metadata: authors: 0: author: Escribano Saez,Alvaro
1: author: Guasch, J. Luis
2: author: Pena Izquierdo,Jorge
count: Africa
docna: 0: docna: Assessing the impact of infrastructure
            quality on firm productivity in Africa : cross-country
            comparisons based on investment climate surveys from 1999 to 2005
docty: Policy Research Working Paper
subtopic: Technology Industry,Technology Innovation,Telecommunications Infrastructure,Energy Policies & Economics,Hydrology,Transport Services
teratopic: Transport,Infrastructure Economics and Finance,Private Sector Development,Macroeconomics and Economic Growth,Social Protections and Labor
abstracts: cdata!: This paper provides a systematic,
            empirical assessment of the impact of infrastructure quality
            on the total factor productivity (TFP) of African
            manufacturing firms. This measure is understood to include
            quality in the provision of customs clearance, energy,
            water, sanitation, transportation, telecommunications, and
            information and communications technology (ICT).
            Microeconometric techniques to investment climate surveys
            (ICSs) of 26 African countries are carried out in different
            years during the period 20026, making country-specific
            evaluations of the impact of investment climate (IC) quality
            on aggregate TFP, average TFP, and allocative efficiency.
            For each country the impact is evaluated based on 10
            different productivity measures. Results are robust once
            controlled for observable fixed effects (red tape,
            corruption and crime, finance, innovation and labor skills,
            etc.) obtained from the ICSs. African countries are ranked
            according to several indices: per capita income, ease of
            doing business, firm perceptions of growth bottlenecks, and
            the concept of demeaned productivity (Olley and Pakes 1996).
            The countries are divided into two blocks:
            high-income-growth and low-income-growth. Infrastructure
            quality has a low impact on TFP in countries of the first
            block and a high (negative) impact in countries of the
            second. There is significant heterogeneity in the individual
            infrastructure elements affecting countries from both
            blocks. Poor-quality electricity provision affects mainly
            poor countries, whereas problems dealing with customs while
            importing or exporting affects mainly faster-growing
            countries. Losses from transport interruptions affect mainly
            slower-growing countries. Water outages affect mainly
            slower-growing countries. There is also some heterogeneity
            among countries in the infrastructure determinants of the
            allocative efficiency of African firms.
repnme: repnme: Assessing the impact of infrastructure
            quality on firm productivity in Africa : cross-country
            comparisons based on investment climate surveys from 1999 to 2005
display_title: Assessing the impact of
            infrastructure quality on firm productivity in Africa :
            cross-country comparisons based on investment climate
            surveys from 1999 to 2005
listing_relative_url: /research/2010/01/11699361/assessing-impact-infrastructure-quality-firm-productivity-africa-cross-country-comparisons-based-investment-climate-surveys-1999-2005
topicv3: Machinery and Equipment m1326517 1916,Water m1326522 137,Transport m1326661 135,Power and Electricity Sector m1327094 515,Telecommunications and Broadband Access
            m1327340 286
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Transport,Infrastructure Economics and Finance,Private Sector Development,Macroeconomics and Economic Growth,Social Protections and Labor
no_of_pages: 117
author_keys: 0: author_key: 744317
1: author_key: 799157
model_extractions: WPS5191 Policy Research Working Paper 5191 Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa Cross-Country Comparisons Based on Investment Climate Surveys from 1999 to 2005 _Alvaro Escribano J. Luis Guasch Jorge Pena_ The World Bank Latin American and the Caribbean Region Sustainable Development Department January 2010
Policy Research Working Paper 5191

# **Abstract**

This paper provides a systematic, empirical assessment of the impact of infrastructure quality on the total factor productivity (TFP) of African manufacturing firms. This measure is understood to include quality in the provision of customs clearance, energy, water, sanitation, transportation, telecommunications, and information and communications technology (ICT). Microeconometric techniques to investment climate surveys (ICSs) of 26 African countries are carried out in different years during the period 2002–6, making country-specific evaluations of the impact of investment climate (IC) quality on aggregate TFP, average TFP, and allocative efficiency. For each country the impact is evaluated based on 10 different productivity measures. Results are robust once controlled for observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, etc.) obtained from the ICSs. African countries are ranked according to several indices: per capita income, ease of doing business, firm perceptions of growth bottlenecks, and the concept of demeaned productivity (Olley and Pakes 1996). The countries are divided into two blocks: high-incomegrowth and low-income-growth. Infrastructure quality has a low impact on TFP in countries of the first block and a high (negative) impact in countries of the second. There is significant heterogeneity in the individual infrastructure elements affecting countries from both blocks. Poor-quality electricity provision affects mainly poor countries, whereas problems dealing with customs while importing or exporting affects mainly fastergrowing countries. Losses from transport interruptions affect mainly slower-growing countries. Water outages affect mainly slower-growing countries. There is also some heterogeneity among countries in the infrastructure determinants of the allocative efficiency of African firms.

This paper—a product of the Sustainable Development Department, Latin American and the Caribbean Region—is part of a larger effort in the department to advocate and support the development and improvement of infrastructure services. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at jguasch@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Assessing the Impact of Infrastructure Quality on Firm Productivity in Africa<sup>*</sup> Cross-Country Comparisons Based on Investment Climate Surveys from 1999 to 2005 by Alvaro Escribano,<sup>†</sup> J. Luis Guasch<sup>‡</sup> and Jorge Pena<sup>§</sup>

**Key words:** Africa, Infrastructure, Total Factor Productivity, Investment Climate, Competitiveness.

> * This study is part of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. Financing for AICD is provided by a multi-donor trust fund to which the main contributors are the Department for International Development (United Kingdom), the Public Private Infrastructure Advisory Facility, Agence Française de Développement, and the European Commission. Inquiries concerning the availability of datasets should be directed to vfoster@worldbank.org. The authors are indebted to Vivien Foster and her group of researchers at the World Bank for providing data sets and further insight into the African region. All errors in this report are the sole responsibility of the authors.

> † Telefónica-UC3M chair in the economics of telecommunications, Department of Economics, Universidad Carlos III de Madrid; alvaroe@eco.uc3m.es

> ‡The World Bank and the University of California, San Diego; jguasch@worldbank.org.

> § Department of Economics, Universidad Carlos III de Madrid; jpizquie@eco.uc3m.es.
# **1. The wide-ranging effects of infrastructure quality**

For Africa’s awaited growth resurgence to occur, a broad range of factors—political, institutional, and economic—must be improved. The World Bank’s landmark Africa Competitiveness Report (ACR) (2004 and 2007) focuses on problems that, in the words of Artadi and Sala-i-Martin (2003), constitute the most important growth tragedy of the twentieth century—a phenomenon that has received special attention in recent growth literature, such as that of Ndulu and O’Connell (2005). It is agreed that improving Africa’s infrastructure is a crucial step toward penetrating international markets and meeting the goals of continuous growth and poverty reduction.

Infrastructure quality has a pervasive influence on all areas of an economy. Low-quality infrastructure and limited transport and trade services increase logistical and transaction costs, rendering otherwise competitive products uncompetitive, as well as limiting rural production and people’s access to markets—with adverse effects on economic activity and poverty reduction. A large number of empirical studies illustrate the impact of infrastructure on economic performance, including those of Calderón et al. (2003a and b), Calderón and Serven (2003), Canning (1998), Reinikka and Svensson (1999), Prud’homme (2004), Escribano and Guasch (2005), Escribano et al. (2005), and Guasch (2004). All suggest that Africa’s infrastructure gap is an important growth bottleneck with a negative impact on productivity and the overall competitiveness of the region. Furthermore, several studies using the methodology of Escribano and Guasch (2005, 2008) and Escribano et al. (2008a and b and 2009) have found empirical evidence—in cases such as Brazil, Chile, Costa Rica, Mexico, Turkey, and several southeast Asian countries—that improvements in investment climate (IC) conditions in general, and in infrastructure quality in particular, may lead to important gains in productivity and in other economic performance measures: employment, real wages, exporting activities, and foreign direct investment (FDI) inflows.

Disentangling the ways that infrastructure affects Africa’s economic growth poses several difficulties because of the special characteristics of the African region. The comprehensive analysis found in Estache (2005) takes stock of the basic characteristics of infrastructure in Sub-Saharan Africa and the impact of 1990 reforms, pointing out that the impact of infrastructure in Africa may be different than in other regions. As Brunel (2004) signals, the colonial period has had a lasting effect on the use of space in the region, resulting in a productive structure that consists, in most cases, of coastal cities connected inland by railways designed to carry raw materials to main ports. This and other factors that are progressively modifying the continent’s productive structure—such as continuous urbanization, the movement of economic activity from the agricultural to manufacturing and service sectors, and the increasing openness of African economies—has caused both a quantitative and qualitative mismatch between the current supply of infrastructure and ever-increasing demand. Factors such as inequality across income levels (affecting the affordability of infrastructure services), large and unoccupied areas, and regional variations in climate are increasingly becoming a concern for African policy makers managing infrastructure.

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<!-- Start of picture text --> = -<br>ie "a - =<br>= Moroccoln5 os ; ton Sy 'F)fs<br>i<br>:rate £ ; +7, os<br>—.. _ Algeria A.R. of<br>Mauritania| Mali I<br>ang o i Niger| , ‘Eritrea<br>Cape wt UE Cameroon weg ae—_<br>‘i Burundi Granzaniahe<br>Lilies — “,<br>:<br>' Madagascar<br>Namibiz ee Ly<br>\ea G7 Swaziland waurtius<br>South Africa<br>i Lesotho<br><!-- End of picture text -->
Figure 1.1 shows the geographical distribution of the countries considered in this study, both in North and Sub-Saharan Africa. The countries studied are divided into five main geographical areas, identified in some cases by the major multilateral organization of each region: (a) the North African region, or Maghreb, includes Morocco, Algeria, and Egypt; (b) the Economic Community of West African States (ECOWAS) includes Mauritania, Mali, Niger, Senegal, Burkina Faso, Benin, Cameroon, and Cape Verde; (c) the Horn of Africa region is composed of two countries, Eritrea and Ethiopia; (d) the East African Community (EAC) includes Tanzania, Kenya, Uganda, and Burundi; and (e) the South African Development Community (SADC), for which we have data for Malawi, Zambia, Namibia, Botswana, Swaziland, Lesotho, and Madagascar. South Africa and Mauritius are the last two individual countries included in the report.

The 26 countries show enormous heterogeneity due to (a) geographical factors, such as whether a nation is landlocked (Cape Verde, Madagascar, and Mauritius), tropical (with landmass for the most part covered by rainforests), or dominated by deserts (such as the North African countries Mauritania and Namibia); (b) social or political factors, such as civil wars, armed conflicts, early democracies, dictatorships, and colonial heritage; and (c) economic factors, which this paper discusses for all countries, from the most affluent (Mauritius) to the poorest (Eritrea).

Figure 1.2 clarifies the different evolutions of per capita income across the countries included in this analysis. Of the 26 African countries analyzed, Mauritius was, in 1950, the country with the highest per capita income (measured in terms of per capita gross domestic product, GDP), followed closely by South Africa, and, by a wider gap, Namibia and Algeria. But the per capita income situation in 2003 was somewhat different; Mauritius was still ranked first, followed by Swaziland, South Africa, and Botswana—and, by a wider gap, Algeria, Cape Verde, Egypt, and Morocco. Panel B of figure 1.2 shows the five-year growth rate of per capita income. Mauritius and Botswana are the countries that have experienced the highest, sustained per capita income growth during the recent years. Lesotho is the median country, splitting the cross-section into two blocks. The first block comprises countries with faster and steadier growth rates (Mauritius, Swaziland, South Africa, Botswana, Namibia, and Lesotho in the south; Algeria, Morocco, and Egypt in the north; and Cape Verde and Cameroon in central Africa). In the second are countries with lower and more irregular growth rates (Mauritania, Senegal, Benin, Mali, Niger, and Burkina Faso in the central west; Uganda, Kenya, Zambia, Tanzania, Malawi, Burundi, Madagascar, Ethiopia, and Eritrea from the central east), periods of positive expansion fluctuate with those of persistent reductions in per capita income.

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<!-- Start of picture text --> US$<br>15000<br>10000<br>.<br>| ) 10 0 iat i, oha<br>MUS SWZ ZAF BWA DZA NAM CPV EGY MAR CMR LSO MRT SEN BEN KEN MLI UGA BFA ZMB TZA NER MWI BDI MDG ETH ERI<br><!-- End of picture text -->

<!-- Start of picture text --> % 5 | |<br>40 | growthAverage rate of i<br>t less sucpesstu<br>| ofrelatively *<br>2 I j ] | | | | t |<br>10 i | 1 le t it | tT} f | |<br>if atl % a te _{hl I| i + | 5 | ’ | h j | f . J | hy ij it *<br>‘ PUT MES AT EL ie ey AUT UT<br>1 stilt ait yy iit ‘ eel ST TF<br>.<br>a ah JASE | UME HE ALY<br>ver | , : i |<br>growth of relatively ie<br>20 ge rate of | | ‘| J<br>||| | |<br>0 comes : | | J<br>| ‘<br>40<br>-0<br>se &€ S£ ST F SF FF FH F FSF EF F FHKE ST KF F SF TF FS FSF SF Sse<br>Pt tT | EE tT<br>Pot | | | te tT<br>Pot tT | EEE EE EE EE EE E T<br><!-- End of picture text -->
These per capita income rankings are correlated with the rankings obtained from the World Bank’s 2007 Doing Business Report (DBR), presented in panel C of figure 1.2. In 2007 Mauritius, Swaziland, South Africa, Botswana, and Namibia rank 32nd, 76th, 29th, 48th, and 42nd in the world based on the ease-of-doing-business indicators. This index considers questions such as the number of days required to start a business and the ease of dealing with licenses, registering a property, trading across borders, employing workers, and so on. Other 2007 rankings include 83rd for Kenya, 97th for Ethiopia, 165th for Egypt, and 170th for Eritrea.

To better understand the convergence or divergence of trends, we plotted the per capita income of each African country relative to the per capita income of the United States (see panel A of Figure 1.3). Convergence is observed only in Mauritius, Swaziland, and Botswana. For all other study countries, including South Africa, per capita income is diverging from the United States, while in a few (Egypt, Morocco, and Cape Verde) the ratio has remained stable. While persistently positive GDP growth allowed Mauritius’s per capita income to reach 45 percent of the United States in 2003, this is clearly the exception (together with Swaziland and Botswana). For the rest of the countries, including South Africa, relative per capita income was much lower in 2003 than in 1960 (indicating divergence). In fact, the 2003 per capita income of several countries was no larger than 5 percent of the per capita income of the United States. As expected, labor productivity is the main factor explaining this divergence in per capita income in Africa (panel B of Figure 1.3), given that labor force participation has a steady influence (panel C of Figure 1.3).<sup>5</sup> Since TFP is usually a key factor explaining the evolution of labor productivity, in this paper we seek to use investment climate surveys (ICSs) to identify the main infrastructure-related TFP bottlenecks in Africa.

> 5 The _per capita income_ of country J (YJ/PJ) is decomposed into labor productivity (YJ/LJ) and the employment-population rate (L<sup>J</sup> /P<sup>J</sup> ) by following the expression: (Y<sup>J</sup> /P<sup>J</sup> ) = (Y<sup>J</sup> /L<sup>J</sup> )*(L<sup>J</sup> /P<sup>J</sup> ), where Y is GDP, L is total labor force, and P is total population.

