INFORMAL ENTERPRISES IN KENYA workshops jua kali handicrafts autoparts furniture furniture handicrafts jua kali workshops enterprises INFORMAL ENTERPRISES IN KENYA January, 2016 TABLE OF CONTENTS Acknowledgements ........................................................................................................................................................................................................... i Introduction.............................................................................................................................................................................................................................. ii Section 1. Background and Overview...................................................................................................................................................................... 1 Section 2. Financing Informality ................................................................................................................................................................................. 7 Section 3. Productivity ...................................................................................................................................................................................................... 11 Section 4. Firm Dynamics ................................................................................................................................................................................................ 17 Section 5. Remaining Informal...................................................................................................................................................................................... 19 Section 6. Summary and Policy Advice .................................................................................................................................................................. 25 LIST OF FIGURES Figure 1: Access to finance is the top obstacle in all regions............................................................................................................... 5 Figure 2: Use of Bank finance for working capital is more common among firms with more educated owners and among the more productive and large firms .............................................................................................. 8 Figure 3: Larger and more productive firms are less likely to be financially constrained .................................................. 9 Figure 4: Informal enterprises are less productive than formal enterprises................................................................................ 11 Figure 5: Labor productivity is lower among informal firms compared with formal micro firms, but the gap varies by region............................................................................................................................................................................................ 12 Figure 6: Variations in labor productivity of informal firms ................................................................................................................... 13 Figure 7: Education level of the manager is positively correlated with labor productivity of the informal firms .... 15 Figure 8: Percentage of firms that increased number of employees, machines, or space used over the last three years varies across regions....................................................................................................................................................... 18 Figure 9: Willingness to register is higher among firms that consider the various obstacles as severe for their business operations .................................................................................................................................................................................. 20 Figure 10: Reasons for not registering vary across regions and by education level of the manager ............................ 20 Figure 11: Perceived benefits of registration vary by region and firms’ perceived severity of the obstacles............ 22 Figure 12: Ease of registering a business is associated with greater willingness among informal firms to register.. 23 Figure 13: Better contract enforcement in Mombasa is associated with more firms reporting being able to issue receipts to customers and suppliers as a benefit of registration ..................................................................... 23 Figure 14: On average, labor productivity increases with greater ease of registering a business.................................... 24 LIST OF TABLES Table 1: General firm characteristics......................................................................................................................................................................... 10 Table 2: Firm ownership characteristics................................................................................................................................................................. 11 Table 3: General management of the business ................................................................................................................................................ 11 Table 4: Key obstacles faced by informal firms.................................................................................................................................................. 12 LIST OF ANNEXES Annex 1: Summary statistics and regressions .................................................................................................................................................... 29 Annex 2: Kenya – Survey of informal firms (2013)............................................................................................................................................ 38 Annex 3: Business environment and productivity............................................................................................................................................ 40 ACKNOWLEDGEMENTS T his report is the outcome of collaborative efforts of the World Bank’s Trade & Competitiveness and Finance & Markets Global Practices. It is a part of the Kenya Investment Climate Assessment ESW (P151793), supported by the Kenya Investment Climate Program-II, which is generously supported by DFID and the Netherlands. The team was led by Mehnaz Safavian (Lead Financial Sector Specialist), Joshua Seth Wimpey (Private Sector Development Specialist) and Mohammad Amin (Senior Economist). i KENYA INFORMAL ENTERPRISES INTRODUCTION J ua Kali means fierce sun in Swahili. It is also the name given to Kenya’s informal sector, the thousands of workshops where people part of the World Bank’s Enterprise Survey initiative for Kenya. The purpose of the note is to assess the main constraints facing informal bang out pots, pans, autoparts, furniture, firms, identify patterns of productivity and and handicrafts, literally under the hot sun, firm dynamics, and better understand drivers day in and day out. In low and middle-income for formalization. Section one provides an countries, informal firms make up the majority overview of key characteristics and main of all enterprises. In Kenya, this is also true, investment climate constraints facing informal with the Kenya National Bureau of Statistics firms. In section two, patterns of informal firm estimating that, as of 2014, the informal sector finance are explored, while in sections three represents 82.7 percent of employment. and four, labor productivity and drivers of firm growth are analyzed. Section five examines While the domination of the informal incentives to remain informal and policies that sector is well known, its implications, costs, can catalyze formalization. This is followed by a reforms, and impact are less well known, conclusion. Due to the sampling methodology and questions abound. What are the main used, all results pertain to the sample of constraints facing informal firms? Why do surveyed firms; hence, due caution is necessary firms choose to remain informal, and what in extrapolating the results to the broader are the benefits to formalization? How much informal sector in Kenya. does informality ‘cost’ in terms of lost revenue and lower productivity? As firms grow in size, Nevertheless, the assessment of the surveyed do they stay informal? Do policies to boost firms could provide important information formalization work and are they worth the cost on identifying policies as well as firm-level to design and implement? support that could boost productivity and catalyze formalization. This could have This note draws from an emerging literature important implications for economic growth on firm informality as well as data collected and job creation in Kenya. on micro enterprises and informal firms as KENYA INFORMAL ENTERPRISES ii SECTION ONE BACKGROUND AND OVERVIEW T he informal sector across Africa is ubiquitous, with a significant number of people engaged in small and household characteristics of firms and their owners, their main investment climate challenges and obstacles to growth, and firm dynamics.1 enterprises outside formal wage employment. The World Bank’s Informal Enterprise Surveys A World Bank review of household enterprises (IFS) collect data on non-registered business in Sub-Saharan Africa (Fox and Sohnesen activities in every region of the world, and an 2012) confirms that the informal nonfarm informal enterprise survey was conducted in sector is an important contributor to economic Kenya in April and May of 2013. The Kenya development in low-income Sub-Saharan IFS used a standardized survey instrument Africa as a source of employment, earnings, designed to assess the business environment and household livelihoods. Nearly 70 percent for non-registered businesses within a well- of employment outside farming is in the defined universe of activities, which have been informal sector. Improving the productivity identified using information from previous of informal enterprises is therefore essential iterations of the studies. The IFS covered for employment, income growth, and poverty business environment topics including general reduction in the region. business characteristics, infrastructure, crime, sales and supplies, finance, labor, registration, Kenya’s informal sector is large and dynamic business environment, and assets. In Kenya, a - 95 percent of the country’s businesses and total of 533 firms were interviewed. The urban entrepreneurs are found here. According to centers identified were Nairobi (137 firms), 2015 Economic Survey, the total number of Mombasa (110), Central (103), Nyanza (93), and persons enrolled in both formal and informal Nakuru (90). sectors increased from 13.5 million in 2013 to 14.3 million in 2014, and of the 799,700 new jobs, The IFS in Kenya allows for comparison across 700,000 were created by the informal sector. different dimensions, including sector of Men account for a majority of employment in activity (manufacturing vs. services), firmsize the informal sector of Kenya and more than (number of employees in a regular month), two-thirds of informal sector jobs are in trade, location (Nairobi, Mombasa, Central, Nyanza, restaurants, and hotels. Employment in the and Nakuru), gender of the main decision informal sector is associated with significantly maker/owner, whether the firm operates from lower levels of poverty than those experienced inside or outside of household premises, and in farming. education level of the primary owner. A full set of summary statistics of all variables are provided Data recently collected can fill some important gaps in information on the informal sector 1 See Annex 2 for a detailed description of the data and in Kenya and provide some insight into the methodology. KENYA INFORMAL ENTERPRISES 1 1. Background and Overview in Annex 1.2 As mentioned above, the lack of typically smaller than 50m2 in size and largely a proper sampling frame for the universe of located outside of the household premises. Of informal firms in Kenya implies that the sample these, 45 percent of the premises were fixed, we use is not necessarily representative of the permanent structures and owners who did not broader informal economy in Kenya or in the own the premises rented these in almost 82 cities covered. Hence, all our results apply to the percent of the cases. sample of surveyed firms and extrapolation to the broader informal economy requires due caution. For the full sample, 27.1 percent of firms had expanded in the last three years (increase in Excerpts from the summary data in Table 1 employees, machinery, or space occupied) reveal that the average age of firms covered but higher growth was seen in companies in the IFS survey was six years and almost half where owners had a secondary education the businesses operated in the manufacturing (32 percent of firms) vs. owners who had no sector. Only 1.3 percent of the sampled primary education (16.6 percent of firms). firms were registered when they started and Similarly, firms managed by males expanded over 40 percent of employees were family in more cases than those managed by females members of the owners. Firm premises were (31.2 percent vs. 20.9 percent). Table 1: General firm characteristics Measure Result Measure Result Average age of the firm 6.5 years Total area occupied by the business or activity 45m2 % of firms that were registered at start up 1.3% Firms located within household premises 13% Firms that belong to the manufacturing sector 48% For firms located inside household premises, % 60% reporting main reason is that it costs less to run the business from home Firms with increase in employees, machinery or space 27% For firms located outside of household premises, 45% occupied during the last 3 years % of firms that have fixed premises and with permanent structure Business is located in an industrial zone or cluster 16% Among businesses whose owners do not own the 82% space occupied by the business, % who pay rent for the space occupied Business is located in the city center 7% Number of family members of the owners working 44% at the firm as a percentage of all workers during the last month As shown in Table 2, firms were typically 2 For all variables covered by IFS, regression analysis was owned by an individual who had an average conducted by regressing each of the variables covered by IFS on various cuts (sector, firmsize, education level of of eight years of experience in the sector. primary owner, etc.) listed above. OLS regression is used The average age of the owner was 35 years where the dependent variable is a continuous variable and logit model is used for categorical (dummy) variables. of age and almost 40 percent of owners were All regression results use Huber-White robust standard female. In 94.3 percent of the cases, the main errors. As we find below, significant regional differences are found in many IFS variables. This is not surprising since owner had started the business themselves (or the informal sector often operates at the local rather than the national level. Hence, all our regression results are run with a partner), and in many instances (66.3 with region fixed effects (dummy variables indicating the percent), these owners came from homes in region to which a firm belongs). 