places a fast infrastructure catch-up beyond the financial reach of most African countries. Even a more gradual approach to infrastructure catch-up would pose considerable demands on fiscal resources over several years, competing with other pressing expenditure needs – such as education and health. In the end, the relative priority of infrastructure is likely to vary greatly across the region, depending on a host of country-specific factors – including their current infrastructure performance, which shows considerable cross-country variation.


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Table 1

Synthetic infrastructure indices: correlation with underlying measures


1.1  Synthetic quantity index



Synthetic Quantity Index

Indicator              [IK1]              [IK2]


Total telephone lines (main lines and mobile phones) 0.935 ..
(per 100 workers) (0.000)
Main telephone lines .. 0.943
(per 100 workers) (0.000)
Electricity generating capacity 0.950 0.956
(in MW per 1000 workers) (0.000) (0.000)
Total road length 0.644 0.644
(in km. per sq. km. of arable land) (0.000) (0.000)


1.2  Synthetic quality index

Synthetic Quality Index

Indicator              [IQ]              [IQ2]


Quality of telecommunication services 0.813 ..
(based on waiting time for main line installation) (0.000)
Quality of electricity 0.746 0.839
(based on technical losses of transmission and distrib.) (0.000) (0.000)
Quality of roads 0.754 0.810
(Share of paved in total roads) (0.000) (0.000)

Note: The numbers in parenthesis under the correlation coefficients are the corresponding p- values. The first synthetic index of infrastructure quantity [IK1] is given by the formula: IK1 = 0.603*K1 + 0.613*K2 + 0.51 * K3, where K1, K2, and K3 denote the logs of (standardized) physical measures of infrastructure in telecommunications (main lines and mobile phones), electricity (electricity generating capacity), and roads (total road length). The measures of telecommunications and electricity are normalized by working population of the country, while roads are normalized by the area of arable land. The second synthetic index of infrastructure quantity [IK2] is obtained as follows: IK2 = 0.606*K1A + 0.614*K2 + 0.506 * K3. The synthetic index IK2 uses the number of main phone lines per 100 workers (K1A) instead of the total number of phone lines (K1). The first synthetic index of infrastructure quality [IQ1] is obtained as follows: IQ1 = 0.608*Q1 + 0.559*Q2 + 0.564 * Q3, where Q1, Q2 and Q3 are (standardized) physical measures of quality in telecommunications (waiting time in years for the installation of main telephone lines), power (the percentage of transmission and distribution losses in the production of electricity) and roads (the share of paved roads in total roads). The second synthetic index of infrastructure quality [IQ2] omits Q1 from the set of underlying indicators, and is given by IQ2 = 0.7*Q2 +0.71* Q3.


Table 2

Infrastructure and Economic Development: Correlation Analysis

Synthetic Infrastructure Index

Economic                Gini                Infrastructure Quantity                Infrastructure Quality


Variables              Growth              Coefficient              [IK1]              [IK2]              [IQ1]              [IQ2]


Infrastructure Quantity (IK1)              0.3397              -0.4667              1.0000

(synthetic index )            (0.000)            (0.000)

Infrastructure Quantity (IK2)              0.3384              -0.4649              0.9962              1.0000

(synthetic index )            (0.000)            (0.000)            (0.000)

Infrastructure Quality (IQ)              0.4213              -0.5613              0.7287              0.7269              1.0000

(synthetic index )            (0.000)            (0.000)            (0.000)            (0.000)

Infrastructure Quality (IQ2)              0.348              -0.5681              0.6286              0.6361              0.9328              1.0000

(synthetic index )            (0.000)            (0.000)            (0.000)            (0.000)            (0.000)


Synthetic Infrastructure Index

Economic Gini Infrastructure Quantity Infrastructure Quality
Variables Growth Coefficient [IK1]                   [IK2] [IQ1]                   [IQ2]
Access to Sanitation 0.3553 -0.3763 0.8299 0.8275 0.6418 0.6046
(% population with access to sanitation) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Access to Safe Water 0.4370 -0.3023 0.7442 0.7423 0.6152 0.5317
(% population with access to safe water) (0.000) (0.003) (0.000) (0.000) (0.000) (0.000)
Access to Rural Roads 0.4732 -0.4929 0.7992 0.7957 0.6496 0.5867
(% population with access to rural roads) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Access to Electricity 0.3494 -0.3651 0.6929 0.7122 0.2078 0.1219
(% households with access to electricity) (0.022) (0.047) (0.000) (0.000) (0.330) (0.915)

Note: The numbers in parenthesis below the correlation coefficients represent their corresponding p-values.

Correlations between growth, infrastructure quantity and infrastructure quality are computed over country averages in the period 1990-2005

Access information is generally not available prior to 2000; hence the correlations of access measures with the other variables refer to averages over 2000-2005.


Table 3

Infrastructure and Economic Growth

Dependent Variable: Growth in GDP per capita (annual average, percent) Sample: 97 countries, 1960-2005 (non-overlapping 5-year period observations) GMM-IV System Estimation


 Variable  [1]  [2]  [3]  [4]
 Infrastructure Development (synthetic indexes):Infrastructure Quantity (IK1) 1/   2.6641 **   2.1927 **   2.0260 *   1.0609
(1.105) (0.981) (1.328) (1.403)
IK1 squared .. .. -0.0403 ..
 IK1 * Sub-Saharan Africa  ..  .. (0.247)..  0.2897
 Quality of Infrastructure Services (IQ1) 2/  ..  1.9581 **  1.9373 ** (1.450)1.5233 *
(0.549) (0.598) (0.800)
IQ1 squared .. .. -0.0265 ..
 IQ1 * Sub-Saharan Africa  ..  .. (0.298)..  1.3582
 Control Variables (1.281)
Initial Output per capita / per worker(in logs)Education(secondary enrollment, in logs) -4.3056 **(1.099)1.9914 *(1.095) -6.2404 **(1.285)2.7857 **(1.160) -5.9773 **(1.815)2.8253 **(1.175) -5.2489 **(1.635)2.9420 **(1.376)
Financial Development 0.4856 -0.0147 -0.0231 -0.0489
(private domestic credit as % of GDP, logs) (0.605) (0.492) (0.508) (0.640)
Trade Openness 1.2705 1.0965 1.1278 0.9347
(trade volume as % of GDP, logs) Lack of Price Stability (inflation rate) (1.053)-0.0990 **(0.036) (1.410)-0.0510 *(0.033) (1.380)-0.0511 *(0.033) (1.363)-0.0618 **(0.031)
Government Burden -1.3229 -1.9217 * -2.0330 * -1.2706
(Government consumption as % GDP, logs) (1.274) (1.281) (1.297) (1.363)
Institutional Quality 0.4748 -0.3029 -0.2769 0.2056
(ICRG Political risk index, logs) (2.418) (1.735) (1.632) (2.408)
Terms of Trade Shocks 0.0197 0.0944 * 0.0991 * 0.0768
(first differences of log terms of trade) (0.066) (0.051) (0.053) (0.055)
 Observations  582  582  582  582
Specification Tests (p-values)(a) A-B test for 2nd-order serial correlation  (0.360)  (0.482)  (0.484)  (0.481)
  • Hansen test of overidentifying restrictions
  • Difference-Sargan tests

All instruments for levels equation

(0.241) (0.166) (0.275) (0.340) (0.211) (0.290) (0.190) (0.197)

Numbers in parentheses are robust standard errors. Our regression analysis includes an intercept and period-specific dummy variables.

