metadata: authors: 0: author: Garrido,Leonardo
1: author: Lundstrom, Susanna
count: Togo
docna: 0: docna: Togo growth diagnostics
docty: Policy Research Working Paper
theme: Analysis of economic growth
majtheme: Economic management
subtopic: Economic Conditions and Volatility,Private Sector Economics,Labor Markets,Health Service Management and Delivery,Private Sector Development Law,Marketing,Inequality
teratopic: Macroeconomics and Economic Growth,Poverty Reduction,Finance and Financial Sector Development
abstracts: cdata!: This paper starts by analyzing past
            growth trends and sources of growth in Togo, as well as the
            structure of the economic actors in the country. The second
            part explores the current binding constraints to rapid and
            sustainable growth. For increased international
            competitiveness and growth, Togo will need to raise efforts
            to streamline its costly and cumbersome business procedures.
            However, for this to have a sizable impact, Togo must prove
            to potential investors that political stability is permanent
            and that corruption, poor budget execution and mismanagement
            of state owned enterprises belongs to the past. As a result
            of the new government's reform agenda and the return of
            international aid, a window of opportunities for high
            returns to the still limited public investments has opened
            up. This is especially true in infrastructure and
            connectivity services, which would not only take advantage
            of Togo's geographical location as a regional hub, but
            also make growth in Togo more inclusive. And as economic
            opportunities arise for the private sector, there is a need
            to restructure the banking sector, which has already
            started, to smooth distortions in the credit market.
            Promising sectors within agriculture that are vital to
            economic growth, employment opportunities, and poverty
            reduction remain important, but will need to overcome a
            number of coordination failures. Not least due to the
            history of government interventions causing economic
            distortions, the government must allow for a stronger role
            for private operators and encourage it wherever possible.
            Finally, although education does not exhibit constraints to
            economic activity in Togo today, it is of importance to
            improve the quality of education, not least to profit from
            and catalyze the opportunities related to Togo's
            potential as a regional hub.
geo_reg_key: 1927320,517191
repnme: repnme: Togo growth diagnostics
display_title: Togo growth diagnostics
listing_relative_url: /research/2010/12/13264204/togo-growth-diagnostics
topicv3: Macroeconomic Vulnerability and Debt
            m1326057 1104,Private Sector Development m1326060 1921,Poverty m1326687 695,Diagnostics m1504698 3625,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2010-12-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Poverty Reduction,Finance and Financial Sector Development
no_of_pages: 63
author_keys: 0: author_key: 777232
model_extractions: WPS5509 Policy Research Working Paper 5509

# Togo Growth Diagnostics

_Susanna Lundstrom Leonardo Garrido_ The World Bank Poverty Reduction and Economic Management Network Economic Policy and Debt Unit December 2010
Policy Research Working Paper 5509

# **Abstract**

This paper starts by analyzing past growth trends and sources of growth in Togo, as well as the structure of the economic actors in the country. The second part explores the current binding constraints to rapid and sustainable growth. For increased international competitiveness and growth, Togo will need to raise efforts to streamline its _costly and cumbersome business procedures_ . However, for this to have a sizable impact, Togo must prove to potential investors that political stability is permanent and that corruption, poor budget execution and mismanagement of state owned enterprises belongs to the past. As a result of the new government’s reform agenda and the return of international aid, a window of opportunities for high returns to the still limited public investments has opened up. This is especially true in _infrastructure and connectivity services,_ which would not only take advantage of Togo’s geographical location as a regional hub, but also make growth in Togo more inclusive. And as economic opportunities arise for the private sector, there is a need to _restructure the banking sector,_ which has already started, to smooth distortions in the credit market. Promising sectors within agriculture that are vital to economic growth, employment opportunities, and poverty reduction remain important, but will need to overcome a number of _coordination failures._ Not least due to the history of government interventions causing economic distortions, the government must allow for a stronger role for private operators and encourage it wherever possible. Finally, although education does not exhibit constraints to economic activity in Togo today, it is of importance to improve the quality of education, not least to profit from and catalyze the opportunities related to Togo’s potential as a regional hub.

This paper is a product of the Economic Policy and Debt Unit, Poverty Reduction and Economic Management Network. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The authors may be contacted at slundstrom@worldbank.org, and lgarrido@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
# **Togo Growth Diagnostics**

## **By**

## **Susanna Lundstrom and Leonardo Garrido**

**PREM Economic Policy and Debt, The World Bank**

_JEL codes_ : O10, O53 _Key Words_ : Economic Growth, Growth analytics, Country Specific Analysis, Growth Diagnostics, Growth constraints, Competitiveness, Togo.
# Acknowledgement

This report was prepared by a core team comprising of Susanna Lundstrom (team leader and principal author), Leonardo Garrido (co‐author), Juan Pedro Schmidt and Pooja Kacker. The report was prepared under the guidance of Philip English, Lead Economist for Togo. Valuable comments were provided by Olivier Cadot, Edgardo Favaro, Timothy Gable, Elena Ianchovichina, Elke Kreuzwieser, Marshall Mills, Lorraine Ocampo, Gallina Vincelette and the Togo country team.

A shorter, revised version of this study was published as the first chapter in the 2010 Country Economic Memoranda “TOGO: Reviving the Traditional Sectors and Preparing for the Future: An Export‐Led Growth Strategy”, Report No 53711‐TG, The World Bank.

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent.

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# **Table of Contents**

|1<br>Introduction .......................................................................................................................................... 4|
|---|
|2<br>Methodology ......................................................................................................................................... 6|
|3<br>The Current Economic Framework in Short ........................................................................................ 10|
|4<br>Development Dynamics ...................................................................................................................... 11|
|4.1<br>Overall Growth Decomposition and Employment Dynamics ..................................................................... 11|
|4.2<br>Capital and Investment Dynamics .............................................................................................................. 12|
|4.3<br>Demand Distribution of Growth ................................................................................................................ 15|
|4.4<br>Economic Sector Dynamics ........................................................................................................................ 16|
|4.5<br>Exports and Economic Diversification ........................................................................................................ 19|
|4.6<br>Conclusions from and Implications of the Development Dynamic Analysis .............................................. 21|
|5<br>Profile of the Economic Actors ........................................................................................................... 22|
|5.1<br>Demographic Dynamics ............................................................................................................................. 22|
|5.2<br>Literacy and Education Level of the Working Age Population ................................................................... 23|
|5.3<br>Employment ............................................................................................................................................... 24|
|5.4<br>Employment, Education and the Incidence of Poverty .............................................................................. 25|
|5.5<br>Conclusions from and Implications of the Profile of the Economic Actors ................................................ 26|
|6<br>Analysis of Potential Constraints to Growth ....................................................................................... 27|
|6.1<br>Cost of and Access to Capital ..................................................................................................................... 28|
|6.2<br>Rate of Return on Capital ........................................................................................................................... 31|
|6.3<br>Geography and Regional Potential ............................................................................................................. 32|
|6.4<br>Infrastructure ............................................................................................................................................. 34|
|6.5<br>Human Capital ............................................................................................................................................ 38|
|6.6<br>Macroeconomic Stability ........................................................................................................................... 41|
|6.7<br>Governance, Corruption and Government Efficiency ................................................................................ 44|
|6.8<br>Barriers to and Costs of Doing Business ..................................................................................................... 46|
|6.9<br>Market Failures .......................................................................................................................................... 52|
|_6.9.1_<br>_Self‐Discovery ......................................................................................................................................... 53_|
|_6.9.2_<br>_Coordination Failures ............................................................................................................................. 55_|
|7<br>Conclusions ......................................................................................................................................... 56|
|8<br>References .......................................................................................................................................... 58|
|9<br>Appendices .......................................................................................................................................... 59|

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# **1 Introduction**

**Togo is one of the poorest countries in the world.** With a per capita GDP equal to 437 current US$ in 2008, the economy was ranked within the world’s lowest income decile and in Purchasing Power Parity (PPP) terms it was the world’s 10<sup>th</sup> poorest economy.<sup>1</sup> Similarly and in the same year, Togo was ranked 159<sup>th</sup> among 179 countries appraised in 2008 UN’s Human Development Index.<sup>2</sup> Low aggregate per capita income level has translated into widespread poverty. In 2006, 62% of the population was poor, with poverty headcount rates in the north exceeding 90% in most rural areas (CWIQ, 2006). Furthermore, an additional 20% of the population technically considered non‐poor, was deemed as vulnerable, which raised the fraction of the population that may eventually fall into poverty to over 80% (PRSP, 2009).

<!-- Start of picture text --> Figure 1: GDP per capita development of Togo and LICs 1990‐2007.<br>( $ )<br>400<br>Low Income Avg.<br>350<br>Togo<br>300<br>250<br>200<br>150<br>100<br>50<br>0<br>Real US$ of 2000<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source:_ Authors’ calculations. DDP Data, World Bank.

**However, Togo has not always exhibited a grim social and economic performance.** In fact, almost half a century ago, Togo began its post‐independence economic journey with a fast‐paced sustained growth, which permitted its per‐capita income to almost double in a span of two decades. An economic collapse after 1980 had wiped out most of the post‐independence improvements in living standards by the early 1990s. If Togo had maintained its average growth rate from the 1960s (5.8% per year per capita), it would have achieved middle‐low‐income status today, with a current US$ per capita GDP comparable to that of Philippines, Honduras and Egypt in 2008.<sup>3</sup> Instead of catching up, per capita income in Togo has diverged from the average of peer low‐income countries, Sub‐ Saharan Africa and the rest of the world (See Figure 1 and Table A1, Appendix).

> 1 Among 192 countries with data reported for 2008, and out of 233 registered by World Bank. Cross country comparisons of income levels, regarded as proxies of individual welfare, are more accurate when presented in Purchasing Power Parity terms (PPP) than in current or real US$. In simple terms, PPP is the ratio of prices for the same quantity of a particular commodity in two countries, when the prices are expressed in each country’s currency. In 2007, the PPP conversion factor was 224.6 XOF (Communaute Financiere Africaine Francs) per US$, implying that, to buy 1 US$ worth of the PPP basket in Togo, one would have needed 224.6 XOF. In turn, the official exchange rate in Togo was 479.3 XOF/US$. At this rate one would have purchased more of the selected PPP basket in Togo  using XOF, than the amount of that basket purchased by 1 US$ in the United States. This is why a per capita income in Togo equal to 379 US$ in 2007 (239 real US$ of 2000) translates into a 808 US$ PPP per capita in 2007 (763 real US$ in PPP of 2005).

> 2 The Human Development Index (HDI) is a summary measure of human development that is published by the United Nations Development Programme (UNDP). The HDI provides an alternative to the common practice of evaluating a country’s progress in development based on per capita Gross Domestic Product (GDP). HDI measures the average achievements in a country in 3 basic dimensions of human development: 1) “A long and healthy life” proxied by the average life expectancy at birth; 2) “Knowledge”, proxied by adult literacy rate, combined with primary, secondary and tertiary enrollment ratio; and, 3) “A decent standard of living”, proxied by GDP per capita at PPP in US$.

> 3 Income level classification is that of the World Bank. According to this, low‐income countries are those with GNI per capita (calculated from World Bank’s “Atlas method”) under US$ 935. Middle low‐income countries are those with GNI per capita ranging from 935$ up to 3,705 US$.

4
**Togo’s post‐independence economic performance has mostly reflected the country’s economic policies, with international and local shocks playing a secondary but not unimportant role in outcomes.** The country registered a trend of decelerating growth since independence, with long periods of negative growth rates that were briefly interrupted by a period of small pick‐up in the mid 1990s after the 100% devaluation of the CFA franc **.** As can be seen in Figure 2, average real per capita income growth was 5.8% in the 1960s, a period characterized by “prudent and market‐friendly policies macroeconomic policies with efforts to modernize the (economic) administration and to adjust the economy from colonial to post‐independence structures and a stable international environment” (Gogué and Evlo, 2004). In the 1970s, a decade that witnessed major shift in economic policy, growth fell to 0.1% as a commodity price boom resulted in increased government revenues, which, in turn, prompted a higher participation and intervention of the public sector. Specifically, higher public revenues were used to increase public investment, implement ill conceived projects, and expand the parastatal sector by creating publicly owned manufactures behind heavy import protection. During this period, government expansionary policies and a liquid international capital market led to a substantive increase in public debt. Togo was heavily affected by the onset of the oil shock and debt crisis in the early 1980s. During this period, and despite economic reforms sponsored by the international financial and development community, real per capita income shrank further at a pace of 2 percent per year. The 1990s was a period characterized of high political instability and continued stop‐and‐go in the reform agenda, as the stabilization and adjustment program faced severe obstacles for implementation. Government effectiveness was severely compromised, not only by people’s opposition to reforms, but also to increased and widespread corruption. During this period, per capita income recovered at a rate of 0.7% per year. A relatively high population growth rate in Togo (2.3% between 1970 and 2000) contributed to the observed low or negative per capita growth rates during the period. Another factor to be considered is the turbulent political situation that characterized the country since the early 1990s<sup>4</sup> and resulted in the withdrawal of international aid until mid 2000s. The lack of aid together with the meager government revenues, resulted in a long period of very low public investment and hence deterioration of public goods.

**Figure 2: GDP and GDP per capita growth rates and Development of GDP per capita in Togo, 1961 to 2008 (LHS), and Per capita GDP growth rates, Togo and selected ECOWAS countries 2004‐2008 (Real US dollars of 2000) (RHS).**

<!-- Start of picture text --> 12% 400 7%<br>10% 350 6%<br>5%<br>8% 300<br>4%<br>6% 250 3%<br>4% 200 2%<br>2% 150 1%<br>0%<br>0% 100<br>‐1%<br>‐2% 50 ‐2%<br>‐4% 0<br>GDP per capita (constant 2000 US$) Tota real GDP growth (US$ of 2000). HP filter<br>Per capita real GDP growth (US$ of 2000) HP filter<br>US$ of 2000 per capita<br>annual growth from Real US$ 2000<br>1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 Cape Verde Ghana Sierra Leone Nigeria Liberia Gambia, The Niger Burkina Faso Mali Guinea Senegal Benin Guinea‐Bissau Togo Cote d'Ivoire SSA LICs MICs<br><!-- End of picture text --> _Source:_ DDP data, World Bank.

**Togo’s more recent real per capita income growth rate of ‐0.1%, 2004‐2008, was the second lowest of the ECOWAS countries**<sup>**5**</sup> **and much lower than the average growth rate of Sub‐Saharan Africa (3.2%) as well as that of**

> 4 After the one‐party system, people started their fight for democracy in the beginning of the 1990s. President Gnassingbe Eyadema died in February 2005 after 38 years in power and immediately replaced by his son Faure Gnassingbe. International pressure forced him to hold an election, but his victory was soon disputed by the opposition and some international observers. This triggered violence, hundreds of lives were claimed and tens of thousands fled to neighboring countries. In August 2006 an agreement, Accord Politique Global (APG) was signed and parliamentary elections were held in October 2007, starting a new period of reconciliation and focus on economic reforms.

> 5 The Economic Community of West African States (ECOWAS) is a regional group of 15 West African countries founded in 1975, with the main mission of promoting economic integration among its members. It includes: Benin, Burkina Faso, Cape Verde, Côte d'Ivoire, Gambia, Ghana, Guinea (suspended after 2008 coup d’etat), Guinea‐Bissau, Liberia, Mali, Niger, Nigeria, Senegal, Sierra Leone and Togo. Out of this group, seven Francophone countries (Benin, Burkina Faso, Côte d’Ivoire, Mali, Niger, Senegal,

5
**the LICs (4.3%) (** Figure 2 **).** IMF (2009b) predicts a GDP growth rate of 1.7% in 2009, which corresponds to a 0.8% fall in per capita terms.<sup>6</sup> This means a continued divergence compared to other low‐income countries. Predictions for 2011, given commitments to planned reforms, are slightly more positive with a 4% GDP growth (IMF, 2009a), corresponding to a per capita growth rate of 1.5%. However, that is still a very low pace for a country that wishes to achieve higher income standards within in the span of one generation. In fact, for Togo to reach a middle income status by, say 2030, it would need to grow at per capita growth rate of 4.1%, which would correspond to a GDP growth of 6.6% (see further comparisons in Table A1 in Appendix I).<sup>7</sup>

**In the 2000s Togo has had more success in terms of both political stability and economic reforms, even if a significant impact on growth is not yet seen.** Increasing political liberties coupled with higher macroeconomic discipline<sup>8</sup> have restored cooperation with the international financial community. As a consequence, total aid grew from 8.2 current USD per capita in 2001 to 18.4 in 2007, which is still low compared to the average aid per capita in LICs (37) and SSA (44) the same year. Moreover, at the end of 2008 Togo qualified for the HPIC debt relief and normalized relations with the international donor community. The Government of Togo has in 2008 achieved nearly all of its structural reform targets agreed with IMF. Missed growth targets are in part due to the global financial crisis and the negative impacts from food and energy price increases, but one needs to take into consideration the nature of the reform so far. Up till now the focus has been on getting some fundamentals correct, such as reinforcing public finances and reforming state‐owned banks and enterprises. Next steps involve policy actions expected to have more direct effects on the private sector and growth. However, as will be further argued, this will depend on to what extent the government manages to target Togo’s _binding_ constraints to growth.

**The purpose of this study is to better understand the structural problems that remain in Togo and binding constraints for higher, sustainable growth.** The growth analytics is based on a number of approaches and methods, covering the more traditional tools combined with a growth diagnostic approach _à la_ Hausmann, Rodrik and Velasco (2005). A central question asked is why the levels of private investment and entrepreneurship are not higher. Possible answers include limited investment and entrepreneurship because of high financing costs or low returns to capital, defined in a broad sense. The latter will in turn point to problems either in the underlying potential of the country in terms of complementary factors (social returns), market failures or government failures such as lack of public investments or involvement in the market that causes distortion. In order to answer this question different types of data are needed, macro and micro, firm and household level, quantitative and qualitative, data will be thoroughly used. To search for the underlying questions as to what constraints to economic growth in Togo today are, involves first an understanding of the negative growth in the 1980s and its consequences today in terms of the investment environment of Togo. In addition, further understanding is reached by looking at what happens to economic actors that manage to circumvent the constraints that are still present.

