An assessment of the investment climate in Namibia (Vol. 2 of 2) : Detailed results and econometric methodology

 71817 v2 An Assessment of the Investment Climate in Namibia June 2007 Volume 2: Detailed Results and Econometric Methodology 1  ABBREVIATIONS CMA Common Monetary Area GDP Gross Domestic Product HIV/AIDS Human Immunodeficiency Virus/ Acquired Immunodeficiency Syndrome ICA Investment Climate Assessment IMF International Monetary Fund LAD Least Absolute Deviation LLC Limited Liability Company MBA Master of Business Administration MW Megawatt NDP National Development Plan OLS Ordinary Least Squares SACU Southern African Customs Union SADC Southern African Development Community SMLEs Small, Medium and Large Enterprises TFP Total Factor Productivity VAT Value-Added Tax VIP Very Important Person 2  Table of Contents ABBREVIATIONS ........................................................................................................................2 CHAPTER 1: INTRODUCTION.................................................................................................6 I. MACROECONOMIC BACKGROUND.............................................................................6 II. THE INVESTMENT CLIMATE ASSESSMENT ................................................................9 II.1 The World Bank Enterprise Survey..............................................................9 II.2 Comparator Countries ...............................................................................11 CHAPTER 2: AN ANALYSIS OF FIRM PERFORMANCE.................................................14 I. FIRM PERFORMANCE ...............................................................................................14 I.1 Labor Productivity .....................................................................................14 I.2 Labor Costs................................................................................................18 I.3 Capital Productivity...................................................................................20 I.4 Total Factor Productivity ..........................................................................23 I.5 Cross-Country Comparisons .....................................................................24 I.6 Differences by Firm Type...........................................................................25 II. INTERNATIONALIZATION .........................................................................................26 II.1 Exporting....................................................................................................26 II.2 Importing....................................................................................................29 III. COMPETITION..........................................................................................................30 IV. PROFITABILITY .......................................................................................................31 ECONOMETRIC ANNEX: TOTAL FACTOR PRODUCTIVITY ....................................................35 Methodology ..........................................................................................................35 Results ....................................................................................................................36 CHAPTER 3: PERCEPTIONS ABOUT THE INVESTMENT CLIMATE IN NAMIBIA.44 I. MAIN PERCEIVED CONSTRAINTS..............................................................................45 II. DIFFERENCES IN PERCEPTIONS FOR DIFFERENT TYPES OF FIRM. ..............................48 II.1 Ownership ..................................................................................................49 II.2 Exporters....................................................................................................50 II.3 Size .............................................................................................................52 II.4 Sector. ........................................................................................................52 ECONOMETRIC ANNEX: PERCEPTIONS ABOUT THE INVESTMENT CLIMATE. .......................54 CHAPTER 4: LABOR MARKETS AND HUMAN CAPITAL .............................................57 I. EMPIRICAL STRATEGY ............................................................................................57 I.1 Training-Firm Level...................................................................................57 I.2 Training-Individual Level ..........................................................................58 I.3 Remuneration-Firm level ...........................................................................59 I.4 Remuneration-Individual level...................................................................60 II. RESULTS .................................................................................................................61 II.1 Training-firm level regressions..................................................................61 II.2 Training-individual level regressions ........................................................62 II.3 Wages-firm level ........................................................................................64 3 CHAPTER 5: ACCESS TO FINANCE.....................................................................................69 I. ACCESS TO FINANCE IN AN INTERNATIONAL PERSPECTIVE .....................................69 II. EFFECT OF SIZE ON ACCESS TO CREDIT...................................................................73 II.1 Objective and Subjective Access Indicators ..............................................73 II.2 Characteristics of Loan products...............................................................74 II.3 Loan Applications and Rejections .............................................................75 II.4 Sources of Finance for Working Capital and Investment..........................76 III. ACCESS TO FINANCE BY DIFFERENT CATEGORIES OF FIRMS .....................................76 III.1 Firm Age ....................................................................................................76 III.2 Registration and Legal Status....................................................................77 III.3 Industry ......................................................................................................78 III.4 Ownership characteristics .........................................................................78 ECONOMETRIC ANNEX: ACCESS TO FINANCE .....................................................................80 Access indicators ...................................................................................................80 Methodology ..........................................................................................................80 Results for Access indicators .................................................................................81 Results for Investment ............................................................................................82 CHAPTER 6: OTHER ASPECTS OF THE INVESTMENT CLIMATE .............................85 I. CRIME .....................................................................................................................85 I.1 Evidence from the World Bank Enterprise Survey ....................................85 I.2 Differences by firm type .............................................................................87 II. TAX RATES .............................................................................................................88 III. ACCESS TO LAND IN NAMIBIA ................................................................................90 III.1 Problems of Land Access ...........................................................................91 IV. COMPETITION FROM THE INFORMAL SECTOR ..........................................................92 IV.1 Overview of the Informal Private Sector in Namibia ................................93 IV.2 Informal practices as a constraint to formal sector growth ......................94 IV.3 The Microenterprise Survey.......................................................................94 V. BUSINESS LICENSING ..............................................................................................97 VI. HIV/AIDS IN NAMIBIA.........................................................................................100 VI.1 Overview of the Current Situation ...........................................................100 VI.2 HIV/AIDS in the Private Sector ...............................................................101 VI.3 Prevention Activity by Country................................................................102 VI.4 Government Spending by Country ...........................................................102 VI.5 Absenteeism by Country...........................................................................103 VI.6 Worker Responses....................................................................................104 VII. LABOR REGULATION.............................................................................................105 VIII. CORRUPTION .........................................................................................................106 IX. INFRASTRUCTURE .................................................................................................107 IX.1 Power .......................................................................................................107 IX.2 Transportation .........................................................................................109 IX.3 Telecommunications ................................................................................110 ECONOMETRIC ANNEX: FIRM RESPONSES TO HIV/AIDS .................................................111 Methodology ........................................................................................................111 Results for HIV Response Determinants..............................................................111 4 REFERENCES...........................................................................................................................113 5  CHAPTER 1: INTRODUCTION Improving living standards and cutting poverty depends on broad-based economic growth, which will only take place when firms improve worker productivity by investing in human and physical capital and technological capacity (defined broadly to include investment in knowledge, equipment and organizational structure). But firms will only invest when the investment climate is favorable. The investment climate is the aspects of the environment that affect the decisions of firms, entrepreneurs and investors on hiring and firing workers, investing in physical and human capital and developing new technologies (Stern, 2002a; Stern, 2002b; Stern, 2002c). In its broadest definition, this includes fixed factors such as a country’s climate, endowment of natural resources, and location. For operational purposes, however, the Investment Climate Assessment (ICA) focuses on factors that are directly affected by government policies. These include things such as macroeconomic stability, regulation, the state of a country’s infrastructure, and incentives embodied in institutional arrangements, such as the security of property rights, the rule of law and governance. These policy-related factors affect the expected return to investment and the uncertainty and risk of this investment. Defined in this way, the investment climate affects the returns to and risks associated with different economic activities. The goal of the Investment Climate Assessment (ICA) for Namibia is to evaluate the investment climate in Namibia in all its operational dimensions and to promote policies to strengthen the private sector. The ICA is largely be based on results from a large firm-level survey that collects information on firm performance, the cost of doing business, the regulatory environment, the labor market, the financial sector, the trade regime and investment. I. MACROECONOMIC BACKGROUND Namibia is a small (in terms of population), resource-rich, upper middle income country. In purchasing power parity adjusted terms, per capita Gross Domestic Product (GDP) is US$6,980. Namibia is a member of both the South African Customs Union (SACU) and the Common Monetary Agreement (CMA). Although mining is important in terms of its contribution to GDP, accounting for about 11 percent of GDP in 2004 (International Monetary Fund, 2006c), it is relatively less important than it is in Botswana, where mining accounts for 33 percent of GDP (International Monetary Fund, 2006a). Manufacturing and agriculture and fishing are also significant, contributing 13 percent, 5 percent and 4 percent of GDP in 2004. The most important sub-sector of manufacturing is food processing, which accounts for over half of total manufacturing production (International Monetary Fund, 2006c). Under Vision 2030 Key Results Area 4 (and 7), the Government of Namibia envisages transforming Namibia into an industrialized economy that is globally competitive . Diamonds are the most important export good, accounting for about 45 percent of total exports in 2004 (International Monetary Fund, 2006c), compared to over three-quarters of exports in Botswana (International Monetary Fund, 2006a). Other minerals accounted for an additional 13 percent of exports. Processed fish and other manufactured goods accounted for 6 about 23 percent of total exports, with live animals, unprocessed fish, other commodities, and other food products making up most of the remainder. Namibia has performed reasonably well in economic terms in recent years. Between 2000 and 2005, GDP grew at an average rate of 4.2 percent, higher than the target rate of 4 percent and faster than average growth in the mid-late 1990s (see Figure 1). Per capita growth has also been positive, if modest, averaging 2.6 percent per year since 2000—considerably faster than in the previous half decade. Figure 1: Namibia has managed to sustain positive, although modest, growth over the past decade. 9 Average Growth 6 3 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 GDP growth (annual %) GDP per capita growth (annual %) Source: World Bank (2006b). In addition to its relatively good growth performance, the macroeconomy has also been relatively stable. The Namibian dollar’s peg to the South African rand has helped regulate inflation by linking Namibia’s monetary policy to South Africa’s inflation targeting framework (see Figure 2) 7  Figure 2: Namibia has managed to sustain positive, although modest, growth over the past decade 8 7.3 7 5.9 6 5 4.2 4 3.4 3 2.0 2 1.4 1 0 South Africa Namibia 2003 2004 2005 Source: Central Bureau of Statistics, Government of Namibia and World Bank (2006b) Despite Namibia’s good macroeconomic performance, some concerns remain. In particular, unemployment remains high, with estimates from the 2002 Labor Force Survey suggested that about 20 percent of the economically active population is unemployed. Broader definitions that include discouraged workers yield estimates as high as 54 percent (International Monetary Fund, 2006c). The fact that unemployment has been persistently high, despite economic growth, emphasizes that unemployment is not high only due to short-run business cycle fluctuations. The IMF also suggests that the unemployment problem primarily affects the unskilled workforce (International Monetary Fund, 2006c). Under these circumstances, it is not surprising that many people consider unemployment to be the most important problem facing the country, with close to 70 percent of respondents identifying it among the top 3 problems that the government should address (Bratton and Cho, 2006; Keulder and Wiese, 2005). Another issue is that although the peg to the Rand has helped to regulate inflation, exchange rate instability remains high—the Rand has been unstable against other major currencies over the past decade—especially against the U.S. dollar. In a 2004 survey, 44 percent of South African enterprises that exported reported that macroeconomic instability was a serious problem for their enterprises operations (World Bank, 2005). The Rand—and therefore the Namibian dollar—has been more stable in the years following 2004 than in the four years before 2004, suggesting that it might not be such a significant problem currently. 