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<!-- Start of picture text --> 06 A. GDP per capita<br>05<br>0.4<br>0.3<br>0.2:0 | | | | | | | lLot H | ot Da Dn rn Mt tt a<br>MUS SWZ ZAF BWA DZA NAM CPV EGY MAR CMR LSO MRT SEN BEN KEN MLI UGA BFA ZMB TZA NER MWI BDI MDG ETH ERI<br><!-- End of picture text -->

<!-- Start of picture text --> 06 B. Labor productivity (GDP per worker)<br>0.5<br>0.4<br>0.3<br>0.2:0 | lilly all Ut | ll Mh Mt om Ua Dorr Mt mH MH ote OT Mh mo<br>MUS SWZ ZAF BWA DZA NAM CPV EGY MAR CMR LSO MRT SEN BEN KEN MLI UGA BFA ZMB TZA NER MWI BDI MDG ETH ERI<br><!-- End of picture text -->

<!-- Start of picture text --> C. Labor force participation<br>1.6<br>14<br>12<br>1<br>08<br>0.6<br>04<br>0.2<br>0<br>MUS SWZ ZAF BWA DZA NAM CPV EGY MAR CMR LSO MRT SEN BEN KEN MLI UGA BFA ZMB TZA NER MWI BDI MDG ETH ERI<br>@Year 1960 @ Year 1970 OYear 1980 OD Year 1990 @ Year 2003<br><!-- End of picture text -->
of electricity provision is a major problem for more than 50 percent of firms in more than half of the countries in our sample. In Burundi, Cameroon, Benin, Burkina Faso, and Cape Verde, the percentage of firms considering electricity as a severe or very severe obstacle exceeds 80 percent; on the other hand, only 20 percent of firms in Morocco, South Africa, Botswana, and Namibia consider electricity a severe obstacle. Customs clearance is considered an acute problem in Benin, Kenya, Madagascar, Senegal, and Algeria. Finally, transportation is considered a severe obstacle by more than 70 percent of firms in Burkina Faso and Benin.

**Figure 1.4 Percentage of firms that consider telecommunications, electricity, customs, and**

**transport as severe or very severe constraints on economic performance (by country)**

<!-- Start of picture text --> A. Telecommunications B. Electricity<br>Benin Burundi<br>Kenya Cameroon<br>Zambia Benin<br>Niger Burkina Faso<br>Ethiopia Cape Verde<br>Lesotho Tanzania<br>Madagascar Malawi<br>Burkina Faso Kenya<br>Algeria Madagascar<br>Malawi Uganda<br>Cameroon Ethiopia<br>Tanzania Mauritania<br>Swaziland Zambia<br>Mali Eritrea<br>Cape Verde Lesotho<br>Eritrea Senegal<br>Botswana Mali<br>Burundi Swaziland<br>Mauritania Niger<br>Namibia Egypt<br>Uganda Algeria<br>Mauritius Mauritius<br>South Africa Morocco<br>Senegal South Africa<br>Egypt Botswana<br>Morocco Namibia<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Percentage of firms Percentage of firms<br>C. Customs clearance D. Transport<br>Benin Burkina<br>Kenya Benin<br>Madagas Cameroon<br>Senegal Kenya<br>Algeria Senegal<br>Cameroon Zambia<br>Tanzania Malawi<br>Ethiopia Niger<br>Zambia Burundi<br>Uganda Madagascar<br>Niger Tanzania<br>Malawi Uganda<br>Mauritius Cape Verde<br>Mauritania Lesotho<br>Mali Mali<br>Egypt Mauritania<br>South Namibia<br>Lesotho Swaziland<br>Cape Ethiopia<br>Burundi Mauritius<br>Burkina South Africa<br>Morocco Botswana<br>Namibia Eritrea<br>Swaziland Morocco<br>Botswana Egypt<br>Eritrea Algeria<br>0 10 20 30 40 50 60 70 80 90 100 0 10 20 30 40 50 60 70 80 90 100<br>Percentage of firms Percentage of firms<br><!-- End of picture text --> _Source:_ Authors’ calculations based on IC data. _Note:_ No data are available for perceptions of transport in Algeria .

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Figure 1.5 offers another view of the state of infrastructure in Africa. The World Bank’s ACR (2007) evaluates a wide range of factors related to economic activity, infrastructure among them. Once again there are clearly different performance levels across the two blocks of countries. While in Namibia, South Africa, Botswana, Egypt, and Morocco the quality of infrastructure exceeds the approval level; in the remaining countries this quality is rated low in most cases. The same holds for the disaggregated results, including the number of telephone lines and the quality of ports, air transport, and electricity supply.

**Figure 1.5 The state of infrastructure in Africa, at first glance**

<!-- Start of picture text --> A. Quality of overall infrastructure B. Quality of railroad infrastructure<br>Namibia Namibia<br>South Africa South Africa<br>Mauritius Egypt<br>Botswana Botswana<br>Egypt Morocco<br>Morocco Tanzania<br>Algeria Burkina<br>Tanzania Algeria<br>Uganda Lesotho<br>Mali Kenya<br>Malawi Mauritania<br>Kenya Malawi<br>Madagasca Cameroon<br>Lesotho Mauritius<br>Ethiopia Mali<br>Benin Uganda<br>Burkina Madagasca<br>Burundi Ethiopia<br>Cameroon Benin<br>Mauritania Burundi<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Score (1-7) Score (1-7)<br>C. Quality of port infrastructure D. Quality of air transport infrastructure<br>Namibia South Africa<br>Mauritius Mauritius<br>South Africa Kenya<br>Morocco Namibia<br>Egypt Egypt<br>Tanzania Morocco<br>Kenya Ethiopia<br>Algeria Botswana<br>Botswana Tanzania<br>Benin Algeria<br>Mauritania Uganda<br>Madagascar Madagascar<br>Cameroon Burkina<br>Uganda Malawi<br>Lesotho Mali<br>Burundi Benin<br>Burkina Mauritania<br>Malawi Burundi<br>Mali Lesotho<br>Ethiopia Cameroon<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7<br>Score (1-7) Score (1-7)<br>E. Quality of electricity supply F. Telephone lines<br>Mauritius Mauritius<br>Namibia Egypt<br>Morocco South Africa<br>Egypt Algeria<br>Botswana Botswana<br>Algeria Namibia<br>South Africa Morocco<br>Ethiopia Lesotho<br>Mali Mauritania<br>Lesotho Benin<br>Kenya Malawi<br>Burkina Fasso Kenya<br>Mauritania Ethiopia<br>Benin Mali<br>Malawi Burkina<br>Cameroon Cameroon<br>Tanzania Tanzania<br>Madagascar Madagascar<br>Burundi Burundi<br>Uganda Uganda<br>0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30<br>Score (1-7) Total number of telephone lines per 100 inabitants<br><!-- End of picture text --> _Source:_ Africa Competitiveness Report (2007), World Bank, Washington, DC. _Note:_ No data are available for Cape Verde, Eritrea, Niger, Senegal, Swaziland, or Zambia .

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The difference between the two blocks becomes even clearer in figure 1.6, where the cross-plots between GDP per capita relative to the United States and firms’ perceptions are presented. A preliminary analysis of the cross-plots suggests two points: first, that there is an intuitive and negative relation between income level and infrastructure constraints; and, second, that the diversion of the two blocks of countries remains intact, showing now the largest dispersion in the constraint perceptions of figure 1.6 for the lowest per capita income group.

**Figure 1.6 A simple illustration (cross-plots) of the relation between per capita GDP and infrastructure perceptions of severe or very severe obstacles to growth in Africa**

GDP per capita relative to United States <!-- Start of picture text --> A. Telecommunications vs. GDP per capita B. Electricity vs. GDP per capita<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>GDP per capita (% of US) GDP per capita (% of US)<br>B. Customs clearance vs. GDP per capita D. Transport vs. GDP per capita<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>GDP per capita (% of US) GDP per capita (% of US)<br>obstacle obstacle<br>% of firms considering telecoms as a severe % of firms considering electricity as a severe<br>obstacle obstacle<br>% of firms considering customs as a severe % of firms considering transport as a severe<br><!-- End of picture text --> _Source_ : Authors’ calculations based on IC data. _Note_ : No data are available on perceptions of transport in Algeria .

The objective of this paper is to assess the impact of the quality of existing infrastructure on the TFP of African firms. This measure is understood to include quality in the provision of the following services: customs clearance, energy, water, sanitation, transportation, telecommunications, and 10
information and communications technology (ICT). We also want to identify infrastructure factors with statistically significant impacts on TFP, country by country. In the econometric evaluation we use 10 different measures of TFP and show that the results are robust—no matter what measure of TFP is used—if we follow the econometric methodology of Escribano and Guasch (2005, 2008), and Escribano et al. (2008).

For the empirical analysis of infrastructure’s constraints on TFP, we go down to the firm level since infrastructure is one of the key elements of a country-specific IC, and a significant component of country competitiveness. To provide reliable and robust estimates of the impact of infrastructure on economic performance is not a straightforward task. As we will see later on, we have to deal with the heterogeneity of the countries included in our sample, and the endogeneity of explanatory variables (inputs and IC variables) in several dimensions due to unobservable fixed effects, measurement errors, missing observations, and so on. To solve these problems, we take advantage of the useful and rich firm-level information provided by the ICSs undertaken by the World Bank in Africa from 2002 to 2006. These surveys capture firm-level information in a range of areas related to economic performance: infrastructure, financing, governance, corruption, crime, regulation, tax policy, labor relations, conflict resolution, supplies and marketing, quality, technology, and training, among others. These surveys offer information on the production function (PF) variables over one, two, or three years, depending on the African country. But for infrastructure and other IC and plant control (C) variables they only provide information for a single year. We will see how we can use this valuable information to evaluate how firms operate in Africa and to identify the main obstacles to productivity improvements.

Section 2 of this report clarifies the link between this type of empirical work and existing literature on infrastructure and productivity. The properties and quality of the ICSs are analyzed in section 3. Why we classify the IC factors in broad categories or groups will also be discussed, together with the infrastructure variables (INFs) used. In section 4 we present the econometric methodology we use to estimate the impact of infrastructure and other IC variables and C characteristics on TFP. Once we have estimated the infrastructure and other IC elasticities and semi-elasticities on productivity, we evaluate the effects of infrastructure on aggregate productivity and on allocative efficiency, using the Olley and Pakes (O&P, 1996) decomposition. The main empirical results are described in the remaining sections. In particular, section 5 focuses on the relative importance of infrastructure in the IC of each country. Section 6 presents the empirical results country by country, and section 7 includes the main conclusions. Most of the tables and figures are included in the Appendix at the end of the paper.

# **2. How does infrastructure quality affect economic performance?**

Much literature discusses the different ways that infrastructure affects growth and other development outcomes at the macroeconomic level. For example, the World Bank’s landmark World Development Report (1994) highlighted multiple links between infrastructure and development and emphasized how 11
policy can improve not only the quantity, but also the quality, of infrastructure services in developing countries.

As Straub (2008) signals, macro level literature has too often sought to obtain the elasticity of infrastructure capital and compare it with the elasticity of private capital. Few papers go beyond measures of infrastructure spending and infrastructure stocks to consider the issue of infrastructure efficiency. Since the seminal paper of Aschauer (1989) found that infrastructure capital has a large impact on aggregate TFP, this finding has been replicated by a number of earlier studies: Munnell (1990a, 1990b, 1992) for the United States, Mitra et al. (2002) for that of India, and Easterly and Rebelo (1993) for cross-sectional country data. Loayza, Fajnzylber, and Calderón (2002) find that a telecommunications indicator is robustly related to growth in a large panel data set that includes both industrial and developing countries.

For the case of Africa, studies exploring the relation between infrastructure and growth are scarce.<sup>6</sup> Traditionally, infrastructure services have been viewed as public goods in Africa, with their provision entrusted to government monopolies. The overall performance of government-owned providers of infrastructure in Africa has been very poor. This sector is characterized by high inefficiency, a lack of technological dynamism, and very poor service provision. In addition, the provision of infrastructure-related services in most African countries is characterized by high prices and long waits between the time of application for services and actual connection. Many African economies are also endowed with adverse natural and geographical attributes, such as lack of access to sea ports and tropical climates.

The Economic Commission for Africa (ECA) Report (2005) and Sachs et al. (2004) have explored the African need for new investments in infrastructure, but without a properly systematic cross-country analysis. Estache et al. (2005) makes one of the first attempts to conduct a more systematic, quantitative assessment of the importance of Sub-Saharan Africa’s infrastructure. The main finding of this paper is that electricity, water, roads, and telecommunications are crucial factors in promoting growth, with colonial and postcolonial histories also being important factors explaining some of the differences among countries. On the other hand, Esfahani and Ramirez (2003) estimate that Sub-Saharan Africa’s poor growth performance is, in part, related to under-investments in electricity and telecommunications infrastructure. Estache (2005) estimates that if Africa had enjoyed Korea’s quantity and quality of infrastructure, it would have raised its annual growth per capita by about 1 percent. Hulten (1996) finds that differences in the effective use of infrastructure resources explain onequarter of the growth differential between Africa and East Asia, and more than 40 percent of the growth differential between low- and high-growth countries.

Empirical explorations of infrastructure’s effect on growth and productivity, however, have been characterized by ambiguous results with little robustness. The possible endogeneity of infrastructure measures has been advanced as a reason for contradictory findings of the impact of public

> 6 Estache (2005) points out the two main reasons for ignoring the role of infrastructure as one of the most important drivers of economic growth in Africa: (a) econometric focus on human capital, and (b) low quality of available data.

12
capital on long-run economic development indicators. Literature has signaled that endogeneity in this context might come from three sources: (a) measurement errors stemming from the use of public capital figures as proxies for infrastructure; (b) omitted variables, which may arise when there is a third variable, unobserved, that affects the infrastructure and growth measure; and (c) the fact that under the simultaneous determination of infrastructure and productivity or output, the bias and inconsistency of standard estimators would follow where infrastructure provision itself positively responds to productivity gains.<sup>7</sup> Possible reasons for such feedback would arise with increased reliance on the private sector for the provision of infrastructure services, or with successful lobbying by industry interest groups that experience either positive productivity gains or constraints on performance due to infrastructure provision.

Various panel data and country studies have tried to address these issues. Röller and Waverman (2001) explicitly model and estimate the impact of telecommunications under simultaneity. In a crosscountry panel estimation, Calderón and Serven (2003, 2005) employ generalized method of moments (GMM) panel estimation methods to control for the possibility of endogeneity, reporting significant improvements in results. Dessus and Herrera (1999) allow for simultaneity in a panel data set for 28 countries. Country-specific time series also confirm the presence of simultaneity between output and infrastructure measures—see Frutos et al. (1998) for Spain, and Fedderke et al. (2005) for South Africa. Also for South Africa, Fedderke and Bogetic (2006)—controlling for the potential endogeneity of infrastructure in estimation—robustly eliminate nearly all evidence of possible overinvestment in infrastructure. Indeed, controlling for the possible endogeneity of infrastructure measures renders the impact of infrastructure capital positive. Romp and Haan (2005) indicate that when simultaneity is taken into consideration, the elasticity estimates found in earlier studies considerably decrease.

Another possibility behind the ambiguous results obtained from empirical studies of public capital impacts on output might simply be that aggregate measures of infrastructure hide the productivity impact of infrastructure at a more disaggregated level. A second batch of studies, focusing mainly on micro data, reveals the existence of the possible indirect impact of infrastructure on economic growth and economic performance beyond the effect of the simple accumulation of capital. Thus, for instance, Shioji (2001) finds that the positive impact of infrastructure arises in panel data on U.S. and Japanese industries once public capital is properly disaggregated. Agénor and MorenoDodson (2006) point out that improvement in the stock of infrastructure can reduce the adjustment costs of private capital by (a) lowering the logistical cost of the investment in private capital, and (b) allowing the replacement of unproductive private investments such as electricity generators or boreholes and wells with more productive investments in machinery and equipment. This assumption has been tested in the context of investment climate assessments (ICAs) with firm-level information. Reinikka and Svensson (1999) show that improvements in the infrastructure stock in Uganda make

> 7 Notice that we avoid using the terms _causality_ or _reverse causality_ , since there is no control group to compare against and the temporal dimension is not large enough to consider Granger-causality concepts _._ Therefore, we use the terms _simultaneity_ and _identification_ , which are more appropriate for ICSs.

13
infrastructure services more reliable, reducing the necessity of investing in less productive substitutes (such as generators) in order to avoid potential service interruptions, and thus freeing funding of private productive investments.

Relationships at a more disaggregated level tend to be obscured by aggregated data, and are unobservable with country-level data. Another channel of infrastructure impact is via improvements in labor productivity through (a) improved transport between home and work, and (b) more efficient work processes. Another way that better infrastructure might increase labor productivity is through improvements in health and education, making existing human capital more efficient, and promoting successive investments in human capital (Galiani et al., 2005).