# **2 Methodology**

**The growth analytics in this study is based on a number of approaches and methods ‐ it will cover the more traditional tools combined with the HRV growth diagnostic approach (Hausmann, Rodrik and Velasco, 2005).** There are three main steps involved in this growth analytics:

> and Togo) plus Guinea Bissau created the West African Economic and Monetary Union (WAEMU or UEMOA from its French name), an organization established to promote economic integration among countries that share a common currency, the CFA franc.

> 6 Assuming a constant population growth of 2.5 % per year.

> 7 Authors’ calculations using the Atlas method and World Bank, DDP data. The income group limits are assumed constant at the values in 2007.

> 8 See for example the letter on intention with IMF in April 2009.

6
<!-- Start of picture text --> Po<br><!-- End of picture text --> <!-- Start of picture text --> P<br>ST<br><!-- End of picture text --> <!-- Start of picture text --> a<br><!-- End of picture text -->
returns’ to capital as a result of ‘government failures’ (such as taxes, poor property rights, corruption, labor‐capital conflicts, macro instability, etc) and ‘market failures’ (such as coordination externalities and learning externalities affecting negatively the country’s ability to adopt new technologies). The _cost of finance_ may be high because the country has limited access to external capital markets or problems in the domestic financial market. Difficulties in accessing external capital markets arise for diverse reasons such as high country risk, unattractive FDI conditions, vulnerabilities in the debt maturity structure, and excessive regulations of the capital account. Bad local finance may be due to low domestic saving and/or poor domestic financial intermediation.

**An exercise in growth diagnostics consists of reviewing and analyzing the factors found along the branches of the growth diagnostic tree in order to ascertain which of these factors are the binding constraints to growth.**<sup>**11**</sup> Although all factors may matter for growth and welfare, those that are binding are likely to provide the largest positive direct effect when released, so that even after taking into account second‐best interactions, the net impact of a policy change is positive and sizable. While this methodology seems simple, in practice it is challenging to identify indicators of scarcity that make possible a trade‐off between the potential constraints to growth. When a constraint is binding, one should be able to observe a number of phenomena: (i) the (shadow) price should be high, (ii) movements in the constraint should produce significant movements in growth, (iii) agents in the economy should be attempting to overcome or bypass the constraint, (iv) agents less intensive in that constraint should be more likely to perform well, and vice versa.<sup>12</sup> Hence, for each potential constraint it is not enough to just conclude that the levels are low but it is also necessary to observe signs of scarcity, i.e. signs of a current need for the constraint to be released in the particular economic environment of the country in question.

**Benchmarking the performance of an economy is one way of identifying factors that hold investment and economic activity in a specific country back.** First of all, it is important to select an appropriate group of comparative countries. The core group of comparators should consist of countries that share similar characteristics with the country of interest, and are going through similar development states (see Box 1). More so this set of countries should also include good performers in the region (as well as regional and income groups’ averages) to see in what dimensions they differ from the studied country. Though the group of comparators may, in fact, vary depending on the question one needs to address.<sup>13</sup> While useful for identifying problem areas, benchmarking for growth diagnostics has limitations and should be used with caution and jointly with other methods to shed light on constraints to growth. For example, comparisons are meaningful only if the indicators are constructed following procedures that are consistent across countries, and a change in the set of comparator countries for an indicator may change the conclusions from a benchmarking exercise. Moreover, evidence that a country ranks low on a given indicator does not imply that the area is a ‘binding’ constraint to growth – it is only an indication that this might be the case. There might be worse constraints that are the binding ones, or the structure of the economy in the specific country of study is simply not sensitive to that potential constraint.

**Another popular source of information is enterprise survey data, but this type of data must also be interpreted with caution.** Besides general information about the firm (including size, ownership, location, sector of economic activity, etc.) these surveys record perceptions, indicators of performance and objective business indicators, in areas such as infrastructure and services, access to land, crime, finance, business government relations, labor regulations and environment, etc. So called Investment Climate Assessment (ICA) surveys are extracted mostly from private business enterprises and aim to understand, precisely, what are the main constraints to economic activity. However, there are a number of caveats such as:

> 11 Note that we do not assume we will find one binding constraint but rather a bundle of constraints that all need to be addressed to trigger growth. However, the goal is not to present a laundry list but a few prioritized areas of concern. 12 See Hausmann, et al. (2005), Hausmann, Klinger and Wagner (2008), Rodrik (2004a and 2004b).

> 13 For example, when one would like to assess whether the cost of capital is high in a country, the group of comparators must include countries with similar credit worthiness ratings. When addressing questions about logistics and transport costs, the group of comparators must include land‐locked countries with similar types of traded goods.

8
- 1) The sample of existing firms included in ICAs is a biased sample excluding the firms that would have existed if the relevant constraints were removed.<sup>14</sup>

- 2) Comparing hedonic scales across individuals and countries is rifled with cultural issues, as well as the particular economic structure in a country<sup>15</sup> .

- 3) The business enterprise sample may not be representative of the span of business enterprises across the country<sup>16</sup> .

Thus, a growth diagnostics exercise has to be undertaken to confirm or reject the reliability and interpretation of these types of surveys.

**Box 1: Is Togo in a Poverty Trap?**

**<mark>When a country is poor, everything seems to be binding and in addition, having a very low level of income may in itself constitute an impediment to growth.</mark>** <mark>Economic literature refers to these cases as</mark> _<mark>poverty traps</mark>_ **<mark>,</mark>** <mark>a situation where growth cannot be ignited in a country just because productive inputs are so scarce that sustained growth cannot kick in. The HRV approach does not explicitly deal with poverty trapped countries. The framework assumes well behaved aggregate production function, which are not amenable to explain a country’s stagnation at very low level of income. Extending the analytical approach to include potential poverty traps is a challenging task, but maybe a necessary one for gaining understanding on constraint to growth in countries at very low levels of income – such as in Togo.</mark>

**<mark>The identification and possible growth implications of being in a poverty trap or another particular growth state could be analyzed by approaching growth analytics as suggested in Pritchett (2008).</mark>** <mark>The key question in all growth analytic processes is: “What are the feasible actions</mark> _<mark>in the country’s current circumstances</mark>_ <mark>that would initiate (or sustain) an episode of sustained, broad‐based, rapid growth?” Hence, a necessary first step, prior to a growth diagnostic exercise, is the “Identification of the country’s</mark> _<mark>current growth state</mark>_ <mark>based on its past and recent performance”. Pritchett (2008) defines 6 possible (exhaustive) growth states</mark> 17 <mark>, which are used to generate empirical transition probabilities from past to current states or from current to desired states. These probabilities are shown to depend on the set of policy actions leading to the next state, but also the initial state.</mark> **<mark>If one were to use Pritchett’s criteria, Togo could be classified as a country that has in the course of the last three decades transitioned from a phase of fast‐growing pre‐independence state, to economic decline post‐1980, and from there to a state of stagnation in the 2000s</mark>** <mark>. Now, being stagnant at a low level of income is not the same as being in a poverty trap. The difference is subtle, yet, important. Being in a poverty trap implies that a country may not be able to endogenously foster and sustain a moderate to high growth rate even if it properly and systematically address main, binding constraints to growth with the available resources and adequate political support. This is so because the low quality, scarce productive factors and the very low level of technology associated with mostly traditional, low scale, subsistence activities; coupled with extremely weak local demand (due to low income) and foreign demand (due to low competitiveness) make business unable to consistently growth, even when good policies are in place. Some exogenous intervention is needed to help the country jump towards a higher growth path.</mark>

**<mark>The question whether Togo is in a poverty trap or simply stagnant at low level of income is not an easy one, but there exist a few indications that Togo is</mark>** **_<mark>not</mark>_** **<mark>poverty trapped</mark>** <mark>. First, per capita income in Togo in 2008 remains 30% below its post‐independence historical maximum, but still 34% above its pre‐independence level when a period of strong growth started. Second, Togo is better equipped in terms of endowment such as human capital per capita which has steadily increased in the last half a century. Physical capital has been added at a much slower pace than the growth of employment, resulting in a physical capital stock per worker at the same low level as at independence. However, not lower than the amount available when country’s growth began to accelerate in the 1960s. Also, it is perceived that the conditions that were present when post‐independence growth ignited in the 1960s are nowadays still in place.</mark>

**<mark>This growth diagnostics exercise will hence identify constraints that prevent a shift from current stagnation towards a desired state of rapid, converging growth in Togo</mark>** <mark>. This is not to say that all countries in a given state will have to follow similar recipes (as the extrapolation of lessons is precisely what GD is</mark> _<mark>not</mark>_ <mark>about) but, as Pritchett explains “the set of diagnostics and therapeutics recommended are likely to be more similar across countries starting from similar states”.</mark>

14 As Hausmann et al (2008) explain: “The surviving firms may be politically well connected; form part of financial conglomerates; or be exempt from taxation because of some specific provision, making them less sensitive to problems of governance, finance or taxation, respectively”.

15 For cultural reasons, what is outrageous corruption in Sweden may not raise too many eyebrows elsewhere (Hausmann et al. 2008). Also, a specific growth determinant such as education may be more important in the US than in Zambia due to their different economic structures.

16 For instance, Togo’s business enterprise survey was restricted to firms in Lomé. Also, the agricultural sector or the informal sector is normally not surveyed, posing problems for lower income countries that rely mostly upon primary activities. 17 The six possible growth states according to Pritchett (2008) are: (i) poverty traps, (ii) a status of economic decline or collapse, (iii) stagnation (or non converging growth), (iv) modest converging growth, (v) rapid converging growth and (vi) growth leaders.

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# **3 The Current Economic Framework in Short**

**Box 2: Togo's Economy in Short**

**Together with other countries from the West African Economic Monetary Union (WAEMU) Togo maintains a fixed exchange rate regime** (at a rate of , a hard peg to the Euro, with own legal tender (the CFA Franc, at a rate CFAfr 655.957 : 1€). The country’s real exchange rate has followed a slightly appreciating trend ever since 1994. In that year, the nominal exchange rate depreciated 100% against the euro, a factor that significantly affected prices (62% of consumer inflation between the end of 1993 and 1995) and to an increase in poverty. Terms of trade have been volatile, linked to international prices of the country’s main export commodities, and those of imported food and energy (the country imports 100% of its petroleum needs). Terms of trade have slightly deteriorated in the last decade. The country sustains current account deficits that are financed by inflows through the capital and financial account, in particular project grants and concessional loans. As IMF (2009) explains, Togo’s current account deficits are in line with the norm for a low‐income African country that receives aid inflows. Nonetheless, the country’s ability to sustain the projected current deficits (5 6 percent of GDP) over the medium‐term depends on the expected increase in aid inflows (consistent with donor pledges) or other, non‐debt‐creating inflows such as FDI.

**Monetary policy is determined by the regional central bank, the Central Bank of West African States (BCEAO, by its French name), which prioritizes the control of inflation and the maintenance of the peg to the euro, which is guaranteed by the French Treasury** . Monetary policy is therefore influenced by that of the European Central Bank (ECB). The BCEAO’s discount rate broadly reflects movements in the ECB’s key interbank rate, although BCEAO rates are typically higher than ECB rates and take account of the prevailing circumstances of member states, such as inflation and bank liquidity.

**The country’s fiscal stance has improved in recent years, especially since the introduction of an IMF Staff Monitored Program (SMP) from October 2006 to June 2007 and a subsequent three‐year poverty reduction and growth facility (PRGF), in the second trimester of 2008** . Donor funding (especially from EU and the World Bank) has increased after full IMF reengagements in the country, following free and fair parliamentary elections in 2007. In fact, Togo began clearing external debt arrears under the SMP, paving the way for fresh funding, and reached a decision point under the heavily indebted poor countries (HIPC) debt initiative at the end of 2008.

**Togo is essentially a rural, agricultural economy: Half of the output, two thirds of workers and over 80% of exports are linked to the primary sector** . Most food crops are produced by subsistence farmers who operate on small family farms and produce items such as corn, manioc, sorghum, yams, cotton and coffee. Leading cash crops are coffee and cocoa, followed by cotton, palm kernels, copra and peanuts. Over 40% of land in the country is arable. There exists a state marketing board for Cotton (Société togolaise de cotton‐Sotoco) that is under a restructuring process under the PRGF.

**The services sector employs about 1/5th of the workforce, with key subsectors including commerce, public services and transport and communications** . The port of the capital, Lomé, is a key asset and plays an important part in regional trade, especially after conflict in neighboring countries caused a shift of international trade through Togo. The banking sector suffered from the dismal economic performance in the 1980s and 1990s, as they had heavily lent to para‐statal enterprises. The subsector comprises nowadays just 1% of GDP. **Together with agriculture, trade and transport, secondary sector activities in phosphate mining, agro‐processing, cement and clinker constitute the main economic activities in Togo.** In 2007, The government returned the mines to full state control with the formation of the Société nouvelle des phosphates de Togo (SNPT). Industrial production was expected to rise with the return in donor support and improvements in electricity supply18, but these prospects were halted by the spike in energy prices and the international financial and economic crisis of 2008‐2009.

**The 1989 Free Zone (FZ) law gave companies the advantages of a tax holiday, duty‐free imports of materials for production, a less restrictive labor code, and the ability to hold foreign‐currency accounts** . By 2005 there were about 60 firms operating in the FZ, employing 9,000 workers. Changes in FZ’s fiscal regime were introduced in 2009 that attempt to improve tax collection and a new FZ law is being finalized.

**Togo’s current economic policy framework has as the overarching goals of contributing to poverty reduction and modernizing the economy through promotion of increased participation of the private sector and productive diversification** . For this purpose, its government has maintained ‐ already for over a decade ‐ an earnest process of market oriented, progressively liberalizing economic reforms that attempt to encourage both local and foreign investment. To this end, the current economic policy performance of Togo was commended by the IMF in 2009, which is especially impressive when considering current demanding local and international circumstances. Fiscal and monetary policies have been prudent in the face of exogenous shocks and slower than expected growth in 2008 and 2009. Low recent growth rates have even prompted authorities to accelerate key reforms to promote

> 18 It is expected that a new, 100‐mw power station in Lomé, which will roughly double domestic generating capacity will be completed in early 2010.

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<!-- Start of picture text --> 10%><br>8% > \<br>6% > Of<br>2% + os \~ hh<br>-2%4% +4 ‘ \Y ‘4<br>ox|<br>sx|<br>-10%+BRREEBREBRBERHRESEEERESHEEEEESEERSRRRERSRSSSRSESSSSSSSSSSSSSRRRRRRERRRR RRERRRSSSSSSESBRSARSRRSSSSSRRSRSSSSSSRSES<br>——GDP Growth == PhysicalCapital === Labor —3*—TFP Cobb Douglas «**** Human Capital<br><!-- End of picture text -->
**effect on GDP growth is more or less exclusively explained by growth in labor.** Figure 4 suggests that less output has been generated with additional units of input, that is, the growth of the residual TFP has been negative. In fact, Figure 4 shows a loss of almost two thirds in cumulative productivity since the end of the 1960s. Togo’s cumulative loss of productivity, as approximated by this residual, is the highest compared to a sample of benchmark countries (Bosworth and Collins, 2000). In what follows, we take a closer look at each of the proxy determinants of growth, namely, physical capital, labor, TFP and in later sections human capital.

# **4.2 Capital and Investment Dynamics**

**As Figure 5 shows, for the period 1981‐2005, the decline in output per worker has been largely explained by the fall in the capital‐labor ratio, and to a lesser extent by a shift of resources across sectors.** The former ratio has declined due to the significant increase in labor and the very low increase in capital (a total of only 9.5%, 1981 to 2005). The capital stock per unit of worker currently stands at the same level as in the 1960’s. It increased steadily in the 1960s and 1970s at a pace much faster than employment growth but was followed by a substantial deceleration in real investments.

**Figure 5: Decomposition of Changes in Output per worker, Togo 1981‐2005 (LHS), and Capital Stock Values per Unit of Worker, Togo, 1960‐2007 (RHS).**

<!-- Start of picture text --> 800<br>700<br>600<br>500<br>400<br>‐51.64 300<br>200<br>100<br>‐30.47 0<br>‐55.00 ‐45.00 ‐35.00 ‐25.00 ‐15.00 ‐5.00 5.00<br>Output per worker net of inter‐sectoral shifts Capital Labor Ratio TFP residual net of inter‐sectoral shifts<br>T h o u san d s o f CF A o f 2000<br>1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005<br><!-- End of picture text --> _Note_ : Capital stocks have been estimated following the perpetual inventory method, whereby the capital stocks values in a year “t” is obtained as the sum of capital stocks in year “t‐1” minus depreciation, plus the investment (gross capital formation) in “t”. In turn, the initial value of capital stocks is estimated under a steady state assumption on the growth rate of capital, as the value of initial gross capital formation divided by the sum of the depreciation rate and the growth rate of investment. _Source_ : Authors’ calculations based on WDI data, World Bank.