8 II. THE INVESTMENT CLIMATE ASSESSMENT II.1 The World Bank Enterprise Survey The main source of information for Investment Climate Assessment is an enterprise level survey that was conducted in June 2006. Information from the survey will be supplemented with information from other sources including the Doing Business Report; analytical reports by the World Bank, the International Monetary Fund, other international organizations and the Government of Namibia; and academic papers and reports. The survey was conducted in two locations, Windhoek and Walvis Bay, and covered the entire urban economy. The sampling methodology varied by strata. In the manufacturing sector, since the size of the universe was relatively small in both locations, a census was undertaken. A comprehensive list of manufacturing establishments was complied based upon an exhaustive search through the areas of each location that contained manufacturing enterprises. An attempt was then made to contact each establishment on the list. In the retail trade and services sectors in Windhoek, a list of establishments that were registered with the municipality was provided by the municipality. After eliminating establishments with fewer than 5 employees, firms were randomly sampled from the remaining list of 1253 retail establishments and 1079 other establishments. In Walvis Bay, similar lists were not available. Because the region was small, an exhaustive count of business establishments in the areas was conducted. Based upon the count, a skip rule was established, which was implemented by the enumerators, to collect the target number of enterprises. Because of the large number of informal microenterprises, an exhaustive count of all areas was not conducted for the microenterprise sample. Rather, based upon discussions with Government officials, Chambers of Commerce, districts and zones with large numbers of informal firms were identified. The survey team then went into the field to verify these sources and to count microenterprises. Within each zone, all microenterprises were counted following a predetermined route. Based upon the count, a skip pattern was established for each area and enumerators were sent to each zone with instructions on how to apply the skip rule for that zone. Because a complete count was not done for the entire city—only for the zones included in the microenterprise survey—this data cannot be combined with the data from the manufacturing, retail and service surveys. That is, since the total number of microenterprises in Windhoek and Walvis Bay is not known, weights that would allow us to merge the SMLE and microenterprise surveys cannot be constructed. Table 1: Unweighted Sample Size, by Sector SMLE Microenterprises Total 329 100 Manufacturing 106 34 Retail 110 44 Other Services 113 22 Source: World Bank Enterprise Survey. Table 1 presents unweighted sample sizes by sector. The sample contains 329 SMLEs and 100 microenterprises. For the purpose of this study, SMLEs are defined to be firms with 9  over 5 employees and consists of very small (5-9 employees), small (10-49 employees), medium (50-99 employees) and large enterprises (at least 100 employees). In most countries where World Bank Enterprise Surveys are completed, the sampling frame only includes firms that are registered with the government. Because comprehensive lists were not available for all groups of firms in this sample, it is possible that some of the firms might be ‘informal’ to some degree. 1 Microenterprises are firms with between 1 and 4 employees. As noted earlier, the sample was selected through an area sampling-type approach not from a list of registered enterprises and so should include both formal and informal microenterprises. Table 2 presents weighted sample characteristics of the SMLEs. Although manufacturing firms make up about one-third of the unweighted sample, they account for only 6 percent of the weighted sample—reflecting the greater importance of retail trade and other services in the economy. Most of the firms in the sample are small—close to half have fewer than 10 employees and over 90 percent have fewer than 50 employees. The manufacturing firms in the sample are larger than the retail and services enterprises—close to 25 percent have more than 50 employees. Close to three-quarters of all SMLEs are black-owned. About 22 percent have a white owner and smaller shares have Asian, or Lebanese/Middle Eastern owners. About one-third of SMLEs have a female owner and about 22 percent were at least partly foreign-owned. About one-third of manufacturing firms export some part of their output. Other SADC countries, particularly South Africa, were the most important export destinations, although close to half of all exporters reported that they exported some of their output to developed economies. Table 2: Sample characteristics of SMLEs, weighted Percent of Sample Percent of Sample (Weighted) (Weighted) Manufacturing 6% Micro (less than 5 employees) 0% Retail 51% Very Small (5-9 employees) 49% Other Services 43% Small (10-49 employees) 44% Medium (50-99 employees) 4% Windhoek 92% Large (100 and up) 4% Walvis Bay 8% Foreign-owned 22% Any female owner 33% Domestically owned 78% Any black owner 78% Any white owner 22% Exporters (manufacturing) 34% Any Asian owner 5% Non-Exporters (manufacturing) 66% Any Lebanese owner 1% Source: World Bank Enterprise Survey. Table 3 presents similar data for microenterprises. Microenterprises were more likely to have female and black owners (52 percent and 97 percent respectively). Very few of the 1 Other possible terms are also problematic (e.g., large or larger enterprises) can give the impression that the sample only includes large firms (e.g., with over 100 employees) 10  microenterprises had white or Asian owners and far fewer were foreign-owned. Microenterprises in the manufacturing sector were far less likely to export. However, close to one in ten of the microenterprises exported at least part of their output—in comparison, none of the microenterprises in a similar sample for South Africa exported any part of their output. Table 3: Sample characteristics of microenterprises. Percent of Sample Percent of Sample Manufacturing 34% Micro (less than 5 employees) 100% Retail 44% Very Small (5-9 employees) 0% Other Services 22% Small (10-49 employees) 0% Medium (50-99 employees) 0% Windhoek 79% Large (100 and up) 0% Walvis Bay 21% Foreign-owned 7% Any female owner 52% Domestically owned 93% Any black owner 97% Any white owner 2% Exporters (manufacturing) 9% Any Asian owner 0% Non-Exporters (manufacturing) 91% Any Lebanese owner 0% Source: World Bank Enterprise Survey. II.2 Comparator Countries One of the advantages that World Bank Enterprise Surveys have over other firm-level surveys is that because similar surveys have been conducted in a wide range of other countries, it is possible to benchmark Namibia against other countries with respect to both firm productivity and measures of the investment climate. Outside of the Southern African Customs Union (SACU), it is difficult to find relevant comparators within Sub-Saharan Africa—most African countries where World Bank Enterprise Surveys have been completed are both considerably poorer than Namibia (see Figure 3). A natural set of comparator countries for Namibia is the other countries in SACU (Botswana, Lesotho, South Africa and Swaziland). Because of their geographical proximity to Namibia and because of three of the four are middle-income, they provide a natural set of comparators. Figure 3: Per capita income is lower in Namibia than it is in most of the proposed comparator countries. 15000 Per Capita GDP, PPP ($) 10000 5000 0 a nd a o s le a ia a a iu an si th tin ric ric hi ib ila ay rit so am C n Af Af w az au ge al ts Le n h N M Sw Bo M Ar ut ra So ha Sa 11 b- Su Source: World Bank (2006b). Surveys were completed in Lesotho and South Africa in 2004 and in Botswana and Swaziland in 2006. An additional middle-income African country that might be an attractive comparator is Mauritius. Although manufacturing is more important in Mauritius than Namibia, its success in this sector make it an interesting comparator (see Figure 4). Similarly, Malaysia—another strong performing middle income country with a strong manufacturing base—might also make an interesting comparator. Malaysia is also interesting because it is a large mineral producer (mostly oil and gas) that has successfully diversified its economy. Finally, two upper middle- income economies in Latin America are proposed as comparators—Chile and Argentina. Chile is particularly interesting given the importance of mining in its economy and its successful diversification into manufacturing. As noted earlier, under Vision 2030, the Government of Namibia intends to transform Namibia into an industrialized economy. Figure 4: Mining is slightly less important in Namibia than in some of the comparator countries such as Malaysia, Chile and Botswana. Swaziland Botswana Malaysia Chile Argentina Malaysia Mauritius Namibia South Africa South Africa Lesotho Argentina Chile Lesotho Namibia Swaziland Botswana Mauritius 0 10 20 30 40 0 10 20 30 40 Value added in manufacturing (% of GDP) Value added in mining (% of GDP) Source: World Bank (2006b) Although per capita GDP is slightly lower in Namibia than in the proposed comparator countries and manufacturing is slightly less important in terms of its contribution to GDP, most of the proposed countries have relatively strong investment climates making them interesting comparators in terms of potential areas for improvement. The following potential comparator countries are therefore proposed: 1. Southern African Customs Union : Botswana, Lesotho, South Africa and Swaziland 2. Middle Income Countries: Argentina, Chile, Mauritius and Malaysia 12  Similar comparators are being proposed for the Investment Climate Assessment for Botswana. The proposed comparators for the Swaziland ICA are slightly lower income and more focused on manufacturing. 13  CHAPTER 2: AN ANALYSIS OF FIRM PERFORMANCE This chapter compare firm performance in Namibia with performance in the other SACU economies and in other middle economies. The different measures of firm performance are intended to provide an indication of how competitive firms are in both international and domestic markets. While this chapter provides an overview of how well firms in Botswana perform, later chapters assess how elements of the investment climate affect their competitiveness. I. FIRM PERFORMANCE As a preliminary analysis of firms’ competitiveness, this section examines several traditional measures of firm productivity. The chapter first looks at labor productivity—the amount of output per worker that firms produce. It then looks at capital stock and capital productivity in the manufacturing sector and compares labor productivity with labor costs, to obtain the unit labor costs. These measures are compared across different firms within Namibia, and also to others internationally. This is followed by an analysis of total factor productivity, a measure of firm performance that takes into account use of both capital and labor. To ensure that the results are comparable across countries, and because the standard methodology is only appropriate for the manufacturing sector—results in this section only cover SMLEs in that sector. I.1 Labor Productivity Value-added per worker is a basic measure of labor productivity. It is the amount that that the firm produces less the cost of the raw materials (such as iron or wood) and intermediate inputs (such as engine parts or textiles) used to produce the output divided by the number of workers in the firm. Labor productivity is higher in firms that produce more output with less raw materials and fewer workers. Differences in labor productivity can be the result of differences in technology, differences in organizational structure, differences in worker skills, differences in management ability, or differences in the amount of capital that the firm uses. Because labor productivity does not take the use of capital (i.e., machinery and equipment) into account, it will generally be higher in firms that use capital in place of labor (i.e., firms that are capital intensive). Labor productivity is higher in Namibia than in most other countries in Sub-Saharan Africa. Of the 31 countries in the region where World Bank Enterprise Surveys have been conducted, the median manufacturing SMLE in Namibia is more productive than similar SMLEs in 30 of them. The one exception is South Africa (see Figure 5). The differences between Namibia and the other economies, including other middle-income economies, are large. Labor productivity is close to twice as high as labor productivity in middle income countries such as Botswana, Mauritius and Swaziland and is over four times as high as most low income countries. 14  Figure 5: Manufacturing SMLEs in Namibia—and the other middle income countries in SACU—are considerably more productive than SMLEs in most low-income countries in Africa. Value-added per worker (2005 US Dollars) $30,000 $20,000 $10,000 $0 Swaziland Malawi Kenya Botswana Rwanda Tanzania Mozambique Eritrea Guinea-Conakry Mauritius South Africa Cape Verde Angola Lesotho Benin Uganda Nigeria Guinea-Bissau Senegal Zambia Gambia Namibia Cameroon Source: World Bank Enterprise Surveys Note: Data were collected between 2002 and 2005 depending on survey period for each country. Data collected prior to 2005 is converted to 2005 figures using GDP deflators. See Table 4 for description of variables. Several low-income African countries with investment climate date on labor productivity are excluded from the graph for presentational purposes. All excluded countries have lower productivity than Botswana. Namibia also compares favorably with the middle-income comparator countries. The median manufacturing SMLE is about as productive as the median SMLE in Malaysia and is considerably more productive than median SMLEs in China, Thailand, or Mauritius. Although labor productivity is lower than in the two Latin American comparator countries, this might not be surprising however given that per capita GDP is also higher in these countries. One issue regarding labor productivity is that it is generally lower in firms that are more labor intensive (i.e., firms that use less capital per worker). Since firms in some sectors (e.g., garments) tend to more labor intensive than others, if industry in a country is a concentrated in these sectors, then productivity might appear to be artificially low. One way of dealing with this is to calculate total factor productivity—a measure of productivity that takes the firms’ use of capital into account. Another approach is to focus on a single sub-sector of manufacturing. In general, since labor intensity will vary less within a single sub-sector than in than in the entire manufacturing sector, this will reduce these concerns. Results are broadly similar when looking at a single sector—garments, an internationally traded good with a relatively well established production technology. Labor productivity is lower in the garment sector in Namibia than in South Africa or Argentina and very close to labor productivity in Malaysia. But it is higher than in any of the other middle-income comparators, including local competitors such as Swaziland and Botswana. 15  Figure 6: Although lower than in the best performing middle-income economies, labor productivity is relatively high in Namibia. Value-added per worker (2005 US Dollars) $30,000 $20,000 $10,000 $0 Swaziland Malaysia Botswana Lesotho China Cape Verde Chile Argentina Mauritius South Africa Thailand Namibia All Garments Source: World Bank Enterprise Surveys Note: See Figure 5 for description of exchange rates and deflators and Table 4 for description of variables. As in most countries, labor productivity is higher for exporters than it is for non-exporters (see Table 4). The difference is quite large— value added per worker is over $25,000 for the median exporter, but only $10,000 for the median non-exporter. This difference is statistically significant (i.e., it does not appear to be due to random or sampling variation). Exporters also tend to pay higher wages and to be more capital intensive (i.e., they tend to use more capital per worker). As discussed in more detail in the section on total factor productivity, exporters might be more productive either because only the most productive firms can enter export markets or because exporting encourages firms to become more productive. Firms with university educated managers tend to be more productive than firms with less well educated managers. This suggests that manager education and training might be important for firm productivity. Given that firms with university educated managers do not use significantly more capital per worker than other firms (i.e., they do not appear to be more capital intensive), this suggests that the difference between firms with and without university educated managers is not due to firms with better educated managers being more capital intensive. Firms with managers with vocational training appear on average to be slightly more productive than firms with managers with only a secondary education, but the difference is relatively small and is not statistically significant. Labor productivity is also higher for the median firm where the manager has an MBA (about $30,000) than for a similar firm with a university educated manager without an MBA (about $18,000), but the difference is not statistically significant. In many countries, labor productivity tends to be higher in large firms than in small firms. In general, a clear pattern does not emerge in Namibia. Although medium-sized firms are more productive than small firms or very small firms, large firms do not appear to be more productive 16 than medium-sized firms. It is important to note however than the samples tend to be relatively small and hence the differences are not generally statistically significant. Table 4: Median Productivity by industry, size, and ownership. Value-added Labor costs Unit labor Capital per Capital per worker per worker costs worker (book) Productivity (book) All $14,863 $3,538 0.29 $5,616 2.45 Sector Furniture $9,610 $2,896 0.23 $4,717 2.15 Food and Beverage $17,512 $3,700 0.20 $7,945 2.19 Metal and Machinery $19,694 $6,107 0.31 $3,145 3.00 Other Manufacturing $12,746 $3,538 0.35 $5,550 2.79 Size Very Small $16,259 $3,145 0.24 $4,914 3.00 Small $10,319 $3,490 0.32 $6,290 1.81 Medium $25,535 $6,338 0.24 $3,622 8.70 Large $17,512 $4,938 0.29 $8,888 2.19 Manager Education Secondary or less $8,545 $3,145 0.34 $4,914 1.63 Vocational $10,319 $3,490 0.34 $6,290 2.43 University $19,243 $4,193 0.25 $5,661 3.05 Exports Exporters $25,444 $6,107 0.29 $8,888 2.34 Non-exporters $9,931 $3,145 0.31 $5,242 2.45 Ownership Foreign Owned $24,755 $4,938 0.20 $8,276 3.66 Domestic $13,756 $3,490 0.30 $5,242 2.45 Any African Owner $14,863 $3,494 0.30 $5,661 2.43 Any White Owner $17,934 $4,088 0.22 $7,548 2.43 Source: World Bank Enterprise Survey Notes: See Figure 5 for description of exchange rates used. All values are medians for enterprises with available data. Value added is calculated by subtracting intermediate inputs and energy costs from sales from manufacturing. Workers include both permanent and temporary workers. Capital is the book value of machinery and equipment. Labor cost is the total cost of wages, salaries, allowances, bonuses and other benefits for both production and non-production workers. Unit labor costs are labor costs divided value-added and capital productivity is value added divided by the book value of machinery and equipment. Labor productivity is also higher for foreign-owned firms than for fully domestic firms. Similar patterns can be observed in both Botswana and South Africa. The difference in Namibia is statistically significant and large—the median foreign-owned manufacturing firm is close to twice as productive as a similar domestic firm. Foreign-owned firms are about as productive as domestically owned firms in South Africa. Given that many of the foreign-owned firms are South African owned, this might not be surprising. 