WPS5552 Policy Research Working Paper 5552 Services Liberalization in Preferential Trade Arrangements The Case of Kenya Edward J. Balistreri David G. Tarr The World Bank Development Research Group Trade and Integration Team January 2011 Policy Research Working Paper 5552 Abstract Given the growing importance of commitments to all business services by Kenya with its African partners foreign investors in services in regional trade agreements, would be somewhat beneficial for Kenya. If a preferential it is important to develop applied general equilibrium agreement with African partners is combined with an models to assess the impacts of liberalization of barriers to agreement with the European Union, the gains would multinational service providers. This paper develops a 55 more than triple the gains of an Africa only agreement. sector applied general equilibrium model of Kenya with Multilateral reduction of services barriers, however, would foreign direct investment and Dixit-Stiglitz productivity yield gains about 12 times the gains of an agreement effects from additional varieties of imperfectly with the Africa region alone. These results suggest that competitive goods or services, and uses the model to preferential liberalization in the region is a valuable first assess its regional and multilateral trade options, focusing step, but wider liberalization, with larger partners and on commitments to foreign investors in services. To liberal rules of origin or multilaterally, will yield much assess the sensitivity of the results to parameter values, the larger gains due to providing access to a much wider set model is executed 30,000 times, and results are reported of services providers. The largest gains would come from as confidence intervals of the sample distributions. domestic regulatory reform in services, as this would The analysis reveals that a 50 percent preferential almost triple the gains of multilateral liberalization. reduction in the ad valorem equivalents of barriers in This paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at dtarr@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 Services Liberalization in Preferential Trade Arrangements: The Case of Kenya by Edward J. Balistreri and David G. Tarr January 27, 2011 Abstract: Given the growing importance of commitments to foreign investors in services in regional trade agreements, it is important to develop applied general equilibrium models to assess the impacts of liberalization of barriers to multinational service providers. This paper develops a 55 sector applied general equilibrium model of Kenya with foreign direct investment and Dixit-Stiglitz productivity effects from additional varieties of imperfectly competitive goods or services, and uses the model to assess its regional and multilateral trade options, focusing on commitments to foreign investors in services. To assess the sensitivity of the results to parameter values, the model is executed 30,000 times, and results are reported as confidence intervals of the sample distributions. The analysis reveals that a 50 percent preferential reduction in the ad valorem equivalents of barriers in all business services by Kenya with its African partners would be somewhat beneficial for Kenya. If a preferential agreement with African partners is combined with an agreement with the European Union, the gains would more than triple the gains of an Africa only agreement. Multilateral reduction of services barriers, however, would yield gains about 12 times the gains of an agreement with the Africa region alone. These results suggest that preferential liberalization in the region is a valuable first step, but wider liberalization, with larger partners and liberal rules of origin or multilaterally, will yield much larger gains due to providing access to a much wider set of services providers. The largest gains would come from domestic regulatory reform in services, as this would almost triple the gains of multilateral liberalization. Edward J. Balistreri is in the economics department at Colorado School of Mines; and David G. Tarr (corresponding author, dtarr@Worldbank.org) is a consultant for DECRG, the World Bank. We thank Ana Margarida Fernandes, Jesper Jensen, Thomas Rutherford, Christopher Worley, Josaphat Kweka, Nora Dihel, Francis Ng and Grigol Modebadze for their contributions to this project, and Paul Brenton, Paulo Zacchia and Maryla Maliszewska for valuable suggestions. Financial support from the Bank-Netherlands Partnership Program under the Regional Services in Africa project is gratefully acknowledged. The views expressed are those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, the Government of Kenya or those acknowledged. 1 Services Liberalization in Preferential Trade Arrangements: The Case of Kenya by Edward J. Balistreri, Colorado School of Mines and David G. Tarr, The World Bank I. Introduction Both economic theory and empirical literature have shown that wide availability of business services results in productivity gains to the manufacturing sector and contributes to its international competitiveness.1 International commitments to national treatment and market access for foreign investors in key business services sectors may help developing countries obtain better access to these services that contribute to the productivity gains. Some developing countries, however, are hesitant to make substantial multilateral commitments, but may be more inclined to proceed in regional arrangements with other neighboring developing countries, rather than with a major Northern partner. Commitments in services, however, are often limited in South-South arrangements. 1 Arnold et al. (2007), Fernandes (2007) and Fernandes and Paunov (2008) have provided econometricestimates of the gains from services liberalization. Marshall (1988) shows that in three regions in the United Kingdom (Birmingham, Leeds and Manchester) almost 80 percent of the services purchased by manufacturers were bought from suppliers within the same region. He cites studies which show that firm performance is enhanced by the local availability of producer services. In developing countries, McKee (1988) argues that the local availability of producer services is very important for the development of leading industrial sectors. Both the urban economics literature (Vernon, 1960; Chinitz, 1961) and the modern economic geography literature (e.g., Krugman, 1991; Fujita, Krugman and Venables, 1999) have focused on the fact that related economic activity is economically concentrated due to agglomeration externalities (e.g., computer businesses in Silicon Valley, ceramic tiles in Sassuolo, Italy). Evidence comes from a variety of sources. Ciccone and Hall (1996) show that firms operating in economically dense areas are more productive than firms operating in relative isolation. Caballero and Lyons (1992) show that productivity increases in industries when output of its input supplying industries increases. Hummels (1995) shows that most of the richest countries in the world are clustered in relatively small regions of Europe, North America and East Asia, while the poor countries are spread around the rest of the world. He argues this is partly explained by transportation costs for inputs since it is more expensive to buy specialized inputs in countries that are far away for the countries where a large variety of such inputs are located. 2 Since the early 1990s, regional trade agreements have surged; 283 have been notified to the WTO and were in force as of February 2010. 2 Given the inclusion of services in modern FTA agreements negotiated with the EU, the US and in some other agreements, economists need to be able to assess the impact of services commitments as part of their advice to governments regarding preferential trade agreements. Services commitments in regional agreements could lead to substantial productivity improvements. But is there an analogy to trade diversion in goods whereby preferential commitments in services could be immiserizing? Are developing countries likely to obtain substantially larger gains from an agreement with a developed country, rather than a developing country? How do the gains of preferential versus global liberalization compare? Given that preferential trade liberalization discriminates against excluded countries, it is well known from the vast theoretical literature that preferential liberalization of goods may lead to either gains or losses of welfare. Perhaps motivated by this uncertainty of outcomes of their agreements, policy-makers have expressed considerable demand for analysis of these agreements. Applied modelers have responded with applied general equilibrium models that focus on goods. So the literature now contains a substantial number of studies that examine regional agreements in goods.3 But this paper and the Jensen and Tarr (2010) paper are the first 2 See http://www.wto.org/english/tratop_e/region_e/region_e.htm. This does not include a significant number of regional agreements that are in force (among developing countries) that have not been notified to the WTO. 3 The more prominent studies include Harris (1984), Cox and Harris (1986), Harrison, Rutherford and Tarr (1996), Smith and Venables (1988), Baldwin, Forslid and Haarland (2000), several in the Francois and Shiells (1994) volume, Levy and van Wijnbergen (1995), Harrison, Rutherford and Tarr (1997a), Rutherford, Rutstrom and Tarr (1993), Rutherford, Rutstrom and Tarr (1995), Harrison, Rutherford and Tarr (2002), Rutherford and Tarr (2003) and Harrison, Rutherford, Tarr and Gurgel (2004). See Jensen and Tarr (2010) for a review of this literature. 3 numerical studies of regional arrangements that assess the impact of commitments to multinational firms who will undertake foreign direct investment in services.4 Given substantial demand by governments for advice on their prospective trade policies in the regional and multilateral arenas, it is unfortunate that the profession does not have a framework for assessing the ex ante welfare impact of preferential reduction of barriers against foreign direct investment in business services. We attempt to fill that gap in this paper. Crucial to the analysis, we incorporate the Dixit-Stiglitz-Ethier mechanism of endogenous productivity gains from additional varieties of imperfectly produced goods and services. Jensen and Tarr (2010) have shown that if domestic firms capture rents from the barriers against multinational providers of services, then these rents are analogous to tariff losses in goods, and losses may occur from preferential liberalization of services. Moreover, with imperfect competition, preferential liberalization will lead to a gain of varieties from the partner countries, but a loss of varieties from excluded countries; the lost varieties can lead to a net loss of welfare. We simulate examples of cases where there is a loss of welfare due to the loss of varieties and show how key elasticities in the model influence the result. 4 Earlier papers that examined the impact of barriers to foreign direct investment in services include the following. Markusen, Rutherford and Tarr (2005) developed a stylized model where foreign direct investment is required for entry of new multinational competitors in services, but they did not apply this model to the data of an actual economy. Brown and Stern (2001) and Dee et al. (2003) employ multi- country numerical models with many of the same features of Markusen, Rutherford and Tarr. Their models contain three sectors, agriculture, manufacturing and services, and are thus also rather stylized. The Dixit-Stiglitz endogenous productivity effect from the impact of service sector liberalization on product variety is not mentioned in the results of Brown and Stern and are interpreted as of little relevance in Dee et al. 4 Konan and Maskus (2006) develop a small open economy constant returns to scale model with an initial monopoly in services sectors that results in monopoly rents and increased costs. They examine the impact of exogenously allowing additional firms to enter. The papers by Jensen, Rutherford and Tarr (2007) and Rutherford and Tarr (2008) on Russian WTO accession are the closest to this model; but there was only one aggregate rest of the world in those models, so there was no possible consideration of regional impacts in those papers. 4 Kenya is an example of a country facing most of the policy choices mentioned above. In many of its business services sectors, including maritime and road transportation, banking, insurance and professional services, the regulatory regime imposes significant burdens on the cost of providing services, both by Kenyan service providers and by multinationals. In 2010 it was involved in negotiations of commitments in services in various regional arrangements, including the Economic Partnership Agreements with the European Union, COMESA and the East African Customs Union.5 And in the context of its international negotiations under the Doha Development Agenda, Kenya may be called upon to make further commitments in the business services area. Kenya is proceeding cautiously regarding services commitments in all these areas, but is taking steps to adopt a mutual recognition agreement in professional services within the East African Customs Union. In this paper we develop a 55-sector small open economy comparative static computable general equilibrium model of Kenya that we believe is appropriate to evaluate the impact of Kenyan liberalization of services barriers. We build on the model of Balistreri, Rutherford and Tarr (2009), but we decompose the rest of the world into three regions: the European Union; our Africa region; and the Rest of the World. All foreign regions are sources of foreign direct investment in some of the business services sectors. We find that a 50 percent preferential reduction in the ad valorem equivalents of barriers in all business services with respect to its African regional partners would be slightly beneficial for Kenya in our central elasticity case. But an agreement with the EU is worth more than twice as much as an agreement with the Africa region, and if preferential liberalization with the Africa region is combined with an agreement with the EU, the gains would more triple. If the 5 See table 1 for a list of COMESA (Common Market of East and Southern Africa) and East AfricanCustoms Union countries. 5 liberalization of business services commitments is extended to all foreign partners (called Unilateral in the tables), the gains would increase by twelve times compared with an African agreement alone. Thus, these results suggest that preferential liberalization in the region is a valuable first step, but wider liberalization, with larger partners or multilaterally will yield much larger gains due to providing access to a much wider set of services providers. Finally, we estimate that a serious effort to reduce non-discriminatory regulatory barriers (that is, barriers that raise the costs of Kenyans as well as foreign services providers in Kenya) would almost triple the benefits of multilateral liberalization in Kenya. Multilateral liberalization yields larger gains than preferential liberalization since with preferential liberalization Kenya will not obtain additional service sector suppliers from excluded countries, and, in fact, Kenya will suffer losses of service sector suppliers in excluded countries. Moreover, we summarize research below that shows that small countries gain more technological spillovers from trade with technologically advanced countries (at least in research and development intensive products). Our model allows for differentiated rates of technological spillover by region and product, and this is the main explanation for why the estimated gains from a preferential agreement with the EU will yield larger gains for Kenya than a preferential agreement with the African region. Combining African regional liberalization with regional liberalization with the EU would capture the gains from agreements with both regions. Kenya could combine an agreement with the EU and the Africa region with liberal rules of origin, and the results would then come closer to the gains from unilateral liberalization with the whole world. 6 We devote considerable attention to the sensitivity of our results to uncertainty in the parameters. First, to understand the model better, we conduct piecemeal sensitivity of the results, where we isolate the impact of each of the parameters to ascertain which parameters most strongly impact the results. Second, to assess the robustness of the results to parameter uncertainty, we conduct systematic sensitivity analysis, where we execute the model 30,000 times. Each simulation is based on a random draw of all the parameter values; we then present sample distributions and sample confidence intervals of the key variables.6 Regarding Kenya's preferential arrangement with the Africa region, we find, from the systematic sensitivity analysis, that there is a 9.5 chance that Kenya would lose. If Kenyans are assumed to capture the rents from barriers in services, then the mean estimate is that Kenya would lose. We conduct sensitivity on a range of values of key parameters that determine the productivity impacts in imperfect competition. In the Kenya-Africa scenario, we show that there is a set of plausible parameter values that result in sufficient loss of varieties from the rest of the world and the EU region that there would be negative welfare effects for Kenya. This shows that under imperfect competition there is an extension of the trade diversion results of perfect competition. The paper is organized as follows. In section II, we provide an overview of the Kenyan services sectors. We discuss how we estimated the tariff equivalents of the barriers in services in section III. We provide an overview of the model in section IV and a discussion of the data in section V. The central results are presented in section VI and sensitivity results are presented in section VII. Given the initiative of the East African Customs Union to begin to include services 6 The systematic sensitivity analysis has been conducted with the range of parameters values shown in table 22, with the exception of the elasticity of firm supply with respect to price. For this latter elasticities, , the systematic sensitivity analysis was done with these elasticities equal to 2 for the Africa and Kenyan regions, ten for the EU and 15 for the Rest of the World (for all sectors). 7 in their agreements by negotiating mutual recognition agreements in professional services, we focus on a range of possible policy options in professional services. These are presented in section VIII. Conclusions are presented in section IX. In appendix A, we discuss the trade and tariff data in some detail. We document the calculation of ownership shares by sector and region in appendix B. How we obtained estimates of the Dixit-Stiglitz elasticities in goods is described in appendix C. The explanation of the estimation of the ad valorem equivalents of barriers in professional services, done by Nora Dihel and Josaphat Kweka, is explained in appendix D. II. Overview of the Kenyan Service Sectors In this section, we summarize the key institutional and policy issues in telecommunications, banking, insurance and transportation. This discussion is based on several policy notes written on the Kenyan business services sectors. Transportation One bright spot in the Kenyan transportation network is its air transportation services. In recent years, Kenya allowed private sector development (both Kenyan and foreign) to develop the air transportation links. The efficient air transportation services facilitate the important tourism sector and have been instrumental in the development of the Kenyan cut flower industry, which in turn has contributed to growth and poverty reduction. However, Kenya's port, rail and road transportation facilities are significant problems for transportation of its goods and for the competitiveness of its exports (for details see Helu, 2007; Ochieng, 2007; and World Bank, 2007). Its principal port, Mombassa, is plagued by poor infrastructure and complicated bureaucratic procedures. As a result, it takes an average of two weeks to clear a container at the port and more than four weeks for over 5 percent of the containers. The cost of importing a container into Kenya exceeds USD 1300, while it is under USD 1,000 per container in Tanzania and South Africa 8 and under USD 500 per container in Malaysia and Singapore.7 Uncertainty over delivery times is a significant cost burden on manufactured exports. The port at Dar es Salaam, Tanzania, is regarded as more efficient and container throughput has been growing much faster there. Due to a lack of investment, Kenya's railways have significantly declined and are considered rather poor providers of freight transportation services since the 1980s.8 Road transportation is the primary means of overland transport. But some sections of the key Northern Corridor are in very poor condition. Kenya's problems with its ports, rail and road transportation facilities were highlighted by Kenya's ranking on the international Logistics Perception Index of 2004.9 Of the 70 countries in the 2004 survey, Kenya was ranked as the least logistically friendly (World Bank, 2007). In Africa, the survey included South Africa, Zambia, Ghana and Nigeria. For 2006, the survey expanded to include 150 countries, and Kenya ranks at number 75--below several African countries, but above average for the region. Telecommunications As of 2007, Kenya's telecommunications services were expensive compared with other Sub- Saharan African countries and even more when compared with those of East and South Asia. Relative to countries with comparable income, Kenya had fewer fixed lines per capita, less than half the level of international calls per subscriber and higher Internet charges. Perhaps more important is the low efficiency of service provision (see World Bank, 2007, pp.45-47). Kenya currently requires that telephone companies must be at least 30 percent owned by Kenyan nationals. Problems related to the licensing of the third mobile telephone provider10 and the Second National Operator were primarily due to this 7 World Bank staff estimates. 8 In the hope of improved performance, Kenya's railways were turned over to Rift Valley Railways, a South African company, in November 2006. 9 The Logistics Perception Index measures the perceptions of managerial level personnel of international freight forwarding companies. It is published by the Global Facilitation Partnership for Transportation and Trade and available at: www.gfptt.org. 10 Regarding the third mobile telephone operator, a consortium of a local investor (Kenya National Federation of Cooperatives, KNFC) and foreign investors (Econet Wireless) won the tender in February 2004. But the consortium was put together to meet the 30 percent local ownership requirement, not because of business reasons. Citing deals 9 restraint. In fact, the Government has acknowledged that the 30 percent ownership requirement is delaying licensing of additional telecom operators.11 By the end of 2009, however, the duopoly of Zain and Safaricom was broken by the addition of two additional operators to the market: Orange Kenya and Essar Telecom Kenya. Subscribers increased to 19.4 million, or 49.7 percent of the population, almost all of whom were prepaid subscribers. About 85 percent of the population lived in a region where service was provided. According to the Communications Commission of Kenya, prices of mobile services were declining due to competition among the four operators. Safaricom was the leader in mobile banking software for its telephones and thus succeeded in capturing back significant market share. Data transmissions are especially expensive by international standards. The primary explanation for the high cost of these services is that until recently East Africa was the only major coastline in the world without access to a fiber-optic cable network. In early 2008, these services were provided by satellite services, which are more expensive than fiber optic seabed cable. However, the completion of the SEACOM and TEAMS fiber optic cable systems in 2009 and the expected operation of the EASSy system in mid-2010 should lower the costs of internet and data transmission services. A reduction in the costs of internet transmission services will likely result in more internet service users. More internet users will likely allow achievement of economies of scale in production that would further reduce costs. However, according to one expert, as of early 2010, the main consequence of the TEAMS and SEACOM project completion has been increasing speed, but not lowering of costs.12 While there are obviously serious economic problems in the telecom sector, the government has implemented significant reforms in the sector in the last ten years. The government's strategy for the made by a previous CEO of KNFC, KNFC at one point wrote to the Communications Commission of Kenya (CCK) withdrawing from the consortium. KNFC later withdrew its letter of objection, but lost its controlling share of the consortium. CCK, nonetheless, awarded the license to Econet Wireless and court battles ensued. The Government eventually suspended the entire CCK board and its Director General, and suspended the license of Econet. In April 2007, Econet has agreed to withdraw its court case and settle the matter out of court. Ultimately, it began operating under the brand name Yu and changed its name to Essar Telecom Kenya in April 2009. . 11 SNO to get a year to meet local ownership rule, The Saturday Standard, Business section, April 14, 2007. 12 See www.moseskemibaro.com/2010/01/02/a-review-of-kenyas-ict-position-in-2009/ 10 sector is outlined in the Postal and Telecommunications Policy Statement of 1997.13 The strategy outlines a more liberal and private sector led strategy designed to optimize the sector's contribution to economic growth of Kenya (Matano and Njeru, 2007). The Kenya Communications Act of 1998 created the Communication Commission of Kenya as an independent regulator of the sector. The monopoly rights of Telekom Kenya Ltd expired on June 30, 2004. Since then significant competition has been introduced into the sector and the Communication Commission of Kenya introduced the modern and efficient Unified Licensing Framework for licensing of telecommunications companies. The estimates of this paper can be taken as an assessment of the effective implementation of this reform program. Banking and Insurance Banking. Relative to other countries in Africa, Kenya has a well developed financial sector. The cost of credit does not appear to be a major constraint for large enterprises. Nonetheless, medium, small and micro enterprises have severe problems accessing credit.14 Only about 1.5 percent of the credit these enterprises receive is from banks, and about 90 percent of them have no access to credit. Their problems accessing credit is because of: the high costs to banks of evaluating and monitoring credit to small enterprises; the absence of credit rating agencies, deficiencies in the legal system that make enforcement of debt contracts difficult and push collateral requirements too high for small firms; many small firms lack the capacity to process bank paperwork; and many small firms do not have access to insurance that would significantly reduce the risk to banks and the collateral required. Foreign banks can operate in Kenya, either by acquiring a Kenyan bank or by obtaining a license to operate as a Kenyan affiliate bank of a multinational bank. In practice, affiliates of multinational banks are provided full market access and national treatment, but Kenya has not bound this practice at the WTO. The European Union has requested that Kenya commit to national treatment of foreign investment 13 This statement is consistent with the government's Economic Recovery Strategy for Wealth Creation (ERS) 14 Despite the credit problems, it is the medium and small enterprises that are the fastest growing part of the Kenyan economy. They increased their share of GDP from 13.8 in 1993 to 18.4 in 1999. 