Business Taxation in a Low-Revenue Economy a study on Uganda in Comparison With Neighboring Countries
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C. METR IN OTHER INPUTS AND COST OF PRODUCTIONMETR on LaborFor this study, we assume that only payroll taxes paid by employers are effective labor taxes borne by employers. Another assumption is that the marginal unit of labor input is an average worker. Therefore, the METR on labor is the total payroll taxes paid by employers on average labor costs. Since the payroll taxes in Uganda and Tanzania are imposed on total payrolls, the statutory tax rate itself can be seen as the effective tax rate on labor. In the case of Kenya, the ceiling of taxable payroll is K Sh 80 per month, which is well below the monthly payroll. As a result, the METR on labor in Kenya is estimated as low as 0.1 percent. According to 1997 ILO Yearbook of Labour Statistics, the average monthly payroll in Kenya was K Sh 3,324 for manufacturing industry and tourism (1991 figure). METR on Other InputsThe METR on other inputs for production is the transaction taxes firms have to pay on these inputs. In our study, motor fuel is the only other input included apart from capital and labor. The average transaction tax rate, i.e., the fuel tax rate is used as the METR. METR on Cost of ProductionBy using the augmented Cobb-Douglas production function, the METR on cost of production T can be estimated as: T = P (1+ti) 1 (9) In the formula, i indicates an input, i.e., capital, labor, and fuel, ti = the METR on each input i, and ai = share of total cost for input i. The detailed derivation may be found in McKenzie, Mintz, and Scharf (1992). APPENDIX B: DATA SOURCES A. TAX PARAMETERSThe formal tax parameters for Kenya, Tanzania and Uganda are obtained from their income tax laws and related official documents (e.g., the 1991 Foreign Investment Code in the case of pre-1997 tax holiday regime for Uganda). Apart from the formal rates that are directly used for the METR calculation (e.g., corporate income tax and property tax rate), there are mainly two types of tax parameters that require derivation. The first is the combined tax depreciation rate for machinery by industry. It is estimated as a weighted average of tax allowances by class for each industry based on the actual URA data on large firms. The second is the combined fuel tax rate for each country. In Uganda, the ad valorem rate on the “total CIF destination warehouse cost”, including all handling charges, ranges from 100 percent to over 200 percent for paraffin, diesel and petroleum products, respectively. A weighted-average rate was estimated as 174 percent based on the data, provided by the URA on fuel sales by product in 1997. For Kenya and Tanzania, the fuel tax rate by product was estimated based on the tax and the price per liter, while Uganda’s shares of various products in total sales were used as weights to estimate the combined fuel tax rate. B. NON-TAX PARAMETERSThe expected inflation rates and interest rates are obtained from the IMF and the World Bank. The expected inflation rate is based on the consumer price index, while the interest rate is the bank lending rate in each country. The debt-to-assets ratio is estimated based on the 1998 World Bank-Private Sector Foundation of Uganda firm survey data for large and medium-sized firms (over 20 employees). The economic depreciation rates for buildings and machinery by industry are adopted from the International Centre Tax Studies (University of Toronto) METR model for small-sized firms in Canada. Considering the differences between Uganda’s economy and the Canadian one, we assume that the average capital investment size for Uganda’s large- and medium-sized firms is equivalent to that for small Canadian firms. The capital structure by industry shown in Table 2 is estimated based on financial statistics by industry provided by the URA. As buildings and land are grouped into a single category in the URA data, this category was disaggregated based on the Canadian proportional relationship between buildings and land by industry. The cost structure by industry is estimated based on Uganda’s 1992 input-output table (IO table), the latest one available. The capital input within an industry is estimated as the given industry’s total inputs of building materials, machinery and metal products, and operating surplus. The labor input is estimated as the wages and salaries. The fuel input is estimated based on the