What determines whether preferential liberalization of barriers against foreign investors in services are beneficial or immizerising: Application to the case of Kenya

Balistreri, Edward J.; Jensen, Jesper; Tarr, David Article What determines whether preferential liberalization of barriers against foreign investors in services are beneficial or immizerising: Application to the case of Kenya Economics: The Open-Access, Open-Assessment E-Journal Provided in Cooperation with: Kiel Institute for the World Economy – Leibniz Center for Research on Global Economic Challenges Suggested Citation: Balistreri, Edward J.; Jensen, Jesper; Tarr, David (2015) : What determines whether preferential liberalization of barriers against foreign investors in services are beneficial or immizerising: Application to the case of Kenya, Economics: The Open-Access, Open-Assessment E- Journal, ISSN 1864-6042, Kiel Institute for the World Economy (IfW), Kiel, Vol. 9, Iss. 2015-42, pp. 1-134, https://doi.org/10.5018/economics-ejournal.ja.2015-42 This Version is available at: https://hdl.handle.net/10419/123096 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/3.0/ Received May 19, 2015 Published as Economics Discussion Paper July 1, 2015 Revised September 25, 2015 Accepted October 12, 2015 Published November 24, 2015 © Author(s) 2015. Licensed under the Creative Commons License - Attribution 3.0 Vol. 9, 2015-42 | November 24, 2015 | http://dx.doi.org/10.5018/economics-ejournal.ja.2015-42 What Determines Whether Preferential Liberalization of Barriers against Foreign Investors in Services Are Beneficial or Immizerising: Application to the Case of Kenya Edward J. Balistreri, Jesper Jensen, and David Tarr Abstract Despite the fact that many modern preferential trade agreements include commitments to foreign investors in imperfectly competitive services sectors, the literature has not established conditions under which these agreements are beneficial or harmful. The authors fill that void by developing a model with monopolistic competition and foreign direct investment in services with Dixit-Stiglitz endogenous productivity effects from additional varieties. They specify a numerical model, with probability distributions of all parameters. The model is executed 30,000 times, and results are reported as probability of an outcome, based on the sample distribution. In order to ground the results in reality, the authors apply the model to Kenya. They show that preferential commitments in services could be immizerising. Losses are more likely the greater the share of initial rent capture on the services barriers in the home country and the more technologically advanced are the excluded regions relative to the partner region. JEL F12 F13 F14 F15 F23 F47 C68 L16 Keywords Immizerising services liberalization; preferential liberalization; multinationals; monopolistic competition; foreign direct investment; endogenous productivity effects Authors Edward J. Balistreri, Colorado School of Mines, 1500 Illinois Street, Golden, CO 80401, USA, [email protected] Jesper Jensen, Jensen Analysis, Agern Alle 3, 2970, Hørsholm, Denmark, [email protected] David Tarr, Former Lead Economist, The World Bank, 1818 H Street, NW, Washington, DC 20433, USA, [email protected] Citation Edward J. Balistreri, Jesper Jensen, and David Tarr (2015). What Determines Whether Preferential Liberalization of Barriers against Foreign Investors in Services Are Beneficial or Immizerising: Application to the Case of Kenya. Economics: The Open-Access, Open-Assessment E-Journal, 9 (2015-42): 1—134. http:// dx.doi.org/10.5018/economics-ejournal.ja.2015-42 www.economics-ejournal.org 2 1 Introduction Since the early 1990s, regional trade agreements have surged; 283 have been notified to the WTO and were in force as of February 2010.1 Commitments to foreign investors in services are now key aspects of modern FTA agreements negotiated with the EU and the US, and in some other agreements. The literature, however, contains neither analytical nor numerical results on the general equilibrium welfare impacts of preferential commitments to foreign investors in the presence of imperfect competition in services sectors.2 Given that commitments to foreign investors in services sectors (many of which are imperfectly competitive) are key aspects of modern FTA agreements, the objective of this paper is to determine if such agreements can be immizerising, and the conditions that make it more or less likely the agreements are beneficial. Further, we develop a numerical general equilibrium framework to assess these agreements in practice. It is well known that the welfare effects of preferential trade in goods are ambiguous, with welfare losses possible in perfectly competitive models due to the loss of tariff revenue on the decline in imports from excluded countries. In services, however, there typically is no tax revenue on barriers to foreign investors, leading some experts to suggest that gains from preferential liberalization of services are much more likely than in goods (Mattoo and Fink, 2002). But Mattoo and Fink acknowledge that if the home country is capturing rents from the barriers, these rents play the same role in preferential liberalization of services as tariffs in goods, leading to possible losses.3 And despite the fact that key sectors in the negotiations are characterized by imperfect competition (like banking, insurance _________________________ 1 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. 2 There have been several numerical modeling papers in recent years that examine FDI in services, without a regional dimension, including Markusen et al. (2005), Konan and Maskus (2006), Rutherford and Tarr (2008), Brown and Stern (2001), Dee et al. (2003), Jensen et al. (2007, 2010), and Balistreri et al. (2009). 3 See Jensen and Tarr (2010) for a detailed analytical treatment. www.economics-ejournal.org 3 and telecommunications), there has not been any analytical work assessing the welfare impacts with imperfect competition.4 Any modeling effort must take into account the mounting evidence on the productivity gains of FDI in services.5 The essential features of the problem, however, (general equilibrium, imperfect competition, foreign direct investment and endogenous productivity effects) make the model sufficiently complex that analytic solutions are exceedingly difficult. Consequently, we construct a numerical model which contains these features (endogenous productivity effects from Dixit-Stiglitz variety effects) and specify probability distributions of all parameters. We execute the model 30,000 times, where each simulation is based on a random draw of all the parameter values. The results are reported as probability of an outcome, based on the sample distribution. In order to ground the results in reality, we apply the model to Kenya, a developing country that is facing a range of regional trade agreements that include services, including the Economic Partnership Agreements with the European Union and the Tripartite Free Trade Agreement among the Common Market for Eastern and Southern Africa (COMESA), the East African Customs Union and the South African Development Community (SADC).6 We build on the 55 sector small open economy model of Kenya by Balistreri et al. (2009), but decompose the rest of the world into the European Union, our Africa region and the Rest of the World. In each imperfectly competitive sector, firm types differ by sector and region. Based on the now extensive econometric literature begun by Coe and Helpman (1995), we allow the Dixit-Stiglitz endogenous productivity effects to vary by the level of development of the partner region, and by sector. _________________________ 4 Mattoo and Fink (2002) develop analytic results that show that due to “first mover” advantages, preferential liberalization in services could result in reduced gains from subsequent multilateral liberalization. But they do not show a case of where the preferential liberalization, ceteris paribus, results in welfare losses. 5 See Francois and Hoekman (2010) for a survey of more than a dozen empirical studies that support this finding. Also see the survey in Jensen and Tarr (2010) for additional studies. Support comes from a variety of sources including studies that use firm level data, such as Arnold et al. (2011) for the Czech Republic and Fernandes and Paunov (2012) for Chile, and studies that use cross country growth regressions, e.g., Mattoo et al. (2006) and Fernandes (2009). 6 See Appendix Table 1 for a list of COMESA and East African Customs Union countries. www.economics-ejournal.org 4 Preferential liberalization of services barriers results in an increase in varieties (with productivity gains) from regional partners, but losses of varieties (and lost productivity) from excluded countries. The possible losses for Kenya in a services agreement with our Africa region show that, with some plausible parameter values, there is an imperfect competition analogy to trade diversion in goods whereby preferential commitments in services could be immizerising due to a loss of varieties of services from excluded countries combined with lost domestic rents. Piecemeal sensitivity analysis shows that the two most important parameters in the model are the share of rents captured by domestic agents and the parameter that captures the capacity of a region to transfer technology to Kenya. We present results of detailed sensitivity analysis with these parameters that show that the gains are both larger and more likely to be positive the more technologically advanced is the partner region relative to the excluded regions, and the less the rent capture on initial barriers in services. While there are no tariffs or taxes on FDI in services, if Kenyans are assumed to capture the rents from barriers in services, then, even in a constant returns to scale version of our model, the mean estimate is that Kenya would lose from preferential liberalization with the Africa region. The paper is organized as follows. In Section 2, we provide an overview of the Kenyan services sectors. We discuss how we estimated the tariff equivalents of the barriers in services in Section 3. We provide an overview of the model in Section 4 and a discussion of the data in Section 5. The central results are presented in Section 6 and sensitivity results are presented in Section 7. Conclusions are presented in Section 8. 