The effect of infrastructure on firms’ international integration has also been tested. Recent literature affirms that improvements in transportation services and infrastructure can lead to improvements in export performance. Thus, for instance, Francois and Manchin (2006) explore the role that infrastructure plays (among other factors such as policy reforms, institutional development, colonial history, development assistance, and general north-south differences) in the different trade performances observed in the so-called _globalizer_ countries such as India and China, as well as other developing countries (many located in Africa and with a very different story to tell regarding the integration of the global economy). Limão and Venables (2001) show that infrastructure is quantitatively important in determining transport costs, concluding that poor infrastructure accounts for much of the different transport costs observed in coastal and landlocked countries. Bougheas et al. (1999), in the context of gravity models, find evidence in the European economy of a positive relationship between the level of infrastructure and the volume of trade. Wilson et al. (2004) consider ports, customs, regulations, and e-businesses as proxies of trade-facilitation efforts, finding that the scope and benefit of unilateral trade-facilitation reforms are very large, and that the gains fall disproportionately to exports.

In a world where governments compete to attract more FDI inflows through a variety of investment and tax incentives and other policy preferences, the availability of good-quality physical infrastructure could also increase the inflow of FDI by subsidizing the cost of total investment by foreign investors and thus raising the rate of return. The favorable role of physical infrastructure in influencing patterns of FDI inflows has been corroborated by recent studies, such as those of Loree and Guisinger (1995), Mody and Srinivasan (1996), and Kumar (2001), among others. Multinational enterprises may consider the quality of available infrastructure especially important while deciding to relocate export-platform production undertaken for efficiency considerations. In other words, the quality of physical infrastructure could be an important consideration for multinationals in their location choices, for FDI in general, and for efficiency-seeking production in particular.

As has been pointed out, the main concern of this paper is to offer a robust assessment of the various channels through which infrastructure quality may impact TFP. Thus, instead of the quantity of macro variables, we use, as an explanatory variable, the quality of existing infrastructure stock. Instead of aggregate infrastructure measures usually included in macro models, such as kilometers of paved roads or total number of telephone lines, we incorporate measures that allow us to identify direct 14
relationships between infrastructure and economic performance at a more disaggregated level. Additionally, by going down to the firm level, we avoid the endogeneity problems of the macro level variables. Nevertheless, micro level data have specific endogeneity problems, and several variables cannot be considered to be exogenously determined; for instance, public investment decisions are likely to be affected by expected returns on investment, and firms faced with different quality and availability of infrastructure services would choose different technologies. The solutions proposed in this methodology allow us to obtain a robust assessment of the impact of infrastructure quality on TFP.

# **3. Country-level data and their treatment in the study**

Produced by the World Bank, ICSs of private enterprises explore the difficulties that firms located in developing countries encounter in starting and running businesses. More precisely, the surveys capture firms’ experiences in a range of areas related to economic performance: financing, governance, corruption, crime, regulation, tax policy, labor relations, conflict resolution, infrastructure, supplies and marketing, quality, technology, training, and so on. For that purpose, we classify IC factors in five categories to evaluate the impact of each group on economic performance. In the first group— infrastructure—we include all related variables such as customs clearance, power and water supply, telecommunications (including phone connection and information technology, IT), and transportation. In the second group—red tape, corruption, and crime—we include IC factors relating to tax rates, conflict resolution, crime, bureaucracy, informalities, corruption, and regulations. The next group comprises financial and corporate governance and includes factors related to management, investments, informalities in sales and purchases, access to finance, and accountability (or auditing). The last group of IC variables includes quality, innovation, and labor skills, as well as quality certifications, technology usage, product and process innovation, research and development (R&D), quality of labor, training, and managers’ experience and education. The last group—other C variables—are not properly a group of IC factors, but a group of other firms’ control characteristics. We classify in this group all the factors that may have an important impact on economic performance but are not considered IC factors: exports and imports, age, FDI, number of competitors, firm size, and so on. Table A.2 (see the Appendix) includes the whole list of IC and C variables, as well as a description of how each is measured. Likewise, not all surveys provide the same information on ICSs, although there is a common group of variables in each group that is available for all the countries; although the regressions among them are slightly heterogeneous, we can use this common group as a benchmark for comparison purposes.

The ICSs provide information on TPF variables, output (sales), employment, intermediate materials, capital stock, and labor costs. Table A.1 includes information on these variables and indications of how they were measured. The ICSs do not provide information on prices at the firm level, so the production function (PF) variables were deflated by using the World Bank’s countryspecific consumer price index (CPI), base 2000. The information on the net book value of the capital stock (NBVC) is not available for Algeria, Kenya, Mali, Senegal, and Uganda; in these cases the 15
NBVC is substituted by the replacement cost of machinery and equipment, which, in the surveys, is only available for a single year. We thus recursively estimate the missing values of the NBVC from the information on the replacement cost of and the net investment in machinery and equipment by using the permanent inventory method, according to which the capital stock at moment _t_ is given by _Kit=K it-1(1δ)+ Iit_ . By inverting this formula we can obtain the value of the capital at moment _t-1_ as _Kit-1=(K it - Iit)/(1-δ)_ where _Kit_ is approximated with the replacement cost of machinery and equipment, Iit is the net investment in machinery and equipment, and _δ_ is the depreciation rate of the machinery and equipment.<sup>8</sup> In this paper we focus on the manufacturing sector, and while classifying the establishments by their international standard of industrial classification (ISIC) code, we end up with establishments from the following eight sectors: (a) food and beverages; (b) textiles and apparels; (c) chemicals, rubber, and plastics; (d) paper, printing, and publishing; (e) machinery and equipment/metallic products; (f) wood and furniture; (g) nonmetallic products; and (h) other manufacturing.

# _Classification of countries by geographical area_

For the classification of countries by groups used in the regression analysis, we take into account the following facts: (a) the surveys provide different information on PF variables and on IC and C variables; (b) the surveys were carried out in different years during the period 2002–6; (c) the quality of the data varies across surveys; and (d) not all the surveys provide panel data information (recall data) for the PF variables. Thus, we end up with two types of country databases. For those countries with a large enough number of observations available for regression analysis (see column 6 of table 3.1) and with panel data information for the PF variables (for more than one year), we carry out the analysis country by country. For the countries in which surveys were collected in 2006 (which only offer one year of information for PF variables) and the number of firms surveyed was lower than in the previous surveys, we follow the estimation strategy of pooling the information according to the similarity of geographical and economic factors—thus gaining efficiency in the parametrical estimation of the IC parameters (with more observations in the regressions) at the cost of having common IC parameters for some countries.

We end up with two pools of 2006 countries: (a) ECOWAS countries, such as Mauritania, Cameroon, Niger, and Burkina Faso; and (b) SADC countries, such as Botswana, Namibia, and Swaziland. Finally, since Eritrea has only 179 observations available, we consider this country as a special region of Ethiopia and carry out a joint analysis of the two, constituting the third pool of countries considered in the analysis.

> 8 The depreciation rate used is 15 percent, a standard percentage commonly applied in other works. Other percentages were also used in order to check robustness. Alternatively, to check whether the results were robust for other ways of constructing the NBVC, we used the next formula Kit-1=K it(1-ΔIit), where Kit is approximated by the replacement cost and ΔIit=(Iit-Iit-1)/Iit1<sup>is the rate of growth of the net investment in machinery and equipment. In both cases the main results were maintained.</sup>

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# **Table A.3 Summary of the investment climate assessment (ICA) surveys, sorted by geographical area**

|||Year of<br>survey|Years of production<br>function (PF)<br>variables|Total number of<br>observations<sup>1</sup>|Final number of<br>observations available for<br>regression analysis<sup>2</sup>|
|---|---|---|---|---|---|
||Algeria|2002|2000–1|952|706|
|North Africa|Egypt|2004|2001–3|2,931|2,629|
||Morocco|2003|2000–2|2,550|2,422|
||Senegal|2003|2000–2|783|535|
||Benin|2004|2001–3|591|475|
||Mali|2003|2000–2|462|309|
|Economic Community of<br>|Cape Verde<sup>3</sup>|2006|2005|47|47|
|West African States<br>(ECOWAS)|Mauritania*|2006|2005|80|80|
||Burkina Faso*|2006|2005|51|51|
||Niger*|2005|2004|64|48|
||Cameroon*|2006|2005|119|118|
|Horn of Africa|Ethiopia**|2002|1999–2001|1,281|1,142|
||Eritrea**|2002|2000–1|237|179|
||Kenya|2003|2000–2|852|577|
|East African Community|Uganda|2003|2001–2|900|635|
|(EAC)|Tanzania|2003|2000–2|828|561|
||Burundi<sup>3</sup>|2006|2005|102|101|
||Malawi|2005|2004–5|320|288|
||Madagascar|2005|2002–4|870|623|
|Southern African|Zambia|2002|1999–2001|564|417|
|Development Community|Lesotho<sup>3</sup>|2003|2000–2|225|79|
|(SADC)|Botswana***|2006|2005|114|112|
||Namibia***|2006|2005|106|104|
||Swaziland***|2006|2005|70|69|
|Mauritius||2005|2002–4|636|417|
|South Africa||2003|2001–2|1,737|1,492|

_Source:_ Authors ́ calculations **;** ICA data **.** Note: 1 Total number of observations is equal to the total number of firms surveyed multiplied by the total number of years.

2 The observations available for regression analysis are the total number of observations minus the observations with any PF variable missing and/ or outlier after the cleaning process.

3 Countries for which no regression analysis was conducted.

* Countries pooled for regression analysis: Mauritania, Burkina Faso, Niger, and Cameroon.

** Countries pooled for regression analysis: Ethiopia and Eritrea.

*** Countries pooled for regression analysis: Botswana, Namibia, and Swaziland.

Table A.3 offers an initial overview of the data we use in the analysis. We have data for 26 countries from five different geographical regions. Cape Verde, Lesotho, and Burundi are special cases. The PF information for Lesotho is rather poor and it is impossible to make reliable statistical inferences 17
with only 79 observations. We did not group Lesotho with the pool of SADC countries because the survey of this country is from 2003 and the information on the IC and C variables is quite different. Burundi presents similar problems—the information on the PF for this country is for a single year (2005), and the number of observations is only 101. Although Burundi belongs to the EAC—along with Kenya, Uganda, and Tanzania—we did not pool Burundi with any of these countries because the information on the PF and the IC comes from different years and with different information on the IC and C variables. Cape Verde is another country with information for a single year (2006) and with only 47 observations available for regression analysis. Because of its obvious difference from the rest of the ECOWAS countries—different per capita income and its condition as an insular state, as well as other geographical considerations—we did not include Cape Verde in this pool. As a result, no regression analyses were conducted for Cape Verde, Lesotho, and Burundi.

By running the regressions country by country we can use as many infrastructure and other IC and C variables as are available. This allows us to gain heterogeneity estimating the impact of infrastructure on productivity. In addition, we can use more variables as proxies for firm-level, unobservable fixed effects, and we do not have to constrain ourselves to the subset of IC variables common to all the countries.

# _Cleaning the data_

The IC databases are, in some respects, troublesome. From table B.1 (see the Appendix) it is clear that out of the total number of establishments surveyed there are a considerable number of observations with at least one PF variable missing, and/or with outlier observations in the PF variables.<sup>9</sup> This problem becomes more acute for some countries—such as Algeria, Senegal, Eritrea, Tanzania, and Mauritius—where more than half the observations are missing for the regression analysis (see the upper panel of table B.1), which results in the sample representativity being lost. To reduce the effects of this sample selection bias, we apply a preliminary data-cleaning process that allows us to retrieve a considerable number of establishments for the analysis; it is based on a robust simple version of the EM-algorithm of Dempster et al. (1977) (for more details see Little and Rubin, 1987; Escribano et al., 2008). First, we exclude those plants with missing values in all the PF variables—sales, materials, capital stock, and labor cost. We convert outlier observations of PF variables into missing observations, then proceed as follows: (a) we replace the missing values by the corresponding (cells) industry-regionsize median of the variables keeping from 15 to 20 observations in each cell; (b) if we do not have enough observations in some cells we replace them with the corresponding industry-size medians; (c) if we still do not have enough observations in those cells we replace them with the region-size medians; and (d) if still necessary, in the last step we compute the medians only by size and/or by industry to replace those missing values. Table B.1 shows that the number of available observations in all the countries considerably increases with the application of this data-cleaning process. Tables B.2.1 and B.2.2 (see the Appendix) show the distribution of the observations (by country and year, and by

> 9 By _outlier_ s we mean those observations with ratios of materials to sales and/or labor cost to sales greater than 1.

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country and industry, respectively) before and after the cleaning process. From these tables it is clear that this process does not alter much of the original representativity of the ICSs.

# _The importance of infrastructure among IC variables_

As has been previously pointed out, we classify the IC factors in several categories to evaluate the impact of each group on economic performance. The infrastructure group of variables (INFs) is intended to be part of the country-specific IC. Within the infrastructure group we consider the next list of IC variables: customs clearance, energy, water, telecommunications, ICT, and transportation. Table B.4 describes the main INFs used in the empirical analysis.

The variables listed in table B.4 are common to almost all the countries considered, and are therefore intended to be a benchmark for comparison purposes; however, there are other countryspecific variables not listed. For a description of the complete set of variables, along with the countries for which they are available and the response rate of the variables, see table B.3 in the Appendix.

Within each infrastructure subgroup we consider different factors. Thus, in the _customs clearance group_ the factor considered is the time required to clear customs for exports and imports, and the time to get an import license. In the _energy group_ we consider variables that describe the quality of power provision (number and average duration of power outages, and subsequent losses), the use of a generator as a substitute for the public provision of power, the price of energy either from the public grid or from private generators, and the average time waiting to be hooked up for electricity supply. Similarly, for the _group of water_ we consider provision quality, price, the use of alternative supplies of water (such as private wells or boreholes), and the time to get water supply. In the _telecommunications and ICT group_ , the variable considered is the quality of the phone provision and the time to obtain a phone connection, as well as the use of ICT technologies (such as Internet or e-mail) in communications with clients and suppliers. The _transport group_ mainly incorporates a description of the quality of transportation services and dummy variables for the use of own-transport services (roads, transportation for workers, and so on).

From the econometric point of view we use three types of variables: (a) variables in logs, whose coefficients are interpreted as elasticities; (b) variables in percentages, whose coefficients can be interpreted as semi-elasticities; and (c) dummy variables, for which coefficients from the regressions are interpreted as semi-elasticities.

Finally, some of the variables in the same group are likely to be correlated since they provide similar information; for instance, the number and average duration of power outages and subsequent losses. In order to avoid multicolinariety problems we do not simultaneously use all variables in the regressions, but in the final model specification we test for possibly omitted variables.