total fuel imports in 1992 and the transportation share by industry based on the 1992 IO table.37 Then the three inputs are summed up as the total cost of production, which is used to arrive at the input share of capital, labor and fuel. C. THE 1998 FIRM SURVEYA private enterprise survey for Uganda was carried out between February and July 1998 jointly by the World Bank and the Ugandan Private Sector Foundation. The survey design benefited from the Regional Program for Enterprise Development (RPED) model, particularly the Ghana and Zimbabwe surveys, but it is more limited in scope, focusing mostly on physical investment, exports, infrastructure services, taxation, policy credibility, regulation, and corruption. However, the survey in Uganda covered a wider range of industrial sectors than the RPED. Apart from manufacturing (which was divided into agro-processing and other manufacturing), the survey included firms from tourism, commercial agriculture and construction as these sectors are expected to have substantial growth potential. Data were collected for the period of 1995-97. Given that the survey required confidential information, such as the firm's costs, sales and tax payments, interviews were carried out by the Uganda Manufactures Association to obtain maximum cooperation of the firms. Special emphasis was laid on enumerator training and the questionnaire was carefully piloted beforehand. In addition to quantitative data, the survey also collected information on firms’ perceptions on various constraints to investment. The latter component was modeled on a similar survey carried out in 1994 by the World Bank, allowing an examination of dynamics of the business environment and constraints, as perceived by the private sector. The latest complete industrial census in Uganda dates back to 1989. An updated industrial census was carried out in 1996 but it includes only eight (out of 45) districts. Despite its limited geographical coverage, the districts included in the 1996 update actually represent 80 percent of value-added in the private industrial sector and 70 percent of employment, based on the 1989 census. It was thus decided to base the sampling frame of the survey on the 1996 update instead of the complete but much older census, particularly as the number of new enterprises has increased dramatically in the past decade. Based on the 1996 update, 37 percent of the firms active today started up since 1990. Although the district of Mbarara was not included in the census update, it was added to the survey, given its importance as a regional business center today. As mentioned above, the firm survey was confined to five sectors—commercial agriculture (includes fishing), agro-processing, other manufacturing, construction and tourism. Table 19 shows the distribution of establishments and employment by firm size and sector in the 1996 updated industrial census. Firm size is defined by employment. Neither the update nor the 1989 census includes firms with less than five employees, so the initial size breakdown was small (5-20 employees), medium (21-100 employees), large (101-500 employees) and very large (over 500 employees). Subsequently, large and very large firms were treated as one group. The five sectors selected for the survey comprise 52 percent of all enterprises included in the census update and almost 80 percent of employment. Table 20 shows the distribution of establishments and employment within the five selected industrial sectors by firm size and sector. The within-sector distribution of employment shows large variations across sectors. Most of the employment within commercial agriculture and construction is concentrated in two to three very large firms, while most of the employment in tourism is in the small firms. Employment in agro-processing and other manufacturing is relatively evenly distributed across firm size. We constructed a stratified random sample for the survey. The following criteria were taken into account:
The final sample consisted of 243 surveyed firms and was similar with respect to size and regional distribution to the stratified sample constructed initially [see World Bank (1998)]. The characteristics of the sampled firms are set out in Table 21 by firm size, sector, location and ownership. Over 80 percent of large firms, about 30 percent of medium-sized firms and about 10 percent of small firms in the five sectors were included. Five different geographical areas were covered: Kampala, Jinja/Iganga, Mbale/Tororo, Mukono, and Mbarara. The first four make up 98 percent of total employment in the five selected sectors reported in the 1996 census update. In terms of ownershipwhich was not a criteria for sample selection70 percent of firms were Ugandan owned, 16 percent foreign owned and 14 percent in joint ownership. The survey typically consisted of at least two visits to each firm by one or two enumerators. While the manager's perceptions were relatively easy to obtain during a single interview, quantitative data on costs, sales and taxation which were collected for three years, usually required another visit to consult the accountant. During the course of the survey it was found that a number of firms had changed business activity since 1996, for example, by shifting to trading instead of manufacturing. Similarly, a number of firms were difficult to locate, which indicates that either they had exited since 1996, moved to another address, or that the 1996 industrial census update may have contained firms from the 1989 census which had exited before 1996. A few firms refused to participate in the survey. For all these reasons, 39 percent of the firms in the final sample were randomly chosen alternates to the initially drawn random sample. BIBLIOGRAPHY Ablo, E. and R. Reinikka, 1998, Do Budgets Really Matter? Evidence from Public Spending on Education and Health in Uganda, World Bank Policy Research Working Paper 1926, Washington, D.C. Bagchi, A., R. Bird and A. Das-Gupta, 1995, An Economic Approach to Tax Administration Reform, International Centre for Tax Studies Discussion Paper No. 3, University of Toronto, Canada Bartolome, C.A.M., 1995, Which Tax Rate Do People Use: Average or Marginal? Journal of Public Economics, 56, pp. 79-96 Biggs, Tyler and Pradeep Srivastava, 1996, Structural Aspects of Manufacturing in Sub-Saharan Africa. Findings from a Seven Country Enterprise Survey, World Bank Discussion Paper No. 346, Africa Technical Department Series, Washington, D.C. Broadway, R., Bruce, N. and Mintz, J. M., 1984, Taxation, Inflation, and the Effective Marginal Tax Rate in Canada, Canadian Journal of Economics, Vol. 27, pp. 286-99 Chen, D. and J. M. Mintz, 1993, Taxation of Capital in Canada: An Inter-Industry and Inter-Provincial Comparison, in Business Taxation in Ontario, University of Toronto Press City Council of Kampala, All About Property Rates, Kampala Coopers & Lybrand and Deloitte, 1991, Government of Uganda Planning for a Revenue Authority for Uganda, report to the Overseas Development Administration (ODA) Das-Gupta, A. and D. Mookherjee, 1998, Incentives and Institutional Reform in Tax Enforcement. An Analysis of Developing Country Experience, Oxford University Press Dunn, D. and A. Pellechio, 1990, Analyzing Taxes on Business Income with the Marginal Effective Tax Rate Model, World Bank Discussion Papers, No. 79, Washington, D.C. Henstridge, M., forthcoming, Macroeconomic Management in Uganda, IMF Working Papers, Washington, D.C. ILO, 1997, Yearbook of Labour Statistics, Geneva Leechor, C. and J. M. Mintz, 1993, On the Taxation of Foreign Corporate Investment when the Deferral Method is Used by the Capital Exporting Country, Journal of Public Economics, pp. 75-96 McKenzie, K. J., M. Mansour, and A. Brule, 1997, The Calculation of Marginal Effective Tax Rates, Working Paper 97-14, Technical Committee on Business Taxation, Department of Finance, Canada McKenzie, K. J., J. M. Mintz and K. Scharf, 1992, Measuring Effective Taxes in the Presence of Multiple Inputs: A Production Based Approach, International Tax and Public Finance, Vol. 4, No. 3, pp. 337-357 Mintz, J. M., 1990, Tax Holidays and Investment, World Bank Economic Review, Vol. 4, pp. 81-102 Reinikka, R. and J. Svensson, 1999a, Confronting Competition: Firms' Investment Response and Constraints in Uganda, unprocessed, Macroeconomics2, Africa Region and Development Research Group, The World Bank. Reinikka, R. and J. Svensson, 1999b, Private Investment and Complementary Capital: The Effects of Deficient Public Capital Provision, unprocessed, Macroeconomics2, Africa Region and Development Research Group, The World Bank. The Republic of Uganda, 1995, Input/Output Tables for Uganda (1989 & 1992), Statistics Department, Ministry of Finance and Economic Planning, Entebbe, September Shah, A. (ed.), 1995, Fiscal Incentives for Investment and Innovation, Oxford University Press. Svensson, J., 1999, Who pays Bribes and How Much? Evidence from