2 Overview of the Kenyan service sectors7 2.1 Transportation Kenya’s port, rail and road transportation facilities are plagued by significant bureaucratic and regulatory problems (on which we focus) as well as investment problems—problems that raise the costs of transportation of its goods. In both 2011 and 2012, Kenya was ranked 141st out of 183 countries on the Doing _________________________ 7 For more details of the services sectors in Kenya, see Balistreri and Tarr (2011). www.economics-ejournal.org 5 Business Survey category known as “Trading Across Borders.” In 2011, the costs of exporting a container were $2055 and the costs of importing a container were $2190.8 While these costs are about average for sub-Saharan Africa, Freund and Rocha (2011) have shown that transit delays and costs have significantly impeded Africa’s exports, especially on inland transportation. In maritime services, foreign executives and specialists can work in Kenya only after the immigration officer has certified that there is no Kenyan national who can perform the work. In order for foreign firms to supply shipping services in Kenya, they must be represented by a Kenyan agent. There is a de facto limitation on foreign ownership that does not permit full foreign ownership of shipping services firms in Kenya or of onshore services to shipping companies. The Kenya Ports Authority, that manages the port of Mombassa, lacks sufficient flexibility to respond to market demand changes due to extensive intervention by the Government in major decision-making. 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) of 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. 2.2 Telecommunications Kenya’s telecommunications services have been expensive compared with other sub-Saharan African countries and even more when compared with those of East and South Asia. Data transmissions are especially expensive by international standards.9 Perhaps more importantly, is the low efficiency of service provision (see World Bank, 2007, pp.45–47). Kenya has required that telephone companies must be at least 30 percent owned by Kenyan nationals, a constraint that likely leads to some rent capture by Kenyans. Problems related to the licensing of the third mobile telephone provider and the “Second National Operator” were _________________________ 8 See http://www.doingbusiness.org/data/exploretopics/trading-across-borders. Francois and Wooten (2010) have shown that competition in distribution services affects the volume of trade in goods. 9 Surprisingly, this does not appear to have improved in 2010 after the completion of the underwater fiber-optic cable connection to Kenya. www.economics-ejournal.org 6 primarily due to this restraint. In fact, the Government has acknowledged that the 30 percent ownership requirement has delayed licensing of additional telecom operators. 2.3 Banking and insurance Relative to other countries in Africa, Kenya has a well-developed financial sector. Nonetheless, medium, small and micro enterprises have severe problems accessing credit and obtaining insurance (World Bank, 2007). Most regulatory barriers are non-discriminatory in nature in the banking sector. All banks must obtain permission from the Minister of Finance to open a new outlet. Banks are prohibited from providing insurance services. Only foreign banks can provide currency exchange services, and branch banking by foreign banks is not permitted. Regarding the regulatory environment in insurance, 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 onethird of the members of the Boards of Directors must be Kenyan (restraints that may allow Kenyans to capture rents on incumbent multinational enterprises operating in Kenya). Insurance companies are prohibited from providing banking or security services. 2.4 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. Foreign professionals working in Kenya must typically do so in the office of a licensed Kenyan, providing rents to Kenyans. 3 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. Our methodology builds on a series of studies supported by the www.economics-ejournal.org 7 Australian Productivity Commission, especially the papers by Warren (2000) in telecommunications, Kalirajan et al. (2000) in financial services, Kang (2000) in transportation services and Nguyen-Hong (2000) in engineering services. For each of these service sectors, the authors first developed a matrix to evaluate and score the regulatory environment in the sector they were studying. The regulatory regimes are evaluated on criteria such as ease of getting a license; measures that restrict a form of commercial presence; maximum ownership shares allowed for foreign investors; and whether senior executives are allowed to work in the country either permanently or temporarily. They collected data and assessed the regulatory regimes of many countries. Evaluations of each criterion were transformed into a quantitative score and weights were assigned to each criterion so that the regulatory regimes of each country were transformed into a “restrictiveness index.” They then regressed the price of services against their restrictiveness index and other relevant variables to determine the impact of the regulatory barriers on the price of services.10 Through this regression, it is possible to obtain an ad valorem equivalence of the regulatory barriers in the countries of their sample. Our methodology assumes that the international regression estimated by these authors applies to Kenya. To build on their regression estimates, it is necessary to score the identical matrix of regulatory barriers used by the Australian authors, since their index was used as an independent variable in their regressions on the price of the service. For this task, we assessed the regulatory environment in the services sectors in our model. We first commissioned a Kenyan law firm to complete the World Bank survey instrument of the regulatory regimes in key Kenyan business services sectors, namely, insurance, banking, fixed line and mobile telecommunications services, air transportation, maritime transportation and professional services.11 We also used separate surveys done in professional services; see World Bank (2010). We could not use the World Bank Services Trade Restrictiveness Indices (STRIs), since the STRIs are not ad valorem _________________________ 10 Warren estimated quantity impacts and then using elasticity estimates was able to obtain price impacts. 11 See Borchert et al. (2014) for a discussion of the World Bank dataset on services barriers against foreign firms. We thank Ms. Sonal Sejpal of the Kenyan law firm of Anjarwalla & Khanna Advocates for leading the research work on the general effort. Nora Dihel led the survey in engineering services. www.economics-ejournal.org 8 equivalents.12 Further, our experience in conducting such assessments in more than seven countries is that interviews and in-country research typically result in modifications and improvements of the assessment of the regulatory barriers of the survey. In fact, we supplemented the information obtained from the World Bank survey instrument questionnaire 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 (attended by one of the authors); this included papers by Oresi (2007) and Ochieng (2007) on transportation services, Helu (2007) on maritime services, Kiptui (2007) on financial services and Matano and Njero (2007) on communication services. In Appendix D, we discuss our use of additional surveys conducted in professional services by the World Bank (2010). Our scoring was also informed by meetings with Kenyan experts and use of World Bank reports, including World Bank (2007) and the report of the Telecommunications Management Group (2007). We define a barrier as non-discriminatory if it applies equally to domestic as well as foreign firms. Examples include the following. In banking, restrictions on the rights of banks to sell insurance or facilitate security trading; restrictions on banking outlets; restrictions on the type of ownership structure and ease of licensing. In telecoms, there are restrictions on the type of telephone and internet services that are permitted that apply to domestic as well as foreign firms. In Kenya, a foreign firm was awarded a monopoly right to operate railway services. Based on the information obtained, Mircheva (2007) scored the regulatory regimes in fixed line and mobile telecommunications, banking, insurance, air transportation and maritime transportation services sectors and produced two trade restrictiveness index for each sector: one discriminatory and one nondiscriminatory. Mircheva then used her calculation of the restrictiveness indices for the various Kenyan services sectors in the regression for the corresponding services sector to obtain the price impact of the regulatory barriers. From the price impact estimate, she calculated the ad valorem equivalents of the discriminatory and non-discriminatory barriers in her services sectors. In the case of professional services, we used engineering services as a proxy for all professional services and _________________________ 12 Recently, Jafari and Tarr (forthcoming) have converted the World Bank dataset into ad valorem equivalents. www.economics-ejournal.org 15 number of firms could exist.17 Given that services cannot be stored, FDI to achieve a domestic presence (what is known as the proximity burden) has historically been crucial to the effective delivery of services. While technological change has progressively allowed more services to be supplied on a cross-border basis, to effectively compete in services “trade,” it still is likely that it requires more of a domestic presence than trade in goods, which suggests that cross border services are not good substitutes for service providers who have a domestic presence. 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. Crucial to the results, we allow multinational service firms to establish a presence in Kenya to compete with Kenyan firms directly. As in the goods 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 the same pricing rules as in the imperfectly competitive goods sectors. Thus, again there are no rationalization impacts. For domestic firms, costs are defined by the costs of primary factors and intermediate inputs. When multinationals service providers decide to establish a 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, _________________________ 17 See Tarr (2012) for references and a brief discussion of econometric papers that estimate economies of scale in all of these sectors. 