* (**) denotes statistical significance at the 10 (5) percent level. Standard errors are computed using the small-sample correction by Windmeijer (2005) 1/ See the notes to Table 1 for the definition of the synthetic indices of infrastructure quantity and quality.


Table 4

Infrastructure and Income Inequality

Dependent Variable: Gini Coefficient (end-of-period, in logs)

Sample: 87 countries, 1960-2005 (non-overlapping 5-year period observations) Estimation: GMM-IV System Estimation


 Variable  [1]  [2]  [3]  [4]
 Infrastructure Development (synthetic indexes):Infrastructure Quantity (IK1) 1/   -0.0828 **   -0.0485 *   -0.0489 *   -0.0537
(0.034) (0.026) (0.029) (0.045)
IK1 squared .. .. -0.0120 ..
 IK1 * Sub-Saharan Africa  ..  .. (0.010)..  0.1815
Quality of Infrastructure Services (IQ1) 2/ .. -0.0387 ** -0.0274 -0.0312
(0.017) (0.019) (0.026)
IQ1 squared .. .. 0.0086 ..
 IQ1 * Sub-Saharan Africa  ..  .. (0.006)..  -0.0349
 Control Variables (0.080)
Income per capita 0.4305 ** 0.2731 * 0.1571 -0.0123
(Real output per capita, in logs) (0.162) (0.160) (0.245) (0.240)
Income per capita squared -0.0213 ** -0.0112 -0.0049 0.0058
 Education (0.009)-0.0031 ** (0.009)-0.0032 ** (0.014)-0.0029 ** (0.014)-0.0034 **
(secondary enrollment) (0.001) (0.001) (0.001) (0.001)
Financial Development -0.0178 -0.0079 0.0043 -0.0115
(private domestic credit as % of GDP, logs) (0.016) (0.017) (0.022) (0.025)
Lack of Price Stability -0.0116 -0.0103 -0.0058 -0.0106
(inflation rate) (0.016) (0.015) (0.018) (0.016)
Trade Openness 0.0365 * 0.0431 * 0.0552 * 0.0485
(exports and imports as % of GDP, logs) (0.021) (0.027) (0.030) (0.035)
 Observations  476  476  476  476
Specification Tests (p-values)(a) A-B test for 2nd-order serial correlation  (0.314)  (0.347)  (0.320)  (0.551)
  • Hansen test of overidentifying restrictions
  • Difference-Sargan tests

All instruments for levels equation

(0.821) (0.423) (0.681) (0.669) (0.758) (0.396) (0.602) (0.521)

Numbers in parenthesis are robust standard errors. Our regression analysis includes an intercept and period-specific dummy variables.

* (**) denotes statistical significance at the 10 (5) percent level. Standard errors are computed using the small-sample correction by Windmeijer (2005 1/ See the notes to Table 1 for the definition of the synthetic indices of infrastructure quantity and quality.


Table A.1

List of Countries

 Industrial countries (23)

Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Switzerland

Latin America and the Caribbean (22)

Argentina, Bahamas, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad and Tobago, Uruguay, Venezuela

East Asia and the Pacific (12)

China, Hong Kong, Indonesia, Republic of Korea, Malaysia, Mongolia, Papua New Guinea, Philippines, Singapore, Taiwan, Thailand, Vietnam


Eastern Europe and Central Asia (18)

Belarus, Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Poland, Romania, Russian Federation, Slovak Rep., Slovenia, Turkey, Ukraine, Yugoslavia (Serbia)


Middle East and North Africa (20)

Algeria, Bahrain, Cyprus, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Malta, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab Emirates, Yemen


South Asia (5)

Bangladesh, India, Nepal, Pakistan, Sri Lanka

Sub-Saharan Africa (36)

Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Central African Republic, Chad, Congo, Dem. Rep., Congo, Rep., Cote d'Ivoire, Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mauritania, Mauritius, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South Africa, Sudan, Tanzania, Togo, Uganda, Zambia, Zimbabwe


The Great Escape - Health, Wealth, and the Orgins of Inequality

Reviewed by Professor Uwe Reindhart, Princeton.

The causes and economic effects of inequality in income and wealth have long been a focus of economic research. As a series of papers on the topic in this year’s summer issue of The Journal of Economic Perspectives illustrates, the issue is much more complex than might be imagined by the simplistic talking heads on television who seek to reduce each and every issue to “good or bad.”
A truly elegant exploration of the topic is offered in a new book, “The Great Escape: Health, Wealth and the Origins of Inequality” by my Princeton colleague, the economist Angus Deaton. It offers an erudite sojourn through history, all the way to the domestic and international policy issues pressing in on us today. Unusual for scholarly works in economics, this book is rendered in easily accessible prose, supported by fascinating statistics presented graphically.

An abstract of the book, so to speak, can be had by viewing a recent presentation by Professor Deaton. A lecture, however, is no substitute for the much richer narrative in the book, which in passing offers many historical nuggets that may be new to readers.

The book demonstrates that assessments of income inequality are basically meaningless without the backdrop of the origins of a prevailing inequality in income and wealth.

Suppose, for example, that in one nation all but a handful of members of society were poor. Income inequality, as we now measure it, would then be quite low. If in a second state a substantial segment of that society had managed to create great wealth for itself, assuming the rest of society remained in its previous state of poverty, would this development be welcomed or deplored?

Leaning on a principle on economic welfare first clearly articulated by the Italian economist Vilfredo Pareto (1848-1923), most economists probably would contend that a change that makes some people in society better off without making anyone worse off must ipso facto represent an increase in economic welfare. It follows that inequality in income and wealth engendered by economic growth typically is not to be deplored.