**Figure 6: Capital Formation in Togo and Benchmark Countries, averages 2000‐2005 (LHS) and Real Gross Capital Formation per capita (in logs) versus Real  per capita GDP growth, Averages 2000‐2008 (RHS).**

<!-- Start of picture text --> 45% 400 20%<br>40% 350<br>35% 300<br>30% 250 15%<br>25%<br>200<br>20%<br>15% 150 10%<br>10% 100<br>5% 50 5%<br>0% 0<br>0%<br>Togo<br>‐5%<br>0 2 4 6 8 10<br>Real Gross Capital Formation per capita (US$ of 2000) Real Gross Capital Formation (Real US$ of 2000) per capita,<br>Ratio of Gross Capital Formation to GDP (Real US$ of 2000) 2000‐2008, in logs<br>% of GDP Per capita<br>rate, 2000 ‐ 2008<br>Cape Verde Ghana Senegal Mali Benin Gambia, The Cote d'Ivoire Togo Burkina Faso Guinea‐Bissau Sub S. Africa Low Income<br>Real GDP per capita (US$ of 2000) growth<br>Lower‐Middle Inc.<br><!-- End of picture text -->

**_Source:_ WDI data, World Bank.**

**In terms of real gross capital formation per capita, Togo has the 11**<sup>**th**</sup> **lowest ratio out of 152 countries with data in the world.** The average for the period 2000 to 2005 was 46 USD per capita, compared to 99 USD per capita on average in SSA, and as much as 355 USD per capita in the lower part of the middle income country group (Figure 6,

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left side). Figure 6, on the right side, plots the investment ratio to total population versus real per capita GDP growth for the period 2000‐2008. Interestingly, one can observe an almost total lack of correlation between the investment ratio to population and per capita GDP growth, i.e. a higher level of investment per capita is not associated with the growth rate of income.<sup>20</sup> On the other hand, investment _growth_ and GDP _growth_ are strongly correlated.<sup>21</sup>

**Most of the investment in Togo during the 2000s has been undertaken by the private sector, as the government has not been able to obtain resources ‐ neither from abroad (ODA) nor from domestic sources as a result of a low fiscal revenue base.** Grants (current transfers to government) averaged just over 1% of GDP between 2000 and 2008. More recently, as Togo has been able to re‐engage with the international financial community, official aid has increased to 3.9% of GDP in 2009 and an expected 4.5% in 2010. Fiscal revenues (excluding grants) from central government remained under 17% of GDP during the same period, but are at least expected to remain at the same level during these two years. As a consequence, the public contribution to capital formation has and is expected to increase compared to historical levels (Figure 7).

**Figure 7: Public and Private Gross Capital Formation and National Savings, Togo, 2001‐2010.**

<!-- Start of picture text --> 18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>GFCF, Private GFCF, Public Gross National Savings<br>% of GDP<br><!-- End of picture text --> _Source:_ International Financial Statistics, IMF.

**Togo has a substantially lower than expected gross national saving rate given its level of per capita income (** Figure 8 **).** This fact translates into a persistent current account deficit (Figure 9) that needs to be financed by external sources.

> 21 Real GDP growth vs Real Gross Fixed Capital Formation Growth (USD of 2000), using averages 2000‐2005, gives y = 1.461x – 0.005 with R<sup>2</sup> = 0.424.

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<!-- Start of picture text --> ©<br>High income<br>o fo} ° Middle income<br>> o ° Low income<br>> So Seo8 % & °2 2 ef So Burkinakina BeninF Faso<br>© 259 & eS 2 °} Niger<br>° © 7 9. Spee ©. og Cote d'lvoire<br>fo rs 8 os? mS So. $08BO ©-O-Ogy} SenegalTogo<br>> C2Ry ewes & ° 2 80-5 Ce LoSg2 2 x GhanaMali<br>° eo ° Gambia,T<br>-0.7 -0.2 0.3 0.8<br><!-- End of picture text --> <!-- Start of picture text --> —<br>a<br>——- TULL<br>— oe _ a. . ; a<br><!-- End of picture text --> <!-- Start of picture text --> aee<br>—<br>i= ARAL<br><!-- End of picture text -->
**Figure 11: Workers' remittances and compensation of employees, received, Togo, 1974‐2007 (LHS), and Workers' remittances and compensation of employees received, Togo and Benchmarks, 2007 (RHS) (% of GDP).**

<!-- Start of picture text --> 12<br>High income<br>Middle income<br>10 Low income<br>Cape Verde<br>Togo<br>8 Sierra Leone<br>Liberia<br>6 Senegal<br>Guinea‐Bissau<br>Gambia, The<br>4 Nigeria<br>Benin<br>2 Mali<br>Niger<br>Cote d'Ivoire<br>0 Ghana<br>Burkina Faso<br>0 1 2 3 4 5 6 7 8 9 10<br><!-- End of picture text --> _Source:_ DDP Data, World Bank.

# **4.3 Demand Distribution of Growth**

**An initial post‐independence decade of prudent macroeconomic policies in the 1960s was followed by a period of expansionary fiscal policies, debt growth and a questionable public investment program in the 1970s** . Government expenditures and (mainly public) capital formation increased in the period at rates close to 10% per year (see Table 1), but did not result in substantial growth as most of purchased goods were of foreign origin. The sudden oil price shock in the early 1980s put an end to an era of easy access to international capital markets at a time when Togo’s external debt had already become unsustainable. The 1980s were a time period of contracted government expenditures and public and private investments, with private consumption providing most of the contribution to growth in aggregate demand. The structural adjustment program included measures to reduce the budget deficit mainly through a control of government expenditure and a gradual disengagement of the government from some sectors of activity. However, it contributed to a recession as the reduced government expenditure was not compensated by increased private activity.

**Capital formation fell in the 1990s while government expenditures remained mostly stagnant, as the ongoing economic liberalization program and reforms were, at best, only partially applied** . Policy inconsistencies were a result of social unrest and economic mismanagement, and a wider perception of corrupt public officials led to increasing dysfunction of the public authority. During this same time period, exports also remained stagnant, represented mainly by traditional agriculture commodities and phosphates. Private consumption was, again in the 1990s, the main driver of aggregate demand.

**Government expenditures, investment and exports have slightly recovered in the 2000s, following a period of increased macroeconomic and political stability and continued liberalization and reforms.** A significant portion of the registered growth of aggregate demand in the 2000s can be attributed to the exportable sector (more on this on the following section). However, as a significant proportion of supply has been provided by imported goods, real GDP growth has remained low, and even negative in per capita terms.

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**Table 1: Growth Rates of Aggregated Demand/Supply, by Demand Components, by decade from 1970s.**

<!-- Start of picture text --> Growth Rates of Aggregate Demand / Supply by Demand Components. By Decades.<br>Period Real GDP Goods and Imports of Services Aggregate DemandSupply / ConsumptionPrivate ConsumptionGovernment Gross Capital Formation Goods and Exports of Services<br>1970s 2.8% 10.9% 5.8% -1.1% 9.8% 11.9% 8.5%<br>1980s 1.1% 1.0% 1.0% 4.9% -2.3% -0.2% -1.0%<br>1990s 2.3% -1.2% 0.9% 4.1% -0.5% -4.8% 0.3%<br>2000s 1.8% 4.0% 2.6% 1.2% 3.9% 3.4% 5.6%<br>Source: Staff Calculations based on World Bank, WDI<br>Shares in Real Aggregate Supply of GDP and Demand Components<br>Period Real GDP Goods and Imports of Services Aggregate DemandSupply / ConsumptionPrivate ConsumptionGovernment Gross Capital Formation Goods and Exports of Services<br>1970s 64.3% 35.7% 100.0% 40.3% 9.9% 13.4% 25.4%<br>1980s 58.5% 41.5% 100.0% 45.8% 8.9% 9.9% 26.4%<br>1990s 65.2% 34.8% 100.0% 60.4% 7.8% 7.3% 22.8%<br>2000s 65.0% 35.0% 100.0% 60.5% 7.2% 7.7% 24.8%<br>Source: Staff Calculations based on World Bank, WDI<br>Contribution to Growth in Aggregate Demand / Supply by Demand Components. By Decades.<br>Period Real GDP Goods and Imports of Services Aggregate DemandSupply / ConsumptionPrivate ConsumptionGovernment Gross Capital Formation Goods and Exports of Services<br>1970s 1.8% 3.9% 5.8% -0.4% 1.0% 1.6% 2.2%<br>1980s 0.6% 0.4% 1.0% 2.2% -0.2% 0.0% -0.3%<br>1990s 1.5% -0.4% 0.9% 2.5% 0.0% -0.4% 0.1%<br>2000s 1.2% 1.4% 2.6% 0.7% 0.3% 0.3% 1.4%<br><!-- End of picture text --> _Note:_ Contributions of each variable to aggregate demand growth are computed multiplying period growth rates by period average share of the variable in total aggregate demand. _Source:_ Authors’ calculation, WDI data, World Bank **.**

# **4.4 Economic Sector Dynamics**

**Togo’s steep economic decline and subsequent stagnation since 1980 has reversed initial trends in structural transformation and has created a domestic economy increasingly dependent on primary activities, mainly at the expense of its tertiary sector (** Figure 12 and Table 2 **).** In fact, primary activities have doubled their share in value added GDP between 1980 and 2009 and represent almost 50% of value added in this latest year. Services have gone the other way, from 51% of GDP in 1980 down to 25% in 2009, while industry has maintained a relatively constant share of 25% of GDP during the same period. On one hand, value added from _primary activities_ has increased at a decreasing pace in the last three decades, boosted mainly by subsistence activities and the animal sector. On the other, traditional coffee and cotton sectors follow declining long term trends of ‐2.1% and 2.0% respectively since 1990. Among traditional primary sectors only cacao exhibits a slightly increasing trend during the period. Within the _secondary secto_ r, traditional extractive industries also show a downward trend. Since 1990, value added generated by the phosphates sector has declined at an average rate of 6.2% per year. This has been compensated by the significant increase in clinker (lime and cement) production, which has performed well at rates over 9% since 1998. Similarly, most manufacturing activities have been on an increasing trend between 1990 and 2008. Among them, the production of non‐mineral metals and metallurgic products have risen each at 12.4% and 11.2% per year during the period, elevating their share in manufacturing production from 12.4% in the former year to over 37% in the latter. Other good manufacturing sub‐sectors are chemicals, beverages, and, to a lesser degree, textiles, electricity gas and water. Value added in the _tertiary sector_ has fallen at an average pace of 0.7% per year since 1990, mainly driven by a debacle in the banking sector at a pace of 11.9% per year and in other commercial services (‐5.2%). In fact, Banking and insurance sector has declined from an already low 5.9% of tertiary GDP (and 2.6% of Value Added GDP) to an incredibly low 0.2% in 2008. The commercial sector has stagnated and, reduced its contribution to total GDP. On the flipside, transport and communication activities have performed moderately well in the last two decades growing at a 2% rate per year.

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**Figure 12: Sector shares in total value added in Togo (percent), 1965 to 2009 (LHS) and Trends in Growth Rates in Value Added GDP, Togo, 1966‐2009 (RHS).**

<!-- Start of picture text --> factor Costs. 1965 2009<br>100% 15%<br>80% 10%<br>60%<br>5%<br>40%<br>20% 0%<br>0% -5%<br>Primary Secondary Tertiary Primary, HP filter Secondary, HP filter Tertiary, HP filter<br>% Chg.<br>1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008<br><!-- End of picture text --> <!-- Start of picture text --> Primary<br><!-- End of picture text --> <!-- Start of picture text --> Secondary<br><!-- End of picture text --> _Notes_ : All calculations use data in constant LCUs. There are small differences in Gross value added at factor cost in the table and the DDP data due to incomplete information on some activities.

_Source_ : Authors' calculations based on data from the World Bank, DDP data, and Government of Togo.

**Table 2: Shares, Growth rates and Contribution to Growth, Togo, 1960s‐2000s.**

|**,**<br>**, g**<br>**Shares in Value Added GDP**|<br>**g**<br>**,**<br>**y**<br>**1960s**|<br>**1970s**|**, ,**<br>**,**<br>**1980s**|<br>**y**<br>**1990s**|<br>**2000s**|
|---|---|---|---|---|---|
|Primary|32.2|26.2|29.4|44.31|46.50|
|Secondary|18.9|21.3|21.5|21.24|24.90|
|Tertiary|48.8|52.5|49.1|34.45|28.60|
||100.0|100.0|100.0|100.0|100.0|
|**Growth in VA GDP**||||||
|Primary|6.2%|2.8%|4.5%|3.1%|2.5%|
|Secondary|11.2%|7.8%|-0.2%|2.6%|3.6%|
|Tertiary|7.3%|3.6%|-0.4%|-0.8%|-2.0%|
|Total VA GDP|7.7%|4.3%|1.1%|1.5%|1.4%|
|**Contribution to Growth**||||||
|Primary|2.0%|0.7%|1.3%|1.4%|1.2%|
|Secondary|2.1%|1.7%|0.0%|0.6%|0.9%|
|Tertiary|3.6%|1.9%|-0.2%|-0.3%|-0.6%|
|Total VA GDP|7.7%|4.3%|1.1%|1.5%|1.4%|

_Notes:_ All calculations use data in constant LCUs. _Source:_ Authors' calculations based on data from the World Bank, DDP and Government of Togo.

**The main conclusion from this discussion is that any incremental growth in the Togo economy in the last three decades, has been achieved out of growth of the country’s labor force.** Unable to find _productive_ employment in tertiary activities, or employment opportunities in general in industry, Togolese citizens have found employment in subsistence agriculture or have used their resource endowments in other primary activities. Subsistence activities and the animal subsector generated over 85% of primary GDP and 40% of overall value added in the 2000s (compared to 29% in 1990). However, the ability of several manufacturing activities to thrive must also be highlighted; the cement sector making up for decreased activities in extractive industries from phosphates, and the transportation and communication sectors ability to maintain a modest growth rate.

**An alternative way to observe sector dynamics and their contribution to GDP is the so called Shapley decomposition, which highlights changes in average productivity, employment or labor reallocation by sector (** World Bank, 2007b **)**<sup>**23**</sup> **.** It is confirmed that agriculture and, to a lesser extent, services, mostly absorbed the main part of the increased labor force between 1981 and 2005, combined with an increased (+17%) but still low labor productivity in agriculture and significantly lowered productivity (‐64%) in services (Figure 13). Labor productivity was highest within the industry sector, growing by 6% during the period, Even though growth in employment was high for the industry sector itself, the absolute number of job opportunities created was small. In 1981, agriculture

> 23 Historic employment data in Togo is quit scarce. In the case of value added GDP, this decomposition necessitates of at least two data points for each sector value added variable and for the employment levels, as well as two data points for indicators of population, working age population, labor force and employment. As one is interested in long term trends, it is recommended for data points to be at least 10 years apart, and that they correspond to normal (non‐shock) years. Fortunately, employment data by sector in Togo is available for 1981 and 2005 by combining information from the World Bank DDP data base and the CWIQ 2006 survey, and real Value Added data by main economic sectors from Government of Togo.

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<!-- Start of picture text --> 4,400,000<br>1,200,000 ———_—_—_—§<br>2& 1,000,000 _— _—___<br>§ a 1981<br>g 92005<br>$ 800,000 — ——<br>a<br>ao<br>= 600,000 —<br>2<br>c<br>400,000 —<br>200,000 —<br>0<br>Agricufture Industry Services<br><!-- End of picture text --> <!-- Start of picture text --> rT<br>:<br><!-- End of picture text -->
# **4.5 Exports and Economic Diversification**

**This section presents an overview of the export structure and behavior in recent years, since it adds important information on Togo’s stage of economic transformation and growth prospects.** Export growth of 5.6% per year explains almost half of the increase in aggregate supply between 2000 and 2007 (see Table 1)<sup>24</sup> . The ability of Togo to sustain the higher growth rates observed between 2000 and2007 therefore hinges on its capacity to sustain a growing exporting sector.

**Growth of exports has been accompanied by diversification of the export basket, as shown by the decrease in the Herfindhal‐Hirschmann index of export diversification (** Figure 14 **)**<sup>**25**</sup> **.** Togo is actually the second most diversified country within the WAEMU, something that is owed to a substitution of traditional products such as cotton, coffee and phosphates in favor of an array of new emerging products (Table 4).

**Figure 14: Non‐Oil Herfindahl Index for Togo, 1980‐2006 (LHS), and Compared to WAEMU Countries, 2006 (RHS).**

<!-- Start of picture text --> 0.4 0.9<br>0.8<br>0.35<br>0.7<br>0.3 0.6<br>0.25 0.5<br>0.4<br>0.2<br>0.3<br>0.15 0.2<br>0.1 0.1<br>0<br>0.05<br>0<br>Scale 0‐1 (1=no diverisifcation)<br>Scale 0‐1 (Lower = Higher Diversification)<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**Table 4: Export Value of Selected Products (% of total exports), Togo, 1980‐2006.**

<!-- Start of picture text --> product code 1980 1985 1990 1995 2000 2005 2006<br>71 Coffee and coffee substitutes 11.0% 14.3% 6.0% 12.0% 5.6%<br>72 Cocoa 13.9% 7.7% 12.0% 22.0%<br>223 Oils seeds and oleaginous fruit, wh 2.4%<br>263 Cotton 12.6% 25.0% 26.7% 22.7% 10.5% 7.2%<br>271 Fertilizers,crude 46.7% 51.2% 52.1% 28.9% 18.1% 10.1% 14.1%<br>333 Petrol.oils,crude,& c.o.obtain.from 9.8%<br>334 Petroleum products,refined 18.5% 5.0% 2.5% 18.1% 13.6% 15.3%<br>661 Lime,cement,and fabricated construc 8.5% 20.3% 12.2%<br>667 Pearls,precious& semi‐prec.stones,u 2.3% 8.2%<br>Grand Total 92.4% 90.8% 88.0% 85.6% 72.9% 66.5% 70.7%<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**The shift of the export basket away from classic to new, emerging products has been accompanied by an increase of the income value of such basket in Togo, as defined by EXPY**<sup>**26**</sup> **‐ an indicator of export sophistication of a country (Hausmann and Klinger, 2006)** . Higher EXPY, authors hypothesize, allows countries to move more easily in the “product space” towards their “technology frontier”. Togo is one of the WAEMU countries that has shown one of the faster increases in the income value of the exportable basket (Figure 15).

> 24 Notwithstanding, imports of goods and services have also increased, at a pace of 4.0% per year.

> 25 Explained in Appendix 2.

> 26 Explained in Appendix 2.