18 Data on the sales of foreign affiliates of U.S. firms suggests that sales through FDI are the most important channel for U.S. firms to sell services to foreigners (Francois and Hoekman, 2010, p.655). See Francois and Hoekman (2010), Francois (1990) and Markusen (1989) for elaboration of the proximity burden in services. www.economics-ejournal.org 16 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 entry19 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). 4.4 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 and greater variety of intermediate goods as a vehicle for technological spillovers that allow less developed countries to close the technological gap with industrialized countries.20 Winters et al. (2004, 84) 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.” Beginning with the pathbreaking work of Coe and Helpman (1995), a rich empirical 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. Several papers, such as Coe, Helpman, and Hoffmaister (1997) and Keller (2000), show that for small developing countries, trading with large technologically advanced countries is crucial for TFP growth. Schiff et al. (2002) show that developing country trade with technologically advanced countries is very important in technology intensive sectors, but trade with developing countries can be important for productivity _________________________ 19 The data in Table 2 reveal that the Africa region has a zero market share in four of the business services sectors. Our model assumes that the market share of the Africa region will remain at zero in any counterfactual simulation. 20 Trade or services liberalization may increase productivity and growth indirectly through its positive impact on the development of institutions. 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. www.economics-ejournal.org 17 spillovers in less technologically complex products in which developing countries have comparative advantage. Regarding foreign direct investment, we have cited several papers above that show that FDI that leads to a diverse set of services suppliers improves total factor productivity. Although FDI in the same sector has ambiguous effects on productivity, several papers have found significant productivity spillovers from FDI in both upstream (supplying) industries (e.g., Javorcik, 2004; Blalock and Gertler, 2008; and Javorcik and Spatareanu, 2008) and downstream (using) industries (e.g., Wang, 2010; Jabbour and Mucchielli, 2007). A more detailed summary of this literature is provided in Jensen and Tarr (2010, Appendix E). 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 Schiff et al. (2002), 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 2. We conduct extensive sensitivity analysis on this parameter, both piecemeal and systematic. 5 Data of the model 5.1 Social accounting matrix The key data source for our study is the social accounting matrix taken from Kiringai,Thurlow and Wanjala (2006). Given our focus on services, we found it necessary to disaggregate the single transportation sector into five sectors and the single financial services sector into insurance, and banking and other financial services.21 A full listing of the sectors is provided in Table 1. _________________________ 21 The decomposition was based on value of output data of the various transportation sectors published in the Economic Survey, 2006 and Statistical Abstract, 2006 by the Kenyan Central Bureau of Statistics. www.economics-ejournal.org 18 Table 2: Market Shares in Kenyan services sectors with FDI (%) and estimates of elasticity of firms' supply with respect to price for Kenya by sector and by Kenyan trading partner region Kenya EU Africa ROW Africa EU ROW Communication 26 49 025 2.5 13.4 20 52-high Insurance 85 4 0 11 3.3 3.3 10 4-low Banking 62 29 0 9 3.3 3.3 10 4-low Professional services 94 2 2 2 2.5 13.4 20 116-high Road services 80 214 43.3 3.3 10 low Railway transport** 0 0 0 100 1.9 10 15 medium Maritime transport** 45 25 15 15 1.9 10 15 medium Pipeline transport** 70 013 18 1.9 10 15 medium Airline transport** 30 30 10 30 1.9 10 15 medium MANUFACTURING beverages and tabacco 3.3 3.3 10 14-low grain milling*** 3.3 3.3 10 7-low sugar&bakery&confectioners*** 3.3 3.3 10 7-low petroleum 3.3 3.3 10 2-low chemicals 1.9 10 15 34-medium metals and machines*** 1.9 10 15 33-medium non-metallic products*** 3.3 3.3 10 0-17-low *Based on average R&D expenditures for the years 2004 and 2005. The average for all US industries was 36. ***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. Source: Authors' estimates. For details, see Balistreri and Tarr (2011). BUSINESS SERVICES Market Shares in Services Sectors with FDI **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. Moreover, about two-thirds of all R&D expenditurs 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. Elasticity of supply with respect to price by Kenyan trading partner region R&D expenditures divided by sales (times 1000) for the US* 5.2 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. We www.economics-ejournal.org 19 mapped two digit sectors from the COMTRADE database into the sectors of our model.22 5.3 Tariff data We started with MFN tariff rates at the eight digit level taken from the website of the Kenyan government. These tariff rates were then aggregated to the sectors of our model, using simple averages. At MFN rates, however, the implied tariff revenues were larger than reported collections. This is largely due to tariff preferences to regional partners and other preference items or tariff exemptions. In 2005, the ratio of total taxes on imports to the total value of imports was 8.4 percent.23 Since zero tariffs apply on all imports from the East African Customs Union and from COMESA, 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. The resulting tariff rates (applied only to non-East African imports) are reported in Table 1. 5.4 FDI data: 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. 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 provided most of our information, but websites of the banks had to be consulted to _________________________ 22 See Appendix A for the mapping of sectors and countries and results for both exports and imports. 23 Economic Survey (2006, pp. 103, 115). www.economics-ejournal.org 20 allocate ownership shares in several cases. In insurance, we used the Axco database in a manner similar to Bankscope in banking. In telecommunications, we had the number of subscribers by company from the website of the Communication Commission of Kenya which we took as the market share data. In professional services, we used the study by Nora Dihel and Josaphat Kweka, with engineering services as the proxy. In railroad and pipeline transportation services, the papers for the transport sector cited above and various websites and articles were employed as cited in Appendix B. The results, by region and sector, are presented in Table 2.24 5.5 Estimates of the Dixit-Stiglitz elasticities of substitution for goods Broda et al. (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. Of the 34 goods sectors in our model, seven are imperfectly competitive. These are the goods sectors in which the Dixit-Stiglitz elasticity of substitution is less than six. One exception was metals and machines, where production function estimates indicate this is an increasing returns to scale sector (see, for example, Tarr, 1984). The elasticity of substitution values are shown in Table 4 in Section 7 and details are in Appendix C. 6 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 _________________________ 24 See Appendix B for full documentation. www.economics-ejournal.org 21 reduced by fifty percent with respect to the region with which Kenya has an agreement. 25 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 et al. 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 nondiscriminatory liberalization. Finally, given our earlier result on the importance of reducing non-discriminatory barriers against investors in services, we examine the impact of a fifty percent reduction of non-discriminatory barriers against service providers combined with unilateral liberalization of discriminatory barriers. As discussed in the introduction, who captures the rents from the services barriers is very important for the welfare results. If the home country is capturing rents from the barriers, these rents play the same role in preferential liberalization of services as tariffs in goods, leading to possible losses. That is, preferential liberalization, by inducing exit of third country suppliers, can lead to loss of rents that were earned from the presence of these multinational service suppliers. If there is no initial rent capture, the rents are dissipated in rent seeking or costly compliance measures; then the gains from preferential liberalization will be larger, since reducing the barriers frees up the resources. Consequently, for each policy _________________________ 25 There is a question of how feasible it is to exclude third countries from preferential liberalization in services. If the preferential agreement grants equivalent rights to third country firms located in the partner region, the preferential arrangement becomes somewhat multilateral. The rules of origin would impact how multilateral the preferential liberalization becomes. What rules of origin apply in practice is an unsettled question both in the literature and in practice. Fink and Jansen (2009) note that typically, FTAs require that enterprises eligible for the agreement’s preference are incorporated under the laws of one of the partner countries. Further, to qualify for preferences, the enterprise must have "substantial business activities" within the region. This indicates that preferences do not extend to enterprises located in third countries if they are not incorporated with substantial business interests in the region. As an example of these principles, Fink and Molinuevo (2007) note that in East Asia non-parties can benefit from the preferences provided in the FTA, as long as they establish a juridical person in one of the FTA member countries and are commercially active in that country. But again, the preferences for non-parties are enterprise specific and do not extend to enterprises without a commercial preference with substantial business interest. www.economics-ejournal.org 22 Table 3: Summary of results (results are percentage change from initial equilibrium, unless otherwise indicated) No initial rent capture case except numbers in parantheses.Values in parantheses are for the initial rent capture case. Scenario definition Benchmark EU FTA EU Discriminatory Services EU Tariffs Africa FTA EU-Africa FTA Unilateral Unilateral Discrimina tory Services Unilateral Tariffs Unilateral & 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 firm 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) 0.5 (0.3) 0.2 (0.2) 0.3 (0.1) 1.0 (0.5) 3.6 (2.9) 1.5 (0.9) 2.0 (2.0) 10.3 (7.0) Welfare (EV as % of GDP) 0.6 (0.4) 0.4 (0.3) 0.1 (0.1) 0.2 (0.0) 0.8 (0.5) 3.0 (2.5) 1.3 (0.7) 1.7 (1.7) 8.6 (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 -0.3 -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. www.economics-ejournal.org 23 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. These results are presented in Table 3. In our systematic sensitivity analysis, in each of the 30,000 scenarios, we allow the share of rents captured by Kenyans to vary stochastically between zero and one. 6.1 Aggregate Effects26 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 sectors. 