In his sobering Chapter 5 on the United States, however, Professor Deaton asserts that economists routinely apply Pareto’s principle too narrowly, overlooking that the wealthy in societies with highly unequal distributions of income and wealth may capture the country’s systems of governance. They may then use this power to rig market processes in their favor or to exploit taxpayers through what economists call “rent seeking” – that is, profit made on government contracts that is not matched by commensurate value delivered to society. That linkage can easily make the rest of society worse off.

“There is a danger that the rapid growth in top incomes can become self-reinforcing through the political process that money can bring,” Professor Deaton warns — a process that can turn democracy into plutocracy.

This causal flow from wealth to politics and thence a perpetuation of wealth has been noted by others – among them my fellow Economix blogger Simon Johnson, formerly chief economist of the International Monetary Fund, in his “The Quiet Coup” and the economist Luigi Zingales of the University of Chicago Booth School of Business, who contends that American capitalism has been slouching more and more toward crony capitalism.

The perpetuation of wealth among top-income families can be further enhanced through the educational system. “The United States is not particularly good at actually delivering equality of opportunity,” notes Professor Deaton.

Eduardo Porter of The New York Times made the same point in his Economic Scene column this week, noting, “The United States is one of the few advanced nations where schools serving the better-off children usually have more educational resources than those serving poor children.”

Much of Professor Deaton’s overall positive book chronicles and celebrates the enormous increase in wealth, health and well-being in many parts of the world during the last two and a half centuries. That story is told in the earlier chapters of the book, supported by many fascinating visual presentations.

“The Great Escape” is a splendid story of how the creation of wealth and better health and longevity have marched forward hand in hand, although not evenly. There remains the mystery of why longevity and health still vary considerably among nations with roughly equal gross domestic product per capita and why the United States, for example, does not rank favorably among nations on that score, as the Institute of Medicine of the National Academy of Sciences noted in a report this year.

Professor Deaton does not ignore the billions of people who have remained excluded from the great escape. His lengthy concluding chapter, “How to Help Those Left Behind,” is thought-provoking and likely to be hotly debated as he explores alternative approaches to helping those who have not escaped the misery of poverty.

It may be intuitively appealing to assume that if only the rich countries taxed themselves a little more and transferred funds to the poor countries, the latter would grow out of their poverty. The sizable flow of foreign aid to the developing countries during the decades after World War II has rested on that theory.

Professor Deaton, though, comes to quite the opposite conclusion. “We often have such a poor understanding” of what the poor in the poor countries “need or want, or how their societies work,” he writes, “that our clumsy attempts to help on our terms do more harm than good.”

A great deal of foreign aid to poor countries derives from economic or political special interests in the donor countries, he says, and has propped up corrupt governments in the poor countries. By providing revenue that frees the government from having to raise taxes, foreign aid hinders the development of the social and political institutions that are the sine qua non of economic growth, in this view.

A better approach, Professor Deaton says, is for donor countries to remove trade policies that obstruct economic development — for example, import quotas or high tariffs on commodities harvested or produced and exported by developing countries. Or to take the money disbursed as foreign aid and fund more research on diseases afflicting people in poor countries or on enhancing agricultural yields. And while the military-industrial complex in rich countries profits greatly from the arms sales to poor countries, it is hard to see how that helps the residents of poor countries escape the clutches of poverty, he says.

And so on.

Professor Deaton concludes this particular chapter by telling his students “to work on and within their own governments, persuading them to stop policies that hurt poor people,” adding, “These are our best opportunities to promote the Great Escape for those who have yet to break free.”

Bad Samaritans by Ha-Joon Chang

By Antoine Cerisier.

Ha-Joon Chang is a South Korean economist currently teaching at Cambridge University, as well as a follower and friend of Nobel laureate Joseph Stiglitz, whose main areas of interest are development economics and international trade. His books have had considerable impact in the developing world: Rafael Correa, the current Ecuadorian president, cited Dr Chang as his main influence for economic policy. Bad Samaritans was released in 2007 and was Dr Chang’s most successful work to date, receiving numerous accolades from the press and from renowned scholars – including Noam Chomsky and his mentor Joseph Stiglitz.

The scourge of neo-liberalism

The first few chapters of Bad Samaritans assess the ‘official’ history of globalisation, as narrated by free trade economists and most international institutions. According to this mainstream view, the United Kingdom was the first country to adopt free trade policies in the early 19th century – largely inspired by Adam Smith’s influential theories. The benefits of trade liberalisation were so apparent that most Western countries followed suit and started to liberalise their trade and domestic economies as early as 1850. The so-called ‘Golden Age’ of globalisation lasted until 1914 and the start of the First World War. In a context of increased tensions and economic downturn, states turned their back on free trade and adopted more protectionist measures – the ultimate sin which, according to liberal scholars, contributed to the outbreak of the Second World War. Thankfully, trade was liberalised again after 1945 with the help of the Bretton Woods system and the creation of the General Agreement on Tariffs and Trade (GATT), now called the WTO. This trend was confirmed in the 1980s with the rise of neoliberalism; and in today’s world, in the words of a free trade economist quoted by Dr Chang, you’re either neoliberal or “neo-idiotic”.

Dr Chang rejects this fairytale and argues there are several major flaws – and lies – in the official history of globalisation. For instance, during the ‘Golden Age’, the United States was the most protectionist nation on the planet; high tariffs allowed American industries to prosper and be protected from international competition. This leads us to the book’s most powerful argument: Western states have been consistently “kicking away the ladder from which they have climbed” by deterring developing countries from using protectionist measures which have been beneficial to the West – and sometimes still are. The author denounces this hypocrisy and reminds us that trade barriers and state intervention were instrumental for the emergence of competitive industries in his home country, South Korea. Protectionism may not directly trigger economic development, but both can certainly coexist, as economic history has taught us. Dr Chang deplores this dishonest rewriting of history as it can be used to justify harmful policies. The remaining chapters are dedicated to other preconceived ideas criticised by the economist. He defends public investment and state enterprises as powerful tools for economic development both in the West and in the global South; admits foreign direct investment (FDI) is beneficial but only if it is well regulated; and rejects the idea that some nations or cultures are in essence more capable than others. The book’s conclusion remains optimistic but calls for major changes in the international system as regards trade and state intervention.

Dr Chang’s book provides a fresh perspective on international development, addressing the root causes rather than the symptoms. His argumentation is precise but still rests on specific and sometimes amusing examples. For example, I discovered that British writer Daniel Defoe, the celebrated author of Robinson Crusoe, was also a distinguished economist advocating protectionism and state intervention as early as the mid-18th century. In fact, Bad Samaritans prolongs the old debate on trade and state intervention, which already involved Alexander Hamilton, Daniel Raymond and Adam Smith in the early 19th century; more generally, Ha-Joon Chang adds his name to the long list of globalisation critics.