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**Figure 15: Trends in EXPY for Togo and WAEMU countries (LHS), and other benchmark countries (RHS), 1980‐2006,.**

<!-- Start of picture text --> p<br>7,000 12000<br>Benin<br>6,000 Burkina Faso 10000 Malaysia<br>Cote D'Ivoire China<br>5,000 Cape Verde 8000 South Africa<br>Ghana India<br>The Gambia 6000<br>4,000<br>Guinea‐Bissau Mauritius<br>Liberia 4000<br>3,000<br>Mali TOGO<br>Niger 2000<br>2,000 Nigeria<br>Senegal 0<br>1,000 Sierra Leone<br>1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006<br>Togo<br>Real US$ of 2000<br>Real US$ of 2000 per capita<br>1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006<br><!-- End of picture text --> _Source:_ Export diversification data, PRMED, World Bank.

**Given the country’s income level, Togo’s EXPY remains below expectations.** As reported by Hausmann et al (2005) there exist an empirical relation between EXPY and a country’s income level that is explained not only by construction of the indicator. Once a set of controls ‐ as rationalized by the authors ‐ have been accounted for, the income value of Togo’s export basket remain below that predicted by the country’s per capita GDP (Figure 16).

**Figure 16: Residual of EXPY regression after controlling for independent variables suggested by Hausmann et al (2005).**

<!-- Start of picture text --> Measure of Unobserved Factors Affecting EXPY (Residuals of OLS<br>Regressions of EXPY on Controls Suggested by Hausmann et al.). 2006<br>100%<br>50%<br>0%<br>-50%<br>-100%<br>-150%<br>Togo<br>Observed EXPY<br>Observed EXPY - Predicted EXPY as % of<br><!-- End of picture text --> _Note_ : A negative value indicates that a country’s EXPY is below that suggested by its income level once one factors in the values for that country of selected explanatory variables. _Source_ : Authors’ calculations.

**It is however important to note that a significant part of Togo’s imported goods are directly re‐exported to Nigeria as well as neighboring land‐locked countries, to the extent that aggregated export and export diversification indicators may not reflect actual economic activity.** During the 2000s, according to IMF (2007) about 16% of exports (11% of imported goods) were re‐exported, mainly to neighboring West African countries. It is however very difficult to estimate any numbers and thereby effects of this type of transit trade. Also, it is important to differentiate between the re‐exportation to the land‐locked countries, which is a type of activity that Togo will continue engaging in and where the Lomé Port is part of the value chain for these products. However, the re‐exportation to Nigeria is a consequence of unfavorable trade policies in Nigeria, which means that this re‐ exportation is not based on Togo’s comparative advantage and may be disrupted as policies in Nigeria changes. Also, the re‐exportation to Nigeria takes many forms and is therefore less measurable than the other types of re‐ exportation.

**Finally, over the long term, Togo has experienced deterioration on its term of trade due to stagnant price trends in the country’s main exportable products from the 1970s through the early 2000s; although import prices have consistently increased during the same period (** Figure 17 **).** However, during the 2000s both prices of traditional commodities and of main imports (including food and oil products) have shown sharp increases leading to an overall stagnation in the terms of trade.

20
**Figure 17: Export prices, Import prices and Terms of Trade in Togo (Base 2000=100), 1960‐2005.**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Exports price index (goods and services, 2000 = 100) Imports price index (goods and services, 2000 = 100)<br>Terms of trade (goods and services, 2000 = 100)<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004<br><!-- End of picture text --> _Source_ : WDI data, World Bank.

# **4.6 Conclusions from and Implications of the Development Dynamic Analysis**

- Togo’s immediate post independence period (1960‐1973) of prudent, modernizing‐oriented and market‐ friendly economic policies were followed by a decade of interventionist, protectionist and expansionary policies that ended with the international oil shock and external debt crisis in the early 1980s. Economic reforms under an IMF‐sponsored financial stabilization program in the 1980s did not result in increased and sustained growth, mainly because of the burden of debt, economic mismanagement, and confrontations between population and the government, which ended up in removing the political elite in the early 1990s. Social unrest, public authority dysfunction, economic mismanagement and perception of corruption of public officials remained until late 1990s.

- The 2000s has constituted a decade of market oriented economic reforms that have been commended by international financial institutions (IMF, 2009). Prudent fiscal and monetary policies have been set in place. The structural reforms have stressed fiscal governance, financial sector restructuring and public enterprise reform, all with an emphasis on measures that more directly impact economic growth. Yet, reforms have not yielded the expected growth. Consecutive food, energy price increases and the recent international economic and financial crises have been identified as at least partially responsible for this lack of growth.

- There has been very limited capital investments in Togo since the 1980s and total factor productivity has declined rather than improved. Any positive effect on growth can almost exclusively be explained by growth in labor, explaining the negative growth _per capita_ numbers. A potentially positive effect on growth per capita from an improved dependency ratio was negated by a decline in the labor force participation rate.

- In terms of sector dynamics since 1980, the _employment share_ has increased for services, and decreased for agriculture and industry, while at the same time, the _value added share_ has decreased for services, increased for agriculture and stayed approximately the same for industry. When looked at in its entirety, this indicates that little progress in economic transformation has actually taken place. For instance, the increased labor force either stayed in low productivity agriculture or moved to low‐productivity self‐ employment in the service sector, resulting in a drastic reduction of average productivity in that sector.

- Export growth explains almost half of the increase in aggregate supply since 2000 and the ability of Togo to sustain these recent higher growth rates mostly hinges on its capacity to maintain this sector. Togo’s exports have shown an increasing trend during the last two decades, though not as large as that of its peers from Sub‐Saharan Africa and low‐income countries. In contrast, Togo is the most diversified exports and ranks highly in terms of the growth rate of the export basket’s income value.

- Public capital investments are expected to rise as Togo re‐engages with the international community. The remaining question is whether the government will direct these investments in a way that removes Togo’s binding constraints to growth – not least through continued progress within the export sector.

21
# **5 Profile of the Economic Actors**

**The second step in the analysis involves forming a picture of the economic actors in terms of their resources and income generating activities.** This is important as it not only tells us about the potential embedded in the production factors labor and human capital, but also about the feasible growth trajectories that would include a major part of the work force without jeopardizing productivity growth.

**Togo is a mostly rural country with 37% of the population residing in urban areas, of which over 60% live in Lomé.**<sup>**27**</sup> As will be discussed below, socio‐economic conditions, employment and living standards differ widely between urban and rural areas with Lomé standing out even compared to other urban areas in the country. For instance the headcount ratio of poverty is 24.5% in Lomé but varies between 28 and 79% in the urban areas in the other regions. Poverty is even higher in rural areas, with Savanes having an incidence of 90%.

**However, Togo has still a relatively equal income distribution with a GINI in 2006 of 34% compared to the 44% average for SSA (** see also Figure 18 **).** Given the very low average income level, this means – as we have seen ‐ that a major part of the population is poor. It also means that there is a limited share of the population with enough capital to make major, risky investments even though opportunities may be there.

**Figure 18: Income shares of the highest and lowest 10%, West African Countries (2001‐2007).**

<!-- Start of picture text --> 45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>Togo Guinea‐Bissau Liberia Senegal Mali Nigeria Burkina Faso Ghana Sierra Leone Niger Gambia, The Cote d'Ivoire Cape Verde Cape Verde Gambia, The Cote d'Ivoire Nigeria Ghana Niger Liberia Senegal Sierra Leone Mali Benin Guinea‐Bissau Burkina Faso Togo<br><!-- End of picture text --> _Note:_ The first (second) group of bars is for the highest (lowest) 10%. Data points were from different years for different countries but all within the period 2001‐2007. Togo data is from 2006. _Source:_ DDP data, World Bank.

## **5.1 Demographic Dynamics**

**Togo has a high fertility rate.** However, 4.8 births per woman is not higher than expected in a country with the same per capita income level (see Figure 19).

> 27 Most of the analysis in this section is based on the Core Welfare Indicators Questionnaire (QUIBB/CWIQ) 2006. The poverty line applied is FCFA 164842, using the FAO/ WHO equivalence scales (see World Bank (2007a)).

22
<!-- Start of picture text --> 8<br>674.46NyAs‘ *f +> +<br>45 4 SSev"o. + + * *<br>+ ne ay<br>* ~~ td * %<br>iJ o,* B¢ oe ne<br>«* *¢ 3 % % toa +<br>- & the “at hep; “ii<br>o+<br>45 55 65 7S 85 95 10.5<br><!-- End of picture text --> <!-- Start of picture text --> eer t nen nen<br>PPP SP? SDD P ? SPKHKH PPPPPP SPSSPSP GS MH SSFPFP IF SoSFSF SF<br>-<---- —_<br><!-- End of picture text --> <!-- Start of picture text --> a<br>an} DN aN ~<br>\ | ‘2 ar ><br>ra<br>eer t nen nen iw<br>SX PIS SM SP PP Sr MH PH GO SG<br>y<br>&<br>PPP SP? SDD P ? SPKHKH PPPPPP SPSSPSP GS MH SSFPFP IF SoSFSF SF<br>-<---- —_ ae<br><!-- End of picture text -->
**Figure 21: Literacy rates and Education Level in rural and urban areas, for age group 15‐45, by regions, Togo, 2006.**

<!-- Start of picture text --> 120% 120%<br>100% 100%<br>80% 80%<br>senior high and above<br>60% 60% junior high<br>40% 40% primary<br>none<br>20% 20% literacy<br>0% 0%<br>maritime plateaux centrale kara savanes Total lomé maritime plateaux centrale kara savanes Total<br><!-- End of picture text --> _Source_ : Core Welfare Indicators Questionnaire (QUIBB/CWIQ) 2006 and author’s calculations.

# **5.3 Employment**

**As in other developing countries, most of the workers are employed in agriculture as self‐employed or unpaid family workers.** Table 5 presents the sector and status of employment in terms of wage and self employment in Togo, and two things stand out from the table. First, as we have seen, agriculture employs almost 60% of workers, followed by Commerce and trade, 22%. Second, the high share of self employment and family helpers (91%) points to a high incidence of low‐skilled and low‐productivity informal employment for those who cannot find other types of employment. Only in education and health and public administration it is more common to be wage employed, and the share is relatively high also for the mining, construction and transport sector. However, the share of these sectors in total employment is very low. As a result, only around 24% of urban workers receive a salary or are paid on a piece rate basis (27% in Lomé). The reminder are apprentices, non‐paid family workers or self‐employed.

**Table 5: Wage and Self Employment (LHS, and) Wage and Self Employment and Share of Laor Force (RHS), by Sector, Togo, 2006.**

||Wage or<br>piece‐rate<br>earner|Self‐<br>employed/<br>family<br>helper|Total||Wage or<br>piece‐<br>rate<br>earner<br>Shares ac|Self‐<br>employed<br>/ family<br>helper<br>ross sectors|Total<br>across<br>sectors|Total<br>Share on<br>total<br>workforce|
|---|---|---|---|---|---|---|---|---|
|Agriculture|0.9%|58.0%|58.9%|Agriculture|5.8%|94.3%|100.0%|10.6%|
|Mining|0.2%|0.4%|0.6%|Mining|63.4%|36.6%|100.0%|0.5%|
|Manufacturing|0.5%|3.2%|3.7%|Manufacturing|22.0%|77.9%|100.0%|7.1%|
|Construction|0.6%|1.2%|1.7%|Construction|37.1%|62.9%|100.0%|4.2%|
|Transport|1.1%|1.5%|2.6%|Transport|49.0%|51.1%|100.0%|6.9%|
|Commerce and Trade|1.3%|20.2%|21.5%|Commerce and Trade|8.3%|91.7%|100.0%|44.5%|
|Education/ Health|2.0%|0.6%|2.6%|Education/ Health|83.5%|16.5%|100.0%|5.5%|
|Administration|0.8%|0.1%|0.9%|Administration|97.1%|2.9%|100.0%|2.4%|
|Financial Services|1.7%|4.4%|6.1%|Financial Services|34.4%|65.6%|100.0%|14.2%|
|Other Services|0.3%|1.1%|1.4%|Other Services|22.7%|77.2%|100.0%|4.1%|
|Total|9.4%|90.6%|100.0%|Total|23.9%|76.2%|100.0%|100.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**Again as in other poor countries, workers tend to be underemployed rather than unemployed.** As Table 6 shows, overall unemployment rates are 7.4 and 1.4% in urban and rural Togo, respectively. At the same time 19% of rural and 25% of urban workers, are underemployed ‐ defined as those workers who declared to have tried to increase their incomes in the last 7 days.

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**Table 6: Unemployment and Underemployment by Region, Togo, 2006.**

||**Unemp**|**loyed**|**Undere**|**mployed**|
|---|---|---|---|---|
||**Urban**|**Rural**|**Urban**|**Rural**|
|**Lomé**|8.7%|‐|19.7%|‐|
|**Maritime**|5.9%|2.5%|28.7%|30.0%|
|**Plateaux**|6.1%|1.0%|12.2%|31.1%|
|**Centrale**|4.5%|1.2%|8.0%|10.7%|
|**Kara**|5.0%|1.4%|18.4%|10.6%|
|**Savanes**|4.9%|0.2%|19.3%|30.0%|
|**Total**|7.4%|1.4%|18.4%|25.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**With regard to the sectors of employment, the main difference between Lomé and other urban areas is in the role that agriculture plays for employment (** Figure 22 **).** Less than 1% of workers are employed in agriculture in Lomé, with the same number being 10% and higher in the other urban areas. Otherwise the distribution of urban workers among sectors is similar across regions. Trade and commerce usually employs most of urban workers, followed by services, agriculture, manufacturing and transport.

**Figure 22: Employment by Sector, Urban areas (LHS) and Rural areas (RHS) in Different Regions, Togo, 2006.**

<!-- Start of picture text --> 100% 100%<br>90% Mining 90% Other Services<br>80% Administration 80% administ<br>70%<br>Other Services 70% mines /<br>60%<br>50% Construction 60% construc<br>40% Education 50% transpor<br>30% Transport 40% educatio<br>20% Manufacturing 30% fabricat<br>10% Agriculture 20% services<br>0%<br>10%<br>Services commerce<br>0%<br>Commerce and Trade agricult<br>maritime plateaux centrale kara savanes Total<br><!-- End of picture text --> _Source_ : CWIQ 2006 and author’s calculations.

**Most of the rural workforce is employed in agriculture with the share varying from 56% in Maritime to 92% in Savanes (** Figure 22 **).** Other sectors with important shares in some regions include commerce, financial services and manufacturing.

# **5.4 Employment, Education and the Incidence of Poverty**

**The incidence of poverty is – not surprisingly ‐ higher for workers with a lower education level in both rural and urban areas (** Table 7 **).** Similarly, there are certain sectors that have higher incidences of poverty. In urban areas the ones standing out are Agriculture, and Agriculture and Transport in rural areas.

**Table 8 presents the distribution of poor by education levels and sectors and shows that among the urban (rural) sectors, Commerce (Agriculture) has the highest number of poor workers.** 38% of urban poor work in Commerce, of which more than half has not finished primary school. Commerce and Agriculture together concentrate 69% of the poor in the urban areas. Agriculture together with Commerce, concentrate 91% of the rural poor, with low‐ skilled agricultural workers employing the highest share of poor people in rural areas (54%). 41% of the poor in urban areas have no education and in rural areas 65%.

25
**Table 7: Incidence of Poverty by Sector and Education, in Rural and Urban areas, Togo, 2006.**

|||Poverty Inc|idence urb|an workers|||Poverty In|cidence ru|ral workers||
|---|---|---|---|---|---|---|---|---|---|---|
||none|primary|junior high|r high and a|**Total**|none|primary|junior high|r high and a|**Total**|
|agricult|68%|66%|59%|42%|**64%**|76%|69%|65%|60%|**73%**|
|mines /|100%|15%|8%|0%|**16%**|67%|55%|64%|38%|**61%**|
|fabricat|37%|17%|6%|0%|**16%**|65%|55%|62%|33%|**60%**|
|construc|33%|12%|13%|9%|**14%**|60%|41%|26%|41%|**45%**|
|transpor|31%|10%|7%|0%|**8%**|42%|44%|54%|75%|**46%**|
|commerce|28%|16%|8%|2%|**18%**|61%|43%|37%|30%|**54%**|
|educatio|41%|27%|20%|6%|**14%**|61%|62%|36%|27%|**37%**|
|administ|0%|36%|15%|6%|**14%**|100%|47%|51%|41%|**48%**|
|services|41%|22%|6%|4%|**17%**|67%|57%|43%|15%|**56%**|
|autres s|21%|6%|23%|5%|**11%**|55%|37%|32%|0%|**41%**|
|Total|37%|21%|13%|6%|**21%**|73%|63%|56%|39%|**68%**|

Note: Using poverty line at 164842 and FAO/WHO equivalence scales. _Source_ : CWIQ 2006 and author’s calculations.

**Table 8: Distribution of the Poor by Sector and Educational Level, Rural and Urban Areas, Togo, 2006.**

||||<br>**Urban**||||<br>**Rural**||
|---|---|---|---|---|---|---|---|---|
||**none**|**primary **|**junior high & above**|**Total**|**none**|**primary **|**junior high & above**|**Total**|
|**Agriculture**|15%|11%|6%|**32%**|54%|21%|5%|**80%**|
|**Mining**|0%|0%|0%|**0%**|0%|0%|5%|**1%**|
|**Manufacturing**|2%|3%|1%|**5%**|1%|1%|5%|**2%**|
|**Construction**|0%|1%|1%|**3%**|0%|0%|5%|**1%**|
|**Transport**|1%|2%|1%|**3%**|0%|0%|5%|**1%**|
|**Commerce**|20%|14%|4%|**37%**|8%|2%|5%|**11%**|
|**Education**|0%|1%|3%|**4%**|0%|0%|5%|**1%**|
|**Administration**|0%|1%|1%|**2%**|0%|0%|5%|**0%**|
|**Services**|3%|6%|2%|**11%**|1%|1%|5%|**2%**|
|**Other Services**|1%|1%|1%|**2%**|0%|0%|5%|**0%**|
|**Total**|**41%**|**40%**|**19%**|**100%**|**65%**|**27%**|**5%**|**100%**|

_Note_ : Using poverty line at 164842 and FAO/WHO equivalence scales. _Source_ : CWIQ 2006 and author’s calculations.