17  Figure 7: Foreign-owned enterprises than domestic enterprises are more productive in most SACU economies. Value-added per worker (2005 US Dollars) $40,000 $30,000 $20,000 $10,000 $0 Swaziland Botswana South Namiba ForeignAfrica Domestic Source: World Bank Enterprise Surveys Note: See Figure 5 for description of exchange rates and deflators and Table 4 for description of variables.. I.2 Labor Costs As with labor productivity, labor costs are higher in Namibia than elsewhere in Sub- Saharan Africa. For the median SMLE in Namibia, labor costs, which include wages, salaries, and benefits were about were about $3,500 per worker. This is significantly lower than in South Africa (about $14,000 per worker) and similar to Mauritius (about $3,700 per worker), but considerably higher than most low-income countries, where labor costs average between about $500 and $1,500 per worker. Wages are also higher than in China (about $1,300 per worker). The relatively high labor costs observed in Namibia suggest that Namibia will find it hard to compete in industries that rely heavily upon unskilled workers unless labor costs are reduced. Although wages are higher than in most other countries in Sub-Saharan Africa, they are not as high when compared to the middle-income comparator countries. Per worker labor costs are similar to costs in Mauritius and Malaysia. But there are considerably lower than in Argentina or Chile, where labor costs are almost twice as high as in Namibia. 18  Figure 8: Average labor costs are relatively low in Namibia relative to other middle-income countries in both dollar terms and as percent of value-added. Chile Mauritius Argentina Argentina Malaysia Chile Mauritius Malaysia China Lesotho South Africa South Africa Botswana Swaziland Swaziland Lesotho Botswana Namibia Namibia $0 $5,000 $10,000 $15,000 0% 20% 40% 60% labor cost per worker (2005 US$) labor cost per worker (% of value added) Source: World Bank Enterprise Surveys Note: See Figure 5 for description of exchange rates and deflators and Table 4 for description of variables For the most part, labor costs follow similar patterns in Namibia to labor productivity. For example, labor costs are higher for foreign-owned firms, exporters, firms with better educated managers, and medium and large firms. The differences, however, are often quite small, although the differences are statistically significant for exporters and firms with university educated managers. Because the retail and services surveys do not collect detailed information on productivity, it is not possible to estimate most measures of productivity for non-manufacturers. It is, however, possible to calculate wages costs for these SMLEs. Labor costs are slightly lower in retail trade (about $3200 per worker) and other services (about $2900 per worker). The differences, however, are relatively small and are statistically insignificant. Although labor cost per worker gives some indication of how expensive labor is, differences in labor costs can reflect differences in things such as worker education and worker skills. If labor costs are high because workers are highly skilled or highly educated—and this is reflected in higher productivity—firms can remain competitive despite paying high wages. In contrast, if labor costs are high because of scarcity or that unions manage to extract most of the rents, high labor costs might hurt productivity. Because wages are relatively low in Namibia while productivity is relatively high, firms could be competitive when compared to even the most productive middle income economies. Unit labor costs (labor costs as a percent of value-added) make it easier to assess the net impact of labor costs on competitiveness by taking differences in productivity into account when 19 assessing differences in labor costs. Unit labor costs are higher when higher labor costs are not fully reflected in higher productivity. When this is the case, firms will find it difficult to compete on international markets all else equal. Unit labor costs, however, are not the only factor that affects competitiveness. For example, firms that employ few workers because they have substituted capital for labor will generally have lower unit labor costs than equivalent firms that use more workers and less capital—that is their labor costs are low not because they are efficient but because they do not have many workers. Similarly, if non-labor costs are high (e.g., if indirect costs such as transportation costs are high), firms might remain uncompetitive despite having low unit labor costs. However, even with these provisos, unit labor costs are a better measure of competitiveness than per worker labor costs. Given that productivity is relatively high, while per worker labor costs are more modest, it is not surprising that unit labor costs are relatively modest in Namibia—about 30 percent. This is about the same as in Botswana and Swaziland, but is lower than in the other middle income comparator countries. It is also lower than in most low-income countries in Sub-Saharan Africa, where unit labor costs were mostly greater than 35 percent. It is important to note, however, that SMLEs in low-income countries are likely to be less capital intensive than SMLEs in middle- income countries and, therefore, all else equal will likely have higher unit labor costs. In this respect, SMLEs in Namibia appear to remain relatively competitive despite per worker labor costs being higher than elsewhere in Sub-Saharan Africa. I.3 Capital Productivity Although differences in labor productivity can reflect differences in firm efficiency due to things such as firm organization, technology or worker skills, they can also reflect differences in capital intensity. Labor productivity will generally be higher in firms that substitute capital for workers—that is, firms with more or better capital will generally be able to produce more output with fewer workers. For this reason, this chapter also looks at capital intensity, how much capital the firm has per worker, and capital productivity, how much the firm produces relative to the capital that it has. Although these measures provide some context for the previous results, it is important to note that it is more difficult to measure capital than it is to measure labor (e.g., it is relatively easy to measure wages and number of workers). Most machinery is long-lived, providing services over a long period of time. As a result it is difficult to measure its contribution to output in a single year. As capital ages, it becomes less productive (i.e., it depreciates in value) and will eventually stop providing any services (i.e., when it becomes obsolete or worn out). Although accounting rules for depreciating machinery and equipment exist, these often bear little resemblance to true rates of economic depreciation—and can vary across countries. The book value of capital (i.e., the value of capital included in company accounts) is therefore not an especially accurate measure of the value of capital—especially for small firms that do not keep detailed audited accounts. As an alternate measure of the value of capital, recent World Bank Enterprise Surveys have also asked firm managers how much it would cost to replace their equipment in its current 20 condition. Although this is a useful measure of capital—and provides an additional check on results—in practice, markets for used capital are thin meaning that firm managers might not know the true value of their capital—especially if the equipment is old or if they have not purchased similar equipment for several years. Because these questions were not asked in older surveys, this section focuses on the book value of capital—although results in this section and the section on total factor productivity are qualitatively similar for those countries where both measures are available. Although capital per worker is lower in Namibia than in South Africa, it is higher than in the other SACU economies. In particular, SMLEs are far more capital intensive than SMLEs in Swaziland and Lesotho. They are also capital intensive compared to several of the middle- income comparator countries in other regions. For example, the median SMLE in Namibia has about the same amount of capital per workers as the median SMLE in Malaysia and has slightly more than the median SMLEs in Argentina and Mauritius. The median SMLE in Chile has significantly more capital per worker than the median SMLE in Namibia. Figure 9: SMLEs in Namibia are not relatively capital intensive and capital productivity is only modest.. Chile Argentina Malaysia Mauritius Argentina Chile Mauritius Malaysia South Africa Swaziland Botswana South Africa Lesotho Botswana Swaziland Lesotho Namibia Namibia $0 $2,500 $5,000 $7,500 0% 200% 400% 600% 800% capital per worker (US$) capital productivity (capital over value added) Source: World Bank Enterprise Surveys Note: See Figure 5 for description of exchange rates and deflators and Table 4 for description of variables. Although capital per worker gives some idea about how much capital firms have, it does not provide any information on whether that capital is being used efficiently. Capital productivity, the ratio of value added to capital, does this. It is analogous to (the inverse of) unit labor costs. Capital productivity is higher in firms that produce a lot of output with a small amount to machinery and equipment. Hence, capital productivity is generally higher in firms that are labor intensive (i.e., that use labor rather than capital in their production). 21  Although labor productivity is relatively high in Namibia, even compared to other middle income economies, capital intensity is also high—SMLEs have more capital per worker than in most of the comparator countries. The net impact is that capital productivity is relatively modest. Although capital productivity is higher than in Botswana or Lesotho and similar to Chile and Malaysia, it is lower than South Africa, Swaziland, Mauritius or Argentina. The World Bank Enterprise Survey also asks SMLEs about capacity utilization, the ratio of actual output to the maximum that the SMLE could produce given the size of their workforce and the machinery and equipment that they use. SMLEs with lower capacity utilization are not using workers and capital efficiently because they could produce more than they do with the same workers and equipment. Low capacity utilization could reflect that demand is lower than forecast (i.e., that workers and equipment are idle) or could reflect problems in the investment climate. For example, unreliable power might prevent firms from operating as many shifts as it could or might mean that workers are idle for parts of the day when power is out. The information on capacity utilization is slightly different from much of the other information in the section which is mostly based upon information from the firm’s accounts. Managers are asked to estimate capacity utilization rather than measuring it in more objective terms. So, for example, if it were possible to improve output by re-designing the production process or improving organizational efficiencies, the manager might not be aware of this and so might overestimate capacity utilization. That is, capacity utilization will usually reflect the manager’s opinions rather than being calculated based upon numbers from the firm’s accounts On average, Namibia is in the middle of the pack with respect to capacity utilization. Capacity utilization is higher than in the other small SACU economies, but is lower than in the best performing economies. Capacity utilization is lower in most countries in Sub-Saharan Africa than in the middle-income comparator countries however. As a result, Namibia compares more favorably with other countries in Sub-Saharan Africa (6th of 31 countries). Figure 10. Capacity utilization is modest in Namibia 80 Capacity utilization 60 40 20 0 nd a a a a s ho le ia iu si an ric in ib hi ila ot ay rit nt C am Af w s az au ge al ts Le h N M Sw Bo M Ar ut So Source: World Bank Enterprise Surveys. Note: Capacity utilization is directly reported by enterprise managers and is defined as the amount of output that is actually produced relative to the maximum amount that could be produced given current capital stock and employment. 22 I.4 Total Factor Productivity Although the results presented in the previous subsection provide useful measures of SMLE performance and competitiveness, they have some drawbacks. The main problem is that when considered in isolation, labor and capital productivity can present incomplete, and sometimes contradictory, evidence. For example, although labor productivity is relatively high in Namibia, capital productivity is lower. Total factor productivity (TFP) avoids some of these problems by taking into account both capital and labor use at the same time. Differences in TFP between groups of SMLEs (that is between SMLEs in different countries or between exporters and non-exporters) are due to differences in things other than capital or labor. For example, differences in TFP might be due to differences in firm organization, differences in management efficiency, or differences in worker skills or education. To the extent that differences in technology are not embedded in the machinery and equipment that the firm uses, differences in total factor productivity can also reflect this. In addition to taking into account both capital and labor use, TFP has several additional advantages over the other measures of SMLE performance presented earlier: 1. Because TFP is calculated in a regression framework (see Annex 2.1), it is possible to control for multiple things when calculating it. For example, when comparing average TFP across countries it is possible to control for differences in sector composition. 2. The regression framework also makes it possible to estimate an augmented production function. This makes it possible to estimate differences between different types of firms while controlling for other factors. For example, foreign-owned firms tend to be more productive than other firms. However, if foreign-owned firms are more likely to work in some sectors than others—and there are sectoral differences in productivity—then it is difficult to know whether it is the sectoral differences or other differences between foreign and domestic firms that are causing the differences in productivity. Within a regression framework it is possible to control for multiple factors (e.g., sector, ownership, or export status) simultaneously. Some problems, however, remain. 1. As with labor productivity, labor costs per worker, capital per worker and other measures of firm performance denominated in US dollars, cross-country comparisons of TFP are vulnerable to exchange rate fluctuations. If the exchange rate is overvalued relative to its long-run equilibrium then TFP might look artificially low. Measures such as capital productivity or unit labor costs that are ratios avoid this problem. 