Despite largely favourable reviews, the book has been criticised in the Financial Times and The Economist – as one might expect. Critics argued that empirical evidence usually supports the main argument put forward by free trade economists, namely that trade liberalisation is good for growth and development. In a 2002 journal article entitled “Growth is good for the poor”, Dollar & Kraay concluded that free trade triggers growth and helps alleviate poverty in the global South. Nonetheless, as Chang would respond, it is probably the opposite: states are more willing to liberalise trade once they reach a certain level of economic development. A more convincing argument came from The Economist. The journalist admits that the East Asian example can illustrate the use of protectionism and state intervention in the economy. However, such policies were not particularly successful in other parts of the world, perhaps due to Asia’s high levels of education and relative political stability. In any case, as I argued in a recent contribution to this blog, unimpeded trade and one-size-fits-all approaches usually do more harm than good. In that regard, Bad Samaritans is a good wake-up call for all those interested in economic development and international politics.

Council on African Security and Development Takes Roots

The Council on African Security and Development (CASADE) is a non-profit research-driven collectivity of experts and academics dedicated to a holistic advancement of Africa and its inhabitants. It serves as a reservoir of experts that African ministries, agencies, universities, and international organizations may confidently and readily access for specific projects, guidance, and counsel. Its vision for Africa is multi-faceted: to see an end to ethnic and religious tensions that lead to civil strife, violence and instability with the potential to disable and discourage sustained human and economic development; to bring to an end pervasive hunger and privation that have singularly defined the lots of individuals and groups in the continent; and ultimately to witness an African renaissance with its own brand of democracy in which human rights and dignity are advanced and protected, and its economies are less dependent on primary commodities and natural resources. Given these preferred visions for Africa, the means to them must equally be multi-faceted.

While countries in Europe, Asia, and North America have benefited immensely from the Industrial and Green Revolutions, those in Africa remain trapped in debilitating poverty, inadequate or non-existent infrastructures, and poor healthcare delivery systems. Received wisdom amongst development experts is that Africa, while unfortunate, is perhaps destined to remain in this state of social and economic affairs so long as enabling domestic institutions are not re-aligned with best practices, and do not self-correct. Evidence that inhabitants of Africa have fared very poorly since the late 1980s is incontestable; but it is also in evidence that certain African countries have shown remarkable social and economic improvements in subsequent years. But what are the causes of the positive experiences some African countries have enjoyed so far? Can these experiences be repeated elsewhere in Africa, and if so, to what extent are the causes transmittable, and sustained in the continent?

These questions, in substance, implicate the following public policy issues: are observable improvements in some African countries in the areas of finance, telecommunication, agriculture, and low-scale manufacturing isolated instances or harbingers of a broader process in place that may, with proper stewardship of natural resources, reduction of civil conflicts, transparency in public finances, improvements in the supply of electricity and healthcare services lead to sustained economic development and security?

To these ends, the Council engages the expertise of internationally recognized academics, policy analysts, and scientists in innovative research and discovery of new knowledge that liberate and inform public policy and its implementation. Through partnerships and alliances with universities and development-oriented centers in Africa, Europe, Asia, and North America, the Council would enhance its technical abilities to deliver beneficent blends of high research and policy analysis. The acquired knowledge generated by the council would be consistently disseminated to governmental agencies, policy makers, and social actors in the fields of development and security through seminars, international conferences, and publications. One of the primary objectives of the Council is to provide linkages between scholars and researchers whose professional interests are in African development and security. Such effort would necessarily lead to active collaborative scholarship between African and non-African experts and specialists with the singular goal of making African issues part of mainstream international concern and attention.

Guiding Philosophy

The Council’s philosophical leaning is largely defined by the belief, informed by evidence and scholarship, that all social and economic problems in any country cannot be solved by governments alone. Contrary to received and pervasive practices in many modern African societies, the government’s role in public and private affairs should be limited but robust enough to address its primary institutional responsibilities such as national security, formulation and implementation of effective laws and policies that govern behavior, protect human rights, the right to religious worship, and enforcement of legal contracts. These provisions, in conjunction with sound fiscal and monetary policies, create enabling environments in which the private sector may flourish and nourish its entrepreneurial spirit.

It is for this reason that the Council calls for institutional shifts and re-alignments that place more emphasis on industrialization, and market-led development strategies that rely less on governments, and revenues derived from primary commodities and natural resources. For, with advances in economic development countries tend enjoy stability and security, and these in turn beget further advances in economic and social welfare. The establishment of enduring institutions for creative economic and public policy dynamics that propel growth in national economies is at the core of the council’s overarching objectives for an emerging Africa.

The Shackling Effects of Bureaucratic Corruption, And New Tools to Recover National Assets

To argue that misappropriation of social resources is the norm in both developed and emerging economies is to state the obvious. And to argue that economic development inevitably takes hold in spite of resource mismanagement by public officials is historically accurate but discounts the fact that such development would have been achieved much sooner would not be so obvious. Advanced Western societies are replete with the latter observation. The long-held view that all manners of bureaucratic corruption are detrimental to efficient resource allocation is factually incorrect, for in instances where bribery of officials would eliminate burdensome bureaucratic red tapes and facilitate productivity have been put in evidence in emerging economies of Africa and Asia, and one would be hard pressed to condemn such practice on grounds of economic efficiency (the moral implications notwithstanding). Thus a distinction must be drawn between petty corruption that are benign and perhaps beneficial to economic growth, and grand corruption that involves massive looting of the public treasury. In the later, its distortionary effects come from internal misalignment of personal and social interests if the misappropriated funds are expended domestically (this is less harmful); the most serious and damaging effects arise if looted funds are stashed abroad thus depriving the domestic economy of both direct benefits and subsequent externalities. The pervasive presence of grand corruption in Africa remains a major source of concern, for it deprives, and marginalizes national economies to the point where its citizens are kept in near perpetual state of poverty, high crime rates, social instability, and dysfunctional institutions; other consequences on this standard list of the nasty side effects of bureaucratic corruption are just as troubling.

How to contain grand corruption, and return ill-gotten gains to the countries of origin have been the focus of numerous international bodies, and Western governments that see their aid packages to Africa returned to them as fruits of bureaucratic corruption. In the following article entitled “What’s Yours Is Mine,” Andrew Marshall provides guidance on how to minimize bureaucratic corruption, and recover misappropriated national assets.