11 in the sector by binding this commitment at the WTO (Kiptui, 2007). Branch banking by foreign banks, however, is not permitted. Insurance. The insurance market in Kenya is small, but is considered one of the more developed in Africa. Similar to banking issues, however, medium, small and micro enterprises have little access to insurance (World Bank, 2007). Regarding the regulatory environment, cross border provision of insurance is limited to cargo insurance and reinsurance services. In addition, the ownership of an insurance company must be at least one-third Kenyan and one-third of the members of the Boards of Directors must be Kenyan. Distribution Services Distribution services are the wholesale and retail trade sector of the economy. In Kenya in 2004 this sector accounted for about 10 percent of GDP, there were 217,000 retail outlets and about 66 percent of these retail outlets were either small retail stores or kiosks. Only one-half a percent of the outlets are super markets or very large stores. It is necessary to distinguish agricultural marketing from the marketing of manufactured goods. Prior to 1993, many agricultural products, including maize, coffee and tea had to be sold to State Marketing Boards. The State Marketing Boards had an exclusive right to purchase, distribute and import these products. Since the reforms of 1993, farmers are now free to sell to private traders or to mills or the final consumer directly, but they still have the option to sell to the State Marketing Board if they choose. On the other hand, distribution of manufacturing goods has traditionally been handled by the private sector. Presently numerous business licenses are required and many are considering damaging forms of government regulation (Onyango, 2007). The Government established a committee to review 1335 licenses. Draft laws and regulations have been prepared to implement the recommendations of the committee but have not yet been implemented as of mid-2007. In addition, restrictions on large scale outlets, shop opening hours and zoning restraints on business have been criticized as unnecessary burdens on business. 12 With respect to discriminatory restraints on foreign investors, Kenya requires that foreigners conduct business only in areas designated as general business areas. Local partners are encouraged, but not required. Expatriates employees are limited and the company must demonstrate that the skills are not available locally. Professional Services There are rather severe restrictions on the rights of foreigners to operate with a license in many of the professional services sectors, including legal, accounting, auditing and engineering services. The East African Customs Union is making its first foray into commitments in the services areas by encouraging mutual recognition agreements among the members. In appendix D, we provide details on the situation in engineering services sectors. III. Estimation of the Tariff Equivalence of the Regulatory Barriers Estimates of the ad valorem equivalents of the regulatory barriers in services are key to the results. In order to make these estimates, we first need to assess the regulatory environment in the services sectors in our model. We commissioned a 54 page survey of the regulatory regimes in key Kenyan business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services and maritime transportation services. 15 We supplemented this information based on a good set of studies on the services sectors that were presented at the conference on Trade in Services in Nairobi, Kenya on March 26, 27, 2007. In particular, we examined the papers by: Kiptui (2007) on financial services; Ndaro (2007) or communication services; Helu (2007) on maritime services; Ochieng (2007) on transport services; and Oresi (2007) on railway services. The study by the World Bank (2007) provided additional detail on the key issues in the sectors and the Telecommunications Management Group (2007) provided extensive details on telecoms. Tarr (2007a, 2007b) summarized this information in telecommunications 15 We thank Ms. Sonal Sejpal of the law firm of Anjarwalla & Khanna Advocates for leading this research effort. 13 and transportation. These questionnaires and papers provided us with data and descriptions and assessments of the regulatory environment in these sectors. Mircheva (2007) then estimated the ad valorem equivalents of barriers to foreign direct investment in fixed line and mobile telecommunications, banking, insurance and maritime transportation services. The process involved converting the answers and data of the questionnaires into an index of restrictiveness in each industry. Mircheva followed the methodology of Kimura, Ando and Fujii (2004a, 2004b, 2004c) to generate these estimates. In the case of professional services, we used engineering services as a proxy for all professional services.16 The details of the regulatory regime and the scoring are listed as appendix D. This methodology further involves building on the estimates and methodology explained in the volume by C. Findlay and T. Warren (2000), notably papers by Warren (2000), McGuire and Schulele (2000) and Kang (2000). For each of these service sectors, the authors evaluated the regulatory environment across many countries. The price of services is then regressed against the regulatory barriers to determine the impact of any of the regulatory barriers on the price of services. Mircheva (and Kweka in the case of engineering services) then assumed that the international regression applies to Kenya in the case that the above mentioned restrictiveness indexes are used. Applying that regression and their assessments of the regulatory environment in Kenya from the questionnaires and other information sources, she estimated the ad valorem impact of a reduction in barriers17 both for discriminatory and non-discriminatory barriers. Mircheva then weighted her fixed line and mobile telecommunications estimates by their market shares to obtain her estimate for communications. The results of the estimates of the ad valorem equivalents of the barriers are listed in table 4. In the case of professional services, we used engineering services as a proxy for all professional services. In engineering services, we have the regression results from the paper by Ngyuen-Hong (2000). Based on an international data set, he estimates the ad valorem equivalents of barriers on trade in engineering services. No such estimates are available for other 16 The estimates were done by Josaphat Kweka, senior economist in the World Bank office in Tanzania in collaboration with Nora Dihel, Trade Coordinator for East Africa in the World Bank. 17 Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts. The estimates by Mircheva that we employ are for discriminatory barriers against foreign direct investment. 14 professional services. Since the methodology we employ requires the existence of a cross- country regression estimate of the impact of barriers to foreign direct investment, we must use engineering services as our proxy. The scoring was done by Josaphat Kweka, senior economist in the World Bank office in Tanzania. The details of the regulatory regime and the scoring are listed as appendix D. IV. Overview of the Model This paper builds on the algebraic structure of the model of Balistreri, Rutherford and Tarr (2009). Here we provide a general description of the structure described there and provide more details where we depart from that structure. There are 55 sectors in the model shown in table 1. Primary factors include skilled, semi-skilled and unskilled labor; mobile capital; sector-specific capital in imperfectly competitive sectors; and primary inputs imported by multinational service providers, reflecting specialized management expertise or technology of the firm. The existence of sector specific capital in several sectors implies that there are decreasing returns to scale in the use of the mobile factors and supply curves in these sectors slope up. We explain this further in the appendix. As in Balistreri, Rutherford and Tarr (2009), there are three categories of firms in the model: (1) perfectly competitive goods and services sectors: (2) imperfectly competitive goods sectors; and (3) imperfectly competitive services sectors with foreign direct investment. The cost, production and pricing structures in the three categories differ widely. The principal extension is that we disaggregate the rest of the world region of Balistreri, Rutherford and Tarr (2009) into three regions: (1) the European Union; (2) the union of the East African Customs Union and COMESA, which we call our African region; and (3) the Rest of the World. In the imperfectly competitive sectors, this requires introducing different firm types with distinct cost structures for each region. We retain the small open economy model framework, so only Kenya is modeled fully. 15 Perfectly Competitive Goods and Services Sectors Regardless of sector, all firms minimize the cost of production. In the competitive goods and services sectors, goods or services are produced under constant returns to scale and where price equals marginal costs with zero profits. This includes all 20 of the agriculture sectors and 19 manufacturing or services sectors, including some food processing sectors such as meat and dairy products and grain milling, and services such as construction, hotels and restaurants, postal communication, real estate, public administration, health and education. In these sectors, products are differentiated by country of origin, i.e., we employ the Armington assumption. All goods producing firms (including imperfectly competitive firms) can sell on the domestic market or export. Firms optimize their output decision between exports and domestic sales based on relative prices and their constant elasticity of transformation production function. Having chosen how much to allocate between exports and domestic sales, firms also optimize their output decision between exports to the three possible export regions, based on relative prices the three regions and their constant elasticity of transformation production function for shifting output between the regions. Goods Produced Subject to Increasing Returns to Scale Goods in these seven sectors are differentiated at the firm level. We assume that manufactured goods may be produced domestically or imported for firms in any region in the model. Firms in these industries set prices such that marginal cost (which is constant) equals marginal revenue; and there is free entry, which drives profits to zero. For domestic firms, costs are defined by observed primary factor and intermediate inputs to that sector in the base year data. Foreigners produce the goods abroad at constant marginal cost but incur a fixed cost of 16 operating in Kenya. The cif import price of foreign goods is simply defined by the import price, and, by the zero profits assumption, in equilibrium the import price must cover fixed and marginal costs of foreign firms. Domestic firms set prices using the Chamberlinian large group monopolistic competition assumption within a Dixit-Stiglitz framework, which results in constant markups over marginal cost for both foreign firms and domestic firms. We have made one significant modeling extension in the imperfectly competitive sectors compared to the Balistreri, Rutherford and Tarr (2009) model. In the Balistreri, Rutherford and Tarr model, domestic firms faced a perfectly elastic demand curve on export markets and they exported at marginal costs. In this model, all imperfectly competitive domestic firms (both goods and services producers) face a downward sloping demand curve in each of their three export markets. Consistent with firm level product differentiation, we assume that the elasticity of demand in each of the export markets is the Dixit-Stiglitz elasticity of demand. Firms then set marginal revenue equal to marginal costs in each of the three export markets; then the export market contribute to the quasi-rents of the firm and affect the entry and exit decisions of firms. Introducing downward sloping demand curves into the model means that there are possible terms of trade affects to consider in this model that were not present in the Jensen, Rutherford and Tarr model. Balistreri and Markusen (2009) have shown, however, that there should be virtually no role for optimal tariffs to exploit terms of trade effects. The reason is that, unlike perfectly competitive firms, imperfectly competitive firms are pricing such than marginal revenue equals marginal costs on export markets, which is the objective of optimal tariffs. For simplicity we assume that the composition of fixed and marginal cost is identical in all firms producing under increasing returns to scale (in both goods and services). This assumption in a our Dixit-Stiglitz based Chamberlinian large-group model assures that output per 17 firm for all firm types remains constant, i.e., the model does not produce rationalization gains or losses. The number of varieties affects the productivity of the use of imperfectly competitive goods based on the standard Dixit-Stiglitz formulation. The effective cost function for users of goods produced subject to increasing returns to scale declines in the total number of firms in the industry. Service Sectors That Are Produced under Increasing Returns to Scale and Imperfect Competition These nine sectors are telecommunications, banking and insurance services, various transportation services and professional business services. In these services sectors, we observe that some services are provided by foreign service providers on a cross border basis analogous to goods providers from abroad. But a large share of business services are provided by service providers with a domestic presence, both multinational and Kenyan.18 Our model allows for both types of foreign service provision in these sectors. There are cross border services allowed in this sector and they are provided from abroad at constant costs--this is analogous to competitive provision of goods from abroad. Cross border services, however, are not good substitutes for service providers who have a domestic presence.19 Crucial to the results, we allow multinational service firm providers that choose to establish a presence in Kenya in order to compete with Kenyan firms directly. As in the goods 18 One estimate puts the world-wide cross-border share of trade in services at 41% and the share of trade in services provided by multinational affiliates at 38%. Travel expenditures 20% and compensation to employees working abroad 1% make up the difference. See Brown and Stern (2001, table 1). 19 Daniels (1985) found that service providers charge higher prices when the service is provided at a distance. 18 sectors, services that are produced subject to increasing returns to scale are differentiated at the firm level. Firms in these industries set prices such that marginal cost (which is constant) equals marginal revenue; and there is free entry, which drives profits to zero. We assume firm level product differentiation and employ the Chamberlinian large group monopolistic competition assumption within a Dixit-Stiglitz framework. Given our assumption on the composition of fixed and variable costs, we have constant markups over marginal cost for both foreign firms and domestic firms, i.e., no rationalization impacts. For domestic firms, costs are defined by observed primary factors and intermediate inputs to that sector in the base year data. When multinationals service providers decide to establish a domestic presence in Kenya, they will import some of their technology or management expertise. That is, foreign direct investment generally entails importing specialized foreign inputs. Thus, the cost structure of multinationals differs from national only service providers. Multinationals incur costs related to both imported primary inputs and Kenyan primary factors, in addition to intermediate factor inputs. Foreign provision of services differs from foreign provision of goods, since the service providers use Kenyan primary inputs. Domestic service providers do not import the specialized primary factors available to the multinationals. Hence, domestic service firms incur primary factor costs related to Kenyan labor and capital only. These services are characterized by firm-level product differentiation. For multinational firms, the barriers to foreign direct investment affect their profitability and entry. Reduction in the constraints on foreign direct investment will induce foreign entry that will typically lead to productivity gains because when more varieties of service providers are available, buyers can obtain varieties that more closely fit their demands and needs (the Dixit- Stiglitz variety effect). 19 Evidence on the Role of Trade and FDI in Increasing Total Factor Productivity through Technology Transfer Grossman and Helpman (1991) have developed models of economic growth that have highlighted the role of trade in a greater variety of intermediate goods as a vehicle for technological spillovers that allow less developed countries to close the technological gap with industrialized countries. Similarly, Romer (1994) has argued that product variety is a crucial and often overlooked source of gains to the economy from trade liberalization. In our model, it is the greater availability of varieties that is the engine of productivity growth, but we believe there are other mechanisms as well through which trade may increase productivity. 20 Consequently, we take variety as a metaphor for the various ways increased trade can increase productivity. Winters et al. (2004) summarize the empirical literature by concluding that the recent empirical evidence seems to suggest that openness and trade liberalization have a strong influence on productivity and its rate of change. Some of the key articles regarding product variety are the following. Broda and Weinstein (2004) find that increased product variety contributes to a fall of 1.2 percent per year in the true import price index. Hummels and Klenow (2005) and Schott (2004) have shown that product variety and quality are important in explaining trade between nations. Feenstra et al. (1999) show that increased variety of exports in a sector increases total factor productivity in most manufacturing sectors in Taiwan (China) and Korea, and they have some evidence that increased input variety also increases total factor productivity. In business services, because of the high cost of using distant suppliers, the close availability of a diverse set of business services may be even more important for growth than in goods. The evidence for this was cited in the introduction section. Beginning with the path-breaking work of Coe and Helpman (1995), a rich literature now exists that shows that important mechanisms for the transmission of knowledge and the increase in total factor productivity are the purchase of imported intermediate goods and inward foreign direct investment. The literature shows that for small developing countries, trading with large technologically advanced countries is crucial for TFP growth.21 A summary of this literature is 20 Trade or services liberalization may increase growth indirectly through its positive impact on the development of institutions (see Rodrik, Subramananian and Trebbi, 2004). It may also induce firms to move down their average cost curves, or import higher quality products or shift production to more efficient firms within an industry. Tybout and Westbrook (1995) find evidence of this latter type of rationalization for Mexican manufacturing firms. 21 Schiff et al., (2002, table 1) have shown that for R&D intensive sectors, trade with industrialized countries contributes significantly to total factor productivity in developing countries, but trade with developing countries 20 provided in Jensen and Tarr (2010, Appendix E). Given the importance of foreign direct investment in services in our model, we mention here that Arnold, Mattoo and Javorcik (2007) show that in the Czech Republic, services sector liberalization led to increased productivity of downstream industries, and the key channel through which reform led to increased productivity was allowing foreign entry. Fernandes and Paunov (2008) found a positive and significant effect of foreign direct investment in services on productivity growth in Chile. Fernandes (2007) finds a positive and significant effect of services liberalization in both finance and infrastructure on the productivity of downstream manufacturing in the fifteen Eastern European countries. In our model, the parameter that reflects the ability of a region to increase total factor productivity through the transmission of new technologies is the elasticity of varieties with respect to the price. Based on the literature summary in Jensen and Tarr (2010, Appendix E), we assign central values to this elasticity based on the region and the research and development intensity of the sector. The assigned central values for these parameters by sector and region are in table 6B. We conduct extensive sensitivity analysis on this parameter, both piecemeal and systematic. V. Data of the Model Social Accounting Matrix The key data source for our study is the social accounting matrix taken from Kiringai. Thurlow and Wanjala (2006). This is a social accounting matrix (SAM) for the year 2003. The table is very rich in agricultural detail, with 20 agricultural sectors. Given our focus on services, we found it necessary to disaggregate the single transportation sector into five sectors (based on value of output data of the various transportation sectors published in the Economic Survey, 2006 for Kenya by the Central Bureau of Statistics (2007a, p. 198)) and the single financial services sector into insurance, and banking and other financial services, from data in Central Bureau of Statistics (2007a, pp. 95-98. We assumed that the input output structure for all sectors using these services was identical for the disaggregated sectors. A full listing of the sectors and factors of production is provided in table 1. Kiringai et al. (2007) also provide a does not. On the other hand, for sectors that are low in R&D intensity, their results suggest that for technology diffusion trade with developing countries can be as important as trade with industrialized countries. 21 set of 20 household accounts integrated into the social accounting matrix. Of these 20 households, ten are rural and ten are urban, ranked according to income. Due to some problems with the consistency of the household data, however, we employ one representative household in this model. . Trade Data by Regional Partner and Sector To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In appendix A, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. Excluding those East African Customs Union countries that are also COMESA members, COMESA includes Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius, Seychelles, Sudan, Swaziland, Zambia and Zimbabwe.22 Trade shares for the Africa region in our model are the sum of East Africa Customs Union plus COMESA. Rest of the World is the residual. We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in appendix A. We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in tables A2 and A3 of appendix A. Tariff Data Tariff and Sales Tax Data. We started with MFN tariff rates at the eight digit level taken from the website of the Kenyan government: www.kra.go.ke/customs/customsdownloads.php. These tariff rates were then aggregated to the sectors of our model, using simple averages. We obtained data on the total taxes on imports and the total value of imports and took the ratio to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent. 23 That is, 22 To avoid double counting we exclude from COMESA the members of the East African Customs Union. 23 Economic Survey (2006, pp. 103, 115). 22 on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production. As we reported in Balestreri, Rutherford and Tarr (2009), the MFN tariff rates, multiplied times the trade flows, exceed the collected tariff rates. That is, using MFN tariff rates for all trade, the weighted average tariff rate exceeds the collected tariff rate of 8.4 percent for the economy as a whole. Thus, they exaggerate the protection received by Kenyan industry and agriculture. This is due to tariff preferences to regional partners and due to other preference items or tariff exemptions. We assume that zero tariffs apply on all imports from the East African Customs Union and from COMESA. 24 Thus, we apply the MFN tariff rates only on the trade flows from outside of these African regions (EU and Rest of World in our model) and take a weighted average tariff rate of the MFN rates on the non-East African regions. The resulting weighted average tariff rate on non-East African imports still exceeds 8.4 percent. We then equi- proportionally reduced all the MFN tariffs in our model so that the estimated collected tariffs on imports from the EU and Rest of World divided by the total value of import is 8.4 percent. Share of Market Captured by Multinational Service Providers It was necessary to calculate the market share of multinational firms in the services sectors by region of the model. Take the banking sector as an example. We need to know the share of the market captured by Kenyan, EU, African and Rest of the World firms, where the countries in the regions are defined in table 1. This entailed acquiring a list of all banks operating in Kenya along with their market share, and, when the bank is owned by multiple parties, allocating the ownership across the regions of our model. The database Bankscope was sufficient for this task in most cases, but websites of the banks had to be consulted to allocate ownership shares in several cases. The results, by region and sector, are presented in table 6. Documentation 24 Kenya agreed to implement zero tariffs on East African Customs Union imports as of January 1, 2005. See Michael-Stahl (2005). 