2. As discussed earlier, capital is more difficult to measure than labor for both theoretical and practical reasons. Since TFP calculations use capital, TFP might be mismeasured when capital is mismeasured. 3. Because estimates are calculated in a regression framework, it is less straightforward than the measures in the previous sections. One issue is that estimates of TFP for groups of firms do not have natural units. For cross country comparisons, TFP is shown as % of TFP in South Africa—one of the most productive countries in Africa. For other group 23  (e.g., exporters) differences are presented in terms of a base category (e.g., non- exporters). A second issue is that estimates of TFP can depend upon the estimation method (e.g., ordinary least squares, frontier estimation, or least absolute deviations (LAD) estimation) that is used and assumptions about the production function. In practice, however, the results in this section-especially those related to cross-country rankings—do not appear to be highly sensitive to different estimation methodologies. The econometric methodology is described in greater detail in an Annex, which explains how TFP numbers are constructed and provide more detail on the econometric results in the chapter. I.5 Cross-Country Comparisons Total factor productivity is relatively high in Namibia, when compared to the mostly low income countries in Sub-Saharan Africa where World Bank Enterprise Surveys have been completed. Except for South Africa, TFP is higher than in any of the other countries in Sub- Saharan Africa (see Figure 18). Figure 11: Total factor productivity is lower than in the best performing middle-income economies TFP (as % of South Africa) 100% 75% 50% 25% 0% nd a a a o a le nd ia si ric in th an hi ib ila la ay nt so C am Af sw ai az ge al Le Th h t N M Sw Bo Ar ut So All Sectors Food Source: World Bank Enterprise Surveys. Note: See Annex 2.1 for a description of the econometric methodology. Namibia also compares favorably when compared to the other middle income countries. For example, TFP is higher in Namibia than in Botswana, Swaziland, Lesotho, and Thailand. As with labor productivity, TFP is slightly lower than in Chile and Argentina and very close to TFP in Malaysia (see Figure 11) Results are similar when considering a single sector—food processing. This sector is chosen because it accounts for a relatively large proportion of SMLEs in the sample for Namibia. 24 I.6 Differences by Firm Type. For the entire sample of SMLEs from Africa, several patterns emerge. Exporters, foreign-owned firms, firms that use the internet, firms that have bank credit, firms that have technology licensed from a foreign company and firms with university educated managers are more productive than other firms. Intuitively, most of the correlations make sense (see Econometric Annex for full discussion). For example, firms that receive bank credit might be more productive because they find it easier to invest in modern equipment or in the human capital of their management or workforce. Or they might be more productive because banks only give credit to firms that are already relatively productive. Similarly, firms that export might be more productive because exposure to foreign buyers might give them access to advice or new technologies. Or alternatively, only SMLEs that are already efficient might be able to enter export markets. For the most part, the variables of interest appear to have similar effects in Namibia as in other countries in Sub-Saharan Africa. There are however a couple of differences that appear relatively interesting. First, older firms are more productive in Namibia than younger firms. Second, as noted earlier, firms with university educated managers appear more productive than firms with less well educated managers for the entire sample. But the effect appears larger in Namibia—firms with university educated managers are 66 percent more productive in Namibia compared to only 13 percent more productive in other countries in Sub-Saharan Africa. Table 5: Comparison of Point Estimates for Whole Sample and for Namibia Point estimate for Point estimate Is point estimate for Namibia whole sample for Namibia Namibia different? Age of Establishment -1% 30% Yes Firm has bank credit 20% 34% No Firm exports 18% 13% No Firm is foreign-owned 21% 30% No Firm has technology licensed from foreign company 31% 1% No Firm uses internet 22% 41% No Firm has training program 0% 13% No Firm's workers are unionized 14% -21% No Firm's manager has university education 15% 66% Yes Firm's manager has an MBA 7% 3% No There are a couple of other interesting differences between Namibia and other countries that are not statistically significant (i.e., they could be due to sampling variation). Most interestingly, although training programs do not appear to be more beneficial overall in Africa, firms in Namibia with formal training programs are about 13 percent more productive than firms without similar programs. This might suggest that training is more effective in Namibia than in other countries in Sub-Saharan Africa. Another interesting factor is that although, on average, firms with unionized workers are more productive than non-unionized firms in other countries, this does not appear to be the case in Namibia. 25 II. INTERNATIONALIZATION This section of the chapter looks at how well manufacturing SMLEs from Namibia are integrated into international markets—how much they export and how much they use imported inputs. As well as providing information on integration in international markets, this also gives an idea about how well firms can compete in international markets—as opposed to domestic markets where they are better protected from competitors by location and other factors. II.1 Exporting Many previous studies have pointed out that international trade tends to be relatively important in small economies (Briguglio, 1995; Easterly and Kraay, 2000; Streeten, 1993). Because of their small size—and therefore their limited ability to produce everything they need, small countries will tend to import more than larger countries do. Because they need to be able to earn hard currency to do this, exports will also tend to be important unless the country has other ways of earning foreign exchange (e.g., remittances, tourism or foreign aid). This, however, does not mean that individual manufacturing enterprises will export. If the country can earn enough hard currency through exports from the primary sector or tourism— and mineral exports are certainly important in Namibia (although less important than in Botswana)—the manufacturing firms might find it difficult to reach international markets. In particular, without a large domestic market, manufacturing enterprises might find it difficult to achieve the economies of scale in domestic markets to be able to become efficient enough to break into international markets. This said, some small economies (e.g., Mauritius, Lesotho, and to a lesser extent Swaziland) have become quite successful at exporting manufactured goods. Compared to the middle income comparator countries, Namibian firms export relatively modest amounts (see Figure 12). Only about one-third of firms exported anything—lower than in any of the comparator countries except for Botswana (about one-quarter of firms). In comparison, close to two-third of firms in Mauritius, Malaysia and South Africa exported some part of their output. 26  Figure 12: Compared to manufacturing firms from other middle-income economies, relatively few firms from Namibia export—and those that do, do not export very much. Mauritius Mauritius Malaysia Malaysia Argentina Chile Chile Argentina South Africa Lesotho Lesotho Swaziland Swaziland South Africa Botswana Botswana Namibia Namibia 0% 20% 40% 60% 80% 0 10 20 30 40 50 % of firms exporting exports (% of sales) Source: World Bank Enterprise Surveys Note: Data varies between 2002-2005, depending on survey period for each country. For firms that do export, they usually export only a relatively modest part of their output. The average firm exported only about 14 percent of their output—more than in Botswana (9 percent) and similar to South Africa (14 percent) and Argentina (13) percent, but far lower than in many other countries (e.g., 44 percent in Lesotho and 39 percent in Mauritius). Only about 12 percent of Namibian firms—or about one-third of exporters—exported more than half of their output. Given the small size of the Namibian domestic economy, this is relatively low. In comparison, nearly one-quarter of Swazi firms exported more than half their output—about two thirds of Swazi exporters. Although Namibian firms are less likely to export than firms in most of the middle- income comparator countries, they generally perform better when compared to other countries in Sub-Saharan Africa (see Figure 13). Firms were more likely to export in Namibia than in 20 of 31 countries where World Bank Enterprise Surveys have been completed. Rankings based upon the average amount that firms export are similar—Namibia ranks 10th. Previous studies have noted that manufacturing firms in Sub-Saharan Africa are less likely to export than firms in other regions (Clarke, 2005). 27  Figure 13: But Namibia does compares more favorably with other countries in Sub-Saharan Africa with respect to exporting. 100% % of firms exporting 75% 50% 25% 0% Kenya Rwanda Botswana Mali Ethiopia Angola Mozambique Madagascar Niger Mauritius Zambia Source: World Bank Enterprise Surveys Note: Data varies between 2002-2005, depending on survey period for each country. Several low-income African countries with investment climate date on exports are excluded from the graph for presentational purposes. Namibia’s relative position is not affected significantly by these omissions. Like firms in Botswana and Swaziland, firms that do export mostly export to other countries in SACU. For the average exporter, about 46 percent of exports went to other SACU economies. This was slightly lower than in Botswana (48 percent) and Swaziland (54 percent). In all three countries, South Africa was the main export destination in SACU. About one-third of exporters said that South Africa was their most important export market. About 6 percent said that Botswana was their most important export market and no firms said that Swaziland or Lesotho was their most important export market. Developed countries, however, were also important export markets for Namibian firms. For the average exporter, about 40 percent of exports went to developed economies—mostly to Europe. This was far higher than in Botswana (23 percent) or Swaziland (24 percent). Namibian firms tended to export less to other SADC economies however, with Angola being the most important market in this respect. Few firms reported exporting to developing countries not in SADC (about 3 percent of exports). Foreign-owned firms were more likely to export (58 percent of foreign-owned firms) and exported more on average (35 percent of their output) than domestic firms (only 27 percent of firms and only 8 percent of output). This is broadly consistent with the previous results that suggest that foreign-owned firms are more productive and more competitive in terms of unit labor costs (see Table 4). 28 II.2 Importing Although Namibian firms do not export much of their output when compared with firms in the middle-income comparator countries, they are tied into international markets in other ways. The average Namibian manufacturing enterprises imports about 60 percent of its inputs. This is far higher than firms in South Africa (24 percent) or the other middle-income countries from other regions. It is, however, closer to the levels observed in Swaziland (45 percent), Botswana (56 percent) and Lesotho (73 percent). One reason for the high level of imports is the small size of the Namibian economy. Firms from other small economies, such as The Gambia, Cape Verde, and Mauritius, also import large amounts of their inputs. Given the remoteness of Cape Verde and Mauritius from other countries, and the proximity of Namibia to South Africa, it is might be surprising that firms in Namibia do not import more of their inputs. Figure 14: Like firms in most small economies, firms in Namibia depend heavily on imported inputs. 80 imports as % of sales 60 40 20 0 Bo nd Ar sia Le a e ca a s le o ia a iu an rd in th bi ib hi ri ila ay rit nt am so Ve C am Af w az au ge al ts G h e N M Sw M ut ap So C Source: World Bank Enterprise Surveys. Note: All values are means for enterprises with available data. Data comes from between 2002 and 2005 for sample countries, depending on the survey date. On average, foreign-owned firms in Namibia import more of their inputs (about 70 percent) than domestic firms (about 56 percent). Given that some of these firms will have parent companies in other countries, this is not surprising. Exporters are also more likely to depend upon imported inputs than non-exporters (67 percent compared to 57 percent). In some ways this might be more surprising since importing and the re-exporting would seem to be more expensive than using domestic products (i.e., it seems like Namibian firms would be more likely to have a comparative advantage in areas where they could rely upon domestic inputs). On the other hand, exporters might be more likely to be tied in international supply chains or might need to purchase inputs that meet international standards than domestic firms. The differences between foreign and domestic and exporters and non-exporters, although relatively large, are not statistically significant suggesting caution when interpreting results. 29 III. COMPETITION It is difficult to measure how much competition firms face. The World Bank Enterprise Survey, therefore, asks several questions that approach the issue in different ways. First, the survey asks about market share in national markets. Market share is defined as the establishment’s sales for its main product line divided by total sales of all firms in these product lines in local markets. In general, competition is lower when average market share is higher. A second question, which was added only in the more recent round of investment climate assessments, asks about the number of competitors in the firms main market for its main product line. This question is not available for most of the comparator countries, but is available for Botswana, Namibia and Swaziland and a set of middle-income countries in Latin America. Figure 15: As in other small countries and other countries in the region, firms in Namibia face little competition in local markets Mauritius2005 Mexico Panama Malaysia Bolivia China2003 Argentina Thailand2004 Uruguay Colombia Peru Lesotho Paraguay Swaziland Swaziland SouthAfrica2003 Namibia Namibia Botswana Botswana 0% 20% 40% 60% % of firms 0 20 40 60 Share of Local market One or fewer Five or fewer Source: World Bank Enterprise Surveys Note: Data varies between 2002-2005, depending on survey period for each country. Market share is the average reported market share in local markets. There are problems with both questions. First, it is generally difficult to define a firm’s market very precisely (e.g., does it include just a very local area around the firm, the entire metropolitan area where the firm is located, one or two large metropolitan areas or the entire country). It is also difficult to define the firm’s main product line very precisely. For example, does a firm selling beer compete only against other beer makers, against firms that sell all types of alcoholic beverages, against all firms that make beverages of any type, or even against all leisure goods. These problems affect both measures of competition. Second, especially by themselves, neither measures competition perfectly. If three or four firms divide a market between them (e.g., based upon geography), they might face relatively low levels of competition while only having a small market share. Similarly, if a large domestic firm competes with several import brands that all have tiny market shares and that face high barriers to entry, it may 30 face a large number of competitors but only a modest level of competition. Looking at the two measures simultaneously reduces some of these concerns, but does not eliminate them. Compared to its immediate neighbors in SACU, Namibian firms appear to face relatively high levels of competition (see Figure 15). The average firm reports having a market share of 23 percent, similar to South Africa (25 percent), but lower than in Botswana or Swaziland. Similarly, firms were less likely to say that they had one or fewer competitors than firms in Botswana or Swaziland, but more likely to say that they had five or fewer competitors. Namibia does less well when compared to other middle-income economies. On average, firms in Thailand and China reported lower market shares than firms in Namibia, although firms in Malaysia reported higher market shares. Moreover, firms in Namibia were more likely to say that faced a single competitor or no competitors than firms in six of eight middle income economies in Latin America and that