Andrew Marshall:


This study is about recovering money stolen by corrupt politicians and officials. Asset recovery is a key element in deterring and punishing the corrupt, and the reduction of corruption is critical to development. The money can be put to better uses once recovered, and it amounts to billions. But there’s another reason why this is significant for those who are primarily focused on development: amongst the key issues in asset recovery are greater accountability and transparency, which are also increasingly regarded as key to long-term development success. The main argument of this study is that corruption investigations and asset recovery are being tackled in new ways by new actors from the private sector, civil society and media, and that this can help improve the prospects for justice. It would be too much to call this a revolution: it’s an evolutionary process. It needs long-term support if it is to prosper as a policy choice, and it raises some issues for policymakers and those who carry out the recoveries. But if the agenda for accountability is to advance at the same pace as transparency, the prosecution of the corrupt and the return of the money they stole are critical.

The main recommendations of this study for policy makers are:

The U.S. and U.K. Governments should build support for asset recovery at home, and find allies in emerging financial and political powers abroad: financial integrity and transparency need a broader underpinning.

Build on new approaches to recovering stolen loot: Donors should explore new ways of helping start, fund and staff asset recoveries, using capabilities from other governments or the private sector. The U.S. Department of Justice should explore new targets for its Kleptocracy Initiative. Media, civil society, NGOs and victim groups have new roles to play.

Fix the problems with global financial intelligence: It is too easy for corrupt politicians to hide money, and too hard for investigators to find it. The U.S. and U.K. need to fix the due diligence process, and work with banks and others to make the global financial information system function better.

Get tougher with corrupt politicians and countries that won’t co-operate: Pursuing corrupt politicians is important. Refusing visas to the corrupt and their enablers should be encouraged – and publicized. Development and policy officials need to get more comfortable with punishment and recovery.

Build greater global support for recovering the proceeds of corruption:

A lot of excellent policy work has already been done in this area. Notably, the World Bank and the United Nations Office on Drugs and Crime have an initiative called Stolen Assets Recovery Initiative (StAR1). This study quotes from its work: StAR’s work has been groundbreaking. It also relies on the work of three NGOs: Transparency International, Global Witness and the International Center for Asset Recovery at the Basel Institute on Governance. Individual countries have also come up with new strategies, with the U.S. in particular playing a leading role. The UK, too, has developed new thinking, as has Switzerland – both states where stolen assets have been laundered and hidden.

What new can be said? Previous analyses have primarily focused on one critical actor – government – and one critical area – rules and laws. The author’s background is in media, and in an investigative company. Because of my experience, I want to focus on:

  • The emerging global information system around banks, financial institutions and intermediaries, and the risk, compliance and investigations industry
  • Media, and their role in identifying and punishing corruption.
  • NGOs, and their prospects for a more prominent role. The first section looks at the rationale and role of asset recovery: why it has become a significant issue.

The core argument that has been made in the last twenty years is about the importance of the legal and procedural issues that have bedeviled the best known cases (the Duvaliers, for example) and the second section of the study deals with these. But there are other areas that are increasingly receiving attention. Another central issue is the investigative process: following the money. While it is too much to expect to remove the obstacles that exist to financial investigation, it is possible to apply existing rules better, and to develop new approaches. The third section looks at this.

Most studies indicate that there is nothing that can be done without political commitment to prosecute, investigate, seize and return money. Leadership does not come from politicians only: civil society and the media play significant roles, and external attitudes are pivotal. The fourth section deals with these.

The fifth section wraps up the discussion with some conclusions and recommendations. What can be observed about progress in developing asset recovery, and what can be said about future steps? A key theme is that asset recovery needs to move out of the shadows, with investigators and prosecutors making new alliances, and development and policy professionals accepting the importance of the recovery agenda, enforcement and the accountability it involves. The recovery of assets, and investigations– in the media or public sector – deserve more attention and more funding.

The results of co-operation in asset recovery can be impressive. Failure can be profoundly morale-sapping. Michela Wrong’s excellent book “It’s Our Turn To Eat” documents the failure of John Githongo, Kenya’s anti-corruption campaigner - despite his strenuous efforts, and the danger he placed himself in. Her book isn’t especially optimistic about tackling corruption; but it is clear-sighted about the reasons for pursuing it. Some of the author’s thoughts on the subject came from being involved, peripherally, with the aftermath of Githongo’s campaign. Enormous progress has been made in finding the guilty, prosecuting them, and getting back the money they have stolen. At its root this is because of wide-ranging changes in how the world sees accountability and transparency: for that to continue will require effort and imagination. The broader theme of this paper is advancing that agenda, in the longer term.

Why stolen asset recovery matters

More than two years later, officials from the different countries are still fighting to get back money stolen by these officials and their friends and families. A house in London; executive jets; stakes in leading Italian companies have all been the subject of litigation. The good news is that the efforts got started promptly with some signal successes. This is by now a process that banks and governments understand well. Action Plan on Asset Recovery showed real commitment, marked by the Arab Forum on Asset Recovery held in Doha. The bad news: remarkably little has yet been settled, despite the time and effort and money involved. There are recriminations and accusations between the countries that have lost money and those where it may have ended up, and a lot of frustration.

Why corruption became a “problem”

It was only in the late twentieth century that corruption abroad, rather than at home, became an issue. The U.S. first made international corruption into a political issue in the 1970s, with the Foreign Corrupt Practices Act, a landmark piece of legislation. Other countries took decades to follow (in some cases, they still haven’t) with legislation outlawing bribery abroad. Pressure from U.S. companies concerned that they alone would be penalized for bribery has helped to propel the agenda. But wider shifts helped.

One key is the end of the Cold War. Dictators who had hitherto been regarded as “regional strongmen” rapidly became regarded as what they were: simply corrupt dictators. At the same time, the rapid spread of capitalism created new opportunities for corruption, and new concerns about it. NGOs, especially Transparency International which was founded in 1993 by a former World Bank official, made exposing and punishing corruption their cause. There was a normative shift in the 1990s: corruption was not an inevitable evil, like bad weather. It was something to outlaw and punish. This took international legal form - “The (Deauville Partnership with Arab Countries in Transition -- Action Plan on Asset Recovery, 2012); (UNCAC Coalition, 2011).

On the 17 December 2010, a Tunisian street vendor called Mohammed Bouazizi set himself on fire, precipitating demonstrations that would spread across the Middle East. Within days, President Zine El Abidine Ben Ali was removed from power. Within weeks, Egyptian President Hosni Mubarak would follow, and within months, Libyan President Muammar Gadaffi.

The G8 put asset recovery as one of the main goals of transition, and its codification into 2012’s “Beyond shedding light on the devastating impact of grand corruption, the Arab spring has revealed major anti-money laundering deficiencies, and the huge difficulties of getting the money back even after the dictator has been pushed from power,” said one group of NGOs working on the issue.