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**Table B.4 Classification of the main infrastructure variables (INFs)**

||**Name of the variable**|**Description of the variable**|
|---|---|---|
|**Customs**|Days to clear customs to import|Average number of days to clear customs when importing (logs)|
|**clearance**|<br>Days to clear customs to export|Average number of days to clear customs when exporting directly (logs)|
||Wait for an import license|Number of days waiting for an import license (logs)|
|**Energy/**|Dummy for own power infrastructure|Dummy taking value 1 if the firm provides its own power infrastructure, excluding generators|
|**Electricity**|Dummy for own generator|Dummy variable taking value 1 if the firm has its own power generator|
||Electricity from own generator|Percentage of the electricity used by the plant provided by the own generator|
||Cost of electricity from generator|Estimated annual cost of generator fuel as percentage of annual sales|
||Cost of electricity from public grid|Average cost per kilowatt-hour (Kw/H) when using power from the public grid (logs)|
||Dummy for equipment damaged by<br>power fluctuations / Equipment<br>damaged by power fluctuations|Dummy taking value 1 if any machine or equipment was damaged by power fluctuations / Value of the<br>losses of machinery and equipment damaged by power fluctuations as a percentage of the net book<br>value of machinery and equipment (NBVC)|
||Power outages / Average duration of<br>power outages / Sales lost due to same|Total number of (logs) / Average duration of (logs) / Percentage of sales loss due to power outages<br>suffered by the plant in the last fiscal year (LFY) (conditional on the plant reports having power outages)|
||Power fluctuations / Average duration of<br>power fluctuations|Total number of (logs) / Average duration of (logs) power fluctuations suffered in hours (conditional on<br>the plant reports having power fluctuations)|
||Wait for electric supply|Number of days waiting to obtain an electricity supply (logs)|
|**Water**|Water outages / Average duration of<br>water outages /Losses due to same|Total number of (logs) / Average duration of (logs) / Percentage of sales lost due to water outages<br>suffered by the plant in LFY (conditional on the plant reports having water outages)|
||Dummy for own well or water<br>infrastructure|Dummy taking value 1 if the plant has its own or shared borehole or well or builds its own water<br>infrastructure|
||Water from own well or water<br>infrastructure|Percentage of firm’s water supply from its own or shared well|
||Cost of water from own well|Total annual cost of self-provided water as a percentage of total annual sales|
||Cost of water from public system|Unit cost of using water from the public water system (logs)|
||Wait for a water supply|Number of days waiting for a water supply (logs)|
|**Telecom.**<br>**and ICT**|Phone outages / Average duration of<br>phone outages / Losses due to same|Total number of (logs) / Average duration of (logs) / Percentage of sales lost due to phone outages<br>suffered by the plant in LFY (conditional on the plant reports having phone outages)|
||Wait for phone connection|Number of days waiting to obtain a phone connection (logs)|
||Dummy for e-mail|Dummy variable taking value 1 if the plant mainly uses e-mail to communicate with clients and suppliers|
||Dummy for web page|Dummy variable taking value 1 if the plant uses its own Web page to communicate with clients and<br>suppliers|
|**Transport**|<br>Transport failures / Average duration of<br>transport failures / Sales lost due to<br>same|Total number (logs) of / Average duration of (logs )/ Percentage of sales lost due to transport failures<br>suffered by the plant in LFY (conditional on the plant reporting on transport failures)|
||Dummy for own roads|Dummy taking value 1 if the firm provides its own roads|
||Dummy for own transportation for<br>workers|Dummy taking value 1 if the firm provides its own transportation for workers|
||Dummy for contract with transportation<br>company|Dummy taking value 1 if the firm arranges transport services for the delivery of finished products or raw<br>materials by directly contracting with the transportation company|
||Dummy for own transportation|Dummy taking value 1 if the firm arranges transport services for the delivery of finished products or raw<br>materials with its own transportation|
||Products with own transport|Percentage of products delivered with firm’s own transport|
||Transport delay|Percentage of times that transport services are late in picking up sales for domestic (or international)<br>markets at the plant for delivery|
||Shipment losses|Percentage of the consignment value of the products shipped for domestic (or international)<br>transportation lost while in transit because of theft, breakage, or spoilage|
||Sales lost due to delivery delays|Percentage of domestic (or international) sales lost due to delivery delays from suppliers in LFY|
||Low quality supplies|Percentage of domestic inputs/supplies that are of lower than agreed-upon quality|

_Source:_ ICS data.

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# **4.   Evaluating the impact of infrastructure on total factor productivity (TFP)**

Escribano and Guasch (2005, 2008) relate infrastructure and other IC and C variables with firm-level productivity (TFP) according to the following observable fixed-effects system of equations: where, _Y_ is firms’ output (sales), _L_ is employment, _M_ denotes intermediate materials, _K_ is the capital stock, _INF_ is a time-fixed vector of observable infrastructure variables, _IC_ is a time-fixed effect vector of other investment climate and other control variables, and _D_ is a vector of industry and year dummies.

The usually unobserved time fixed effects (<sup>_a_</sup> _i_<sup>) of the TFP equation (4.1b) is here proxy by the</sup> set of observed time-fixed components **INF** and **IC** variables of (4.1c) and a remaining unobserved random effect (<sup>ε</sup> _i_<sup>). The two random error terms of the system,ε</sup> _i_<sup>and</sup> _wit_ , are assumed to be conditionally uncorrelated with the explanatory L, M, K, **INF** and **IC** variables<sup>10</sup> of equation (4.2): ′ ′ ′ log _Yit_ = α _L_ log _Lit_ + α _M_ log _M it_ + α _K_ log _Kit_ + α _INF INFi_ +α _IC ICi_ +α _DDi_ + α _P_ + _uit_ . (4.2) Therefore, the regression equation (4.2) is representing the _conditional expectation_ plus a composite RE error term equal to _uit_ = ε _i_ + _wit_ .

Providing reliable and robust estimates of the impact of infrastructure on productivity is not a straightforward task. First, because the functional form of the PF is not observed and there is no available single salient TFP measure. Second, there is an identification issue separating TFP from PF. When any PF inputs are influenced by unobserved common causes affecting productivity—such as a firm’s fixed effects—there is a simultaneous equation problem in equation (4.1a). Third, we could expect that several IC variables have at least some degree of endogeneity, questioning therefore the conditional lack of correlation of (4.2). In what follows of this section, we briefly review the solutions to these questions suggested in Escribano and Guasch (2005, 2008) and Escribano et al. (2008).

## _Estimating infrastructure’s impact on productivity_

TFP or multifactor productivity refers to the effects of any variable different from the inputs—labor (L), intermediate materials (M), and capital (K)—affecting the production (Y) process. Since there is no

> 10 Under this formulation (and other standard conditions) the OLS estimator of the productivity equation (4.2) with robust standard errors is consistent, although a more efficient estimator (GLS) is given by the random effects (RE) estimator that

> takes into consideration the particular covariance structure of the error term, ε _i_ + _wit_ , which introduces a certain type of heteroskedasticity in the regression errors of (4.2).

21
single salient measure of productivity (or _logTFPi_ ), any empirical evaluation of the productivity impact of INFs might critically depend on the particular productivity measure used. Escribano and Guasch (2005, 2008) suggested—following the literature on _sensitivity analysis_ of Magnus and Vasnew (2006)—to look for empirical results (elasticities) that are robust across several productivity measures. This is also the approach we follow in this paper.

In particular, we want the elasticities of INFs on productivity (TFP) to be robust (with equal signs and similar magnitudes) for the 10 different productivity measures used. Alternative productivity measures come from considering:

- Different functional forms of the PFs (Cobb-Douglas and Translog)

- Different sets of assumptions (technology and market conditions) to get consistent estimators based on Solow’s residuals, ordinary least squares (OLS), or random effects (RE), and so on

- Different levels of aggregation in measuring input-output elasticities (at the industry level or at the aggregate country level)

**Box 4.1 Summary of productivity measures and estimated investment climate (IC) elasticities**

|1. Solow ́s Residual|Two-step<br>estimation|1.1 Restricted coefficient<br>1.2 Unrestricted coefficient|1.1.a OLS 1.1.b RE<br>1.2.a OLS 1.2.b RE|2 (Pit) measures<br>4 (IC) elasticities|
|---|---|---|---|---|
|2. Cobb-Douglas|Single-step<br>estimation|2.1 Restricted coefficient<br>2.2 Unrestricted coefficient|2.1.a OLS 2.1.b RE<br>2.2.a OLS 2.2.b RE|4 (Pit) measures<br>4 (IC) elasticities|
|3. Translog|Single-step<br>estimation|3.1 Restricted coefficient<br>3.2 Unrestricted coefficient|3.1.a OLS 3.1.b RE<br>3.2.a OLS 3.2.b RE|4 (Pit) measures<br>4 (IC) elasticities|
|Total||||10 (Pit) measures<br>12 (IC) elasticities|

_Source:_ Author’s estimations. _Note:_ Restricted coefficient = equal input-output elasticities in all industries. _Unrestricted coefficient = different input-output elasticities by industry._ Box 4.1 summarizes the productivity measures used for the IC evaluation. The two-step estimation starts from the nonparametric approach based on cost shares from Hall (1990) to obtain Solow’s residuals in logs under two different assumptions:<sup>11</sup> (a) the cost shares are constant for all plants located in the same country (restricted Solow residual), and (b) the cost shares vary among industries in the same country (unrestricted by industry Solow residual). Once we have estimated the Solow residuals ( _logTFPi_ ) in the first step, in the second step we can estimate equation (4.3) by OLS with robust standard errors for the countries that have a single year of data (2006) on PF variables. For the remaining 14 countries and for the blocks of countries described in section 3, we can also estimate (4.3) by RE to obtain the corresponding IC elasticities and semi-elasticities,

> 11 The advantage of the Solow residuals is that they require neither the inputs (L, M, K) to be exogenous nor the input-output elasticities to be constant or homogeneous (Escribano and Guasch, 2005 and 2008). The drawback is that they require having constant returns to scale (CRS) and, at least, competitive input markets.

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where _INF_ is the observable fixed effects vector of infrastructure variables, and _IC_ is the observable fixed effects vector of other IC and control variables listed in table A.2 in the Appendix. In all the panel data regressions, we always control for several sector-industry dummies (Dj, j = 1, 2, . . ., qD), and in the cases having more than one year of observations we also include a set of time (Dt, t = 1, 2, . . ., qT) dummy variables and always a constant term ( α _P_ ).

For cross-country comparisons based on TFP we use the concept of demeaned TFP,<sup>12</sup> which gets rid of the constant term as well as the constant effects by industry and by year, concentrating therefore on the part of TFP that is influenced by INF, IC, and the other plant-level control variables, In the single-step estimation approach, we consider the parametric estimation by OLS and RE of the extended PF (4.2). To address the well-known problem of the endogeneity of inputs, we follow the approach proposed by Escribano and Guasch (2005, 2008). That is, we proxy the usually unobserved firm-specific fixed effects (which are the main cause of inputs’ endogeneity) by a long list of observed firm-specific fixed effects coming from the ICSs. Controlling for the largest set of IC variables and plant characteristics, we can get—under standard regularity conditions—consistent and unbiased least squares estimators of the parameters of the PF and the INF and IC elasticities. Furthermore, we use two different functional forms of the PF—Cobb-Douglas and Translog—under two different assumptions on the input-output elasticities: equal input-output elasticities in all industries (restricted case) and different input-output elasticities by industries (unrestricted case).

Notice that even if we are only interested in assessing the impact of infrastructure on TFP, we do not limit the scope of the control analysis to only this subset of IC variables. We include (and therefore control for) all the IC factors because of the crucial role IC variables play as proxies for the unobserved fixed effects; this is the key feature of this methodology in order to provide robust empirical results. If we tried to estimate only the impact of infrastructure, without controlling for the other blocks of IC variables, we might get different signs on certain coefficients because of the omitted variables problem (Escribano and Guasch, 2008).

Another econometric problem we have to face when estimating the parameters of the INF and IC variables—either from the two-step or single-step procedure—is the possible endogeneity of some of these variables. That is, many INF or IC variables are likely to be determined simultaneously along with any TFP measure. With these productivity equations, the traditional instrumental variable (IV) approach is difficult to implement, given that we only have information for one year, and therefore we cannot use the natural instruments for inputs, such as those provided by their own lags. As an alternative correction for the endogeneity of the INF and IC variables, we use the region-industry-size

> 12 Notice that the demeaned TFP concept of equation (4.3b) corresponds to the observable part of the fixed effects equation (4.1c).

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average of firm-plant-level INF and IC variables instead of the crude variables,<sup>13</sup> which is a common solution in panel data studies at the firm level<sup>14</sup> (see Veeramani and Goldar, 2004, for other use of industry-region averages with IC variables).

Using industry-region-size averages also mitigates the effect of having certain missing individual INF and IC observations at the plant level, which—as mentioned in section 3—represent one of the most important difficulties of using ICSs. As an alternative, we also follow a second strategy to deal with the missing values of some INF and IC variables. In order to keep as many observations in the regressions as possible to avoid losing efficiency, when the response rate of the variables is large enough, we decided to replace those missing observations with the corresponding industry-region-size 15 average. Thus, we gain observations, efficiency, and representativity at the cost of introducing measurement errors into some variables.<sup>16</sup> The econometric methodology applied for the selection of the variables (INF and IC) goes from the general to the specific. The otherwise omitted variables that we encounter—starting from a toosimple model—generate biased and inconsistent parameter estimates. We start the selection of variables with a wide set compounded by up to 90 variables (depending on the country). We avoid simultaneously using time variables that provide the same information and are likely to be correlated, mitigating the problem of multicollinearity that could otherwise arise. We then start removing the less significant variables from the regressions one by one, until we obtain the final set of variables, all significant in at least one of the regressions and with parameters varying within a reasonable range of values. Once we have selected a preliminary model we test for omitted INF and IC variables.

The robust coefficients of the INF and IC variables in productivity, along with their level of significance, are available upon from request. The parameters estimated in the two step procedure with restricted input-output elasticites can be found in figures 6.1 to 6.23.

# _Infrastructure assessment based on O&P decompositions_

According to the O&P (1996) decomposition, aggregate productivity for a given country, industry, or region may be decomposed into two terms: (a) average productivity, and (b) a covariance term measuring whether the economy is able to efficiently reallocate resources from less productive establishments to more productive ones. Once we have estimated a robust set of parameters for the IC

> 13 Because of the low number of available regions in most of the countries, we had to use the industry-region-size variables instead of the region-industry averages. For the creation of cells a minimum number of firms are imposed—there must be at least 15 to 20 firms in each industry-region-size cell to create the average, otherwise we apply the region-industry averages. If the problem persists, we apply the industry-size or the region-size average.

> 14 This two-step estimation approach is a simplified version of an instrumental variable (IV) estimator (two-stage least squares, 2SLS).

> 15 Notice that this replacement strategy has a straightforward weighted least squares interpretation since we are giving a greater weight to those observations with more variance (Escribano et al., 2008).

> 16 The measurement error introduces a downward bias in the parameters that depends on the ratio between the variances of the variables and the measurement error. Since those explanatory variables are constant within regions, sizes, and industries we expect their variances will be small.

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factors with statistically significant impacts on firms’ productivity, we exploit the exact relation, proposed by Escribano et al. (2008a), between the terms of the O&P decomposition, and the IC factors affecting productivity. The IC infrastructure variables affect both the average productivity of African establishments (or firms) as well as their allocative efficiency component. It is well known that competitive markets efficiently allocate resources under certain conditions. But in a world of imperfect information a turbulent IC introduces distortions into markets, and, as a result, affects the efficiency of the economy as a whole. The allocative efficiency term of the O&P decomposition should therefore reflect those imperfections.

In the second part of this analysis—taking advantage of the robustness of the INF, IC, and C elasticities estimated—our aim is to concentrate on the TFP measure that comes from the restricted Solow’s residuals in order to evaluate the infrastructure effects on average productivity and on allocative efficiency based on the O&P decomposition of aggregate productivity in levels, Furthermore, we want to exploit the log-linear properties of the following mixed<sup>17</sup> O&P decomposition for each of the African countries considered in order to obtain closed form O&P decompositions in terms of IC and C variables: Aggregate log productivity of country _q_ , say (logTFPq), is equal to the sum of the sample average log productivity of the establishments of country _q_ , and the covariance between the share of sales (s<sup>Y</sup> q<sup>) and log productivity of that country (allocative efficiency of country</sup><sup>_q_). The index</sup><sup>_q_could</sup> also indicate a particular industry, region, size, and so on. The useful additive property of equation (4.2) in logarithms allows us to obtain an exact closed form solution of the decomposition of aggregate log productivity according to equation (4.5). Following Escribano et al. (2008), we can express aggregate log productivity as a weighted sum of the average values of the IC, dummy D variables, the intercept, and the productivity average residuals ( ö _u_ ) from (4.2), and the sum of the _covariances_ between the share of sales and investment climate variables IC, dummies D, and the productivity residuals ( ö _u_ ): where the set of estimated parameters used comes from the two-step TFP estimation, having the restricted Solow’s residual as a dependent variable in (4.2).

From equation (4.5) each INF and IC variable may affect the aggregate log productivity through both its average and covariance (with respect to the share of sales). This complements the information provided by the marginal effects (INF and IC elasticities). Suppose that an INF variable with a low

> 17 It is called a _mixed_ Olley and Pakes (O&P) decomposition because in the original O&P decomposition both TFP and the share of sales were in levels, while TFP in (4.4b) is in logs (log P).

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impact—in terms of marginal effects (elasticities)—affects most of the firms in a given country; the impact of such an IC variable in terms of average productivity could be very high. It is therefore very important for policy analysis to combine the empirical evidence from the estimated IC elasticities on productivity with their corresponding INF impact on the two components of O&P decompositions: average productivity and allocative efficiency.

A variable with a negative marginal effect on average productivity (or logTFP) may have either a positive or a negative effect on allocative efficiency. If the covariance of that IC variable and the market share is positive, then the greater proportion of sales in the hands of establishments with high levels of that variable, the larger the negative impact on aggregate productivity will be, therefore decreasing the allocative efficiency. In contrast, a negative covariance means that those establishments with the highest levels of the IC variable have the lowest market shares, and therefore the negative effect of the IC variable on average productivity is somehow compensated through the effect on the reallocation of resources among firms.