Uganda, unprocessed, Development Research Group, The World Bank The World Bank, 1994, The Private Sector in Uganda: Results of the World Bank Enterprise Survey, unprocessed, Eastern Africa Department The World Bank, 1996, Performance and Perceptions of Health and Agricultural Services in Uganda, A Report Based on the Findings of the Baseline Service Delivery Survey, Economic Development Institute and CIET International, Washington, D.C. The World Bank, 1998, Note on the Construction of the Sample of Ugandan Industrial Enterprises, unprocessed, Macroeconomics2, Africa Region and Development Research Group 1 The World Bank (1996), and Ablo and Reinikka (1998) provide qualitative and quantitative evidence on problems in service delivery in Uganda. 2 Biggs and Srivastava (1996). 3 It is important to note that there are, of course, many other factors than taxation that affect investment decisions. For example, in Uganda the single most important constraint upon firms is poor and expensive infrastructure services. [Reinikka and Svensson (1999a,b)]. 4 Coopers & Lybrand and Deloitte (1991). 5 It is important to note that GDP in Uganda includes the non-monetary (subsistence) sector. Domestic revenue was 7.6 percent of monetary GDP in 1986 and 14.7 percent in 1998. 6 Henstridge (forthcoming). 7 World Bank (1994). 8 Reinikka and Svensson (1999a). 9 The method used to estimate the METR has been extensively documented. See, for example, Broadway, Bruce and Mintz (1984), Chen and Mintz (1993), McKenzie, Mintz and Scharf (1992), and Mintz (1990). Other references include Dunn and Pellechio (1990) and Shah (ed.) (1995). For a brief discussion of the method see also Appendix A. 10 Apart from the METR and the AETR, other analytic tools include cost of capital frameworks and computable general equilibrium models. For consumer behavior see de Bartolome (1995). 11 The 1998 firm survey, which was carried out by the World Bank and the Ugandan Private Sector Foundation, covered 243 firms in commercial agriculture, agro-processing, manufacturing, tourism and construction: 38 percent were small firms (5-20 employees), 36 percent were medium-scale (21-100 employees), and 26 percent large firms (over 100 employees). About 5 percent of the sample were ‘very large’ firms with several thousand employees. For details see the World Bank (1998) and Appendix B. 12 The Republic of Uganda (1995). A more detailed discussion of the data sources appears in Appendix B. 13 The World Bank (1994). 14 As our focus is on real rather than financial capital investment, we take into account only those taxes that affect real capital and production decisions. Taxes targeted to financial capital investment, such as tax treaties on repatriation of interest income, are beyond the scope of this study. 15 The initial allowance for investment in machinery and plant (except for vehicles) is 50 percent in five main industrial locations (Kampala, Entebbe, Namanve, Jinja and Njeru), and 75 percent elsewhere in Uganda. The annual depreciation rate is 40, 35, 30 and 20 percent for four different classes of machinery and plant, respectively. For example, when a Kampala-based firm purchases a computer (Class 1) for business use, it can claim 70 percent of the purchasing cost during the first year. That is, the firm is entitled to a 50-percent initial allowance plus a 40 percent annual depreciation allowance based on the balance. The remaining 30 percent of the cost can then be depreciated annually at 40 percent of the unclaimed balance. 16 Prior to the 1997 tax reform, the annual depreciation rate for structures was 4 percent, while machinery and plant were divided into three classes, with the annual depreciation rate at 50, 40, and 20 percent, respectively. The classification of machinery was also changed significantly in 1997. For example, computers that now enjoy the most generous tax depreciation allowance (40 percent), were allowed the smallest annual allowance (20 percent) under the previous system. Although there was an initial depreciation allowance under the previous tax system, it was only for ‘approved businesses’ as designated by the Minister of Finance. In practice, the Minister had never approved any firm for such an incentive. 17 In the case of an investment project of at least US$50,000 for domestic firms and US$300,000 for foreign investors, the tax holiday was three years. For larger investments the holiday was typically five years. It could be extended for an additional year for an investor operating in any of the priority areas specified in 1991 Investment Code, that is, agro-processing, manufacturing, construction, transportation, and tourism but not commercial agriculture or communications. 