they faced five or fewer competitors than firms in seven of eight middle income economies in Latin America. Thus, although Namibia compares favorable with other middle income countries in the region, it compares less well with middle income countries in other regions. Do firms in Namibia face little competition because they are in a small country? Studies of small countries often stress that there is less scope for competition in small countries. Firms in some small countries, including Cape Verde and Mauritius, also report that they face little competition (see Figure 15). These countries, however, are not just small—they are also remote islands. In contrast, firms in another small country in Africa, the Gambia, which is also located next to a larger country report that they face a relatively high amount of competition. Even if the Namibian market can only support a few domestic producers, the larger SACU market, which includes South Africa, should be able to support a large number of firms in most industries. In this respect, the low level of competition might simply reflect that competition is modest throughout the region, even in the relatively large economy of South Africa. Previous studies have suggested that competition is relatively modest even in South Africa (Fedderke and others, 2003; Fedderke and Szalontai, 2005). Consistent with this, competition does not appear to be particularly high even in South Africa, where it appears broadly similar to competition in Namibia. IV. PROFITABILITY At the enterprise-level, profitability is associated with better firm performance. Firms that are more productive and that have lower overhead costs will be more profitable than other firms because they manage to produce more output at lower cost. But at an industry or country level, high profitability could also reflect a lack of competition—especially in countries like Botswana where few firms sell mostly in domestic markets. When markets are less competitive, firms will be able to earn higher profits than in more competitive markets where profits will typically be competed away. Although the World Bank Enterprise Survey does not collect any data on taxation, meaning that profits can only be calculated before taxation, it is possible to calculate several before-tax measures of profitability in a consistent way for the most recent set of World Bank 31 Enterprise Surveys in Africa and Latin America. Unfortunately, similar numbers can not be calculated for any of the earlier surveys—including for the comparator countries. Because, as discussed earlier, capital is difficult to measure accurately, this section focuses more heavily on return on sales (profits over sales) than on return on assets (profits over capital). Since information on depreciation is not collected in the Latin American surveys, the comparisons with Latin America exclude depreciation while the comparisons with other countries in Sub-Saharan Africa include them (although comparisons with other countries in Sub-Saharan Africa would be qualitatively similar omitting depreciation). Excluding depreciation from the calculations for Africa would raise measured return on assets slightly. The median return in the country in Latin America with the lowest return (Peru) would, however, still be higher than the median return in 10 of 18 countries in Sub-Saharan Africa. Firms in Namibia appear to be relatively profitable when compared to firms in either Latin America or elsewhere in Africa. The median firm in Namibia reported that profits were equal to about 28 percent of sales—higher than in any of the other 17 countries for which comparable data were available. About 20 percent of firms reported than return on sales was negative before taxes (i.e., that they lost money), higher than in several countries where median losses were lower (e.g., Tanzania or Rwanda). Results are similar when firms in Namibia are compared to firms in middle income countries in Latin America. The median firm in Namibia reports that returns of sales excluding depreciation are higher than in six of the eight countries in Latin America where comparable data are available. 32  Figure 16: Profitability (return on sales) is relatively high in Namibia, compared to either other countries in Sub-Saharan Africa or middle income countries in Latin America. Mauritania Gambia, The Peru Guinea-Bissau Cape Verde Uruguay Niger Argentina Rwanda Uganda Colombia Burkina Faso Botswana Congo, DR Botswana Paraguay Malawi Bolivia Angola Cameroon Namibia Guinea-Conakry Panama Burundi Tanzania Swaziland Namibia Mexico Swaziland 0% 10% 20% 30% 40% 0% 10% 20% 30% 40% 50% Return on sales (incl depreciation) Return on sales (excl. depreciation) Source: World Bank Enterprise Surveys Note: Data varies between 2002-2005, depending on survey period for each country. Because firms in Namibia are relatively capital intensive, it is not surprising that when ranked by median return on assets, firms in Namibia look slightly less profitable compared to countries in Sub-Saharan Africa (5th of 18th) or Latin America (lower than 4 countries) than based upon return on sales. However, as noted earlier, measuring capital is relatively difficult, so these results should probably be treated cautiously. Firms that export report lower return on sales than firms that do not (33 percent compared to 26 percent). Since better performing firms are more likely to export than worse performing firms, this might seem surprising. One plausible explanation is that there are relatively high fixed costs associated with operating in export markets (i.e., although returns in domestic and export markets are similar, the high costs of operating in export markets for firms from Namibia are higher). Another is that the low level of competition observed in domestic markets gives some firms operating in domestic markets the possibility of earning higher rates of return. Otherwise, the results for profitability are broadly similar to the results for productivity. For example, return on sales are higher for the median foreign-owned firm than for the median domestic firms (30 percent compared to 27 percent) and for firms with university educated managers than for firms with less educated manager (34 percent compared to 20 percent). Although the first difference is not statistically significant, the second is. The higher return for firms with managers with university education suggest that the additional cost of hiring well- educated managers is not as high as the potential benefits. However, if profits are an important 33 part of manager compensation, especially where the manager is also the owner, this might overestimate the importance of the effect on returns. Is the high level of profitability in Namibia linked to the low level of competition in the region? At the firm level, there is little evidence of this. Firms in Namibia that report high market share do not appear to be more profitable than firms that report lower market share. Similarly, firms that report facing no competitors or only a single competitor also do not appear to be more profitable. There is, however, a fairly strong correlation at the country level. For the firms in Latin America and Botswana, Namibia, and Swaziland, on average firms are more profitable in countries where they are more likely to report fewer competitors (correlation with profitability of 0.77 for one or less competitors and 0.69 for five or less competitors). 34 ECONOMETRIC ANNEX: TOTAL FACTOR PRODUCTIVITY Although the measures of firm productivity such as labor and capital productivity provide useful information on firm performance, they can be misleading when considered in isolation. To get an overall assessment of productivity, it is necessary to take both capital and labor use into account. This can be done by calculating total factor productivity (TFP). Differences in TFP are differences in output that cannot be explained by differences in the use of labor, capital and other intermediate inputs. Differences in TFP can be due to the quality of workers, the quality of management, the technology used (as long as it isn’t embodied in capital), or firm organization. Firms for which TFP is higher are more efficient than other firms because they produce more with less capital and workers. Methodology Mechanically, TFP is calculated by estimating a Cobb-Douglas production function, using data for enterprises from twelve different manufacturing sub-sectors, and looking at the residuals and coefficients on various variables in an augmented production function. To allow us to compare TFP between Namibia and the low- and middle-income comparator countries, we pool the observations for many countries into a single regression including all countries in Sub- Saharan Africa where data are available and the middle-income comparator countries used in this study. As a robustness check, the analysis includes regressions only including countries in Africa and tests to check that results for Namibia are similar to results for the larger sample. In addition to making it possible to compare TFP in Botswana with TFP in the comparator countries, this also gives us a sufficiently large sample of enterprises to compare total factor productivity across firms of different types, making it possible to assess the impact of various firm characteristics on TFP. The production function is estimated using a stochastic frontier approach. 2 As a robustness check, we also estimate the production function using a Least Absolute Deviations (LAD) estimator. This is used rather than Ordinary Least Squares (OLS), because it is more robust to outliers. 3 The dependent variable is the natural log of value added and all regressions control for the enterprises’ use of capital and workers. 4 All models include country dummies to pick up differences in TFP between the different countries and also to reduce problems associated with exchange rates. The regressions also include a full set of sector dummies. These dummies are 2 See Kumbhakar and Lovell (2000) for a description of stochastic frontier models. The model estimated assumes that technical inefficiency component is distributed with an exponential distribution, while the white noise component is distributed with a normal distribution. Firms that are outliers for value added per worker and capita per workers (i.e., enterprises in the top and bottom 5 percent of firms) are dropped in this part of the analysis so that results will not be driven by outliers and to ensure convergence. 3 Due to concerns about outliers, LAD estimators are often used when estimating production functions. See, for example, Greene (2000, pp. 449-450). 4 Following Caves (1990), value-added rather than sales is used as the dependent variable. 35 interacted with the measures of labor and capital to allow different technologies in different sectors—that is labor and capital intensities are not assumed to the be the same across sectors. The augmented production function is: log(VA ijk ) = ∑ μ k + ∑ (α j + β j log(Capitalijk ) + γ j log(workers ijk )) + ∂ Firm Characteristics ijk + ε ijk + ν ijk j where VA is value-added in firm i in sector j in country k. Labor and capital are the number of workers and the book value of machinery and equipment. The coefficients on labor and capital, β and γ, are assumed to vary between sectors. Sector and country dummies, α and μ, are included to allow for systematic differences in productivity across countries and sectors. In some specifications, a series of enterprise level controls (such as dummies indicating whether the firm exports and the log of the firm’s age) are included. Finally, two error terms, a one sided term assumed to have a half normal distribution, ν, representing technical efficiency and a two- sided, ε, a two-sided normally-distributed error term representing temporary shocks to productivity and measurement error, is also included. The model is estimated using maximum likelihood estimation. In the LAD regressions, a single error term is assumed (ε). The LAD model does not require strong distributional assumptions about the error terms. Results Sector specific production technologies. A joint test of the significance of the interaction terms rejects the null hypothesis that the coefficients on labor and capital are identical across sectors at a one-percent level or higher in both the frontier regressions and the LAD regressions. This strongly suggests that these terms need to be included in the estimation. This implies that capital and labor intensities are significantly different across sectors and that it is inappropriate to pool enterprises into a single model without controlling for this. Differences between countries. After controlling for differences in productivity due to differences in capital use, firm size, the use of different factors of production, and sector of operations (i.e., using results from column 1 of Table 8), enterprises in Namibia remain relatively productive (see Figure 17). Although the median SMLE in South Africa Chile and Argentina remain more productive than the median SMLE in Namibia. TFP, like value-added per worker remains very close to TFP in Namibia and is higher than in the other comparator countries. Results are similar when considering a single sector—food processing. This sector is chosen because it accounts for a relatively large proportion of firms in the sample for the three small SACU economies. 36  Figure 17: Total factor productivity is lower than in the best performing middle-income economies TFP (as % of South Africa) 100% 75% 50% 25% 0% nd a a Sw na a am o le nd ia n si in ric th oo hi ib ila a la ay nt so C am Af sw er ai az ge al Le Th h t N M Bo Ar ut C So All Sectors Food Source: World Bank Enterprise Surveys. Note: See Annex 2.1 for a description of the econometric methodology. It is important to keep an important caveat in mind when comparing total factor productivity across countries. Although including country dummies is useful because doing so means that the coefficients on other variables will not be affected by assumptions about exchange rates (i.e., the dummies will control for exchange rates in cross-country regressions where monetary variables are in logs), exchange rates can make the coefficients on the country dummies difficult to interpret. That is, the coefficients on the country dummies will depend upon the exchange rate as well as on productivity differences. If a country’s exchange rate is overvalued relative to its long-run equilibrium value, the coefficient on that country’s dummy will appear artificially large (as will value added per worker). Economies of Scale. If large enterprises were consistently more productive than small enterprises, the sum of the coefficients on labor and capital would be greater than one. If this were the case, total production would more than double if the number of workers and amount of capital were doubled. In practice, the sum of the two coefficients is generally close to one for most of the sectors. For the base sector, food and beverage, the sum of the coefficients is 1.13 suggesting modest economic of scale. That is, large firms appear to be more productive than smaller firms. 