2 KENYA INFORMAL ENTERPRISES 1. Background and Overview Table 2: Firm ownership characteristics Measure Result Measure Result Number of owners in the business 1.1 For firms with largest owner who has not spent 64.4% his/her entire life in the city, % of firms where owner migrated from a smaller city Number of years of experience that the main decision 8.1 Number of people who live in the largest owner’s 3.8 maker has working in the sector household premises Age of the largest owner 35.0 Firms with largest owner’s parents having no 66.3% education or primary education % of owners of the firm that are female 37.8% Firms with largest owner employed in the same 23.4% activity prior to current business Largest owner acquired ownership of the firm by 94.3% Prior to starting this business, % of firms with the 21.7% starting the business alone or with partners largest owner being unemployed Largest owner migrated to the city where the 78.8% Number of businesses or activities started by the 1.0 business is located from another city or from another largest owner in the last three years country which parents had no education or a primary average firm operates for approximately 65 education. Almost 80 percent of the largest hours per week and this remains constant across owners migrated to the city in which the sector, region, type of ownership, and stage business is located and, of these, the majority of maturity of the business. A large majority (64.4 percent) migrated from smaller cities. of the businesses (86.8 percent) use their own About a fifth of the owners of the surveyed funds to finance the day-to-day operations, businesses were unemployed prior to starting with only 8.7 percent using banks. However, their respective businesses. 16 percent of firms managed by individuals with a vocational or university degree make Table 3 provides further insight into the use of bank financing for this purpose vs. only management of the day-to-day operations 3 percent of managers with no education or a of the businesses surveyed. As shown, the primary education. On average, 34.4 percent of Table 3: General management of the business Measure Result Measure Result Firms where the largest owner is also the main 96.8% % of firms that have a bank account to run the 34.4% decision maker business Hours of normal operation of the firm per week 64.8 For firms that have a bank account to run the 52.6% business, % of them that use separate bank account for their household % of firms who use electricity 51.8% Total cost of workers for the last month Ksh 12,679 % of firms that use water for business purposes 36.9% % of firms who experienced losses due to crime 7.0% during the last month % of firms that used own funds to finance their day- 86.8% Losses due to crime during the month as % of 46.7% to-day operations monthly sales among firms who had positive losses due to crime in the last month % of firms that used banks to finance their day-to- 8.7% Losses due to crime during the last month as a 2.9% day operations percentage of sales in a regular month including zero losses for firms that had no such losses KENYA INFORMAL ENTERPRISES 3 1. Background and Overview firms use a bank account to manage their funds education), by number of employees (22.1 and the use of bank accounts is doubled when percent for multiple employee businesses comparing level of education (vocational or vs. 60.7 percent for single employee), and by university degree vs. no/primary education). Of gender (29.1 percent for female managed those that make use of bank accounts, just over businesses vs. 54.1 percent for male managed half the firms separate business and household businesses). bank accounts. Once again, level of education is a large driver of separation (70 percent vs. Firms were provided with a list of eight 25.7 percent with no/primary education). obstacles in running their business and asked to choose the most important one. The Just over half the firms surveyed use electricity obstacles include access to finance, access to to operate their businesses (51.8 percent) and land, corruption, power supply or electricity, only 37 percent use water. The average cost crime, water supply, access to technology, of workers per month is Ksh 12,679, although and inadequately educated workers. Access there are substantial differences by sector to finance was the top obstacle, cited by 59 (Ksh 16,448 in manufacturing vs. Ksh 9,056 in percent of firms surveyed. This was followed by services), by level of education of owners (Ksh electricity problems (10.3 percent), access to 16,178 with university degree vs. Ksh 8,937 land (9.3 percent), and corruption (9.3 percent). with no/primary education), and by gender of manager (Ksh 15,613 for males vs. Ksh 8,022 for As seen in table 4, 63.8 percent of firms cite females). Seven percent of firms experienced access to finance as a severe obstacle, and losses due to crime in the month prior to being limited access to land is also a severe stumbling surveyed. Of those firms, the losses represented block for 41.3 percent of firms surveyed. almost 47 percent of sales for the month. There Corruption appears to be widespread, with were differences in this percentage by level 33 percent of the sampled firms reporting it of education (31.0 percent with secondary as a severe obstacle, 60 percent reporting education vs. 72.5 percent with no/primary harassment by government officials during the Table 4: Key obstacles faced by informal firms Measure Result Measure Result % of firms that consider limited access to finance as a 63.8% Limited access to land is a severe obstacle to firm’s 41.3% severe obstacle to their current operations operations (% of firms) % of firm that rank limited access to finance as the 59.3% % of firms reporting electricity problems as a 38.5% most important obstacle within the set of eight severe obstacle to their current operations obstacles % of firms who report crime as a severe obstacle for 28.0% For firms that use electricity, % of firms that 83.6% their operations experienced power outages during the last month % of firms who report corruption as a severe obstacle 33.0% % of firms reporting water problems as a severe 22.9% for their operations obstacle to their current operations Business experienced harassment by government 60.0% For firms that use water for business purposes, 43.0% officials during the last month (% of firms) % of firms that experienced insufficient supply during the last month % of firms who believe that firms like themselves give 52.9% % of firms that would like their business to be 53.0% informal payments or bribes or protection payments registered with the Registrar General in order to stay in business 4 KENYA INFORMAL ENTERPRISES 1. Background and Overview last month, and 53 percent reporting that they firms surveyed in Mombasa region (Figure believe bribes are required to stay in business. 1). This is significantly higher than in Nakuru This figure is significantly higher among (51 percent) at the low end. Nyanza and surveyed firms in the manufacturing sector Mombasa stand out with a significantly higher vs. the services sector (80.0 percent vs. 31.6 proportion of firms that rank access to land as percent). Access to services is also a challenge the top obstacle (21 percent and 14 percent, as almost 40 percent of firms surveyed face respectively) compared with each of the electricity problems (over 80 percent of firms remaining regions. In contrast, no surveyed using electricity experienced power outages in firm in Nyanza considers corruption as the top the prior month), and almost a quarter of firms obstacle compared with 11 percent on average face severe water problems (over 40 percent elsewhere, and no surveyed firm in Mombasa of those using water experienced insufficient considers crime as the top obstacle compared supply in the prior month). with 8 percent of firms on average surveyed elsewhere. The Central and Nakuru regions Access to finance continues to be the top stand out with a significantly larger proportion obstacle even within sub-samples such as of firms surveyed reporting poor power supply sector of activity, region, gender of manager, single vs. multiple employee firm, etc. as the most important obstacles (20 and 17 percent, respectively) than firms in any of By region, at the high end, access to finance the other regions (average for the remaining is the top obstacle for 65 percent of the regions is 5.6 percent). Figure 1: Access to finance is the top obstacle in all regions 70 65 62 60 59 60 56 51 50 Percentage of rms 40 30 20 21 20 17 14 15 13 14 12 10 10 9 7 8 9 9 5 6 4 6 3 3 3 3 0 0 0 Central Nyanza Mombasa Nairobi Nakuru All rms Access to nance Access to land Corruption Crime Electricity Source: Kenya Informal Enterprise Survey, 2013 KENYA INFORMAL ENTERPRISES 5 SECTION TWO FINANCING INFORMALITY proportion of firms that find access to finance C ommon in the literature on informality is the consistent pattern that access to finance (among other variables) is a as the top obstacle and firmsize measured by monthly sales of the firm. key determinant of the rate of formality. The proportion of surveyed firms that use their Furthermore, the greatest perceived obstacle own internal funds to finance operations does for both informal and formal firms is access not vary much by firm-size, labor productivity, to finance, although this could often be gender of the manager, education level of the interpreted more fundamentally as an issue of manager, whether a firm operates from inside limited human capital (LA Porta, Shliefer, 2014). or outside of household premises, industrial sectors, or whether or not the firm expanded In Kenya, an overwhelming majority of informal over the last three years. There are, however, firms surveyed use their own funds to finance some significant differences in other categories. working capital requirements; internal funds Younger firms are significantly more likely to serve as a source of financing for working use their own funds than older firms. This result capital for 87 percent of firms surveyed. seems to be largely driven by firms that are 10 This is followed by money from friends and years or older (about 20 percent of the sample). relatives (used by 35 percent of firms), credit For instance, 81 percent of the firms surveyed and advances from suppliers and customers that are 10 years or older use their own funds to (19 percent), micro-finance institutions (16 finance operations compared with 89 percent percent), moneylenders (9 percent), and banks of the firms surveyed that are younger. (9 percent). The sampled firms in the furniture industry There is also a fair amount of literature showing are an anomaly as they are less likely to use that financial constraints are particularly their own funds (75 percent) than the sampled acute for relatively smaller firms. Data from firms in the rest of manufacturing (92 percent) the informality survey in Kenya are consistent as well as services sector (85 percent). This in this respect. That is, the proportion of may suggest that the furniture industry enjoys firms that consider access to finance their top somewhat greater access to finance. Regional obstacle is significantly higher as firmsize, differences for the full sample are noticeable. measured by the number of employees, Specifically, firms surveyed in the Central and decreases. For example, 62 percent of the Mombasa regions have a higher proportion single employee firms rank access to finance of firms using their own funds (98 percent in as the top obstacle, compared with only 55 Central region and 94 percent in Mombasa) percent of multiple employee firms. However, than firms in Nyanza (77 percent), Nairobi (84 there is no noticeable relationship