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 in services 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 _________________________ 26 Discussion of additional scenarios in the table may be found in Balistreri and Tarr (2011). In order to facilitate the interpretation of results, in the appendices, we provide additional tables that report results on output, imports, exports and number of firms by sector and by scenario. www.economics-ejournal.org 24 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 central results table, 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 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 2.27 In Balistreri and Tarr (2011) we show that the number of varieties from the EU substantially increases as a result of preferential liberalization with the EU, while 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. The results in the column EU-Africa FTA show that Kenya can _________________________ 27 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 industry marginal cost 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 industry marginal cost curves in that sector. www.economics-ejournal.org 31 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 it also leads to a loss of service providers (varieties) from all excluded regions and the home country, which results in a loss of productivity. The lost productivity from lost varieties from the regions excluded and the home country from the 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 www.economics-ejournal.org 32 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 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, thereby making it more likely that the preferential liberalization is welfare www.economics-ejournal.org 33 enhancing. 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 (the excluded country impact is more significant in Figure 2). In addition to the variety impacts in imperfect competition, the rent and terms of trade impacts (which are present in perfect competition) 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. 7.4 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 randomly select values of all the parameters in the model (the parameters in Table 4), based on the specified 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.29 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 sample distributions of the results for preferential reduction of barriers with African partners on welfare and output, respectively, are shown in Figures 3 and 5. Figure 4 and Appendix Figure 7 are similar for the welfare and output impacts, respectively, of a preferential trade agreement with the EU. For the Africa-Kenya FTA, we find that 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 be immizerising. A 95 _________________________ 29 For a given range, the uniform distribution implies lower probabilities close to the mean and higher probabilities close to the bounds when compared with the normal distribution (which underlies the statistical work by the Australian authors on whom we rely). Our design thus tends to overestimate the probability of extreme values and underestimate central values. By allocating more probability to values further from our central point estimates, we believe our design adds to the robustness of the qualitative results. www.economics-ejournal.org 34 Figure 3: Sample Frequency Distribution of the Welfare Results of Kenyan Preferential Reduction of Services Barriers against African Partners—30,000 simulations Figure 4: Sample Frequency Distribution of the Welfare Results of Kenyan Preferential Reduction of Services Barriers Against EU Partners—30,000 simulations. 0 0.5 1 1.5 2 2.5 3 3.5 4 -0.25 0.00 0.25 0.50 0.75 Percentage of solutions Percentage of consumption Distribution of welfare results 0 0.5 1 1.5 2 2.5 3 3.5 0.25 0.55 0.85 1.15 1.45 Percentage of solutions Percentage of consumption www.economics-ejournal.org 35 Figure 5: Means, 50 and 95 Percent Confidence Intervals of the Sample Frequency Distributions of the Output Changes by Sector 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. -4 -2 0 2 4 6 8 10 Tea Wheat Other cereals Textile & clothing Pulses & oil seeds Sheep goat and lamb for slaughter Other livestock Dairy Fishing Beef Poultry Real estate Forestry Leather & footwear Health Water Rice Hotels Education Maize Fruits Non metallic products Metals and machines Sugar & bakery & confectionary Construction Barley Others crops Adminsitration Meat & dairy Grain milling Cotton Chemicals Vegetables Roots & tubers Other manufactures Beverages & tobacco Printing and publishing Communication Wood & paper Electricity Trade Banking and other financial services Insurance Petroleum Other services Other manufactured food Coffee Mining Pipeline transport Maritime transport Airline transport Road services Railway transport Sugarcane Cut flowers % change Aggregate Output Impact www.economics-ejournal.org 36 percent confidence interval for equivalent variation as a percent of consumption is: 0.008 to 0.417 around a sample mean of .203.30 For a free trade agreement with the EU that includes services, there are no negative welfare results. A 95 percent confidence interval for equivalent variation as a percent of consumption is: 0.37 to 0.94 around a sample mean of 0.63.31 To further establish the relative importance of technology transfer in the choice of partners in preferential trade arrangements, we executed a second systematic sensitivity analysis of 30,000 runs. In this alternative systematic sensitivity analysis, we choose uniform pdfs for εAFR, εEU and εROW with lower and upper bounds for εAFR of 1 and 3, for εEU of 5 and 15 and for εROW of 7.5 and 22.5. All other probability distributions for all other parameters are unchanged, i.e., are as in Table 4. Our estimate of the median gains from a preferential agreement with the Africa region falls, and the chance of the agreement yielding negative welfare results increases to 9.5 percent. Our piecemeal sensitivity analysis above suggests that the key change is the lower pdf for εAFR. In Figure 5, we show “box and whisker” diagrams for the sample distribution of the percentage change in output by sector for a preferential services agreement with African partners. (See Appendix Figure 2 for the similar figure for the EU.) Sectors are on the horizontal axis and the percentage change in output is shown on the vertical axis. The bars in the boxes are the means of the distributions. Fifty percent confidence intervals are depicted by the boxes, while the vertical lines show 95 percent confidence intervals. Regarding the means of the distributions, the striking result is, where there are declines in sector output, the contractions are generally very moderate. This contrasts with our results (not shown) that there are somewhat larger output declines for the agreement with the European Union and much more substantial output declines for these sectors in the unilateral scenario. This follows from the less substantial increase in competition or drop in overall protection to any sector in a preferential trade arrangement with the African countries. Regarding the sensitivity analysis at the sector level, for the Africa agreement we see that the confidence intervals are rather tight for most sectors. But they _________________________ 30 90 percent and 99 percent confidence intervals are 0.033 to 0.384 and –0.029 to 0.479, respectively. 31 90 and 99 percent confidence intervals are 0.41 to 0.89 and 0.30 to 1.07, respectively. www.economics-ejournal.org 37 reveal a large range of uncertainty for five sectors (other manufactured food, coffee, mining, road services and maritime services) where 50 percent confidence intervals indicate the sectors will expand; but 95 percent confidence intervals contain negative values. We conclude the predicted output changes for these five sectors are not robust. 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 (on the negative side) sugarcane, other manufactures and metals and machines. We can have confidence in the sign of the direction of change, but not in the magnitude of the mean estimate for these sectors. 8 Conclusions In this paper we have shown that under imperfect competition with foreign direct investment and the Dixit-Stiglitz variety externality, welfare losses from preferential reduction of services barriers are possible. We showed that the losses are more likely the more technologically advanced are the excluded regions relative to the partner region and the more the home country captures rents from the existing services barriers. Our systematic sensitivity analysis shows that the mean estimate of the gains to Kenya from preferential reduction of barriers in services with the Africa region is very small, and there is a 1.9 percent chance that it would lose from such an agreement. Estimated gains for the agreement with the European Union are two to three times larger and occur with probability one. We estimate that multilateral liberalization dominates preferential liberalization, as it would yield gains five times greater than a preferential agreement with the European Union. Acknowledgements We thank Thomas Rutherford, Christopher Worley, Josaphat Kweka, Nora Dihel, Francis Ng, Ana Margarida Fernandes 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. 