A 1997 OECD Convention marked the beginning of an international movement based on the premise that we all have a stake in the integrity of the global marketplace that deserves the protection of law,” writes one academic.

An agenda, policy recommendations, and action have followed, including the landmark United Nations Convention against Corruption (UNCAC). The G20 countries have also committed themselves to recovering the proceeds of corruption, and at their 2010 summit passed a surprisingly ambitious anti-corruption plan. Aid agencies have built anti-corruption into their policies, including references in the Accra Agenda for Action.

Why recovery is an issue

Recovery of stolen assets has taken its place as one of the pillars of anti-corruption action. Why? “Three incentives drive the asset recovery agenda: a resource mobilization incentive; a law enforcement incentive; and moral and reputational considerations, which encompass both the belief that it is wrong for corrupt officials to benefit from stolen loot and the concern that the reputations of those who fail to act will be tarnished,” says the Stolen Asset Recovery Initiative. “Similar incentives drive public policy on the proceeds of crime. There are parallels between these agendas, not least because efforts to tackle the proceeds of corruption use the institutional and legal framework established for broader law enforcement purposes.”

It is not just that a problem (corruption) has been recognized, but also a solution (legal action to freeze and recover the proceeds). In the 1980s, it became more common to use asset forfeiture against criminals, going against the economic basis of criminality and in particular drug trafficking. Controls on money laundering were stepped up. In the same way, the tightening of the rules after 9/11 affected the way that countries and governments went after the corrupt. The fight against corruption has become part of a broader effort to establish rules and definitions for dealing with international and transnational criminality in a context of globalization and concern about its risks.

The first landmark in the recovery of assets, the case of former Philippine President Ferdinand E. Marcos precedes the end of the Cold War, in 1986.7 The task was tough, but some valuable precedents were laid down. Since then, there have been dozens of cases, with more or less success: Haiti and the Duvaliers, the second major case, came soon after, and it continues to this day. Other, more successful cases came in the 2000s, including Nigeria and Sani Abacha, and Vlademiro Montesinos in Peru. (Carrington, 2010), (World Bank and UNODC, 2009), (World Bank and UNODC)

Increasingly the work has been codified. In 2007 the World Bank with the United Nations Office on Drugs and Crime launched the Stolen Asset Recovery (StAR) Initiative, “an initiative to help developing countries recover assets stolen by corrupt leaders, help invest them in effective development programs and combat safe havens internationally.” It was a bold and interesting move, putting ideas, people, money and action behind the concept, and combining a development and financial institution with one focused on criminality and law enforcement. The project aimed to build institutional capacity in developing countries, strengthen the integrity of financial markets, assist asset recovery, and monitor the use of recovered assets. Yet the record of successes is less than might be expected. In the first case, the record has been poor. “Marcos and associates made off with an estimated $5-$10 billion through a variety of corrupt schemes,” notes the Bank. “To date, the Philippines has managed to recover about US$684 million from foreign jurisdictions.” Early in 2013, it looked likely that the search would finally sputter out, as lawmakers in the Philippines proposed disbanding the Presidential Commission on Good Government, the asset-recovery program launched in 1987. The cost and returns were not matching up. “There remains a huge gap between the results achieved and the estimated billions of dollars that are stolen from developing countries,” says StAR. ”A total of $1.225 billion assets were frozen between 2006 and 2009 and $277 million assets were returned to the country of origin. These amounts are only a tiny fraction of the estimated $20 billion to S40 billion that are stolen annually from developing countries and hidden in financial centers.”

Why tackling recovery is tough

After the Arab Spring, there were promises that asset recovery would be a priority. But two years later, the Egyptian government complained that too little had been done by the British government. Assem al-Gohary, head of Egypt's Illicit Gains Authority, told the BBC: “The British government is obliged by law to help us. But it doesn't want to make any effort at all to recover the money. It just says: ‘Give us evidence’. Is this reasonable? We are in Egypt, looking for money in the UK.”9 There were accusations that Britain had allowed former officials and their cronies to keep millions of pounds of property and business assets in the UK, because “ministers are more interested in preserving the City of London's cozy relationship with the Arab financial sector than in securing justice.”10 A British minister retorted in the politest possible way that rules are rules. “It will take time to achieve the results we all want. Asset recovery requires painstaking work and we must ensure proper judicial processes are followed,” wrote Jeremy Browne, the UK's Minister of

State for Crime Prevention, in Al-Ahram. “Whilst there is a moral imperative for this work to be carried out swiftly, it should not be at the cost of depriving individuals of their rights.”

Most international co-operation is hard if it is worth anything. But in its complexities, legal and financial niceties, and adversarial nature, asset recovery presents problems that tax the brain and sap the spirit. An asset recovery is like engaging in a knife fight while conducting a divorce case by telex, in Latin. It requires brains, patience and aggression. Finding those with the required skills is not easy. “Few countries have expertise in this area and governments tend not to prioritise it, whether on the requesting or requested side.”11. It is a specialist area, or more accurately several specialist areas. The processes can be described as: Instigation, tracing, freezing, confiscation or forfeiture, and return.

The legal—procedural obstacles are usually primary. Asset recovery involves working across borders, between governments, which is tough at the best of times. There are reasons why it is hard to find and seize someone’s assets: individuals have rights and amongst them, in most societies, are rights to privacy, due process, protection of private property and equal and fair treatment under the law.12 This applies to ex-dictators too. It may even apply more, since international law reserves certain prerogatives to states and their representatives. It is no small matter to abandon these: most people don’t believe that an individual should have their bank accounts taken by the U.S. government on arbitrary grounds, or just because it has taken a dislike to a foreign politician.

The second set of obstacles is informational and financial. Investigators are trying to find money that has been deliberately concealed by a disciplined, well-funded, well-informed individual with access to state power, global banks and smart lawyers. And money can be moved very rapidly.

There are other layers of difficulty that are primarily political. In many cases, the pursuing government will lack the capability to investigate. In the case of Libya, for example, the state had almost vanished. Even had it not, the Libyan government had not built capabilities to chase funds stolen by its rulers. And in cases where anti-corruption efforts have been pursued subsequent governments have not always been supportive. In the countries where assets have been transferred, there will also be politics.