18 City Council of Kampala. For rating purposes, Kampala is divided into 15 rating zones that classify various properties by location. 19 As a firm is taxed as a whole rather than by asset type or at the margin, this tax subsidy on machinery can be thought of as reducing the tax on income generated by other type of investment. 20 This holds provided that firms are not allowed to defer their depreciation allowance until after the holiday has expired. 21 It is measured by the METR dispersion which is a weighted standard deviation across industries (Appendix A). 22 As the payroll tax in Uganda is imposed on the total payroll without ceilings, the statutory payroll tax rate can be seen as the marginal rate. By ignoring the shift effect, we also assume the employer’s share of payroll tax is fully borne by the employer. 23 Based on the Uganda Investment Authority data. 24 The annual depreciation allowance in Kenya is 2.5 percent for buildings over 40 years. Machinery and equipment are grouped into four classes with the annual depreciation allowance of 37.5, 30, 25 and 12.5 percent, respectively, based on the declining balance. 25 This rate is estimated based on the aggregate fuel tax per liter (including import duty, excise duty, road maintenance levy and petroleum development levy) using Uganda’s sales by product as weights. 26 The estimate is based on the latest year available for the annual average salary in manufacturing (1991), published in the ILO Yearbook of Labour Statistics (1997). 27 The annual depreciation allowance is 4 percent for industrial buildings, and 6 percent for hotels. Machinery and equipment are grouped into three classes, with the annual depreciation allowance of 37.5, 25 and 12.5 percent, respectively. 28 The valuation of inventory is based on “cost or market, whichever is the lower”. It is not clear whether this valuation is for writing-off or evaluating in-house inventory. If this is for writing off inventory, it indicates FIFO during the period of inflation, otherwise LIFO. 29 This rate is estimated based on the aggregate fuel tax per liter (including import duty, excise duty, the road toll tax on petroleum and diesel fuels) using Uganda's sales by product as weights. 30 Should buildings also be exempted from the municipal property tax in Uganda, Uganda could gain a tax advantage over Kenya and Tanzania in manufacturing , and over Tanzania in tourism. 31 See Das-Gupta and Mookherjee (1998). 32 The World Bank (1994). 33 The tax return form currently used in Uganda is based on the 1974 decree and hence out of date. It contains only limited information on the taxpayer’s business, income, and deductions. Individuals and firms actually share the same tax return form, which is mainly tailored for the former. For firms, the main drawback is its lack of standardized format to conduct a reconciliation of net income per financial statements with net income for income tax purpose. In other words, the form does not provide observability of tax liabilities or benefits which makes resolving disagreements in a case-by-case format very time-consuming. 34 The METR model can, of course, incorporate the actual practice of postponing the depreciation instead of using the mandatory tax allowance that is not being enforced. In that case, the METR would be significantly lower than that presented for the tax holiday firm in Table 3. 35 When the input tax credit is not refunded at all, the VAT could be modeled as a sales tax on capital or any other taxable input. In the case where the refund period is abnormally long and no interest is paid by the revenue authority, the interest cost could be modeled as an increment on the cost of financing. 36 It appears that when offset procedures are being used, no supporting documentation is required and the approval is granted after a desk review, subject to an audit at some later date. However, such a loose arrangement can lead to major difficulties at the audit stage. 37 More specifically, an industry’s transportation share is estimated by dividing its total transportation cost by the total national transportation cost. This industry’s transportation share is then used to disaggregate the total fuel imports in the same year to arrive at the fuel cost by industry. |