By operating in (4.5) Escribano et al. (2008) obtained the next expression, which allows us to obtain a direct decomposition of the impacts of each INF and IC variable on aggregate productivity (logTFPq): There are several advantages of using equation (4.6). First, we can compare net contributions by isolating the impact of INF and other IC variables from the impact of industry dummies, the intercept, and the residuals. Second, we can express what portion of aggregate productivity is explained by INF, IC, and C variables (demeaned log TFP), and what proportion is due to the constant term, industry dummies, and so on. To make cross-country comparisons based on IC impacts on TFP and to avoid the problem of comparing apples and oranges, it is desirable to create an index (demeaned TFP). After subtracting the mean (that is, the constant term, time effects, industry effects, and country-specific effects) from aggregate productivity we can concentrate on the contributions of IC variables to the demeaned TFP.

Similarly, we can construct the demeaned counterparts of expressions (4.5) and (4.6) and compute the percentage contribution of each INF variable or block of IC variables—as in equations (4.7) and (4.8), respectively—obtaining the following _demeaned mixed O&P decomposition_ : So far, we have exploited the linear properties of the logarithm form of the mixed O&P decomposition of TFP. But the original O&P decomposition is based on TFP and the share of sales (in 26
levels), and is therefore also capturing nonlinear relations between market shares and IC variables coming from (4.3a). To know to what extent these nonlinear terms are affecting this relation, we perform simulation experiments<sup>18</sup> on INF, IC, and C variables, and evaluate the consistency of the results with the ones obtained from the previous mixed O&P decomposition—see (4.4b). The IC simulations are done variable by variable (one at a time) keeping the rest of the variables constant; that is, we propose a scenario in which the level of one of the IC variables improves by 20 percent in all establishments (20 percent less power outages, 20 percent less shipment losses, etc). We compute the corresponding rate of change of aggregate productivity, average productivity, and allocative efficiency caused by such a 20 percent improvement. We repeat the same experiment for the rest of the IC and C variables, and, for comparative purposes, we also evaluate the relative group of IC variables.

# **5. The contribution of infrastructure to the investment climate (IC) of Africa**

In section 4 we described the econometric methodology used in section 6 to assess the impact of infrastructure on productivity. We suggested three key elements of empirical evaluations of infrastructure and other IC and C impacts on productivity: the marginal productivity effects, the percentage contributions of infrastructure to aggregate log productivity (mean and efficient components), and the simulations of infrastructure improvements on aggregate productivity (in levels).

In this section we focus on presenting the results of infrastructure contributions to aggregate productivity from simulation experiments. In addition to the results of the econometric analysis, we consider African firms’ perceptions of the main obstacles to economic performance. In the first subsection the objective is to assess how African firms perceive infrastructure quality. This is followed in the next subsections by the results of the econometric analysis, focusing on the infrastructure’s effect on productivity after controlling for other IC factors. Finally, to complement the robustness of the results we check the consistency of the conclusions obtained from both the IC contributions to average log TFP and from the TFP simulation experiments.

_Do African firms perceive infrastructure as an obstacle to growth?_ In ICSs, firms are asked to rate a number of IC factors as obstacles to economic performance. The survey options offered are _no obstacle_ , _minor obstacle_ , _moderate obstacle_ , _major obstacle_ , or _very severe obstacle_ on a broad range of IC aspects: infrastructure, red tape, corruption and crime, finance, and labor skills.

Figure 5.1 shows the degree to which each group of IC factors is perceived by firms as an obstacle to economic development. These perceptions are sorted in descending order by their perceived contribution to the total, after being normalized to 100. For example, in panel A of figure 5.1, we observe that in Cape Verde 25 percent of firms believe infrastructure to be a major or very severe obstacle to economic growth; 40 percent find red tape, corruption, and crime as a major or very severe

> 18 We are indebted to Ariel Pakes for this suggestion.

27
<!-- Start of picture text --> A Relative IC block contribution depending on the number of questions *<br>90%<br>100% FTW ea a<br>60%<br>50%<br>40%<br>30%<br>N N N N N N N N N<br>cPV BDI BFA MRT CMR ETH NER KEN LsO MW BEN SWZ ERI UGA ZMB TZA MDG SEN NAM ML DZA BWA ZAF MAR MUS EGY<br><!-- End of picture text --> <!-- Start of picture text --> B. IC block contribution to the mean of each block<br>100%<br>7<br>80% |<br>TT<br>70%<br>60%<br>40%<br>30% N N S&S NN NS N y y .<br>NNNNNNNNNSNNABSB RK 7<br>MNNNNNNNNVNNNVVNVNNVVNVNNVVVNANANN A N VN VN VN VN VN AN AN VN AN WN WN WN AWN AW AWN AWN WN VN AN CN AN ESA<br>CPV CMR BFA BOI ETH NER KEN BEN MRT MW TZA ZMB LSO MDG UGA SEN SWZ Mu ERI DZA NAM ZAF BWA EGY MUS MAR<br><!-- End of picture text -->
The factor group with the largest number of sub-factors is red tape, corruption, and crime. So it is not surprising that this has the largest percentage of all. In order to isolate this effect, we normalize after computing the mean of each IC factor type. For example, in the infrastructure group there are four IC sub-factors (telecommunications, customs, electricity, and transportation), whereas the red tape group contains eleven sub-factors. But the importance of infrastructure is very similar across countries, although obviously the relative contribution of the red-tape group is more balanced, gaining relatively more relevance to the other IC groups.

In the next subsection the results of the econometric analysis—estimating the relative impact of infrastructure on average productivity using simulations—are compared with firms’ perceptions of obstacles to growth. The question of interest is: are the econometric results consistent with firm perceptions?

# _Impact of infrastructure on productivity_

The impact of infrastructure factors on productivity is evaluated here in terms of their effect on the O&P decomposition. Infrastructure’s contribution to the aggregate productivity of each country’s manufacturing sector is decomposed into its contribution to (a) average productivity and (b) allocative efficiency (the ability of markets to reallocate resources from less productive to more productive establishments).

Figure 5.2 presents the two alternative country-by-country O&P decompositions given by equations (4.4a) and (4.4b), sorted by aggregate productivity in descending order. The productivity measure used to calculate the O&P decomposition is the restricted Solow residual obtained from the two-step estimation approach (see section 4). We present two sets of results with O&P decompositions. Panel A of figure 5.2 shows the O&P decomposition with the restricted Solow residual in levels, and panel B shows the mixed O&P decomposition with the restricted Solow residual, in logs, weighted by the share of sales. This is important because the results of the simulations are associated with the O&P decomposition in levels, and the results from the percentage contributions to the average use the convenient additive property of the TFP equation in logs. Notice that both panels A and B preserve the rankings of average productivity, but this is not necessary true for aggregate productivity. The reason is clear: aggregate productivity is simply the sum of average productivity and allocative efficiency, and this second term depends somewhat on whether we use TFP in levels or in log form.<sup>19</sup> From panel A of figure 5.2 we observe a positive reallocation of output. That is, output is moving from less productive establishments to more productive ones, since in all the countries the allocative efficiency is positive, with the greatest effects found in Benin, Burkina Faso, Cameroon, Niger, and Eritrea.

> 19 For a deeper discussion of this issue see Escribano et al. (2008).

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Notice that we avoid direct comparisons of TFP across countries but we suggested in section 4 to compare demeaned productivity decompositions (see figure 5.2, panels A and B, ). Remember that the demeaned productivity (or demeaned TFP) at the firm level is simply firm-level productivity minus the constant term of the productivity equation and the industry and year dummies (see equation 4.7).<sup>20</sup> We assume that all the productivity differences resulting from units of measurement, different deflators, and so on are contained in the constant, industry, and time-fixed effects, and therefore what is left in the productivity measure are only the TFP effects of the infrastructure, IC, and other C variables.

We obtain the O&P decomposition using the demeaned productivity either in levels or in logs. This demeaned TFP set of cross-country comparisons is presented in figure 5.3 . Panel A shows the decomposition of the demeaned productivity in levels; it is interpreted as the productivity that stems from IC conditions after controlling for all the other elements. The results are not at all surprising since they are basically consistent with those provided by the per capita income and by the DBR (2007). Rankings based on demeaned productivity are topped by South Africa and Mauritius, closely followed by Botswana, Algeria, Egypt, Namibia, and Swaziland. The lowest-ranked countries are those with the most antiproductive IC, in other words, those whose IC conditions pose difficulties to economic development. These countries are Tanzania, Malawi, Uganda, Benin, Mauritania, and Zambia. Symmetrically, as for the regular O&P decompositions, the contribution of the IC to aggregate demeaned productivity is decomposed into its contributions to average demeaned productivity and the allocative demeaned efficiency term (see equation 4.7) _._ Notice that, in Africa, the allocative efficiency component is always lower than the effect of average productivity. Nevertheless, in Madagascar, Botswana, Mauritius, and other countries, the IC has a considerable effect on the efficient reallocation of resources among establishments.

Alternatively, this demeaned productivity may be interpreted as a sum of pro- and antiproductive infrastructure, as well as other IC and C factors. Examples of pro-productive infrastructure factors are the use of e-mail and websites. Negative or anti-productive infrastructure factors include the number of power outages, the average duration of water outages, and so on. As a consequence, productivity will decrease as the importance of anti-productive factors becomes larger and larger; this picture becomes even clearer in panel B of figure 5.4 . The demeaned O&P decomposition of TFP in logs (see panel B) shows how aggregate productivity may be negative (in Tanzania, Benin, Malawi, and so on) when the negative TFP aspects of IC dominate over the positive (pro-productive IC factors weigh more than the negative ones), as in the case of South Africa, Mauritius, Egypt, Botswana, and so on.

In sum, in African countries the IC has important effects on the aggregate productivity of the manufacturing industry, and this net effect may be positive or negative depending on which IC aspect matters more—the pro-productive or the anti-productive. The aim now is to know to what extent such decreases or increases in productivity are due to infrastructure or other IC groups.

> 20 Obviously, the year dummies are only subtracted from the productivity measure of the countries with panel data.

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The same holds for the rest of the groups of IC and C variables.

Figure 5.4, panel A, provides the decomposition of demeaned productivity in levels using simulations of improvements to IC variables. When the IC factor improves by 20 percent it could mean, for example, that 20 percent more firms are using e-mail, or that there is a 20 percent reduction in power outages, and so on—which implies decreases in the negative IC factors and increases in the positive ones. The total effect of improving each IC by 20 percent, maintaining the rest of IC factors constant, implies that aggregate productivity could increase in South Africa by 55 percent, in Mauritius by 30 percent, and so on. From panel A of figure 5.4 it is clear that there are some economies that are more likely to be affected by the IC. These are therefore more sensitive to changes in IC conditions. This is the case in Kenya and Benin, where the aggregate productivity could increase by 70 percent and 85 percent, respectively. At the other extreme are Egypt, Morocco, and Eritrea. Lastly, improvements to aggregate productivity come in almost all countries via improvements to average productivity, and, to a lesser extent, allocative efficiency, with the exception of Algeria, Kenya, and Benin. The role of infrastructure in the composition of aggregate productivity is considerable in all the countries, but is the greatest in Uganda, Benin, Malawi, Cameroon, and Zambia. This suggests that these countries are the most sensitive to changes in infrastructure quality.

Panel B of figure 5.4 shows a more static interpretation using the O&P decomposition in logs by group of variable. In particular, panel B offers information on the actual and current situation of the IC and its effect on aggregate productivity; in other words, gains and losses generated by the average IC conditions (O&P decomposition of TFP in logs decomposed by groups of INF, IC, and C variables). For example, in South Africa, aggregate _demeaned_ TFP is 0.83 (see panel B of figure 5.3). Out of this level of productivity -0.9 is explained by the overall contribution of the infrastructure factors; 0.95 by red tape, corruption, and crime; and the rest by the remaining IC and C variables. Notice that in panel B the contributions of the different groups are not in absolute value, so the positive effect of the proproductive factors compensate for the negative effect of the anti-productive ones. Even taking this into account, the overall contributions of the infrastructure group are negative in all the countries, implying that the pro-productive infrastructure IC factors never compensate for the negative IC effects, with the exception of Kenya (where it is slightly positive) and Madagascar, Ethiopia, and Algeria (where the contribution of the infrastructure group is close to zero and almost negligible). As expected, the largest and most negative infrastructure effect is found in Benin, followed by Malawi, Uganda, Mauritania, Cameroon, and Zambia.

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IC to aggregate productivity, average productivity, and allocative efficiency, we find via simulations that the relative 20 percent improvement of infrastructure in Malawi reaches 58 percent, in Eritrea 50 percent, and in Uganda 45 percent (as panel A of figure 5.5 shows). The same holds for average productivity (panel B) where the rankings do not change, and for allocative efficiency (panel C), where, once again, Malawi, Senegal, and Uganda show the largest contributions of the infrastructure group. **Figure 5.6 Infrastructure absolute effects on productivity: Mixed** **_demeaned_ O&P decomposition** <!-- Start of picture text --> A. Aggregate productivity<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI UGA ZMB ETH BEN SEN DZA MLI CMR ERI TZA NER MAR MRT MDG EGY ZAF BFA MUS NAM KEN SWZ BWA<br>Infrastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br>B. Average productivity<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI BEN SEN UGA ETH ZMB DZA ERI MLI CMR MRT BFA NER TZA MA R MDG KEN ZAF MUS EGY SWZ BWA NA M<br>Infrastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br>C. Allocative efficiency<br>100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>MWI SEN NA M DZA UGA TZA MDG BFA MLI ZMB CMR EGY ERI ETH MRT KEN ZAF MUS MAR SWZ BEN NER BWA<br>Inf rastructures Red tape, corruption and crime Finance and corporate governance Quality, innovation and labor skils Other control variables<br><!-- End of picture text --> _Source_ : Authors’ calculations from IC data.

_Note_ : The percentage contribution of the infrastructure group is computed as the sum of the absolute values of the percentage contributions of the individual infrastructure variables, divided by the cumulative sum in absolute terms of the percentage contributions of all the IC and C variables, including infrastructure. The holds for the rest of the IC blocks of variables.

A similar picture is provided by figure 5.6, where, instead of simulations, we consider the relative contributions by groups of variables to average demeaned log productivity and to the demeaned 36
efficiency term—see equation (4.8). In this case we sum up the different contributions of the INF, IC, and C factors of equation (4.8), but in absolute value so that the negative effects do not compensate for the positive ones and vice versa _,_ and we compute the relative contribution of each group within the IC group as a whole. Therefore, the relative contribution of the infrastructure group is the sum in absolute value of all individual infrastructure variables divided by the total absolute contribution of all INF and IC variables—multiplied by 100. The largest relative effects of infrastructure on aggregate log productivity are found in Malawi (60 percent), Uganda (50 percent), Benin (50 percent), Zambia (47 percent), and Ethiopia (46 percent). The lowest contributions are in Kenya, Swaziland, and Botswana. A similar ranking is provided by panel B, where the effects on the average log productivity are isolated from those from the allocative efficiency, as seen in panel C. Once again, Malawi, Benin, Senegal, Uganda, and Ethiopia lead a ranking closed by Mauritius, Egypt, Swaziland, Botswana, and Namibia. Panel C offers the results for allocative efficiency. In Malawi, Senegal, Namibia, and Algeria, the effects of infrastructure on the efficient reallocation of results among firms appear to be very significant, reaching the relative contributions of 54 percent, 48 percent, and 46 percent, respectively.<sup>21</sup>

# _Cross-country comparisons_

Table C.1 summarizes the empirical results discussed in previous sections. The first column shows ranking of African countries based on per capita income, the second based on the DBR (2007), the third column based on quality of overall infrastructure (1 minimum, 7 maximum) given in the ACR (2007), the fourth column the demeaned aggregate productivity, and the fifth column shows the ranking of firms’ perceptions of the quality of infrastructure (from 23rd being the poorest quality to 1st being the best quality in our sample). Columns 6 and 7 show the percentage of absolute contributions of infrastructure to average log productivity and to allocative efficiency, with TFP in logs, while columns 8 and 9 show the percentage absolute contributions of infrastructure to average productivity in levels and to allocative efficiency via simulations.