23 of the results, with listing by firm, sector and region, and the data sources are presented in appendix B. Share of Expatriate Labor Employed by Multinational Service Providers The impact of liberalization of barriers to foreign direct investment in business services sectors on the demand for labor in these sectors will depend importantly on the share of expatriate labor used by multinational firms. We explain in the results section that despite the fact that multinationals use Kenyan labor less intensively than their Kenyan competitors, if multinationals use mostly Kenyan labor, their expansion is likely to increase the demand for Kenyan labor in these sectors.25 We obtained estimates of the share of expatriate labor or specialized technology not available to Kenyan firms that is used by multinational service providers in Kenya from the survey mentioned above. We found that multinational service providers use mostly local primary factor inputs and only small amounts of expatriate labor or specialized technology. Our estimated share of foreign inputs used by multinationals in Kenya is presented in the table on sensitivity analysis. Estimates of the Dixit-Stiglitz Elasticities of Substitution for Goods It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. Among the 73 countries, there were four in Sub- Saharan Africa: the Central African Republic, Madagascar, Malawi and Mauritius. We judged 25 See Markusen, Rutherford and Tarr (2005) for a detailed explanation on why FDI may be a partial equilibrium substitute for domestic labor but a general equilibrium complement. 24 that Madagascar was the country closest in characteristics to Kenya, so we took the values of the elasticities estimated for Madagascar as a proxy for the elasticities for Kenya. We explain in appendix C, how we mapped the 3 digit elasticities for 130 goods sectors estimated by Broda et al. into the sectors of our model. The mapping and resulting elasticities by relevant sector in our model are shown in table C1. VI. Results for Preferential Reduction of All Services Barriers--Central Elasticity Case We execute several scenarios to assess the impacts of Kenya entering into a bilateral free trade agreement that includes services with the European Union, and similarly with the Africa region. In these scenarios we assume that Kenyan ad valorem equivalents of the barriers against foreign investors in services are reduced by 50 percent with respect to the region with which Kenya has an agreement. We assume that Kenya already offers tariff free access to goods originating from its African trade partners, so in the scenario where we evaluate the agreement with the Africa region we include only liberalization of discriminatory barriers against foreign investors in services. Insofar as combining preferential trade agreements could potentially reduce trade diversion inherent in separate agreements (see, e.g., Harrison, Rutherford and Tarr, 2002, 2004), we examine the impacts of the combination of free trade agreements with both the Africa region and the European Union. We compare these impacts with unilateral non-discriminatory liberalization. Finally, given our earlier result on the importance of reducing non-discriminatory barriers against investors in services, we examine the impact of a 50 percent reduction of non- discriminatory barriers against service providers combined with unilateral liberalization of discriminatory barriers. As discussed in Jensen and Tarr (2010), who captures the rents from the barriers is very important for the welfare results. Consequently, for each policy scenario, we execute two versions of the model with our central elasticities. In one case, we assume that Kenyans do not capture any rents from the barriers. In the second scenario, we assume that the discriminatory barriers generate rents that are captured by Kenyans. In our systematic sensitivity analysis, in each of the 30,000 scenarios, we allow the share of rents captured by Kenyans to vary 25 stochastically between zero and one. In a section below, we focus on the impacts in professional services by considering the same set of policy experiments where we allow reduction in services barriers only in professional services. Aggregate Effects We first discuss (and present results in tables 7 and 8) our estimates of the full reform scenario in our central elasticities case. In these tables we present results on the impacts on aggregate variables including welfare, the real exchange rate, aggregate exports and imports, the return to capital, skilled labor and unskilled labor and the percentage change in tariff revenue. In order to obtain an estimate of the adjustment costs, we estimate the percentage of each of our factors of production that have to change industries. Significant gains with the EU--deriving primarily from services liberalization. We estimate that the preferential arrangement with the EU that includes both goods and services would generate gains for Kenya of 0.7 percent of consumption with no initial rent capture and 0.5 percent of consumption if there is initial rent capture by Kenyans. The gains come primarily from the preferential liberalization of services, although the relative contribution is much larger with no initial rent capture. That is, the gains to Kenya from preferential liberalization of tariffs with the EU are invariant to the rent capture assumption at 0.2 percent of consumption. But, if there is initial rent capture, the gains to Kenya of preferential liberalization of services fall from 0.5 percent of consumption to 0.3 percent of consumption. Small gains from preferential liberalization with the Africa region. In the case of preferential liberalization with the Africa region, the gains are smaller--0.3 percent of consumption in the case of no initial rent capture and 0.1 percent of consumption in the case of rent capture initially by Kenya. The agreement with the EU includes tariff reduction, while tariff free access in the Africa region is considered part of the status quo; so the appropriate scenario for comparison of the relative gains for Kenya is the scenario in the second column of the results tables, labeled EU discriminatory services. With no initial rent capture, the gains for Kenya of an agreement with the EU are 60 percent greater than the gains from an agreement with the Africa region. With initial rent capture, gains of an agreement with the EU are three times greater than the gains from an agreement with the Africa region. We show in the sensitivity section that 26 there is a possibility of losses from an agreement with the Africa region in the initial rent capture case. Why are the gains larger for the agreement with the "northern" region. As we discussed above, trade with and FDI from large technologically advanced regions can be expected to lead to technology diffusion that increases total factor productivity. Although trade and FDI from small developing countries can contribute to technology diffusion, it has been estimated to do so to a significantly lesser extent, at least for research and development intensive sectors. The elasticity of the number of varieties (firms) with respect to price is the parameter in our model that captures that effect, and the values we have chosen are in table 6B.26 Table 21 shows that we estimate that the number of varieties from the EU substantially increases as a result of preferential liberalization with the EU, while table 18 shows that the estimated expansion of varieties from the Africa region is much more modest in response to preferential liberalization with respect to the African region. We show in the sensitivity analysis below that this elasticity of supply parameter is very important for the results: preferential agreements in services are more likely to be beneficial the higher the supply elasticities of the partner country's services suppliers and the lower the supply elasticities of the excluded countries services suppliers. More substantial gains from combining the Africa FTA with a FTA with Europe. In tables 7 and 8, in the column labeled EU-Africa FTA, we show our estimates for the impacts of agreeing to a FTA with both the EU and the Africa region. The estimated gains are approximately the sum of the separate agreements. This shows that Kenya can significantly augment the gains it may realize from an agreement with the Africa region, by adding a FTA with the EU. Harrison, Rutherford and Tarr (2002) found that, for Chile, the gains from combining free trade agreements would be more than additive. Harrison, Rutherford, Tarr and Gurgel (2004) found similar results for Brazil. That is, the gains of the two agreements combined exceeded the gains of the two separate agreements. The reason is that if Chile, for example, agreed to a free 26 The elasticity of supply corresponds to the share of the sector's costs that are due to a specific factor of production. In all of the imperfectly competitive sectors, we assume there are four specific factors: one for each region in the model. Then, as industry output expands, the price of the specific factor necessary for production of that variety increases, thereby increasing the cost of production of firms. Since the cost of production of firms increases as the industry supply increases, the supply curve of each region will slope up in each of these sectors. And higher cost shares of the specific factor will lead to less elastic supply curves in that sector. 27 trade agreement with the U.S., then competition from the U.S. would greatly reduce the trade diversion associated with an agreement with neighboring developing countries. But there are the possibilities of trade diversion with the rest of the world region, so the gains from combined agreements are not necessarily greater than the gains from the separate agreements. Non-discriminatory liberalization would result in a five-fold increase in the gains compared with preferential liberalization with the EU. With non-discriminatory liberalization, Kenyans would be able to access goods and services from the least cost supplier in the world. This would eliminate all trade diversion losses, reduce any adverse terms of trade losses and result in the maximum number of new foreign varieties for productivity improvement from trade and FDI liberalization. Consequently, the gains are much larger in this case. Because the rest of the world has a much larger share of the goods market in Kenya than it enjoys in the services sectors, the gains from non-discriminatory liberalization come more from liberalization of goods than from services. The largest gains come from reduction in the barriers that domestic as well as foreign firms face. Consistent with the work of Jensen, Rutherford and Tarr (2009) in a model with an aggregate rest of the world, we find that the largest gains for Kenya would come from liberalization of the non-discriminatory barriers in services. That is, when we estimate the impact of a 50 percent reduction in the non-discriminatory services barriers on top of unilateral liberalization of all discriminatory services barriers, the estimated gains are 10.3 percent of consumption with no rent capture or 7.0 percent of consumption with initial rent capture. Sector Impacts In table 9, we present results for the percentage change in output by sector for four scenarios: an FTA with the EU; and FTA with the Africa region; and FTA with the EU and the Africa region combined; and unilateral liberalization. Details of what is included in these scenarios are explained in table 7. In general we see an expansion of the output of the business services sectors in all scenarios. Multinational firms in the business services sectors are located in the home country and their output is defined as part of industry output. Reduction of barriers against one partner generally reduces the number of firms from the other three regions in the model, but on balance the output of the sector expands. To see what happens to EU firms, versus Kenyan and other 28 firms, it is necessary to view the tables that report the change in the number of firms by scenario. Outside of business services, we estimate that mining, coffee and other manufactured food sectors are the sectors that will expand the greatest. These sectors are intensive users of business services, such as transportation and banking services. Regulatory reforms will decrease the price and allow for quality improvements in these business services, which permits these sectors to operate more cheaply and offer better quality services. Given that we assume that total employment and the capital stock are fixed in the medium term, if labor expands in some sectors, it must contract in other sectors. Given the large expansion in several sectors, especially services, we must have declines in others in the medium term. Although we estimate small declines in output in several sectors, especially those that use business services less intensively, the striking result is that there is very little output decline in response to any regional initiative. On the other hand, since we assume zero tariffs in our unilateral reform scenario, output declines are much more pronounced in some cases. Sugarcane, which is the one of the more highly protected sectors, is estimated to decline substantially along with wheat and rice in the unilateral scenario. The stark result from table 9 is that for sectors that experience output declines, the contractions are generally much more moderate in the regional preferential scenarios than in the unilateral scenario. This follows from the less substantial drop in overall protection to any sector in a preferential trade arrangement. VII. Sensitivity Analysis In this section we assess the impact of parameter values and key modeling assumptions on the results. Through our piecemeal sensitivity analysis we will determine the most important parameters for the results, and we will assess how important for the results are rent capture or additional varieties from reform in services sectors under increasing returns to scale. In the piecemeal sensitivity analysis, we change the value of a single parameter while holding the values of all other parameters unchanged at our central elasticity values. We present piecemeal sensitivity analysis of the two most relevant policy scenarios. In table 22, we examine the 29 prospective free trade agreement with the EU, and in table 23, we examine the agreement with the Africa region. Given uncertainty of parameter values and the rent capture assumption, point estimates of the results may be viewed with skepticism. In our systematic sensitivity analysis, we execute 30,000 simulations. In each simulation, we allow the computer to randomly select the values of all parameters, subject to the specified probability distributions of the parameters. Through the systematic sensitivity analysis we will be able to assess how robust the results are and obtain confidence intervals of the results. Rent Capture Assumption In the row labeled share of rents captured we retain the increasing returns to scale assumption in the services sectors and selected goods sectors, but allow the initial rent capture share in the services sectors to be either zero (central value ) or 1 (upper value). We see that there is approximately a 40 percent reduction in the welfare gain from a free trade agreement with the EU if rents are captured initially (from a welfare gain of 0.67 percent of consumption to 0.49 percent of consumption). In the case of an agreement with the African region, the gains fall even more dramatically, from a welfare gain of 0.25 percent of consumption to a gain of 0.05 percent of consumption in our central elasticity case. Impact of Constant Returns to Scale--Possible Negative Welfare Effects In the row labeled CRTS--share of rents captured--we assume constant returns to scale in all sectors, which eliminates the Dixit-Stiglitz externality from additional varieties. We allow the initial rent capture share in the services sectors to be either zero (central value) or 1 (upper value). We see that without the Dixit-Stiglitz variety externality, the gains from an agreement with the EU fall dramatically. With no initial rent capture, the gains for the EU agreement would be .09 percent of consumption, and would fall to negative values (-0.06 percent of consumption) with initial rent capture. In the case of an agreement with the Africa region, the gains are 0.14 percent of consumption with no initial rent capture and are negative (-0.06 percent of consumption) with initial rent capture. 30 Piecemeal Sensitivity Analysis Four parameters stand out as having a strong impact on the results. The elasticity of substitution between firm varieties in imperfectly competitive services sectors, (q i, qj) has a very strong impact. At the low end of the elasticity range, the estimated gains are almost 10 percent of consumption from a preferential agreement with the EU and 5 percent of consumption from an agreement with the Africa region. Following from the Le Chatelier principle, larger elasticities typically lead to larger welfare gains in response to welfare improving reforms, as the economy can adapt more readily. Unlike other elasticities, however, a lower value of (qi,qj) increases the welfare gains. This is because lower values of this elasticity imply that varieties are less close to each other, so additional varieties are worth more. Since the policy shocks in goods are much less, the same elasticity variation in goods has a much smaller impact, but its impact is nonetheless significant. The elasticity of substitution between value-added and business services, (va, bs), also has a strong impact. The better firms are able to substitute business services for labor and capital, the more the economy will gain from the reforms that reduce the quality adjusted price of business services. Finally, for the agreement with the EU, there is a strong impact from changes in the value of EU , the elasticity of multinational service firm supply with respect to the price of output. For the agreement with Africa, there is a strong impact of the parameter AFR .Larger values of this parameter mean that tariff preferences that open opportunities for EU service firms to provide new varieties, will not be so quickly choked by the increased cost of the specific factor required for EU firm expansion. We investigate the sensitivity of the results to changes in the value of this parameter in more detail below. Impact of Partner and Excluded Country Elasticities of Multinational Service Firm Supply--Why It Is More Likely to Obtain Gains from Large Technologically Advanced Partners In figures 1 and 2, we depict the impact and interrelationship of the elasticities of firm supply from partner and excluded countries. In figure 1, we examine the estimates for the welfare effects in Kenya of a 50 percent preferential reduction of barriers in services against African partners. On the vertical axis is the set of elasticities of firm supply of African partners with respect to price. We scale this set of elasticities from between one-half to twice their central values. On the horizontal axis we scale the central values of the elasticities of firm supply of all 31 excluded countries from one-half of their central values to twice their central values. Excluded regions are the EU and Rest of the World. In figure 2, we do analogous simulations, except that since the preferential liberalization is with the EU, the EU elasticities are on the vertical axis and we scale the elasticities of the African region and the Rest of the World on the horizontal axis. In the left hand side panel, we present results with no initial rent capture, but initial rent capture is shown on the right hand side panel. Regarding preferential reduction of barriers with African partners, we see in figure 1 that, with initial rent capture, there is a significant range of elasticities that result in losses for Kenya. Without initial rent capture, however, there are gains for all these values. We see from figures 1 and 2 that the gains to the home country increase the higher the elasticity of supply of firms in partner countries and the lower the elasticity of supply of firms in excluded countries, with the partner country elasticity being by far the more important. Preferential reduction of barriers, leads to an increase in firms (varieties) and productivity from partner countries, but a loss of service providers (varieties) from all excluded regions and the home country, results in a loss of productivity for sectors using these services. The lost productivity from lost varieties from the regions excluded and the home country from the preferential liberalization in services is analogous to the trade diversion losses in perfect competition. When firm elasticities in partner countries are high, the after tax price increase for firms in partner countries from preferential reduction of barriers induces a large increase in partner country varieties, boosting productivity. For excluded countries, the price decrease of partner countries shifts in demand for their products and lowers their price; but the lower price induces fewer lost varieties when firms in excluded countries have low elasticities. In addition to the variety impacts in imperfect competition, as explained above, in perfect competition the rent and terms of trade impacts reinforce the argument that high elasticities of partners and low elasticities of excluded countries increase the likelihood of welfare gains from a preferential agreement in services. Systematic Sensitivity Analysis In the systematic sensitivity analysis, we execute the model 30,000 times and harvest the results for desired variables. In each individual simulation, we allow the computer to select values of all the parameters in the model (the parameters in table 22), based on the specified 32 probability density functions (pdfs) of the parameters. We assume uniform probability density functions, with upper and lower values of the pdfs given by the upper and lower values in the piecemeal sensitivity analysis table. We include initial rent capture in the systematic sensitivity analysis, with the rent capture parameter allowed to take values between zero and one with a uniform pdf. The results for preferential reduction of barriers with African partners on welfare, output and labor are shown in figures 3-5 and similar figures for the preferential trade agreement with the EU in figures 6-8.. The sample distribution of the welfare results for preferential reduction of barriers in services with the Africa region is depicted in figure 3. We find that in 1.9 percent of the 30,000 simulations yield a negative welfare result, which we interpret as a 1.9 percent probability that preferential liberalization with the Africa region will yield a negative result. A 95 percent confidence interval for equivalent variation as a percent of consumption is: 0.008 to 0.417 around a sample mean of .203.27 The sample distribution of the welfare results for a free trade agreement with the EU that includes services are depicted in figure 6. In the 30,000 simulations, there are no negative values. A 95 percent confidence interval for equivalent variation as a percent of consumption is: 0.37 to 0.94 around a sample mean of 0.63.28 In figures 4 and 7, we show box and whisper diagrams for the sample distribution of the percentage change in output by sector. Sectors are on the horizontal axis and the percentage change in output is shown on the vertical axis. The bars in the box are the means of the distributions. Fifty percent confidence intervals are depicted by the boxes, while the vertical lines show 95 percent confidence intervals. The means of the systematic sensitivity results show a similar pattern to the point estimates regarding the expansion of the services sectors. While the confidence intervals are rather tight for most sectors, they reveal a large range of uncertainty for several sectors. With respect to the EU agreement, while the sign of the direction of change does not change within the 95 percent confidence interval, the confidence intervals of expected output change are large for other manufactured food, maritime transportation, coffee and mining (among the expanding sectors) and sugarcane, non-metallic products and other manufactures (on the negative side). For 27 90 percent and 99 percent confidence intervals are 0.033 to 0.384 and -0.029 to 0.479, respectively. 28 90 and 99 percent confidence intervals are 0.41 to .89 and 0.30 to 1.07, respectively. 33 several sectors, where the mean change in output is close to zero, notably wood and paper, and chemicals, 95 percent confidence intervals reveal that the estimated sign of the change is not robust. For the Africa agreement, it is remarkable how tight the 95 percent confidence intervals are for those sectors where the mean predicted change is small (with the exception again of wood and paper). Confidence intervals are much less tight for sectors with significant predicted changes, but the sign of the change does not change throughout the confidence intervals. VIII. Results for Preferential Reduction of Barriers in Professional Services Liberalization In these scenarios we consider the impact of a preferential reduction in barriers in professional services, but assume that there is no reduction of barriers in other services sectors. We execute the same policy scenarios that we executed in the earlier section, where all services barriers are reduced on a preferential basis. The results are shown in tables 24-26. In table 24, we assume no initial rent capture, but in table 25, we assume initial rent capture. We find that preferential liberalization of professional services with respect to its African regional partners would result in small positive welfare gains (0.02 percent of consumption) in the case of no initial rent capture, but virtually no welfare impact with initial rent capture. 29 But, it may be seen as positive if it is an important first step toward wider liberalization. Preferential liberalization of professional services with the EU will yield positive gains of 0.06 percent of consumption (in the no initial rent capture case). If liberalization in professional services is extended to all foreign partners (called unilateral in the tables), the gains would increase to 0.16 percent of consumption.. Thus, these results suggest that preferential liberalization in the region may be a useful, but wider liberalization, with larger partners or multilaterally will yield much larger gains. Finally, we estimate that the largest gains in professional services reform would come from a serious effort to reduce non-discriminatory regulatory barriers (that is, barriers that raise 29 Taken to a third decimal place, the impact is negative with initial rent capture. 