# **5.5 Conclusions from and Implications of the Profile of the Economic Actors**

- The importance of looking at the business environment both from the perspective of small firms as 91% of the population is self‐employed and large firms with the purpose of increasing scarce wage‐employment opportunities.

- Trade and commerce employs most of urban workers, followed by Services, Agriculture, Manufacturing and Transport. Agriculture employs most of rural workers followed by Commerce. Poor in the urban areas are concentrated in Commerce (about one third) and Agriculture (about one third), and in rural areas in Agriculture (about two thirds). Hence, constraints for the agriculture and commerce/trade sectors need to be given attention, at least as important sectors in the medium run. At the same time, the concentration of the poor in these sectors point to a questioning of these sectors as the main, long‐run vehicle out of poverty.

- 41% of the poor in urban areas have no education and in rural areas 65%. Still, to what extent education is the constraint to growth and a way out of poverty in Togo at this point needs to be further analyzed (see Section 5.4).

26
# **6 Analysis of Potential Constraints to Growth**

**Recent enterprise survey data**<sup>**28**</sup> **for Togo offers an important source of information about the main business constraints faced by firms in Lomé, as declared by interviewed firm representatives**<sup>29</sup> . However, as discussed in Section 2, perception data has a number of caveats and limitations so the reliability and interpretation of each of these will be discussed below, when combined with other types of data.

**Figure 23 suggests that Political Instability, Access to Finance, Corruption, and Informal practices of competitors, are the main constraints to businesses in Togo.** Other constraints often mentioned are Trade and customs, Electricity and Tax rates and administration. Interestingly, potential problems such as Crime/Theft/Disorder, Court, Labor regulations and Labor Skills are more or less never mentioned as major or severe constraint. Even Access to land, Business licensing and permits, and Transport are mentioned by a surprisingly low number of firms, but this result will be further discussed below. When breaking down the ICA result by size of firms (Figure 24) we see that micro and small firms see political instability as the main constraint, while medium firms mostly complain about access to finance and large firms about political instability.

**Figure 23: Main Constraints to Business (ICA), Togo, 2009. Percent of Firms perceiving a category as the main constraint.**

<!-- Start of picture text --> 30%<br>25%<br>20%<br>15%<br>10%<br>5%<br>0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

**Figure 24: Main Constraints to Business (ICA), by Size of Firms, Togo 2009. Percent of Firms perceiving a category as the main constraint.**

<!-- Start of picture text --> 40%35%30% Micro and Small Firms 40%35%30% Medium Firms 40%35%30% Large Firms<br>25% 25% 25%<br>20% 20% 20%<br>15% 15% 15%<br>10% 10% 10%<br>5% 5% 5%<br>0% 0% 0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

> 28 <u>www.enterprisesurveys.org</u> Enterprise survey data is available for over 100,000 firms in 117 countries. The surveys include indicators that benchmark the quality of the business environment across countries. They are commonly referred to as “Investment Climate Assessment” (ICA) indicators. Togo’s business enterprise survey was concentrated in the capital city, Lomé, and includes 300 enterprises, of which 145 are micro firms (0‐4 workers); 99 are small (5‐19 workers); 41 are medium (20‐99 workers); and 15 are large (over 100 workers). These firms are also broken down into 19 firms within the Free Zone (FZ) and 281 non‐FZ. Considering the survey structure, the 300 firms in sample represent over 1,700 firms in Togo.

29 Enterprise surveys always include this type of question, providing a list of 15 candidates from the topics mentioned above. The interviewed can provide up to 3 answers: The top, second and third most serious obstacle to business. Note that we in this study report the number of firms expressing a category as the most severe constraint, while the Investment Climate Assessment of Togo 2010 report both the firms that saw it as a main and a severe constraint.

27
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<!-- Start of picture text --> ZAGOGEOBGAAZEASL<br>s & & s .oe aw & s a , we<br>Oo S os se & ” & , s &<br><!-- End of picture text -->
**Figure 31: Rate of Return to Capital, Togo, 1960‐2008.**

<!-- Start of picture text --> ogo: ate o etu o Cap ta : 960 008<br>18%<br>16%<br>14%<br>12%<br>10%<br>8%<br>6%<br>4%<br>2%<br>0%<br>Capital Stock<br>Income Accruing to Capital / Estimated<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008<br><!-- End of picture text --> _Source_ : Authors’ calculations. WDI data, World Bank.

# **6.3 Geography and Regional Potential**

**Togo’s economic prospects are not constrained by its geographic characteristics.** That is, when we regard geography in a narrow sense, involving the country’s physical characteristics, access to international markets, resource endowments and its degree of exposure to natural hazard phenomena. However, our opening statement needs to be made with modesty when we extend the concept to consider the economic geography dimension. That is, the one that involves the country’s current access to dynamic, high income markets with high income, growth‐enhancing products. We develop these ideas below.

**Togo is a small, modestly population‐dense country resulting in a small domestic market, but many other characteristics compensate for this.** With an area of 56,785 square km it only ranks as the 132<sup>nd</sup> biggest country in the world. This feature, however, does not impose a physical limitation to conduct business, as the country has excellent access to neighboring Ghana, Burkina Faso and Benin; is a coastal economy with a relatively well developed deep sea port in  Lomé; is a mostly flat country with savannas in the north, not very sloped central hills, southern plateaus and low coastal plains, all of which extend through 6 distinct geographic regions; and in spite of being small, is not as densely populated as one may think. With 120 people per square km in 2007, population density is higher than the average for LICs (58.5 people/sq. km), ECOWAS (53.3) and SSA (32.3), but below the world simple country average. In this regard Togo ranks 80<sup>th</sup> among 225 economies. Togo is also relatively urbanized compared to other low‐income countries with almost 40 percent of its population living in cities (20 percent in Lomé). This is an advantage in terms of public investments that may have a very high pay‐off as they efficiently can reach a large number of the population.

**Figure 32: Arable Land, WAEMU countries, 2006.**

<!-- Start of picture text --> 50 1.2<br>45<br>40 1.0<br>35 0.8<br>30<br>25 0.6<br>20<br>15 0.4<br>10 0.2<br>5<br>0 0.0<br>Arable land (% of land area) Arable land (hectares per person)<br>% of lanf area<br>Hectares / person<br>Togo Nigeria Gambia, The Benin Ghana Burkina Faso Senegal Niger Cape Verde Cote d'Ivoire Guinea‐Bissau Sierra Leone Liberia Mali<br><!-- End of picture text --> _Source_ : WDI data, World Bank.

32
**Togo has also the highest fraction of arable land to country area within ECOWAS (** Figure 32 **).** Despite its population density, the amount of arable land per person is second highest in the region, only behind Niger. At the same time, Togo has relatively limited access to water resources, with the fraction of irrigated land (as a percentage of total croplands) being one of the lowest in the world. Water pollution is also a matter of concern in terms of health hazard and impact on the fishing sector, and deforestation linked to agricultural practices is also an issue to be accounted for.

**Based on Collier and O’Connell (2004)’s classification of countries, Togo is considered to be a “resource‐poor coastal economy” which is generally considered as positive.**<sup>33</sup> The authors’ cross‐country empirical work shows that, overall, countries in this category tend to grow faster than resource‐rich economies and than resource‐poor, landlocked economies. However, in the reduced sample of SSA countries, resource‐poor coastal economies still do better than resource‐poor landlocked economies but grow slower than resource‐rich countries, indicating that the impact of “syndromes”<sup>34</sup> and of the Dutch disease associated with availability of primary commodities is less strong in SSA countries. Yet, another classification by World Bank (2008) has Togo as an agricultural based economy<sup>35</sup> showing its continued reliance on primary commodities and traditional activities and the mostly rural character of its population. Note however, that many countries that are resource‐poor are in Collier and O’Connell’s classification also considered to be agricultural based economies. These concepts are not at odds as those countries with a sizeable base of minerals, energy and forest, and exports (not GDP) counts for the resource rich country classification.

**Togo is not immune to hazards of natural origin, but natural disasters recorded in the country have not been as frequent or as severe as in other Sub‐Saharan African countries.** EMDAT<sup>36</sup> , the most comprehensive database on disasters of natural and technological records a total of about 1,200 deaths and 770,000 affected in Togo between 1965 and 2005, most of them linked to epidemics, droughts and floods (Table 9). This is not an insignificant number, but as shown in the table the number of killed and affected taking population size into account is much smaller than the average for SSA.

**Table 9: Summary of Natural Disasters since 1990, Togo.**

||**Togo **|**SSA excl. Togo **|
|---|---|---|
|Population 2000|5,247,486|670,817,822|
|Killed 65-05|1,172|1,061,604|
|Affected 65-05|766,161|420,367,666|
|Killed/year|29|26,540|
|Affected/year|19,154|10,509,192|
|Killed/1000000people|6|40|
|Affected/1000 people|3,650|15,666|

_Source_ : EM‐DAT: The OFDA/CRED International Disaster Database.

> 33 A country is deemed to be resource‐rich if all the following are true: 1) Current rents from energy, minerals and forests exceed 5% of GNI; 2) A forward moving average of these rents exceeds 10% of GNI; and, 3) The share of primary commodities in exports exceeds 20% for at least a 5‐year period following this initial year.

> 34 “Syndromes” are linked, in general, to the absence of political stability and of market friendly policies.

> 35 World Bank’s 2008 World Development Report that focus on the subject of Agriculture for Development, classifies countries as “Agricultural based”, “Transforming” or “Urbanized” based on the contribution of agriculture GDP real growth to an economy’s total GDP growth, and on the fraction of rural poor to total poor population in the country. Thus way, countries where agriculture contributes with more than 33% of income growth and have a fraction of rural poor population to total poverty headcount higher than 85% are considered to be agricultural based. Togo’s figures are 42% in the former variable and 86% in the latter by 2007 and 2006 respectively.

> 36 Centre for Research on the Epidemiology of Disasters (CRED) at the University of Louvain, Belgium,

33
<!-- Start of picture text --> 4s<br>++<br>w+ *<br>ost? re<br>35 é ae ae<br>ee + ral<br>, ot”<br>* + + he<br>2S ¢_<*8 a<br>24 °+ ry Son,+a atY ++ aeee.°4 Re¢¢?: +<br>+ sees aS id<br>—eee+ * a2 *<br>15 +<br>+<br>5 6 7 8 a 10 14 12<br><!-- End of picture text -->
<!-- Start of picture text --> Niger =<br>Mali mm<br>Guinea-BissauLiberia ——E|<br>SierraLeone | ,<br>Nigeria } . :<br>Togo i, ' i<br>Coted'voire }<br>Ghana }<br>CapeVerde }<br>Burkina Faso |<br>Gambia, The | : z<br>0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4<br><!-- End of picture text -->
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# **6.5 Human Capital**

**Some of Togo’s human capital indicators are relatively weak but do not stand out compared to the rest of the region.** Figure 39 suggests that Togo fares relatively well with respect to health – infant mortality rate is the second lowest in the group and the share or birth attended by a skilled health worker is the second highest. With regard to education, Togo has a relatively high primary enrollment rate but the primary completion rate is relatively weak, which indicates high repetition rates and a high number of dropouts.

**Figure 39: Health and Mortality Indicators, Togo and benchmark countries, 2007.**

<!-- Start of picture text --> 180<br>160 Mortality rate, infant (per 1,000 live births) 10090 Total enrollment, primary (% net)Primary completion rate, total (% of relevant age group)<br>140 Births attended by skilled health staff (% of total) 80<br>120 70<br>60<br>100<br>50<br>80 40<br>60 30<br>40 20<br>10<br>20<br>0<br>0<br><!-- End of picture text --> _Source:_ DDP data, World Bank.

**Despite the prolonged economic stagnation, public expenditures per capita have been maintained at least a few years back.** Public health expenditures are more or less constant and even though public education expenditures as a share of government revenues and GDP may have a downward trend, the expenditure per capita is more or less constant (Figure 40).

**Figure 40: Public health and education expenditure, Togo 2002‐2006.**

<!-- Start of picture text --> 2 Health expenditure, public (% of GDP) (lhs) 12 5 25<br>Health expenditure, public (% of government expenditure) (rhs)<br>1.8 Per capita public health expenditures (const US$) (rhs) 10 4.5<br>1.6 4 20<br>1.4 8 3.5<br>1.2 3 15<br>1 6 2.5<br>0.8 2 10<br>4<br>0.6 1.5<br>0.4 1 Public spending on education, total (% of GDP) (lhs) 5<br>2<br>Public spending on education, total (% of government expenditure) (rhs)<br>0.2 0.5<br>Public spending on education, per capita (const US$) (rhs)<br>0 0 0 0<br>2002 2003 2004 2005 2006 2000 2001 2002 2003 2004 2005 2006 2007<br>Source: DDP data, World Bank.<br><!-- End of picture text -->

**Total health expenditure per capita is relatively low, and the share of private health expenditures to total expenditures is high compared to the benchmark countries (** Figure 41 **).** The high share of private expenditure indicates a response to a lack of public health care but may be an expected result of a conscious health policy. Around half of the population in urban areas seeking treatment chooses a private facility. In rural areas, the same number is one third. Access to health facilities seems not to be a major issue in most parts of the country ‐ the absence of a health facility as a reason for not seeking treatment in rural areas was only given in around 10% of the cases. The vast majority of those who considered treatment necessary but did not seek treatment rather did so because they considered treatment to be too expensive. Satisfaction with the treatment is higher in private clinics, especially in urban areas where 82% of those treated in a private facility are satisfied with the treatment versus 65% in public facilities.

38
**Figure 41: Public and Private Health Expenditure per Capita, 2006 (RHS), and Type of health specialist visited, satisfaction with treatment and reasons for not seeking treatment, Togo 2006 (LHS).**

|50|**Went to see health specialist**<br>|Total Urban|Total Rural|
|---|---|---|---|
|45<br>Public|**Type of institution**|||
|35<br>40<br>Private|**Yes**<br>public|6.3%|7.3%|
|30|_of which satisfied with treatment_|_65.4%_|_73.5%_|
|25|private|7.4%|3.7%|
|15<br>20|_of which satisfied with treatment_|_81.9%_|_77.1%_|
|10<br>|**No**|86.2%|88.9%|
|5|**Reason for not seeing health spe**|**cialist**||
|0|not necessary|88.0%|87.4%|
||too expensive|11.4%|11.7%|
||not available/ too far|0.2%|1.5%|
||other|0.7%|0.5%|

_Source:_ DDP data, World Bank, and author’s calculations using CWIQ 2006.

**The high repetition rate has created a vicious circle, leading to an additional drain on limited public funds.** The repetition rate is the highest of all the benchmark countries for primary and the third highest for secondary schooling, and is part of the reason for ‐ as well as an implication of ‐ the high pupil teacher ratios (Figure 42).

**Figure 42: Repetition Rate and Pupil‐Teacher Ratio for Primary and Secondary Schooling, 2007.**

<!-- Start of picture text --> 60<br>40 Pupil‐teacher ratio, secondary<br>35 Primary Secondary 50 Pupil‐teacher ratio, primary<br>30 40<br>25<br>20 30<br>15 20<br>10<br>5 10<br>0 0<br>Source: DDP data, World Bank.<br><!-- End of picture text -->

**Despite some problems, Table 10 below suggests that skills are not a current constraint to economic activity as both unemployment and underemployment are high for all levels of education.** However, there is an important difference across education levels. Unemployment increases with the education level, indicating that the better educated, and probably better off, prefer to be unemployed looking for employment than to work and be underemployed. Conversely, the lower educated and poorer segments of the population have to accept whatever employment they can find and, therefore, are more often under‐employed. So it seems that Togo still has potential to use more of the educated workers and to use the ones already employed in a more productive fashion.

**Table 10: Un‐and Underemployed by Education Level for Rural, Urban Areas and Lomé, Togo 2006.**

||**U**|**nemploye**|**d**|**Un**|**deremplo**|**yed**|
|---|---|---|---|---|---|---|
||**Urban**|**Rural**|**Lomé**|**Urban**|**Rural**|**Lomé**|
|**None**|3.9%|0.6%|5.4%|19.4%|24.5%|22.5%|
|**Primary**|7.4%|2.3%|7.8%|20.7%|25.2%|21.2%|
|**Junior high**|7.4%|3.9%|8.3%|15.7%|21.4%|17.6%|
|**Senior high & above**|11.4%|2.3%|12.2%|13.7%|18.6%|14.6%|
|**Total**|7.4%|1.4%|8.5%|17.8%|24.2%|19.0%|

_Source_ : CWIQ 2006 and author’s calculations.

**There is a strong correlation between low education levels and poverty, already discussed in the Profile of the Economic Actors section and confirmed in the regression results presented in Table 11.** Compared to households

39
in which the household head has not finished primary education, the risk of poverty decreases for households in which the head of household has finished at least primary school and junior high school, in rural as well as urban areas. However, the main reason for the substantial number of poor is probably again the absence of productive employment possibilities. Three main reasons speak for this: First, the probit regressions indicate that the risk of poverty in urban areas depends statistically significantly on the region, even after controlling for education. This indicates that workers with the same level of education face different possibilities for productive employment across the regions. Similarly, rural workers in sectors other than agriculture also face a lower risk of poverty than workers in agriculture. Second, the substantial share of workers who are self‐employed or work as unpaid family helpers points to the absence of more productive employment opportunities. Third, and as mentioned, Table 10 shows that workers of all levels of education are affected by under‐ and unemployment.