between the percent), and Nakuru (81 percent). KENYA INFORMAL ENTERPRISES 7 2. Financing Informality Although close to 20 percent of firms in the with firmsize (sales, employment), labor full sample use advances and credit from productivity, and education level of the suppliers and customers, the percentage manager (Figure 2). It is also higher for increases significantly with firmsize (sales, manufacturing vs. services sector firms (11 and employment), labor productivity, firm’s age, 6 percent, respectively), for firms that expanded and the level of education of the manager. workers, machines, or space, used over the For example, 26 percent of the firms surveyed last three years vs. others (13 percent vs. 7 with above median level of labor productivity percent, respectively). As might be expected, use advances/credit compared with 12 percent surveyed firms that currently use bank finance of the firms surveyed with below median labor are less likely to report that they would benefit productivity. Use of this source of finance also from better access to finance as a result of differs significantly between the sample of registration. Among firms that use bank finance, firms in the furniture industry (38 percent), rest 63 percent report better access to finance as a of manufacturing (23 percent) and services benefit from registering; the corresponding (13 percent), and it is significantly higher for figure for firms that do not use bank finance is dynamic firms that increased workers, machines, significantly higher at 78 percent. or space used over the last three years vs. those that did not (33 and 14 percent, respectively). The survey provides information on whether Last, there is not much regional variation with or not a firm applied for a loan during the the exception that firms surveyed in Mombasa last year, and if not, the main reason for not use advances/credit from suppliers/customers doing so. We define a firm to be financially less compared with each of the other regions constrained if it did not apply for a loan during (8 percent vs. 22 percent). the last year and the main reason for not doing so is either high interest rates, lack of required While only 9 percent of firms in the sample guarantees, complex application procedures, use banks to finance working capital, the it did not think it would be approved, and the proportion of such firms increase significantly residual category of other reasons. Figure 2: Use of Bank finance for working capital is more common among firms with more educated owners and among the more productive and large firms 18 16 16 Percentage of rms that use banks to 14 13 nance working capital 12 11 10 8 6 6 6 6 4 3 2 0 Manager has no or Manager has secondary Manager has vocational Firm has single Firm has more than one Below median labor Above median labor primary education education training or university employee employees productivity productivity degree Source: Kenya Informal Enterprise Survey, 2013 8 KENYA INFORMAL ENTERPRISES 2. Financing Informality According to this measure, about 60 percent of labor productivity. Across regions, sampled the sampled firms are financially constrained. firms that are financially constrained are more This proportion does decline with increases common in the Mombasa region. Figure 3 in firmsize (sales), firm age, labor productivity, provides more detail with respect to regions, and firm growth. firm age, productivity, and sales. As might be expected, firms that consider access to finance Surveyed firms with higher labor productivity as an obstacle are more likely to be financially are less financially constrained (at 53 percent) constrained vs. those that are not (76 vs. 39 compared with 67 percent of firms with lower percent, respectively). Figure 3: Larger and more productive firms are less likely to be financially constrained 90 82 80 70 67 65 61 63 60 56 Percentage of rms 60 53 53 53 55 50 47 40 30 20 10 0 Full sample Central Nyanza Mombasa Nairobi Nakuru Above median Below median Older than Median or Above median Below median labor labor median age below median month sales monthly sales productivity productivity (4 years) age (4 years) Source: Kenya Informal Enterprise Survey, 2013 KENYA INFORMAL ENTERPRISES 9 SECTION THREE PRODUCTIVITY G iven that a large proportion of workers in the informal sector belong to the low- income category, increasing labor productivity Consistent with the broader literature, in Kenya, formal or registered micro firms show a much higher level of labor productivity than in the informal sector may be crucial for their informal firm counterparts surveyed, reducing poverty, increasing income equality, but the gap varies by region. The mean value and improving the living conditions of relatively of labor productivity for micro firms is about poorer sections of society. 8.4 times that of informal firms surveyed. The corresponding figure for median level of labor In general, it is well understood that informal productivity is lower, but sill 3.8 times that of firms are much less productive than formal informal firms surveyed. firms, with productivity calculated as value added per employee. La Porta and Shliefer Figure 4: Informal enterprises are less productive than formal enterprises (2014) present evidence that this is an accurate representation and not just under- 200 190 180 reporting by informal firms. The low value- Monthly sales per worker (KES, '000) 160 added per employee reflects the low quality 140 of products produced by informal firms, which 120 is also indicated by the concerns informal 100 entrepreneurs report about competition from 80 60 50 the formal sector. Low productivity is also 40 reflected in the growth rates of informal firms 22 20 13 (La Porta and Shiliefer, 2014). 0 Mean Median Informal Micro We define labor productivity as the (log of) ratio Source: Kenya Informal Enterprise Survey, 2013 of sales to employment in a regular month. Note: All the micro firms belong to the formal or registered sector. Regression analysis was performed to analyze the relationship between labor productivity The productivity gap between formal micro and various firm-characteristics. Unless stated firms and informal firms surveyed grows at otherwise, all the results for labor productivity higher levels of labor productivity. Focusing continue to hold even after accounting for on the mean level of labor productivity, differences in basic firm characteristics including there is no significant difference in the gap firm-size (log of number of employees), age of between micro and informal firms surveyed the firm, sector of activity, regional location, with respect to firm’s age, sector of activity and the number of years of experience of the (manufacturing vs. services), and firm-size main decision-maker. (number of employees). However, the gap does vary significantly across regions (Figure 5). KENYA INFORMAL ENTERPRISES 11 3. Productivity Figure 5: Labor productivity is lower among informal firms compared with formal micro firms, but the gap varies by region 12.0 11.2 11.3 10.8 10.9 9.7 9.7 10.1 10.0 9.6 Monthly sales per worker (KES, logs) 9.2 9.2 8.0 6.0 4.0 2.0 0 Central Nyanza Mombasa Nairobi Nakuru Informal Micro Source: Kenya Informal Enterprise Survey, 2013 Labor productivity is significantly higher for lower in Mombasa and Nyanza compared with formal micro firms compared with the sampled the other three regions (panel A, Figure 6).4 informal firms in all the regions, but the gap For instance, in Nairobi, labor productivity is significantly smaller in Nakuru than in any of is almost twice the level in Mombasa. These the other four regions. results are robust to some basic controls such as firmsize (number of employees at the firm), While there is substantial work on firm’s age, sector (manufacturing vs. services), determinants of labor productivity for firms in gender of the manager, and the level of the formal or registered sector, there is little education of the manager. work in this area for informal sector firms. For instance, studies of formal sector firms show There is a fair amount of research on the that labor productivity and other measures of impact of firmsize on firm productivity. Large firm-performance are much higher for older firms enjoy economies of scale while small firms, firms that are larger, and firms managed firms tend to be more flexible and adapt more by men rather than women. Regional or sub- quickly to new market opportunities. While the national differences have also been found in a majority of the evidence in this area suggests number of studies. that large firms have higher productivity than small firms, the contrary evidence cannot be For the informal firms surveyed in Kenya, the neglected. The issue of firmsize is of special mean value of labor productivity equals KES interest to the informal sector. One view is that 22,481, and the median value is KES 13,000.3 informal firms are inefficiently small and hence However, there are sharp differences in labor not capable of contributing to vibrant growth productivity along a number of dimensions. of the private sector. Across regions, labor productivity is significantly Labor productivity is defined as value of sales per 3 employee in a regular month over the last one year. While 4 Unless stated otherwise, all the results discussed below this is only one measure of firm performance, it provides are statistically significant at the 10 percent level or better useful information on how productive labor is on average. and are robust to region fixed effects. 12 KENYA INFORMAL ENTERPRISES 3. Productivity Can we expect firm productivity to improve than large informal firms where firmsize is as informal firms get bigger? There is very measured by the number of employees at little by way of formal work on this issue, and the firm. They conclude that even though the studies that do exist show mixed results. poor performance of informal firms is typically For example, Benjamin and Mbaye (2012) use attributed to their small size vis-à-vis registered survey data of 900 formal and informal firms that or formal sector firms, incremental increases in they collected in West Africa. They distinguish the size of informal firms do not necessarily between the relatively large vs. small informal imply a narrowing of the formal-informal firm firms and find that the large informal firms productivity gap. have much higher productivity (labor and total productivity) than the small informal firms. While a proper analysis of the firm-size and The authors suggest that the large informal productivity relationship for Kenya would firms are at the fringes of the formal-informal require a rigorous empirical analysis beyond divide and therefore much closer to the formal the scope of this note, preliminary results sector firms in terms of productivity and other for Kenya show that increasing firmsize may characteristics than the small informal firms. A not necessarily translate to higher labor similar result is found by McKenzie and Sakho productivity. That is, for the informal firms (2010) who find that owners of large firms that surveyed in Kenya, labor productivity is lower have managed to stay informal have higher for the relatively larger firms and significantly entrepreneurial ability than owners of formal so, once region specific and sector specific firms, potentially indicating higher productivity differences in labor productivity are taken into of large informal firms over small informal account. For example, labor productivity for firms. However, Amin and Islam (2015) use firms with a single employee averages KES data for over 500 informal or unregistered firms 24,096 while labor productivity for firms with in seven countries in Africa and find different more than one employee averages KES 21,628 results. They find robust evidence that small (panel B, figure 6). informal firms have higher labor productivity Figure 6: Variations in labor productivity of informal firms Panel A Panel B Panel C 35 25 24 30 28 31 24 30 25 Montly sales per worker (KES '000) Montly sales per worker (KES '000) Montly sales per worker (KES '000) 26 24 22 25 21 23 20 20 20 18 23 17 15 15 22 22 22 10 10 21 5 5 21 0 20 0 More than one Single asa za ru al bi Services Rest of Furniture ntr employee iro employee rm ku an mb Na Na Ny Ce manufacturing Mo Source: Kenya Informal Enterprise Survey, 2013 KENYA INFORMAL ENTERPRISES 13 3. Productivity One explanation here could be that a larger among relatively older firms.6 The importance firmsize raises evasion costs associated with of human capital and the level of education being informal and this evasion expenditure for overall economic development is now affects firm performance. However, it is also well established. Some work is also beginning possible that the most productive large firms to emerge explaining differences in labor formalize, biasing labor productivity among productivity between formal and informal the remaining large informal firms towards a firms based on the level of education of firm lower level. managers. Sector specific differences in labor For the case of informal firms surveyed in productivity are also observed in the sample Kenya, as predicted above, labor productivity of informal firms. Labor productivity is much increases with a firm’s age (panel A, figure higher in the manufacturing sector compared 7). For example, labor productivity for with the services sector. Further, sampled firms firms above the median age of four years in the furniture industry stand out with a labor averages KES 25,505 compared with a much productivity level that is significantly higher lower KES 19,649 for the remaining firms. than for firms surveyed in the services sector There is no difference in the age-to-labor and the rest of manufacturing (panel C, figure productivity relationship between firms in the 6). For example, labor productivity for firms in manufacturing and services sector, by firmsize the furniture industry is about 1.3 times the (number of employees), and the gender of the level in the rest of the sample. Differences manager. However, firms in the furniture sector in location of firms, firmsize, age of the firm, again stand out with younger firms showing a and the education level of the manager do much higher level of labor productivity than not seem to the driving force behind these older firms (panel B, figure 7). productivity differences. The education level of manager is highly Labor productivity for firms surveyed is higher correlated with the level of labor productivity among relatively older firms and firms with of the surveyed firm (panel C, figure 7). For more educated managers.5 example, labor productivity for firms with managers that have no education or only A fairly large literature exists on differences primary education is only 72 percent of that in firm productivity depending on the age of of firms with managers that have vocational the firm. Natural selection, whereby the less training or a university degree. Education efficient firms are weeded out, and learning- matters for labor productivity for the sampled by-doing effects that favor longer tenures firms in both the manufacturing and services suggest that firm productivity should be higher sector. 