34 the costs of Kenyans as well as foreign professional services providers in Kenya). This would more than quadruple the benefits of multilateral liberalization in Kenya. These results are consistent with the work of Dihel et al., (2010). They have documented extensive regulatory barriers in professional services in Kenya and other East African countries that prevent entry of potential suppliers and limit competition among existing professional service suppliers. As we mentioned in section III, we used engineering services as a proxy for all services when we estimated the barriers and calculated the market shares of different regions. Experts in this field in East Africa have suggested that engineering services are likely less constrained than some other services, notably legal services. This would imply that the ad valorem equivalents of the barriers would be higher if averaged with the other more protected sectors, and the gains from non-discriminatory liberalization would be higher. In some other sectors, like accounting, there is a much greater foreign presence. With higher barriers, discriminatory liberalization with the Africa region in accounting could displace a larger number of varieties in a sector like accounting and increase trade diversion. IX. Conclusions In this paper we have developed an innovative small open economy computable general equilibrium model of the Kenyan economy that is capable of assessing the impact of the preferential liberalization of barriers against multinational service providers. We have provided a discussion of the welfare economics of services liberalization, in which we argue that it is likely that the gains from preferential reduction of barriers against multinational services will be larger if the partner region is large. We find that gains to Kenya from preferential reduction of barriers in services with the Africa region are negligible, and could be negative under some plausible parameter assumptions. We show that under imperfect competition with the Dixit-Stiglitz 35 variety externality, welfare losses from preferential reduction of services barriers are possible due to a loss of varieties from excluded countries. Gains from a free trade agreement with the EU that includes a preferential 50 percent reduction in the ad valorem equivalents of the barriers in the services sectors will produce significant gains for Kenya, deriving primarily from services commitments. Kenyan gains from liberalization with the EU region are considerably larger than the gains from an agreement with the African region because a small increase in the price to EU producers can be expected to induce a relatively large increase in the number of varieties. In addition, when rents are captured initially, the terms of trade loss on partner services and the lost rents on services from the Rest of the World are larger when partners have low supply elasticities. We have conducted extensive sensitivity analysis to determine confidence intervals for the results and found plausible ranges of key parameters that lead to estimated losses for preferential liberalization of services with the Africa region. References Arnold, Jens, Aaditya Matoo and Beata Javorcik (2007), Does Services Liberalization Benefit Manufacturing Firms: Evidence from the Czech Republic, World Bank Policy and Research Working Paper Number 4109. Baldwin, Richard E., Forslid, Rikard and Haaland, Jan (1999), "Investment Creation and Investment Diversion: Simulation Analysis of the Single Market Programme," in R. Baldwin and J. Francois (eds.), Dynamic Issues in Applied Commercial Policy Analysis, Cambridge: Cambridge University Press. Balistreri, Edward J. and James Markusen (2009), Sub-national differentiation and the role of the firm in optimal international pricing, Economic Modellling, 26(1), 47 - 62. Balistreri, Edward J., Thomas F. Rutherford and David G. Tarr (2009) Modeling Services Liberalization: The Case of Kenya, Economic Modeling, Vol. 26 (3),May, 668-679. Broda, Christian and David Weinstein (2004), Variety, Growth and World Welfare, American Economic Review, 94 (2), May, 139-144. Broda, Christian, Josh Greenfield and David Weinstein (2006), From Groundnuts to Globalization: A Structural Estimate of Trade and Growth, National Bureau of Economic Research Working Paper No. 12512. Available at: http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/BrodaGroundnuts.pd f . 36 Brown, Drusilla, Alan Deardorf, Alan Fox and Robert Stern (1996), Liberalization of Services Trade, in W. Martin and L. A. Winters, eds., The Uruguay Round and the Developing Countries, Cambridge: Cambridge University Press. Brown, Drusilla and Robert Stern (2001), Measurement and Modeling of the Economic Effects of Trade and Investment Barriers in Services, Review of International Economics, 9(2): 262-286. Central Bureau of Statistics (2007a), Economic Survey, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya. Central Bureau of Statistics (2007b), Statistical Abstract, 2006, Nairobi: Ministry of Planning and National Development, Republic of Kenya. Chinitz, B. (1961), Contrast in agglomeration: New York and Pittsburgh, American Economic Review, Papers and Proceedings, 51:279-89. Cox, David and Richard G. Harris (1986), A Quantitative Assessment of the Economic Impact on Canada of Sectoral Free Trade with the United States, The Canadian Journal of Economics, Vol. 19 (3), August, 377-394. Daniels, P.W. (1985), Service Industries: A Geographical Appraisal, New York: Methuen & Co. Dee, Philippa, Kevin Hanslow and Tien Phamduc (2003), Measuring the Costs of Barriers to Trade in Services, in Takatoshi Ito and Anne Krueger (eds.), Trade in Services in the Asia-Pacific Region, Chicago: University of Chicago Press. Dihel, Nora, Ana Margarida Fernandes, Aaditya Mattoo and Nicholas Strychacz (2010), Reform and Regional Integration of Professional Services in East Africa, Economic Premise No. 32, September, The World Bank. Available at: www.worldbank.org/economicpremise. Dixit, A. and J. Stiglitz (1977), Monopolistic Competition and Optimum Product Diversity, American Economic Review, 76(1):297-308. Ethier, W.J. (1982), National and International Returns to Scale in the Modern Theory of International Trade, American Economic Review, 72(2):389-405. Fernandes, Ana M (2007), Structure and performance of the services sector in transition economies, World Bank Policy and Research Working Paper Number 4357. Available at: http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK= 64165421&menuPK=64166093&entityID=000158349_20070919101422 Fernandes, Ana M and Caroline Paunov (2008), Foreign direct investment in services and manufacturing productivity growth: evidence for Chile, World Bank Policy and Research Working Paper Number 4730. Available at: http://econ.worldbank.org/external/default/main?pagePK=64165259&theSitePK=469372&piPK= 64165421&menuPK=64166093&entityID=000158349_20080929085154. Francois, Joseph and Clint Shiells (eds., 1994), Modeling Trade Policy: Applied General Equilibrium Assessments of North American Free Trade, Cambridge: Cambridge University Press. 37 Fujita, Masahisa, Paul Krugman and Anthony J. Venables (1999), The Spatial Economy: Cities, Regions, and International Trade, Cambridge MA: MIT Press. Harris, Richard (1084), Applied General Equilibrium Analysis of Small Open Economies with Scale Economies and Imperfect Comp, The American Economic Review, Vol. 74 (5), December, 1016- 1032 Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997), Quantifying the Uruguay Round, Economic Journal, Vol 107 (444), September, 1405-1430. Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1993), "Trade Reform in the Partially Liberalized Economy of Turkey," The World Bank Economic Review, May. Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr. (1996), AIncreased Competition and Completion of the Market in the European Community: Static and Steady-State Effects,@ Journal of Economic Integration, 11(3), September 1996, 332-365. Harrison, Glenn H., Thomas F. Rutherford and David G. Tarr (1997a), AEconomic Implications for Turkey of a Customs Union with the European Union,@ European Economic Review, 41(3-5), 861-870. Harrison, Glenn H., Thomas F. Rutherford, David G. Tarr and Angelo Gurgel (1997a) Trade Policy and Poverty Reduction in Brazil, The World Bank Economic Review, Vol. 18, 289-317, 2004. Helu, Samuel (2007), Maritime Services in Kenya, paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27. Heston, Alan, Robert Summers and Bettina Aten (2006), Penn World Table Version 6.2, Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania, September. Holmes, T. (1995), Localization of Industry and Vertical Disintegration, Federal Reserve Bank of Minneapolis. Hummels, D (1995), Global Income Clustering and Trade in Intermediate Goods, Graduate School of Business, University of Chicago. Ikiara, Gerrishon K., Moses I. Muriira and Wilfred Nyangena (2000), Kenya's Trade in Services: Should the Country Fully Liberalize, in Services in the International Economy, Robert Stern (ed.), Ann Arbor: University of Michigan Press. Jensen, Jesper, Thomas F. Rutherford and David G. Tarr (2007), The Impact of Liberalizing Barriers to Foreign Direct Investment in Services: The Case of Russian Accession to the World Trade Organization, Review of Development Economics, Vol 11 (3), August, 2007, 482-506. Jensen, Jesper and David G. Tarr (2010), Can Preferential Liberalization of Services Reduce Welfare: The Case of Tanzania, The World Bank, draft mimeo. 38 Kang, Joog-Soon (2000), Price Impact of Restrictiveness on Maritime Transportation Services, in Findlay, Christopher and Tony Warren (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge). Kimura, Fukunari, Mitsuyo Ando and Takamune Fujii (2004a), Estimating the Ad Valorem Equivalent of Barriers to Foreign Direct Investment in the Telecommunications Services Sectors in Tanzania . Available at http://www.worldbank.org/trade/russia-wto. Kiptui, Moses (2007), Financial Services in Kenya, paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27. Kiringai, Jane , James Thurlow and Bernadette Wanjala (2006), A 2003 Social Accounting Matrix (SAM) For Kenya, Kenya Institute for Public Policy Research and Analysis (KIPPRA) and International Food Policy Research Institute (IFPRI), August. Konan, Denise Eby and Keith E. Maskus (2006), Quantifying the impact of services liberalization in a developing country, Journal of Development Economics, Vol. 81, 142-162. Levy, Santiago & van Wijnbergen, Sweder (1995), "Transition Problems in Economic Reform: Agriculture in the North American Free Trade Agreement," American Economic Review, Vol. 85(4), September, 738-54. Markusen, James R, Thomas Rutherford and David Tarr (2005), Trade and Direct Investment in Producer Services and the Domestic Market for Expertise, Canadian Journal of Economics, Vol 38 (3), 758-777. Marshall, J.N. (1988), Services and Uneven Development, London: Oxford University Press. Matoo, Aaditya and Carsten Fink (2003), Regional agreements and trade services - policy issues, Volume 1, World Bank Policy and Research Working Paper Number 2852. Available at: http://www- wds.worldbank.org/external/default/WDSContentServer/IW3P/IB/2002/07/09/000094946_020621 04102344/Rendered/PDF/multi0page.pdf. Matoo, Aaditya and Pierre Sauve (2008), Regionalism in Services Trade, in A Handbook of International Trade in Services, Aaditya Mattoo, Robert M. Stern and Gianni Zanini (eds.), Oxford: Oxford University Press. McGuire, Greg and Michael Schuele (2000), Restrictiveness of International Trade in Banking Services, in Findlay, Christopher and Tony Warren (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge). McKee, D.L. (1988), Growth, Development, and the Service Economy in the Third World, New York: Praeger Publishers. Mircheva, Bladislava (2007), Ad valorem equivalence to FDI restrictiveness, Kenya Washington D.C.: The World Bank, mimeo. 39 Matano, M. Ndaro and James Njeru (2007), Communication Services in Kenya, paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27. Ng, Francis and Alexander Yeats (2005), Kenya: Export Prospects and Problems, Africa Region Working Paper Number 90, Washington DC: World Bank. Nguyen-Hong, D. (2000), Restrictions on Trade in Professional Services, Productivity Commission Staff Research Paper, Ausinfo, Canberra. Available at: http://www.pc.gov.au/research/staffresearch/rotips Ochieng, David (2007), Transport Services in Kenya, paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27. Oresi, Samuel (2007), Railway Services in Kenya, paper presented at the Trade in Services Workshop in Nairobi, Kenya, sponsored by the Ministry of Industry and Trade of Kenya and International Lawyers and Economists against Poverty, Nairobi, March 26, 27. Rutherford, Thomas F. and David Tarr (2008), "Poverty Effects of Russian WTO accession: modeling real households with endogenous productivity effects, Journal of International Economics, Vol 75 (1), 131-150. Rutherford, Thomas F. and David Tarr (2003), "Regional Trading Arrangements for Chile: Do the Results Differ with a Dynamic Model?" Integration and Trade, Vol. 7, Number 18, 117-139. Rutherford, Thomas F. and David Tarr (2002), "Trade Liberalization and Endogenous Growth in a Small Open Economy," Journal of International Economics, 56 (2), March, 247-272 Rutherford, Thomas F., Rutstrom, E.E. and Tarr, David G. (1993). Morocco's free trade agreement with the European Community: A quantitative assessment, Economic Modelling, Vol. 14, No. 9, 237- 269. Rutherford, Thomas, Rutstrom, E.E. and Tarr, David (1995), The free trade agreement between Tunisia and the European Union, World Bank, Washington, DC Mimeo. Reprinted as Rutherford, Thomas, Rutstrom, E.E. and Tarr, David (2000) A Free Trade Agreement Between the European Agreement and a Representative Arab Mediterranean Country: A Quantitative Assessment, in Bernard Hoekman and Jamel Zarrouk (eds.), Catching Up with the Competition: Trade Opportunities and Challenges for Arab Countries, Ann Arbor: University of Michigan Press. Smith, Alasdair, and Venables, Anthony J., "Completing the Internal Market in the European Community: Some Industry Simulations", European Economic Review, 32, 1988, 1501-1525. Tarr, David (2002), "On the Design of Tariff Policy: Arguments for and Against Uniform Tariffs, in B. Hoekman, A. Mattoo and P. English (eds.), Development, Trade and the WTO: A Handbook, Washington: World Bank. Tarr, David (2007a), Policy Note on the Kenyan Telecommunications Sector, in World Bank (2007). Tarr, David (2007b), Policy Note on the Kenyan Transportation Sector, in World Bank (2007). 40 Telecommunications Management Group, Inc.(2007), Trade in Information and Communication Services: Opportunities for East and Southern Africa, Chapter on Kenya, Draft report to the World Bank, January 31, pp.15-39. United Nations Conference on Trade and Development and World Bank (1994), Liberalizing Trade in Services: A Handbook, New York and Geneva: United Nations. United Nations Conference on Trade and Development, Division on Transnational Corporations and Investment (1995 and 1996), World Investment Report 1995 and 1996, New York and Geneva: United Nations. Warren, Tony (2000), The Impact on Output of Impediments to Trade and Investment in Telecommunications Services, in Findlay, Christopher and Tony Warren (eds), Impediments to Trade in Services: Measurement and Policy Implications, (London: Routledge). World Bank (2007), Kenya: Unleashing the Potential for Trade and Growth, Report No. 37688-KE, World Bank: Washington, DC. Available at: http://imagebank.worldbank.org/servlet/WDSContentServer/IW3P/IB/2007/03/16/000310607_20 070316095914/Rendered/PDF/376880KE.pdf 41 42 Table 1 -- List of Sectors in the Kenya Model Business Services Agriculture (CRTS) Communication Maize Insurance Wheat Banking and other financial services Rice Professional business services Barley Road services Cotton Railway transport Other cereals Maritime transport Sugarcane Pipeline transport Coffee Airline transport Tea Roots & tubers IRTS Goods Pulses & oil seeds Beverages & tobacco Fruits Grain milling Vegetables Sugar & bakery & confectionary Cut flowers Petroleum Others crops Chemicals Beef Metals and machines Dairy Non metallic products Poultry Sheep goat and lamb for slaughter Factors of Production Other livestock Skilled labor Semi-skilled labor Other CRTS Unskilled labor Fishing Capital Forestry Land Mining Meat & dairy Regions Other manufactured food Kenya Textile & clothing Africa (East African Customs Union + COMESA) Leather & footwear EU (27) Wood & paper Rest of World Printing and publishing Other manufactures Water Electricity Construction Trade Hotels Real estate Adminsitration Health Education Note: East African Custom Union includes (besides Kenya) Burundi, Rwanda, Tanzania and Uganda. COMESA includes Burundi, Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Libya, Madagascar, Malawi, Mauritius, Rwanda, Seychelles, Sudan, Swaziland, Zambia and Zimbabwe. 43 Table 2 -- Sectoral value-added (%, unless otherwise indicated) Labor GDP Skilled Semi- Unskilled Capital Land BKS (Billions % of total labor skilled labor of Kenyan labor Shillings) Business Services Communication 3.7 19.7 13.7 62.9 30.6 3.1 Insurance 1.2 5.4 19.3 74.0 21.1 2.2 Banking and other financial services 1.2 5.4 19.3 74.0 45.7 4.7 Professional business services 23.1 4.4 14.3 58.3 94.5 9.7 Road services 9.9 34.6 5.5 50.0 42.0 4.3 Railway transport 9.9 34.6 5.5 50.0 1.2 0.1 Maritime transport 9.9 34.6 5.5 50.0 4.6 0.5 Pipeline transport 9.9 34.6 5.5 50.0 2.1 0.2 Airline transport 9.9 34.6 5.5 50.0 16.9 1.7 Dixit-Stiglitz Goods Beverages & tobacco 0.7 34.0 65.2 13.7 1.4 Grain milling 2.1 9.5 2.9 85.5 9.6 1.0 Sugar & bakery & confectionary 7.9 36.8 11.7 43.6 4.4 0.5 Petroleum 0.4 1.3 98.4 3.9 0.4 Chemicals 16.4 5.4 29.7 48.5 7.1 0.7 Metals and machines 2.8 55.0 2.9 39.2 8.2 0.8 Non metallic products 0.5 9.8 89.7 23.1 2.4 Agriculture Maize 10.7 48.0 0.2 10.7 30.4 28.9 3.0 Wheat 0.7 25.0 20.6 53.7 0.4 0.0 Rice 24.8 21.2 22.6 31.3 1.1 0.1 Barley 1.1 24.9 20.6 53.4 0.7 0.1 Cotton 17.4 26.3 0.1 12.7 43.5 0.3 0.0 Other cereals 8.6 24.6 0.2 23.5 43.2 0.1 0.0 Sugarcane 7.6 37.6 0.3 11.5 43.1 1.8 0.2 Coffee 14.6 30.1 0.2 12.2 42.8 5.6 0.6 Tea 13.9 45.3 0.2 10.6 30.0 35.0 3.6 Roots & tubers 11.6 38.3 0.3 31.9 18.0 10.0 1.0 Pulses & oil seeds 12.0 38.0 0.5 11.9 37.7 19.0 1.9 Fruits 15.3 34.0 0.2 10.6 39.9 13.5 1.4 Vegetables 14.7 38.7 0.3 29.8 16.5 22.0 2.2 Cut flowers 35.2 19.7 0.1 10.3 34.7 11.7 1.2 Others crops 15.3 36.5 0.6 27.3 20.3 7.3 0.7 Beef 24.8 36.2 0.5 38.5 13.9 1.4 Dairy 26.1 35.7 0.2 38.1 23.6 2.4 Poultry 15.3 43.4 0.8 40.5 15.2 1.6 Sheep goat and lamb for slaughter 28.2 36.9 0.2 34.6 5.1 0.5 Other livestock 6.5 35.4 0.2 58.0 3.8 0.4 Other CRTS Fishing 3.7 7.4 88.8 3.9 0.4 44 Table 2 -- Sectoral value-added (%, unless otherwise indicated) continue Forestry 3.1 23.2 73.7 7.0 0.7 Mining 16.4 30.9 52.7 3.2 0.3 Meat & dairy 3.2 27.6 0.0 69.2 11.9 1.2 Other manufactured food 8.3 36.1 0.5 55.1 0.9 0.1 Printing and publishing 44.8 55.2 5.7 0.6 Textile & clothing 57.0 9.3 0.6 33.1 5.4 0.6 Leather & footwear 13.9 2.3 83.9 5.2 0.5 Wood & paper 4.4 7.1 27.1 61.4 2.9 0.3 Other manufactures 3.3 63.9 0.6 32.3 29.8 3.0 Water 28.8 10.9 60.3 13.1 1.3 Electricity 0.7 25.4 1.5 72.3 12.9 1.3 Construction 1.5 14.9 2.5 81.1 51.8 5.3 Trade 16.6 5.6 7.0 70.8 63.6 6.5 Hotels 51.1 5.0 0.9 43.1 9.8 1.0 Real estate 0.3 29.8 13.0 57.0 56.2 5.8 Adminsitration 1.1 12.1 8.0 78.8 49.3 5.1 Health 1.6 2.6 92.5 3.2 21.2 2.2 Education 0.8 2.9 66.4 30.0 74.9 7.7 45 Table 3 -- Trade Flows Imports Exports BKS % of total % of supply BKS % of total % of output Business Services Communication 1.9 0.8 4.1 Insurance 2.4 0.7 7.5 0.4 0.2 1.5 Banking and other financial services 5.1 1.5 7.6 0.9 0.4 1.5 Professional business services Road services 29.9 9.0 30.7 20.3 8.3 23.1 Railway transport 1.0 0.3 29.7 Maritime transport 3.7 1.1 29.8 2.6 1.1 23.1 Pipeline transport 1.7 0.5 29.7 1.2 0.5 23.1 Airline transport 12.9 3.9 30.1 9.0 3.7 23.1 Dixit-Stiglitz Goods Beverages & tobacco 1.4 0.4 5.1 12.1 4.9 30.4 Grain milling 0.7 0.2 2.1 Sugar & bakery & confectionary 2.9 0.9 14.6 2.0 0.8 10.8 Petroleum 60.0 18.0 56.8 14.7 6.0 49.0 Chemicals 50.4 15.1 67.2 12.9 5.2 71.2 Metals and machines 48.0 14.4 69.4 5.0 2.0 55.8 Non metallic products 2.9 0.9 8.7 3.8 1.5 11.1 Agriculture Maize 0.7 0.2 2.0 0.3 0.1 0.6 Wheat 10.9 3.3 96.1 0.1 0.0 14.6 Rice 3.9 1.2 53.7 Barley 0.1 0.0 11.0 Cotton 0.0 0.0 7.4 Other cereals 0.0 0.0 41.2 Sugarcane 1.5 0.4 42.5 1.5 0.6 33.7 Coffee 11.7 4.8 86.6 Tea 0.4 0.1 9.0 47.1 19.1 91.5 Roots & tubers Pulses & oil seeds 0.5 0.1 3.4 8.1 3.3 38.3 Fruits 2.0 0.8 18.2 Vegetables 0.5 0.1 2.7 7.9 3.2 31.0 Cut flowers 21.3 8.7 98.4 Others crops 0.7 0.2 6.0 4.5 1.8 29.9 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 0.4 0.1 31.5 6.1 2.5 95.2 Meat & dairy 1.0 0.3 2.9 12.8 5.2 25.7 Other manufactured food 22.9 6.8 76.4 2.8 1.2 69.6 Printing and publishing 11.1 3.3 34.9 Textile & clothing 9.4 2.8 43.6 4.4 1.8 31.2 Leather & footwear 1.6 0.5 9.9 3.5 1.4 20.4 Wood & paper 2.9 0.9 43.4 8.4 3.4 88.9 Other manufactures 35.4 10.6 43.9 14.7 6.0 22.2 Water Electricity Construction Trade Hotels Real estate 7.4 2.2 10.1 1.5 0.6 2.3 Adminsitration Health Education 46 Table 4 -- Benchmark Distortions (%) Regulatory barriers Tariff Sales Tax All firms Foreign firms Business Services Communication 6.0 4.0 Insurance 0.6 13.0 26.0 Banking and other financial services 0.6 17.0 Professional business services 3.7 11.9 Road services 15.0 30.0 Railway transport 25.0 Maritime transport 57.0 40.0 Pipeline transport Airline transport 2.0 2.0 Dixit-Stiglitz Goods Beverages & tobacco 30.4 44.0 Grain milling 25.8 9.4 Sugar & bakery & confectionary 23.5 19.5 Petroleum 10.4 22.4 Chemicals 8.8 4.8 Metals and machines 9.5 5.2 Non metallic products 19.3 0.7 Agriculture Maize 29.6 Wheat 11.0 Rice 27.6 Barley 9.9 Cotton 12.5 12.5 Other cereals 9.9 Sugarcane 64.2 19.4 Coffee 19.7 Tea 19.7 5.1 Roots & tubers Pulses & oil seeds 6.7 0.0 Fruits 19.5 Vegetables 19.7 0.1 Cut flowers 19.7 Others crops 2.7 3.4 Beef 19.7 Dairy 28.9 Poultry 19.7 Sheep goat and lamb for slaughter Other livestock 19.7 Other CRTS Fishing 19.7 Forestry Mining 1.2 4.1 Meat & dairy 27.6 15.5 Other manufactured food 15.8 5.5 Printing and publishing 12.1 Textile & clothing 14.4 8.5 Leather & footwear 13.8 14.5 Wood & paper 9.2 5.9 Other manufactures 17.2 3.0 Water Electricity Construction Trade 1.9 Hotels 13.9 Real estate Adminsitration Health Education Source: Authors' estimates. See Balistreri, Rutherford, and Tarr (2009) for details. 47 Table 5 -- Trade Flows by Trading Partner (%) Imports Exports European Union Africa Rest of the World European Union Africa Rest of the World Business Services Communication 66 0 34 Insurance 23 0 77 23 0 77 Banking and other financial services 75 1 24 75 1 24 Professional business services Road services 10 70 20 10 70 20 Railway transport 0 0 100 Maritime transport 45 27 27 45 27 27 Pipeline transport 0 41 59 0 41 59 Airline transport 43 14 43 43 14 43 Dixit-Stiglitz Goods Beverages & tobacco 23 58 20 7 57 37 Grain milling 13 32 55 Sugar & bakery & confectionary 20 15 65 3 73 24 Petroleum 3 2 94 0 58 41 Chemicals 28 6 66 0 69 30 Metals and machines 27 2 70 3 78 19 Non metallic products 24 4 72 5 86 9 Agriculture Maize 0 91 9 0 27 73 Wheat 3 0 97 0 28 72 Rice 0 16 84 Barley 0 100 0 Cotton 12 2 86 Other cereals 1 64 35 Sugarcane 4 65 31 0 98 2 Coffee 59 1 40 Tea 0 1 99 19 24 57 Roots & tubers Pulses & oil seeds 1 76 24 60 2 38 Fruits 76 6 18 Vegetables 11 43 46 89 2 9 Cut flowers 81 6 13 Others crops 14 58 28 15 53 32 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 5 5 90 28 43 29 Meat & dairy 12 17 71 1 74 26 Other manufactured food 7 16 77 34 56 10 Printing and publishing 35 19 45 Textile & clothing 3 7 89 1 18 80 Leather & footwear 3 1 96 18 48 35 Wood & paper 34 16 50 4 87 10 Other manufactures 36 2 61 14 70 17 Water Electricity Construction Trade Hotels Real estate 33 33 33 33 33 33 Adminsitration Health Education Source: Authors' estimates. 48 Table 6A -- Market Shares in Sectors with FDI (%) European Rest of the Kenya Union Africa World Business Services Communication 26 49 0 25 Insurance 85 4 0 11 Banking and other financial services 62 29 0 9 Professional business services 94 2 2 2 Road services 80 2 14 4 Railway transport 0 0 0 100 Maritime transport 45 25 15 15 Pipeline transport 70 0 13 18 Airline transport 30 30 10 30 Source: Authors' estimates. See appendix for details. 49 Table 6B: Estimates of elasticity of firms with respect to price for Kenya by sector and by Kenyan trading partner region R&D intensity R&D expenditures divided by Elasticity Estimates sales (times 1000) for the US* Africa EU ROW SERVICES telecommunications 52-high 2.5 13.4 20 banking 4-low 3.3 3.3 10 insurance 4-low 3.3 3.3 10 professional services 116-high 2.5 13.4 20 air transport** medium 1.9 10 15 road transport low 3.3 3.3 10 rail transport** medium 1.9 10 15 water transport** medium 1.9 10 15 MANUFACTURING beverages and tabacco 14-low 3.3 3.3 10 grain milling*** 7-low 3.3 3.3 10 sugar&bakery&confectioners*** 7-low 3.3 3.3 10 petroleum 2-low 3.3 3.3 10 chemicals 34-medium 1.9 10 15 metals and machines*** 33-medium 1.9 10 15 non-metallic products*** 0-17-low 3.3 3.3 10 *Based on average R&D expenditures for the years 2004 and 2005. The average for all US industries was 36. **We evaluate transportation as a medium R&D sector since three sectrors dominate R&D expenditures of US multinationals operating abroad. These are transportation, chemiicals and computers and electronics. M oreover, about two-thirds of all R&D expenditures of foreign multinationals operatingi in the US was performed in the same three sectors. See "U.S. and International Research and Development: Funds and Technology Linkages," at 'http://www.nsf.gov/statistics/seind04/c4/c4s5.htm. ***Food is the proxy for grain mlling and sugar, bakery and confectioners; machinery is used for metals and machines; for non-metallic products, we used plastics, rubber, mineral and wood products. Foundation, Division of Science Resources Statistics, Survey of Industrial Research and SOURCE: R&D and sales data from National Science Development: 2005, Data Tables . Available at: http://www.nsf.gov/statistics/nsf10319/content.cfm?pub_id=3750&id=3. See appendix E for details of the calculations. 50 Table 7: Summary of Results (results are percentage change from initial equilibrium, unless otherwise indicated) No initial rent capture case Unilateral EU Discrimina Unilateral Discriminatory EU-Africa tory Unilateral & Scenario definition Benchmark EU FTA Services EU Tariffs Africa FTA FTA Unilateral Services Tariffs Domestic 50% reduction of discriminatory barriers on EU services firms No Yes Yes No No Yes Yes Yes No Yes 50% reduction of discriminatory barriers on African services firms No No No No Yes Yes Yes Yes No Yes 50% reduction of discriminatory barriers on ROW services firms No No No No No No Yes Yes No Yes 50% reduction of regulatory barriers for all services firms No No No No No No No No No Yes Removal of tariffs on EU sourced goods No Yes No Yes No Yes Yes No Yes Yes Removal of tariffs on ROW sourced goods No No No No No No Yes No Yes Yes Aggregate welfare Welfare (EV as % of consumption) 0.7 0.5 0.2 0.3 1.0 3.6 1.5 2.0 10.3 Welfare (EV as % of GDP) 0.6 0.4 0.1 0.2 0.8 3.0 1.3 1.7 8.6 Government budget Tariff revenue (% of GDP) 3.6 2.1 2.9 2.1 2.9 2.1 2.9 Tariff revenue -29.0 -0.1 -28.9 -0.1 -29.1 -100.0 -0.3 -100.0 -100.0 Aggregate trade Real exchange rate 0.9 0.3 0.6 0.2 1.2 4.0 0.9 3.1 5.8 Aggregate exports 3.2 0.1 3.1 0.3 3.5 12.6 0.5 11.9 15.4 Factor Earnings Skilled labor 2.2 0.7 1.5 0.5 2.7 9.0 2.2 6.5 15.3 Semi-skilled labor 1.1 0.5 0.6 0.3 1.4 5.6 1.5 4.1 10.3 Unskilled labor 1.5 0.6 0.9 0.3 1.9 7.4 1.9 5.3 14.3 Capital 1.5 0.5 0.9 0.3 1.8 7.0 1.7 5.1 12.4 Land 2.6 0.4 2.2 0.5 3.0 7.7 1.4 6.1 10.0 Factor adjustments Skilled labor 0.5 0.3 0.3 0.2 0.7 2.1 0.9 1.3 4.2 Semi-skilled labor 0.7 0.2 0.7 0.1 0.8 2.5 0.6 1.9 4.5 Unskilled labor 0.2 0.1 0.1 0.0 0.2 0.7 0.2 0.5 1.3 Capital 0.3 0.1 0.3 0.0 0.3 1.3 0.3 1.2 2.2 Land 1.0 0.5 0.7 0.4 1.4 3.7 1.4 2.2 7.2 Source: Authors' estimates. 