**Table 11: Marginal effects for probit regression on the determinants of poverty, urban and rural areas, Togo 2006**

||Urb|an|Ru|ral|
|---|---|---|---|---|
||Coef.|z|Coef.|z|
|primary|‐0.04|‐1.87|‐0.07|‐2.48|
|junior high|‐0.05|‐2.11|‐0.09|‐2.56|
|senior high and above|‐0.11|‐6.09|‐0.38|‐2.79|
|Agriculture|0.07|1.86|||
|Mining|0.02|0.29|‐0.10|‐0.89|
|Manufacturing and utilites|0.00|‐0.07|‐0.01|‐0.19|
|Construction|‐0.01|‐0.43|‐0.12|‐1.33|
|Transport/ communication|‐0.01|‐0.20|‐0.12|‐1.83|
|Commerce and hotelery|||‐0.18|‐3.48|
|Education/ health|0.00|‐0.08|‐0.17|‐2.33|
|Public Administration|0.03|0.71|‐0.21|‐2.17|
|Services/ banks/ insurances|0.02|0.52|‐0.17|‐1.32|
|Other services|0.00|0.04|‐0.11|‐0.86|
|HH size|0.04|7.65|0.10|15.33|
|Lomé|**‐0.53**|**‐7.52**|||
|Maritime|**‐0.08**|**‐3.55**|**‐0.16**|**‐2.66**|
|Plateaux|**‐0.10**|**‐5.75**|**‐0.37**|**‐6.54**|
|Centrale|**‐0.07**|**‐3.28**|‐0.09|‐1.62|
|Kara|‐0.04|‐1.12|‐0.06|‐1.22|
|Age|0.00|3.13|0.00|2.21|
|sick last 4 weeks|0.04|2.03|0.12|5.55|
|underemployed|0.02|1.14|‐0.05|‐2.00|
|Land per capita|||‐1.67|‐1.98|

_Source_ : CWIQ 2006 and author’s calculations.

**Mincerian wage regressions reveal the existence of some premium – as expected ‐ for additional years of education, but these are very low compared to other SSA countries**<sup>**39**</sup> **.** This regression of earnings on a proxy for human capital (the education of the labor force), controlling for a set of individual, community, household and regional, characteristics, shows a positive non‐linear relation between educational attainments and the dependent variable (Figure 43 and Table A4 in Appendix 1). For the average population, the mean marginal return to education stands at 5.2%, which is quite close from the findings of Kuepie et al (2008) who, by means of a similar specification estimated returns to education to be around 5% in Lomé. Figure 43 also shows that wages, given the level of primary education completion rate, are very low in Togo compared to other WAEMU countries further indicating a low demand from firms for more educated work force (See Appendix, Table A4 for summary econometric results from the Mincerian specification).

> 39 There are a number of methodological problems when comparing regressions on returns to education across countries, but Psacharopoulos (1994) can give us a sense that in most SSA countries the return for different levels of education is usually above 15%.

40
**Figure 43: Returns to Education in Togo (Mincerian Analysis) (LHS), and Wages vs Primary Education Rate in Togo and benchmark countries (RHS).**

<!-- Start of picture text --> 0.6 700<br>Education and Wages in WAEMU Countries BEN<br>0.5 600 COT<br>0.4 500 SEN<br>0.3 400<br>0.2 300 BUR MAL TOGO<br>0.1 200 NIG<br>0.0 100 GNB<br>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19<br>0<br>Years of Education 0 10 20 30 40 50 60 70<br>Primary Completion Rate (%)<br>E a rn in g s<br>% C h a n g e in<br>Avg.Monthly Salary (USD) .<br><!-- End of picture text --> Source: Authors’ calculations, LSMS data, and IMF (2009b).

**Finally, the ICA data reveals that none of the interviewed firms had any concerns regarding skills of the labor force.** In fact very low proportion of interviewed enterprises revealed much concern regarding the availability of skilled workers or labor regulations in Togo. In addition to this it is worth mentioning that over the long run, as the economy of Togo deteriorated, human capital has improved – suggesting a low correlation between human capital and growth for Togo at this point in time. In light of this, we cannot conclude that education is currently binding constraint to growth in Togo. That, of course, does not exclude a need for specific skills or a need for reforms given expected future demand as the economy develops.

# **6.6 Macroeconomic Stability**

**Togo has been affected by the financial crisis, but its impact has been moderate.** Before analyzing potential macroeconomic constraints it needs to be clear what in the current situation is due to the global crises and what is due to actual constraints in the Togolese economy (see Box A 1 in Appendix 1 for more details on the effect of the crises).<sup>40</sup> Growth has been estimated at 3.1% in 2009, 0.9 percentage points lower than the previous growth projection for 2009. The dependence on commodity exports and official and private capital inflows has to some extent constrained growth during the crisis (IMF, 2009a). Remittances, a significant source of income for Togo (almost 10% of GDP), are expected to be further affected by lower global demand as well as unemployment and lower wages in Europe and the US. Another possible negative impact is a lower than expected level of FDIs. On the other hand, Togo’s limited integration with international finance and cross‐country linkages within the banking system may soften that negative impact of the crisis. Also, donor support is not expected to drop significantly. Finally, even though the potential growth rate will be affected, this may not translate into lower overall growth than pre‐crises as public investments and a rebound in agricultural production is expected. Hence, the impact of the financial crises on Togo is expected to be more of a delay in rapid growth and poverty reduction ‐ but this can be serious enough given the current low levels of economic activity (IMF, 2009b).<sup>41</sup>

**As mentioned, Togo has made significant progress in terms of macroeconomic stability.** Togo is in line with the PRGF targets and the membership in WAEMU, as well as the regional central bank (BCEAO) that controls its monetary policy creates stability in terms of both monetary and fiscal policies. Table 12 confirms what was already discussed in the section on cost of capital ‐ that Togo has a current account deficit that needs to be covered from

> 40 The trade balance and the current account is according to IMF expected to improve in 2010 as global demand recover in combination with increased productivity, led by sectors such as phosphate and cement (IMF, 2009a).

> 41 The current financial crisis is expected to hurt low‐income countries as a second wave after the more advanced economies have been hit affecting the demand on the global market.  However, to what extent different low‐income countries will be affected by this lower demand and lower commodity prices depends on factors as the level of international integration (goods, services, migration, etc), the import or export dependency of affected commodities (such as oil, food, and minerals), tax structures, etc. Also, the short term effect may be very different to the medium term effects as country‐specific factors may aggravate, mitigate or offset the direct effects.

41
foreign sources. Even though FDI and remittances have been on the rise, there is still a gap that is now predicted to be filled by an increasing amount of foreign aid.

**Table 12: Balance of Payment Items, Togo, 2003‐2007.**

|**Column1**|**2003**|**2004**|**2005**|**2006**|**2007**|**2008**|
|---|---|---|---|---|---|---|
|Domestic PrimaryBalance(% of GDP)|4.5|3.2|‐0.5|‐1.1|0.2|0.4|
|Trade Balance(% of GDP)|‐14|‐15|‐16.9|‐19.6|‐10||
|Current Account Balance(% of GDP)|‐4.2|‐3|‐5.3|‐6|‐3.9||
|Gross int. reserves (months of imp)|2.3|3.6|2|3.2|3||

_Source:_ International Financial Statistics, IMF.

**Fiscal policy has meet and even gone beyond its targets, although this is partly a consequence of limited execution on the expenditure side.** The domestic primary balance was +0.4% of GDP in 2008, and tax and custom revenue collection were higher than expected. Current expenditures represented 87% of total expenditures while investment spending represented 13%. Capital spending was under executed (among the lowest in SSA as reflected in the low public investments in Figure 44), mainly due to capacity constraints and complex procurement procedures. This may turn out to be an important indirect binding constraint as the public investments expenditures are supposed to finance crucial elements of future growth in Togo (see further Section 5.6 below).

**Figure 44: Public Capital Investments, WAEMU, latest available data (% of GDP)**

<!-- Start of picture text --> 14.0<br>12.0<br>10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>-<br>Niger Burkina Mali Benin Senegal Cote WEMU Togo<br>Faso d'Ivoire<br>Total Domestically financed Foreign financed<br><!-- End of picture text --> _Source_ : IMF (2009a).

**Togo qualified for the HIPC initiative and will upon completion point qualify for the Multilateral Debt Relief Initiative (MDRI)** . Togo’s NPV of debt‐to‐revenue ratio in end‐2007 was 309%, and to reach 250% requires 270 million USD in HIPC relief. The MDRI is then expected to reduce the debt with another 404 million USD. The development of an interim PRSP and now a PRSP, as well as the progress on triggers related to macroeconomic stability, public financial management, governance, debt management and social sectors – monitored under the IMF’s PRGF program and IDA’s Development Policy Operations – have made it possible for Togo to make significant progress towards the completion point, which the government is hoping to reach in early 2010. Togo stands out in terms of the share of arrears in the external debt, but many of these arrears have been cleared during 2008 and the remaining is under process of being cleared or rescheduled (World Bank, 2008).

**Both trade partners and sources of remittances are relatively diversified, lowering Togo’s macroeconomic vulnerability.** As previously reported, remittances have started to make a significant contribution to Togo’s economy. Of the remittances, 30% come from Europe (mainly France), 20% from the US and 15% from within the WAEMU. The diversity of countries is also reflected in the number of trading partners (Figure 45).

42
<!-- Start of picture text --> France, 15.8<br>, - 105<br>_ Belgium, 4.9— = Gams 70<br><!-- End of picture text --> <!-- Start of picture text --> eees ee<br>ee<br>e e |<br>TSOU A\VeNN\ALAAA|<br>le<br><!-- End of picture text -->
<!-- Start of picture text --> 120<br>100A\<br>140 - f-\<br>VAN I<br>a ee<br>a<br>1580 135 1) 1565 70 27005<br><!-- End of picture text -->
**Box 3: Governance Indicators: Togo and Benchmark Countries**

<!-- Start of picture text --> 0.75 Voice & Accountability 0.35 Government Effectiveness<br>Ghana<br>0.25 Benin 0.00<br>Senegal<br>‐0.35<br>‐0.25 MIC<br>Burkina Faso Nigeria ‐0.70<br>‐0.75 SSA SSA<br>‐1.05 Nigeria<br>‐1.25 Togo<br>‐1.40 Togo<br>‐1.75 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br>Political Stability Regulatory Quality<br>0.35<br>0.75<br>Benin<br>Ghana<br>0.00<br>0.25<br>Ghana Senegal MIC<br>Burkina Faso<br>‐0.35<br>‐0.25 MIC Burkina Faso<br>Senegal Benin<br>‐0.75 ‐0.70 SSA<br>SSA<br>‐1.25 Togo ‐1.05 Togo<br>Nigeria<br>‐1.75 ‐1.40<br>‐2.25 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br>Rule of Law<br>0.35 Control of Corruption<br>0.35<br>Burkina Faso<br>0.00 0.00 Ghana<br>Senegal Senegal<br>MIC<br>‐0.35 Benin ‐0.35<br>Benin<br>‐0.70 SSA ‐0.70<br>‐1.05 Togo ‐1.05 Togo<br>‐1.40 ‐1.40 Nigeria<br>Nigeria<br>‐1.75 ‐1.75<br>2000 2002 2003 2004 2005 2006 2007 2008 2000 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source_ : Governance Database, World Bank.

**The ownership structure and management of state owned enterprises (SOEs) have also been a reflection of the poor quality of governance.** The phosphate sector is operating at less than a quarter of its capacity and IMF (2009a) argues that GDP would increase by as much as 2% if full capacity is reached. The phosphate society OTP/IFG has been replaced by the Societe Nouvelle des Phosphates du Togo (SNPT), but the authorities have so far resisted proposals to identify a new strategic partner which will be needed to bring in the necessary capital and technical expertise. The main cotton company (SOTOCO) was highly criticized and has now been replaced by a new company in which the producers hold 40% of the capital. However, here too the government has delayed plans to

45
<!-- Start of picture text --> Index of Economic Freedom, 1999-2009<br>80<br>o| oF<br>65<br>55 [SS eSSa<br>$= Ban ee SSA<br>45 X——*ogo<br>Ks —e ——K<br>40<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009<br>—— BurkinaFaso —— Ghana —>— Nigeria ——~— Senegal<br>—*— Togo —*— Benin —e— MIC —*— SSA<br><!-- End of picture text -->
<!-- Start of picture text --> High income<br>Middle income<br>Low income<br>Senegal<br>Gambia, The<br>Burkina Faso<br>Cape Verde<br>Sierra Leone<br>Ghana<br>Nigeria<br>Céte d'ivoire<br>Benin<br>Mali<br>Niger<br>Togo<br>Guinea-Bissau<br>0 20 40 60 80 100<br><!-- End of picture text -->
<!-- Start of picture text --> High income z<br>Middle income —<br>Low income TS|<br>Senegal<br>BurkinaGambia, FasoThe<br>SierraCape‘ VerdeLeone aTS|<br>Ghana SS<br>Coteeed'ivoire<br>Benin<br>Mali<br>Niger<br>eee<br>Guinea-Bissau<br>0 20 40 60 80 100<br><!-- End of picture text -->

<!-- Start of picture text --> Gambia, the<br>Senegal<br>Niger<br>Benin<br>Céte d'ivoire<br>Togo<br>Liberia<br>Burkina Faso<br>Guinea-Bissau7 e<br>Cape Verde<br>Sub-Saharan Africa<br>Sierra Leone<br>Nigeria<br>0 2 4 6 8<br><!-- End of picture text -->
<!-- Start of picture text --> 2<br>® Documents for import (number)<br>10 - — ® Documentsfor export (number)<br>Pa<br>re<br>re |<br>04 ' ’ , .<br>ea al A * sse &e ra ¥eS ev we Peee ra<br>ff & ro & Ff<br><!-- End of picture text -->

<!-- Start of picture text --> 4500 5 1m Cost to export (USS per container)<br>4000 | = Cost to import (USS per container)<br>3500 |<br>3000 |<br>2500 |<br>2000 |<br>1500 |<br>1000 |<br>o + : 7 + - + + 7 7<br>Poxe SF° &* Fol roo se2 &¢ £ ca Fs ‘ ¥e > a we<br>& FS Se &<br><!-- End of picture text -->
<!-- Start of picture text --> 40<br>35 +<br>30 +<br>- +e +o ¢<br>25 ooo @ * 6<br>20 rs + ° ¢ #, o &e<br>15 @ 4% S 4? Sd q& S Woeie> Xe¢<br>ct, *%Sbs oeoe oe* es pa o° “"#<br>: °<br>5<br>0<br>6 7 8 9 10 14 12<br><!-- End of picture text -->
<!-- Start of picture text --> 30,00<br>25.00 fh.<br>20.00 A<br>ri<br>‘1e-00:10.00 ie —+—@— PartPart Exp. Tot inp.Tot<br>=<br>§.00 —_—= x a e __-* f<br>0.00 age ee<br><!-- End of picture text -->
**Table 15: Factor Intensity** **<u>per Sector (%).</u>**

|**Sector**|**Formal**<br>**workers**|**Informal**<br>**workers**|**Private**<br>**capital**|**Public**<br>**capital**|
|---|---|---|---|---|
|Agriculture: Food|0,07|24,48|75,45||
|Agriculture: Annual Crops||30,90|69,10||
|Industry: Food, non‐FZ|6,80|67,70|25,49||
|**Industry: Food, FZ**|**53,15**|**32,43**|**14,41**||
|Industry: Chemicals, non‐FZ|2,98|43,01|54,01||
|**Industry: Chemicals, FZ**|**11,99**|**7,59**|**80,41**||
|Other Industries, non‐FZ|16,11|71,69|12,19||
|**Other Industries, FZ**|**68,70**|**,18**|**31,12**||
|Services, merchandize|15,02|81,49|3,49||
|Services, non‐merchandize|70,32|||29,68|
|**_Total_**|_17,92_|_48,94_|_28,93_|_4,31_|

_Source_ : Agbodji (2009), data from MCS, Togo.

**The ICA data seems to suggest that the main reason for firms to join the Free Zone is the fiscal and administrative advantage.** ICA data in Figure 55 shows that firms in the Free Zone tend to first of all have concerns in fewer areas than non‐FZ firms. FZ and non‐FZ firms are more or less equally concerned about issues of access to finance and political instability, but non‐FZ firms are concerned about for example corruption, tax administration, custom and trade regulation, and business licensing while these are hardly mentioned as the main constraint by FZ firms. Given the higher growth of firms within the zone, this suggests that easier administrative procedures for running a business are binding constraints for many firms in Togo. Note however that the Togo ICA (2010) looks at the number of firms identifying a category as a main as well as a severe constraint and then compares the result between FZ and non‐FZ firms and conclude that complaints about business related constraints are not significantly different between the two groups. Given a number of differences when including both the main and severe constraint, the result should be taken with caution. Moreover, in the survey it was never specified if the FZ are responding having their particular investment climate in mind, or the general investment climate in Togo.

**Figure 55: Main Constraints to Business, FZ firms vs non‐FZ firms, Togo, 2009.**

<!-- Start of picture text --> 35%<br>FZs<br>30%<br>25% Non FZs<br>20%<br>15%<br>10%<br>5%<br>0%<br><!-- End of picture text --> _Source_ : ICA data, World Bank.

# **6.9 Market Failures**

**Undertaking profitable economic activities requires the availability of inputs specifically needed for such activities, but these inputs may not be available in a market despite a strong demand (current or potential).** These are tradable inputs, such as raw materials, equipment and other capital goods; and non‐tradable inputs, such as skilled workers, able entrepreneurs, infrastructure services and other services. In most of the cases, the provision of tradable inputs implies less of a problem for firms, as long as there is a good enough infrastructure (roads, ports, etc.) to make available these goods from far away when they are not locally produced. On the other hand, the provision of non‐tradable services represents more of a problem as it may take long time for a market for these services to clear ‐ if at all ‐ in response to a shortage of an input. In particular, markets may not even send signals for scarcity of certain production factors if there is no perceived demand for them, as the activities that

52
would actually require them in their production process never get to come to life, precisely due to the lack of these factors. It is the typical chicken and egg vicious circle, as explained by Hausmann et al. (2008).