37  Table 6: Test for Constant Returns to Scale Sum P-value Food and Beverage 1.133 0 Furniture 1.162 0 Metal 1.132 0 Other Manufacturing 1.163 0.1148 Table 6 presents sums of the coefficients for each sector and the test of the null hypothesis that they sum to one (model from column 1 of Table 8). These results suggest in most sectors modest economies of scale. Larger enterprises appear to be consistently more productive than smaller firms. In the context of this study, this suggests that small firms are disadvantaged relative to larger firms. Given the small size of the domestic market in Namibia this suggests that firms will find it harder to compete on international markets unless they can expand their operations sufficiently to start benefiting from these economies of scales It is important to note, however, that the estimates of productivity by country take size into account. That is, firms in Namibia do not appear to be less productive than firms in South Africa or Chile because they are small. Other enterprise characteristics. In addition to capital, labor, the sector dummies, the interaction terms, and the country dummies, the regressions also include a series of variables to control for other enterprise characteristics. These include variables indicating the age of the firm, whether the firm exports, whether the firm is foreign-owned, whether the firm has bank credit (a loan, line of credit or overdraft) whether the firm licenses technology from abroad, whether the firm’s workers belong to a union, and the education of the manager. Results are similar for the frontier and LAD regressions, so only results from the frontier regressions are presented. When the results from the LAD regressions differ significantly from the results from the frontier analysis, this is noted in the text. Some of these variables—whether the firm exports, is foreign-owned, or uses the internet—are available for most of the countries in the sample. Others such as whether the firm has bank credit, licenses technology from abroad, and manager education are available only for a sub-sample of countries. These variables are included in groups to allow for the larger samples for the variables with greater coverage. Four variables, firm age, a dummy variable indicating that the firm exports, a dummy variable indicating that the firm is foreign-owned, and a dummy variable indicating that the firm uses the Internet are available for most of the countries in the sample. These variables are included first. Several other variables are only available a sub-sample of countries. These include a dummy variable indicating the firm has bank credit, a dummy variable indicating that the firm licenses technology from abroad, and a dummy variable indicating that the firm’s workers are unionized. These variables are included next. Finally, variables indicating whether the firm’s manager has an MBA and whether the manager has a university education are available only for a smaller subset of African countries. These variables are included last. 38  Exporters are about 16 to 27 percent more productive than similar enterprises that do not export. The coefficients are statistically significant in both the frontier analysis and the LAD model, and remain statistically significant as additional variables are added. This is consistent with other studies that have found similar results for different sets of countries.5 Economists have suggested two possible explanations for the correlation. One is that exporting might result in productivity improvements for the firms that are doing it (the ‘learning-by-exporting’ hypothesis). The discipline of competing in international markets might encourage enterprises to improve their productivity or might expose them to foreign technologies or modes of production. The other explanation is that since firms have to be efficient to compete on international market, only firms that are already efficient are able to export (the ‘self-selectivity’ hypothesis). Although inefficient firms might be protected from international competition in domestic markets by natural barriers (e.g., high transportation costs) and policy barriers (e.g., government tariffs and quotas or inefficient ports or customs administration), they are unable to enter international markets. It is important to note that the two hypotheses are not mutually exclusive. Even if efficient firms are more likely to start exporting, this does not rule out the possibility that exporting will help them increase their productivity further. 6 Due to the cross-sectional nature of the data included in this study, we are unable to distinguish between the two competing hypotheses in this analysis. Foreign-owned enterprises are also more productive, on average, than similar domestic enterprises. The point estimates for the parameters in columns 2 through 5 of Table 8 indicate that majority foreign-owned enterprises are about 17 to 27 percent more productive than similar domestic enterprises. The coefficients are statistically significant in all model specifications. This is also consistent with previous results that have often found that foreign-owned enterprises are more productive than domestic enterprises in developing countries (Saggi, 2002). Previous studies have found that firms that use the internet to communicate with clients and suppliers are more productive than firms that do not. 7 Firms that use the internet to communicate with other enterprises appear more productive than other firms. In reality, it seems unlikely that internet use alone results in higher productivity. That is, even in the United States, a country where internet use is far more common than in the low- and middle-income countries in this study, most studies have found only modest gains related to internet use. 8 Rather than reflecting actual internet use, it seems likely that this reflects difference in the intensity of 5 The large literature on this topic is summarized in Tybout (2003) and Keller (2003). 6 The evidence appears to support both hypotheses. For example, several econometric studies that have looked at whether enterprises improve their productivity before or after they start exporting have found that productivity improvements precede exporting, providing support for the self-selectivity hypothesis. See for example, Clerides et al. (1998), Bernard and Jensen (1999), Liu et al. (1999) and Aw et al. (2000). However, case studies often support the ‘learning by exporting’ hypothesis. Studies of exporters in Korea and Taiwan found that export buyers were an important source for new technologies, which they provided in various forms including complete blueprints, information about manufacturing processes and quality control methods, technical advice and on-site plant inspections, and training for technical and production staff (Westphal, 2002) 7 Quiang, Clarke, and Halewood (2006) find similar results for a larger sample of developing countries using World Bank Enterprise Surveys 8 For example, Oliner and Sichel (2000) estimate that e-commerce has increased multifactor productivity growth in the US by considerably less than 0.1 percent per year. 39 technology use rather than internet use. Enterprises that use technology more intensively are between 23 and 30 percent more productive than similar firms that do not. Since the econometric estimates controls for capital use in the main regression, this does not simply reflect that firms that are more technologically advanced are also more capital intensive. Another variable to proxy for technology use is also correlated with total factor productivity. Firms that license technology from foreign companies are more productive than firms that do not. The point estimates of the coefficient suggest that they are between 20 and 29 percent more efficient than other firms—even after controlling for technology use by including the internet dummy. Together these results suggest that technology use is strongly correlated with productivity. Enterprises with bank credit are more productive than enterprises without credit—based upon the point estimates of the coefficients in Table 8 they are between 14 and 20 percent more productive than firms with credit. Although this might imply that firms with access to credit are able to use that credit to improve firm performance, it is possible that causation runs in the opposite direction. That is, banks might only be willing to lend to enterprises that are already productive. As with exporting, the two hypotheses are not mutually exclusive. Even if banks are only willing to lend to the most productive enterprises, these enterprises might still benefit from bank credit. Enterprises with better educated managers are more productive than other enterprises. An enterprise with a manager with a university education is about 21 percent more productive than a similar enterprise where the manager has only a primary or secondary education. Firms with managers with MBAs, in contrast, do not appear to be significantly more productive than firms with university educated managers without Previous studies of firm performance in Africa have stressed the importance of formal training on firm performance. 9 The analysis based upon the samples in this study, however, does not show that training has a large impact on total factor productivity. The coefficient on a dummy variable indicating that the firm has a training program is positive in all specifications, but is never statistically significant in the frontier analysis and is only statistically significant in one regression in the LAD estimation. Replacing the dummy with a variable indicating the number of workers trained gives similar results—the coefficient is positive but is not statistically significant in most specifications. Are results the same in Africa? The regressions included countries from Sub-Saharan Africa, as well as countries from other regions (e.g., the middle income comparator countries). A natural question, therefore, is whether results hold in Africa specifically or whether they hold only in other regions. As noted earlier, to test this, the main regression is repeated including only countries from Africa (see columns 4 and 5 in Table 8). The results for the African countries are mostly similar to the pooled results—exporters, foreign-owned firms, firms that use technology more intensively, and firms with access to credit are more productive. 9 See, for example, Regional Program on Enterprise Development (2005b; 2005c) 40  The main differences are that older firms do not appear to be any more productive than younger firms in Africa and firms with unionized workers appear more productive in Africa than other firms. Once again, the direction of causation is not clear—this could be that unionization improves firm performance of this could be because firms that are more productive, and hence more profitable, are more likely to unionize Are results the same in Namibia? Finally, an additional question is whether firms from Namibia specifically appear like firms from the other countries in the sample. To test whether this is the case, a set of interaction terms are added to the regression in column 5. If the coefficients on these interaction terms were statistically significant, this would suggest that these variables affect firms in Swaziland differently than in other countries. For the most part, the variables of interest appear to have similar effects in Namibia as in other countries in Sub-Saharan Africa. There are however a couple of differences that appear relatively interesting. First, older firms are more productive in Namibia than younger firms. Second, as noted earlier, firms with university educated managers appear more productive than firms with less well educated managers for the entire sample. But the effect appears larger in Namibia—firms with university educated managers are 66 percent more productive in Namibia compared to only 13 percent more productive in other countries in Sub-Saharan Africa. Table 7: Comparison of Point Estimates for Whole Sample and for Namibia Point estimate for Point estimate Is point estimate for Namibia whole sample for Namibia Namibia different? Age of Establishment -1% 30% Yes Firm has bank credit 20% 34% No Firm exports 18% 13% No Firm is foreign-owned 21% 30% No Firm has technology licensed from foreign company 31% 1% No Firm uses internet 22% 41% No Firm has training program 0% 13% No Firm's workers are unionized 14% -21% No Firm's manager has university education 15% 66% Yes Firm's manager has an MBA 7% 3% No There are a couple of other interesting differences between Namibia and other countries that are not statistically significant (i.e., they could be due to sampling variation). Most interestingly, although training programs do not appear to be more beneficial overall in Africa, firms in Namibia with formal training programs are about 13 percent more productive than firms without similar programs. This might suggest that training is more effective in Namibia than in other countries in Sub-Saharan Africa. However, it is important to note that this is not statistically significant. 41  Table 8: Determinants of Firm-Level Productivity for Manufacturing Enterprises Frontier Regression Value Added (natural log) Observations 10497 8910 3991 1851 1453 Sample All All All Africa-only Africa-only Production Function Capital (book value) 0.275*** 0.250*** 0.212*** 0.245*** 0.230*** (natural log) (21.57) (19.69) (11.78) (9.83) (8.39) Workers 0.858*** 0.801*** 0.846*** 0.776*** 0.775*** (natural log) (44.41) (41.11) (28.49) (17.96) (15.35) Firm Characteristics Age of Establishment 0.079*** 0.054*** 0.010 0.005 (natural log of years) (7.30) (3.67) (0.45) (0.19) Firm exports 0.240*** 0.151*** 0.165*** 0.171** (dummy) (10.89) (4.44) (3.11) (2.46) Firm is foreign-owned 0.240*** 0.154*** 0.191*** 0.197*** (dummy) (7.76) (3.44) (3.42) (2.96) Firm uses internet 0.236*** 0.262*** 0.228*** 0.205*** (dummy) (10.03) (7.20) (4.47) (3.62) Firm has bank credit 0.148*** 0.127*** 0.185*** (dummy) (4.48) (2.68) (3.16) Firm has technology licensed from foreign company 0.179*** 0.201*** 0.251*** (dummy) (4.11) (3.25) (3.08) Firm has training program 0.026 0.025 -0.001 (dummy) (0.91) (0.58) (0.02) Firm's workers are unionized 0.051 0.123** 0.117* (dummy) (1.33) (2.34) (1.88) Firm's manager has university education 0.159*** (dummy) (2.91) Firm's manager has an MBA 0.057 (dummy) (0.67) *** Significant at 1 percent level ** Significant at 5 percent level * Significant at 10 percent level. t-statistics in parentheses. Dependent variable is log of value-added. a Coefficients are reported for firms in the food sector. In addition, sector dummies and sector specific production functions are included for 12 additional sectors (Textiles; Construction materials; Garments and Leather; Food and Beverages; Metals and Machinery; Electronics; Chemicals and Pharmaceuticals; Wood and Furniture; Plastics; Paper, Printing and Publishing; other Manufacturing; and autos and auto parts). The dummies are interacted with capital and labor to allow sector-specific production technologies. Coefficients on sector dummies and interaction terms are omitted for presentational reasons. 42  TFP (as % of South Africa) 0% 20% 40% 60% 80% 100% South Africa Namibia Swaziland Botswana Source: World Bank Enterprise Survey Angola Cameroon Cape Verde Burkina Faso Tanzania Bolivia Benin Indonesia All Guinea-Conakry 43 Niger Food Zambia Mauritania Congo, DR Malawi Lesotho Uganda Rwanda Guinea-Bissau Burundi Figure 18: Although lower than in South Africa, TFP is high in Namibia relative to other countries in Sub-Saharan Africa Eritrea Gambia Ethiopia  CHAPTER 3: PERCEPTIONS ABOUT THE INVESTMENT CLIMATE IN NAMIBIA In addition to collecting information on firm productivity, the Enterprise Survey for Namibia also collects information on the investment climate—including on topics such as crime, taxation, worker education and skills, corruption, finance, regulation, and infrastructure. Two types of information are collected: (i) subjective information on what managers see as the major obstacles that their firm faces; and (ii) objective indicators such as production lost due to power outages, whether the firm has a loan or overdraft facility, and amount of time managers spend dealing with government regulations. The report uses both types of data—and supplementary information from other sources—to assess constraints to enterprise operations and growth in Namibia and compare these with constraints in the comparator countries. I. Perceptions about Constraints to Enterprise Operations and Growth The perceptions of managers about the constraints that they face are a good starting place for an analysis of the investment climate. An enterprise manager probably knows more about the immediate problems facing his or her business than government officials, academic researchers, or other outside experts. Because of this, it makes sense to take their concerns about the investment climate seriously. Perception-based measures do, however, suffer from several problems. First, it is difficult aggregate perception-based data across firms. Constraints will affect different firms to different degrees. For example, firms not involved in international trade will be far less affected by problems at the ports than firms that do trade across borders. Firms that export might see poor ports as a very serious problem, while firms that do not might see it as a minor inconvenience. In contrast, all firms might agree that corruption is a fairly serious problem. If you rely upon measures that count the number of enterprises that said each was a serious problem, the results might be quite different from a measure that take into account the intensity of the problem. For this reason, this section percent two measures of perceptions—the share of firms that say each issue is a serious problem and the share of enterprises that say it is the biggest obstacle that they face. Second, although managers may be aware of a problem, they might not be aware of the underlying causes. For example, managers might know that it is difficult get bank loans to finance new investment, but might not know the underlying reasons for this (e.g., lack of competition in the banking sector, government debt issues crowding out private investment or problems with land registration that prevent firms from using land as collateral). As a result, additional information is needed to assess how to release any constraint. Third, enterprise managers’ interests might not always be consistent with society’s interests. Most managers would like subsidized credit or to be charged less for power or water than the amount it costs to produce them if they believed that the cost of providing these services would be borne by someone else. Similarly, most managers would be happy to face less competition even if the cost to society outweighed the benefits to their firm.  