51 Table 8: Summary of Results (results are percentage change from initial equilibrium, unless otherwise indicated) Initial Rent Capture Case Unilateral EU Discrimina Unilateral Discriminatory EU-Africa tory Unilateral & Scenario definition Benchmark EU FTA Services EU Tariffs Africa FTA FTA Unilateral Services Tariffs Domestic 50% reduction of discriminatory barriers on EU services firms No Yes Yes No No Yes Yes Yes No Yes 50% reduction of discriminatory barriers on African services firms No No No No Yes Yes Yes Yes No Yes 50% reduction of discriminatory barriers on ROW services firms No No No No No No Yes Yes No Yes 50% reduction of regulatory barriers for all services firms No No No No No No No No No Yes Removal of tariffs on EU sourced goods No Yes No Yes No Yes Yes No Yes Yes Removal of tariffs on ROW sourced goods No No No No No No Yes No Yes Yes Aggregate welfare Welfare (EV as % of consumption) 0.5 0.3 0.2 0.1 0.5 2.9 0.9 2.0 7.0 Welfare (EV as % of GDP) 0.4 0.3 0.1 0.0 0.5 2.5 0.7 1.7 5.9 Government budget Tariff revenue (% of GDP) 3.6 2.1 2.9 2.1 2.9 2.1 2.9 Tariff revenue -29.0 -0.1 -28.9 -0.1 -29.1 -100.0 -0.4 -100.0 -100.0 Aggregate trade Real exchange rate 0.9 0.3 0.6 0.2 1.1 4.0 0.9 3.1 5.5 Aggregate exports 3.2 0.1 3.1 0.2 3.4 12.4 0.4 11.9 14.3 Factor Earnings Skilled labor 2.2 0.7 1.5 0.5 2.7 8.9 2.2 6.5 14.7 Semi-skilled labor 1.1 0.5 0.6 0.3 1.4 5.6 1.5 4.1 10.0 Unskilled labor 1.5 0.6 0.9 0.3 1.8 7.4 1.9 5.3 14.6 Capital 1.4 0.5 0.9 0.3 1.7 6.9 1.6 5.1 12.2 Land 2.5 0.3 2.2 0.4 2.9 7.5 1.1 6.1 8.5 Factor adjustments Skilled labor 0.6 0.4 0.3 0.3 0.9 2.3 1.1 1.3 5.0 Semi-skilled labor 0.8 0.3 0.7 0.2 0.9 2.5 0.8 1.9 4.9 Unskilled labor 0.2 0.1 0.1 0.1 0.3 0.8 0.4 0.5 2.0 Capital 0.3 0.1 0.3 0.1 0.4 1.4 0.4 1.2 2.7 Land 1.0 0.4 0.7 0.4 1.4 3.7 1.5 2.2 7.2 Source: Authors' estimates. 52 Table 9: Output and Employment Impacts from Liberalisation (% change from benchmark) No initial rent capture case Unilateral FTA EU-Africa FTA Africa FTA EU FTA Output Labor income Output Labor income Output Labor income Output Labor income Business Services Communication 3.0 8.3 1.1 2.3 0.2 0.3 0.9 2.0 Insurance 4.1 9.8 0.9 2.0 0.2 0.4 0.7 1.6 Banking and other financial services 2.4 7.7 0.9 2.1 0.2 0.4 0.7 1.7 Professional business services 4.1 10.5 1.5 3.1 0.2 0.4 1.3 2.6 Road services 6.5 9.4 2.8 3.0 0.4 0.5 2.3 2.4 Railway transport 12.6 14.3 6.1 5.7 1.8 1.4 4.2 4.2 Maritime transport 14.3 16.8 8.2 8.2 -0.2 -0.6 8.2 8.7 Pipeline transport 5.5 7.0 2.7 2.3 0.8 0.4 1.9 1.9 Airline transport 6.6 8.4 3.2 2.8 0.9 0.4 2.3 2.4 Dixit-Stiglitz Goods Beverages & tobacco 6.2 12.1 0.6 1.9 0.1 0.3 0.5 1.6 Grain milling 2.7 10.0 0.5 2.4 0.1 0.4 0.4 1.9 Sugar & bakery & confectionary -2.4 4.0 0.4 2.1 0.1 0.4 0.3 1.7 Petroleum 0.7 3.5 3.4 4.0 0.2 0.2 3.2 3.7 Chemicals 1.5 7.3 -0.4 1.0 0.0 0.3 -0.4 0.7 Metals and machines -8.4 -3.3 -3.7 -2.5 0.0 0.3 -3.7 -2.7 Non metallic products -14.2 -9.7 -1.0 0.6 0.0 0.3 -1.0 0.3 Agriculture Maize 1.7 7.1 0.6 2.1 0.1 0.3 0.6 1.8 Wheat -27.7 -24.9 -2.7 -0.9 -0.2 0.1 -2.4 -1.0 Rice -29.8 -27.0 0.6 2.0 0.1 0.3 0.5 1.7 Barley 3.3 10.0 0.1 2.2 0.0 0.4 0.1 1.8 Cotton 2.5 7.6 0.5 1.9 0.1 0.3 0.4 1.6 Other cereals -2.1 3.9 -0.9 1.1 -0.2 0.1 -0.6 0.9 Sugarcane -31.0 -30.2 -3.2 -3.4 2.3 1.9 -5.5 -5.4 Coffee 52.4 60.9 15.5 17.4 0.4 0.7 15.1 16.8 Tea -7.3 -2.1 -1.6 0.0 -1.2 -1.0 -0.3 1.1 Roots & tubers 0.6 4.9 0.3 1.4 0.1 0.3 0.2 1.1 Pulses & oil seeds 0.3 5.7 0.1 1.7 0.0 0.3 0.1 1.4 Fruits -0.4 5.0 -0.1 1.4 0.1 0.3 -0.2 1.1 Vegetables -0.7 4.8 0.3 1.8 0.1 0.3 0.2 1.5 Cut flowers 21.1 27.1 11.2 12.7 4.8 4.9 6.1 7.4 Others crops 1.0 5.6 1.2 2.5 0.1 0.3 1.2 2.2 Beef 2.2 9.3 0.6 2.5 0.0 0.4 0.6 2.1 Dairy 0.4 7.1 0.1 1.9 0.0 0.3 0.1 1.5 Poultry 0.6 7.1 0.1 1.8 0.0 0.3 0.1 1.5 Sheep goat and lamb for slaughter 0.9 7.9 0.1 2.0 0.0 0.4 0.1 1.7 Other livestock -0.5 6.1 0.0 1.7 0.0 0.3 -0.1 1.3 Other CRTS Fishing 0.3 7.3 -0.1 1.7 0.0 0.4 -0.1 1.3 Forestry 0.1 6.8 0.0 1.8 0.0 0.4 0.0 1.4 Mining 81.3 96.4 9.0 10.8 0.8 1.0 8.1 9.7 Meat & dairy 7.1 13.6 0.9 2.4 0.1 0.3 0.8 2.0 Other manufactured food 49.6 63.3 8.1 10.5 0.7 1.1 7.4 9.3 Printing and publishing 6.2 12.6 0.8 2.1 0.1 0.3 0.7 1.8 Textile & clothing -4.4 3.1 -0.1 2.2 -0.1 0.4 -0.1 1.8 Leather & footwear 4.7 12.8 0.4 2.3 0.0 0.4 0.4 2.0 Wood & paper 4.3 11.6 -0.8 0.8 0.1 0.5 -0.9 0.4 Other manufactures -12.1 -7.3 -6.2 -5.1 0.0 0.3 -6.3 -5.4 Water -0.5 5.9 0.1 1.8 0.0 0.3 0.1 1.4 Electricity 0.5 6.7 0.4 1.9 0.1 0.4 0.2 1.5 Construction 0.0 6.0 0.0 1.5 0.0 0.3 0.0 1.2 Trade 3.4 7.6 1.1 2.0 0.1 0.2 1.0 1.7 Hotels 0.4 5.6 0.0 1.3 0.0 0.3 0.0 1.1 Real estate -2.3 3.5 -0.5 1.0 0.0 0.3 -0.5 0.7 Adminsitration 0.0 6.4 0.0 1.6 0.0 0.3 0.0 1.3 Health -0.3 7.0 53 -0.2 1.6 0.0 0.3 -0.2 1.3 Education -0.3 6.7 -0.1 1.7 0.0 0.3 -0.1 1.4 Source: Authors' estimates. Table 10: Impacts on Imports from Unilateral Liberalisation (% change from benchmark) No initial rent capture case European Rest of the Union Africa World Business Services Communication Insurance -1.0 5.5 Banking and other financial services 3.4 3.4 3.9 Professional business services Road services -6.3 -6.3 -4.2 Railway transport -3.0 Maritime transport -20.9 -25.2 -19.1 Pipeline transport -0.9 -0.5 Airline transport -2.6 -3.2 -2.4 Dixit-Stiglitz Goods Beverages & tobacco 67.0 -6.6 148.7 Grain milling 59.6 -13.9 218.3 Sugar & bakery & confectionary 43.7 -20.8 118.6 Petroleum 2.8 -25.4 6.5 Chemicals 5.1 -14.2 6.0 Metals and machines 6.0 -19.8 9.1 Non metallic products 37.4 -24.2 187.9 Agriculture Maize 173.2 -3.1 173.2 Wheat 4.2 -31.4 4.2 Rice 65.3 -37.6 65.3 Barley Cotton Other cereals Sugarcane 216.2 -56.5 216.2 Coffee Tea 58.3 -22.9 58.3 Roots & tubers Pulses & oil seeds 31.3 1.4 31.3 Fruits Vegetables 98.7 -3.2 98.7 Cut flowers Others crops 11.6 0.4 11.6 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining -26.0 -29.4 -26.0 Meat & dairy 107.8 -21.6 107.8 Other manufactured food 16.5 -35.3 16.5 Printing and publishing -3.4 -3.4 -3.4 Textile & clothing 29.2 -24.5 29.2 Leather & footwear 44.1 -14.1 44.1 Wood & paper 17.9 -17.2 17.9 Other manufactures 26.6 -32.8 26.6 Water Electricity Construction Trade Hotels Real estate 4.3 4.3 4.3 Administration 54 Health Education Source: Authors' estimates. Table 11: Impacts on Exports from Unilateral Liberalisation (% change from benchmark) No initial rent capture case European Rest of the Union Africa World Business Services Communication 0.2 0.2 Insurance -6.6 -6.6 Banking and other financial services -1.6 -1.6 -1.6 Professional business services Road services 5.1 5.1 5.1 Railway transport 23.8 Maritime transport 3.4 3.4 3.4 Pipeline transport 6.8 6.8 Airline transport 6.4 6.4 6.4 Dixit-Stiglitz Goods Beverages & tobacco 13.8 13.8 13.8 Grain milling Sugar & bakery & confectionary 15.0 15.0 15.0 Petroleum 16.2 16.2 16.2 Chemicals 7.5 7.5 7.5 Metals and machines 52.8 52.8 52.8 Non metallic products 20.1 20.1 20.1 Agriculture Maize 6.0 6.0 6.0 Wheat -25.3 -25.3 Rice Barley -3.5 Cotton 1.9 1.9 1.9 Other cereals -5.1 -5.1 -5.1 Sugarcane -15.5 -15.5 -15.5 Coffee 55.7 55.7 55.7 Tea -7.0 -7.0 -7.0 Roots & tubers Pulses & oil seeds -0.7 -0.7 -0.7 Fruits -3.3 -3.3 -3.3 Vegetables -0.8 -0.8 -0.8 Cut flowers 21.4 21.4 21.4 Others crops 1.3 1.3 1.3 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 85.2 85.2 85.2 Meat & dairy 23.5 23.5 23.5 Other manufactured food 77.4 77.4 77.4 Printing and publishing Textile & clothing 6.6 6.6 6.6 Leather & footwear 18.1 18.1 18.1 Wood & paper 5.5 5.5 5.5 Other manufactures 3.6 3.6 3.6 Water Electricity Construction Trade Hotels Real estate -8.2 -8.2 -8.2 Administration 55 Health Education Source: Authors' estimates. Table 12: Impacts on Number of Firms from Unilateral Liberalisation (% change from benchmark) No initial rent capture case European Rest of the Kenya Union Africa World Business Services Communication -1.8 5.3 6.2 Insurance -6.3 33.4 91.4 Banking and other financial services 1.4 1.7 1.7 3.5 Professional business services 1.1 50.7 13.7 61.1 Road services -0.3 39.8 39.7 128.3 Railway transport 7.5 Maritime transport -9.0 86.4 16.7 115.9 Pipeline transport 3.9 3.0 12.2 Airline transport 3.3 9.1 2.7 11.1 Dixit-Stiglitz Goods Beverages & tobacco 5.5 50.6 -5.4 116.4 Grain milling 2.2 45.5 -11.5 169.9 Sugar & bakery & confectionary -2.3 33.4 -17.1 92.2 Petroleum 0.6 2.2 -20.6 5.1 Chemicals 1.4 3.9 -11.3 4.7 Metals and machines -7.3 4.6 -15.9 7.0 Non metallic products -10.5 28.9 -20.1 144.9 Source: Authors' estimates. 56 Table 13: Impacts on Imports from combined EU and Aftrica FTAs No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication Insurance 3.4 -0.4 Banking and other financial services 0.6 0.6 0.8 Professional business services Road services -3.5 -3.5 -4.3 Railway transport -1.9 Maritime transport -11.8 -18.1 -20.7 Pipeline transport -0.8 -0.7 Airline transport -1.4 -1.9 -1.8 Dixit-Stiglitz Goods Beverages & tobacco 75.3 -1.5 -2.5 Grain milling 79.3 -1.7 -3.4 Sugar & bakery & confectionary 72.5 -3.6 -7.0 Petroleum 36.0 -0.8 -1.7 Chemicals 43.7 -4.4 -14.3 Metals and machines 129.4 -8.5 -43.3 Non metallic products 72.1 -2.6 -6.8 Agriculture Maize 178.7 -1.1 -1.1 Wheat 51.1 -0.6 -0.6 Rice 164.2 -0.3 -0.3 Barley Cotton Other cereals Sugarcane 521.0 -14.6 -14.6 Coffee Tea 104.5 -0.4 -0.4 Roots & tubers Pulses & oil seeds 29.9 0.3 0.3 Fruits Vegetables 102.5 -1.4 -1.4 Cut flowers Others crops 11.5 0.4 0.4 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 1.5 -3.1 -3.1 Meat & dairy 153.6 -4.3 -4.3 Other manufactured food 72.7 -4.1 -4.1 Printing and publishing -0.6 -0.6 -0.6 Textile & clothing 69.5 -0.9 -0.9 Leather & footwear 67.6 0.0 0.0 Wood & paper 32.1 -7.2 -7.2 Other manufactures 59.8 -15.1 -15.1 Water Electricity Construction Trade Hotels Real estate 0.5 0.5 0.5 Administration 57 Health Education Source: Authors' estimates. Table 14: Impacts on Exports from Combined EU-Africa FTA No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication 0.2 0.2 Insurance -0.2 -0.2 Banking and other financial services 0.4 0.4 0.4 Professional business services Road services 2.6 2.6 2.6 Railway transport 11.8 Maritime transport 1.7 1.7 1.7 Pipeline transport 3.8 3.8 Airline transport 3.6 3.6 3.6 Dixit-Stiglitz Goods Beverages & tobacco 1.9 1.9 1.9 Grain milling Sugar & bakery & confectionary 3.6 3.6 3.6 Petroleum 4.6 4.6 4.6 Chemicals 1.2 1.2 1.2 Metals and machines 26.0 26.0 26.0 Non metallic products 2.6 2.6 2.6 Agriculture Maize 2.4 2.4 2.4 Wheat -4.5 -4.5 Rice Barley -2.4 Cotton 0.5 0.5 0.5 Other cereals -2.1 -2.1 -2.1 Sugarcane 2.8 2.8 2.8 Coffee 16.6 16.6 16.6 Tea -1.7 -1.7 -1.7 Roots & tubers Pulses & oil seeds -0.1 -0.1 -0.1 Fruits -1.0 -1.0 -1.0 Vegetables 1.0 1.0 1.0 Cut flowers 11.4 11.4 11.4 Others crops 1.7 1.7 1.7 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 9.4 9.4 9.4 Meat & dairy 3.5 3.5 3.5 Other manufactured food 11.9 11.9 11.9 Printing and publishing Textile & clothing 0.2 0.2 0.2 Leather & footwear 0.6 0.6 0.6 Wood & paper -0.5 -0.5 -0.5 Other manufactures -0.2 -0.2 -0.2 Water Electricity Construction Trade Hotels Real estate -1.3 -1.3 -1.3 Administration Health Education 58 Source: Authors' estimates. Table 15: Impacts on Number of Firms from Combined EU-Africa FTA No initial rent capture case (% change from benchmark) European Rest of the Kenya Union Africa World Business Services Communication -1.4 7.0 -5.4 Insurance -0.3 42.2 -0.6 Banking and other financial services 0.6 0.6 0.6 1.2 Professional business services 0.2 46.4 12.8 0.6 Road services -0.5 41.1 41.0 -4.2 Railway transport 3.6 Maritime transport -7.0 120.3 20.3 -35.0 Pipeline transport 2.0 1.4 5.6 Airline transport 1.8 7.1 2.1 2.2 Dixit-Stiglitz Goods Beverages & tobacco 0.6 56.5 -1.2 -2.0 Grain milling 0.4 59.5 -1.3 -2.8 Sugar & bakery & confectionary 0.4 53.9 -2.9 -5.6 Petroleum 3.2 27.2 -0.6 -1.4 Chemicals -0.4 33.3 -3.4 -11.1 Metals and machines -3.2 96.9 -6.7 -33.9 Non metallic products -0.7 53.8 -2.1 -5.5 Source: Authors' estimates. 59 Table 16: Impacts on Imports from African FTA No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication Insurance 0.1 0.1 Banking and other financial services 0.1 0.1 0.2 Professional business services Road services -3.0 -2.3 -3.1 Railway transport -0.6 Maritime transport -2.5 -1.7 -2.5 Pipeline transport -0.3 -0.3 Airline transport -0.4 -0.4 -0.4 Dixit-Stiglitz Goods Beverages & tobacco 0.0 0.0 0.1 Grain milling 0.0 0.0 0.0 Sugar & bakery & confectionary 0.0 0.0 0.1 Petroleum 0.0 0.0 0.0 Chemicals 0.1 0.0 0.1 Metals and machines 0.0 0.0 0.1 Non metallic products 0.1 0.1 0.2 Agriculture Maize 0.2 0.2 0.2 Wheat 0.1 0.1 0.1 Rice 0.1 0.1 0.1 Barley Cotton Other cereals Sugarcane -1.1 -1.1 -1.1 Coffee Tea -0.4 -0.4 -0.4 Roots & tubers Pulses & oil seeds 0.2 0.2 0.2 Fruits Vegetables 0.0 0.0 0.0 Cut flowers Others crops 0.2 0.2 0.2 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining -0.2 -0.2 -0.2 Meat & dairy 0.0 0.0 0.0 Other manufactured food 0.0 0.0 0.0 Printing and publishing 0.1 0.1 0.1 Textile & clothing 0.3 0.3 0.3 Leather & footwear 0.2 0.2 0.2 Wood & paper 0.9 0.9 0.9 Other manufactures 0.2 0.2 0.2 Water Electricity Construction Trade Hotels Real estate 0.2 0.2 0.2 Administration Health Education 60 Source: Authors' estimates. Table 17: Impacts on Exports from African FTA No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication 0.1 0.1 Insurance 0.1 0.1 Banking and other financial services 0.1 0.1 0.1 Professional business services Road services -1.1 -1.1 -1.1 Railway transport 3.6 Maritime transport 1.3 1.3 1.3 Pipeline transport 1.2 1.2 Airline transport 1.2 1.2 1.2 Dixit-Stiglitz Goods Beverages & tobacco 0.2 0.2 0.2 Grain milling Sugar & bakery & confectionary 0.1 0.1 0.1 Petroleum 0.3 0.3 0.3 Chemicals 0.0 0.0 0.0 Metals and machines 0.0 0.0 0.0 Non metallic products 0.0 0.0 0.0 Agriculture Maize 0.0 0.0 0.0 Wheat -0.5 -0.5 Rice Barley -0.3 Cotton 0.1 0.1 0.1 Other cereals -0.5 -0.5 -0.5 Sugarcane 4.1 4.1 4.1 Coffee 0.5 0.5 0.5 Tea -1.2 -1.2 -1.2 Roots & tubers Pulses & oil seeds -0.1 -0.1 -0.1 Fruits 0.0 0.0 0.0 Vegetables 0.2 0.2 0.2 Cut flowers 4.9 4.9 4.9 Others crops 0.0 0.0 0.0 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 0.8 0.8 0.8 Meat & dairy 0.2 0.2 0.2 Other manufactured food 0.8 0.8 0.8 Printing and publishing Textile & clothing -0.3 -0.3 -0.3 Leather & footwear -0.1 -0.1 -0.1 Wood & paper 0.1 0.1 0.1 Other manufactures -0.1 -0.1 -0.1 Water Electricity Construction Trade Hotels Real estate -0.1 -0.1 -0.1 Administration Health Education 61 Source: Authors' estimates. Table 18: Impacts on Number of Firms from African FTA No initial rent capture case (% change from benchmark) European Rest of the Kenya Union Africa World Business Services Communication 0.0 0.1 0.2 Insurance 0.1 0.1 0.2 Banking and other financial services 0.1 0.1 0.1 0.2 Professional business services 0.0 -0.1 12.6 -0.1 Road services -2.2 -2.8 40.2 -5.5 Railway transport 1.1 Maritime transport -0.1 -2.8 28.3 -3.4 Pipeline transport 0.6 0.4 1.6 Airline transport 0.7 0.9 1.9 1.0 Dixit-Stiglitz Goods Beverages & tobacco 0.1 0.0 0.0 0.0 Grain milling 0.1 0.0 0.0 0.0 Sugar & bakery & confectionary 0.1 0.0 0.0 0.1 Petroleum 0.2 0.0 0.0 0.0 Chemicals 0.0 0.1 0.0 0.1 Metals and machines 0.0 0.0 0.0 0.0 Non metallic products 0.0 0.1 0.1 0.2 Source: Authors' estimates. 62 Table 19: Impacts on Imports from EU FTA No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication Insurance 3.3 -0.5 Banking and other financial services 0.5 0.5 0.6 Professional business services Road services -0.6 -1.3 -1.3 Railway transport -1.2 Maritime transport -9.6 -17.2 -18.8 Pipeline transport -0.6 -0.4 Airline transport -1.0 -1.4 -1.4 Dixit-Stiglitz Goods Beverages & tobacco 75.2 -1.6 -2.6 Grain milling 79.3 -1.7 -3.5 Sugar & bakery & confectionary 72.4 -3.7 -7.1 Petroleum 36.0 -0.8 -1.8 Chemicals 43.5 -4.4 -14.4 Metals and machines 129.3 -8.5 -43.3 Non metallic products 71.9 -2.7 -7.0 Agriculture Maize 178.2 -1.3 -1.3 Wheat 51.0 -0.7 -0.7 Rice 163.9 -0.4 -0.4 Barley Cotton Other cereals Sugarcane 527.5 -13.7 -13.7 Coffee Tea 105.4 0.0 0.0 Roots & tubers Pulses & oil seeds 29.6 0.1 0.1 Fruits Vegetables 102.5 -1.4 -1.4 Cut flowers Others crops 11.4 0.2 0.2 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 1.7 -2.9 -2.9 Meat & dairy 153.5 -4.4 -4.4 Other manufactured food 72.6 -4.1 -4.1 Printing and publishing -0.7 -0.7 -0.7 Textile & clothing 68.9 -1.3 -1.3 Leather & footwear 67.3 -0.2 -0.2 Wood & paper 30.9 -8.1 -8.1 Other manufactures 59.5 -15.3 -15.3 Water Electricity Construction Trade Hotels Real estate 0.3 0.3 0.3 Administration Health Education 63 Source: Authors' estimates. Table 20: Impacts on Exports from EU FTA No initial rent capture case (% change from benchmark) European Rest of the Union Africa World Business Services Communication 0.2 0.2 Insurance -0.2 -0.2 Banking and other financial services 0.3 0.3 0.3 Professional business services Road services 3.6 3.6 3.6 Railway transport 7.9 Maritime transport 0.4 0.4 0.4 Pipeline transport 2.6 2.6 Airline transport 2.5 2.5 2.5 Dixit-Stiglitz Goods Beverages & tobacco 1.7 1.7 1.7 Grain milling Sugar & bakery & confectionary 3.5 3.5 3.5 Petroleum 4.4 4.4 4.4 Chemicals 1.2 1.2 1.2 Metals and machines 25.9 25.9 25.9 Non metallic products 2.6 2.6 2.6 Agriculture Maize 2.4 2.4 2.4 Wheat -3.9 -3.9 Rice Barley -2.1 Cotton 0.4 0.4 0.4 Other cereals -1.6 -1.6 -1.6 Sugarcane -1.3 -1.3 -1.3 Coffee 16.2 16.2 16.2 Tea -0.4 -0.4 -0.4 Roots & tubers Pulses & oil seeds 0.0 0.0 0.0 Fruits -1.0 -1.0 -1.0 Vegetables 0.8 0.8 0.8 Cut flowers 6.2 6.2 6.2 Others crops 1.7 1.7 1.7 Beef Dairy Poultry Sheep goat and lamb for slaughter Other livestock Other CRTS Fishing Forestry Mining 8.5 8.5 8.5 Meat & dairy 3.4 3.4 3.4 Other manufactured food 10.9 10.9 10.9 Printing and publishing Textile & clothing 0.5 0.5 0.5 Leather & footwear 0.7 0.7 0.7 Wood & paper -0.6 -0.6 -0.6 Other manufactures -0.1 -0.1 -0.1 Water Electricity Construction Trade Hotels Real estate -1.2 -1.2 -1.2 Administration Health Education 64 Source: Authors' estimates. Table 21: Impacts on Number of Firms from EU FTA No initial rent capture case (% change from benchmark) European Rest of the Kenya Union Africa World Business Services Communication -1.4 6.9 -5.6 Insurance -0.4 42.0 -0.8 Banking and other financial services 0.5 0.5 0.5 0.9 Professional business services 0.2 46.5 0.2 0.7 Road services 1.7 44.3 0.7 1.4 Railway transport 2.5 Maritime transport -7.0 127.4 -12.2 -33.1 Pipeline transport 1.4 1.0 3.8 Airline transport 1.2 6.2 0.3 1.1 Dixit-Stiglitz Goods Beverages & tobacco 0.4 56.5 -1.3 -2.1 Grain milling 0.4 59.5 -1.4 -2.8 Sugar & bakery & confectionary 0.3 53.9 -3.0 -5.7 Petroleum 3.0 27.1 -0.6 -1.4 Chemicals -0.4 33.2 -3.5 -11.2 Metals and machines -3.2 96.8 -6.7 -33.9 Non metallic products -0.7 53.7 -2.2 -5.6 Source: Authors' estimates. 65 Table 22: Sensitivity Analysis of Kenya-EU FTA Parameter Value % Welfare Change (EV) Parameter Lower Central Upper Lower Central Upper (q i , q j ) ­ services sectors 1.5 3 4.5 9.99 0.67 0.50 (q i , q j ) ­ goods sectors see below 1.06 0.67 0.59 (va, bs) 0.625 1.25 1.875 0.55 0.67 0.82 (D, M) 2 4 6 0.65 0.67 0.69 (L, K) 0.5 1 1.5 0.64 0.67 0.70 (A 1 ,...A n ) 0 0 0.25 0.67 0.67 0.67 (D, E) 2 4 6 0.65 0.67 0.69 KEN Central values of all 4 sets of eta 0.61 0.67 0.72 EU parameters are listed in table 6B 0.25 0.67 0.96 AFR Lower values are 0.5 central values and 0.68 0.67 0.67 ROW upper values are 1.5 times central values 0.90 0.67 0.55 share of rents captured 0 0 1 0.67 0.67 0.49 CRTS--share of rents captured NA 0 1 NA 0.09 -0.06 m 0.025 0.05 0.075 0.67 0.67 0.67 (q i , q j ) ­ goods sectors sugar and bakery 2.12 2.93 3.74 beverages and tabacco 1.52 2.33 3.14 chemicals 2.01 2.82 3.63 metals and machines 8.345 16.69 25.035 gain milling 2.43 3.24 4.05 nonmetallic products 2.805 5.61 8.415 petroleum 2.75 3.56 4.37 Source: Authors' estimates 66 Table 23: Sensitivity Analysis of Kenya-Africa FTA Parameter Value % Welfare Change (EV) Parameter Lower Central Upper Lower Central Upper (q i , q j ) ­ services sectors 1.5 3 4.5 5.02 0.29 0.16 (q i , q j ) ­ goods sectors see below 0.32 0.29 0.28 (va, bs) 0.625 1.25 1.875 0.25 0.29 0.33 (D, M) 2 4 6 0.28 0.29 0.29 (L, K) 0.5 1 1.5 0.28 0.29 0.29 (A 1 ,...A n ) 0 0 0.25 0.29 0.29 0.29 (D, E) 2 4 6 0.28 0.29 0.29 KEN Central values of all 4 sets of eta 0.31 0.29 0.27 EU parameters are listed in table 6B 0.29 0.29 0.29 AFR Lower values are 0.5 central values and 0.14 0.29 0.43 ROW upper values are 1.5 times central values 0.29 0.29 0.29 share of rents captured 0 0 1 0.29 0.29 0.05 CRTS--share of rents captured NA 0 1 NA 0.14 -0.06 m 0.025 0.05 0.075 0.29 0.29 0.29 (q i , q j ) ­ goods sectors sugar and bakery 2.12 2.93 3.74 beverages and tabacco 1.52 2.33 3.14 chemicals 2.01 2.82 3.63 metals and machines 8.345 16.69 25.035 gain milling 2.43 3.24 4.05 nonmetallic products 2.805 5.61 8.415 petroleum 2.75 3.56 4.37 Source: Authors' estimates 67 Table 24: Summary of Results for Professional Services --No Initial Rent Capture Case (results are percentage change from initial equilibrium, unless otherwise indicated) Domestic & Unilateral EU Africa Africa-EU Rest of World Discriminatory Domestic Discriminatory Discriminatory Discriminatory Discriminatory Discriminatory Scenario definition Services Services Services Services Services Services Services 50% reduction of discriminatory barriers on EU services firms Yes No Yes Yes No Yes No 50% reduction of discriminatory barriers on African services firms Yes No Yes No Yes Yes No 50% reduction of discriminatory barriers on ROW services firms Yes No Yes No No No Yes 50% reduction of regulatory barriers for all services firms Yes Yes No No No No No Aggregate welfare Welfare (EV as % of consumption) 0.71 0.54 0.16 0.06 0.02 0.08 0.07 Welfare (EV as % of GDP) 0.60 0.45 0.13 0.05 0.02 0.07 0.06 Government budget Tariff revenue (% of GDP) 2.9 2.9 2.9 2.9 2.9 2.9 2.9 Tariff revenue -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 Aggregate trade Real exchange rate 0.2 0.1 0.1 0.0 0.0 0.0 0.0 Aggregate exports 0.6 0.4 0.2 0.1 0.1 0.1 0.1 Factor Earnings Skilled labor 1.0 0.6 0.4 0.2 0.0 0.2 0.2 Semi-skilled labor 0.5 0.4 0.1 0.1 0.0 0.1 0.1 Unskilled labor 0.8 0.5 0.3 0.1 0.0 0.1 0.1 Capital 0.7 0.4 0.2 0.1 0.0 0.1 0.1 Land 1.2 0.8 0.4 0.1 0.1 0.2 0.2 Factor adjustments Skilled labor 0.5 0.3 0.2 0.1 0.0 0.1 0.1 Semi-skilled labor 0.4 0.2 0.1 0.0 0.0 0.1 0.1 Unskilled labor 0.1 0.1 0.1 0.0 0.0 0.0 0.0 Capital 0.2 0.1 0.1 0.0 0.0 0.0 0.0 Land 1.1 0.7 0.3 0.1 0.0 0.2 0.2 Source: Authors' estimates. 68 Table 25: Summary of Results for Professional Services, initial rent capture case (results are percentage change from initial equilibrium, unless otherwise indicated) Domestic & Unilateral EU Africa Africa-EU Rest of World Discriminatory Domestic Discriminatory Discriminatory Discriminatory Discriminatory Discriminatory Scenario definition Services Services Services Services Services Services Services 50% reduction of discriminatory barriers on EU services firms Yes No Yes Yes No Yes No 50% reduction of discriminatory barriers on African services firms Yes No Yes No Yes Yes No 50% reduction of discriminatory barriers on ROW services firms Yes No Yes No No No Yes 50% reduction of regulatory barriers for all services firms Yes Yes No No No No No Aggregate welfare Welfare (EV as % of consumption) 0.63 0.52 0.09 0.04 0.00 0.08 0.05 Welfare (EV as % of GDP) 0.53 0.44 0.08 0.04 0.00 0.07 0.04 Government budget Tariff revenue (% of GDP) 2.9 2.9 2.9 2.9 2.9 2.9 2.9 Tariff revenue -0.1 -0.1 0.0 0.0 0.0 0.0 0.0 Aggregate trade Real exchange rate 0.2 0.1 0.1 0.0 0.0 0.0 0.0 Aggregate exports 0.6 0.4 0.2 0.1 0.0 0.1 0.1 Factor Earnings Skilled labor 1.0 0.6 0.4 0.2 0.0 0.2 0.2 Semi-skilled labor 0.5 0.3 0.1 0.0 0.0 0.1 0.1 Unskilled labor 0.8 0.5 0.3 0.1 0.0 0.1 0.1 Capital 0.6 0.4 0.2 0.1 0.0 0.1 0.1 Land 1.1 0.8 0.3 0.1 0.0 0.2 0.2 Factor adjustments Skilled labor 0.5 0.3 0.3 0.1 0.0 0.1 0.1 Semi-skilled labor 0.4 0.2 0.1 0.0 0.0 0.1 0.1 Unskilled labor 0.2 0.1 0.1 0.0 0.0 0.0 0.0 Capital 0.2 0.1 0.1 0.0 0.0 0.0 0.0 Land 1.1 0.7 0.3 0.1 0.0 0.2 0.1 Source: Authors' estimates. 69 Table 26: Impacts on Number of Firms from Liberalisation of Barriers in Professional Services No initial rent capture case % change from benchmark Domestic & Unilateral EU Africa Africa-EU Rest of World Discriminatory Domestic Discriminatory Discriminatory Discriminatory Discriminatory Discriminatory Services Services Services Services Services Services Services Kenya 0.5 1.7 -1.1 -0.5 -0.1 -0.6 -0.6 European Union 49.2 5.1 40.2 43.3 -0.4 42.7 -1.6 Africa 13.4 1.7 11.4 -0.5 12.5 12.0 -0.6 Rest of the World 59.2 6.0 48.2 -1.6 -0.4 -2.0 51.4 Source: Authors' estimates. 70 Figure 1 Sensitivity Analysis of Kenyan Preferential Liberalization of Services with African Partners: Impact of Partner and Excluded Country Supply Elasticity, with and without Rent Capture Case I: No initial rent capture by Kenya Case II: Initial rent capture by Kenya 71 Figure 2: Sensitivity Analysis of Kenyan Preferential Liberalization of Services with the EU: Impact of Partner and Excluded Country Supply Elasticity, with and without Rent Capture Case I: No initial rent capture by Kenya Case II: Initial rent capture by Kenya 72 Figure 3: Sample Frequency Distribution of the Welfare Results of Kenyan Preferential Reduction of Services Barriers Against African Partners--30,000 simulations. 4 3.5 Percentage of solutions 3 2.5 2 1.5 1 0.5 0 -0.2 0.0 0.2 0.4 0.6 Percentage of consumption 73 % change -2 -1 0 1 2 3 4 5 Tea Wheat Other cereals Textile & clothing Pulses & oil seeds Sheep goat and lamb for slaughter Other livestock Dairy Fishing Beef Poultry Real estate Forestry Health Fruits Hotels Maize Water Rice Barley Education Sugar & bakery & confectionary Meat & dairy Leather & footwear Grain milling Construction Adminsitration Cotton Non metallic products Roots & tubers Vegetables Others crops Metals and machines Aggregate Output Impact Beverages & tobacco Chemicals Communication Other manufactures Electricity Insurance Petroleum Printing and publishing Banking and other financial services Other services Trade Wood & paper 74 Other manufactured food Coffee Pipeline transport Road services Maritime transport Airline transport Mining Railway transport Sugarcane Cut flowers from Kenyan Preferential Reduction of Services Barriers Against African Partners--30, 000 simulations. Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles. Figure 4: Means, 50 and 95 Percent Confidence Intervals of the Sample Frequency Distributions of the Output Changes by Sector % change -1 0 1 2 3 4 5 Tea Wheat Other cereals Roots & tubers Pulses & oil seeds Petroleum Fruits Beverages & tobacco Hotels Cotton Real estate Maize Rice Vegetables Poultry Meat & dairy Other livestock Trade Fishing Dairy Construction Health Others crops Sheep goat and lamb for slaughter Forestry Communication Beef Water Education Adminsitration Metals and machines Chemicals Textile & clothing Labor Income (% change) Barley Sugar & bakery & confectionary Grain milling Non metallic products Insurance Printing and publishing Leather & footwear Banking and other financial services Other services Other manufactures Electricity Pipeline transport Airline transport Maritime transport Wood & paper 75 Other manufactured food Coffee Road services Reduction of Services Barriers Against African Partners--30,000 simulations. Mining Railway transport Sugarcane Cut flowers Figure 5: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Labor Payment Changes by Sector from Kenyan Preferential Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles. Figure 6: Sample Frequency Distribution of the Welfare Results of Kenyan Preferential Reduction of Services Barriers Against EU Partners--30,000 simulations. 3.5 3 Percentage of solutions 2.5 2 1.5 1 0.5 0 0.25 0.55 0.85 1.15 1.45 Percentage of consumption 76 % change -10 -5 10 15 20 0 5 Sugarcane Non metallic products Other manufactures Metals and machines Wheat Sugar & bakery & confectionary Other cereals Tea Real estate Fruits Barley Cotton Grain milling Health Fishing Education Other livestock Electricity Hotels Forestry Chemicals Dairy Construction Adminsitration Pulses & oil seeds Poultry Sheep goat and lamb for slaughter Vegetables Water Roots & tubers Textile & clothing Beverages & tobacco Maize Aggregate Output Impact Rice Beef Others crops of Services Barriers Against EU Partners--30,000 simulations. Banking and other financial services Leather & footwear Meat & dairy Trade Other services Communication Printing and publishing Insurance Wood & paper 77 Petroleum Pipeline transport Airline transport Road services Railway transport Cut flowers Maritime transport Coffee Mining Other manufactured food Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles. Figure 7: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Output Changes by Sector from Kenyan Preferential Reduction % change -10 -5 10 15 20 25 0 5 Sugarcane Non metallic products Other manufactures Metals and machines Wheat Sugar & bakery & confectionary Real estate Tea Cotton Other cereals Hotels Roots & tubers Fruits Chemicals Electricity Construction Grain milling Beverages & tobacco Adminsitration Fishing Other livestock Forestry Vegetables Education Barley Pulses & oil seeds Health Water Poultry Dairy Maize Sheep goat and lamb for slaughter Others crops Labor Income (% change) Trade Rice Banking and other financial services Beef Pipeline transport Textile & clothing Meat & dairy Petroleum Communication Leather & footwear Other services Printing and publishing Airline transport Insurance Road services Reduction of Services Barriers Against EU Partners--30,000 simulations. Wood & paper 78 Railway transport Cut flowers Maritime transport Coffee Mining Other manufactured food Figure 8: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Labor Payment Changes by Sector from Kenyan Preferential Note: The boxes are limited vertically by the 25% and 75% quartiles. The bars in the box are the means. The vertical lines extend to the 2.5% and 97.5% percentiles. 79 Appendices Appendix A: Trade Share Data and Tariff Rates for Kenya's Trade Partners Appendix B: Documentation of the Calculation of Ownership Shares for Kenya Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution for Kenyan Imperfectly Competitive Goods Appendix D: Engineering Services in Kenya - Restrictiveness Index Appendix E: Data on Research and Development Expenditures and Sales for the United States in 2004 and 2005. Appendix F: Kenya Model with Multiple FDI and Trade Partners (Algebraic Strcuture) Appendix G: A Note of the Relationship Between Sector Specific Capital and the Elasticity of Supply in Applied General Equilibrium Models of Imperfect Competition 80 Appendix A: Trade Share Data and Tariff Rates for Kenya's Trade Partners Trade Share Data To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In this appendix, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. For COMESA, in order to avoid double counting, we took the COMESA countries less those in the East African Customs Union, i.e., Comoros, Congo, Djibuti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius Seychelles, Sudan, Swaziland, Zambia and Zimbabwe. Trade shares for the Africa region in our model is the sum of East Africa Customs Union plus COMESA as defined above. Rest of the World is the residual. We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in the first table below. We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in the subsequent three tables, by CRTS and IRTS goods in our model separately. Tariff Rate Calculations Tariff and Sales Tax Data. We started with MFN tariff rates at the eight digit level taken from the website of the Kenyan government: www.kra.go.ke/customs/customsdownloads.php. These tariff rates were then aggregated to the sectors of our model, using simple averages. We obtained data on the total taxes on imports and the total value of imports and took the ratio to obtain the average value of import taxes in the Kenyan economy. In 2005, this was 8.4 percent. 30 That is, on average, Kenyan importers paid 8.4 percent of the value of imports on import taxes that did not apply to domestic production. As we reported in Balestreri, Rutherford and Tarr (2009), the MFN tariff rates, multiplied times the trade flows, exceed the collected tariff rates. That is, using MFN tariff rates for all trade, the weighted average tariff rate exceeds the collected tariff rate of 8.4 percent for the economy as a whole. Thus, they exaggerate the protection received by Kenyan industry and agriculture. This is due to tariff preferences to regional partners and due to other preference items or tariff exemptions. We assume that zero tariffs apply 30 Economic Survey (2006, pp. 103, 115). 81 on all imports from the East African Customs Union and from COMESA. 31 Thus, we apply the MFN tariff rates only on the trade flows from outside of these African regions (EU and Rest of World in our model) and take a weighted average tariff rate of the MFN rates on the non-East African regions. The resulting weighted average tariff rate on non-East African imports still exceeds 8.4 percent. We then equi- proportionally reduced all the MFN tariffs in our model so that the estimated collected tariffs on imports from the EU and Rest of World divided by the total value of import is 8.4 percent. 31 Kenya agreed to implement zero tariffs on East African Customs Union imports as of January 1, 2005. See Michael-Stahl (2005). 82 Notes on Product/Sector Classifications in SITC Revision 2 Product SITC Classiifcation (Rev. 2) All goods 0 to 9 Dixit-Stiglitz Goods Beverages and tobacco 1 Food manufactures (excl. bev & tob) ** 012+014++0224+023+024++0252+037+046 to 048+056+058+0612+ 0615+0619+062+0712+0722+0723+073+0812 to 0918+09+41+42+43 Printing and publishing 64 Mineral fuels 3 Chemicals 5 Metals and machines 67+68+69+7 Non-metallic products 66 Other manufactures (excl. CRTS sectors) 62+81+82+83+87+88+89 Agriculture (excl. food manuf & bev, tob) 0+1+2+4-27-28-1-above food manufacturing products Other goods All goods-Dixit/Stiglitz goods-above agriculture Agricultural Products Maize 044 Wheat 041 Rice 042 Barley 043 Other cereals 045 Cotton 263 Sugar 061 Coffee 071 Tea 074 Roots and tubers 0548 Oil seeds and pulses 22 Fruits 057+058 Vegetables 054+056 Cut flowers 2927 Other crops 072+075+081 Beef 0111 Dairy products 02 Poultry 0114 Meats of sheep and goats 0112 Other livestock 00+0113+0115+0116+0118 Other CRTS Goods Fishing 03 Forestry 24+25 Mining 27+28 Meats and dairy 01+02 Grain milling 046+047 Sugar & bakery confectionary 062+073+048 Textiles and clothing 65+84 Leather and footwear 61+85 Wood and papers 63+64 Note: ** based on all processed and manufacturing food products 83 Kenyan Exports Values and Shares of Agricultural and Other CRTS Products in 2007 Export value ($ '000) export shares Product COMESA15 EAC5 EU27 ROW WLD COMESA15 EAC5 EU27 ROW WLD AGRICULTURE Maize 671 2,694 7 9,096 12,468 0.054 0.216 0.001 0.730 1.000 Wheat 2 43 0 119 164 0.013 0.264 0.000 0.723 1.000 Rice 203 318 5 86 613 0.332 0.519 0.009 0.140 1.000 Barley 0 654 0 0 654 0.000 1.000 0.000 0.000 1.000 Other cereals 453 107 8 309 877 0.517 0.122 0.009 0.352 1.000 Cotton 4 0 18 126 148 0.025 0.000 0.120 0.855 1.000 Sugar 10,573 8,616 19 336 19,545 0.541 0.441 0.001 0.017 1.000 Coffee 1,093 780 98,647 65,708 166,228 0.007 0.005 0.593 0.395 1.000 Tea 170,298 238 131,530 396,147 698,213 0.244 0.000 0.188 0.567 1.000 Roots and tubers 1 24 7 0 32 0.022 0.739 0.229 0.010 1.000 14 Oil seeds and pulses 157 4,831 3,007 8,009 0.002 0.020 0.603 0.375 1.000 Fruits 2,335 4,878 85,188 20,397 112,797 0.021 0.043 0.755 0.181 1.000 Vegetables 987 4,610 256,893 26,590 289,080 0.003 0.016 0.889 0.092 1.000 Cut flowers 22,982 8 316,343 50,929 390,262 0.059 0.000 0.811 0.130 1.000 Other crops 737 3,739 1,233 2,733 8,442 0.087 0.443 0.146 0.324 1.000 Beef 287 528 0 484 1,299 0.221 0.406 0.000 0.372 1.000 Dairy products 3,002 10,337 25 3,340 16,704 0.180 0.619 0.001 0.200 1.000 Poultry 101 8 0 9 118 0.856 0.067 0.000 0.077 1.000 101 Meats of sheep and goats 283 0 86 469 0.214 0.603 0.000 0.183 1.000 Other livestock 150 1,876 69 1,013 3,108 0.048 0.604 0.022 0.326 1.000 OTHER CRTS GOODS Fishing 411 162 34,837 25,757 61,167 0.007 0.003 0.570 0.421 1.000 Forestry 412 483 4 169 1,068 0.386 0.452 0.004 0.159 1.000 Mining 2,305 29,358 21,162 21,545 74,369 0.031 0.395 0.285 0.290 1.000 Meats and dairy 3,821 14,847 131 6,576 25,375 0.151 0.585 0.005 0.259 1.000 Grain milling 415 538 49 59 1,062 0.391 0.507 0.046 0.056 1.000 confectionary Sugar & bakery14,420 33,297 1,912 16,008 65,637 0.220 0.507 0.029 0.244 1.000 22,415 Textiles and clothing 32,212 3,996 238,463 297,087 0.075 0.108 0.013 0.803 1.000 14,512 Leather and footwear 28,989 15,930 31,441 90,872 0.160 0.319 0.175 0.346 1.000 Wood and papers 16,394 47,045 2,587 7,287 73,314 0.224 0.642 0.035 0.099 1.000 Source: Based on UN COMTRADE Statistics. 84 Kenyan Imports of Agricultural and Other CRTS Products in 2007 Import value ($ '000) Import shares Product COMESA15 EAC5 EU27 ROW WLD COMESA15 EAC5 EU27 ROW WLD AGRICULTURE Maize 625 14,194 0 1,445 16,265 0.038 0.873 0.000 0.089 1.000 Wheat 62 2 3,618 140,505 144,187 0.000 0.000 0.025 0.974 1.000 Rice 8,919 2,563 12 58,559 70,054 0.127 0.037 0.000 0.836 1.000 Barley 0 0 1 0 1 0.000 0.000 1.000 0.000 1.000 Other cereals 0 9,083 3 53 9,139 0.000 0.994 0.000 0.006 1.000 Cotton 214 4,322 0 119 4,655 0.046 0.929 0.000 0.026 1.000 Sugar 72,342 1,914 4,939 35,055 114,249 0.633 0.017 0.043 0.307 1.000 Coffee 41 635 78 1,347 2,101 0.020 0.302 0.037 0.641 1.000 Tea 0 86 22 8,088 8,196 0.000 0.011 0.003 0.987 1.000 Roots and tubers 0 29 662 205 896 0.000 0.032 0.739 0.228 1.000 Oil seeds and pulses 803 16,126 164 5,296 22,388 0.036 0.720 0.007 0.237 1.000 Fruits 1,492 2,848 2,444 7,358 14,141 0.105 0.201 0.173 0.520 1.000 Vegetables 1,589 19,450 5,546 22,592 49,177 0.032 0.396 0.113 0.459 1.000 Cut flowers 0 1,844 7 161 2,012 0.000 0.917 0.003 0.080 1.000 Other crops 55 9,461 2,337 4,599 16,452 0.003 0.575 0.142 0.280 1.000 Beef 0 0 0 0 0 0.000 0.000 1.000 0.000 1.000 Dairy products 693 458 779 3,437 5,367 0.129 0.085 0.145 0.640 1.000 Poultry 0 0 0 0 0 0.000 0.000 1.000 0.000 1.000 Meats of sheep and goats 0 0 0 8 8 0.000 0.000 0.000 1.000 1.000 Other livestock 67 36 246 1,787 2,136 0.031 0.017 0.115 0.836 1.000 OTHER CRTS GOODS Fishing 3,155 640 194 4,326 8,315 0.379 0.077 0.023 0.520 1.000 Forestry 1,084 16,979 4,388 9,851 32,301 0.034 0.526 0.136 0.305 1.000 Mining 518 1,272 1,774 33,094 36,658 0.014 0.035 0.048 0.903 1.000 Meats and dairy 781 458 868 5,143 7,249 0.108 0.063 0.120 0.709 1.000 Grain milling 10,092 1,341 4,728 19,656 35,817 0.282 0.037 0.132 0.549 1.000 Sugar & bakery confectionary 3,151 1,400 6,280 20,475 31,307 0.101 0.045 0.201 0.654 1.000 Textiles and clothing 4,815 18,592 10,903 279,109 313,418 0.015 0.059 0.035 0.891 1.000 Leather and footwear 170 117 551 20,191 21,029 0.008 0.006 0.026 0.960 1.000 Wood and papers 30,504 7,720 79,746 115,781 233,751 0.130 0.033 0.341 0.495 1.000 Source: Based on UN COMTRADE Statistics. 85 Kenyan Exports and Imports of Dixit-Stiglitz Goods and Other Products in 2007 Trade value ($ '000) Trade Share Product COMESA15 EAC5 EU27 ROW WLD COMESA15 EAC5 EU27 ROW WLD EXPORTS EXPORTS All goods 664,849 952,788 1,084,812 1,378,351 4,080,800 0.163 0.233 0.266 0.338 1.000 tobacco 47,692 Beverages and46,796 11,535 61,085 167,109 0.280 0.285 0.069 0.366 1.000 79,712 98,905 Food manuf (excl. bev & tob) 106,990 31,678 317,284 0.251 0.312 0.337 0.100 1.000 9,987 Printing and publishing 41,596 129 3,635 55,347 0.180 0.752 0.002 0.066 1.000 Mineral fuels 15,225 86,515 139 72,263 174,143 0.087 0.497 0.001 0.415 1.000 Chemicals 68,878 175,389 1,057 106,367 351,691 0.196 0.499 0.003 0.302 1.000 129,528 198,787 Metals and machines 11,782 80,253 420,350 0.308 0.473 0.028 0.191 1.000 10,513 Non-metallic products 87,666 5,697 10,639 114,515 0.092 0.766 0.050 0.093 1.000 45,774 Other manufactures 88,777 26,412 32,468 193,431 0.237 0.459 0.137 0.168 1.000 211,253 29,739 877,333 Agriculture (excl. food manuf & bev, tob) 627,966 1,746,291 0.121 0.017 0.502 0.360 1.000 Other goods 47,183 97,723 43,737 351,997 540,640 0.087 0.181 0.081 0.651 1.000 IMPORTS IMPORTS All goods 332,205 191,598 1,812,340 6,653,119 8,989,262 0.037 0.021 0.202 0.740 1.000 tobacco 27,881 Beverages and11,958 15,716 13,650 69,204 0.173 0.403 0.227 0.197 1.000 73,603 19,352 Food manuf (excl. bev & tob) 38,219 436,903 568,077 0.130 0.034 0.067 0.769 1.000 30,462 Printing and publishing 7,634 69,199 88,868 196,163 0.155 0.039 0.353 0.453 1.000 Mineral fuels 45,727 427 60,393 1,811,868 1,918,415 0.024 0.000 0.031 0.944 1.000 Chemicals 58,989 4,172 322,652 754,982 1,140,796 0.052 0.004 0.283 0.662 1.000 60,085 Metals and machines 12,273 958,236 2,461,164 3,491,757 0.017 0.004 0.274 0.705 1.000 5,118 Non-metallic products 491 30,219 90,373 126,201 0.041 0.004 0.239 0.716 1.000 7,117 Other manufactures 2,616 152,026 257,025 418,784 0.017 0.006 0.363 0.614 1.000 33,340 96,683 64,962 Agriculture (excl. food manuf & bev, tob) 328,230 523,215 0.064 0.185 0.124 0.627 1.000 Other goods 5,804 20,070 100,720 410,055 536,649 0.011 0.037 0.188 0.764 1.000 Source: Based on UN COMTRADE Statistics. 86 Appendix B: Documentation of the Calculation of Ownership Shares for Kenya I. Telecommunications Shares in Kenya The primary source of data was various publications of Paul Buddle Communications, including Kenya--Telecoms Market Statistics and Forecasts, March 20, 2008. Table 10 contains mobile phone subscription statistics by company and Table 2 lists the number of fixed-line phone subscribers. We defined market share as the share of total subscribers, summing fixed-line and mobile subscribers. The telecommunications companies are: Telkom Kenya, Safaricom and Celtel. Ownership shares are as follows. France Telecom purchased 51% of Telkom Kenya in 2007 with the Government of Tanzania holding the remaining 49 percent.32. Vodafone held 35% of Safaricom network, with the remainder held by Telkom Kenya (60%) and a local company Mobitelea (5%). 33. Celtel was acquired by MTC of Kuwait for US$3.4 billion in March 2005. MTC was later renamed Zain Group.34 The results for market share by country (in percent) are as follows: Kenya, 26; EU, 49; EAC, 0; COMESA, 0; Rest of World, 25. II. Bank Shares in Kenya. Bank Market Shares The data source for bank market shares was Bankscope, an on-line data source for about 29,000 banks world-wide.35 Through Bankscope, we obtained data on total assets by bank in Kenya, owners -shareholders of the bank and the percent of the bank owned by each owner-shareholder. Market share of each bank was defined based on the bank's assets as a share of total bank assets in the country. We divided the regions into the European Union, East African Customs Union, COMESA and Rest of the World.36 Ownership Shares of Banks Each bank's market share was then allocated among geographic regions according to the shares of ownership of the bank. We then summed across the banks to obtain total market shares by region. In many cases, however, the Bankscope data were inadequate to allocate ownership shares by region. In these cases, we investigated bank websites, to obtain the required ownership information. The results of our supplementary inquiries are listed below. The results we get are that owners of the banking sector of Kenya are as follows, in percent: Kenya, 61.8.; EU, 28.7; EAC, 0; COMESA, 0.2; ROW, 9.3. Detailed results on the ownership of the banks are in the tables below. 32 http://www.orange.com/en_EN/press/press_releases/cp080917uk.html Accessed 17 April 2009 33 See Paul Buddle Communications, The Kenya Regulatory and Fixed-Line Telecoms Overview, March 20, 2008. 34 See Paul Buddle Communications, The Kenya Mobile Market Overview, March 20, 2008. 35 It combines data from the main information provider, Fitch Ratings, and nine other sources, with software for searching and analysis. Each bank report contains balance sheet and income statements with up to 200 data items. 36 Although we calculated data for the U.S. and the U.K. separately, these were aggregated into the Rest of the World and the European Union, respectively. 87 Table 1: Kenya Banking Sector Ownership Shares, by Region (1 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW ABN AMRO Ba nk NV Abn Amro Hol di ng Nv (NL) Afri ca n Ba nki ng Corpora ti on Li mi ted Queens Hol di ngs Ltd (KE) 25.00 77,200 0.56% 0.56% Afri ca n Merca nti l e Ba nki ng Compa ny Li mi ted - AMBANK Ba nk of Afri ca Kenya Li mi ted 93,493 0.68% 19.89 Afri ca n Fi na nci a l Hol di ng Sa -Afri ca n Fi na nci a l Hol di ng/Ba nk Of Afri ca (LU) 0.16% Ba nk Of Afri ca - Ma da ga s ca r (MG) 20.00 0.16% 20.00 Nederl a nds e Fi na nci eri ngs -Ma a ts cha ppi j Voor Ontwi kkel i ngs l a nden N.V. (NL) 0.16% Ba nk Of Afri ca - Côte D'Ivoi re (CI) 15.00 0.12% Ba nk Of Afri ca - Beni n (BJ) 10.11 0.08% Ba nk of Ba roda (Kenya ) Ltd Ba nk Of Ba roda (IN) 86.70 169,651 1.23% 1.23% Ba rcl a ys Ba nk of Kenya Ltd 1,700,672 12.30% Ba rcl a ys Ba nk Pl c (GB) 68.50 8.43% Kenya n Publ i c & Ins ti tuti ons (KE) 31.50 3.88% Bi a s ha ra Ba nk of Kenya Li mi ted Ca l yon Ca l yon (FR) Centra l Ba nk of Kenya Government Of Kenya (KE) 100.00 3,067,136 22.19% 22.19% CFC Sta nbi c Hol di ngs Li mi ted Sta nbi c Afri ca Hol di ngs Li mi ted (GB) 60.00 581,708 4.21% 4.21% Cha rterhous e Ba nk Li mi ted Cha s e Ba nk (Kenya ) Li mi ted Cha s e Ba nk (Kenya ) Li mi ted (US) 100.00 59,405 0.43% 0.43% Ci ti ba nk NA Ci ti ba nk Na (US) 100.00 544,612 3.94% 3.94% Ci ty Fi na nce Ba nk Li mi ted Commerce Ba nk Li mi ted Commerci a l Ba nk of Afri ca Commerci a l Ba nk of Afri ca (KE) 100.00 539,477 3.90% 3.90% Cons ol i da ted Ba nk of Kenya Li mi ted Cons ol i da ted Ba nk of Kenya (KE) 100.00 49,528 0.36% 0.36% Co-opera ti ve Ba nk of Kenya Ltd 831,354 6.01% Co-Opera ti ves Soci eti es (??) 83.82 Indi vi dua l Members Of Co-Opera ti ves (??) 16.18 Credi t Ba nk Li mi ted 37,606 0.27% Da i ma Ba nk Li mi ted Devel opment Ba nk of Kenya Ltd Devel opment Ba nk of Kenya (KE) 100.00 47,115 0.34% 0.34% 88 Table 1: Kenya Banking Sector Ownership Shares, by Region (2 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW Di a mond Trus t Ba nk Ke nya Li mi te d 313,234 2.27% 17.32 Aga Kha n Fund For Economi c De ve l opme nt Sa (CH) 0.76% Ba rcl a ys (Ke nya ) Nomi ne e s Ltd (KE) 9.85 0.43% Ha bi b Ba nk Li mi te d (PK) 9.72 0.43% The Jubi l e e I ns ura nce Compa ny Ltd (KE) 8.77 0.39% Di a mond Jubi l e e I nve s tme nt Trus t (GB) 1.87 0.08% Cra ys e l l I nve s tme nts Ltd (KE) 1.62 0.07% Noora l i Moha n Ma nji (KE) 1.27 0.06% Ame e ra l i Na za ra l i Es ma i l (KE) 0.92 0.04% Duba i Ba nk Ke nya Li mi te d EABS Ba nk Li mi te d 128,389 0.93% Pri va te Sha re hol de rs (KE) 65.59 0.61% LP Hol di ngs (KE) 16.95 0.16% Ra jmuk Hol di ngs (KE) 9.41 0.09% Empe ror Hol di ngs (KE) 8.05 0.07% Ea s t Afri ca n Bui l di ng Soci e ty - EABS Equa tori a l Comme rci a l Ba nk Li mi te d Equi ty Ba nk Li mi te d 11.06 288,544 2.09% 2.09% Bri ti s h-Ame ri ca n I nve s tme nts Compa ny (Ke nya ) Li mi te d (KE) Euro Ba nk Li mi te d Fa ul u Ke nya Li mi te d Fa ul u Ke nya Li mi te d (CH) 70.00 29,829 0.22% 0.22% Fi de l i ty Comme rci a l Ba nk Li mi te d Fi na Ba nk Li mi te d 141,005 1.02% Entre pri s e Ba nki ng Group (BW) 20.75 0.21% Dha ba ri a Ltd (KE) 19.81 0.20% Ra re Ltd (KE) 17.83 0.18% Si rus Ltd (KE) 15.85 0.16% Snow Poi nt (K) Ltd (KE) 9.91 0.10% Ha rupa Ltd (KE) 3.96 0.04% Kus ha n Ltd (KE) 3.96 0.04% Re e na Ltd (KE) 3.96 0.04% 89 Table 1: Kenya Banking Sector Ownership Shares, by Region (3 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW Fi rs t Ameri ca n Ba nk of Kenya Fi rs t Na ti ona l Fi na nce Ba nk Ltd. Gi ro Commerci a l Ba nk Li mi ted Gua rdi a n Ba nk Li mi ted Gui l ders Interna ti ona l Ba nk Li mi ted Ha bi b Ba nk Li mi ted Ha bi b Ba nk Li mi ted (PK) Hous i ng Fi na nce Compa ny of Kenya Li mi ted 142,700 1.03% Equi ty Ba nk Li mi ted (KE) 20.00 0.44% Na ti ona l Soci a l Securi ty Fund (KE) 7.87 0.17% Government Of Kenya (KE) 7.32 0.16% Ba rcl a ys (Kenya ) Nomi nees Ltd 9347 (KE)4.90 0.11% Northbound Hol di ngs Ltd (??) 4.60 Steel Son Li mi ted (KE) 3.55 0.08% Nomura Nomi nees Ltd A/C Jmm (KE) 3.15 0.07% Ndungu Pa ul Wa nderi (??) 2.35 Ki buwa Enterpri s es Ltd (??) 0.91 Ki ri nya ga Cons tructi on Ltd (KE) 0.52 0.01% Imperi a l Ba nk Li mi ted 135,537 0.98% Abduma l Inves tments Ltd (??) 14.00 Si mba Col t Motors Li mi ted (KE) 14.00 0.38% Ja nco Inves tments Li mi ted (??) 13.50 Kenbl es t Ltd (??) 12.50 Momentum Hol di ngs Li mi ted (KE) 12.50 0.34% Rex Motors Ltd (??) 12.50 11.00 Ea Motor Indus tri es (Sa l es & Servi ces ) Ltd (??) Reynol ds & Co. Li mi ted (IE) 10.00 0.27% Indus tri a l a nd Commerci a l Devel opment Corpora ti on Government Of Kenya (KE) 100.00 Indus tri a l Devel opment Ba nk Li mi ted 90 Table 1: Kenya Banking Sector Ownership Shares, by Region (4 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW Inve s tme nts a nd Mortga ge s Ba nk Li mi ted - I&M Ba nk Li mi ted 322,035 2.33% Bi a s ha ra Se curi ti e s Ltd (KE) 21.55 0.53% Mi na rd Hol di ngs Li mi ted (KE) 17.54 0.43% Te coma Li mi ted (KE) 15.72 0.38% Zi yungi Li mi ted (KE) 15.72 0.38% Mna na Li mi ted (KE) 14.52 0.36% Ci ty Trus t Li mi ted (KE) 10.14 0.25% Sa chi t Sha h (??) 2.40 Sa ri t S. Sha h (??) 2.40 Ke nya Comme rci a l Ba nk LTD 1,333,300 9.64% Pe rma ne nt Se cre ta ry To The Tre a s ury (KE) 26.23 5.87% Na ti ona l Soci a l Se curi ty Fund (KE) 6.80 1.52% 3.49 Sta nbi c Nomi ne e s Ke nya Li mi ted A/C Icdci (KE) 0.78% Suni l Na rs hi Sha h (??) 2.33 Kcb Sta ff Pe ns i on Fund (KE) 2.32 0.52% 1.53 Sta nbi c Nomi ne e s Ke nya Li mi ted A/C R 48701 (KE) 0.34% Nomura Nomi ne e s Ltd A/C Jmm (KE) 1.01 0.23% Ke nya Re i ns ura nce Corpora ti on Li mi ted 0.87 (KE) 0.19% Ba rcl a ys (Ke nya ) Nomi ne e s Ltd A/C 9230 0.82 (KE) 0.18% Ba rcl a ys (Ke nya ) Nomi ne e s Ltd A/C 1256 0.69 (??) Ke nya Comme rci a l Fi na nce Compa ny Li mi ted Ke nya Pos t Offi ce Sa vi ngs Ba nk 100.00 215,015 1.56% 1.56% Ke nya Wome n Fi na nce Trus t K-REP Ba nk 75,223 0.54% Afri ca n De ve l opme nt Ba nk (II) 15.14 0.41% Ne the rl a nds De v. Fi na nce Co (NL) 5.00 0.14% Mi ddl e Ea s t Ba nk Ke nya Li mi ted 49,015 0.35% Forti s Ba nk (BE) 25.03 0.18% 25.00 Ba nque Be l gol a i s e -Be l gol a i s e Ba nk (BE) 0.18% Na ti ona l Ba nk of Ke nya Ltd 520,526 3.77% Na ti ona l Soci a l Se curi ty Fund (KE) 48.00 2.58% Gove rnme nt Of Ke nya (KE) 22.00 1.18% 91 Table 1: Kenya Banking Sector Ownership Shares, by Region (5 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW NI C Ba nk Li mi te d 376,210 2.72% Fi rs t Cha rte re d Se curi ti e s Ltd (??) 16.44 I ce a I nve s tme nt Se rvi ce s Ltd (??) 9.42 Li vi ngs tone Re gi s tra rs Ltd. (KE) 8.13 1.11% Ri ve l Ke nya Ltd (KE) 7.73 1.05% Dunca n Nde ri tu Nde gwa (??) 4.56 Sa i ma r Ltd (KE) 4.13 0.56% Amwa Hol di ngs Ltd (??) 1.97 1.65 Ke nya Comme rci a l Ba nk Nomi ne e s Ltd- A/C 769G (??) Thuthuma Ltd (??) 1.27 Ma ki mwa Cons ul ta nts Ltd (??) 1.26 Ori e nta l Comme rci a l Ba nk Ltd 20,886 0.15% Pa s ha I nve s tme nts Ltd (KE) 13.40 0.08% Sa g I nve s tme nts Ltd (KE) 13.30 0.08% Pa ra mount Uni ve rs a l Ba nk Li mi te d Pri me Ba nk 150,617 1.09% Pri me Ca pi ta l & Cre di t Li mi te d Prude nti a l Ba nk Li mi te d Re l i a nce Ba nk Li mi te d Southe rn Cre di t Ba nki ng Corpora ti on 66,003 0.48% Othe rs (??) 28.00 Fi nci ty I nve s tme nts Ltd (??) 23.00 Sounthe rn Shi e l d Hol di ngs Ltd (??) 20.00 Sounthe rn Shi e l d Se curi ti e s Ltd (??) 19.00 Sa drudi n Ka ri m Kurji (??) 10.00 Sta nbi c Ba nk Ke nya Li mi te d 100.00 372,120 2.69% 2.69% Sta nda rd Cha rte re d Ba nk Ke nya 1,169,151 8.46% 73.81 Sta nda rd Cha rte re d Hol di ngs (Afri ca ) B.V. (NL) 8.11% Ka ba ra k Li mi te d (??) 1.03 0.69 Ol d Mutua l Li fe As s ura nce Compa ny Li mi te d (KE) 0.08% Na ti ona l Soci a l Se curi ty Fund (KE) 0.68 0.07% (??) Ba rcl a ys (Ke nya ) Nomi ne e s Ltd A/C 1256 0.59 0.51 Ke nya Comme rci a l Ba nk Nomi ne e s Ltd- A/C 744 (KE) 0.06% te d Sta nda rd Cha rte re d Afri ca Hol di ngs Li mi0.48 (GB) 0.05% (KE) Ba rcl a ys (Ke nya ) Nomi ne e s Ltd A/C 1853 0.45 0.05% (KE) Ba rcl a ys (Ke nya ) Nomi ne e s Ltd A/C 9230 0.36 0.04% 92 Table 1: Kenya Banking Sector Ownership Shares, by Region (6 of 6) Total Assets Company Market Share by Region (%) Owner (2006 Market COME Bank Shareholder (ISO Country Code) ship % USD) Share KE GB EU EAC SA US ROW The Compa ny for Ha bi ta t & Hous i ng i n Afri ca 71,600 0.52% Tra ns -Na ti ona l Ba nk Li mi ted Fi ve Ke nya n Pri va te Compa ni e s (KE) 88.69 42,967 0.31% 0.31% Trus t Ba nk Li mi ted Uni ve rs a l Ba nk Vi ctori a Comme rci a l Ba nk Ltd. 61732.04 0.45% 35 Othe r Sha re hol de rs (??) 27.24 Ki ngs wa y Inve s tme nts Ltd (KE) 16.43 0.12% Jong-Chul Ki m (KE) 10.81 0.08% Roche s ter Hol di ng Li mi ted (KE) 10.74 0.08% Mone ta ry Cre di t Hol di ngs Ltd (KE) 6.65 0.05% Godfre y C. Omondi (KE) 6.05 0.04% Orchi d Hol di ngs Ltd (KE) 5.83 0.04% Ra ja n Ja ni i & Ka l a pi Ja ni (??) 5.70 Ka nji Da mji Pa ttni (KE) 5.39 0.04% Pa ttni Yoge s h K (??) 