6 Interestingly, for formal firms in the manufacturing sector in Kenya, this does not hold true. In some subsectors—and for the manufacturing sector as a whole—low-productivity firms employ more workers than high-productivity firms. This result contrasts with results for the European Union, 5 The positive relationship between labor productivity and a where low-productivity firms are always smaller than the firm’s age becomes statistically weak and insignificant at the median-productivity firm and high productive firms are 10 percent level when we control for the number of years of 5–12 times larger than the median-productivity firm (see experience of the main decision maker in the industry. Kenya Economic Update, December 2014, Issue 11). 14 KENYA INFORMAL ENTERPRISES 3. Productivity Figure 7: Education level of the manager is positively correlated with labor productivity of the informal firms Panel B: Furniture industry 4 2 Labor productiivty (KES, logs, residuals) Labor productiivty (KES, logs, residuals) 2 1 0 0 -2 -1 -4 -2 -2 -1 0 1 2 -2 -1 0 1 2 coef = .13651961, (robust) se = .05337474, t = 2.56 -.37555935, (robust) se = .13841094, coef = - t = 2.71 Panel C 30 Manufacturing Services 26 26 24 Monthly sales per worker (KES '000) 25 22 21 20 19 16 15 10 5 0 No or primary Secondary Vocational training education education or university degree Source: Kenya Informal Enterprise Survey, 2013 Note: Panel A and B contain partial scatter plots obtained after controlling for regional fixed effects There is also evidence that gender disparity also holds when we look at median values is less among informal firms surveyed than instead of the mean values (as above) of labor among the formal sector firms. That is, while productivity. That is, for the sample of informal labor productivity is significantly lower for firms firms, the median labor productivity for female with a female manager among informal and vs. male managed firms is KES 12,250 vs. KES formal micro firms surveyed, this gender-based 13,167, respectively. For the formal micro firms, gap is significantly smaller for the informal median labor productivity for female managed firms surveyed compared with firms in the firms equals KES 31,250 compared with KES formal sector. Average labor productivity for 61,111 for male managed firms. the surveyed informal firms managed by men is higher by KES 6,881 (KES 25,290 vs. KES This note also provides some analysis to 18,409). The corresponding gap for the formal explore whether improvements in the business micro firms is much larger at KES 125,456 (KES environment translate into higher levels of 219,675 vs. KES 94,219), which in relative terms productivity, replicating a similar analysis by is roughly three times as large. This result for Gelb et al (2009). They speculate that when the gender-based gap in labor productivity the business environment improves, gaps for informal vs. formal micro firms surveyed in productivity between formal and informal KENYA INFORMAL ENTERPRISES 15 3. Productivity firms will emerge. In Kenya, evidence suggests very little distinction between the productivity that between 2007 and 2013 the business of formal and the sample of informal firms environment changed significantly, and over in 2007, formal firms’ productivity became the same period productivity gaps between substantially higher than informal firms by 2013. formal and informal firms surveyed emerged. (See Annex 3 for the methodological approach In other words, the investment climate has and empirical findings). changed in such a way that while there was 16 KENYA INFORMAL ENTERPRISES SECTION FOUR FIRM DYNAMICS F irm dynamics, measured by an increase in employees, machines, and space used by the firm, suggests that firms in the furniture with a firm’s age. Among firms that are older than the median age (four years in our sample), about 32 percent expanded compared with a industry, older firms, firms with more educated much lower 22 percent of younger firms. As we managers, and those located in the Central might expect, education level of the manager and Nairobi regions are more dynamic. is significantly positively correlated with the probability of firm expansion. Seventeen In a survey question, firms were asked if over percent of firms surveyed with managers the last three years they had expanded the that have no education or primary education number of employees, machines, or space expanded over the last three years. The used. In another question, firms were asked corresponding figure for the remaining firms about the current number of workers at the that have managers with secondary education, firm and when the firm started operations. vocational training, or university degrees is Firms that answered in the affirmative to the significantly higher at 31 percent. first question are defined as dynamic firms. A second definition of dynamism is if the number Again, in terms of expansion, manufacturing of employees at the firm increased since it firms outperform services firms with 31 began operations. The two measures overlap percent of the former vs. a significantly but not entirely with correlation coefficient of lower 24 percent of the latter in our sample 0.37. The results discussed below hold for both experiencing expansion. It should be noted measures in the qualitative sense and so we that this difference between manufacturing and focus only on the first measure. It should be services firms is entirely driven by the furniture noted that information on exiting firms or firms industry. Approximately 43 percent of firms in that close down is not available in the survey. the furniture industry surveyed experienced Since exiting firms have different dynamics expansion, compared with a significantly lower than the surviving firms, our results below for 27 percent of firms in the rest of manufacturing firm dynamics are potentially biased as far as and 24 percent of firms in the services sector. the whole sample is concerned. The difference between firms in the services sector and the rest of manufacturing discussed About 27 percent of the informal firms surveyed here is not significant. increased employees, machines, or space used (henceforth, expanded or expansion) over the We also looked at the regional level and found last three years. There is substantial literature firms surveyed in the Central and Nairobi that suggests that younger firms are more regions to be significantly more dynamic dynamic than older firms. We find no evidence than in Nyanza and Mombasa in terms of the of this in our sample. In fact, the probability of percentage of firms that expanded (Figure 8). expansion is significantly positively associated We examined a number of business climate KENYA INFORMAL ENTERPRISES 17 4. Firm Dynamics measures but found no consistent pattern to bribe payments. The percentage of firms of any relationship with the likelihood of firm that expanded is significantly lower among expansion in our sample. For example, the firms that report making informal payments percentage of firms that expanded is only or bribes to remain unregistered (18 percent) poorly correlated with whether or not the compared with the rest of the firms (31 percent). firm faced power outages or water shortages. This finding regarding informal payments does Expansion is also poorly correlated with not hold for our second definition of a dynamic measures of crime and security, and with firm based on employment growth since the various firm perceptions about factors such as firm started operating and may signal a weaker land and access to finance being an obstacle for relationship between bribery and workforce. their business. One exception we find relates Figure 8: Percentage of firms that increased number of employees, machines, or space used over the last three years varies across regions 50 45 43 40 38 36 35 Percentage of rms 30 27 27 26 26 25 20 18 15 15 10 5 0 All rms Central Nyanza Mombasa Nairobi Nakuru Furniture Manufacturing Services Increased workers, machines or space used in last three years Source: Kenya Informal Enterprise Survey, 2013 18 KENYA INFORMAL ENTERPRISES SECTION FIVE REMAINING INFORMAL F ormalization, or bringing the informal firms within the fold of the formal sector, is suggested as a possible solution to low percent of small formal firms, 5 percent of medium formal firms, and only 2 percent of large formal firms were not registered at start- income levels and lack of dynamism in the up. The median length of operations without informal sector. Moving to the formal sector registration for these previously informal firms is expected to improve access to physical is one or two years for all size categories. It infrastructure, finance, and public services; the seems that the opportunity for becoming move also benefits the government through formal may be associated with what takes place better compliance with the laws and more in the earliest year or two of a startup. tax revenue. However, the move to the formal sector has been notoriously difficult to achieve In our sample of informal firms, larger firms (in and slow in most countries. Hence, an important terms of sales, employment) are significantly question here is whether informal firms want to more likely to report willingness to register register, and what sorts of informal firms are than smaller firms. Sixty percent of firms with more likely to do so. more than one employee report wanting to register compared with just 49 percent of firms The informality survey in Kenya asked firm with a single employee. Second, manufacturing owners if they would like their firms to firms report wanting to register significantly be registered. Close to 53 percent of the more than services firms, but again, this firm owners surveyed responded ‘Yes’ to difference is entirely due to the furniture sector. the question. The desire to register is more That is, 70 percent of the surveyed firms in the common in our sample among firms that are furniture industry report wanting to register, larger and more dynamic, firms in the furniture and this is significantly higher than the 53 industry, firms located in Nyanza region, and percent in the remaining manufacturing sector firms that face water, electricity, crime, access and 49 percent in the services sector. to land, access to finance, and corruption constraints. Third, regional differences are noticeable with the proportion of sampled firms wanting Comparing the behavior of firms that remain to register being significantly higher in the informal with formal firms that began in the Nyanza region (80 percent) than in any of informal economy suggests that there may be the other regions. Firms in the Central region little crossover between the groups. La Porta report a desire to register only 33 percent of and Schleifer’s recent paper confirms that very the time, and this is significantly lower than the few firms crossover from the informal to formal corresponding figures for Nyanza, Nairobi and sector;7 21 percent of micro formal firms, 11 Nakuru regions. 