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Journal of Applied Economics 10(1): 115–136. http://econpapers.repec.org/article/cemjaecon/v_3a10_3ay_3a2007_3an_3a1_3ap_3a1 15-136.htm Jafari, Yaghoob, and David G. Tarr (2015). Estimates of the Ad Valorem Equivalents of Barriers against Foreign Suppliers of Services in Eleven Services Sectors in 103 Countries. The World Economy. Available as World Bank Policy and Research Working Paper No. 7096. Article first published online October 12, 2015, DOI: 10.1111/twec.12329. www.economics-ejournal.org 47 Appendix Table 1 continued EU (27) 28.Wood & paper Rest of World 29.Printing and publishing 30.Other manufactures 31.Water; 32.Electricity 33. Construction; 34.Trade 35. Hotels; 36. Real Estate 37. Administration 38. Health; 39. 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. www.economics-ejournal.org 48 Appendix Table 2: Sectoral value-added (%, unless otherwise indicated) Labor GDP Skilled labor Semiskilled labor Unskilled labor Capital Land BKS (Billions of Kenyan Shillings % of total 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 oth er financial services 1.2 5.4 19.3 74.0 45.7 4.7 Professional busines s 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 Appendix Table 2 continued www.economics-ejournal.org 49 Appendix Table 2 continued Labor GDP Skilled labor Semiskilled labor Unskilled labor Capital Land BKS (Billions of Kenyan Shillings % of total 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 CRTS Fishing Forestry Mining Meat & dairy Other manufactured food Printing and publishing Textile & clothing Leather & footwear Wood & paper Other manufactures Water Electricity Construction Trade Hotels Real estate Administration Health Education www.economics-ejournal.org 50 Appendix 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 Nonmetallic 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 Appendix Table 3 continued www.economics-ejournal.org 51 Appendix Table 3 continued Imports Exports BKS % of total % of supply BKS % of total % of output 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 www.economics-ejournal.org 52 Appendix 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 Appendix Table 4 continued www.economics-ejournal.org 53 Appendix Table 4 continued Regulatory barriers Tariff Sales Tax All firms Foreign firms 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. www.economics-ejournal.org 54 Appendix 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 and 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 Appendix Table 5 continued www.economics-ejournal.org 55 Appendix Table 5 continued Imports Exports European Union Africa Rest of the World European Union Africa Rest of the World 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 Administration Health Education Source: Authors' estimates. www.economics-ejournal.org 56 Appendix Table 6A: Market Shares in Sectors with FDI (%) Kenya European Union Africa Rest of the 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. Appendix Table 6B: Estimates of Elasticity of Firms with respect to Price for Kenya by Sector and by Kenyan Trading Partner Region R& D Intensity 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 SOURCE: R&D and sales data from 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. See appendix E for details of the calculations. R&D expenditures divided by sales (times 1000) for the US* *Based on average R&D expenditures for the years 2004 and 2005. The average for all US industries was 36. ***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. **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. Moreover, 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. Elasticity Estimates www.economics-ejournal.org 63 Appendix Table 13: Impacts on Imports from combined EU and Aftrica FTAs No initial rent capture case (% change from benchmark) European Union Africa Rest of the 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 Health Education Source: Authors' estimates. www.economics-ejournal.org 64 Appendix Table 14: Impacts on Exports from Combined EU-Africa FTA No initial rent capture case (% change from benchmark) European Union Africa Rest of the 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 Source: Authors' estimates. www.economics-ejournal.org 65 Appendix Table 15: Impacts on Number of Firms from Combined EU-Africa FTA (% change from be nchmark)—No initial rent capture case Kenya European Union Africa Rest of the 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. www.economics-ejournal.org 66 Appendix Table 16: Impacts on Imports from African FTA (% change from benchmark)—No initial rent capture case European Union Africa Rest of the 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 Source: Authors' estimates. www.economics-ejournal.org 67 Appendix Table 17: Impacts on Exports from African FTA (% change from benchmark)—No initial rent capture case European Union Africa Rest of the 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 Source: Authors' estimates. www.economics-ejournal.org 68 Appendix Table 18: Impacts on Number of Firms from African FTA (% change from be nchmark)—No initial rent capture case Kenya European Union Africa Rest of the 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. www.economics-ejournal.org 69 Appendix Table 19: Impacts on Imports from EU FTA (% change from benchmark)—No initial rent capture case European Union Africa Rest of the 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 Source: Authors' estimates. www.economics-ejournal.org 70 Appendix Table 20: Impacts on Exports from EU FTA (% change from benchmark)—No initial rent capture case European Union Africa Rest of the 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 Source: Authors' estimates. www.economics-ejournal.org 71 Appendix Table 21: Impacts on Number of Firms from EU FTA (% change from benchmark)—No initial rent capture case Kenya European Union Africa Rest of the 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. www.economics-ejournal.org 72 Appendix Table 22: Sensitivity Analysis of Kenya-EU FTA Parameter Value % We lfare Change (EV) Parameter Lower Central Upper Lower Central Upper σ(qi, qj) – s ervices s ectors 1.5 3 4.5 9.99 0.67 0.50 σ(qi, qj) – goods s ectors see below 1.06 0.67 0.59 σ(va, bs ) 0.625 1.25 1.875 0.55 0.67 0.82 σ(D, M) 2 4 6 0.65 0.67 0.69 σ(L, K) 0.5 1 1.5 0.64 0.67 0.70 σ(A 1 ,…A n ) 0 0 0.25 0.67 0.67 0.67 σ(D, E) 2 4 6 0.65 0.67 0.69 ε KEN Central values of all 4 sets of eta 0.61 0.67 0.72 ε EU parameters are listed in Table 6B 0.25 0.67 0.96 ε AFR Lower values are 0.5 central values and 0.68 0.67 0.67 ε ROW upper values are 1.5 times central values 0.90 0.67 0.55 s hare of rents captured 0 0 1 0.67 0.67 0.49 CRTS--s hare of rents captured NA 0 1 NA 0.09 -0.06 θ m 0.025 0.05 0.075 0.67 0.67 0.67 σ(q i , q j ) – goods s ectors s ugar 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 16.6 9 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 ' es timates www.economics-ejournal.org 79 -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 Beverages & tobacco Chemicals Communication Other manufactures Electricity Insurance Petroleum Printing and publishing Banking and other financial services Other services Trade Wood & paper Other manufactured food Coffee Pipeline transport Road services Maritime transport Airline transport Mining Railway transport Sugarcane Cut flowers % change Aggregate Output Impact Appendix Figure 4: Means, 50 and 95 Percent Confidence Intervals of the Sample Frequency Distributions of the Output Changes by Sector from Kenyan Preferential Reduction of Services Barriers Against African Partners—30, 000 simulations www.economics-ejournal.org 80 Appendix Figure 5: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Labor Payment Changes by Sector 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. -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 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 Other manufactured food Coffee Road services Mining Railway transport Sugarcane Cut flowers % change Labor Income (% change) www.economics-ejournal.org 81 Appendix Figure 6: Sample Frequency Distribution of the Welfare Results of Kenyan Preferential Reduction of Services Barriers Against EU Partners—30,000 simulations 0 0.5 1 1.5 2 2.5 3 3.5 0.25 0.55 0.85 1.15 1.45 Percentage of solutions Percentage of consumption www.economics-ejournal.org 82 Appendix Figure 7: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Output Changes by Sector from Kenyan Preferential Reduction of Services Barriers Against EU 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. -10 -5 0 5 10 15 20 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 Rice Beef Others crops Banking and other financial services Leather & footwear Meat & dairy Trade Other services Communication Printing and publishing Insurance Wood & paper Petroleum Pipeline transport Airline transport Road services Railway transport Cut flowers Maritime transport Coffee Mining Other manufactured food % change Aggregate Output Impact www.economics-ejournal.org 83 Appendix Figure 8: Means, 50 and 95 Percent Confidence Intervals of the Sample Distributions of the Labor Payment Changes by Sector from Kenyan Preferential Reduction of Services Barriers Against EU 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. -10 -5 0 5 10 15 20 25 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 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 Wood & paper Railway transport Cut flowers Maritime transport Coffee Mining Other manufactured food % change Labor Income (% change) www.economics-ejournal.org 84 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. 