5.16 KE GB EU EAC COM US ROW Ma rke t Grand Total = 13,824,591 Sha re 59.00% 15.46% 9.18% 0.00% 0.16% 4.37% 3.46% Sca l e d Sha re 64.39% 16.87% 10.02% 0.00% 0.17% 4.77% 3.77% 93 Supplementary Information on Ownership Shares of Tanzanian Banks from Bank Websites (Quotes are from the websites listed.) National Microfinance ­Rabobank, 34.9%; The Government of the United Republic of Tanzania, 30.0%; Public, 21.0%; National Investment Company Limited (NICOL), 6.6%; Exim Bank Tanzania, 5.8%; Tanzania Chambers of Commerce Industries and Agriculture (TCCIA), 1.7%. http://www.nmbtz.com/about_nmb/shareholder_information.html . CRDB Bank Plc ­ TZ 38.8% ­ shareholders are listed as follows: Private individuals, 37.0; Co operatives , 14.0; Companies, 10.2; DANIDA investment fund, 30.0; Parastatals ( NIC & PPF ), 8.8. http://www.crdbbank.com/aboutUs.asp Accessed 3 April 2009. Commercial Bank of Africa ­according to their website they are wholly Kenyan owned. http://www.cba.co.ke/default2.php?active_page_id=117 Citibank NA ­ US 100% Kenya Post Office Savings Bank The bank is wholly owned by the Government of Kenya and reports to the Ministry of Finance. http://www.postbank.co.ke/index.php?do=about. K-REP Bank International Finance Corporation, 16.7%; The African Development Bank, 15.1%; The Netherlands Dev. Finance Co. (FMO), 5.0%; Triodos, 11.0%; ShoreCap International, 8.2%; Kwa (ESOP), 10.0%; K-Rep Group, 25.0%; Founding Members, 5.2%. ICDC-I (Public investment company) 3.8% http://www.k- repbank.com/index.php?option=com_content&task=view&id=71&Itemid=109 . Chase Bank (Kenya) Limited ­ U.S. 100% Development Bank of Kenya Ltd ­ KE 100% - Consequently after forty five years the bank ownership changed to one that is Kenyan owned and directed as follows; Industrial & Commercial Development Corporation (ICDC), 89.3%; Transcentury Ltd, 10.7%. http://www.devbank.com/about.php?subcat=27&title=Shareholders. III. Kenyan Insurance Companies 94 The premium information came from the Insurance Industry Annual Report for 2007 of the Association of Kenya Insurers.37 Table 9 of their report lists premium income by company and type of insurance. We define market share of a company by the company share of total market premia. For ownership shares, we commissioned a survey from a specialist at the Association of Kenyan Insurers. 38 He provided the data on the ownership shares of the Kenyan companies. In the table below, we list the result of these calculations. 37 Available at: http://www.akinsure.com/images/aki-annual-report-2007.pdf 38 We thank Mr. Joseph Luvisia Jamwaka ( a fellow of the Life Management Institute of the U.S. and Associate of the Chartered institute of Insurance of the UK) for providing this information. 95 Table 2: Kenya Insurance Sector Ownership Shares, by Region (1 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Afri ca n Mercha nt As s ura nce Compa ny 563 1.71% 1.71% Hon. Wi l l i a m Ruto (KE) 80.00 Si l a s Si ma two (KE) 20.00 AIG Ins ura nce Compa ny AIG (US) 100.00 1,801 5.48% 5.48% APA Ins ura nce Compa ny 2355 7.17% 7.17% Apol l o Ins ura nce (KE) 60.00 Pa n Afri ca Ins ura nce Hol di ngs (KE) 40.00 Bl ue Shi el d Ins ura nce Compa ny 2,273 6.92% 6.92% Beth Ngonyo Munga i (KE) 40.05 Bermuda Hol di ngs Ltd (KE) 33.10 Afri ca n Thea tres Ltd (KE) 13.55 Ja mes Mui ga i Ngengi (KE) 3.31 Jea n Mui ga i Ngengi (KE) 3.31 Peter Ka ma u Ngengi (KE) 3.31 Ma rtha Vi ncent & Pa ul Vi ncent (KE) 3.31 Si mon Eva ns Gi thi nji (KE) 0.02 Si mon Munyi Ga choki (KE) 0.01 Bri ti s h Ameri ca n Ins ura nce Compa ny 679 2.07% Bri ti s h Ameri ca (K) Ltd (??) 66.67 Ji mna h Mba ru (KE) 25.00 1.55% Peter K Munga (KE) 5.00 0.31% Bens on I Wa i regi (KE) 3.33 0.21% Ca nnon As s ura nce Compa ny 557 1.70% 1.70% Inder Ji t Ta l wa r (KE) 0.00 Ca nnon Hol di ngs (KE) 40.00 Evi s a Inves ments (PVT) Ltd (KE) 28.70 PBM Nomi nees (KE) 31.30 Concord Ins ura nce Compa ny 585 1.78% 1.78% Dors e Gems Interna ti ona l Inc (KE) 32.00 Ki rumba Mwa ura (KE) 36.00 Ja mes Ga cheru (KE) 32.00 96 Table 2: Kenya Insurance Sector Ownership Shares, by Region (2 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Co-opera ti ve Ins ura nce Compa ny 1,028 3.13% 3.13% Ha ra mbee Co-opera ti ve Movement (KE) 9.06 Ae mbu Fa rmers Co-opera ti ve Soci ety Ltd (KE) 8.30 Ki a mbu Uni ty Fi na nce Co-opera ti ve Uni on (KE) 8.15 CIC Sta ff Co-opera ti ve Sa vi ngs a nd Credi t (KE) 7.27 The Co-opera ti ve Ba nk of Kenya (KE) 6.13 Ba nda ri Co-opera ti ve Sa vi ngs a nd Credi t (KE) 3.34 Mwa l i mu Co-opera ti ve Sa vi ngs a nd Credi t (KE) 1.59 Ki ps i gi s Tea chers Sa vi ngs a nd Credi t (KE) 1.32 Na ci co Sa vi ngs a nd Credi t Co-opera ti ve (KE) 1.10 Sti ma Sa vi ngs a nd Credi t Co-opera ti ve (KE) 1.09 Emma nuel Ki pkemboi Bi rech (KE) 1.30 Is a a c Wa i tha ka Ka munya (KE) 1.12 Teres a Wa nji ru Thi mba (KE) 1.10 Leona rd Obura Ol oo (KE) 0.89 Gera l d Mba a bu M'i kunyua (KE) 0.84 Fra nci s Ka ma u Ng'a ng'a (KE) 0.64 Others (KE) 46.76 Corpora te Ins ura nce Compa ny 351 1.07% 1.07% Xa nthi ppe Hol di ngs Ltd (KE) 63.30 Eja x Inves tments Ltd (KE) 36.70 CFC Li fe As s ura nce Compa ny 674 2.05% CfC Sta nbi c Hol di ngs Group (GB) 60.00 1.23% C Njonjo (KE) U P Ja ni (KE) J G Ki erei ni (KE) J H D Mi l ne (UK) M Sounda ra ra ja n (KE) A Munda (KE) R E Lea key (KE) Di rectl i ne As s ura nce Compa ny Ltd 259 0.79% 0.79% Roya l Credi t Li mi ted (KE) 99.70 Sa muel S. K. Ma cha ri a (KE) 0.10 Puri ty G. Ma cha ri a (KE) 0.10 Da n Korobi a (KE) 0.10 97 Table 2: Kenya Insurance Sector Ownership Shares, by Region (3 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Fi de l i ty Shi e l d Ins ura nce Compa ny 684 2.08% 2.08% Southe rn Shi e l d Hol di ngs Ltd (KE) 66.70 Southe rn Cre di t Ba nki ng Corp. (KE) 24.40 Sol i Li mi ted (KE) 6.40 Ke nya Shi ppi ng Age ncy (KE) 1.40 Fi rs t As s ura nce Compa ny 1,038 3.16% 3.16% Fi rs t As s ura nce Inve s tme nt Ltd (KE) 83.00 Syndi ca te Nomi ne e Ltd (KE) 17.00 Ga tewa y Ins ura nce Compa ny 436 1.33% 1.33% Godfre y W Ka ra uri (KE) 21.20 John N Muchuki (KE) 1.40 Be thue l M Ge ca ga (KE) 8.30 Muvoka nza Li mi ted (KE) 1.40 El i ud Ndi ra ngu (KE) 4.30 Je rome P N Ka ri uki (KE) 0.30 Ra ymond Ma ti ba (KE) 0.30 Fra nci s Thuo (KE) 1.80 Ki ha ra Wa i tha ka (KE) 2.10 Mubi ru Hous i ng Compa ny (KE) 0.90 Ma i na Ki me re & Pa rtne rs (KE) 5.40 Is a a c G. Wa njohi (KE) 14.50 Wi l s on Ki ra gu (KE) 1.40 Chi e f Eze ki e l N Onwe re (KE) 7.60 Is a a c Njoroge (KE) 0.60 Ja me s M Ga che ru (KE) 1.10 Ge mi ni a Ins ura nce Compa ny 460 1.40% 1.40% Gi koi De ve l opme nt Co. Ltd (KE) 8.16 Mba gi Li mi ted (KE) 34.70 Sta nl e y M. Gi thunguri (KE) 26.53 Le ona rd M Ka be tu (KE) 0.30 Bi ma l R. Sha h (KE) 5.67 Ha rs ha R. Sha h (KE) 1.19 Ha s i t K Sha h (KE) 1.38 Khe ts hi K Sha h (KE) 1.38 Uni ve rs a l Roa dwa ys (K) Ltd (KE) 5.53 Ki ri ti Sha h (KE) 2.67 Ja y K Sha h (KE) 1.38 Mona D Sha h (KE) 1.38 Mona D Sha h (KE) 5.68 De vcha nd A. Sha h (KE) 2.67 98 Table 2: Kenya Insurance Sector Ownership Shares, by Region (4 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Genera l Acci dent Ins ura nce 682 2.08% 2.08% Ra pun Li mi ted (KE) 49.00 J S Ins ura nce Li mi ted (KE) 49.00 Sha nti l a l Sha h (KE) 2.00 Heri ta ge Al l Ins ura nce Compa ny 1505 4.58% CFC (GB) 64.08 2.94% Afri ca n Li a s on Cons ul ta nt Servi ces (KE) 35.92 1.65% Ins ura nce Compa ny of Ea s t Afri ca Fi rs t Cha rtered Securi ti es Li mi ted (KE) 100.00 1,173 3.57% 3.57% Intra Afri ca As s ura nce Compa ny 402 1.22% Robert T. Ga checheh (KE) 10.50 0.18% Archi ba l d Gi thi nji (KE) 7.50 0.13% Ma hendra Cha ndul a l (KE) 5.00 0.09% Upenra Amba l a l Pa tel (KE) 5.00 0.09% Ji tenra Amba l a l Pa tel (KE) 5.00 0.09% Di nes h Cha ndul a l Pa tel (KE) 10.00 0.17% Henry Mka ngi (KE) 3.00 0.05% Bha ra t Kuma r Pa tel (KE) 5.00 0.09% Jos eph Muri u (KE) 5.00 0.09% Premji Ra tna (KE) 5.00 0.09% Ra nja ben Sures h Pa tel (KE) 5.00 0.09% El eyo Sa w Mi l l s (??) 20.00 Pra ful C Pa tel (KE) 5.00 0.09% Inves co Ins ura nce Compa ny 958 2.92% Jubi l ee Ins ura nce Compa ny 2,450 7.46% Jubi l ee Hol di ngs Ltd (KE) 100.00 7.46% Kenneth Ha mi s h Wool er Sha h (KE) 0.00 Nevi l l e Pa tri ck Gi bs on Wa rren (IN) 0.00 Keni ndi a As s ura nce Compa ny 3,028 9.22% Li fe Ins ura nce Corp. Of Indi a (IN) 10.00 0.92% Genera l Ins ura nce Corp Of Indi a (IN) 9.00 0.83% New Indi a As s ura nce Co. Ltd. (IN) 9.00 0.83% Ori enta l Ins ura nce Co. Ltd. (IN) 9.00 0.83% Uni ted Indi a Ins ura nce Co. Ltd. (IN) 9.00 0.83% Na ti ona l Ins ura nce Co. Ltd. (IN) 9.00 0.83% Pv Ka ri a (IN) 1.39 0.13% M N Mehta (KE) 0.00 0.00% M P Cha nda ri a (KE) 0.00 0.00% Sa da s i v Mi s hra (KE) 0.00 0.00% Si meon Nya cha e (KE) 7.00 0.64% Cha nda ri a Founda ti on Trus tees (KE) 7.01 0.65% Mehta Group Of Compa ni es (KE) 6.02 0.55% Lex Hol di ngs (KE) 3.66 0.34% Others (KE) 20.00 1.84% 99 Table 2: Kenya Insurance Sector Ownership Shares, by Region (5 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Kenya Ori ent Ins ura nce Compa ny 283 0.86% 0.86% Tha na k Inves tments (KE) 90.39 Ra jwi nder Si ngh (KE) 5.95 Avta r Si ngh Ubhi (KE) 1.80 Ka hn Si ngh Ubhi (KE) 1.80 Luka Da udi Ga l ga l o (KE) 0.06 Kenya Al l i a nce Ins ura nce Compa ny Interna ti ona l Control s Li mi ted (??) 100.00 353 1.07% Li on of Kenya Ins ura nce Compa ny Fi rs t Cha rtered Securi ty (KE) 80.00 1,217 3.71% 3.71% Kenya Hol di ngs (KE) 20.00 Ma di s on Ins ura nce Compa ny 1 Amedo Ma di s on Hol di ngs Li mi ted (KE) 00.00 625 1.90% 1.90% Ma yfa i r 273 0.83% 0.83% Adrea Ltd (KE) 27.77 Corpora te Inves tments (KE) 12.48 A 2 Enterpri s es (KE) 9.32 Ti nker Bi rd Securi ti es (KE) 9.15 Ka zka zi Ma ri ti me Ltd (KE) 3.12 Uni on Logi s ti cs (KE) 3.12 Ma renyo Ltd (KE) 8.32 Muhwa i Ltd (KE) 6.55 Ma hes h Dos hi And Shei l a Dos hi (KE) 6.24 Ns p Hol di ngs Ltd (KE) 6.24 La kda wa l l a Inves tments Ltd (KE) 4.16 Bha ra s a Inves tments Ltd (KE) 3.54 Merca nti l e Li fe & Genera l Ins ura nce 369 1.12% 1.12% Ecoba nk Kenya Ltd (KE) 20.00 L.P Hol di ngs (KE) 21.00 Ba rcl a ys Trus t (KE) 24.00 Ea bs Ba nk (KE) 35.00 Occi denta l Ins ura nce Compa ny 740 2.25% 2.25% Pa rk Enterpri s es Ltd (KE) 30.00 Oa k Inves tments Ltd (KE) 15.00 La nds end Kenya Ltd (KE) 15.00 Ha ns i ng Ltd (KE) 15.00 Rock Inves tment Ltd (KE) 15.00 Nga ma cu Ltd (KE) 5.00 Ma ga nl a l La kha ms hi Dodhi a (KE) 2.50 Ka nti l a l Ma ga na l a l Dodhi a (KE) 2.50 100 Table 2: Kenya Insurance Sector Ownership Shares, by Region (6 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Pa ci s Ins ura nce Compa ny Ltd 162 0.49% 0.49% Luna Regi s tered Trus tees (KE) 35.87 Archdi oces e Of Na i robi (KE) 32.56 As s oci a ti on Of Si s terhoods (KE) 5.42 Di oces e Of Na kuru (KE) 4.65 Rel i gi ous Superi or Confrence (KE) 2.34 Di oces e Of Mura nga (KE) 2.20 Di oces e Of Ngong (KE) 2.09 Di oces e Of Ki s i i (KE) 1.71 Di oces e Of Is i ol o (KE) 1.63 Di oces e Of Ma cha kos (KE) 1.12 Di oces e Of Nya hururu (KE) 1.00 Di oces e Of Embu (KE) 0.90 Di oces e Of Ga ri s s a (KE) 1.00 Di oces e Of Ma rs a bi t (KE) 1.00 Archi oces e Of Ki s umu (KE) 1.00 Ca thol i c Uni vers i ty Of Ea s t Afri ca (KE) 1.63 Others (KE) 4.00 Pi oneer Li fe As s ura nce Compa ny 89 0.27% 0.27% Ros e Wa rui nge (KE) 9.00 Mta l a ki Mwa s hi mba (KE) 11.00 Ja mes Ol uba yi (KE) 80.00 Phoeni x of Ea s t Afri ca As s ura nce 525 1.60% 1.60% Tra ns worl d Inves tment Li mi ted (KE) 77.87 Ki ruma Interna ti ona l (KE) 8.93 Ba wa n Li mi ted (KE) 3.40 Others (KE) 10.00 Rea l Ins ura nce Compa ny 746 2.27% Mureka Inves tments (KE) 69.00 1.57% Za ni ki Hol di ngs Ltd (KE) 15.00 0.34% The Gl obe Ins ura nce Compa ny (UK) 15.00 0.34% Kenya Fa rmers As s oci a ti on (KE) 1.00 0.02% Sta nda rd As s ura nce Compa ny 522 1.59% 101 Table 2: Kenya Insurance Sector Ownership Shares, by Region (7 of 7) Premium Income (million Company Market Share by Region (%) Owner KSH Market COME Insurance Company Shareholder (ISO Country Code) ship % 2007) Share KE GB EU EAC SA US ROW Ta us i As s ura nce Compa ny 500 1.52% 1.52% Ra s i k Ka nta ri a (KE) 10.00 Pri me Ca pi ta l Li mi ted (KE) 30.00 Brookwood Inves tment Li mi ted (KE) 7.00 Mukes h Pa tel (KE) 7.14 Sha nti l a l Sha h (KE) 19.30 Ra jni ka t Sa nghra jka (KE) 4.56 Na ya n Na yendra Tha ker (KE) 5.66 Others (KE) 17.00 The Mona rch Ins ura nce Compa ny 140 0.43% 0.43% Va l enci a Hol di ng Li mi ted (KE) 50.00 Ta ma s ha Corpora ti on Ltd (KE) 50.00 Tri dent Ins ura nce Compa ny Tri dent Inves tment Li mi ted (KE) 100.00 360 1.10% 1.10% UAP Provi nci a l Ins ura nce Compa ny 2,000 6.09% 6.09% J N Mugui yi (KE) 10.43 Centum Inves tment Compa ny (KE) 24.07 C J Ki rubi (KE) 24.07 Ba wa n Li mi ted (KE) 35.27 Others (KE) 7.00 Kenya GB EU EAC COM US ROW Ma rket Sha re 79.64% 3.28% 0.00% 0.00% 0.00% 5.48% 5.19% Sca l ed Grand Total (million KSH) = 32,845 Sha re 85.09% 3.50% 0.00% 0.00% 0.00% 5.86% 5.55% 102 IV. Railroad Transportation In the hope of improved performance, in November 2006, Kenya's (and Uganda's) railways were turned over to Rift Valley Railways, a consortium led by South Africa's Sheltam Trade Close. This consortium won the right to operate the railways for 25 years. They are a monopolist, so we infer 100 percent ownership to the Rest of the World. 39 V. Pipeline Transportation The Kenya Pipeline Company operates 800 kilometers of pipeline within Kenya for the transport of refined oil products. The pipeline runs from the refinery at the port of Mombassa to the capital of Nairobi, and with its western extension to Eldoret and to Kisimu. This pipeline is operated by the Kenya Pipeline Company, a wholly owned entity of the Government of Kenya. 40 In addition, there is a 320 kilometer pipeline under construction to extend the pipeline from Eldoret to Kampala Uganda. It is a Public-Private Partnership with the Governments of Uganda and Kenya originally each holding 24.5 percent shares. The remaining 51 percent was to be held by a consortium. Tamoil East Africa, a company registered in Uganda, owns 70 percent of the remainder. Tamoil East Africa is a wholly owned subsidiary of Tamoil Holdings, the Libyan state owned oil firm. The remaining 30 percent in the private consortium is held by Habib Investments, an investment company belonging to Habib Kagimu, a Ugandan businessman. However, in 2008, the Government of Uganda agreed to take only half of its 24.5 percent share and sell the other half to the private sector consortium. Thus, the share of the pipeline extention to Kampala of Tamoil East Africa increased to 44.3 percent and of Habib Investments to 19.0 percent.41 We assume that shares of the market are proportional to the kilometers of the pipeline, and allocate ownership shares accordingly. There are 1120 kilometers of pipeline. The finished pipeline is 60 percent of the total and the Kampala extension is 40 percent. The Kenyan government holds 100 percent ownership interest in 800 kilometers (or 60 percent of the total) and 24.5 ownership interest in the remaining 320 kilometers (or 9.8 of the total) for a total share of 69.8 percent. The Uganda ownership share is the sum of the share of the Government of Uganda and the share of Habib Investments, i.e., 12.5 percent (equals .4 * (12.25 + 19.0)). The results are as follows. Kenya, 69.8; Uganda, 12.5; Rest of World, 17.7. 39 On May 7, 2009, the Kenyan government announced it would like to renegotiate the contract and build (along with the government of Uganda) a second line to haul more cargo to the inland countries like Uganda, Rwanda and Burundi. See The New Vision, May 7, 2009. Available at: http://www.newvision.co.ug/D/8/220/680519. 40 See Kenya Pipeline Company on Wikipedia at: http://en.wikipedia.org/wiki/Kenya_Pipeline_Company, and the company website at: http://www.kpc.co.ke/ 41 See Uganda cedes stake of oil pipeline to Tamoil of Libya, local investors, Libya On-Line, July 21, 2008. Available at: http://www.libyaonline.com/news/details.php?cid=75&id=4830 103 104 Appendix C : Estimates of the Dixit-Stiglitz Elasticities of Substitution Formatted: Right: 0.63" for Kenyan Imperfectly Competitive Goods It was necessary for us to obtain estimates of the Dixit-Stiglitz product variety elasticities of substitution for the imperfectly competitive sectors in our model. Christian Broda, Joshua Greenfield and David Weinstein (2006) estimated Dixit-Stiglitz product variety elasticities of substitution at the 3 digit level in 73 countries. Among the 73 countries, there were four in sub-Saharan Africa: the Central African Republic, Madagascar, Malawi and Mauritius. We judged that Madagascar was the country closest in characteristics to Kenya, so we took the values of the elasticities estimated for Madagascar as a proxy for the elasticities for Kenya. Broda et al., estimate 3 digit elasticities for 130 goods sectors, but there are 34 goods sectors in our model, It was necessary to map the sectors estimated by Broda et al. into the sectors of our model. In table C1 of this appendix, we show the mapping for the imperfectly competitive sectors. (These elasticiteis are not relevant in our model for perfectly competitive sectors.) Next, since there are often multiple sectors from Broda et al. mapped into a single sector in our model, it was necessary to determine a method of weighting the Broda et al. elasticities. There are reasons to use both export shares as well as import shares. A larger share of a subcategory in imports reflects more imports, and more likely there are more varieties of imports. So weighting by the import share of a subcategory is better than an unweighted measure. Domestic varieties are also important. Since we do not have production data for the subcategories, we use export shares as a proxy for domestic production by subcategory. Analogously, weighting subcategories by export shares is better than unweighted categories. Since both import shares and export shares are useful in the weighting, we take one half the shares of both exports and imports as the weights. The resulting elasticities are reported in table C1. Broda, Christian , Joshua Greenfield and David Weinstein (2006), From Groudnuts to Globalization: A Structural Estimate of Trade and Growth, National Bureau of Economic Research Working Paper 12512. Available at: http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElasticities .html. 105 Table C1: Estimated Elasticities of Substitution for Varieties in Kenyan Imperfectly Competitive Goods Sectors Sector in our Model Matching HS-3 Code from Broda et al estimates weighted elasticity of substitution Beverages & tobacco 220, 240 2.3 Petroleum 271 3.6 Chemicals 280-391, 390, 393 2.8 Metals and machines 720-854 16.7 Non metallic products 680-702 5.6 Grain milling 110 3.2 Sugar & bakery & confectionary 170 2.9 Source: Authors calculations based on estimates from Broda, Greenfield and Weinstein (2006). 106 Appendix D: Engineering Services in Kenya - Restrictiveness Index The components of the engineering restrictiveness index as well as the scoring options are presented in Table D1. Table D1: Professions Restrictiveness Index 107 108 109 The scoring for Kenya is described below. It is based on the results of the World Bank Regulatory Survey in East Africa42 and the World Bank Survey on Applied Policies in Services 43. Barriers to establishment Form of establishment Score 0.5 Foreign service providers are required to incorporate or establish the businesses locally. There are no restrictions on forms of incorporation. Foreign partnership/joint venture/association Score 0 No restrictions. Investment and ownership by foreign professionals Score 0 No restrictions. Investment and ownership by non-professional investors Score 0.5 An engineering/ consulting firm must have at least one Partner/Director registered as Consulting Engineer who has in force an Annual Practicing Licence in the specified disciplines. Nationality/citizenship requirements Score 0 No restrictions. Residency and local presence Score 0 No restrictions. Quotas/economic tests on the number of foreign professionals and firms Score 1 Entry permits are issued to non-citizens with skills not available at present in the Kenya (class A entry permits for management and technical staff - horizontal measure in Immigration Act Cap 172). Licensing and accreditation of domestic professionals Score 1 Membership in association is compulsory. Professional examination, practical experience and proof of higher education are required. Licensing and accreditation of foreign professionals Score 0.75 Foreign professionals must be registered members of the Engineers Association. Foreign professionals must be holder of a diploma, degree or other qualification recognized by the Association of Engineers of Kenya. Movement of people - permanent Score 0.5 There are limits on the duration of stay; in general, duration of stay is determined on a case by case basis. On-going operations Activities reserved by law to the profession Score 1 42 The regulatory surveys were conducted by local consultants who interviewed the professional associations in the examined East African countries in 2009. 43 The policy surveys were conducted by DECRG in 2008-2009. 110 The engineering profession has an exclusive right to perform the following services: design and planning, representation for obtaining permits (signature of designs), tender and contract administration, project management including monitoring of execution, planning and managing maintenance, survey sites, testing and certification and expert witness activities. There is no law prohibiting a foreign provider with a commercial presence in Kenya from providing these services. The engineering profession has a shared right to provide the following services: feasibility studies, environmental assessment, and construction cost management. There is no law prohibiting a foreign provider with a commercial presence in Kenya from providing these services. Apart from design and planning, which can be done elsewhere and sent to Kenya, a foreign provider supplying services (i.e., without commercial presence in Kenya) will need a work permit in order to provide these services. Multidisciplinary practices Score 0 There are no restrictions on cooperation between engineering professionals and other professionals. The same applies to foreign suppliers. Advertising, marketing and solicitation Score 1 Advertising and marketing by Kenyan professional engineers as well as foreign suppliers is prohibited. Fee setting Score 0.5 Prices /fees in the engineering services applicable to the private sector and other institutions outside the government are not regulated. In the case of professional engineering services rendered to the government, prices/fees are determined the Ministry in charge of engineering services but as of 2010, this function will be performed by the Engineering Registration Board (ERB). The ERB will set the prices/fees to be paid for professional engineering services rendered to the government; the service providers will be expected to compete on the technical aspect only. Licensing requirements on management Score 0 No restrictions. Movement of people - Temporary Score 0 No restrictions. Other restrictions (Addition categories) Score 0.33 Restrictions on hiring professionals: Investment Promotion Act 2004 (cap 172) section 13.1. The employment of foreign natural persons for the implementation of foreign investment shall be agreed upon by the contracting parties and approved by Government. Sources: Dee, P. (2005), A compendium of barriers to services trade, prepared for the World Bank, http://www.crawford.anu.edu.au/pdf/staff/phillippa_dee/Combined_report.pdf Nguyen-Hong, D. (2000), Restrictions on Trade in Professional Services, Productivity Commission Staff Research Paper, Ausinfo, Canberra. Available at: http://www.pc.gov.au/research/staffresearch/rotips World Bank Regulatory Survey in East Africa conducted in the context of the Project Trade in Professional Services in East Africa in 2009. World Bank Survey on Applied Policies in Services conducted by Development Research Group, in 2008-2009. 111 Formatted: Left, Indent: First line: 0" 112 Appendix E: Data on Research and Development Expenditures and Sales for the United States in 2004 and 2005. TABLE E1. Funds for industrial R&D and sales for companies performing industrial R&D in the United States, by industry: 2004 and 2005 All R&D Sales in $millions Ratio of R&D ex penses Industry and company size NAICS codes 2004 2005 2004-2005 av erage in 2005 to sales (x 1,000) $millions All industries 21­23, 31­33, 42, 44­81 208,301 226,159 217,230 6,119,133 36 Manufacturing industries 31­33 147,288 158,190 152,739 3,998,256 38 Food 311 2,254 2,716 2,485 374,342 7 Bev erage and tobacco products 312 555 i 539 547 38,003 14 Tex tiles, apparel, and leather 313­16 570 816 693 51,639 13 Wood products 321 D D 0 27,002 0 Paper, printing, and support activ ities 322, 323 D D 0 159,608 0 Petroleum and coal products 324 1,603 D 802 404,317 2 Chemicals 325 D 42,995 21,498 624,344 34 Pharmaceuticals and medicines 3254 31,477 34,839 33,158 273,377 121 Plastics and rubber products 326 D 1,760 880 90,176 10 Nonmetallic mineral products 327 787 894 841 50,344 17 Primary metals 331 727 631 679 110,960 6 Fabricated metal products 332 1,512 1,375 1,444 174,165 8 Machinery 333 6,579 8,531 7,555 230,941 33 Computer and electronic products 334 48,296 D 24,148 472,330 51 Electrical equipment, appliances, and components 335 2,664 2,424 2,544 101,398 25 Transportation equipment 336 D D 0 957,051 See note Motor v ehicles, trailers, and parts 3361­63 15,677 D 7,839 646,486 12 Aerospace products and parts 3364 13,086 15,005 14,046 227,271 62 Other transportation equipment other 336 D D 0 83,294 0 Furniture and related products 337 408 400 404 48,534 8 Miscellaneous manufacturing 339 4,388 5,143 4,766 83,103 57 Medical equipment and supplies 3391 3,343 4,374 3,859 56,661 68 Other miscellaneous manufacturing other 339 1,045 769 907 26,442 34 All R&D Industry and company size NAICS codes 2004 2005 2004-2005 av erage $millions #DIV/0! Nonmanufacturing industries 21­23, 42, 44­81 61,013 67,969 64,491 2,120,877 30 Mining, ex traction, and support activ ities 21 D D 0 33,665 0 Utilities 22 202 210 206 223,395 1 Construction 23 1,481 D 741 57,187 13 Wholesale trade 42 D D 0 107,485 0 Retail trade 44, 45 1,596 D 798 232,150 3 Transportation and w arehousing* 48, 49 D D 0 79,436 See Note Information 51 22,593 23,836 23,215 445,489 52 Finance, insurance, and real estate 52, 53 1,708 3,030 2,369 580,380 4 Professional, scientific, and technical serv ices 54 28,709 32,021 30,365 261,500 116 Architectural, engineering, and related serv ices 5413 4,265 4,687 4,476 50,121 89 Computer sy stems design and related serv ices 5415 11,575 13,592 12,584 136,376 92 Scientific R&D serv ices 5417 11,355 12,299 11,827 34,516 343 Other professional, scientific, and technical serv icesother 54 1,514 1,444 1,479 40,487 37 Health care serv ices 621­23 500 989 745 25,076 30 b Other nonmanufacturing 55, 56, 61, 624, 1,595 2,137 1,866 75,115 25 71, 72, 81 *We ev aluate transportation as a medium R&D sector since three sectrors dominate R&D ex penditures of US multinationals operating abroad. These are transportation, chemiicals and computers and electronics. Moreov er, about tw o-thirds of all R&D ex penditures of foreign multinationals operatingi in the US w as performed in the same three sectors. See "U.S. and International Research and Dev elopment: Funds and Technology Linkages," at 'http://w w w .nsf.gov /statistics/seind04/c4/c4s5.htm. SOURCE: Calculated from data in National Science Foundation, Division of Science Resources Statistics, Survey of Industrial Research and Development: 2005, Data Tables . Available at: http://www.nsf.gov/statistics/nsf10319/content.cfm?pub_id=3750&id=3. 