7 Rafael La Porta, and Andrei Shleifer, (2014), “Informality and Development,” Journal of Economic Perspectives, 28(3): 109-126. KENYA INFORMAL ENTERPRISES 19 5. Remaining Informal Figure 9: Willingness to register is higher among firms that consider the various obstacles as severe for their business operations 80 Percentage of rms who would like to register 72 70 64 57 60 58 60 49 49 46 50 43 41 40 30 20 10 0 Access to land Electricity Water supply Access to nance Crime Severe obstacle Not severe obstacle Source: Kenya Informal Enterprise Survey, 2013 Fourth, if informal firms expect formalization firms not registering, but there are sharp to ease the difficulties they face in obtaining differences by region, firm productivity, and finance, accessing electricity, water, and other education level of the manager. In the survey, public services, and dealing with corruption firms were asked if the following were reasons and harassment from public officials, the why they had not registered: cost of registering willingness to register may be higher among (time, fees and paper work required), taxes that firms that consider these problems to be registered business have to pay, inspections more constraining relative to other firms. and meeting with government officials post The survey for Kenya does not reject such a registration, bribes registered businesses need possibility. That is, firms that consider these to pay, and no benefit from registering. obstacles to be severe are significantly more likely to show willingness to register than firms Figure 9 shows how surveyed firms view that do not find these obstacles to be severe. these costs. In the full sample, taxes Figure 8 provides more detail on this issue. following registration are cited as a reason for not registering for 57 percent of the The costs associated with registering and firms, followed by the cost of registering taxes that registered businesses have to pay (56 percent), no benefit from registering (47 are the most common reasons for surveyed percent), inspections and meetings required Figure 10: Reasons for not registering vary across regions and by education level of the manager 100 Reason for not registering (% of rms) 90 85 84 86 80 80 70 65 66 65 66 59 57 58 58 60 53 54 55 57 50 48 52 52 50 44 45 42 40 38 35 33 37 37 36 37 35 31 31 35 30 26 22 20 10 9 7 10 10 0 Central Nyanza Mombasa Nairobi Nakuru No or Primary Secondary education Vocational or education University degree Time, fees and paper work required for registering Taxes paid by registered businesses Inspections and meeting with public o cials Bribes that registered rms need to pay No bene t from registering Source: Kenya Informal Enterprise Survey, 2013 20 KENYA INFORMAL ENTERPRISES 5. Remaining Informal (37 percent), and bribes paid (36 percent). from registering would help further the cause Considered individually, these reasons for not of formalization. registering show significant variation across different firm types. For example, older firms In the survey, firms were asked if registering are significantly more likely to report bribe would bring the following potential benefits: payments and no need to register as reasons better access to finance; better access to raw for not registering compared to younger firms. materials, infrastructure, and government The cost of registering disincentivizes a higher services; less bribes to pay; and being able to proportion of relatively larger firms (sales and issue receipts to customers. About 77 percent employment wise), and controlling for region of firms surveyed consider better access to specific effects, more dynamic firms are more finance as a benefit, followed by better access likely to report taxes that registered businesses to raw materials, infrastructure and government have to pay as a reason for not registering. services (61 percent), issue of receipts (42 Interestingly, 21 percent of firms reported percent) and less bribes to pay (40 percent). having to pay a bribe in order to remain Regional differences abound. For example, unregistered and continue operations. less bribes to pay is sees as a potential benefit for over half of firms in Mombasa, Nairobi, and The most glaring differences in reasons given Nakuru regions. This is significantly higher than for not registering by the surveyed firms are what we find in Nyanza (21 percent) and the seen across regions, labor productivity, and Central region (4 percent). the education level of the manager. Higher labor productivity is associated with a higher Panel A of figure 10 contains the full distribution proportion of firms reporting each of the of regional differences. Controlling for region above as reasons, with the exception of paying specific differences (region fixed effects), taxes, for not registering. Figure 9 shows the firms that are larger in terms of monthly sales distribution by region and the education level and firms that have higher labor productivity of the manager. Many of the differences shown are significantly more likely to report each in these figures are significant. For example, of the above factors as potential benefits of no benefit from registering is a reason for only registering. For example, being able to issue 9.5 percent of the firms in Nyanza region, and receipts is a potential benefit of registering for significantly lower than what we find in each of 41 percent of the firms that exhibit lower labor the other regions. productivity compared with much higher 48 percent of firms with higher labor productivity. The findings in the previous paragraph shed light on the possible course of policy measures The benefits to registering also seem to be to facilitate registration. That is, to the extent reported more frequently among firms that that firm’s perceptions regarding the various feel constrained in their current operations. obstacles discussed above are due to lack of Firms that report access to finance as a severe proper information, policies aimed at providing obstacle for their business are more likely to better information to the firms would be useful; consider better access to finance following and where the perceptions mirror objective registration to be a potential benefit (panel B, reality, policies aimed at reducing registration figure 10). The same holds for firms that report costs, taxes, corruption and improving benefits corruption as a severe obstacle and perceive KENYA INFORMAL ENTERPRISES 21 5. Remaining Informal Figure 11: Perceived benefits ofregistration vary by region and firms’perceived severity of the obstacles Panel A: Bene ts from registering by region 100 90 90 83 82 77 78 75 Bene t from registering (% of rms) 80 71 70 67 61 59 60 55 55 50 50 47 50 44 46 40 40 42 40 30 30 21 20 17 10 4 0 All rms Central Nyanza Mombasa Nairobi Nakuru Better access to nance Better access to raw materials, infrastructure and government services Less bribes to pay Being able to issue receipts Panel B: Better access to nance is a bene t from registering Panel C: Less bribes to pay is a bene t from registering 100 60 53 84 50 80 Percentage of rms Percentage of rms 63 40 37 60 30 40 20 20 10 0 0 Less bribes to pay Access to nance is a severe obstacle Access to nance is not a severe obstacle Corruption is a severe obstacle Corruption is not a severe obstacle Source: Kenya Informal Enterprise Survey, 2013 less bribes to pay as a potential benefit of on firm’s perceptions. One problem with registration (panel C, figure 10), and among such perceptions is that they may not always firms that report access to land as a severe reflect the underlying objective reality of obstacle and better access to raw materials, the costs and benefits of registering. For physical infrastructure, and government instance, lack of proper information may bias services as a potential benefit of registration. a firm’s perceptions. Fortunately, in the case Interestingly, we do not find any significant of Kenya, the World Bank’s Sub-National correlation between the potential benefit of Doing Business project provides information better access to raw materials, infrastructure, on select business environment measures for and government services and whether or Mombasa, Nairobi, and Nakuru regions. The not electricity and water supply are severe Sub-National Doing Business measures cover obstacles for firms’ current operations. areas including starting a business, registering property, enforcing a contract, and dealing with a construction permit. We find some The discussion above as to whether or not evidence that, at least to some extent, firms’ firms would like to be registered, as well perceptions reflect objective reality. That is, as the obstacles to registering are based the proportion of firms surveyed that would 22 KENYA INFORMAL ENTERPRISES 5. Remaining Informal like to be registered is significantly higher in high cost of registering as to why they are not regions where registering a business is less registered is 54 percent, 65 percent, and 84 cumbersome to the firms (overall composite percent in these three regions, respectively. measure of registering based on the number of procedures, time and cost of registering, In terms of the reported benefits from and the minimum paid up capital required). registering, a better contract enforcement Figure 11 provides the details. system, as measured by Sub-National Doing Business (composite measure of procedures, We also find that more cumbersome business time and cost of enforcing contract), is also registration processes are associated with associated with a proportionately larger number proportionately more firms on average that of the sampled firms that report being able to report a high cost of registering as a reason issue receipts to customers and suppliers as for not registering, although this result does a benefit of registration. However, this result not hold for Nakuru and Mombasa (Figure does not hold for Nairobi and Nakuru; the 11). Looking separately at the time and the result is also statistically insignificant in the full monetary cost of registering as measured by sample. Figure 12 provides the details. Sub-National Doing Business project, the proportion of surveyed firms that report high A more cumbersome business registration costs (time, fees, etc.) as reasons why they are system, as measured by Sub-National Doing not registered is significantly higher in regions Business, is associated with lower labor with high time cost (as measured by Doing productivity and a smaller firmsize of informal Business), but there is no such relationship for firms surveyed. While business registration the Sub-National Doing Business’ monetary is not the only element of the business cost of registering. For example, according to environment that may be important to informal Sub-National Doing Business, it takes 32 days sector firms, it is perhaps the most important to register a business in Nairobi, followed by proxy measure of broader institutional 37 days in Mombasa, and 38 days in Nakuru. environment faced by them. As above, we use The percentage of firms surveyed that cite a the composite Sub-National Doing Business Figure 12: Ease of registering a business is associated with greater Figure 13: Better contract enforcement in Mombasa is associated willingness among informal firms to register with more firms reporting being able to issue receipts to customers and suppliers as a benefit of registration 90 56 55 84 80 54 70 65 Percentage of rms 61 52 60 54 Percentage of rms 52 50 50 40 48 40 47 46 30 46 20 44 10 42 0 Nairobi Nakuru Mombasa (Best ranked in ease of registering (Worst ranked in ease of registering 40 a business, Doing Business) a business, Doing Business) Mombasa Nakuru Nairobi (best ranked by Doing Business (worst ranked by Doing Business % of rms that would like to be registered in contract enforcement) in contract enforcement) % of rms that for whom high registration cost is a reason for not registering Being able to issue to receipts is a bene t due to registration Source: Enterprise Surveys Source: Kenya Informal Enterprise Survey, 2013 KENYA INFORMAL ENTERPRISES 23 5. Remaining Informal ranking for starting a business in terms of the in Nairobi, the best ranked region; this is not number of procedures required to register, too different from the mean of 3.9 employees in the time it takes to complete the procedures, the next best region of Nakuru. However, firms the cost of complying with the registration in Mombasa, the worst ranked region, hire only procedures, and the minimum paid up 2.5 employees, significantly less than what we capital required. For this composite measure find in Nairobi as well as in Nakuru. Figure 16 and for the firms surveyed, Nairobi is the provides the details for labor productivity. best ranked region followed by Nakuru and Figure 14: On average, labor productivity increases with greater then Mombasa. We looked at both firmsize ease of registering a business (employment, sales) and labor productivity to 30,000 Labor productivity (KES, median values) see how firm performance compares across 25,000 25,000 regions depending on the ease of registering businesses. 