> 21 It is useful to clarify that the differences between the rankings of the contributions to the aggregate productivity via simulations of panel A of figure 5.5 and the rankings of the contributions to the aggregate log productivity of panel A of figure 5.6 come mainly from the role of the allocative efficiency. Notice that in figure 5.6 the allocative efficiency term based on log TFP does not have the same scale as the efficiency term when TFP is in levels. As a result, since aggregate productivity is simply the sum of the average productivity and the efficiency term in levels, the role of the efficiency term with TFP in levels will increase with respect to its counterpart in logs and therefore, could alter the rankings of countries based on the two alternative measures of aggregate productivity (weighted productivity).

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**Table C.1 Summary of cross-country comparisons based on alternative rankings of economic performance**

|**Ranking**<br>**based on**<br>**per capita**<br>**GDP**|**DBR 2007**<br>**(rank)**|**Ranking**<br>**ACR’07 (rank**<br>**within**<br>**sample)**|**_Demeaned_**<br>**Aggregate**<br>**Productivity**<br>**(rank)**|**Firms'**<br>**perceptions:**<br>**infras. As an**<br>**obstacle**<br>|**Perc. Contributions**<br>**log-productivity**|**of infrastructure to**|**Perc. contributions**<br>**productivity via sim**|**of infrastructure to**<br>**ulations**|
|---|---|---|---|---|---|---|---|---|
|||||**% abs.**<br>**contribution**|**Average log-**<br>**productivity**|**Allocative efficiency**<br>**in logs**|<br>**Average**<br>**productivity**|**Allocative efficiency**|
|||||**(rank)**|**% abs. contribution**<br>**(rank)**|<br>**% abs. contribution**<br>**(rank)**|**% abs. contribution**<br>**(rank)**|**% abs. contribution**<br>**(rank)**|
|**MUS (1)**|32 (2)|2.0 (2)|4.2 (1)|13.9 (2)|26.6 (19)|17.1 (18)|21.8 (6)|12.4 (4)|
|<br>**SWZ (2)**|<br>76 (5)|<br>1.4 (7)|<br>n.a|<br>22.4 (10)|<br>25.6 (21)|<br>14.3 (20)|<br>27.4 (10)|<br>17.6 (10)|
|<br>**ZAF (3)**|<br>29 (1)|<br>2.3 (1)|4 (3)|<br>16.2 (5)|<br>28.6 (18)|<br>19.7 (17)|<br>17.4 (4)|<br>11.0 (2)|
|<br>**BWA (4)**|<br>48 (4)|<br>1.7 (3)|<br>3.4 (6)|<br>15.6 (4)|<br>17.5 (22)|<br>7.41 (23)|<br>23.2 (8)|<br>8.8 (1)|
|<br>**DZA (5)**|116 (12)|1.5 (4)|2.9 (7)|18.3 (7)|48.6 (7)|31.1 (4)|34.9 (18)|26.4 (17)|
|**NAM (6)**|42 (3)|1.5 (6)|4.2 (2)|18.3 (6)|16.5 (23)|32.9 (3)|22.7 (7)|36.7 (20)|
|<br>**EGY (7)**|<br>165 (22)|<br>1.5 (5)|<br>3.7 (4)|<br>14.0 (3)|<br>26.0 (20)|<br>23.8 (12)|<br>19.9 (5)|<br>16.1 (8)|
|<br>**MAR (8)**|<br>115 (11)|<br>1.1 (9)|<br>3.6 (5)|<br>9.9 (1)|<br>31.3 (15)|<br>16.6 (19)|<br>16.2 (3)|<br>14.8 (6)|
|<br>**CMR (9)**|<br>152 (18)|<br>0.8 (16)|<br>1.9 (18)|<br>27.5 (23)|<br>41.6 (10)|<br>25.4 (11)|<br>31.2 (13)|<br>23.2 (13)|
|**MRT (10)**|148 (16)|0.6 (19)|2.1 (15)|25.3 (17)|35.4 (11)|21.1 (15)|28.3 (12)|16.2 (9)|
|**SEN (11)**|146 (15)|0.9 (12)|n.a|22.7 (11)|58.5 (3)|40.9 (2)|52.1 (21)|42.2 (22)|
|<br>**BEN (12)**|<br>137 (13)|<br>0.6 (20)|2.1 (11)|<br>25.6 (18)|<br>59.9 (2)|<br>12.4 (21)|<br>33.3 (17)|<br>23.3 (14)|
|<br>**KEN (13)**|<br>83 (6)|<br>1.0 (11)|<br>2.8 (8)|<br>25.6 (19)|<br>30.3 (17)|<br>19.9 (16)|<br>26.1 (9)|<br>23.2 (12)|
|<br>**MLI (14)**|<br>155 (19)|<br>0.9 (14)|<br>2.1 (14)|<br>21.6 (9)|<br>42.7 (9)|<br>26.8 (9)|<br>42.5 (19)|<br>33.5 (19)|
|<br>**UGA (15)**|107 (9)|0.6 (21)|2 (17)|23.3 (12)|58.4 (4)|29.8 (5)|45.4 (20)|42.0 (21)|
|**BFA (16)**|163 (21)|0.8 (15)|2.1 (12)|26.9 (22)|35.3 (12)|27.0 (8)|27.6 (11)|12.0 (3)|
|<br>**ZMB (17)**|<br>102 (8)|<br>0.7 (18)|<br>n.a|<br>24.0 (14)|<br>50.6 (6)|<br>26.8 (10)|<br>15.4 (2)|<br>15.1 (7)|
|<br>**TZA (18)**|<br>142 (14)|<br>0.2 (23)|2.7 (9)|<br>24.3 (15)|<br>34.1 (14)|<br>28.3 (6)|<br>32.3 (15)|<br>29.1 (18)|
|<br>**NER (19)**|<br>160 (20)|<br>0.8 (17)|<br>n.a|<br>26.2 (20)|<br>34.7 (13)|<br>11.1 (22)|<br>31.6 (14)|<br>22.1 (11)|
|<br>**MWI (20)**|<br>110 (10)|<br>0.4 (22)|2.1 (13)|<br>24.5 (16)|<br>65.9 (1)|<br>45.8 (1)|<br>53.7 (22)|<br>55.2 (23)|
|<br>**MDG (21)**|<br>149 (17)|<br>1.4 (8)|<br>2 (16)|<br>23.5 (13)|<br>30.6 (16)|<br>27.9 (7)|<br>11.1 (1)|<br>14.3 (5)|
|<br>**ETH (22)**|<br>97 (7)|<br>1.0 (10)|<br>2.3 (10)|<br>26.7 (21)|<br>52.6 (5)|<br>21.9 (14)|<br>33.2 (16)|<br>25.0 (15)|
|<br>**ERI (23)**|<br>170 (23)|<br>0.9 (13)|<br>n.a|<br>20.7 (8)|<br>46.1 (8)|<br>22.5 (13)|<br>54.7 (23)|<br>25.3 (16)|

_Source:_ Authors’ calculations using ICA data, DBR (2007), ACR (2007), and Penn World Table. _Note:_ n.a = not available.

The rankings presented in the first five columns are very consistent. In particular, the ranking based on demeaned aggregate productivity (column 4) shows a clear positive correlation to per capita GDP and with the ranking based on the DBR rankings. From the results of the rankings obtained from the first five columns we find two groups of African countries, as was suggested by looking at the growth rates of per capita GDP (see figure 1.2, panel B). That is: (a) countries in the north and south of Africa are relatively more successful, and (b) countries from the central-east and central-west regions of Africa are relatively less successful.

The last four columns show two alternative measures of the percentage absolute contribution of infrastructure to productivity, along with the ranking in parentheses. In particular, column 6 shows a negative correlation between the ranking based on the contribution of IC to average log TFP and per capita GDP and also with the ranking based on the DBR, indicating that low infrastructure quality is one of the key growth bottlenecks in Africa. The results show a great homogeneity among the rankings in the first four columns and the results from the econometric analysis. Thus, for instance, Mauritius is ranked 1st in terms of per capita income and quality of overall infrastructure in the ACR (2007), 2nd according to the DBR (2007) and firms’ perceptions, 19th (out of 23) according to the impact of the INF variables on the average log productivity, and 18th (out of 23) with respect to the allocative efficiency in logs. In these cases the correlation with firm growth is negative, signaling again that infrastructure quality is an important growth bottleneck in Africa.

38
Egypt and Morocco are interesting cases. Both countries show a relatively high quality of infrastructure according to the ACR and perception rankings, and the results of the econometric analysis confirm this. Egypt is 4th and Morocco is 5th in the rankings based on demeaned aggregate productivity, and both have one of the lowest contributions of infrastructure to TFP.

Countries with the poorest infrastructure quality, according to ACR (2007) and firms’ perceptions, are Cameroon, Burkina Faso, Niger, and Ethiopia. Once again this is consistent with the econometric analysis done for these countries, showing a great negative influence of infrastructure on productivity. Cameroon and Burkina Faso are ranked among the countries with the highest contribution of infrastructure to average productivity. The influence of infrastructure on Ethiopia’s manufacturing sector productivity is also very high, with a relative contribution equal to 52.6 percent of the total IC effect.

The following set of figures provides some additional evidence on the relation between measures of countries’ economic performance and TFP based on our econometric analysis. The previous conclusions become more apparent by looking at the cross-plots. Figure 5.7 shows a clear positive correlation between GDP per capita and demeaned aggregate productivity, with a correlation coefficient equal to 0.81. Notice that this positive relationship has a decreasing dispersion as per capita income grows; that is, for those African countries with a per capita GDP lower than 10 percent of that of the United States, demeaned aggregate productivity presents a more heterogeneous behavior. The conclusion obtained from figures 5.8 and 5.9 are similar. Those countries that are high in the ranking based on ease of doing business (DBR) also have a large demeaned aggregate productivity, with a correlation coefficient of 0.77. The same is true for the positive relationship between the quality of overall infrastructure and the ACR (2007)—the more productive the manufacturing firm is, the higher the contribution of overall infrastructure quality to TFP (correlation coefficient equal to 0.76).

A question of interest is whether those countries with the lowest demeaned aggregate productivity levels are also those with the greatest impact of infrastructure on firm’s perceptions, on average productivity, and on allocative efficiency. Figures 5.10 and 5.12 provide clear answers to these questions. Figure 5.10 shows the negative correlation between the mixed demeaned aggregate productivity and firm’s perceptions of growth bottlenecks, with a correlation coefficient of 0.76. The absolute contribution of infrastructure to both average log productivity and average productivity via simulations decreases as the demeaned aggregate productivity increases. This relation is stronger in the case of the absolute percentage contribution to the average log productivity since the corresponding coefficients of correlations are -0.60 (figure 5.11) and -0.49 (figure 5.12).

39
**Figure 5.7. Cross-plot between demeaned aggregate productivity and GDP per capita (% of US)**

**Figure 5.8. Cross-plot between Figure 5.9. Cross-plot between demeaned aggregate productivity demeaned aggregate productivity and ranking on the ease of doing and quality of overall business* infrastructure from ACR 2007 ***

<!-- Start of picture text --> 3.0 2.5 2.5<br>2.5 R2 = 0.66 R2 = 0.6<br>2.0 2.0<br>R 2 = 0.58<br>2.0<br>1.5 1.5<br>1.5<br>1.0 1.0<br>1.0<br>0.5 0.5 0.5<br>0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0 1 2 3 4 0.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5<br>GDP per capita (relative to U.S) Ranking Ease of Doing Business 2007 Quality of overall Infrastructure ACR 2007<br>Demeaned Aggregate Productivity<br>Demeaned Aggregate Productivity Demeaned Aggregate Productivity<br><!-- End of picture text -->

*Rank is computed as: (total number of firms in DBR-Rank)/ total number of firms in DBR Source: Authors ́ calculations with IC data, Doing Business Report (2007) and Penn World Table.

<!-- Start of picture text --> Figure 5.10. Cross-plot between Figure 5.11. Cross-plot between Figure 5.12. Cross-plot between<br>demeaned aggregrate productivity demeaned aggregate productivity demeaned aggregate productivity<br>and firms ́ perceptions on and percentage absolute and percentage absolute<br>infrastructure as an obstacle contribution of infrastructure to contribution of infrastructure to<br>average log-productivity average productivity via<br>simulations<br>2.5 2.5 2.5<br>R2 = 0.47<br>2.0 2.0 2.0<br>R 2 = 0.25<br>1.5 1.5 1.5<br>1.0 1.0 1.0<br>R 2 = 0.36<br>0.5 0.5 0.5<br>0.0<br>0.0 10 20 30 40 50 60 70 0.0 0 10 20 30 40 50 60<br>8 13 18 23 28 33 Percentage Absolute Contribution of Infrastructure to Average Log- Percentage Absolute Contribution of Infrastructure to Average<br>Firms' perceptions of infrastructure as an abstacle Productivity Productivity via simulations<br>Demeaned Aggregate Productivity<br>Demeaned Aggregate Productivity Demeaned Aggregate Productivity<br><!-- End of picture text --> Source: Authors ́ calculations with IC data.

Figures 5.13 and 5.14 show the linear correlations between demeaned aggregate productivity and percentage absolute contribution to allocative efficiency TFP in logs and allocative efficiency via simulations, correspondingly. There is a negative relation in both figures. But the linear correlation is smaller in the case of the average log productivity (correlation coefficient equal to -0.31) than in the case of allocative efficiency with TFP in levels (correlation coefficient equal to -0.49).

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Finally, figures 5.15 and 5.16 show the strong linear positive relation between the infrastructure contributions to the two components of the O&P decomposition based on TFP in logs and TFP in levels. Their corresponding coefficients of correlation are 0.69 and 0.77, respectively.

<!-- Start of picture text --> Figure 5.13. Cross-plot between Figure 5.14. Cross-plot between<br>demeaned aggregate productivity and demeaned aggregate productivity and<br>percentage absolute contributions of percentage absolute contributions of<br>infrastructure to allocative efficiency infrastructure to allocative efficiency via<br>(TFP in logs) simulations<br>2.5 2.5<br>2.0 2.0<br>1.5 1.5<br>1.0 1.0<br>R 2 = 0.1 R 2 = 0.24<br>0.5<br>0.5<br>0.0<br>0.0 0 10 20 30 40 50 60<br>0 10 20 30 40 50 Percentage Absolute Contribution of Infrastructure to Allocative<br>Percentage Absolute Contribution of Infrastructure to Allocative Efficiency via simulations<br>Efficiency (TFP in logs)<br>Figure 5.15. Cross-plot between Figure 5.16. Cross-plot between<br>percentage absolute contribution to percentage absolute contribution to<br>average log–productivity and allocative efficiency (with TFP in logs)<br>contributions via simulations and contributions via simulations<br>70 60<br>60 50<br>50<br>40<br>40<br>30<br>30<br>20 R 2 = 0.47 20<br>R 2 = 0.59<br>10 10<br>0<br>0 10 20 30 40 50 60 0<br>Percentage Absolute Contribution of Infrastructure to Average 0 10 20 30 40 50<br>Productivity via simulations Percentage Absolute Contribution of Infrastructure to Allocative<br>Efficiency via simulations<br>Demeaned Aggregate Productivity Demeaned Aggregate Productivity<br>Average log-Productivity<br>Percentage Absolute Contribution of Infrastruct Allocative Efficiency (TFP in logs)<br>Percentage Absolute Contribution of Infrastruct<br><!-- End of picture text --> Source: Authors’ calculations with IC data.

All effects of infrastructure are not supposed to be negative in all cases. There are positive factors intended to stimulate productivity and economic activity, such as the use of ICT or a firm’s own electricity generator. A question of interest is to what extent the impacts listed in table 5.1 are due to positive factors that enhance economic performance or negative factors that constrain economic activity. Since the absolute percentage contributions are constructed based on absolute values, at this point we still cannot say anything about the direction of the effect of infrastructure on economic performance. But it is easy to analyze the effect of the individual INF factors, and this is one of the aims of the following section.

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# **6. Country-by-country results**

In the preceding section we evaluated the relative weight of infrastructure among IC variables. In this section the objective is to present a summary of the main results, country by country, focusing on the impact of the individual infrastructure factors or variables. We measure the strength of infrastructure’s impact on TFP through three different procedures: (a) elasticities or semi-elasticities, (b) simulations, and (c) evaluation of the IC regressions impact on the sample mean of the variables.

These three sets of results provide complementary information. The elasticities and semielasticities measure the impact of a change in an independent variable (infrastructure factors or other IC and C variables) on the dependent variable (productivity). But elasticities and semi-elasticities are not directly comparable.