**Two categories of market failures are identified within this type of problems: Coordination failures and self‐ discovery.** Coordination problems arise when inputs and activities that are necessary to undertake other economic activities are underprovided or not provided at all, precisely because there is no demand from those yet‐to‐exist activities, or they have the character of public goods and there are hence limited incentives for an individual firm to start those activities. Self discovery problems arise from lack of _information_ about what goods or services, not yet locally provided, can be profitably produced; this is so because the process of “discovering” new goods entails initial costs for entrepreneurs, that may not be able to be recovered, because of high uncertainty on demand, or because the possibility that new entrants in the market (after the product has been found to be profitable) will, by means of competition, wipe away potential excess benefits for the entrepreneur (Hausmann and Rodrik, 2004). Thus way, new profitable products will be under‐provided ex‐ante, and over‐provided (in a sense that free entry will eliminate excess profits) ex‐post. Both, coordination failures and self discovery problems are said to justify some sort of public policy.

# **_6.9.1 Self­Discovery_**

**The task of identifying potential constraints to growth from lack of self‐discovery is a most difficult one, especially as it involves assessing activities that are** **_not_ in the country’s current production sphere.** However, as Hausmann et al. (2008) explain, one can think of a series of tests applicable to actual production data which refer to the degree of sophistication of existing activities; their relative position compared to products closer to the “production frontier”; and the number of new, successful activities. These tests permit to check whether, ex‐post, things are taking place in the country’s innovation arena. If innovation is indeed happening as a widespread phenomenon in the economy, then market failures associated with lack of self‐discovery may not be a major problem. This is, low ex‐ante entrepreneurship (before discovery time) and excess ex‐post entry into the new market (hypothesized under laissez faire conditions) are not a binding constraint in the country. A practical problem that arises in attempting to make these tests is the lack of highly disaggregated production data necessary to pinpoint activities by degree of sophistication and discoveries. That is why it has been suggested (Klinger and Lederman, 2006) the use of export data instead. Having in mind these caveats<sup>49</sup> , a battery of export indicators for diversification, sophistication, proximity to technological frontier and discovery, as defined in works by Hausmann et al. (2005) and Hausmann and Klinger (2006), and Klinger and Lederman (2006), can be used to assess whether new higher quality activities are taking place within a country.

**We have in Section 4 already defined proxies for the degree of sophistication of products (PRODY) and the export basket (EXPY) and concluded that Togo exports low‐income products but is improving.** An additional indicator refers to the “density” of exported products and their position in the “open forest”. Products that are “closer”<sup>50</sup> to other higher income value products (as defined by PRODY) are said to be in a “denser” part of the product space, implying that the ability of the country to “jump” (as one measure of self‐discovery) to these higher income value products is actually higher.

> 49 COMTRADE provides a good source of information, where goods can be disaggregated in thousands of subcategories, depending on the used nomenclature<sup>49</sup> . An additional advantage of using export data, as opposed to production data is that exporting can be seen as a sort of demonstration that a firm was able to pass a “quality” test in the international market. Being purchased in the world market, a locally produced and exported good can thus be regarded as one produced under satisfactory minimum standards, with entrepreneurs being able to break into a more competitive stage. On the flipside, using exports data omits considering the possibility that the goods were actually successfully produced within the country with good quality standards for already a long time and that the inability to bring them to the international market may be linked to some form of obstacle or intervention.  It also restricts the analysis to tradable goods and is silent in the –equally important‐ service area, including the touristic, and information technology sectors.

> 50 “Closeness” between product groups A and B refers to the probability that a country exporting product group “A” will also export product group “B”. In turn, this probability is derived from the export record, by product group, from the rest of the world. It is the minimum of the pairs of conditional probabilities going in both directions as an inverse measure of distance: min{P(A|B),P(B|A)}. See Hausmann and Klinger (2006)

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**The evidence for Togo shows that products who did not have “Relative Comparative Advantages” (RCA) in the early 1980s but were located in the “denser” part of the product space (were close to many other products with higher income value) did “jump” to become products with RCA in the early 2000s.** This in contrast to products that were more isolated in the product space (see Figure 56).

**Figure 56: Histogram of Density for Jumps to New Products (i.e. products with RCA in 2000/04 but not in 1980/84) and No Jumps Products (i.e. no RCA in either period), Togo.**

<!-- Start of picture text --> 90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>-6 -5.5 -5 -4.5 -4 -3.5 -3 -2.5 -2 -1.5<br>ln(density)<br>No RCA in 80-84 and RCA in 00-04 No RCA in 80-84 nor in 00-04<br>Relative Frequency<br><!-- End of picture text --> _Source_ : Authors calculations, based on WITS, COMTRADE data.

**Moreover, between 1980‐1984 and 2000‐2004, Togo’s export basket significantly approached or became surrounded by groups of products of implicit high income value, positioning the country in a much better position to jump to a basket of products with even higher level of sophistication.** In other words, using Hausmann et al (2005) terminology, the “option value” of the countries unexploited opportunities has increased as the so‐called “open forest” for new products has become denser, more attainable. This circumstance provides a key element in the analysis of the role of the export diversification path for growth in Togo. As the empirical records show, the increase of export sophistication (measured by positive changes in EXPY) is affected by the opportunities provided by the current productive structure, and changes in EXPY are in turn positively associated with growth.

**Togo has discovered quite a significant number of new exportable goods already.** We define discovery in a similar (but not identical) way as proposed by Klinger and Lederman (2006)<sup>51</sup> . We use three moving windows to identify whether or not a country was exported by period. If a product was exported in two successive periods (windows), we say the product is an “established” one. If it was not exported in an initial period and was exported in the next period, we call it a “discovery”. If a discovered product in the second window was also exported in the third window, the country is added to the “established” category. If a product was discovered or established in one period but not exported in the next period we call it a “disappearance”.<sup>52</sup> Togo has been able to discover a total of 134 new product groups between 1980 and 2006. It departed from just 43 “established” product groups 1983‐ 1985 up to 173 in 2004‐2006.<sup>53</sup>

> 51 Innovation as defined by Klinger and Lederman (2006) inside the technological frontier, this is, the one that refers to the successful production within a country of goods or services already discovered and produced elsewhere, as opposed to innovation “at the frontier” which involves the generation of new products and services based on R&D.

> 52 We loosely define the time windows to be equal to 3 years each. We consider a product to be “exported” during a period if the two following criteria are met: The product is exported in most of the window (at least 2 out of 3 years in the window) and the average value of exports during the period exceeds 10,000$ per year. This definition more or less resembles that of Klinger and Lederman (2006). In their work, an initial period is used “to confirm that the good was never before exported (1994–96); a window during which time the discovery can emerge (1997–2002); and then a final period when the discovery is confirmed to be an established export (exported for at least $10,000 in both 2002 and 2003)”

> 53 This compares with a maximum number of 266 categories in the SITC at the 3 digit level.

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**Figure 57: Discoveries and disappearing products in Togo, 1985‐2006**

<!-- Start of picture text --> 25<br>20<br>15<br>10<br>5<br>0<br>‐5<br>‐10<br>‐15<br>‐20<br>Total discoveries Total disappearances Total net discoveries<br><!-- End of picture text --> _Note_ : Based on SITC 3 digit data _Source_ : Authors calculations based on COMTRADE data.

**An alternative definition of “emerging products”, confirming Togo’s innovation activities, can be derived from the idea of RCA, already referred to.** About 20% of Togo’s exports comprising 31 product groups from SITC at 3‐ digit level have been identified only in 2000‐2006 (see Table A 2 in Appendix I). Exports of this group have increased 14.5% between the periods 1992‐96 to 2002‐06. The implied income value of this export basket was 30% above the EXPY for the traditional or “classic” products group.

**Despite these positive developments, we cannot go as far as to say that Togo is steadily moving to close the gap with the technology frontier.** The contribution of discoveries to exports remain relatively low and Togo’s growth prospects are highly dependent on its traditional products as judged by the strong correlation observed between the growth rate of real GDP and the evolution of its Terms of Trade explained above. And, as Figure 12 above shows, the share of Togo on world exports is on a declining trend. On the other hand, we cannot argue that self‐ discovery is the current binding constraint to growth in Togo.

# **_6.9.2 Coordination Failures_**

**Turning to the second type of market failures, one would easily suspect coordination activities in Togo to be a constraint as the very existence of successful special economic zones indicates what could happen if those types of constraints are removed.** The Free Zone in Togo has, as argued, shown to be successful but the benefits of the zone is not directly related to overcoming coordination failures but rather a package of lower cost and less cumbersome business regulations (see Box 4 above). The zone is not a geographical area with surrounding infrastructure (even though most of the firms are located in Lomé), do not have special middlemen or marketing services, no government intervention in terms of input markets, no research or product development program, no advisory services, no special credit treatment, etc.

**Moreover, Togo is a relatively densely populated, small country where internal coordination is expected to be less of a constraint compared to other countries ‐ but external coordination is more important.** This has however been acknowledged by the government that has acted, not least through participation in ECOWAS and WAEMU for regional economic and trade coordination (see Section 5.2). And as mentioned, the West African economic integration efforts are viewed as very successful.

**However, coordination failures are clearly a constraint in the agricultural sector.** The sector is dominated by small holders, technically unsophisticated production focused on risk minimization and self‐consumption. Increased agricultural growth requires a number of support structures, such as road infrastructure, market information mechanisms, research, advisory support, etc. Previous government attempts to provide the farmers with this support structure did not succeed, but this is a symptom of government failures rather than a lack of impact from well implemented market coordination effort. For example, the cotton production increased steadily up until 2003 when it dropped sharply, partly due to a drop in the international price of cotton but also due to mismanagement of the national cotton company SOTOCO. Without an alternative coordination function, cotton production has still not recovered and the problems have spread to other crops that are co‐cultivated with cotton. Another example is the coffee/cacao sector that in terms of downstream impact (marketing, packaging, export, etc) profited from the

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liberalization in 1996 and saw increased returns for planters. However, production has stagnated due to the inefficient institutions established to provide technical extension services, cutting of 60 percent of the planters from crucial advisory services. Alternative institutions to support the agricultural sector is yet to be developed and those are expected to be a mix of public and private initiatives (see further a more detailed discussion in the agricultural section of the Togo Country Economic Memoranda, 2010).

# **7 Conclusions**

As is the case in low‐income countries, many issues appear to be constraining economic activity in Togo. However, a first step is to exclude those potential constraints that do not show signs of being binding at this moment. Among those are 1) _human capital_ (however, it may need to be addressed today for future needs), 2) _geography_ , 3) _macroeconomic stability_ , and 4) _self‐discovery_ . It is noteworthy that these conclusions are based on the given level of stocks and flows at the time of the analysis, and any subsequent decrease in these levels may change their status as binding constraints. A second step is to determine if the constraint appears to be on the cost or return side of investments and entrepreneurship, which in the case of Togo, evidence indicates problems related to returns of investments despite a continued need to improve access to capital and financial intermediation.

_Infrastructure_ measures give mixed signals in terms of scarcity at his moment in time. For instance, few firms complain about overall infrastructure and the supply in relation to countries at similar development levels is not exceptional. However, much of the Togo infrastructure was destroyed in recent flooding to the detriment of the greater economy.. The mixed signals are also indicative of the fact that some types of infrastructure present greater and different challenges than others. Moreover, infrastructure as a constraint must be understood in the context of Togo’s growth strategy as a regional trade hub in addition to its goal of broad‐based growth and the need to connect firms outside Lomé. Taken together, Togo must ensure that the increased focus on infrastructure remains as donors re‐engage and support public investments.

_Access to credit_ for the private sector is yet another potential constraint that gives varied signals. Credit emerges as a severe and high‐ranked constraint to business according perception surveys at an enterprise level; however, this is the case in most countries surveyed. To this end, it is unclear if the low amount of credit is a result of lack of demand (i.e. given the level of economic opportunities today, few firms are prepared to take a loan and make risky investments), or a lack of supply mainly in terms of financial intermediation now that international finance has returned to Togo. In this sense, the possibility of financial intermediation as a potential binding constraint is – in a similar fashion to infrastructure – already being addressed through several measures. At this point, it is crucial to, at the very least, implement the necessary banking sector reforms and continue to pay close attention to access to credit as a constraint. Improvements will have to be continuously realized as economic opportunities and demand for credit arises.

A prominent area in terms of constraint to growth in Togo is _governance issues_ , in particular political instability, corruption, and governance efficiency. In nearly every measure of governance, Togo’s performance ranks last when compared to neighboring countries with very little documented progress to date. Political stability has improved since the latter part of the 1990s and more prudent macroeconomic reforms and policies have been implemented. However, if and how these improvements translate into increased trust among investors is debatable according to perception data from domestic investors and the limited FDIs recorded. Not least, the low governance efficiency in Togo has been traced to mismanaged SOEs and lack of public investment execution. As with several of the measures, this may be more connected to previous governments and whether more sustainable and thorough reforms happen in the future remain to be seen.

Governance issues are also related to investor trust in business‐government relations, which according to the analysis is an obvious binding constraint Togo needs to address. Foremost, _barriers and costs of doing business_ are high, especially when starting a business, and compare poorly to countries at the same development level. Even though the over‐arching issue of doing business is critical to tackle, priorities are important. For instance, tax 56
administration is an area that needs continued attention while labor market regulations seem to be less of a problem. Trading across borders is a relative strength compared to other countries and other constraints, however if Togo is able to leverage this strength, with additional improvements, into a role as a regional hub remains to be seen. Moreover, security of property rights does not seem to be binding today but building trust in this area will be crucial for future growth. As an overall indicator of business costs being a constraint in Togo, “informal practices of competitors” – i.e. those not following the formal government rules for businesses ‐ is mentioned as one of the top constraints by firms. Hence, decreasing doing business costs as well as taking measures to ensure that key reforms are trusted by investors emerge as priority when address binding constraints to growth in Togo.

Special attention needs to be given to the _agricultural sector_ because it has a substantially higher output and productivity potential going forward, and is still the most important sector in terms of GDP and employment. The previous institutional structure no longer exists due to several government failures, leaving the sector with coordination issues and lack of infrastructure. The replacement structure is developing slowly at best, but does grant more influence to farmers and the private sector in the organization than in the provision of services, which hopefully will avoid continued government failures in the sector.

Looking ahead, for increased international competitiveness and rapid growth, Togo will need to ramp up efforts to streamline its _costly and cumbersome business procedures_ . However, for this to have a sizable impact, Togo must prove to potential investors that _political stability_ is permanent and that corruption, poor budget execution and mismanagement of SOEs belongs to the past. As a result of the new government’s reform agenda and the return of international aid, a window of opportunities for high returns to the still limited public investments has opened up. This is especially true in _infrastructure and connectivity services_ , which would not only take advantage of Togo’s geographical location as a regional hub but also make growth in Togo more inclusive. Also, as the economic opportunities arise for the private sector, there is a need to _restructure the banking sector_ , which has already started, to smooth distortions in the credit market. Promising sectors within agriculture that are vital to economic growth, employment opportunities and poverty reduction remain important, but will need to overcome a number of _coordination failures_ . Not least due to the history of government interventions causing economic distortions, the government must allow for a stronger role for private operators and encourage it wherever possible. Finally, even though _education_ does not exhibit constraints to economic activity in Togo today it is of importance to improve the quality of education, not least to profit from and catalyze the opportunities related to Togo’s potential as a regional hub.

Togo appears to be slowly but surely diversifying away from traditional business, and in the process discovering new products that have been successfully placed in the international market. These new products present a path to growth for Togo, so the government should pay special attention to issues confronting these products as a guide to reform. Case in point is the success shown in the Free Zone and the positive incentives it created should be encouraged and potentially be applied to firms outside the zone. However, there is a need to look at the cost side of the success of the Free Zone, and to understand the puzzle of why mostly capital‐intensive industries seem to be able to profit from the advantages within the zone, despite Togo’s relatively low wages.

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# **8 References**

Assiongbon, E. and J.P. Chausse (2008). _Togo: Croissance Agricole et Reduction de la Pauvrete_ . Draft, November 8, 2008.

Azariadis, C. (1996). _The Economics of Poverty Traps – Part One: Complete Markets_ . Journal of Economic Growth, 1: 449‐486.

- CWIQ (2006). _Core Welfare Indicators Questionnaire (CWIQ)/ Questionnaire des Indicateurs de Base du Bien‐être (QUIBB)_ , Direction Générale de la Statistique et de la Comptabilité Nationale, Republique Togolaise 2006.

- Gogue, T. A. and K. Evlo (2004). _Explaining African Economic Growth Performance: Togo Case Study_ , Mimeo, Universite de Lome. Lome, Togo.

- Gonzales and Rosenberg (2006). _The State of Microfinance – Outreach, Profitability, and Poverty: Findings from a database of 2600 microfinance institutions_ , CGAP. Memo.

- Hausmann R. and B. Klinger (2006). "Structural Transformation and Patterns of Comparative Advantage in the Product Space" _Working Paper No. 128_ , Kennedy School of Government, Harvard University, Cambridge, MA.

- Hausmann, R. and B. Klinger (2007). “The Structure of the Product Space and the Evolution of Comparative Advantage”, _CID Working Paper No 146_ , Kennedy School of Government, Harvard University, Cambridge, MA.

- Hausmann, R., B. Klinger and R. Wagner (2008). “Doing Growth Diagnostics in Practice: A ‘Mindbook’ “, _CID Working Paper No 177_ , Kennedy School of Government, Harvard University, Cambridge, MA.

- Hausmann, R., D. Rodrik and A. Velasco (2005). _Growth Diagnostics_ . Kennedy School of Government, Harvard University, Cambridge, MA. March 2005.

- IMF (2009a). _Togo: Staff Report for the 2009 Article IV Consultation and Second Review of the Poverty Reduction and Growth Facility Arrangement_ . International Monetary Fund: Washington, DC.

- IMF (2009b). _Togo ‐ Selected Issues_ . International Monetary Fund: Washington, DC.

- Kuepie, Nordman and Roubaud (2008). Education and Earnings in Urban West Africa. _Journal of Comparative Economics_ , September 2008.