20,000 15,000 15,000 15,000 Overall, in our sample of informal firms, 10,000 firmsize and labor productivity are both 5,000 significantly positively correlated with greater ease of registering a business, although the 0 Nairobi Nakuru Mombasa (Best ranked in ease of (Worst ranked in ease of result does not hold for all bilateral regional registering a business, registering a business, Doing Business) Doing Business) comparisons. For example, the mean number Source: Kenya Informal Enterprise Survey, 2013 of employees at the firm equals 3.4 employees 24 KENYA INFORMAL ENTERPRISES SECTION SIX SUMMARY AND POLICY ADVICE T his note provided an overview of the landscape of informal firms surveyed by the World Bank’s Enterprise Surveys from suppliers (19 percent) and microfinance (16 percent). However, the overwhelming majority of informal enterprises surveyed draw in Kenya, with a particular focus on their on finance through internal sources (87 percent) operating characteristics, key constraints, and family/friends (35 percent). Smaller firms access to finance, labor productivity, and (as measured by the number of employees) in constraints and incentives for registration. the survey are more likely to consider access to Very interesting patterns emerged from the finance as a key constraint, while using supplier data and analysis, some of which could inform credit or relying on banks is associated with policy and investment choices of both public larger, more dynamic firms with higher labor and private sector players. productivity, and better educated owners. Firstly, in our sample, attributes of the Regional differences are pronounced. principal owner are important. For example, Mombasa consistently stands out as the a key finding of the analysis is the role played most challenging region for surveyed firms by the education of the owner in almost all to access finance, whereas Nakuru is on the elements of firm performance. More educated opposite end of the spectrum for financial owners have more dynamic and productive access. Labor productivity is significantly lower firms, are less financially constrained, more for firms surveyed in Mombasa and Nyanza, likely to use banks and formal sources of the gap between productivity in the formal finance for their businesses, and even less likely and informal sector is the highest, and firms to experience theft and other security-related from these two regions are the least likely losses. The gender of the owner also matters. to expand and grow. Mombasa and Nyanza That is, in our sample, female owned firms are have the lowest percent of firms that want less productive, less dynamic, and pay their to register. On the positive side, there is no workers less compared to male owned firms. firm in Nyanza that perceives corruption an obstacle, while crime and electricity are not Secondly, access to finance is consistently major constraints in Mombasa, compared to identified as the largest obstacle for informal other regions. Nairobi and Central regions firms surveyed in Kenya, with over 60 percent consistently stand out with the sampled firms ranking it as the number one obstacle. Other having highest labor productivity and most key constraints include electricity, access to dynamic firms, and Nairobi is ranked top in land, and corruption. Bank credit as a source ease of doing business; however, it is also of working capital is low, with only 9 percent where corruption as a constraint stands out of informal firms using banks to finance their relative to other regions. operations, compared to firms using credit KENYA INFORMAL ENTERPRISES 25 6. Summary and Policy Advice Furniture also stands out in many respects when combined with reductions in labor taxes. amongst all sectors. In terms of finance, From an informal firm’s perspective, there are surveyed firms in the furniture sector are less also compelling reasons for both becoming likely to use their own funds, and much more formal and remaining informal. Firms perceive likely to use supplier credit and bank finance. formalization can lead to better access to credit Surveyed firms in the furniture sector have,on and protection of property rights, while taxes, average, the highest labor productivity, the corruption, and bureaucracy are disincentives most dynamic firms, and are more likely to hire to formalize. more employees. Firms in this sector are also more inclined to register their businesses. The question then becomes about identifying the most effective means to foster business The majority of firms surveyed prefer to registration in Kenya. While there is evidence remain informal because of taxes and the that simplifying the process and lowering cost of registration, especially younger firms the costs to start a business are important and those that are more dynamic. Conversely, predictors of firm registrations, overall, efforts the main reason informal enterprises are at formalization through streamlining business interested in formalizing is greater perceived registration processes are mixed (see Kaplan, access to finance. The proportion of firms that Peiro and Siera (2007); Straub (2005); McKenzie want to register in our sample is significantly and Sakho (2007) to name a few. higher in regions where registering a business is less cumbersome, and the converse holds Klapper and Love (2010) find that small reforms true— firms are more likely to not want to (less than a 40 percent reduction in procedures register in regions with more cumbersome or 60 percent reduction in costs) do not have registration processes. In terms of impact, a significant effect on new registrations, and a more cumbersome registration process is that there are important synergies in multiple linked to lower labor productivity. reforms of two or more business environment indicators. A key issue for policy makers is then whether there is a public rationale for attempting to Kaplan et al (2007) suggest that in cases formalize small-scale firms. McKenzie and where the impact of reforms are modest Bruhn (2013) make the case that there are or temporary, it is because of the burden several compelling reasons to try and bring of complementary procedures and overall larger and more profitable informal firms into institutional quality. More inclusive programs the formal system, including increasing revenue could have a much bigger impact on start- mobilization and widening the tax base, and ups. It should also be noted that burdensome leveling the playing field between large informal registration regulations may not be the firms and efficient formal firms which will only important barrier to firm creation or foster growth and productivity. Sharma (2009) formalization. Instead, the cost of paying taxes highlights potential gains in labor productivity may still outweigh the benefits of registering, after business regulation reforms, especially especially when credit is scarce. 26 KENYA INFORMAL ENTERPRISES 6. Summary and Policy Advice Given the experience globally, and the While bringing some of the larger, more context in Kenya, this note suggests some productive firms in to the formal sector can policy recommendations for consideration. benefit Kenya’s growth and employment Firstly, attempts at business registration trajectory, the reality is that there will remain and broader business environment reforms, a large cadre of informal firms for whom the especially at the county level, appear to be costs of registration outweigh the benefits. having an impact on informal firms’ incentives These small enterprises nonetheless provide to register, and are linked to increases in labor income and employment to the vast majority productivity. Therefore, these reforms should of the unemployed, and many of them may be accelerated and broadened regionally. eventually grow into more dynamic enterprises. Secondly, there is a compelling case to be Therefore they also merit support. Increasing made for the impact of business environment the skills of the main owner appears to be the reforms when they are broader, deeper, and most effective means to increasing productivity include stronger institutional capacity and and growth, while lowering barriers to financial stronger enforcement. Therefore, a reform access could further support microenterprises agenda should entail substantial changes to to increase survival rates and maximize their the modus operandi, and include support to opportunity to grow and expand. build the capacity of enforcing institutions. KENYA INFORMAL ENTERPRISES 27 REFERENCES Amin, Mohammad, and Asif Islam. (2015). “Are Large Firms More Productive than Small Informal Firms? Evidence from Firm-level Surveys in Africa.” Mimeograph. Benjamin, Nancy, and Ahmadou Aly Mbaye. (2012). “The Informal Sector, Productivity, and Enforcement in West Africa: A Firm-level Analysis.” Review of Development Economics 16(4): 664-680. Gelb, Alan, Taye Mengistae, Vijaya Ramachandran, and Manju Kedia Shah. (2009). “To Formalize or Not to Formalize? Comparisons of Microenterprise Data from Southern and Eastern Africa.” Working Paper 175, Center for Global Development. La Porta, Rafael, and Andrei Shleifer. (2014). “Informality and Development.” Journal of Economic Perspectives, 28(3): 109-126. Mckenzie, David, and Yaye Seynabou Sakho. (2010). “Does it Pay Firms to Register for Taxes? The Impact of Formality on Firm Profitability.” Journal of Development Economics 91 (2010): 15-24. World Bank. (2013).“Kenya Informal Enterprise Survey.” World Bank. (2014). “Anchoring High Growth.” Kenya Economic Update Issue 11, December 2014. 28 KENYA INFORMAL ENTERPRISES Annex 1: Summary statistics and regressions TABLE 5: Summary Statistics for the Full Sample of Firms in Kenya, Informal Survey (2013) Std. 95% confidence Variable Observations Mean deviation Min. Max. interval % of firms that belong to the manufacturing sector 533 48 50 0 100 44 53 (Log of) Number of workers at the firm during a normal 526 0.3 0.5 0.0 4 0.3 0.4 month (log of) Total sales (LCUs) of the firm during a normal 483 10 1 7 14 10 10 month (Log of) Sales per worker during a normal month 483 9 1 7 13 9 10 % of firms located within household premises 533 13 34 0 100 10 16 % of firms that have more than one business activity 427 18 38 0 100 14 21 % of owners of the firm that are female 530 38 47 0 100 34 42 % of firms that have at least one female owner 530 40 49 0 100 36 45 Largest owner acquired ownership of the firm by 529 94 23 0 100 92 96 starting the business alone or with partners (% of firms) % of firms with a female main decision maker 530 38 49 0 100 34 42 % of firms that have a married largest owner 528 76 43 0 100 73 80 Number of years the largest owner has lived in the city 505 18 13 1 56 17 20 where the business is located Largest owner migrated to the city where the business 505 79 41 0 100 75 82 is located from another city in the country or from another country (% of firms) Largest owner currently has a job in the formal sector 524 15 35 0 100 11 18 or has been looking for one over the past two years (% of firms) For firms that use electricity, number of power outages 233 7 15 0 144 5 9 faced during the last month including no power outages For firms that use electricity, % of electricity from 245 0.2 2 0 25 0.0 0.4 generators including zero for firms that do not own/ share/use a generator For firms that use water for business purposes, number 112 2 3 0 15 1 2 of incidents of water insufficiency during the last month including zero for firms with no such incidents Amount paid for security as a percentage of total sales 509 1 5 0 67 1 2 in a regular month including zero amount for firms that did not pay for security Losses due to crime during the last month as a 528 3 19 0 333 1 5 percentage of sales in a regular month including zero losses for firms that had no such losses Number of crime incidents experienced by the firm in 530 0.1 1 0 6 0.1 0.2 the last month including zero incidents for firms with no such incidents % of firms for whom own funds are the most 481 77 42 0 100 74 81 commonly used source of finance for their day-to-day operations KENYA INFORMAL ENTERPRISES 29 Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval For firms that bought any machinery, vehicles or other 99 76 43 0 100 67 84 means of transport, equipment, land or buildings during the last three months, % reporting own funds as the most important source of finance for the purchase Number of family members of the owners working at 523 44 47 0 100 40 48 the firm as a percentage of all workers during the last month % of firms that have a physical location 533 78 41 0 100 75 82 Number of owners in the business 531 1 0 1 3 1.1 1.1 % of firms that have a female largest owner 530 39 49 0 100 35 43 Number of businesses or activities started by the 523 1 1 0 10 0.9 1.0 largest owner in the last three years For the sample of firms whose largest owner started 414 1 0 0 5 1.0 1.1 a business during the last three years, number of businesses still owned or managed by the largest owner % of firms that had an increase in the number of 528 27 44 0 100 23 31 employees, machinery used or the space occupied during the last three years % of firms where the largest owner is also the main 532 97 18 0 100 95 98 decision maker % of firms with a female main decision maker 530 38 49 0 100 34 42 Number of years of experience that the main decision 523 8 7 0 50 7 9 maker has working in the sector Age of the firm 522 6 6 0 43 6 7 Number of employees at the firm when the firm 520 1 1 1 8 1.3 1.5 started operations % of firms that were registered at start up 528 1 11 0 100 0.3 2.3 Age of the largest owner 520 35 9 18 85 34 36 % of firms that have a married largest owner 528 76 43 0 100 73 80 For the sample of firms with a largest owner who 416 64 48 0 100 60 69 has not spent his/her entire life in the city, % of firms where the largest owner migrated from a smaller city For the sample of firms with a largest owner who 416 26 44 0 100 22 30 has not spent his/her entire life in the city, % of firms where the largest owner migrated from a bigger or same size city in the same country For the sample of firms with a largest owner who 416 10 30 0 100 7 13 has not spent his/her entire life in the city, % of firms where the largest owner migrated from a different country Number of people who live in the largest owner's 522 4 2 0 35 3.6 4.0 household premises Number of people less than six years old who live in the 524 1 1 0 8 0.7 0.8 largest owner's household premises Number of people in largest owner's household 526 0.3 1 0 2 0.2 0.3 premises who have employment under a contract % of firms with largest owner having no education or 516 30 46 0 100 26 34 primary education (completed or not) 30 KENYA INFORMAL ENTERPRISES Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval % of firms with largest owner having secondary 516 34 47 0 100 30 38 education (completed or not) % of firms with largest owner having vocational 516 36 48 0 100 32 40 training or university training (completed or not) % of firms with ether of largest owner's parents having 454 66 47 0 100 62 71 no education or primary education (completed or not) % of firms with ether of largest owner's parents having 454 16 37 0 100 13 19 secondary education (completed or not) % of firms with ether of largest owner's parents having 454 18 38 0 100 14 21 vocational training or university training (completed or not) % of firms with largest owner's parents owning a 499 42 49 0 100 38 46 business in the past or currently Prior to starting this business, % of firms with largest 521 23 42 0 100 20 27 owner employed in the same activity as the current business Prior to starting this business, % of firms with largest 521 22 41 0 100 18 25 owner employed in a different activity than the current business Prior to starting this business, % of firms with largest 521 15 35 0 100 12 18 owner self- employed in a different activity than the current business Prior to starting this business, % of firms with largest 521 14 35 0 100 11 17 owner self- employed in a same type of activity as the current business Prior to starting this business, % of firms with the 521 22 41 0 100 18 25 largest owner being unemployed Prior to starting this business, % of firms with the 521 4 21 0 100 3 6 largest owner's employment status was different from above For firms with largest owner not being unemployed 205 53 50 0 100 46 60 and not being in the same activity as the current business prior to starting this business, % who changed activity because the change offered a more attractive business activity For firms with largest owner not being unemployed 205 13 33 0 100 8 17 and not being in the same activity as the current business prior to starting this business, % who changed activity because change offered better hours or better location For firms with largest owner not being unemployed 205 34 48 0 100 28 41 and not being in the same activity as the current business prior to starting this business, % who changed activity because the owner could not open a business in the same activity or desired location or for other (than above) reasons % of firms with largest owner currently having a job in 523 4 20 0 100 2 6 a formal (registered) business % of firms whose largest owner tried to get a job in the 503 11 31 0 100 8 14 formal sector during the past two years Among firms whose largest owner tried to get a job 55 13 34 0 100 4 22 in the formal sector during the past two years, % of largest owners who got the job % of firms whose largest owner has insurance 513 10 30 0 100 8 13 KENYA INFORMAL ENTERPRISES 31 Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval For firms located inside household premises, % 70 60 49 0 100 48 72 reporting the main reason to be located inside is that it costs less to run the business from home For firms located inside household premises, % 70 29 46 0 100 18 39 reporting the main reason to be located inside is that it is easier to manage family responsibility along with work For firms located inside household premises, % 70 11 32 0 100 4 19 reporting the main reason to be located inside to be other than above or that there is no benefit from locating inside For firms located outside of household premises, % of 462 45 50 0 100 41 50 firms that have fixed premises and with permanent structure For firms located outside of household premises, % 462 42 49 0 100 37 46 of firms that have fixed premises and with temporary structure For firms located outside of household premises, % of 462 13 34 0 100 10 16 firms that have no fixed premises Total area occupied by the business or activity (square 449 45 183 2 3025 28 62 meters) Owner or owners own the location or space occupied 472 13 34 0 100 10 16 by the business (% of firms) Among businesses whose owners do not own the space 409 82 39 0 100 78 86 occupied by the business, % who pay rent for the space occupied For firms whose owners own the space occupied by the 313 18 38 0 100 14 22 business, % of firms whose owners have a title for the space occupied at the land registry Firm changed its main business location over the last 461 5 21 0 100 3 7 12 months due to lack of formal title for its land (% of firms) Business is located in an industrial zone or cluster (% 471 16 37 0 100 13 19 of firms) Business is located in the city center (% of firms) 473 7 26 0 100 5 9 Limited access to land is a severe obstacle to firm's 467 41 49 0 100 37 46 operations (% of firms) % of firms who use electricity 473 52 50 0 100 47 56 For firms that use electricity, % that are connected to 245 76 43 0 100 70 81 the electricity grid For firms that use electricity, % of firms that 244 84 37 0 100 79 88 experienced power outages during the last month For firms that use electricity and report having power 193 8 16 1 144 6 10 outages in the last month, number of power outages faced by the business in the last month For firms that use electricity and report having power 197 7 25 1 336 4 11 outages in the last month, average duration (hours) of power outages in the last month % of firms that own or share a generator 245 2 15 0 100 0.5 4 For firms that own or share a generator, % of electricity 6 8 9 2 25 -1.4 17 that comes from generators % of firms that use water for business purposes 472 37 48 0 100 32 41 32 KENYA INFORMAL ENTERPRISES Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval For firms that use water for business purposes, % who 175 54 50 0 100 47 62 obtain water form public sources For firms that use water for business purposes, % who 175 35 48 0 100 28 42 obtain water from private sources For firms that use water for business purposes, % who 175 11 31 0 100 6 16 obtain water from both public and private sources For firms that use water for business purposes, % of 114 43 50 0 100 34 52 firms that experienced insufficient water supply during the last month For firms that use water for business purposes and 47 4 3 1 15 3 5 those who report insufficient water supply during the last month, number of incidents of water insufficiency in the last month Average duration of insufficient water supply during 45 15 32 1 160 6 25 the last among firms who use water for business purposes and experienced insufficient water supply incidents during the month % of firms reporting electricity problems as a severe 468 38 49 0 100 34 43 obstacle to their current operations % of firms reporting water problems as a severe 468 23 42 0 100 19 27 obstacle to their current operations % of firms who paid for security during the last month 533 19 39 0 100 15 22 For firms who paid for security during the last month, 76 8 12 0 67 5 10 total spending on security during the last month as a percentage of monthly sales % of firms who experienced losses due to crime during 532 7 25 0 100 5 9 the last month Losses due to crime during the month as a percentage 33 47 60 5 333 25 68 of monthly sales among firms who had positive losses due to crime in the last month Number of incidents of crime in the last month among 35 2 1 1 6 1 2 firms who experienced losses due to crime in the last month % of firms who believe that firms like themselves give 34 53 51 0 100 35 71 informal payments or bribes or protection payments in order to stay in business Business experienced harassment by government 35 60 50 0 100 43 77 officials during the last month (% of firms) % of firms who report crime as a severe obstacle for 528 28 45 0 100 24 32 their operations % of firms who report corruption as a severe obstacle 531 33 47 0 100 29 37 for their operations % of firms who produce or sell under contract for 533 9 29 0 100 7 12 another business or person Number of years the firm has worked with its primary 48 3 2 1 10 2 4 supplier of its main input or sales item Hours of normal operation of the firm per week 532 65 20 3 126 63 67 % of firms that presently use cell phones for their 533 76 43 0 100 72 80 operations % of firms that presently use internet for their 533 3 16 0 100 1 4 operations KENYA INFORMAL ENTERPRISES 33 Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval % of firms that presently use machinery, vehicles or 533 47 50 0 100 42 51 other means of transport or equipment For firms that presently use machinery, vehicles, other 237 46 50 0 100 40 53 means of transport or equipment, % of firms reporting these as less than 3 years old For firms that presently use machinery, vehicles, other 237 26 44 0 100 20 31 means of transport or equipment, % of firms reporting these as 3 to 5 years old For firms that presently use machinery, vehicles, other 237 20 40 0 100 15 25 means of transport or equipment, % of firms reporting these as 5 to 10 years old For firms that presently use machinery, vehicles, other 237 8 27 0 100 5 11 means of transport or equipment, % of firms reporting these as more than 10 years old For firms that presently use machinery, vehicles, other 243 35 48 0 100 29 41 means of transport or equipment, % of firms reporting difficulty with finding spare parts in the last year For firms that presently use machinery, vehicles, other 242 42 49 0 100 35 48 means of transport or equipment, % of firms reporting difficulty with repairing in the last year For firms that presently use machinery, vehicles, other 242 32 47 0 100 26 38 means of transport or equipment, % of firms reporting difficulty with maintenance in the last year Business accounts kept separately from household 522 33 47 0 100 29 37 expenses (% of firms) % of firms that used own funds to finance their day-to- 524 87 34 0 100 84 90 day operations % of firms that used credit from suppliers or advances 526 19 40 0 100 16 23 from customers to finance their day-to-day operations % of firms that used money lenders to finance their 517 9 28 0 100 6 11 day-to-day operations % of firms that used microfinance institutions to 518 16 36 0 100 12 19 finance their day-to-day operations % of firms that used banks to finance their day-to-day 520 9 28 0 100 6 11 operations % of firms that used friends or relatives to finance their 517 35 48 0 100 31 39 day-to-day operations % of firms that used other (than above) sources to 515 5 21 0 100 3 6 finance their day-to-day operations % of firms that in the last three years bought any 528 20 40 0 100 17 24 machinery, vehicles or other means of transport, equipment, land or buildings For firms that spent on machinery, vehicles, 97 24147 31043 0 170000 17891 30404 equipment, land or buildings in the last three years, amount spent in the last 3 years on purchase of new or used machinery (LCUs) For firms that spent on machinery, vehicles, 91 10688 14940 0 79000 7576 13799 equipment, land or buildings in the last three years, amount spent in the last 3 years on purchase of new or used equipment's and tools (LCUs) 34 KENYA INFORMAL ENTERPRISES Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval For firms that spent on machinery, vehicles, 83 19049 74962 0 500000 2681 35418 equipment, land or buildings in the last three years, amount spent in the last 3 years on purchase of new or used vehicles and other means of transport (LCUs) For firms that spent on machinery, vehicles, 90 0.1 1 0 9 -0.1 0.3 equipment, land or buildings in the last three years, amount spent in the last 3 years on the purchase of land (LCUs) For firms that spent on machinery, vehicles, 93 1376 8777 0 60000 -431 3184 equipment, land or buildings in the last three years, amount spent in the last 3 years on the purchase or construction of buildings (LCUs) For firms that spent on machinery, vehicles, 104 88 33 0 100 81 94 equipment, land or buildings in the last three years, % of them who financed the purchase through own funds For firms that spent on machinery, vehicles, 105 10 29 0 100 4 15 equipment, land or buildings in the last three years, % of them who financed the purchase through credit from suppliers or advances from customers For firms that spent on machinery, vehicles, 104 8 27 0 100 2 13 equipment, land or buildings in the last three years, % of them who financed the purchase through moneylenders For firms that spent on machinery, vehicles, 102 12 32 0 100 5 18 equipment, land or buildings in the last three years, % of them who financed the purchase through microfinance institutions For firms that spent on machinery, vehicles, 103 14 34 0 100 7 20 equipment, land or buildings in the last three years, % of them who financed the purchase through banks For firms that spent on machinery, vehicles, 104 23 42 0 100 15 31 equipment, land or buildings in the last three years, % of them who financed the purchase through friends/ relatives For firms that spent on machinery, vehicles, 103 4 19 0 100 0.1 8 equipment, land or buildings in the last three years, % of them who financed the purchase through other than above sources % of firms that have a bank account to run the business 520 34 48 0 100 30 39 For firms that have a bank account to run the business, 175 53 50 0 100 45 60 % of them that use separate bank account for their household % of firms that have a loan against the firm or against 523 9 28 0 100 6 11 the largest owner for business purposes % of firms that applied for a loan during the last year 518 10 31 0 100 8 13 For firms that did not apply for a loan during the last 479 33 47 0 100 29 37 year, % of firms reporting the main reason