Fourth, the perceptions of managers of existing enterprises might not reflect all obstacles to private sector growth. Managers of existing enterprises that have already completed registration procedures might not be concerned about entry costs even if they remain high. Similarly, they might rate some issues as lesser problems because they have structured their businesses in ways to minimize those costs. For example, if transportation costs are especially high in some areas, existing firms might only be located close to transportation facilities. Finally, if investment climate constraints are particularly binding, then there might be very few firms that rely heavily upon that area of the investment climate. 10 For example, if the ports and custom facilities are particularly poor, very few firms might operate in export-oriented industries. Any of these factors could affect the ranking of constraints. Finally, although recent work suggests that perception-based measures line up reasonably well with objective macroeconomic indicators, it remains difficult to compare perceptions across countries. 11 That is, cultural differences or persistent differences in expectations about how the investment climate should look might affect perceptions. Finally, expectations about freedom of speech might affect whether managers are willing to complain to outside interviewers about the investment climate more than it affects their willingness to answer objective questions. 12 Because of these concerns, although the ICA uses perception data as a starting point for the analysis, it is supplemented with objective measures of the investment climate taken from the ICS and other sources when appropriate. The report also avoids making cross-country comparisons based only on perception-based data (e.g., comparing the number of firms that complain about an issue between countries) when other information is available. This chapter discusses the perception-based measures, while later chapters discuss specific issues in greater detail, supplementing this information with objective data. The additional objective data allow us to benchmark the investment climate in Namibia against the investment climates in other countries. I. MAIN PERCEIVED CONSTRAINTS The Enterprise Survey asks firms to rank each of 17 areas of the investment climate are a constraint on the current operations of their business. They respond by rating each on a five- point scale between ‘no obstacle’ and a ‘very severe obstacle’. Figure 19 shows the percent of each type of firm that rated each area as a ‘major’ or ‘very severe obstacle’. Among the SMLEs (i.e., firms with more than five employees), firms were most likely to say that crime, tax rates, worker skills, corruption, finance and macroeconomic instability were serious obstacles to their firm’s operations. Very few firms—about 1 in 10 or even fewer—rated 10 Hausmann and Velasco (2005) illustrates this point with an analogy to camel and hippos. He notes that the few animals that you find in the Sahara will be camels, which have adapted to life in the desert, rather than hippos, which depend heavily upon water. 11 See, for example, Gelb et al (2006) for work using data from Africa or Hellman et al (Hellman and others, 1999)for work using data from Eastern Europe and Central Asia 12 Clarke et al (2006) show that firms appear to complain more about access to finance in countries that are more free politically than in other countries after controlling for other country and firm characteristics. 45 any other area of the investment climate as a serious obstacle. In particular, fewer than 1 in 10 firms rated any aspect of either regulation or infrastructure as a serious concern. Figure 19: There are large difference in perceptions between large and small enterprises 60% % of firms saying issue is serious problem 40% 20% 0% es e e rts n eg i o n om ility ty n ns d ns s s y lls g rim nc io or ilit on io an in ci at ki r R atio io ou pt t at s tit b ab tri na ta i L rs R at at en C ta ru r pe C ec or t o st ul ul Fi c is s x ke c or t i Ta In sp In El n eg in Li ss or C Bu mu m an ro al ss R ce C W Ad ic ac m Tr ne e Ac al bo lit ad co M rm x si Po La Ta Tr le fo Te In Source: World Bank Enterprise Surveys. One notable aspect of this is that firms do not seem to consistently be concerned about any area of the investment climate. In most countries where Enterprise Surveys have been completed, more than 60 percent of firms identify at least one area of the investment climate as a serious obstacle. 13 In Namibia, only 28 percent of firms identified crime, the most serious concern, as a serious obstacle. Namibia is similar to South Africa in this respect—only 35 percent of firms rated the top constraint in South Africa as a serious problem and only four areas were rated as a major constraint by more than 20 percent of firms (compared to three in Namibia). 14 One possible reason for this is that firms have relatively few complaints about the investment climate in Namibia. However, as noted above, it is difficult to draw any strong 13 For example, over 90 percent of firms in Uganda reported that electricity was a problem (Regional Program on Enterprise Development, 2007c) and over 70 percent of firms in Kenya reported that corruption was a problem (Regional Program on Enterprise Development, 2004) 14 Regional Program on Enterprise Development (2006). 46 conclusions from this given the difficulty of making cross-country comparisons with perceptions data. For example, differences in the number of complaints might reflects differing cultural norms. As in South Africa, microenterprises in Namibia have a somewhat different view of the investment climate than SMLE. 15 Microenterprise managers most serious concern, by a considerable margin, was access to finance, with over 40 percent of managers saying it was a serious problem. In comparison, only 18 percent of SMLE managers said the same. Microenterprise managers were also far more likely to say that macroeconomic instability, business licensing, informal competition, and access to land were serious problems and were far less likely to say that tax rates or worker skills were serious problems that managers of SMLEs. There are, however, some similarities between the perceptions of SMLE and microenterprise managers. Most notably, although there were significant differences in the number of managers of microenterprise and SMLEs that rated some of the top areas as serious obstacles, four of the top six complaints were identical for the two sets of managers (crime, corruption, finance and macroeconomic instability). In addition, a similar number of managers of both types of firms rated crime and corruption as serious constraints. Finally, managers of few firms of either type were concerned about any area of either infrastructure or regulation (other than business licensing for microenterprises). In addition to being asked about how great an obstacle an issue was on a five-point scale, firms were also asked what they saw as the biggest obstacle. While the first measure gives an idea about the breadth of concern—that is it allows us to see how many firms saw that area of the investment climate as a serious problem—the second gives some idea about the depth of concern. Since firms can only rank one issue as their greatest concern, a relatively small number of firms could give a single issue prominence on this measure. In the first measure, since firms can rate multiple problems as 4s or 5s, these small group issues would be unlikely to standout. For example, if exporters only make up a relatively small part of the population and are particularly concerned about trade regulation or transportation, these areas might not look like they are minor problem on the first measure (i.e., only a small number of firms rated it as a major problem), but as a major problem on the sector. Even a small group, however, can make an issue appear important using the second measure. Although the two questions do not always give identical answers, responses to questions about the biggest problem that the firm faced were similar to responses about whether an issue is a serious problem were similar in Namibia. Most notably, SMLE managers were most likely to say that crime was their biggest problem and microenterprise managers were most likely to say that access to finance was (see Figure 19). Moreover, most of the areas of the investment climate that firms said were the biggest problems were also rated by many firms as serious problems. Microenterprises were most likely to say that access to finance, macroeconomic instability, access to land, crime and competition from the informal sector was the biggest 15 See Regional Program on Enterprise Development (2005a; 2006). In contrast, microenterprise managers in Swaziland and Botswana perceive similar obstacles as their formal counterparts. See Regional Program on Enterprise Development (2007a; 2007b) 47 problem their firm faced and SMLE managers were most likely to say that crime, tax rates, access to finance, macroeconomic instability and corruption was the biggest problem they faced. Figure 20: Responses were different for SMLEs and microenterprise managers when they were asked about the biggest constraint that they faced. Formal Firms Microenterprises Access to Finance Crime Other Other Tax Rates Informal Macro Comp Access to Instability Access to Land Macro Finance Crime Corruption Instability Source: World Bank Enterprise Survey. II. DIFFERENCES IN PERCEPTIONS FOR DIFFERENT TYPES OF FIRM. In addition to differences between the SMLEs and microenterprises, different types of enterprises often have very different perceptions about the investment climate and the major constraints that they face even within these classes. These differences could be due to differences in expectations (e.g., foreign-owned firms might have expectations based upon their experience in their home countries) or differences in experiences (e.g., large firms might find it easier to get loans due to having better connections to bank officials or better access to collateral). This section of the chapter looks at differences in perceptions across different types of firm in more detail. In later chapter, we address whether the differences in objective indicators are consistent with the differences in perceptions. Two issues are important when looking at differences in perceptions among different types of firms. First, because the sub-samples are often quite small—for example there are less than 15 Asian-owned firms in the sample of SMLEs—some apparent differences might be due to random variation rather than systematic differences in perceptions. Second, there are often systematic differences in other firm characteristics between different classes of firms that might affect perceptions about the investment climate in addition to the observed differences. For example, foreign-owned firms are larger on average than domestic firms. If large firms find it easier to get access to credit (e.g., because their managers are better connected), then foreign- owned firms might appear to have better access to credit than domestic firms not because they are foreign-owned, but because they are large. Because of these reasons, this section mostly 48 focuses on differences between firms that are statistically significant after controlling for other firm characteristics. The methodology used in this section is described in detail in Appendix A. II.1 Ownership As in Swaziland and Botswana, managers of female-owned firms did not have significantly different concerns than managers of firms with no female owners. 16 After controlling for other differences between firms, differences in perceptions about the investment climate were not statistically significant at conventional significance levels the coefficients were generally small. This suggests that any differences in the constraints that male and female- owned firms face are probably the result of factors other than gender. Since the sample of Asian-owned firms was relatively small—only about 13 firms—it is not surprising that the perceptions of managers of Asian-owned firms were not statistically different from the perceptions of indigenously owned firms. Given the small size of the sample, it would be difficult to find statistically significant differences between the two sets of firms. In contrast, managers of white-owned firms did report slightly different concerns than managers of indigenously owned firms. Most notably, managers of white-owned firms were less likely to say that access to finance and crime were serious obstacles to their enterprises’ operations. After controlling for other differences between white- and indigenously owned firms, white-owned firms were 11 percentage points less likely to say that access to finance was a serious problem for their firm. Results are similar for the question about the main obstacle that the firm faces—whereas only 6 percent of managers of white-owned firms said that access to finance was the biggest obstacle that their firm faced, 13 percent of managers of indigenously owned firms said that same. The difference in perceptions about access to finance between white and black-owned firms is also visible in the enterprise surveys for Botswana, Swaziland and South Africa (see Figure 21). 17 In all four countries, managers of indigenously owned firm were more likely to say that access to finance was a more serious problem than managers of white-owned firms. An important issue is the extent to which the objective data on access to finance is consistent with the perception-based data. This is discussed in greater detail in Chapter 5. 16 See Regional Program on Enterprise Development (2007a; 2007b) 17 See Regional Program on Enterprise Development (2005a; 2006; 2007a; 2007b). The econometric evidence for microenterprises in South Africa is discussed in greater detail in Clarke and Cull (2007). 49  Figure 21: Managers of white-owned firms are less likely to say access to finance is a serious problem than manager of black-owned firms in most SACU economies. % of firms saying access to finance is serious problem 60% 40% 20% 0% Swaziland South Africa Namibia Botswana White Black Source: Enterprise Surveys. Although white-owned firms were less likely to say that crime was a serious problem than indigenously owned firms were, a significant number of white-owned firms said crime was the biggest problem that they faced (14 percent—more than any other constraint other than tax rates). Although indigenously owned firms were more likely to say that it was the biggest problem that they faced (18 percent—more than any other constraint), the difference was not large. In this respect, the differences in perceptions about crime are less marked than the differences with respect to access to finance. For the most part, foreign- and domestically owned firms had similar concerns— differences between foreign- and domestic firms with respect to the likelihood that they saw different areas of the investment climate as serious obstacles were relatively small and were not statistically significant at a 5 percent level. In most areas where the differences were close to being significant (courts, labor regulation, and access to finance), managers of foreign-owned firms were less likely to see them as significant problems. The one exception was crime—which foreign-owned managers generally saw as a greater problem. II.2 Exporters Exporters were more likely to say that telecommunications, tax administration, trade regulations and the courts were a serious obstacle to operations even after controlling for other factors. It might not be surprising that exporters rated telecommunications and trade regulations as serious obstacles than non-exporters given that they are probably more dependent upon 50 international communications and are more likely to be affected by trade regulation than firms that do not export. Similarly, it is plausible that exporting makes some aspects of taxation (e.g., getting refunds for VAT or dealing with customs administration and tariffs) more burdensome. It is less clear, however, why they would be rate courts as a greater concern. It is important to note, however, that even among exporters, relatively few firms (less than 20 percent) said that any of these areas were serious concerns. Figure 22: Although exporters were more likely to rate telecommunications, trade regulation, tax administration and courts as serious problems than non-exporters, even among exporters only a small number of firms saw these as serious obstacles. % of firms saying access to finance is serious problem 20% 0% Telecommunications Tax Administration Trade Regulations Courts Exporters Non-exporters Source: Enterprise Surveys. Results are also similar for tax administration and trade regulations, when looking at the main obstacle that firms face. Although exporters were more likely than non exporters to say that they were the biggest obstacle that the firm faced (1 percent compared to 0 percent for tax administration and 5 percent compared to 2 percent for trade regulations), relatively few exporters rated either as their biggest problem. For courts, similar number of exporters and non- exporters rated them as their biggest concern (7 percent compared to 8 percent). But for telecommunications, a relatively large number of exporters (9 percent), but few non-exporters (3 percent) rated it as their biggest obstacle. In this respect, telecommunications appears to be the one area of the investment climate where there is both a large difference between exporters and non-exporters and where it rates as a serious concern for exporters. 