The Stolen Asset Recovery Procedure

  • The case may start with the changing of a regime, peacefully or otherwise, a criminal prosecution, whistleblower allegations, or media claims.
  • Investigators trace assets via documents, electronic data, informants, accounting, and information from banks and governments. Some may be available locally or via open sources; much will require help from overseas governments. It is important that this be done discreetly and fast.
  • Once the assets have been located, they must be frozen in place by a prosecutor, magistrate or judge. Different jurdisdictions have different rules. Again, it will be critical to maintain confidentiality until the freeze is in place, and to gain co-oepration with other governments.
  • The assets must now be taken through a confiscation or seizure order. There are different routes depending on jurdisdiction and circumstances, including criminal or civil proceedings. Again, requires working with other jurisdictions to get orders and to enforce them.
  • Assets must be transferred back to where they came from. This raises a number of issues, like costs to other jurisdictions, compensating those who may have lost out in other ways, and ensuring that the proceeds go to the right place (and do not get embezzled again).


Follow the rules: Legal, technical and procedural problems

The toughest, most detailed and least glamorous part of the work that has been done to ease recovery of stolen assets concerns the removal or easing of legal and procedural barriers.

Making the rules work: Removing the barriers

It’s hard for countries to work together. They have different legal systems, police forces, governments, political processes and histories, embedded in different sovereignties (and conceptions of sovereignty). There are processes for requesting help from other countries – Mutual Legal Assistance requests, for example – and the strengths and weaknesses of this government-to-government system account for many of the issues. StAR’s landmark study Removing the Barriers to Recovery13 lists a series of steps to make things easier using official channels, the most established way of proceeding. Most of the barriers identified are in the categories of legal, technical and procedural:

Asking for assistance: MLAs can be large, complex and hard to understand or execute. Some countries may require information that is hard to find, or have very detailed procedures, and a request may seem too vague. It’s important to be flexible, but the word “legal” is there for a reason.

Managing assistance: Simply managing the MLA process can be bureaucratic, slow and frustrating – getting confirmation, responses, addresses and names, processes, timelines, clarity on jurisdiction.

Co-coordinating domestically: MLAs, information and co-operation requests are complex to co-ordinate domestically, and can receive less attention than local matters.

MLATs: A Mutual Legal Assistance Treaty is the basis for legal co-operation. Not every jurisdiction has such agreements with every other jurisdiction, some are old, and some don’t reflect the realities of international asset recovery.

Refusal of MLA: it is too easy for a country to simply refuse an MLA with little response, or a bland reference to “economic interests”.

Informal assistance: in many cases, co-operation will be speedier and more effective if done informally, without an MLA – but through a structured process. Contacting witnesses, temporary freezes, provision of public records can all be done in this way.

Statutes of limitation: many corruption cases go back years. Officials may also remain in office to wait out the limitation. There are ways round this (changing laws, or using alternative offences).

Legal co-ordination: Prosecutors may wish to reach a plea agreement – but that may have big implications for other jurisdictions and cases.

Dual criminality: some extradition laws require that the offence on which an individual is being extradited should be an offence in both countries.

Confidentiality: in some countries, if a financial institution sends information on a customer, it has to notify them. In an asset search, this gives a well-resourced criminal time to shift cash and disappear.

Immunity: In some cases an individual is immune from prosecution because of holding office. Some countries may also extend immunities to foreign officials.

Moving quickly: Sometimes, an expedited procedure is needed on information, assets or people. It is useful to be able to make temporary freezes without an MLA when time is tight.

Non-conviction based confiscation: This allows for confiscation of assets even where the asset holder has not been convicted, but some countries do not permit it. This can be a problem if the corrupt leader is “dead, a fugitive, absent, immune from prosecution.”

Standard of proof: An asset search is a speculative business. Very strict standards may make it practically impossible to get restraint orders – but on the other hand, there needs to be evidence.

Restraint and confiscation: Freezing assets, keeping them frozen and then taking them can be protracted and complex, but the freeze needs to be rapid or money will move. Laws often need to be updated, and to accommodate complex cases. Foreign orders may not be enforceable.

Handling the money at the end: Jurisdictions need legislation for disposing of assets, and selling them promptly, so they hold their value. Who sends money back, and to whom? With what conditions?

To some degree, these issues can be dealt with by gradually changing laws – piecemeal legal reform. To some degree, it requires changes of attitude, which is harder. This classic route – using criminal law, and official machinery – is tough.

One of the biggest and most significant advances has been the use of non-conviction-based civil forfeiture: using civil, not criminal action, and going directly against assets rather than individuals. This avoids problems associated with individuals who haven’t been convicted (or are still in office), or where the criminal trial is still under way. “Because it is against the property, an NCB forfeiture action is not dependent on a criminal conviction and may be pursued even if the corrupt official is dead, a fugitive, has been acquitted of a related

criminal offense, is immune from criminal prosecution, or enjoys residual political influence making criminal prosecution not possible,” wrote Linda Samuel, a senior U.S. official.

One example of new ways to focus skills, money and co-ordination has come about through the U.K. government’s realignment of anti-corruption strategy in the early 2000s, under the Department for International Development. Making DFID the focus emphasised that corruption was a development issue. “The Department for International Development (DFID) is the U.K. Government lead for UNCAC. It also has a specific interest in preventing U.K. individuals and companies from contributing to corruption overseas, especially in developing countries. It funds the Metropolitan Police's Proceeds of Corruption Unit and the City of London Police's Overseas Anti-Corruption Unit, as well as a small corruption intelligence cell in SOCA and part of the asset recovery work of the Crown Prosecution Service.”15 The logic behind the move was that without such funding and support, anti-corruption work wouldn’t get the backing it needed.

How successful has this been? While the results aren’t negligible, they are small in the overall scale of grand corruption. There is evidence that the investment has played a useful role in realigning British government efforts, and in particular in tackling cases of corruption in Nigeria. The US, too, has been coming up with new ideas to make the existing system function better, and investing in resources and institutional capacity. In 2010, Attorney General Eric Holder announced the creation of a new Kleptocracy unit at the Justice Department, focused specifically on this issue. “The unit is housed in the Asset Forfeiture and Money Laundering Section of the department’s Criminal Division and... staffed by five lawyers, Justice Department officials said. The Federal Bureau of Investigation’s Asset Forfeiture and Money Laundering Unit, based in the bureau’s Washington headquarters, has diverted two agents to the effort... They will supplement the work of established anti-corruption groups in U.S. Immigration and Customs Enforcement and the FBI’s Washington field office.”