32 That is, on average, Kenyan importers _________________________ 32 Economic Survey (2006, pp. 103, 115). www.economics-ejournal.org 85 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.33 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. _________________________ 33 Kenya agreed to implement zero tariffs on East African Customs Union imports as of January 1, 2005. See Michael-Stahl (2005). www.economics-ejournal.org 86 Table A1: 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 www.economics-ejournal.org 87 Table A2: 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 79,096 12,468 0.054 0.216 0.001 0.730 1.000 Wheat 243 0119 164 0.013 0.264 0.000 0.723 1.000 Rice 203 318 586 613 0.332 0.519 0.009 0.140 1.000 Barley 0654 0 0 654 0.000 1.000 0.000 0.000 1.000 Other cerea 453 107 8309 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 124 7 0 32 0.022 0.739 0.229 0.010 1.000 Oil seeds a 14 157 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 8316,343 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 0484 1,299 0.221 0.406 0.000 0.372 1.000 Dairy produ 3,002 10,337 25 3,340 16,704 0.180 0.619 0.001 0.200 1.000 Poultry 101 809118 0.856 0.067 0.000 0.077 1.000 Meats of sh 101 283 086 469 0.214 0.603 0.000 0.183 1.000 Other livest 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 4169 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 3,821 14,847 131 6,576 25,375 0.151 0.585 0.005 0.259 1.000 Grain millin 415 538 49 59 1,062 0.391 0.507 0.046 0.056 1.000 Sugar & ba 14,420 33,297 1,912 16,008 65,637 0.220 0.507 0.029 0.244 1.000 Textiles an 22,415 32,212 3,996 238,463 297,087 0.075 0.108 0.013 0.803 1.000 Leather and 14,512 28,989 15,930 31,441 90,872 0.160 0.319 0.175 0.346 1.000 Wood and 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. www.economics-ejournal.org 88 Table A3: 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 01,445 16,265 0.038 0.873 0.000 0.089 1.000 Wheat 62 23,618 140,505 144,187 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 00101 0.000 0.000 1.000 0.000 1.000 Other cereals 09,083 353 9,139 0.000 0.994 0.000 0.006 1.000 Cotton 214 4,322 0119 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 086 22 8,088 8,196 0.000 0.011 0.003 0.987 1.000 Roots and tubers 029 662 205 896 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 01,844 7161 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 00000 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 00000 0.000 0.000 1.000 0.000 1.000 Meats of sheep and goats 00088 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. www.economics-ejournal.org 95 Table B1: Kenya Banking Sector Ownership Shares, by Region (4 of 6) KE GB EU EAC COME SA US ROW Investments and Mortgages Bank Limited - I&M Bank Limited 322,035 2.33% Biashara Securities Ltd (KE) 21.55 0.53% Minard Holdings Limited (KE) 17.54 0.43% Tecoma Limited (KE) 15.72 0.38% Ziyungi Limited (KE) 15.72 0.38% Mnana Limited (KE) 14.52 0.36% City Trust Limited (KE) 10.14 0.25% Sachit Shah (??) 2.40 Sarit S. Shah (??) 2.40 Kenya Commercial Bank LTD 1,333,300 9.64% Permanent Secretary To The Treasury 26.23 5.87% National Social Security Fund (KE) 6.80 1.52% Stanbic Nominees Kenya Limited A/C 3.49 0.78% Sunil Narshi Shah (??) 2.33 Kcb Staff Pension Fund (KE) 2.32 0.52% Stanbic Nominees Kenya Limited A/C 1.53 0.34% Nomura Nominees Ltd A/C Jmm (KE) 1.01 0.23% Kenya Reinsurance Corporation Limit 0.87 0.19% Barclays (Kenya) Nominees Ltd A/C 92 0.82 0.18% Barclays (Kenya) Nominees Ltd A/C 12 0.69 Kenya Commercial Finance Company Limited Kenya Post Office Savings Bank 100.00 215,015 1.56% 1.56% Kenya Women Finance Trust K-REP Ba nk 75,223 0.54% African Development Bank (II) 15.14 0.41% Netherlands Dev. Finance Co (NL) 5.00 0.14% Middle East Bank Kenya Limited 49,015 0.35% Fortis Bank (BE) 25.03 0.18% Banque Belgolaise-Belgolaise Bank 25.00 0.18% National Bank of Kenya Ltd 520,526 3.77% National Social Security Fund (KE) 48.00 2.58% Government Of Kenya (KE) 22.00 1.18% Market Share by Region (%) Bank Shareholder (ISO Country Code) Owner ship % Total Assets (2006 USD) Company Market Share www.economics-ejournal.org 96 Table B1: Kenya Banking Sector Ownership Shares, by Region (5 of 6) KE GB EU EAC COME SA US ROW NIC Bank Limited 376,210 2.72% First Chartered Securiti es Ltd (??) 16.44 Icea Investment Services Ltd (??) 9.42 Livings tone Regis trars Ltd. (KE) 8.13 1.11% Rivel Kenya Ltd (KE) 7.73 1.05% Duncan Nderitu Ndegwa (??) 4.56 Saimar Ltd (KE) 4.13 0.56% Amwa Holdings Ltd (??) 1.97 Kenya Commercial Bank Nominees Lt 1.65 Thuthuma Ltd (??) 1.27 Makimwa Consultants Ltd (??) 1.26 Oriental Commercial Bank Ltd 20,886 0.15% Pasha Investments Ltd (KE) 13.40 0.08% Sag Investments Ltd (KE) 13.30 0.08% Paramount Universal Bank Limited Prime Bank 150,617 1.09% Prime Capital & Credit Limited Prudential Bank Limited Reliance Bank Limited Southern Credit Banking Corporation 66,003 0.48% Others (??) 28.00 Fincity Inves tments Ltd (??) 23.00 Sounthern Shield Holdings Ltd (??) 20.00 Sounthern Shield Securities Ltd (??) 19.00 Sadrudin Karim Kurji (??) 10.00 Stanbic Bank Kenya Limited 100.00 372,120 2.69% 2.69% Standard Chartered Bank Kenya 1,169,151 8.46% Standard Chartered Holdings (Africa) 73.81 8.11% Kabarak Limited (??) 1.03 Old Mutual Life Assurance Company 0.69 0.08% National Social Security Fund (KE) 0.68 0.07% Barclays (Kenya) Nominees Ltd A/C 12 0.59 Kenya Commercial Bank Nominees Lt 0.51 0.06% Standard Chartered Africa Holdings L 0.48 0.05% Barclays (Kenya) Nominees Ltd A/C 18 0.45 0.05% Barclays (Kenya) Nominees Ltd A/C 92 0.36 0.04% Market Share by Region (%) Bank Shareholder (ISO Country Code) Owner ship % Total Assets (2006 USD) Company Market Share www.economics-ejournal.org 97 Table B1: Kenya Banking Sector Ownership Shares, by Region (6 of 6) KE GB EU EAC COME SA US ROW The Company for Habitat & Housing in Africa 71,600 0.52% Trans-National Bank Limited Five Kenyan Private Companies (KE) 88.69 42,967 0.31% 0.31% Trust Bank Limited Universal Bank Victoria Commercial Bank Ltd. 61732.04 0.45% 35 Other Shareholders (??) 27.24 Kingsway Investments Ltd (KE) 16.43 0.12% Jong-Chul Kim (KE) 10.81 0.08% Rochester Holding Limited (KE) 10.74 0.08% Monetary Credit Holdings Ltd (KE) 6.65 0.05% Godfrey C. Omondi (KE) 6.05 0.04% Orchid Holdings Ltd (KE) 5.83 0.04% Rajan Janii & Kalapi Jani (??) 5.70 Kanji Damji Pattni (KE) 5.39 0.04% Pattni Yogesh K (??) 5.16 KE GB EU EAC COM US ROW Grand Total = 13,824,591 Market Share 59.00% 15.46% 9.18%0.00% 0.16% 4.37% 3.46% Scaled Share 64.39% 16.87% 10.02%0.00% 0.17% 4.77% 3.77% Market Share by Region (%) Bank Shareholder (ISO Country Code) Owner ship % Total Assets (2006 USD) Company Market Share www.economics-ejournal.org 98 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.krepbank.com/index.php?option=com_content&task=view&id=71&Itemid =109 . • Chase Bank (Kenya) Limited – U.S. 100% www.economics-ejournal.org 99 • 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. Kenyan Insurance Companies The premium information came from the Insurance Industry Annual Report for 2007 of the Association of Kenya Insurers.39 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. 40 He provided the data on the ownership shares of the Kenyan companies. In the table below, we list the result of these calculations. _________________________ 39 Available at: http://www.akinsure.com/images/aki-annual-report-2007.pdf 40 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. www.economics-ejournal.org 100 Table B2: Kenya Insurance Sector Ownership Shares, by Region (1 of 7) KE GB EU EAC COME SA US ROW African Merchant Assurance Company 563 1.71% 1.71% Hon. William Ruto (KE) 80.00 Silas Simatwo (KE) 20.00 AIG Insurance Company AIG (US) 100.00 1,801 5.48% 5.48% APA Insurance Company 2355 7.17% 7.17% Apollo Insurance (KE) 60.00 Pan Africa Insurance Holdings (KE) 40.00 Blue Shield Insurance Company 2,273 6.92% 6.92% Beth Ngonyo Mungai (KE) 40.05 Bermuda Holdings Ltd (KE) 33.10 African Theatres Ltd (KE) 13.55 James Muigai Ngengi (KE) 3.31 Jean Muigai Ngengi (KE) 3.31 Peter Kamau Ngengi (KE) 3.31 Martha Vincent & Paul Vincent (KE) 3.31 Simon Evans Githinji (KE) 0.02 Simon Munyi Gachoki (KE) 0.01 British American Insurance Company 679 2.07% British America (K) Ltd (??) 66.67 Jimnah Mbaru (KE) 25.00 1.55% Peter K Munga (KE) 5.00 0.31% Benson I Wairegi (KE) 3.33 0.21% Cannon Assurance Company 557 1.70% 1.70% Inder Jit Talwar (KE) 0.00 Cannon Holdings (KE) 40.00 Evisa Invesments (PVT) Ltd (KE) 28.70 PBM Nominees (KE) 31.30 Concord Insurance Company 585 1.78% 1.78% Dorse Gems International Inc (KE) 32.00 Kirumba Mwaura (KE) 36.00 James Gacheru (KE) 32.00 Market Share by Region (%) Insurance Company Shareholder (ISO Country Code) Owner ship % Premium Income (million KSH 2007) Company Market Share www.economics-ejournal.org 101 Table B2: Kenya Insurance Sector Ownership Shares, by Region (2 of 7) KE GB EU EAC COME SA US ROW Co-operative Insurance Company 1,028 3.13% 3.13% Harambee Co-operative Movement (KE) 9.06 Aembu Farmers Co-operative Society Ltd (KE) 8.30 Kiambu Unity Finance Co-operative Union (KE) 8.15 CIC Staff Co-operative Savings and Credit (KE) 7.27 The Co-operative Bank of Kenya (KE) 6.13 Bandari Co-operative Savings and Credit (KE) 3.34 Mwalimu Co-operative Savings and Credit (KE) 1.59 Kipsigis Teachers Savings and Credit (KE) 1.32 Nacico Savings and Credit Co-operative (KE) 1.10 Stima Savings and Credit Co-operative (KE) 1.09 Emmanuel Kipkemboi Birech (KE) 1.30 Isaac Waithaka Kamunya (KE) 1.12 Teresa Wanjiru Thimba (KE) 1.10 Leonard Obura Oloo (KE) 0.89 Gerald Mbaabu M'ikunyua (KE) 0.84 Francis Kamau Ng'ang'a (KE) 0.64 Others (KE) 46.76 Corporate Insurance Company 351 1.07% 1.07% Xanthippe Holdings Ltd (KE) 63.30 Ejax Investments Ltd (KE) 36.70 CFC Life Assurance Company 674 2.05% CfC Stanbic Holdings Group (GB) 60.00 1.23% C Njonjo (KE) U P Jani (KE) J G Kiereini (KE) J H D Milne (UK) M Soundararajan (KE) A Munda (KE) R E Leakey (KE) Directline Assurance Company Ltd 259 0.79% 0.79% Royal Credit Limited (KE) 99.70 Samuel S. K. Macharia (KE) 0.10 Purity G. Macharia (KE) 0.10 Dan Korobia (KE) 0.10 Market Share by Region (%) Insurance Company Shareholder (ISO Country Code) Owner ship % Income (million KSH 2007) Company Market Share www.economics-ejournal.org 102 Table B2: Kenya Insurance Sector Ownership Shares, by Region (3 of 7) KE GB EU EAC COME SA US ROW Fidelity Shield Insurance Company 684 2.08% 2.08% Southern Shield Holdings Ltd (KE) 66.70 Southern Credit Banking Corp. (KE) 