51 II.3 Size Previous studies of firms in Sub-Saharan Africa have found that large firms appear to consistently be more likely to rate most areas of the investment climate as a serious constraint than smaller firms are (Gelb and others, 2006). Although Gelb and others (2006) did not include Namibia is the group of countries that they analyzed, the results from the Namibia survey are broadly consistent with this—either there was no statistically significant difference between the responses of large and small firms or large firms were more likely to say that the area was a serious obstacle. 18 For four areas of the difference between large and small firms was statistically significant. Large firms were more likely to say that electricity, labor regulation, political instability and crime were serious problems. For two of these areas, large firms were also more likely to say that it was the biggest obstacle that they faced. Whereas 25 percent of large firms said that crime was the biggest problem that they faced, only 15 percent of small firms said the same. 19 Similarly, whereas 10 percent of large firms said that labor regulation was the biggest constraint they faced only 2 percent of small firms said the same. In terms of rankings, the difference between the perceptions of large and small firms was more pronounced for labor regulation, which only large firms were concerned about, than for crime, which although large firms were more concerned, was an obstacle for both large and small firms. When constraints were ranked based upon the number of firms that said it was the biggest constraint they faced, crime ranked first for large firms and second for small firms. Labor regulation, in contrast, ranked third for large firms but only 13th for small firms. The story is similar when rankings are based upon the number of firms that saw that area as a serious constraint, the results are similar. Both large and small firms were more likely to rate crime as a serious problem than any other area of the investment climate, while labor regulations ranked third for large firms but only 14th for small firms. II.4 Sector. Differences between sectors (retail, manufacturing, and other services) were generally modest. None of the differences between retail and manufacturing enterprises were statistically significant. In contrast, other service firms were more likely to say that transportation, access to land, corruption and competition from the informal sector were serious problems than manufacturing firms. The differences, however, mostly seem to be because manufacturing firms complain more about most areas of the investment climate more than other firms. Of the 17 areas of the investment climate, after controlling for other differences, manufacturing firms were more likely to say that the area was a serious obstacle than retail or service firms in 14 areas. 18 The effect was more noticeable in Swaziland, where most differences were statistically significant (Regional Program on Enterprise Development, 2007b) and more noticeable than in Botswana (Regional Program on Enterprise Development, 2007a). 19 Because the number of large (over 100 employees) and medium-sized (50-99 employees) firms is so small, for the purpose of these comparisons, large firms are defined to be firms in the 75th percentile of above. Small firms are defined as firms below the median size. 52  As a result, relative rankings were not significantly different for different types of firms. For example, based upon the number of firms that said each area was a serious problem, corruption ranked 3rd for manufacturing firms, 4th for service firms and 3rd for retail trade firms. Thus, even though manufacturing firms were more likely to say it was a serious problem, its ranking was similar for all three types of firms. This was also true for the other three constraints, which ranked between 7th and 11th for all three types of firm as well. 53 ECONOMETRIC ANNEX: PERCEPTIONS ABOUT THE INVESTMENT CLIMATE. An interesting question is whether there are systematic differences in perceptions about the investment climate among firms of different types. One way of doing this would be simply to compare average responses across firms of different types. For example, it would be possible to look at how many firms of different types rated a particular investment climate issue as their biggest constraint or how many firms rated it as a major or very severe constraint. Although this approach is intuitive, there are at least two problems associated with it. First, the sub-samples of different types of firms are often relatively small. For example, there are fewer than 15 Asian-owned firms in the sample and only 17 medium-sized and 21 large firms in the sample. This makes it difficult to assess whether differences are due to random variation in responses or due to actual systematic differences in perceptions. Second, there are also systematic differences in other firm characteristics across types of firms. For example, exporters tend to be both larger than non-exporters (an average of 105 workers compared to an average of 19 workers) and white-owned (40 percent compared to 22 percent). Differences in perceptions between exporters and non-exporters might therefore reflect differences in size or ownership rather than whether they export. To deal with these two issues, this appendix presents econometric results that deal with both these issues. First, by using a multivariate regression approach, it is possible to look at differences in perceptions after controlling for other systematic differences between firms. Second, it is possible to look at the statistical significance of the results (i.e., to see whether it is likely that the differences are due to random variation). Methodology. The methodology used in this section is similar to the methodology used in a recent paper by Gelb, Ramachandran, Shah and Turner (2006) that used data from seven countries in Africa (Kenya, Madagascar, Senegal, South Africa, Tanzania, Uganda, and Uganda) to look at differences in perceptions within and between countries. Because of concerns about pooling the data from the SMLE and microenterprise surveys, the results focus on differences among the SMLEs in the sample. The microenterprise sample, with only about 120 firms, is too small to do a similar analysis. The question of how different factors, including ownership, affect access to credit for microenterprises is examined by estimating different versions of the equation below: Perception about IC i = β 1 + β 2 Ownership i + β 3 Size + β 4 Exporteri + β 5 Sector + ε i The dependent variables are dummy variables indicating whether the manager of firm i rates that area of the investment climate as a major or very severe obstacle. The independent variables are a set of five dummy variables indicating firm ownership (whether the firm has a white, Asian or black owner, whether the firm has a female owner, and whether the firm is foreign owned), firm size (number of workers), a dummy variable indicating whether the firm exports, and a series of dummies indicating sector of operations. The error term is assumed to be 54 normally distributed. Because the dependent variable is a dummy variable, the model is estimated using standard maximum likelihood estimation. Results from the regression for each of the obstacles are shown in Table 9. The results are discussed in detail in Chapter 3. 55  Table 9: Effect of enterprise characteristics on perceptions about different aspects of the investment climate Access to Tax Trade Labor Telecom Electricity Transportation Tax Rates Courts Land Administration Regulations Regulation Observations 305 305 305 318 318 305 318 318 318 Workers -0.02 0.03** 0.01 -0.02 -0.03 -0.01 0.00 -0.01 0.05*** (natural log) (1.41) (1.99) (0.49) (1.19) (1.14) (0.45) (0.18) (0.64) (3.60) Sector - retail -0.01 -0.01 -0.06 -0.06 0.00 -0.01 0.00 -0.00 -0.01 (dummy) (0.27) (0.16) (1.44) (1.54) (0.01) (0.48) (0.08) (0.12) (0.38) Sector - other services -0.00 -0.04 -0.09** -0.12*** -0.10* -0.03 0.05 -0.00 -0.03 (dummy) (0.13) (0.91) (2.28) (2.85) (1.83) (0.97) (1.31) (0.05) (1.03) Foreign-owned 0.05 0.01 0.02 0.01 -0.05 -0.03 -0.01 -0.05* -0.08** (dummy) (1.39) (0.13) (0.37) (0.31) (0.79) (0.96) (0.37) (1.65) (2.38) Exporter 0.09** 0.02 0.03 0.01 -0.02 0.09** 0.12** 0.11** 0.07* (dummy) (2.12) (0.51) (0.59) (0.16) (0.23) (2.21) (2.51) (2.28) (1.67) Ownership - White 0.05 -0.02 -0.04 -0.03 0.08 -0.01 0.01 0.09** -0.02 (dummy) (1.63) (0.42) (0.90) (0.67) (1.40) (0.25) (0.42) (2.33) (0.55) Ownership - Asian -0.01 0.09 0.02 0.07 0.01 (dummy) (0.08) (0.75) (0.26) (0.82) (0.08) Ownership - Female -0.01 0.03 0.03 0.00 0.02 -0.04 0.01 -0.01 0.01 (dummy) (0.52) (0.82) (0.72) (0.08) (0.39) (1.44) (0.28) (0.46) (0.45) Worker Business Political Macroeconomic Informal Finance Corruption Crime education Registration Instability instability Sector Observations 318 305 318 305 318 305 318 318 Workers 0.04 -0.01 0.00 0.01** -0.02 -0.01 0.06** -0.02 (natural log) (1.59) (0.42) (0.07) (2.05) (0.63) (0.38) (2.16) (0.88) Sector - retail -0.07 -0.00 -0.09* 0.00 -0.01 -0.09 0.12* -0.06 (dummy) (1.08) (0.00) (1.71) (0.16) (0.11) (1.62) (1.72) (1.46) Sector - other services -0.05 0.02 -0.07 -0.01 -0.05 -0.11** 0.01 -0.09** (dummy) (0.78) (0.64) (1.30) (0.60) (0.98) (2.04) (0.14) (2.18) Foreign-owned -0.05 0.00 -0.11* 0.02 -0.09 -0.02 0.13* 0.03 (dummy) (0.74) (0.04) (1.79) (0.90) (1.56) (0.28) (1.83) (0.67) Exporter 0.08 0.01 0.09 0.00 0.01 -0.05 -0.02 -0.02 (dummy) (1.14) (0.37) (1.33) (0.12) (0.17) (0.83) (0.22) (0.48) Ownership - White 0.07 0.00 -0.11** 0.00 0.05 -0.08 -0.13** -0.03 (dummy) (1.16) (0.16) (2.01) (0.15) (0.86) (1.35) (2.17) (0.70) Ownership - Asian -0.15 -0.03 0.09 -0.02 -0.06 (dummy) (1.15) (0.26) (0.77) (0.13) (0.61) Ownership - Female -0.07 -0.00 0.08 0.03 0.06 0.06 0.02 0.05 (dummy) (1.35) (0.18) (1.54) (1.48) (1.14) (1.22) (0.31) (1.21) Source: Authors calculations based on Enterprise Survey data. ***, **, * Significant at 1, 5 and 10 percent significance levels Note: Asian-owned firms are dropped from some regression because the dummy variable perfectly predicts the outcome.  CHAPTER 4: LABOR MARKETS AND HUMAN CAPITAL A well-functioning labor market is vital to the success of the government’s policies to establish a globally competitive economy. Labor markets provide important avenues for poverty alleviation and overall competitiveness. However, the success of this mechanism is determined by worker productivity relative to direct and indirect labor costs. This chapter investigates the determinants of two important components of this mechanism in Namibia: on-the-job training and worker remuneration using enterprise data from the 2006 Investment Climate Survey. Both quantity and quality of workers, capital intensity and the wage levels affect competitiveness. Workers can improve their human capital is several ways: they can invest in formal education before employment; they can learn on the job; and they, or their employers, can invest in general or firm-specific skills training (Becker, 1964; Mincer, 1962). For workers that have completed formal schooling, the main way that they can improve their skills in the short and medium term is through firm-based training. Firm-based training is particularly important in economies with historically high inequality in education. This chapter investigates the determinants of firm-provided training. This provides information on policy interventions that might encourage more widespread skills development in Namibia. Worker remuneration affects export competitiveness. However, where monitoring is imperfect and contracts costly or incomplete, worker pay can be used to support higher levels of worker effort or to screen for particular types of workers (Lazear, 1979; Shapiro and Stiglitz, 1984). Regulation can also affect worker pay either directly by setting wage levels or indirectly by making it harder for firms to hire and fire workers and by affecting how flexible working hours are. The chapter also investigates the determinants of wages using both firm-level and matched employer-employee worker-level data. I. EMPIRICAL STRATEGY This section outlines the empirical strategy for answering the two questions set out above. Both topics can be addressed using either firm-level or worker-level data. Because these two approaches provide complementary information—and are a useful cross-check—both approaches are used in this section. I.1 Training-Firm Level At the firm level, firms decide whether they should invest in their workers. They also decide who they will train and the content of the training. The firms’ decisions can be investigated by looking at the characteristics of firms that train. To do this the probability that the firm provides training can be estimated as a probit model. The model is: Tij = η+α Xij + μj + εij (1) where Tij takes the value of 1 if firm i in sector j provides training and 0 otherwise. Xij represents observable firm characteristics thought to affect the provision of training and μj represents fixed sector characteristics that determine the desirability of training such as average levels of capital intensity and skill complementarities in production. εij represents unobserved firm characteristics that potentially affect training. 20 The set of firm characteristics Xij include indicators of firm size to capture the affect of scale economies. Firm size might affect the probability that the firm provides training in a number of ways. First, large firms might have become large because they provide training or a common factor such as ‘high quality management’ or access to liquidity might have affected both employment and training decisions. Second, to the extent that there are fixed costs associated with training (including the space to provide training), larger firms will face lower per-worker training costs. The regression includes size in a flexible way using a set of indicators for four size classes. The regression also controls for export status, foreign ownership and firm vintage. These are proxies for firm quality (Roberts and Tybout, 1996). Firms facing international competition might be more likely to invest in the quality of their workers to remain competitive. Similarly, firms with foreign ownership might be more likely to provide training because of greater liquidity or peer effects. Firm age is a measure of quality or competitive pressure (Hopenhayn, 1992). Given that the effect of age is ambiguous, the regression includes a quadratic to capture non-linear effects of age on the propensity to provide training. The returns to training are likely related to the level of formal education of the worker. The regressions therefore control for the average level of education in the firm. The indicator is a dummy variable indicating that the average worker has more than 6 years of schooling. Bargaining power of workers is likely to affect firm-based training. On the one hand, workers with more bargaining power will induce firms to invest in worker skills. On the other hand, if workers value other non-skills related investments, then training is less likely. The proportion of workers that are seasonal captures the extent to which a firm relies on a stable workforce. A higher proportion of seasonal workers will be associated with a lower propensity to provide training. The regression also include a measure of the extent to which firm’s are engaged in HIV prevention/treatment activities. Firms that invest in prevention/treatment are firms that are sensitive to the skill-composition of their workforce and are more likely to provide training. 21 Finally the regressions include a series of controls that capture other measures of firm competitiveness and liquidity such as capacity utilization and whether the firm’s accounts are externally audited. I.2 Training-Individual Level At the individual level, the Enterprise Survey includes data on whether individual workers have received contemporaneous training and whether they have received training in the 20 In this analysis, we are unable to control for unobserved firm-fixed effects. As a result, it will be difficult to rule out the potential for omitted variable bias of the estimates. 21 Ramachandran and others (2005) find that high-skill intensity firms are more likely to invest in a number of health-enhancing activities. 58 past. It also asks whether the training was external or in-firm and whether it was financed by the firm or by the worker. Because the data includes information on the worker, it is possible to look at how training depends on the individual’s characteristics. In particular, the analysis looks at how formal schooling, gender, union-status, tenure and experience affect whether the worker receives training. The econometric model is a probit model with the following specification: Yijk = η+αWijk + βFjk + πk +