Switzerland, too, has moved to make the rules work better, through the Restitution of Illicit Assets Act (RIAA), also known as Lex Duvalier. “Specifically, the RIAA allows for asset confiscation in situations where the current state of the victim country renders it impossible to conduct a proper exchange procedure via traditional judicial procedures. In these cases, the RIAA would allow a unique “burden shift,” requiring the Swiss government to show that: (1) the funds held in Switzerland by an alleged corrupt official are significantly larger than what someone could have credibly earned in office; and that (2) the country from which the funds originate is known to be corrupt. The burden of proving that the money came from legal sources would then shift to the allegedly corrupt official, rather than the Swiss state.” Making the existing rule set work better is a valuable goal. There’s no doubt that the search for assets of the deposed Arab rulers got off to a swifter start than in most previous transitions. Changes in the rules have had valuable effects in Nigeria, Peru and other asset recoveries, too. But the rules aren’t always right for the situations that anti-corruption campaigners and others find themselves in. This has led governments, NGOs and private actors to seek other ways forward that go beyond the classic route, which is so heavily reliant on official channels.

Going around the rules: Private action and alternative approaches.

Government-to-government action isn’t the only way of tackling asset recovery. Non-state actors and processes play an important role already, and may play an increasing one in the future. Is it plausible to look to this as one way around the logjam that results from government co-operation and its weakness? The use of civil litigation, rather than criminal law, has great promise and potential. As one expert writes, “Civil law, allowing for confiscation and recovery based on the balance of probabilities, has a clear advantage, as the evidentiary threshold is not as demanding as it is with criminal actions. This civil standard or burden of proof also means that in civil proceedings, the link between the assets and the criminal acts at their origin needs be established only on the grounds of a balance of probabilities. Finally, civil recovery also opens alternative approaches as far as civil actions against third parties are concerned and for the participation of victims in the action. It also has the advantage of civil recovery in a totally different jurisdiction or even in several jurisdictions at once.”

Civil cases can be very useful as part of a strategic effort against a large, systematic corruption case that has used specialist means – offshore jurisdictions, trusts and shell companies. Several governments have used civil litigation to pursue stolen assets, as a complement and substitute for criminal approaches. Using the “classic,” government-led route, most evidence and intelligence is generated by government investigators. But that needn’t be the case. “Some techniques may require authorization by a prosecutor or judge (for example, electronic surveillance, search and seizure orders, production orders, or account monitoring orders), but others may not (for example, physical surveillance, information from public sources, and witness interviews),”17 says StAR. In civil cases, private investigators can seek information in many ways. “Private investigators do not have the powers granted to law enforcement; however; they will be able to use publicly available sources and apply to the court for some civil orders (such as production orders, on-site review of records, prefiling testimony, or expert reports); (Bacarese, 2008).

Civil cases often involve expertise from outside government – typically, lawyers, accountants and investigators. This helps address issues of capability. But they are typically expensive, by the standard of government salaries in London and Washington, let alone Cairo and Kinshasa. So their use raises issues of finance and value for money, as well as accountability and management. Civil processes bring other complex questions. They have been associated with negotiated settlements, where former officials return some of their assets while retaining a portion, as in the Abacha case: this is seen as a trade-off for greater speed, though it is of course highly controversial. The growth of civil litigation is also helping to spur the growth of new financial approaches, again not without complexity. Commercial litigation funding for asset recovery in corruption cases has been explored, and it is intriguing if also full of risk.

As Paul Carrington, Professor of Law at Duke University has written, the U.S. has always mixed private and public actions in the area of corruption. He points to the False Claims Act, which incentivizes “relators” to come to court with cases of fraud against the government. There are other routes for recovery that place more emphasis on the restoration of justice in the countries where bribery takes place. Some analysts have suggested action based on claims that corruption breaches human rights. “A recent publication by the International Council on Human Rights Policy and Transparency International outlines a systematic approach and suggest lines of argument that would support the causal links between corruption and a range of human rights violations,” StAR says.

There have been cases. In 2007, the Open Society Justice Initiative together with the Spanish human rights organization Asociación pro Derechos Humanos de España (APDHE) and EG Justice, a U.S.-based organization, filed a complaint to the African Commission on Human and Peoples’ Rights, arguing that the diversion of oil wealth by the rulers of Equatorial Guinea violates the African Charter on Human and Peoples’ Rights.21 And in France, a similar case has been brought successfully. “The initial complaint in the case, filed by anti-corruption groups Transparency International (TI) and SHERPA [advocacy website], accused the late Omar Bongo of Gabon, Denis Sassou-Nguesso of the Democratic Republic of the Congo (DRC), Teodoro Obiang Nguema of Equatorial Guinea and their relatives of acquiring luxury homes and cars in France with African public funds.”22

Going above the rules: Transnational approaches and the ICC

If existing rules don’t work very well, that is in part because they are international rules for a transnational problem. As with many other phenomena linked to globalization, the initiative goes to criminals if the rules rely on traditional interstate mechanisms: the exchange of messages, diplomatic niceties, embassies, protocol. Criminals don’t have to send MLAs. One solution might be to create new machinery at a transnational level, or to repurpose existing institutions. The most-cited potential forum is the International Criminal Court23. As legal theorist Sonja Starr has argued, “International criminal tribunals could contribute meaningfully to the fight against kleptocracy. They have considerable powers to trace, freeze, and seize stolen funds, and can exercise jurisdiction where other domestic or international remedies are unavailable... There is a strong legal argument for treating grand corruption as a crime against humanity based on existing treaties.”

There are reasons, however, why governments have been very hesitant in granting authority to the ICC and why they haven’t created a transnational structure to do more. The United States, of course, is not a participant in the ICC. It might seem logical for StAR, attached as it is to a multilateral organization and an international one, to operate as the global agency to carry out recoveries. That is not how it is designed. StAR cannot be involved in litigation or criminal proceedings, StAR cannot finance or get involved in legal representation, nor can StAR manage cases or be party to confidential communications between states. Its role is to support and assist, to facilitate communications and to help partners make informed decisions. 25 Governments are reluctant to cede power in this area to a “global FBI.”

Rather than aim for a single forum for asset recovery, it seems more useful to move towards a more open playing field – if not a single global regime, then a set of best practices and common approaches, as StAR has done – with many multiple “good” approaches. The ICC was used to seize assets belonging to members of the former Gadaffi regime in 2013, a potentially important precedent.


A lot of thought has gone on into making asset recovery better understood, and the barriers are clearer. But some of the obstacles will remain, because they are difficult and because the process is supposed to be hard. Government-to-government action will continue to be the primary focus of asset recovery, and it will be hard. While traditional government routes to recovery remain bureaucratically challenged, there is every incentive for others – private law firms, investigators, financiers, non-profits and all – to seek new routes. As StAR says, “Developments in this area are unlikely to be driven through a negotiated process in the framework of international agreement. Instead, alternative avenues will be opened through the decisions of national authorities, judiciaries and activist litigants.” In other words: watch this space.