- PRSP (2009). DSRP _Complet 2009‐2011, Document complet de Strategie de Reduction de la Pauvrete_ , Version Preliminaire, Mars 2009. Republique Togolaise.

- Psacharopoulos (1994). Returns to investment in education: A global update. _World Development,_ Volume 22, Issue 9, p. 1325‐ 1343.

- Rodrik, D. (2004a) “Why we learn nothing from regressing economic growth on policies”, Narch 2005, Cambridge MA: Harvard University, pp. 1‐14.

Rodrik, D. (2004b) “Industrial Policy for the Twenty‐first Century,” mimeo.

- Togo Sources of Growth (2008). _Togo: Les Sources de Croissance er le Climat d’Investissement_ .  Working Document for the preparation of the Poverty Reduction Strategy Paper, Version Preliminaire, November 17.

World Bank (2007a). _Profil de la pauvreté et de la vulnérabilité au Togo, 2007_

- World Bank (2007b). _The Role of Employment and Labor Income in Shared Growth: What to Look for and How_ , PREM Poverty Reduction Group, World Bank, Draft December 2007.

- World Bank (2008). _Togo: Enhanced Heavily Indebted Poor Counties (HIPC) Initiative Decision Point Document_ , November 2008, International Development Association (IDA) and International Monetery Fund (IMF), Washington, DC.

- World Bank (2009). _Togo: Mémorandum Economique du Pays sur les Sources de Croissance et Etude Diagnostique sur l’Intégration Commerciale (EDIC) du Cadre Intégré pour l’Assistance Technique Liée au Commerce_ . Mission Principale du 18 mai au 9 juin, 2009, Aide Mémoire.

- World Bank (2010). _Togo Investment Climate Policy Note_ . Draft Report. Finance and Private Sector Development, Africa Region. World Bank, Washington, DC.

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# **9 Appendices**

## **Appendix 1: Additional Figures, Tables and Boxes**

**Table A 1: Togo Income Benchmarking**

||<br>2007 GDP<br>per capita<br>(real US $|<br>1980‐<br>Histor|<br>2000‐<br>ical Grow<br>**g**|2006‐<br>th rate<br>**,**|1980‐<br>Years to<br>countri<br>**,**<br>|2000‐<br>achieve pc<br>es at histor<br>rate:<br>**,**|2006‐<br>growth of<br>ic growth|PC Growth rate<br>required to reach<br>2007 GDPpc of<br>resp. countries in|
|---|---|---|---|---|---|---|---|---|
|Country|OF 2000)|2007|2007|2007|2007|2007|2007|20years|
|Togo|239|‐1.4%|‐0.4%|‐0.5%|||||
|Burkina Faso|260|1.8%|2.4%|1.0%|‐6|‐21|‐16|0.4%|
|Benin|328|0.4%|0.7%|1.5%|‐23|‐78|‐58|1.6%|
|Senegal|509|0.4%|1.6%|1.9%|‐55|‐187|‐140|3.8%|
|Nigeria|472|0.2%|3.6%|4.0%|‐50|‐168|‐126|3.4%|
|Ghana|307|1.0%|3.1%|4.2%|‐18|‐62|‐46|1.2%|
|Mauritius|4700|4.1%|3.2%|3.9%|219|736|550|14.9%|
|LICs|415|1.2%|3.2%|4.3%|‐40|‐136|‐102|2.8%|
|Sub‐Saharan Africa|598|0.0%|2.4%|3.7%|‐67|‐227|‐169|4.6%|
|MICs|1997|2.6%|4.9%|6.9%|156|525|392|10.6%|

_Source:_ Authors’ calculations. DDP Data, World Bank.

### **Box A 1: Expected Transmission Channels of the Global Crisis to Togo**<sup>**.1**</sup>

|**_The global crisis is having or may have the following spillover effects on Togo:_**|
|---|

- _Lower demand and prices for exports:_ Lower global growth may adversely impact demand for traditional commodity exports, such as cotton and phosphate. Their share in total exports declined from 50% at the end of the 1990s to just 13% in 2008 and could fall further with the crisis. Cement exports, which represent about 20% of total exports, are expected to continue to increase, but declining regional demand should not be ruled out.

- _Decreasing trade‐related activities:_ The activity of the Port of Lomé and the related transport sector may suffer from lower transit and re‐export of goods to Burkina Faso, Niger, and Mali. Early in 2009 there was no sign of declining activity, but there may be a delayed impact.

- _Lower remittances:_ If economic conditions worsen in host countries, particularly in continental Europe, remittances, which make up 10 to 15% of GDP, could drop off sharply and jeopardize domestic demand (which is particularly important for construction). As of December 2008, no drop‐off was evident, however.

- _Lack of interest of strategic investors:_ Togo is not highly dependent on private capital flows, but delays in attracting strategic investors, which are key to finalizing restructuring of the banking and phosphate sectors, would constitute lost opportunities to enhance growth. For example, low international demand for phosphate may delay investor interest, which is crucial to obtain fresh capital and raise output.

- _Lower aid flows_ : Togo is not heavily dependent on donors flows—its foreign‐financed investment and social spending are among the lowest in the WAEMU—but it is expecting an increase to support economic revival.

- _Declining fiscal revenues:_ A greater than expected slowdown would likely reduce revenues below projections. The resulting financing gap would pose policy challenges.

- **_Togo’s exposure to the crisis may, however, be mitigated by the following country‐specific factors:_**

- _Low international exposure of the financial sector:_ The banking sector is mostly publicly and regionally owned, with low exposure to the most affected markets. Nonetheless, a recent pick‐up in household credit could reverse, if regionally owned private banks—the most active in this market—cut back lending in Togo to rebalance their portfolios. While there is no evidence so far of disruptions in international trade finance or the interbank market, banks are seeing more strict requirements from correspondent banks. Microfinance institutions, which represent 15% of total credits, have experienced decreasing funding from international institutions, however. Second round effects (e.g., rising NPLs) are also expected to be moderate given the recent restructuring, but the authorities are committed to heightened oversight to minimize the risks.

- _No major decrease in committed donor support_ : Public investment in 2009, financed in large part by donor support, is expected to pick up due to new commitments. Togo is largely reliant on multilateral donors—the World Bank, the EU, and the AfDB—whose disbursement is more stable and which together have committed budget support of about 2% of GDP to help Togo meet its 2009 financing needs.

- _Subsistence agriculture that accounts for about 25% of GDP_ might be an important shock absorber. Staff projects a rebound in subsistence production following the floods in 2008.

- _Declining energy and food prices_ are expected to reduce inflationary pressures, improve the terms of trade, and narrow the current account deficit in 2009.

- **___________________ 1** For further details see Selected Issues Paper.

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**Table A 2: Groups of Products with Revealed Comparative Advantage in Togo, 1992‐96 vs 2002‐06.**

|**"The Marginals" (with export shares>0.1%, each)**<br>**Classification: SITC2-3 Digits**|**No RCA in either 1**<br>**Growth 2000-06 /**|**S**<br>**990-1996 o**<br>**1990-96**|**hare 00/06**<br>**r 2000-2006**|**"The Classics" RCA in both 1**<br>**Classification: SITC2-3 Digits**|**990-1996 & 2000-2**<br>**Growth 2000-06**|**006**<br>**/ 1990-96**|**Share 00/06**|
|---|---|---|---|---|---|---|---|
|**Code**<br>**Product Name**<br>|**Togo**<br>|<br>**World**<br>|<br>**in Togo**|**Code**<br>**Product Name**<br>|**Togo**<br>|**World**<br>|<br>**in Togo**|
|11 Meat,edible meat offals, fresh, chi|46.7%|4.2%|<br>0.19%|35 Fish,dried,salted or in brine ; smo|14.3%|1.9%|<br>0.5%|
|57 Fruit & nuts(not includ. oil nuts),|4.4%|4.7%|0.42%|36 Crustaceans and molluscs,fresh,chil|‐7.5%|3.2%|0.4%|
|98Edible products and preparationsn.|19.1%|8.1%|0.28%|71Coffee and coffee substitutes|‐6.0%|1.2%|1.9%|
|112 Alcoholic beverages|21.7%|6.0%|0.20%|72Cocoa|19.2%|5.3%|7.2%|
|522 Inorganic chemicalelements,oxides|41.8%|6.5%|0.12%|75 Spices|10.0%|5.7%|0.1%|
|553 Perfumery,cosmetics and toilet prep|31.1%|9.9%|0.13%|81 Feed.stuff for animals(not incl.unm|4.2%|4.1%|1.1%|
|634 Veneers,plywood,improved or reconst|39.4%|4.5%|0.11%|222 Oil seeds and oleaginous fruit,whol|23.4%|5.5%|1.6%|
|635 Wood manufactures,n.e.s.|8.2%|7.7%|0.11%|223 Oils seeds and oleaginous fruit, wh|‐5.8%|4.7%|0.1%|
|642 Paper and paperboard,cut to size or|35.3%|6.7%|0.16%|263 Cotton|1.3%|0.5%|14.1%|
|653Fabrics,woven,of man‐madefibres|38.9%|1.5%|0.30%|271 Fertilizers,crude|‐4.2%|1.0%|11.4%|
|657Specialtextilefabrics andrelated|19.1%|5.8%|0.10%|292Crudevegetablematerials,n.e.s.|‐1.0%|3.9%|0.3%|
|674Universals,plates and sheets,of iro|23.1%|7.4%|0.27%|334 Petroleumproducts,refined|27.0%|10.6%|18.1%|
|678 Tubes,pipes and fittings,of iron or|29.8%|6.6%|0.17%|424 Other fixed vegetable oils,fluid or|13.5%|7.3%|1.2%|
|697 Household equipment of base metal,n|31.1%|7.3%|0.20%|661 Lime,cement,and fabricated construc|39.5%|6.2%|10.3%|
|723 Civil engineering & contractors pla|55.3%|8.8%|0.12%|941 Animals,live,n.e.s.,incl. zoo‐anima|‐3.1%|5.4%|0.2%|
|778 Electrical machinery and apparatus,|31.6%|8.4%|0.23%|||||
|<br>781 Passenger motorcars,fortransport|43.9%|7.9%|2.93%||**To**|**tal ------>**|**68.4%**|
|782 Motor vehiclesfortransport ofgoo|33.2%|7.3%|0.85%|||||
|785Motorcycles,motorscooters,invalid|48.6%|6.0%|0.21%|||||
|786 Trailers & other vehicles,not motor|49.9%|8.9%|0.15%|**"Emerging Products" No RCA in 19**|**90-1996 & RCA in**|**2000-2006**||
|<br>821 Furniture and parts thereof|15.5%|9.2%|0.11%|<br>**Classification: SITC2-3 Digits**|<br>**Growth 2000-06**|<br>**/ 1990-96**|**Share 00/06**|
|845 Outer garments and other articles,k|39.5%|6.7%|0.16%|**Code**<br>**Product Name**|**Togo**|**World**|**in Togo**|
|<br>846 Under garments,knitted or crocheted|28.3%|7.8%|0.15%|22 Milk and cream|37.9%|4.9%|0.4%|
|851 Footwear|19.8%|4.6%|0.21%|23Butter|79.8%|3.2%|0.1%|
|893Articles of materials describedin|13.2%|8.0%|0.21%|34 Fish,fresh(live ordead),chilled o|21.5%|4.8%|1.1%|
|971 Gold,non‐monetary|7.6%|5.1%|0.25%|42 Rice|33.0%|4.7%|0.4%|
|||||46 Meal and flour of wheat and flour o|40.7%|2.6%|0.5%|
|**Share of Marginals(each with an export share>0**|**.1%) on total exp **|**orts ----->**|**8.4%**|54 Vegetab.,fresh,chilled,frozen/pres.|7.4%|4.8%|0.4%|
|||||56 Vegetab.,roots & tubers,prepared/pr|10.5%|4.4%|0.1%|
|||||58 Fruit,preserved,and fruit preparati<br>|25.2%|4.5%|0.4%|
|**Marginal Products with Share in Togo's**|**Overall Exports 20**|**00-2006<0.**|**1%**|61Sugarandhoney|33.6%|4.1%|0.3%|
|**Classification: SITC2-3 Digits**|**Growth 2000-06 /**|**S**<br>**1990-96**|**hare 00/06**|62Sugarconfectionery and othersugar|32.6%|7.0%|0.1%|
||**Togo**|**World**|**in Togo**|91 Margarine and shortening|46.6%|10.0%|0.5%|
|**151 groups of products, each with share in Togo's**|||**280%**|111 Non alcoholic beverages,n.e.s.|41.2%|10.1%|0.2%|
|**total exports<0.1% during 2000‐2006**|||**.**|247 Other wood in the rough or roughly|35.8%|0.5%|1.7%|
|||||248 Wood,simply worked,and railway slee|39.1%|3.1%|0.4%|
|**Total share of the marginals -------------------------------**|**---------------------------**|**----------->**|**11.2%**|269 Old clothing and otherold textile|37.8%|5.8%|0.2%|
|||||273 Stone,sand and gravel|45.6%|5.2%|0.3%|
|||||282 Waste and scrapmetalof ironorst|32.4%|9.8%|1.0%|
|**"Disappearances" RCA in 1992-199**|**6 & No RCA in 20**|**00-2006**||288 Non‐ferrous base metal waste and sc|17.4%|6.8%|0.4%|
|**Classification: SITC2-3 Digits**|**Growth 2000-06 /**|**S**<br>**1990-96**|**hare 00/06**|335 Residual petroleum products,nes.& r|62.2%|10.2%|0.2%|
|**Code**<br>**Product Name**|**Togo**|**World**|**in Togo**|<br>423 Fixed vegetable oils,soft,crude,ref|91.2%|6.1%|0.2%|
|245 Fuel wood (excluding wood waste) an|‐23.9%|7.7%|0.0%|554 Soap,cleansing and polishing prepar|40.4%|8.4%|0.3%|
|667 Pearlsprecious& semi‐prec.stonesu|‐11.5%|9.4%|0.5%|562 Fertilizersmanufactured|17.8%|5.1%|0.6%|
|,,<br>844Undergarments oftextilefabrics|‐18.8%|2.0%|0.0%|,<br>583Polymerizationand copolymerization|47.9%|8.3%|2.7%|
|896Works ofart,collectors pieces & an|‐2.5%|4.3%|0.1%|591 Disinfectants,insecticides,fungicid|51.1%|6.1%|1.2%|
|||||652 Cotton fabrics,woven|43.1%|3.0%|2.9%|
|**Total share of disappearances ----------------------------**|**----------------------------**|**--------->**|**0.6%**|656 Tulle,lace,embroidery,ribbons,& oth|41.7%|5.2%|0.1%|
|||||<br>673 Iron and steel bars,rods,angles,sha|51.1%|6.1%|1.6%|
|||||677 Iron/steel wire,wheth/not coated,bu<br>721 Agricultural machineryandparts|51.9%<br>741%|7.3%<br>62%|0.1%<br>03%|
|||||<br>783Roadmotor vehicles,n.e.s.|.<br>59.9%|.<br>7.7%|.<br>0.6%|
|||||899 Other miscellaneousmanufactured ar|15.4%|8.4%|0.5%|
||||||**To**|**tal ------>**|**19.8%**|

_Source_ : PRMED staff calculations based on UN/COMTRADE data.

**Table A4. Mincerian regression of income on education and a set of controls**

|**Mincerian regressions of**|**income per individual, Togo 2006**|||
|---|---|---|---|
||**Number obs.**||**1507**|
||**Prob F>0**||**0**|
|**Dependent variable**|**: Individual income, in logs**<br>**R‐squared**||**0.37**|
||**Coefficient**|**t‐statistic**||
||Years of Education<br>0.05|5.24|*******|
||Age<br>0.03|6.01|*******|
||Age, Squared<br>0.00|‐4.51|*******|
||Male (Dummy=1)<br>0.61|20.01|*******|
||Married (dummy=1)<br>0.05|2.51|*******|
||Literate (dummy=1)<br>0.03|0.10||
|Default is:|Education type, General<br>‐0.08|0.09||
|Professional|Education type, Technical<br>‐0.13|0.11||
||Private Employee (dummy=1)<br>0.10|1.87|*****|
||Salaried worker<br>0.07|3.01|*******|
|default sector is:|Primary Sector<br>‐0.14|0.18||
|Tertiary|Secondary Sector<br>0.12|0.04||
||constant<br>2.32|17.08|*******|
|*** =Significant at|1% ;** =Significant at 5%;* =Significant at10%|||

**_Source_ : Authors’ calculations based on Core Welfare Survey, 2006.**

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# **Appendix 2: Export diversification and sophistication indices**

## **Herfindhal‐Hirschmann index of export diversification**

This indicator is calculated for country “i” as the sum of squared values of product “j” shares in total country export (Xsh), adjusted by the number of products exported by the country in year “t”, as follows: In this equation, the adjustment of 1 minus the sum of exported products “j” is intended to scale the HH index to the ratio 0 to 1, with zero meaning full diversification and 1 meaning a concentration of exports in just one product.

## **EXPY index of export sophistication**

The first step is to construct the so‐called PRODY’s for each category of service exports. “PRODYj” is the income value associated with the export good “j”, and is constructed by using the export (x) share of a country (i) in world exports of “j” divided by the sum of shares of “j” in world exports of “j” across all countries exporting the good.<sup>54</sup> These ratios are multiplied by the exporting countries’ respective per capita income level (Y) and the result is summed up across all countries. In other words, the PRODY becomes the weighted average of per capita GDPs, where the weights represent the revealed comparative advantage in good “j” for each country.<sup>55</sup> PRODYs are constructed for each export goods category, for each year of available data, and are by construction the same for all countries.

EXPY is then the weighted income value of goods exported by a country, computed as the sum of PRODYs using as weights the share of the particular service in the country’s total goods export basket. EXPYs are constructed for each country and for each year with available data.

> _xij_

> 54 _Xi_ is hence the value‐added share of commodity j in the country’s overall export basket.

> 55 The rational for using revealed comparative advantages as weights is to control for country size when ranking the services.

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