for not applying is no need for a loan For firms that did not apply for a loan during the last 479 14 35 0 100 11 17 year, % of firms reporting the main reason for not applying is complex application procedures For firms that did not apply for a loan during the last 479 25 43 0 100 21 29 year, % of firms reporting the main reason for not applying is high interest rates KENYA INFORMAL ENTERPRISES 35 Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval For firms that did not apply for a loan during the last 479 10 30 0 100 7 13 year, % of firms reporting the main reason for not applying is lack of required guarantees For firms that did not apply for a loan during the last 479 2 15 0 100 1 4 year, % of firms reporting the main reason for not applying is that the firm thought the loan would not be approved For firms that did not apply for a loan during the last 479 16 37 0 100 13 19 year, % of firms reporting the main reason for not applying is other than above % of firms that consider limited access to finance as a 470 64 48 0 100 59 68 severe obstacle to their current operations % of firms that are financially constrained where a firm 518 60 49 0 100 56 64 is defined as financially constrained if it did not apply for a loan during the last year for reasons other than "no need for a loan" Number of family members of the owner(s) who were 527 1 1 0 3 0.5 0.6 working in the business in the last month Average monthly salary for an average worker at the 461 5405 3340 1 25000 5099 5710 firm (LCUs) Average monthly salary for a female full-time worker 235 4850 2794 1 17000 4491 5209 at the firm (LCUs) Number of men working at the firm who have social 526 0.1 0.4 0 4 0.1 0.1 security coverage Number of women working at the firm who have social 529 0.1 0.3 0 4 0.1 0.1 security coverage % of firms that would like their business to be 500 53 50 0 100 49 57 registered with the Registrar General % of firms for whom time, fees, and paper work 500 56 50 0 100 52 61 required for registering is a reason for not registering % of firms for whom taxes that registered businesses 494 57 50 0 100 53 61 have to pay is a reason for not registering % of firms for whom inspections and meetings with 486 37 48 0 100 32 41 government officials that follow registration is a reason for not registering % of firms for whom bribes that registered businesses 481 36 48 0 100 32 40 need to pay is a reason for not registering % of firms for whom no benefit from registering is a 493 46 50 0 100 42 51 reason for not registering % of firms that report having to pay gifts, informal 507 19 39 0 100 15 22 payments or bribes to remain unregistered % of firms for whom better access to financing is a 467 77 42 0 100 73 81 benefit from registering % of firms for whom better access to raw materials, 459 61 49 0 100 56 65 infrastructure services and government services is a benefit from registering % of firms for whom less bribes to pay is a benefit from 449 40 49 0 100 36 45 registering % of firms for whom being able to issue receipts to 473 42 49 0 100 38 47 attract customers is a benefit from registering Amount of time (days) the firm thinks it will take to 307 18 51 1 365 12 24 register the business 36 KENYA INFORMAL ENTERPRISES Annexes Std. 95% confidence Variable Observations Mean deviation Min. Max. interval Maximum amount of time (days) the firm thinks it will 303 26 66 1 450 19 33 take to register the business Minimum amount of time (days) the firm thinks it will 309 9 33 1 365 5 13 take to register the business % of firm that rank limited access to finance as the 388 59 49 0 100 54 64 most important obstacle within the set of eight obstacles % of firm that rank limited access to land as the most 388 9 29 0 100 6 12 important obstacle within the set of eight obstacles % of firm that rank corruption as the most important 388 9 29 0 100 6 12 obstacle within the set of eight obstacles % of firm that rank crime as the most important 388 6 24 0 100 4 9 obstacle within the set of eight obstacles % of firm that rank problems with electricity supply 388 10 30 0 100 7 13 as the most important obstacle within the set of eight obstacles % of firm that rank problems with water supply as 388 3 17 0 100 1 5 the most important obstacle within the set of eight obstacles % of firm that rank limited access to technology as 388 1 9 0 100 -0.1 2 the most important obstacle within the set of eight obstacles % of firm that rank inadequately educated workforce 388 2 13 0 100 0.5 3 as the most important obstacle within the set of eight obstacles Total cost of workers for the last month (LCUs) 451 12679 33268 0 600000 9600 15757 Total cost of electricity for the last month (LCUs) 421 796 1813 0 15000 622 970 Total cost of transportation in the last month (LCUs) 455 1064 2665 0 39000 819 1310 Total cost of raw materials for the last month (LCUs; 241 17101 26003 0 250000 13802 20401 only for manufacturing firms) % of firms that use machinery (excluding tools, 533 44 50 0 100 40 49 equipment and computers) in their current operations For firms that use currently use machinery, cost of 194 89163 182336 200 1000000 63344 114983 purchasing machinery and equipment (LCUs) used by the firm in its current condition (excluding tools, equipment and computers) % of firms that use own vehicles or other means of 532 19 39 0 100 15 22 transport in their current operations For firms that currently use own vehicles or other 56 158760 228775 100 1000000 97494 220026 means of transport, cost of purchasing them in their current condition (LCUs) Cost of purchasing all the tools, equipment and 343 48486 188031 100 2000000 28516 68455 computers (excluding machinery and vehicles) in their current condition (LCUs) KENYA INFORMAL ENTERPRISES 37 Annexes Annex 2: Kenya – survey of informal firms (2013) Description of the Informality Survey The World Bank’s Informal Enterprise Surveys (IFS) collect data on non-registered business activities in every region of the world. The IFS are implemented in parallel to the World Bank’s Enterprise Surveys (ES), which interview formal, private, non-agricultural firms in countries around the world (www.enterprisesurveys.org). The IFS use a standardized survey instrument designed to assess the business environment for non-registered businesses within a well-defined universe of activities, which have been identified using information from previous iterations of the studies. The IFS cover business environment topics including: general business characteristics, infrastructure, crime, sales & supplies, finance, labor, registration, business environment, and assets. The objective of the IFS can be summarized as follows: • To provide information about the state of the private sector for informal businesses in client countries; • To generate information about the reasons of said informality; • To collect useful data for the research agenda on informality; and • To provide information on the level of activity in the informal sector of selected urban centers in each country The IFS are conducted using a uniform sampling methodology in order to minimize measurement error and yield data that are comparable across the world’s economies. The primary sampling units of the IFS are non-registered business entities.For consistency, “registration” is defined according to the established convention for the Enterprise Surveys in each country. In these surveys, the requirements for registration are defined on a country-by-country basis consulting information collected by Doing Business and information from the in-country contractors. For the case of Kenya, informal firms were defined as those not registered with the Kenya Revenue Authority (KRA). The survey was conducted between April,18th and May, 11th2013. In each country, the IFS are conducted in selected urban centers, which are intended to coincide with the locations for the implementation of the main Enterprise Surveys. Each urban center is divided into an appropriate number of zones. The zones are identified using regional considerations and the concentration of informal business activity through consulting local knowledge. The overall number of interviews is pre-determined, and these interviews are distributed between the selected urban centers, according to criteria such as the level of business activity and each urban center’s population, etc.In Kenya, a total of 533 firms were interviewed. The urban centers identified were Nairobi (137 firms), Mombasa (110), Central (103), Nyanza (93), and Nakuru (90). These urban centers were divided into 122 zones and at least four interviews were completed for each zone. In order to provide information on diverse aspects of the informal economy, the sample is designed to have equal proportions of services and manufacturing (50:50). These business activity sectors are defined by responses provided by each informal business to a question on the business’s main activity included in the screener portion of the questionnaire. 38 KENYA INFORMAL ENTERPRISES Annexes Due to lack of proper sampling frame and the limited geographical coverage, the informality survey for Kenya (and other countries) is not necessarily representative of the informal sector in the country or even the informal sector in the urban centers covered. Hence, all the results presented below using IFS for Kenya should be treated with due caution as pertaining to the sample of firms surveyed and not necessarily the informal sector more broadly. Nevertheless, Enterprise Surveys take appropriate measures to keep the IFS as truly random so that the results based on these data are not systematically biased in one direction or the other. In the case of Kenya, the following steps were taken to ensure randomness of the selection process: • Each interviewer receives one or more maps of the geographic sectors he/she has to cover with the indication of the starting points and the direction to follow. • The interviewers were instructed to follow the direction of the street. • Four interviews (two services and two manufacturing firms) were completed from each starting point. The instruction was that interviews be conducted in every address (or stall) passed until 4 completed interviews have been achieved. • GPS coordinates of the interviewed business were recorded. KENYA INFORMAL ENTERPRISES 39 Annexes Annex 3: Business environment and productivity To answer this question, we replicated some of the analysis that was produced by Gelb et al in 20099 which examines firm productivity by contrasting informal firms with their formal micro-enterprise and SME counterparts. They speculate that growth and productivity within the informal market is dependent on the quality of the business environment. Their hypothesis is that when the business environment is poor, informal and formal firms will be less distinguishable, and conversely, in a higher quality business environment, differences in growth and productivity between formal and informal firms will emerge. Their hypothesis rests on a differential treatment of firms in a higher quality business environment through “sticks” in the form of tougher enforcement limiting informal activity and/or through “carrots” in the form of improved business service access for formal firms. In particular, they find (using this same Enterprise Survey data from the World Bank but from 2007-2009) that informal firms from four countries in East Africa (Kenya, Tanzania, Uganda, and Rwanda)) exhibit productivity profiles that are indistinguishable from their formal counterparts while informal firms in southern Africa (Botswana, Namibia, and South Africa) are considerably poorer performers than their formal sector counterparts. This uni-modal vs bi-modal finding for the probability density of productivity in each country drives their entire result. The Gelb et al 2009 paper shows Kenya Informal firms in 2007 to be indistinguishable from their formal counterparts (Figure 3b). Using the new data, we find that the informal firms are now distinguishable from their formal counterparts (Figure 3c). This suggests that the quality of the business environment—at least as differentially experienced by formal and informal firms—in Kenya may have changed since the last survey. TABLE 6: Summary of Kenya’s Progress on Doing Business Indicators Measure Result DB 2007 DB 2014 Difference Procedures (number) 13 10 -3 Starting a Business Time (days) 54 32 -22 Cost (% of income per capita) 46.3 38.2 -8.1 Procedures (number) 6 8 +2 Dealing with Construction Permits Time (days) 158 125 -33 Cost (% of income per capita) 1 3.4 +2.4 Payments (number per year) 42 41 -1 Paying Taxes Time (hours per year) 432 307.5 -125 Total tax rate (% profit) 49.8 38.1 -11.7 9 Gelb, Alan and Mengistae, Taye and Ramachandran, Vijaya and Shah, Manju Kedia, To Formalize or Not to Formalize? Comparisons of Microenterprise Data from Southern and East Africa (July 20, 2009 Available at SSRN: http://ssrn.com/abstract=1473273 or http://dx.doi.org/10.2139/ssrn.1473273 40 KENYA INFORMAL ENTERPRISES Annexes Figure 3b: In 2013, informal firms in Kenya exhibited productivity profiles that are quite different from their formal counterparts. Figure 3b: In 2007, informal firms in Kenya exhibited productivity This differential effect has been associated with stronger business profiles that are indistinguishable from their formal counterparts environments in other research. Source: Source: KENYA INFORMAL ENTERPRISES 41 Delta Center, Menengai Road, Upper Hill P. O. Box 30577 – 00100 Nairobi, Kenya Telephone: +254 20 293 7706 www.worldbank.org