24.40 Soli Limited (KE) 6.40 Kenya Shipping Agency (KE) 1.40 First Assurance Company 1,038 3.16% 3.16% First Assurance Investment Ltd (KE) 83.00 Syndicate Nominee Ltd (KE) 17.00 Gateway Insurance Company 436 1.33% 1.33% Godfrey W Kara uri (KE) 21.20 John N Muchuki (KE) 1.40 Bethuel M Gecaga (KE) 8.30 Muvokanza Limited (KE) 1.40 Eliud Ndirangu (KE) 4.30 Jerome P N Kariuki (KE) 0.30 Raymond Matiba (KE) 0.30 Francis Thuo (KE) 1.80 Kihara Waithaka (KE) 2.10 Mubiru Housing Company (KE) 0.90 Maina Kimere & Partners (KE) 5.40 Isaac G. Wanjohi (KE) 14.50 Wilson Kiragu (KE) 1.40 Chief Ezekiel N Onwere (KE) 7.60 Isaac Njoroge (KE) 0.60 James M Gacheru (KE) 1.10 Geminia Insurance Company 460 1.40% 1.40% Gikoi Development Co. Ltd (KE) 8.16 Mbagi Limited (KE) 34.70 Stanley M. Githunguri (KE) 26.53 Leonard M Kabetu (KE) 0.30 Bimal R. Shah (KE) 5.67 Harsha R. Shah (KE) 1.19 Hasit K Shah (KE) 1.38 Khetshi K Shah (KE) 1.38 Universal Roadways (K) Ltd (KE) 5.53 Kiriti Shah (KE) 2.67 Jay K Shah (KE) 1.38 Mona D Shah (KE) 1.38 Mona D Shah (KE) 5.68 Devchand A. Shah (KE) 2.67 Market Share by Region (%) Insurance Company Shareholder (ISO Country Code) Owner ship % Premium Income (million KSH 2007) Company Market Share www.economics-ejournal.org 103 Table B2: Kenya Insurance Sector Ownership Shares, by Region (4 of 7) KE GB EU EAC COME SA US ROW General Accident Insurance 682 2.08% 2.08% Rapun Limited (KE) 49.00 J S Insurance Limited (KE) 49.00 Shantilal Shah (KE) 2.00 Heritage All Insurance Company 1505 4.58% CFC (GB) 64.08 2.94% African Liason Consultant Services (KE) 35.92 1.65% Insurance Company of East Africa First Chartered Securities Limited (KE) 100.00 1,173 3.57% 3.57% Intra Africa Assurance Company 402 1.22% Robert T. Gachecheh (KE) 10.50 0.18% Archibald Githinji (KE) 7.50 0.13% Mahendra Chandulal (KE) 5.00 0.09% Upenra Ambalal Patel (KE) 5.00 0.09% Jitenra Ambalal Patel (KE) 5.00 0.09% Dinesh Chandulal Patel (KE) 10.00 0.17% Henry Mkangi (KE) 3.00 0.05% Bharat Kumar Patel (KE) 5.00 0.09% Joseph Muriu (KE) 5.00 0.09% Premji Ratna (KE) 5.00 0.09% Ranjaben Suresh Patel (KE) 5.00 0.09% Eleyo Saw Mills (??) 20.00 Praful C Patel (KE) 5.00 0.09% Invesco Insurance Company 958 2.92% Jubilee Insurance Company 2,450 7.46% Jubilee Holdings Ltd (KE) 100.00 7.46% Kenneth Hamish Wooler Shah (KE) 0.00 Neville Patrick Gibson Warren (IN) 0.00 Kenindia Assurance Company 3,028 9.22% Life Insurance Corp. Of India (IN) 10.00 0.92% General Insurance Corp Of India (IN) 9.00 0.83% New India Assurance Co. Ltd. (IN) 9.00 0.83% Oriental Insurance Co. Ltd. (IN) 9.00 0.83% United India Insurance Co. Ltd. (IN) 9.00 0.83% National Insurance Co. Ltd. (IN) 9.00 0.83% Pv Karia (IN) 1.39 0.13% M N Mehta (KE) 0.00 0.00% M P Chandaria (KE) 0.00 0.00% Sadasiv Mishra (KE) 0.00 0.00% Simeon Nyachae (KE) 7.00 0.64% Chandaria Foundation Trustees (KE) 7.01 0.65% Mehta Group Of Companies (KE) 6.02 0.55% Lex Holdings (KE) 3.66 0.34% Others (KE) 20.00 1.84% Market Share by Region (%) Insurance Company Shareholder (ISO Country Code) Owner ship % Premium Income (million KSH 2007) Company Market Share www.economics-ejournal.org 104 Table B2: Kenya Insurance Sector Ownership Shares, by Region (5 of 7) KE GB EU EAC COME SA US ROW Kenya Orient Insurance Company 283 0.86% 0.86% Thanak Investments (KE) 90.39 Rajwinder Singh (KE) 5.95 Avtar Singh Ubhi (KE) 1.80 Kahn Singh Ubhi (KE) 1.80 Luka Daudi Galgalo (KE) 0.06 Kenya Alliance Insurance Company International Controls Limited (??) 100.00 353 1.07% Lion of Kenya Insurance Company Firs t Chartered Securi ty (KE) 80.00 1,217 3.71% 3.71% Kenya Holdings (KE) 20.00 Madison Insurance Company Amedo Madison Holdings Limited (K 100.00 625 1.90% 1.90% Mayfair 273 0.83% 0.83% Adrea Ltd (KE) 27.77 Corporate Investments (KE) 12.48 A 2 Enterprises (KE) 9.32 Tinker Bird Securities (KE) 9.15 Kazkazi Maritime Ltd (KE) 3.12 Union Logistics (KE) 3.12 Marenyo Ltd (KE) 8.32 Muhwai Ltd (KE) 6.55 Mahesh Doshi And Sheila Doshi (KE) 6.24 Nsp Holdings Ltd (KE) 6.24 Lakdawalla Investments Ltd (KE) 4.16 Bharasa Investments Ltd (KE) 3.54 Mercantile Life & General Insurance 369 1.12% 1.12% Ecobank Kenya Ltd (KE) 20.00 L.P Holdings (KE) 21.00 Barclays Trust (KE) 24.00 Eabs Bank (KE) 35.00 Occidental Insurance Company 740 2.25% 2.25% Park Enterprises Ltd (KE) 30.00 Oak Investments Ltd (KE) 15.00 Landsend Kenya Ltd (KE) 15.00 Hansing Ltd (KE) 15.00 Rock Investment Ltd (KE) 15.00 Ngamacu Ltd (KE) 5.00 Maganlal Lakhamshi Dodhia (KE) 2.50 Kantilal Maganalal Dodhia (KE) 2.50 Market Share by Region (%) Insurance Company Shareholder (ISO Country Code) Owner ship % Income (million KSH 2007) Company Market Share www.economics-ejournal.org 111 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 www.economics-ejournal.org 112 Table D1 continued www.economics-ejournal.org 113 Table D1 continued www.economics-ejournal.org 114 The scoring for Kenya is described below. It is based on the results of the World Bank Regulatory Survey in East Africa44 and the World Bank Survey on Applied Policies in Services, World Bank (2010). 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 _________________________ 44 The regulatory surveys were conducted by local consultants who interviewed the professional associations in the examined East African countries in 2009. See Borchert et al., (2014). www.economics-ejournal.org 115 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 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. www.economics-ejournal.org 116 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. www.economics-ejournal.org 117 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 Sales in $millions Ratio of R&D expenses Industry and company size NAICS codes 2004 2005 2004-2005 average in 2005 to sales (x1,000) All industries 21–23, 31–33, 42, 44–81 208,301 226,159 217,230 6,119,133 36 Manufacturing industries 31–33 147,288 158,190 152,739 3,998,256 38 Food 311 2,254 2,716 2,485 374,342 7 Beverage and tobacco products 312 555 i539 547 38,003 14 Textiles, apparel, and leather 313–16 570 816 693 51,639 13 Wood products 321 D D 0 27,002 0 Paper, printing, and support activities 322, 323 D D 0 159,608 0 Petroleum and coal products 324 1,603 D802 404,317 2 Chemicals 325 D42,995 21,498 624,344 34 Pharmaceuticals and medicines 3254 31,477 34,839 33,158 273,377 121 Plastics and rubber products 326 D1,760 880 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 D24,148 472,330 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 vehicles, trailers, and parts 3361–63 15,677 D7,839 646,486 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 Industry and company size NAICS codes 2004 2005 2004-2005 average Nonmanufacturing industries 21–23, 42, 44–81 61,013 67,969 64,491 2,120,877 30 Mining, extraction, and support activities 21 D D 0 33,665 0 Utilities 22 202 210 206 223,395 1 Construction 23 1,481 D741 57,187 13 Wholesale trade 42 D D 0 107,485 0 Retail trade 44, 45 1,596 D798 232,150 3 Transportation and warehousing* 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 services 54 28,709 32,021 30,365 261,500 116 Architectural, engineering, and related services 5413 4,265 4,687 4,476 50,121 89 Computer systems design and related services 5415 11,575 13,592 12,584 136,376 92 Scientific R&D services 5417 11,355 12,299 11,827 34,516 343 Other professional, scientific, and technical services other 54 1,514 1,444 1,479 40,487 37 Health care services 621–23 500 989 745 25,076 30 Other nonmanufacturing b 55, 56, 61, 624, 1,595 2,137 1,866 75,115 25 71, 72, 81 All R&D $millions All R&D $millions *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. Moreover, 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. 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. conomics: The Open-Access, Open-Assessment E-Journal Appendix F: Kenya Model with Multiple FDI and Trade Partners (Algebraic Structure) This document presents the algebraic formulation of a general-equilibrium numericsimulation model of the Kenya economy. This model largely follows the structure of our earlier work on developing countries [e.g., Balistreri et al. (2009)]. 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: ( a. ) Business Services, characterized by monopolistic competition and foreign direct investment (FDI), indexed by i∈I⊂G ; ( b. ) Dixit-Stiglitz manufacturing sectors, characterized by monopolistic competition, indexed by j∈J⊂G ; and ( c. ) 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 there are 5 primary factors of production indexed by f∈F. Table F1 summarizes the equilibrium conditions and associated variables. The non-linear system (of 1,364 equations and variables) is formulated in GAMS/MPSGE and solved using the PATH algorithm. We proceed with a description and algebraic representation of each of the conditions itemized in Table F1. Dual representation of technologies and preferences Technologies and preferences are represented in the Kenya model through value functions that embed the optimizing behavior of agents. Generally, any linearlyhomogeneous 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 condition for the activity level of the transformation. 