On the other hand, simulations measure how the dependent variable changes from scenario A, in which the infrastructure and other IC and C factors are as observed by the survey, to scenario B, in which one of the infrastructure factors improves by, say, 20 percent. From this we can make the following assertions: _If the number of power outages suffered by firms in country_ X _is reduced by 20 percent, then the average productivity (or the allocative efficiency) could increase by_ Y _percent, holding everything else constant_ .

Finally, the evaluation at the sample means of the regression variables, as opposed to the simulations, is a static exact decomposition of the terms of the mixed O&P decomposition. We can evaluate the contributions of all the INF, IC, and C factors to the sample mean of average log productivity, identifying the relative importance of each infrastructure variable (for example, losses due to the number of water outages or transport failures) in net terms or in absolute terms.

The next subsection focuses on the results of each of the 23 countries. The results are presented in a series of country-specific figures 6.1–6.23. The figures also report the results for the productivity equation. The first panel of each figure shows the elasticities and semi-elasticities; the second and third panels focus on the relative contribution of the INF variables to the average log productivity and to the allocative efficiency in logs; and the fourth and fifth panels present the results of the simulations (that is, how much the average productivity and the allocative efficiency would increase if we improve the INF variables).

Note that the results on the elasticities and semi-elasticities are not comparable since they use different measurement scales. For purposes of comparison we should rely on the simulations and on the results of the contributions to the average.

In the interest of space, we focus only on the major results for each country.

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# **6.1 Infrastructure impacts on TFP by country**

_High-growth countries in southern Africa_

**Mauritius (MUS).** Mauritius is the top-ranked in terms of per capita GDP and demeaned aggregate productivity, and the second according to the DBR (2007) and firms’ perceptions of the quality of infrastructure (Table C.1). The relative contribution of infrastructure to average log productivity is 26.6 percent—one of the lowest among the African countries considered (see figure 5.5, panel B, and figure 6.14, panel B). The most important constraint on productivity comes from the number of days to clear customs for exports (17 percent on average) (see figure 6.14); 5.2 percent is due to the use of IC technologies (positive factor); and low-quality provision of electricity and water accounts for only 2 percent of the average log productivity.

**Swaziland (SWZ).** Swaziland is ranked second in terms of per capita GDP (Table C.1). Productivity of firms is negatively affected by shipment losses in customs, the number of power outages, and the average duration of transport by road (see figure 6.20). These results are common to Namibia and Botswana since the countries are pooled together for estimation. The use of generators has a positive sign, meaning that it stimulates productivity. Country-specific results for Swaziland show that the largest contribution to average log productivity comes from problems in customs during the export process (10 percent), and from the number of power outages (9 percent).

**South Africa (ZAF).** South Africa is ranked third based on per capita GDP and demeaned aggregate productivity (Table C.1). Productivity is negatively affected by the days to clear customs to import, the sales lost due to power outages, the number of water outages, the time waiting for an electricity supply, and sales lost due to delivery delays. Therefore, the low quality of the customs services, electricity services, and water affects productivity performance at the firm level in South Africa. The contribution to average log TFP of electricity provision is 6.9 percent, and the contribution of water provision is 5.7 percent. Time wasted in customs while importing accounts for 9.4 percent of the average log productivity. Lastly, problems in transport services represent 5.7 percent of average log productivity (see figure 6.19).

**Botswana (BWA).** Botswana is ranked fourth based on per capita income (DBR, 2007) and on perceptions and sixth in terms of demeaned aggregate productivity. The productivity of firms located in Botswana is affected by shipment losses in customs while exporting (negative), power outages (negative), the percentage of electricity that comes from firm’s own generators (positive), and the average duration of transport by road (negative) (see panel A of figure 6.3 and panel B of figure 5.5). These marginal effects are common to Namibia and Swaziland since the countries are pooled together for estimation. Country-specific results show that the largest contributor to both average log productivity and to allocative efficiency is shipment loss in customs while exporting (panel B). Simulations show that the largest productivity improvement comes from reduction in power outages (panel D of figure 6.3). That is, according to our simulations, if the number of power outages suffered by firms in Botswana were reduced by 20 percent, average productivity could increase by 2.1 percent.

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**Namibia (NAM).** Namibia is ranked sixth in terms of per capita GDP and ACR (2007) (Table C.1). Productivity of firms is negatively affected by shipment losses in customs while exporting, the number of power outages, and the average duration of transport by road (see figure 6.16). These results are common to Botswana and Swaziland since the countries are pooled together for estimation. Country-specific results show that the impact of infrastructure on the productivity of manufacturing firms in Namibia mainly comes from problems in customs while exporting—this factor represents 9 percent of the average log productivity. Problems from electricity provision (power outages) and from use of alternative power infrastructure (such as a generator) represent 2.2 percent and 3 percent of the average log productivity, respectively, in absolute terms (figure 6.16).

# _High-growth countries in North Africa_

**Algeria (DZA).** Algeria is ranked fifth in terms of per capita GDP, and seventh in terms of firm perceptions and demeaned aggregate productivity. The results on the **productivity impact** of infrastructure (see figure 6.1) show the total effect on absolute value is as large as 48.6 percent of average log TFP. The quality of infrastructure variables affecting TFP are: cost of exports, having an own generator, number of power outages, losses due to water outages, having an own well, the cost of water from the public system, having e-mail, and low-quality supplies. The largest and most positive effect comes from having e-mail, which could represent 14.5 percent of average log TFP.

**Egypt (EGY).** Egypt is ranked seventh based on per capita GDP, sixth in terms of ACR (2007), fourth in terms of demeaned aggregate TFP, and third in firms’ perceptions of infrastructure quality. From the econometric analysis, the contribution of infrastructure to the average log productivity in Egypt is only 26 percent. The main infrastructure factors affecting firms’ productivity are the average duration of water and power outages (both with negative effects), the percentage of firms with their own generator (positive effect), the dummy for own transportation (positive), shipment losses in exports (negative), and days of inventory of the main supply (negative) (figure 6.6).

**Morocco (MAR).** The perceptions of the managers of the Moroccan firms suggest that infrastructure is not a major concern when compared to other IC constraints; it is ranked first in Table C.1. But according to figure 6.15, the contribution of infrastructure to average log productivity is 31.3 percent, with the largest impacts coming from (a) the average time to clear customs to import, and (b) the time wasted to obtain a phone connection. A 20 percent reduction of average customs delays for imports could increase average productivity by 1.6 percent and allocative efficiency by 0.4 percent. Notice, that the ranking based on the econometric analysis (demeaned aggregate productivity) is consistent with the ranking based on per capita GDP (see Table C.1), which establishes that Morocco is in eighth position, not first.

# _Low-growth countries in central-west Africa_

**Cameroon (CMR).** Cameroon is rank ninth in term of per capita GDP, which is somehow surprising if we compare it with the results of the rest of the rankings based on DBR (2007), ACR (2007), firms’ perceptions, and demeaned aggregate productivity. We found that the productivity of manufacturing

56
firms in Cameroon is reduced by the following factors: number of days required to clear customs for imports, average duration of power and of water outages, shipment losses, and time waiting for a phone connection (panel A of figure 6.5). These factors are common to Mauritania, Burkina Faso, and Niger since the countries are pooled together for estimation purposes. Country-specific results show that the largest contributions to average log productivity come from the number of days waiting to clear customs, duration of power outages, and from the time waiting for a phone connection (panel B of 6.5). Infrastructure represents 41.6 percent of average log TFP.

**Mauritania (MRT).** Mauritania ranks 10th in terms of per capita GDP, 13th in term of the total absolute contribution of infrastructure to average log productivity, 17th in terms of firm perceptions, and 15th in terms of demeaned aggregate productivity. These results are common to Burkina Faso, Cameroon, Niger, Ethiopia, and Eritrea since those countries are pooled together for estimation purposes. Delays in customs while importing represent 22 percent of average log productivity and 12 percent of allocative efficiency. In terms of simulation, a 20 percent improvement in this variable could cause a 13.9 percent increase in average productivity and a 7.8 percent one in allocative efficiency. Low-quality provision of electricity and water and its indirect costs also reduce average productivity in Mauritania (see figure 6.13).

**Senegal (SEN).** Senegal reveals a high infrastructure impact on the TFP of manufacturing firms. The percentage contribution of infrastructure to the average log productivity of this country is 58.5 percent; the indirect costs stemming from the low-quality provision of electricity represent 9.3 percent of this (see figure 6.18). The use of own-power infrastructure partially alleviates the negative impact of the low quality of electricity provision. The relative importance of problems in transport services (such as low-quality supplies) is very high; this variable represents 23.4 percent of average log productivity and 14 percent of allocative efficiency. A 20 percent reduction in the percentage of lowquality supplies received may cause a 3.2 percent increase in average productivity. Notice that the simulation of a 20 percent improvement in the percentage of low-quality supplies received causes a decrease in allocative efficiency of -0.4 percent. The reason for this phenomenon is clear: the allocative efficiency is simply the covariance between productivity at the firm level and share of sales. Therefore, a negative rate of change of the allocative efficiency indicates that the firms receiving a larger share of low-quality supplies are those with the largest market shares.

**Benin (BEN)** . Benin is ranked 12th in per capita GDP, 11th in terms of demeaned aggregate productivity, and 13th in terms of DBR (2007). The time waiting for phone connections and to clear customs in order to export are the two factors that most negatively contribute to average log productivity (see panel B of figure 6.2). An independent 20 percent improvement in these two variables could increase average productivity by 3.8 percent and 4.3 percent, respectively (panel D of figure 6.2). The same holds for the allocative efficiency term (panels C and E).

**Mali (MLI).** Mali is ranked 14th in per capita GDP terms, in demeaned aggregate productivity, and in the ACR (2007). The total contribution of infrastructure to average log productivity in Mali is 42.7 percent. The low quality of electricity, water, and phone provision accounts for almost 32 percent

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of the average log productivity. The use of firms’ own roads is a factor that increases productivity (figure 6.12).

**Burkina Faso (BFA).** Burkina Faso ranks 16th and 12th in terms of per capita GDP and demeaned aggregate TFP, respectively. The main infrastructure problems are clearing customs while importing, the average duration of power and water outages, shipment losses, and time waiting to obtain a phone connection. These results are common to Mauritania, Cameroon, and Niger since these countries are pooled for estimation. In particular, for Burkina Faso, all of these factors reduce productivity at the firm level (see figure 6.4) and can contribute to 35 percent of average log TFP.

**Niger (NER).** Niger is one of the poorest countries in our sample: it ranks 19th based on per capita income, and managers’ perceptions show a great concern regarding quality of current infrastructure (20th in the rank). The absolute contribution of infrastructure to average log productivity is 34.7 percent, with 20.7 percent due to problems  clearing customs while importing, 9.4 percent due to the average time wasted in obtaining a phone connection, and 4.5 percent due to the cumulated negative effect of the low-quality provision of electricity and water and the poor transport system (see figure 6.17).

_Low-growth countries in central-east Africa_

**Kenya (KEN).** Kenya ranked 13th in per capita GDP, 6th in terms of DBR (2007), and 8th in terms of demeaned aggregate productivity. The results of the productivity equation in Kenya show multiple interrelationships between productivity at the firm level, on one hand, and infrastructure, on the other. Factors such as the use of generators, the cost of electricity from the public grid, water outages, sales lost due to power outages, and so on, reduce productivity at the firm level (see figure 6.9). Although the contribution to the average log productivity of any of these factors is over 6 percent, infrastructure as a whole accounts for 30 percent of average log productivity, and for 19.9 percent of allocative efficiency (which illustrates the major influence infrastructure has on Kenyan firms’ productivity).

**Uganda (UGA).** Uganda is ranked 15th in per capita GDP and 17th in demeaned aggregate productivity. Uganda’s average log productivity is strongly influenced by infrastructure conditions, representing 58.4 percent. The two main factors affecting average productivity are the time to clear customs and the provision of electricity (figure 6.22, panel D).

**Zambia (ZMB).** Zambia ranks 17th in terms of per capita GDP, 14th in terms of firm perceptions, and 18th in terms of the ACR (2007) .The contribution of the cost of electricity from the public grid to average log productivity in Zambia is 32.5 percent. The contribution of the average duration of power outages to average log TFP is 9.1 percent (see figure 6.23). The total contribution of infrastructure to average TFP is 50.6 percent.

**Tanzania (TZA).** Tanzania is 18th based on per capita GDP, 14th in terms of DBR (2007), and 15th in terms of firm perceptions. The absolute contribution of infrastructure to average log productivity in Tanzania is 34.1 percent. Out of this, 14.8 percent is due to time wasted waiting for water supply and 5.5 percent is due to the number of transport outages (figure 6.21).

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**Malawi (MWI).** Malawi is ranked 20th in per capita GDP terms and 22nd in terms of ACR (2007). The econometric evidence shows that the aggregate productivity of Malawian’s manufacturing firms are dramatically affected by infrastructure quality (83 percent of average log TFP). Delays in clearing customs while importing account for 25 percent of the average log productivity. A 20 percent improvement in this variable could increase the average productivity by 6 percent and the allocative efficiency by 1.5 percent (see panels D and E of figure 6.11). Symmetrically, productivity decreases as the number of power outages increases; the percentage contribution of this variable to average log productivity is 9.2 percent and to the allocative efficiency is 3.5 percent. The cost of electricity from firms’ own generators is another antiproductive factor. Water provision also impacts the productivity of Malawian firms (figure 6.11).

**Madagascar (MDG).** Madagascar is ranked 21st in per capita GDP terms, and 16th and 17th in terms of demeaned aggregate productivity and DBR (2007), respectively. How infrastructure may impact firm-level productivity is clear from the results obtained in figure 6.10. The factors related to electricity supply are intimately linked to productivity. Water costs and the number of phone outages also reduce productivity at the firm level. The total contribution of infrastructure to average log productivity in Madagascar is 31 percent and to allocative efficiency is 28 percent.

**Ethiopia (ETH).** Ethiopia is ranked 22nd in terms of per capita GDP and 21st in terms of firms’ perceptions. The productivity of Ethiopian manufacturing firms is negatively affected by the days to clear customs to import, the cost of electricity from the public grid, shipment losses while in transit, and the percentage of supplies that are of lower than agreed-upon quality. Positive effects on productivity come from the percentage of electricity from firms’ own generators and from the days of inventory of the main supply. These results are common to Eritrea since the countries are pooled together for estimation. The largest contribution to the average log productivity is by the days of inventory of main supply, days to clear customs to import, the cost of electricity from the public grid, and the electricity from firms’ own generators (figure 6.8). In total, infrastructure represents 32 percent of average log TFP, which seems too low. This might indicate that pooling with Eritrea for estimation of IC elasticities might not be a good idea in this case.

**Eritrea (ERI).** Eritrea is the last country in the ranking, in 23rd position in terms of per capita GDP. The total number of days waiting to clear customs to import, the cost of electricity from the public grid, shipment losses, and low-quality supplies are the factors with negative effects on firms’ productivity (see figure 6.7). Remember that these results are common to Ethiopia since the countries are pooled together for estimation. The cost of energy from the public grid accounts for a 22.9 percent of average log productivity, almost 50 percent more than the second factor (days to clear customs to export) in order of importance. In total, infrastructure represents 48 percent of average log TFP.

# **6.2 Summary of the main empirical results**

The aim of this subsection is to summarize the main empirical results obtained, country by country, including the absolute percentage contribution of the infrastructure group of variables to the sample 59
means of productivity in logs. The _customs clearance_ subgroup includes those variables related to the ease or difficulty of clearing customs when exporting or importing. Within _provision of electricity_ we have grouped all the variables related to low-quality provision of electricity (number of power outages, power fluctuations, cost of electricity, and so on). _Use of power infrastructure_ is intended to enclose all the variables related to the use of alternative sources of energy, such as generators. Similarly, the subgroups _provision of water_ and _provision of phone connections_ includes all the variables related to the quality of the provision of these utilities, whereas _use of water infrastructure_ includes the use of firms’ own wells or boreholes to replace the public provision of water. Obviously, _use of ICT_ takes into account the use of ICT in firms’ commercial operations. Lastly, _transport services_ contains all variables relating to the quality of transportation services, such as shipment losses in transit, transport delays, delivery delays, and so on. _Own transport infrastructure_ includes the use of own transportation for products or workers.