113 Appendix F: Kenya Model with Multiple FDI and Trade Partners Edward J. Balistreri Thomas F. Rutherford David G. Tarr Colorado School of Mines u ETH-Z¨rich The World Bank June 2010 This document presents the algebraic formulation of a general-equilibrium numeric- simulation model of the Kenya economy. The model is currently under development by the authors. An earlier version of this model was used to analyze unilateral services liberalization in Kenya [Balistreri et al. (2009)]. The model now includes features that allow for an analysis of regional trade agreements. The model includes 55 goods and services, which are purchased by households, firms, and the government. Let the goods and services be indexed by g G. Divide these goods and services into the following three categories that define their treatment in the model formulation: (i.) Business Services, characterized by monopolistic competition and foreign direct investment (FDI), indexed by i I G; (ii.) Dixit-Stiglitz manufacturing sectors, characterized by monopolistic competition, indexed by j J G; and (iii.) Constant Returns To Scale (CRTS) goods indexed by k K G. In the current aggregation there are 9 elements in I, 7 elements in J, and 39 elements in K. Goods and services are also classified by their associated region, indexed by r R, where there are 4 regions.1 The accounts track the incomes of 10 rural and 10 urban households, indexed by h H, and 1 The current formulation includes Kenya or the domestic region (D), the European Union (EU ), important African trade partners (AF R), and the rest-of-world region (ROW ), such that R = {D, EU, AF R, ROW }. 1 there are 5 primary factors of production indexed by f F . Table 1 sumarizes the equilibrum conditions and associated variables. The non-linear system (of 1,364 equations and variables) is formulated in GAMS/MPSGE and solved using the PATH algorithum. We proceed with a description and algebraic representation of each of the conditions itemized in Table 1. 1 Dual representation of technologies and preferences Technologies and preferences are represented in the Kenya model through value func- tions that embed the optimizing behavior of agents. Generally, any linearly-homogeneous transformation of inputs into outputs is fully characterized by a unit-cost (or expenditure) function. Setting the output price equal to optimized unit cost yields the equilibrium con- dition for the activity level of the transformation. That is, a competitive constant-returns activity will increase up to the point that marginal benefit (unit revenue) equals marginal cost. In the case of the Kenya model not all transformations are constant returns, so there are exceptions. In general, however, we will use the convention of setting unit rev- enues (left-hand side) equal to unit cost (right-hand side) and associating this equilibrium condition with a transformation activity level. Agents in Kenya wishing to purchase a particular good or service g face an aggregate price PAg . Let tsg equal the sales tax rate such that (1 - tsg )PAg is the net-of-tax price of the aggregate of domestic, foreign, and FDI (if applicable) varieties of g. In constructing the aggregate, we will rely on the following notation for the component prices: PDg Gross price of domestic output (g G), g PMr Gross price of cross-border imports from region r of Business Services and CRTS goods (g (I K)), Prg Dixit-Stiglitz price index on region-r varieties (g (I J)). 2 Table 1: General equilibrium conditions Equilibrium Condition (Equation) Associated Variable Dimensions Dual representation of preferences and technologies: Armington unit-cost functions (1) i I Ag : Armington Activity G (2) j J (3) k K g Dixit-Stiglitz price indexes (4) g (I J) Qr : D-S Activity by region (I + J) × R g Zero Profits for Dixit-Stiglitz firms (5) g (I J) Nr : Number of Firms (I + J) × R g Dixit-Stiglitz composite input prices (6) g (I J) and r = D Zr : IRTS resource use (I + J) × R (7) j J and r = D (8) i I and r = D Input-output technologies (9) g G Y g : Production level G Constant elasticity of transformation (10) k K X g : Index on CET activity G (11) g (I J) (No Export Coefficients for g (I J)) g Exports (12) k K and r = D EXr : Exports G × (R - 1) (13) g (I J) and r = D g Imports (14) g G and r = D IMr : Imports (net of FDI-firm imports) G × (R - 1) Unit expenditure function (15) U : Household utility index 1 Unit cost of public purchase (16) PUB: Government Activity 1 Unit cost of investment (17) INV : Investment Activity 1 Market clearance conditions: Composite goods and services (18) g G P g : Composite price indexes A G g D-S composites (19) g (I J) and r = D Pr : Prices of D-S composites (I + J) × R (20) g (I J) and r = D Markets for IRTS composite input (21) g (I + J) PMC g : Composite input prices (I + J) × R Markets for domestic output (22) k K PDg : Domestic output prices G (23) i I (24) j J k Markets for export output (25) k K and r = D PXr : Export output prices K × (R - 1) Markets for gross output (26) g G PY g : Output prices G g Markets for imports (27) i I and r = D PMr : Import prices G × (R - 1) (28) j J and r = D (29) k K and r = D Factor markets (30) f F PFf : Factor prices F g IRTS specific factors (31) g (I J) PZr : Sector-specific capital price (I + J) × R Fixed real investment (32) PINV : Unit cost of investment 1 Fixed real public spending (33) PG: Unit cost of public good 1 Enterprise owner transfers (34) PE: Price of a claim on firm income 1 Nominal utility equals Income (35) PC: Unit expenditure index 1 Balance of payments (36) PFX: Price of foreign exchange 1 Income balance: Domestic agent income (37) h H A R h : Household Income H Government budget (38) GOVT : Government spending 1 Enterprise income (39) ENT : Enterprise income 1 Foreign Entrepreneur (40) FE: External agent income 1 Auxiliary Conditions: Fixed real public spending (41) T : Index on direct taxes 1 Foreign Savings (42) KA: Capital Account 1 Total Dimensions: 6G + 6[(I + J) × R] + 3[G × (R - 1)] + [K × (R - 1)] + F + H + 13 = 1, 364 3 Assuming a Constant Elasticity of Substitution (CES) aggregation of the components we equate the net-of-tax prices to the CES unit-cost functions: i 1/(1-F ) g i i i (1 - ts )PA = (Pri )1-F + i i (PMr )1-F r (1) r r j 1/(1-F ) j g j (1 - ts )PA = (Prj )1-F (2) r k 1/(1-DM ) g k k k (1 - ts )PA = k (PDk )1-DM D + k (PMr )1-DM r k , (3) r g k where F g (I J) is the Dixit-Stiglitz elasticity of substitution and DM is the Armington elasticity of substitution on CRTS goods. The arguments of these functions are the component prices. The parameters are CES distribution parameters that indicate scale and weighting of the arguments. These are calibrated to the Kenyan social accounts such that the accounts are replicated in the benchmark equilibrium. For the IRTS sectors we have the Dixit-Stiglitz price indexes. These are functions of the number of varieties, firm-level costs, and the optimal markup. Assuming each firm is g small relative to the size of the market the demand elasticity for a firm's variety is F and 1 the optimal markup over marginal cost is given by 1/(1 - g F ). Let marginal cost equal g PMCr g (I J), which is the price of a composite input to the Dixit-Stiglitz firms g associated with region-r, and let the number of varieties by region equal Nr g (I J). The price indexes for the Dixit-Stiglitz goods are thus given by 1-g 1/(1-F ) g g F g PMCr Prg = Nr g (I J). (4) 1 - 1 g F In equilibrium, the number of varieties by region adjusts such that we have zero profits. Denote the Dixit-Stiglitz composite activity level associated with equation (4) by Qg g r (I J). Given the Dixit-Stiglitz aggregation of varieties each firm produces a quantity 4 g g g Qg (Nr )F /(1-F ) . Assuming that fixed and variable costs are satisfied using the same r g input technology, and a firm-level fixed cost of fr (in composite input units), we have the zero profit condition g g Qg (N g )F /(1-F ) g fr - r rg =0 g (I J). (5) F - 1 The technologies for producing the composite inputs for use in the Dixit-Stiglitz sectors depend on the type of sector. For all of the sectors there is a sector-specific capital input g from the respective source region. Let PZr g (I J) be the price of this sector-specific capital input. Domestic firms (producing goods or services) use domestic inputs, so the unit cost function is given by g g g g g 1/(1- g ) r PMCr = Zr (PZr )1- r + Dr (PDg )1- r g , for r = D; (6) g where r is the elasticity of substitution between the sector-specific capital input and other inputs, and the 's are the CES distribution parameters. Imports of Dixit-Stiglitz goods embody the gross of tariff imported inputs: j j 1/(1- j ) r j j j PMCr = Zr (PZr )1- j r + M r (PMr )1- j r , for r = D. (7) FDI firms, on the other hand, use domestic inputs as well as a specialized imported service from the sources region. The price of the specialized imports equals the price of foreign exchange (denoted PFX) times one plus the tariff rate (denoted ti ). The unit cost for r FDI firms is thus given by the following: i i i 1/(1- i ) r PMCr = Zr (PZr )1- r + (Dr PDi + M r (1 + ti )PFX)1- r i i i i r , for r = D. (8) 5 For the CRTS sectors and upstream of the IRTS technologies, we have domestic pro- duction in accordance with the input output data. Denote the price of this output PY s , for s G. The technology includes an upstream Cobb-Douglas value-added nest which then combines with intermediates (trading at PAg ) in fixed proportions. Let PFf indicate the price of primary factor of production f F . The unit cost function is given by s s PY s = VA PFf f + g PAg s (9) f g where the are distribtuion parameters and are value shares within the value-added s nest ( f f = 1s). For the CRTS sectors a constant elasticity of transformation (CET) activity splits domestic output (with a unit value PY k ) into goods destine for domestic versus the region-specific export markets. Let the export price (for goods destine for region r = D) k be PXr then the CET technology is given by 1/(1+ ) k (PD k )1+ + k k r (PXr )1+ = PY k , (10) D r=D where indicates the elasticity of transformation and the are the CET distribution parameters. In the case of IRTS sectors, we assume that domestic firms use domestic output to produce Dixit-Stiglitz varieties. Thus the CET technology collapses without g export coefficients [r = 0 g (I J)]: PDg = PY g g (I J). (11) For CRTS sectors the export commodity is traded for foreign exchange at a fixed rate. Let PFX equal the price of foreign exchange, and with a choice of units such that all unit export prices are one at the benchmark, we have the following specification for the CRTS 6 export activities: k PFX = PXr for r = D. (12) For the IRTS sectors, domestic firms export the firm-level good where foreign agents are assumed to behave according to Dixit-Stiglitz preferences that are the same as domestic g agents. Domestic IRTS firms face an export demand elasticity for their variety of F and thus price their exports using the optimal markup. In aggregate the IRTS export activities by region are characterized by g F g g 1 PFX EXr = r 1- g g g (I J) and r = D. (13) F PMCD Cross-border imports are purchased at the price of foreign exchange times one plus the tariff rate, which sets up the arbitrage condition for each import activity; g PMr = (1 + tg )PFX for r = D. r (14) Final demand includes three categories: household demand, government demand, and investment. The representative agents for each household h are assumed to have identical Cobb-Douglas preferences over the aggregated goods and services. The preferences are specified via a unit expenditure function associated with an economy-wide utility index (U ). Let PC be the true-cost-of-living index indicated by the following unit expenditure function: g PC = (PAg )µC , (15) g where the µ are value shares. The government faces a Leontief price index, PG, for 7 government purchases: PG = µg (PAg ). G (16) g Similarly the price of investment, PINV is a Leontief aggregation of commodity purchases: PINV = µg (PAg ). INV (17) g Equations (1) through (17) define all of the transformation technologies for the model. Next we turn to a specification of the market clearance conditions for each price. 2 Market clearance conditions For each good or service there is a market, and, for any non-zero equilibrium price, supply will equal demand. We will use the convention of equating supply, on the left-hand side, to demand, on the right-hand side. The unit-value functions presented above are quite useful in deriving the appropriate compensated demand functions, by the envelope theorem (Shephard's Lemma). Supply of the composite goods and services, trading at PAg , is given by the activity level, Ag , and demand is derived from each production or final demand activity that uses the good or service. The market clearance condition is given by PC Ag = g Y s + µg U s C g + µg PUB + µg INV G INV (18) s PA For the IRTS sectors we have market clearance for the Dixit-Stiglitz regional composites: g F (1 - tsg )PAg Qg = Ag g (I J), for r = D; (19) r Prg 8 and for domestic firms we include demand for the Dixit-Stiglitz exports g F (1 - tsg )PAg Qg = A g D g + g EXr g (I J). (20) PD r g g The IRTS composite input (trading at PMCr ) is supplied by an activity, denoted Zr g (I J), and is demanded by the firms: g g g g g Zr = fr Nr + Qg (Nr )1/(1-F ) r g (I J). (21) g g g To derive (21) recall that firm-level output is Qg (Nr )F /(1-F ) so the use of the input r g g g g across all firms is Qg (Nr )1/(1-F ) plus the total input use on fixed costs, fr Nr . r Market clearance for the domestic output of CRTS sectors depends on supply from the CET activity and demand from the Armington activity: k DM PDk (1 - tsk )PAk k D X k = k A k D . (22) PY k PDk For IRTS sectors, supply is simply given by the CET activity (as there are no export coefficients in the CET technology for IRTS sectors). Output is then demanded by either the domestic or FDI firms. The market clearance conditions are given by i i i i PMCD D PMCr r i i X i = DD ZD + i i Dr Zr i i (23) PDi r=D Dr PDi + M r (1 + ti )PFX r for the service sectors, and j j j j PMCD D X j = DD ZD (24) PDj for the Dixit-Stiglitz goods sectors. 9 Market clearance for exports of CRTS output is given by the CET supply function and demand is given by the export activity level (export demand is perfectly elastic): k PXr k r X k k = EXr , for r = D. (25) PY k Reconciling gross output with the CET activities, we have market clearance for the com- modities that trade at PY g : Y g = Xg. (26) Import supply is perfectly elastic and import demand is derived from the Armington activities or embodied in the foreign Dixit-Stiglitz firm's inputs. For r = D, we have the following: i F i (1 - tsi )PAi IMr = i A i r i (27) PMr j j r j j j PMCr IMr = M r Zr j (28) PMr k DM (1 - tsk )PAk k IMr = k Ak r k . (29) PMr Factor markets clear, where factor supply is given by the exogenous endowments to households, denoted S f h , and input demands are derived from the cost functions: PY s - g g PAg s Sfh = f Y s s . (30) h s PFf In addition, we have the market for the specific factor used in the IRTS sectors. Denoting 10 g the regional endowments of the specific factors SF r g (I J), we have: g g g g g PMCr r SF r = Zr Zr g g (I J). (31) PZr Real investment equals real savings by households, enterprises, the government, and indirect foreign investment: INV = sav h + sav ent + sav G + sav row . (32) h Real government purchases equal the nominal government budget scaled by the govern- ment price index: GOVT PUB = . (33) PG Enterprises earn net income of ENT , other agents (households, the government, and foreign entrepreneurs) own claims to this income. We set up the transfer of income as a market clearance condition for claims, which trade at a price PE. The claims are indicated by the coefficients and these are serviced by net enterprise income scaled by the price of the claims: ENT h + gov + F E = . (34) h PE Thus the nominal value of enterprise income transferred to households, the government, and foreign entrepreneurs equals ENT . Household utility (U ) equals nominal income across households scaled by the true- cost-of-living index. That is, we represent an aggregate activity U , which supplies utils to the households. For the representative agent of household type h denote nominal income 11 RAh . The market clearance condition for utils is thus hRAh U = . (35) PC The final market clearance condition reconciles the balance of payments. The supply of foreign exchange includes its generation in the export activities and net borrowing from the rest of the world (net capital account surpluses). The real capital account surplus includes an endogenous component, denoted KA, and an exogenous residual transfer (from foreigners to the domestic government), denoted f trn. Foreign exchange is demanded for direct import purchases as well as the payments to foreign agents for their contribution to production. g g EXr + (KA + f trn) = IMr r=D g r=D g i i i i PMCr r + M r Zr i i r=D i Dr PDi + M r (1 + ti )PFX r FE + , (36) PFX where FE equals the nominal claims that the foreign entrepreneurs have on specific factor rents and enterprise income. 3 Income Balance Conditions The representative agent for household h earns income from factor endowments and claims on enterprise income, but disposable income nets out savings and direct taxes. Real sav- ings is held fixed (by the coefficient sav h ). We also hold fixed the real level of government spending, but this requires an adjustment in direct taxes on households. Removal of tar- iffs, for example, impact the government budget and the shortfall is made up for by an 12 endogenous increase in the direct taxes on households. We use the auxiliary variable T to scale the direct taxes appropriately. Household h's budget is given by RAh = PFf S f h f + h PE - sav h PINV - dtaxh PG × T (37) The government budget is given by direct taxes, sales taxes, tariff revenues, and claims on enterprise income. The government budget also captures the capital account balance and its conversion to investment by foreign agents.2 In addition, the government's budget is augmented by foreign transfers of foreign exchange and reduced by direct public savings. The full nominal government budget is GOVT = dtaxh PG × T + dtaxent PG + tsg PAg Ag g + g tg (PFX)IMr r r=D g i i PMCr r + i i ti (PFX)M r Zr r i i r=D i Dr PDi + M r (1 + ti )PFX r + gov PE + (PFX)KA - sav row PINV + f trnPFX 2 Nominal government income is increased by (PFX)KA but this is offset by nominal investments of (sav row )PINV . Equation (42) is added to the equilibrium system to ensure that the nominal value of capital inflows equals the nominal investment by the modeled government agent acting on behalf of the foreign investors. 13 - sav G PINV (38) Again, the index T is adjusted endogenously to hold the real level of public spending fixed. We now turn to enterprise income. Enterprises earn income on the domestic specific factors. Real spending by enterprises on direct taxes and savings are held fixed. The net income is then available to be transferred to one of the agents [via equation (34)]. Enterprise income is given by g g ENT = PZD SF D g - sav ent PINV - dtaxent PG (39) We also need an agent representing the foreign entrepreneur. The foreign entrepreneur's nominal income is FE, which is spent on foreign exchange, and consists of the specific factor payments and the distributed enterprise payments: g g FE = PZr SF r + FE PE (40) r=D g 4 Auxiliary Conditions In addition to the three sets of standard conditions presented above, we need to close the model with a couple of auxiliary conditions. First, we need to determine the index on direct taxes of households. Associated with the variable T is the following condition: PUB = pub. (41) 14 This holds fixed the real size of the government at the exogenous level pub. Lastly, we reconcile relative price changes that affect capital flows. Foreign agents interested in investing in Kenya will consider the relative price of investment to the price of foreign exchange. Associated with the variable KA is the following condition: (PFX)KA = (sav row )PINV. (42) From the perspective of the foreign agent, real indirect investment in Kenya is held fixed, and the term [(PFX)KA - sav row PINV ] drops out of the government budget. Equations (1) through (42) define the general equilibrium simulation model of Kenya. The fuctions are calibrated to the social accounts, such that the social accounts are replicated in the benchmark solution. References Balistreri, Edward J., Thomas F. Rutherford, and David G. Tarr (2009) `Modeling services liberalization: The case of Kenya.' Economic Modelling 26(3), 668­679 15 Appendix G: A note on the relationship between sector specific capital and the elasticity of supply in applied general equilibrium models of imperfect competition Edward J. Balistreri David G. Tarr Colorado School of Mines The World Bank June 2, 2010 The models developed by Balistreri et al. (2009) and Jensen et al. (2008) to analyze ser- vices liberalization in Kenya and Tanzania utilize a specific-factor formulation. The specific- factor formulation facilitates a calibration of the FDI and domestic service responses. This is important because the empirical evidence [Hummels and Klenow (2005)] indicates that varieties expand less than proportionately to market size. The expansion of services bids up the price of the specific factor resulting in increasing costs (upward sloping supply). These increasing costs ensure that the varieties expand less than proportionately to market size. The predetermined elasticity of supply controls the magnitude of these effects. This note outlines the calibration procedure. One can calibrate a linearly-homogeneous (constant-returns) Constant Elasticity of Sub- stitution (CES) technology to an arbitrary price elasticity of supply if some of the input value is allocated to a specific factor. In the context of the Kenyan and Tanzania models the supply elasticity applies to the composite input that is used in both fixed and variable costs associated with the services sectors. To simplify the presentation, consider the composite input for a single type of firm (say domestic firms) and for a single industry (say Communications). Let the quantity of this This note is largely based on lecture notes from Thomas F. Rutherford's graduate course on Computa- tional Economics at the University of Colorado (late 1990's) 1 composite input be denoted y with a market price of p. Denote the associated nested CES unit cost function c(r), where r is a vector of input prices. With competition for the composite input we have p = c(r) min {r x s.t. f (x) = 1} , (1) where x is the vector of inputs and the function, y = f (x), is the CES technology for ¯ aggregating inputs. Denote the fixed quantity of the sector specific input R with price r1 , and assume that all of the mobile inputs can be combined into a separable composite X ¯ with composite price r2 (that is, x = {R, X} and r = {r1 , r2 }).1 We thus have the explicit expression: 1/ ¯ p = c(r1 , r2 ) min r1 R + r2 X s.t. ¯ R R + X X =1 , (2) where indicates the elasticity of substitution, = 1/(1 - ), and R and X are the CES distribution parameters. Choosing units carefully (such that p = r1 = r2 = 1) at the benchmark and solving (2) we have the unit cost function: 1 1- 1- 1- c(r1 , r2 ) = r1 + (1 - )r2 , (3) where is the benchmark value share of the sector specific input. Given that the quantity ¯ R is fixed in supply the price r1 is a residual. The technology de facto exhibits decreasing returns (upward sloping supply) because the only way to increase y is to increase X at 1 ¯ The variable X is a nested CES subcomposite of all of the inputs excluding R. Define z as the vector ¯ of all inputs other than R, and define s as the vector of corresponding input prices. Let X = g(z), so we have r2 = min {s z s.t. g(z) = 1}, where g(z) is a nested CES function and the input vector z may include intermediates. The actual specification of g(z) is not a concern here because the supply elasticity is inherently dependent on the concept of partial differentiation (changes in the elements in s are not considered). In fact, we are only concerned with the supply elasticity local to the benchmark equilibrium, where r2 takes on a specific numeric value. 2 ¯ diminishing marginal product (as the R to X ratio falls). ¯ Using Shepard's lemma to derive demand for R we can represent the overall resource constraint on the specific factor as follows: ¯ c(r) R = y r1 p = y . (4) r1 Solving for the residual price 1/ y r1 = p ¯ , (5) R and then substituting this back into the unit cost function we have: 1- 1- 1- y 1- p = p ¯ + (1 - )r2 . (6) R Solving for y as a function of the resource constraint and the price ratio (r2 /p) we have supply: 1- 1- ¯ 1 1 - (1 - ) r2 y = R -1 . (7) p The supply elasticity is given by y p (1 - ) = -1 , (8) p y -1 + + rp2 and evaluating this local to the benchmark equilibrium (r2 = p = 1) we have (1 - ) = . (9) This equation gives us the fundamental relationship between the local supply elasticity and the CES parameters. 3 Notice that there are many combinations of value shares and substitution elasticities that yield the same local supply elasticity. If the goal is to calibrate the model to a given value of there are a couple of options. For example, one could simply lock down the value of (at say = 1, which is Cobb-Douglas) and then calculate the appropriate overall value share of the specific factor (at = 1 we have = 1/(1 + )). In empirical applications, however, this calibration method can be problematic, because the value of may be constrained by the social accounts. In the Kenya and Tanzania models we choose a different calibration strategy. We observe the value of capital payments in the social accounts, and it is logical that these include payments to the specific factor. Denote the observed capital payments vk and the overall value of output vy. Now if we choose a share of the capital payments that should be allocated to the specific factor, call this k , we can calculate the appropriate elasticity of substitution as follows: = , (10) 1- where = k (vk/vy). In sensitivity analysis on the Kenya and Tanzania models we hold fixed the value of k = 0.5 and vary the value of . As increases the calibrated elasticity of substitution increases and we observe a more elastic supply response. In terms of varieties, we observe that the change in the number of varieties is closer to proportional to the change in market size as increases. One might consider sensitivity analysis on the value of k , but this will not necessarily generate intuitive responses. In fact, as long as the counterfactual is local to the benchmark equilibrium there should be no effect of changing k . As k increases the value of /(1 - ) falls and, according to equation (10), the calibrated value of falls to compensate. So larger value shares will not necessarily generate larger supply responses. In fact, by design, the 4 local impact of a change in k is zero. References Balistreri, Edward J., Thomas F. Rutherford, and David G. Tarr (2009) `Modeling services liberalization: The case of Kenya.' Economic Modelling 26(3), 668­679 Hummels, David, and Peter Klenow (2005) `The variety and quality of a nation's trade.' American Economic Review 95(3), 704­723 Jensen, Jesper, Thomas F. Rutherford, and David G. Tarr (2008) `Modeling services liber- alization: The case of Tanzania.' Policy Research Working Paper, No. 4801, The World Bank 5