1 The current formulation includes Kenya or the domestic region ( D ), the European Union ( EU ), important African trade partners ( AFR ), and the rest-of-world region ( ROW ), such that R={D,EU,AFR,ROW}. www.economics-ejournal.org 118 conomics: The Open-Access, Open-Assessment E-Journal Table F1: 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 Dixit-Stiglitz price indexes (4) ∀g∈(I∪J)Qg r: D-S Activity by region (I+J)×R Zero Profits for Dixit-Stiglitz firms (5) ∀g∈(I∪J)Ng r: Number of Firms (I+J)×R Dixit-Stiglitz composite input prices (6) ∀g∈(I∪J)and r=D Zg r: IRTS resource use (I+J)×R (7) ∀j∈Jand r6=D (8) ∀i∈Iand r6=D Input-output technologies (10) ∀g∈G Y g: Production level G Constant elasticity of transformation (11) ∀k∈K Xg: Index on CET activity G (12) ∀g∈(I∪J)(No Export Coefficients for g∈(I∪J)) Exports (13) ∀k∈Kand r6=D EXg r: Exports G×(R−1) (14) ∀g∈(I∪J)and r6=D Imports (15) ∀g∈Gand r6=D IMg r: Imports (net of FDI-firm imports) G×(R−1) Unit expenditure function (16) U: Household utility index 1 Unit cost of public purchase (17) PUB: Government Activity 1 Unit cost of investment (18) INV : Investment Activity 1 Market clearance conditions: Composite goods and services (19) ∀g∈G PAg: Composite price indexes G D-S composites (21) ∀g∈(I∪J)and r6=D Pg r: Prices of D-S composites (I+J)×R (22) ∀g∈(I∪J)and r=D Markets for IRTS composite input (23) ∀g∈(I+J)PMCg: Composite input prices (I+J)×R Markets for domestic output (24) ∀k∈K PDg: Domestic output prices G (25) ∀i∈I (26) ∀j∈J Markets for export output (27) ∀k∈Kand r6=D PXk r: Export output prices K×(R−1) Markets for gross output (28) ∀g∈G PY g: Output prices G Markets for imports (29) ∀i∈Iand r6=D PMg r: Import prices G×(R−1) (30) ∀j∈Jand r6=D (31) ∀k∈Kand r6=D Factor markets (32) ∀f∈F PFf: Factor prices F IRTS specific factors (33) ∀g∈(I∪J)PZg r: Sector-specific capital price (I+J)×R Fixed real investment (34) PINV: Unit cost of investment 1 Fixed real public spending (35) PG: Unit cost of public good 1 1 Nominal utility equals Income (36) PC: Unit expenditure index 1 Balance of payments (37) PFX: Price of foreign exchange 1 Income balance: Domestic agent income (38) RAh: Household Income 1 Government budget (39) GOVT: Government spending 1 Foreign Entrepreneur (40) FE: External agent income 1 Auxiliary Conditions: Fixed real public spending (41) T: Index on direct taxes 1 Total Dimensions: 6G+6[(I+J)×R] +3[G×(R−1)] + [K×(R−1)]+ F+H+13 =1,364 www.economics-ejournal.org 119 conomics: The Open-Access, Open-Assessment E-Journal 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 revenues (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 . In constructing the aggregate prices, we will rely on the following notation for the component prices: PDgPrice of domestic output (∀g∈G), PMg r Price of cross-border imports from region r of Business Services and CRTS goods (∀g∈(I∪K)), Pg rDixit-Stiglitz price index on region-rvarieties (∀g∈(I∪J)). Assuming a Constant Elasticity of Substitution (CES) aggregation of the components we equate the prices to the CES unit-cost functions: PAi=∑ r (Pi r)1−σi F+∑ r φi r(PMi r)1−σi F1/(1−σi F) (1) PAj=∑ r (Pj r)1−σj F1/(1−σj F) (2) PAk=φk D(PDk)1−σk DM +∑ r φk r(PMk r)1−σk DM 1/(1−σk DM) ,(3) where σg F∀g∈(I∪J) is the Dixit-Stiglitz elasticity of substitution and σk DM is the Armington elasticity of substitution on CRTS goods. The arguments of these functions are the component prices. The φ parameters are CES distribution parameters that indicate scale and weighting of the arguments. These are calibrated to the www.economics-ejournal.org 120 conomics: The Open-Access, Open-Assessment E-Journal 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 r6=D, we have the following: IMi r=φi rAiPAi PMi rσi F (29) IM j r=θj MrZj r PMC j r PM j r!εj r (30) IMk r=φk rAkPAk PMk rσk DM .(31) Factor markets clear, where factor supply is given by the exogenous endowments to households, denoted Sf , and input demands are derived from the cost functions: Sf=∑ s αs fβs vasYsPva s (1+tf s)PFfPvas s Pva sσvas ,(32) where Pva s is the composite value-added price: Pva s=∏fγs f[(1+tf s)PFf]αs f . In addition, we have the market for the specific factor used in the IRTS sectors. Denoting the regional endowments of the specific factors SFg r∀g∈(I∪J) , we have: SFg r=θg ZrZg rPMCg r PZg rεg r ∀g∈(I∪J).(33) Real investment equals real savings by households: INV =sav.(34) Real government purchases equal the nominal government budget scaled by the government price index: PUB =GOVT PG .(35) www.economics-ejournal.org 127 conomics: The Open-Access, Open-Assessment E-Journal 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 RA. The market clearance condition for utils is thus U=RA PC .(36) 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 is held fixed at the exogenous benchmark observation, denoted ftrn . Foreign exchange is demanded for direct import purchases as well as the payments to foreign agents for their contribution to production. ∑ r6=D ∑ g EXg r+ftrn =∑ r6=D ∑ g IMg r +∑ r6=D ∑ i θi MrZi r PMCi r θi DrPDi+θi Mr(1+timp ir )PFX !εi r +FE PFX ,(37) where FE equals the nominal claims that the foreign entrepreneurs have on specific factor rents in the Dixit-Stiglitz manufacturing sectors. Income Balance Conditions The representative agent (household) earns income from factor endowments, but disposable income nets out savings and a direct tax transfer to the government. Real savings is held fixed (by the coefficient savh ). We also hold fixed the real level of government spending, but this requires an adjustment in direct taxes on households. Removal of tariffs, for example, impact the government budget and the shortfall is made up for by an endogenous increase in the direct taxes on households. We use the auxiliary variable T to scale the direct taxes appropriately. In addition, the www.economics-ejournal.org 128 conomics: The Open-Access, Open-Assessment E-Journal household is assumed to hold any benchmark net international capital flows. The household’s budget is given by RA =∑ f PFfSf +∑ g PZg BELSFg BEL −savPINV −dtaxPG ×T +ftrnPFX (38) The government budget is given by net direct and indirect taxes on domestic and international transactions. The full nominal government budget is GOVT =dtaxhPG ×T +∑ g tcons gPAgµg CUPC (1+tcons g)PAg +∑ g tinv gPAgµg INV INV +∑ g tgov gPAgµg GPUB +∑ s ∑ i tint is PAiαs iβs vasYsPsrv s (1+tint is )PAiPvas s Psrv sσvas +∑ s ∑ j tint js PAjβs jYs +∑ s ∑ k tint ks PAkβs kYs +∑ s ∑ f tf sPFfαs fβs vasYsPva s (1+tf s)PFfPvas s Pva sσvas +∑ r6=D ∑ g timp gr (PFX)IMg r +∑ r6=D ∑ i timp ir (PFX)θi MrZi r PMCi r θi DrPDi+θi Mr(1+timp ir )PFX !εi r www.economics-ejournal.org 129 conomics: The Open-Access, Open-Assessment E-Journal +∑ r6=D ∑ i texp i PMCi BEL 1−1 σi F EXi r +∑ r6=D ∑ j texp j PMC j BEL 1−1 σj F EX j r +∑ r6=D ∑ k texp kPXk rEXk r(39) Again, the index T is adjusted endogenously to hold the real level of public spending fixed. In addition to the household and government agents we need an agent representing the foreign entrepreneurs who own the specific factors associated with cross-border Dixit-Stiglitz traded goods. The foreign entrepreneur’s nominal income is FE, which is spent on foreign exchange: FE =∑ r6=D ∑ g PZg rSFg r(40) Auxiliary Condition In addition to the three sets of standard conditions presented above, we need to close the model with an auxiliary condition such that the real size of the government is held fixed. To do this we need to determine the index which scales direct taxes on households. Associated with the variable Tis the following condition: PUB =pub.(41) www.economics-ejournal.org 130 conomics: The Open-Access, Open-Assessment E-Journal Appendix G: A Note on the Relationship between Sector Specific Capital and the Elasticity of Supply in Applied General Equilibrium Models of Imperfect Competition The models developed in this paper, by Balistreri et al. (2009) and by Jensen et al. (2008) to analyze services liberalization in Kenya and Tanzania utilize a specificfactor formulation. 2 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 Substitution (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 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~r0~xs.t. f(~x) = 1,(42) 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 2 The appendix is largely based on lecture notes from Thomas F. Rutherford’s graduate course on Computational Economics at the University of Colorado (late 1990’s) www.economics-ejournal.org 131 conomics: The Open-Access, Open-Assessment E-Journal composite X with composite price r2 (that is, ~x={¯ R,X} and ~r={r1,r2} ). 3 We thus have the explicit expression: p=c(r1,r2)≡minnr1¯ R+r2Xs.t. [αR¯ Rρ+αXXρ]1/ρ=1o,(43) where ρ indicates the elasticity of substitution, σ=1/(1−ρ) , and αR and αX 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: c(r1,r2) = θr1−σ 1+(1−θ)r1−σ 21 1−σ,(44) where θ 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 diminishing marginal product (as the ¯ R to X ratio falls). Using Shephard’s lemma to derive demand for ¯ R we can represent the overall resource constraint on the specific factor as follows: ¯ R=y∂c(~r) ∂r1 =θyp r1σ .(45) Solving for the residual price r1=pθy ¯ R1/σ ,(46) 3 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{~ s0 ~zs.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 r2takes on a specific numeric value. www.economics-ejournal.org 132 conomics: The Open-Access, Open-Assessment E-Journal and then substituting this back into the unit cost function we have: p1−σ=θp1−σθy ¯ R1−σ σ +(1−θ)r1−σ 2.(47) Solving for y as a function of the resource constraint and the price ratio ( r2/p ) we have supply: y=¯ Rθ1 σ−1"1−(1−θ)r2 p1−σ#σ 1−σ .(48) The supply elasticity is given by η≡∂y ∂p p y=σ(1−θ) −1+θ+r2 pσ−1,(49) and evaluating this local to the benchmark equilibrium (r2=p=1) we have η=σ(1−θ) θ.(50) This equation gives us the fundamental relationship between the local supply elasticity and the CES parameters. 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 η there are a couple of options. For example, one could simply lock down the value of σ (at say σ=1 , which is Cobb-Douglas) and then calculate the appropriate overall value share of the specific factor (at σ=1 we have θ=1/(1+η) ). In empirical applications, however, this calibration method can be problematic, because the value of θmay 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 www.economics-ejournal.org 133 conomics: The Open-Access, Open-Assessment E-Journal capital payments that should be allocated to the specific factor, call this θk , we can calculate the appropriate elasticity of substitution as follows: σ=ηθ 1−θ,(51) where θ=θk(vk/vy). In sensitivity analysis on the Kenya and Tanzania models we hold fixed the value of θk=0.5 and vary the value of η . As η 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 ηincreases. One might consider sensitivity analysis on the value of θk , 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 θk . As θk increases the value of θ/(1−θ) falls and, according to equation (10), the calibrated value of σ falls to compensate. So larger value shares will not necessarily generate larger supply responses. In fact, by design, the local impact of a change in θk is zero. www.economics-ejournal.org 134 Please note: You are most sincerely encouraged to participate in the open assessment of this article. You can do so by either recommending the article or by posting your comments. Please go to: http://dx.doi.org/10.5018/economics-ejournal.ja.2015-42 The Editor © Author(s) 2015. Licensed under the Creative Commons Attribution 3.0.