Figure 6.24 shows the prominent influence of low-quality electricity provision on average log productivity in the different countries considered in this report. The percentage absolute contribution of this group of variables to average log productivity ranges from 34.1 percent in Zambia to 0.3 percent in Morocco, being a negative effect in all cases. Only in Tanzania was the low quality of electricity provision not statistically significant, probably due to the significant and very influential effect of water provision in this country. The low quality of electricity and the continuous outages are partially alleviated by the use of own-power infrastructure, as the positive effect of the group _use of power infrastructure_ shows.

Another group of variables with a statistically high impact on average log productivity is _customs clearance._ The contributions of this group are negative and very large in most countries, indicating a clear and pervasive constraining effect of the time wasted in customs when importing or exporting.

**Figure 6.24 Infrastructure’s impact on average log productivity by key factors (I)**

<!-- Start of picture text --> A. Percentage absolute contribution of infrastructure to average log-productivity<br>100.0<br>90.0<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>MWI BEN SEN UGA ETH ZMB DZA ERI MLI CMR MRT BFA NER TZA MAR MDG KEN ZAF MUS EGY SWZ BWA NAM<br>Infrastructures Other IC<br><!-- End of picture text --> <!-- Start of picture text --> 100.0<br>90.0<br><!-- End of picture text --> 60
B. Percentage absolute contribution of infrastructure to average log-productivity by key factors <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> Use of generator or power infrastructures<br>Use of ICT<br><!-- End of picture text --> <!-- Start of picture text --> Provision of water<br>Transport services<br><!-- End of picture text --> <!-- Start of picture text --> Own transport infrastructures<br><!-- End of picture text --> <!-- Start of picture text --> Other<br><!-- End of picture text --> _Source_ : Author’s calculations with IC data.

_Note_ : Customs clearance includes: days to clear customs to export and import; shipment losses in customs; inspections in customs; wait for an import license. Provision of electricity includes: power outages; avg. duration of power outages; losses due to power outages, wait for an electricity supply; power fluctuations; avg. duration of power fluctuations; cost of electricity from the public grid; cost of electricity from private system. Use of power infrastructures includes: dummy for own generator; electricity from own generator; dummy for own power infrastructures (excl. generators). Provision of water includes: water outages; avg. duration of water outages; losses due to water outages, wait for a water supply; cost of water from the public grid; cost of water from private system. Use of water infrastructures includes: dummy for own water infrastructures; water from own well. Provision of phone includes: phone outages; avg. duration of phone outages; losses due to phone outages, wait for a phone connection. Use of ICT includes: dummy for e- mail; dummy for webpage. Transport services includes: sales lost due to transport delays; sales lost due to delivery delays; shipment losses; low quality supplies; transport delays. Own transport infrastructures include: dummy for own roads; dummy for own transportation for workers; products with own transport. Other: inventories, illegal payments to obtain public utilities.

Regarding _provision of water_ , the relative importance of this group of variables is lower when compared to the _provision of electricity_ ; nevertheless, there are some cases (such as Tanzania, Mali, and Kenya) where the contribution of this group of variables is very high, even compared to the provision of electricity. As with the provision of electricity, the use of alternative water infrastructure such as boreholes or wells has a positive impact on plants’ productivity—an effect that only appears to be significant when there is a negative effect of water provision, suggesting the existence of a replacement effect between the public provision of water and alternative supplies of water.

The poor quality of phone provision is negatively related to productivity in 14 countries; nevertheless, the quantitative contribution of this group of variables is, in general, lower than the impact of the electricity provision. Benin is an exception. The contribution to the average log productivity of telephone provision in this country is 40.8 percent—more than 20 times the contribution of electricity provision, which is only 2 percent. The use of IC technologies is positively related to productivity, but the use of these technologies was only significant in the productivity regressions of six countries: Malawi, Algeria, Tanzania, Kenya, Morocco, and Mauritius, with the largest impacts in Algeria (14.5 percent) and Mauritius (5.2 percent).

Problems with product transport are negatively related to productivity in all the cases, with the exception of Botswana, Swaziland, and Namibia, for which no variables for this group were significant in productivity regressions. The largest impacts of this subgroup of variables were seen in Senegal, 61
Tanzania, Madagascar, South Africa, and Zambia; nevertheless, the contributions of transport services to average log productivity are relatively lower than the impact of the _provision of electricity_ or the _customs clearance_ subgroups. On the other hand, the use of own-transport infrastructure stimulates productivity growth; in all the cases in which any variable belonging to this subgroup was significant in the productivity regressions, it appeared with a positive sign. But the positive effects on productivity of these factors were concentrated in only seven countries: Malawi, Benin, Senegal, Eritrea, Tanzania, Kenya, and Egypt.

Figure 6.25 provides similar information, but, in this case, we have grouped the different infrastructure factors in only five groups: _customs clearance, electricity, water, telecommunications and ICT,_ and _transportation_ . From this figure the high influence of _electricity_ factors become even clearer. When we include the _provision of electricity_ and _use of generators_ or _power infrastructure_ into a single group, the resulting block of electricity factors can explain more than half of average log productivity in Uganda, Ethiopia, Zambia, Eritrea, Swaziland, and Botswana. The water group is relatively important in Tanzania, Kenya, and Egypt. The customs clearance group gains importance in those countries with a more patent export orientation, such as Mauritius, Botswana, and Namibia. Finally, as has been signaled, the transportation subgroup explains more than half of the whole infrastructure impact on average log productivity in Senegal and Madagascar.

**Figure 6.25 Infrastructure impact on average log productivity by key factors (II)**

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br><!-- End of picture text --> <!-- Start of picture text --> MWI UGA BEN SEN ETH ZMB ERI MLI CMR DZA MRT BFA NER TZA KEN MAR MDG ZAF EGY SWZ MUS BWA NAM<br>Customs clearance Electricity Water Telecoms and ICT Transportation Other<br><!-- End of picture text --> <!-- Start of picture text --> Customs clearance<br><!-- End of picture text --> <!-- Start of picture text --> Water<br><!-- End of picture text --> <!-- Start of picture text --> Telecoms and ICT<br><!-- End of picture text --> <!-- Start of picture text --> Transportation<br><!-- End of picture text --> <!-- Start of picture text --> Other<br><!-- End of picture text --> _Source_ : Author’s calculations with IC data.

_Note_ : For a description of the variables contained in each group see footnote in Figure 6.24 Figure 6.26 reports the absolute percentage contribution of infrastructure by key factors via simulations. The results are fully consistent with the ones provided by figure 6.24. The relative weights of the electricity factors dominate in more than half the countries, and the water and telecommunications and ICT subgroups tend to play a secondary role in explaining average productivity when compared to electricity. The main difference with respect to figure 6.24 is the lower relative weight of the customs clearance group. Once again, it should be pointed out that the 62
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# **7. Conclusions**

For Africa’s awaited growth resurgence to occur, a broad range of factors—political, institutional, and economic—must be improved. The World Bank’s landmark Africa Competitiveness Reports (2004 and 2007) focus on problems that inhibit economic growth. It is agreed that improving Africa’s infrastructure is a crucial step toward penetrating international markets and meeting the goals of continuous growth and poverty reduction.

Infrastructure quality has a pervasive influence on all areas of an economy. Low-quality infrastructure and limited transport and trade services increase logistical and transaction costs, rendering otherwise competitive products uncompetitive, as well as limiting rural production and people’s access to markets—with adverse effects on economic activity and poverty. A large number of empirical studies illustrate the impact of infrastructure on economic performance. All suggest that Africa’s infrastructure gap is an important growth bottleneck, with a negative impact on productivity and the overall competitiveness of the region. Using the methodology of Escribano and Guasch (2005, 2008) and Escribano et al. (2008), several studies have found empirical evidence—in cases such as Brazil, Chile, Costa Rica, Mexico, Turkey, and several Southeast Asian countries—that improvements in investment climate conditions in general, and in infrastructure quality in particular, may lead to important gains in productivity and in other economic performance measures: employment, real wages, exporting activities, and inflows of foreign direct investment (FDI).

## _Approach and methods_

This paper provided a systematic, empirical assessment of the impact of infrastructure quality on the TFP of African manufacturing firms. We applied micro-econometric techniques to investment climate surveys of 26 African countries to gauge the impact of infrastructure quality on TFP.

For each country we estimated, by regression techniques, the impact of infrastructure quality based on 10 different productivity measures and showed that the results were robust once we controlled for other observable fixed effects (red tape, corruption and crime, finance, innovation and labor skills, and so on) obtained from the investment climate surveys (see Escribano and Guasch, 2005, 2008).

We pooled data from the investment climate surveys only for the few African countries for which we did not have sufficient observations for estimation purposes. Otherwise, we performed a country-by-country estimation to reveal firm and industry information by country. After pooling the data from several countries, the econometric results were then suitably disaggregated following the method of Olley and Pakes (1996) (Escribano et al., 2008), which allowed us to make country-specific evaluations of the impact of investment-climate quality on aggregate TFP, average TFP, and allocative efficiency.

We ranked the African countries in the study according to several aggregate indices: per capita income, ease of doing business, firm perceptions of growth bottlenecks, and the recent concept of 65
_demeaned productivity (demeaned TFP)_ , which overcame the problem of comparing apples and oranges when doing TFP cross-country comparisons (Escribano et al., 2008). We found the concept of demeaned productivity very useful because it is highly correlated with per capita income, ease of doing business indices, firm’s perceptions of growth bottlenecks, and the results of the Africa Competitiveness Reports. Furthermore, the information obtained from the investment-climate determinants of demeaned TFP provided a much deeper insight into the firm-level investment-climate infrastructure elements that are constraining productivity growth in African countries.

We distinguished two clear blocks of countries in Africa.

The first block comprised countries with faster, steadier growth rates. These are mainly in the south, including Mauritius, Swaziland, South Africa, Botswana, Namibia, and Lesotho. The block also included Algeria, Morocco, and Egypt from the north, and Cape Verde and Cameroon from Central Africa. In southern Africa Botswana, Namibia, and Swaziland, were pooled for estimation purposes.

In the second block were Mauritania, Senegal, Benin, Mali, Niger, and Burkina Faso in the central-west; and Uganda, Kenya, Zambia, Tanzania, Malawi, Burundi, Madagascar, Ethiopia, and Eritrea in the central-east. These countries have experienced lower and more irregular growth rates, with periods of both positive increase and persistent decrease in per capita income. Pooled for estimation purposes were the West African states (Burkina Faso, Cameroon, Mauritania, and Niger) and Eritrea and Ethiopia.

Of the 26 African countries analyzed, Mauritius was, in 1950, the country with the highest per capita income (measured in terms of per capita gross domestic product, GDP), followed closely by South Africa, and, by a larger gap, Namibia and Algeria. But the per capita income levels in 2003 were somewhat different; Mauritius was still ranked first, followed by Swaziland, South Africa, and Botswana—and, by a wider gap—Algeria, Cape Verde, Egypt, and Morocco. Mauritius and Botswana experienced the highest sustained per capita income growth during recent years. Lesotho’s rate is the median, splitting the study into two blocks.

To better understand the convergence or divergence of trends, we plotted the per capita income of each African country relative to the per capita income of the United States. Convergence was observed only in Mauritius, Swaziland, and Botswana. For all other study countries, including South Africa, per capita income was found to be diverging from the United States, while, in a few (Egypt, Morocco, and Cape Verde) the ratio was stable. While persistently positive GDP growth allowed Mauritius’s per capita income to reach 45 percent of the United States’ in 2003, this was clearly the exception (together with Swaziland and Botswana). For the rest of the countries, including South Africa, relative per capita income was much lower in 2003 than in 1960 (indicating a divergence). In fact, the 2003 per capita income of several countries was no larger than 5 percent of the per capita income of the United States. As expected, labor productivity was the main factor explaining this divergence, given that labor force participation has a steady influence. Since total factor productivity (TFP) is usually a key factor explaining the evolution of labor productivity, in this paper we used investment climate surveys to identify the main infrastructure-related TFP bottlenecks in Africa.

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The per capita income rankings were correlated with the rankings obtained from the World Bank’s 2007 _Doing Business_ report. In 2007 Mauritius, Swaziland, South Africa, Botswana, and Namibia ranked 32nd, 76th, 29th, 48th, and 42nd in the world based on the ease-of-doing-business indicators. This index considers questions such as the number of days required to start a business and the ease of dealing with licenses, registering a property, trading across borders, employing workers, and so on. Other 2007 rankings include 83rd for Kenya, 97th for Ethiopia, 165th for Egypt, and 170th for Eritrea.

We showed the percentage of firms that _perceived_ telecommunications, electricity, customs clearance, and transport as major obstacles to their economic performance. Only in Benin, Kenya, and Zambia did more than 50 percent of firms identify telecommunications as a severe obstacle. Meanwhile, the quality of electricity provision is a major problem for more than 50 percent of firms in more than half of the countries in our sample. In Burundi, Cameroon, Benin, Burkina Faso, and Cape Verde, the percentage of firms considering electricity as a severe or very severe obstacle exceeded 80 percent; on the other hand, only 20 percent of firms in Morocco, South Africa, Botswana, and Namibia considered electricity a severe obstacle. Customs clearance was considered an acute problem in Benin, Kenya, Madagascar, Senegal, and Algeria. Finally, transportation was considered a severe obstacle by more than 70 percent of firms in Burkina Faso and Benin.

The World Bank’s 2007 Africa Competitiveness Report evaluated a wide range of factors related to economic activity, with infrastructure among them. Once again there were clearly different performance levels across the two blocks of countries. While in Namibia, South Africa, Botswana, Egypt, and Morocco, the quality of infrastructure exceeded the approval level, in the remaining countries this quality was rated low in most cases. The same held for the disaggregated results, including the number of telephone lines and the quality of ports, air transport, and electricity supply.

The difference between the two blocks becomes even more apparent when looking at the crossplots between GDP per capita relative to the United States and firms’ perceptions. A preliminary analysis of the cross-plots suggests two points: first, that there is an intuitive and negative relation between income level and infrastructure constraints; and, second, that the division of the two blocks of countries remains intact, showing now the largest dispersion in the constraint perceptions for the lowest per capita income group.

# _Findings_

Among the countries of the high-income-growth block, infrastructure has a low impact on TFP (see panel B of figures 5.5 and 5.6 and panel A of figure 6.24). Red, tape, corruption, and crime dominate over infrastructure in countries such as Mauritius, Egypt, and South Africa (figure 5.5, panel B). Infrastructure quality has a high impact on TFP in the countries of the low-income-growth block (see panel B of figures 5.5 and 5.6 and panel A of figure 6.24), but the impact is very negative (see panel B of figures 5.3 and 5.4), identifying important bottlenecks for TFP growth.

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We found much heterogeneity among individual infrastructure factors affecting countries in both the high- and low-growth blocks (see figure 6.26). Among related factors that most influence the average productivity TFP of African firms are:

- Poor-quality _electricity_ provision, which affects mainly poor countries, such as Eritrea, Ethiopia, Mali, Senegal, Uganda, Zambia, and Kenya. It also affects countries that are growing faster, in relative terms, such as Botswana, Namibia, and Swaziland.

- Problems dealing with _customs_ during importing or exporting affects mainly fast-growing countries, such as Mauritius, Morocco, and Swaziland. But low quality of customs also affects slow-growing countries, such as Niger, Mauritania, Cameroon, Malawi, Burkina Faso, and others.

- Losses from _transport interruptions_ affect mainly slower-growing countries, such as Madagascar, Kenya, Tanzania, and Senegal.

- _Water outages_ affect mainly slower-growing countries, such as Tanzania, Kenya, Burkina Faso, Mauritania, Niger, and Mali. But it also affects some of the faster-growing countries, such as Egypt.

- Of the infrastructure determinants that most influence the _allocative efficiency_ of African firms there is also some heterogeneity across countries.

- Poor-quality _electricity_ provision affects the allocative efficiency of mainly poor countries, such as Zambia, Mali, Uganda, Eritrea, and Kenya.

- Problems dealing with _customs_ while importing or exporting affects mainly slow-growing countries, such as Mauritania, Niger, and Cameroon. But it also affects the allocation efficiency of countries that are growing fast, such as Morocco, Namibia, and Mauritius.

- _Transport services_ affects the allocative efficiency of mainly slower-growing countries, such as Madagascar, Senegal, and Tanzania.

- _Water provision_ affects the allocative efficiency of mainly slower-growing countries, such as Tanzania, Kenya, and Mali.

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