Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Impact of Investment in Kenya’s Priority Sectors on Gendered Employment Outcomes: A Social Accounting Multiplier Analysis Approach Bernadette Wanjala and Maureen Were (PhD) Kenya Institute for Public Policy Research and Analysis (KIPPRA) P.O Box 56445, Nairobi Kenya Telephone: +25420241380 or +254722319614 or +254722792136 Fax: +254202719951 Email: [email protected] or [email protected] Key words: gender, employment, social accounting matrices JEL classification: J16, J21, Abstract Employment creation has been a central objective of the government of Kenya since the country achieved its independence in 1963. However, sustaining high economic growth while at the same time generating gainful employment remains one of the greatest challenges facing Kenya. The growth in employment has fallen short of the growth in the labour force, leading to high unemployment rates. There also exist considerable gender disparities in the labour market. For instance, females constituted about 50.1percent of the total population in 1999, and yet on average, they constituted only about 30percent of the total formal sector wage employment and earn 33percent less than their male counterparts. This paper sought to analyze the impact of investing in Kenya’s key sectoral priorities on gendered employment outcomes. An analysis of the multiplier effects on compensation of employees for 2003 revealed that skilled labour accounted for the greatest increments, with the increments for male-skilled labour being relatively higher than female skilled labour. Also, the proportion for females was relatively high in the informal sector than formal sector. Overall, the highest increase in employment generation was with investment in manufacturing, with 86% being informal. Thus, although women befitted from the employment creation, the concern is the type and nature of jobs—precarious, informal or casual type of jobs with relatively low wages. Overall, policies aimed at increasing productivity and raising women skills while encouraging women’s participation in the formal labour market are essential. 1 1. INTRODUCTION Employment creation has been a central objective of the government of Kenya since the country achieved its independence in 1963. From the early 1990s, the government’s policy on employment has been focused on creating a conducive environment for the private sector to play a leading role in economic growth and employment generation (Jane Mariara, 2003). However, sustaining high economic growth while at the same time generating gainful employment remains one of the greatest challenges facing Kenya. The growth in employment has fallen short of the growth in the labour force, leading to high unemployment rates. For a country marred with poverty, employment creation is central and critical in poverty reduction initiatives. Kenya’s development policy agenda has emphasized the importance of high economic growth in achieving development goals. The question is whether growth promoting priority sectors necessarily generate equal employment opportunities for both men and women. Arguably, this depends on the institutional structure of the labour market and the economy as a whole. Empirical evidence indicates that there may be an unemployment-growth trade-off in the long run (Patrick Toche 2001; Gordon Robert 1995; Martin Zagler 2000). If economic growth is driven by structural change, which could entail a shift towards capital-intensive production, then a social cost of unemployment is expected. Kenya’s experience shows that the economy has become more capital-intensive. At the same time, the incremental capital--output ratio has also increased, implying declining productivity of capital. This has partly undermined the country’s ability to generate gainful employment. This policy concern was raised as early as 1980s during the onset of the structural adjustment programmes where it was noted that a structural transformation in both the pattern and process of growth towards a path with potential for employment 2 generation and a more efficient structure of production was needed. After over two decades of reforms however, sustainable growth and employment creation have remained elusive. The dual nature of the economy has become more pronounced with the informal sector growing faster than the formal sector. The share of wage employment has been declining since the 1980s. Kenya’s sectoral growth priorities have focused on investment in sectors viewed to be productive (e.g. agriculture, industry and tourism). It is believed that these sectors can generate gainful employment opportunities given the growing unemployment rate. However, there exist considerable gender disparities in the labour market. According to the 1999 census, for instance, females constituted about 50.1percent of the total population, and yet on average, they constitute only about 30percent of the total formal sector wage employment and earn 33percent less than their male counterparts (Maureen Were and Jane Kiringai, 2004). Women’s earnings have been found to be lower than men’s even after making adjustments for the type of employment, occupation and hours of work (Mariara, 2003). In addition, 37percent of households in Kenya are female-headed and the incidence of poverty is slightly higher for females as compared to males. Given less favourable terms for women in labour markets and a high dependency ratio, households headed by females are likely to be more vulnerable to economic shocks. Although the gender gap in overall labour force participation rates seems to be narrowing over time (74.7percent for male and 72.6percent for women based on the Labourforce Survey of 1998/99), the participation rates are higher for women (compared to men) in rural areas, where they are actively involved in subsistence activities and agricultural production, besides 3 the unpaid domestic work. However, these activities have been associated with a higher than average probability of being poor (Miriam Oiro, Germano Mwabu and Damiano Manda 2004). Moreover, women’s expanding labour participation has been accompanied by a relatively higher unemployment rate that surpasses that for men. Estimates for the late 1990s show that the overall urban unemployment was about 25 percent, with female unemployment being 38 percent compared to 12.5percent for males (Damiano Manda 2002). In addition, women also spend less time in wage employment and devote more time to household production than their male counterparts.1 Despite the gender imbalances, analyses of growth and employment outcomes in Kenya have largely been gender-blind (see Damiano Manda 2004; Damiano Manda and Kunal Sen 2004; and Maureen Were 2007 for instance). Although Were and Kiringai (2004) attempt to analyse gender disparities in key dimensions of development including employment, the focus is broad, and is aimed at highlighting key implications for poverty reduction strategies. Other previous research attempts on gender and employment outcomes in Kenya (Mariara, 2003, Rosemary Atieno and Francis Teal, 2006) have shown that increased access to education can ensure equality of outcomes in the labour market, but only in the public sector. Given that the direction of policy reform is aimed towards increasing the role of the private sector, the challenge is how to reduce the gender differentials in the private sector, which is largely informal. Arguably, the large gender gap with respect to unpaid family work ensures that women are much more likely to be in informal and unpaid work than men (Atieno and Teal, 2006). Generally, a rigorous analysis of implications of investment on For instance, an analysis based on the Kenyan Urban Labour force Survey in 1986 revealed that when combining domestic chores with economic activities, women in the working age group worked 50.9 hours per week, compared with only 33.2 hours worked by men (Mariara, 2003). 1 4 employment outcomes by gender is lacking. This paper seeks to shed some light on the gendered employment outcomes of various investment options for Kenya. Using a disaggregated Social Accounting Matrix (SAM) for 2003 with sectoral employment extension, SAM multiplier analysis is employed to identify and analyse the effects of the different sectoral investment policy options (simulations) on generation of sectoral employment in terms of gender and skill levels and broad employment sectors (formal and informal). 2. GROWTH AND EMPLOYMENT TRENDS IN KENYA 2.1 Economic Growth Trends Kenya’s economic growth history, just like many African economies, has been episodic. The country’s growth performance can be sub-divided into three major broad phases: a rapid growth phase over 1964-73; an era of external shocks over 1974-89 dominated by oil price shocks, a coffee boom and structural adjustment and; an era of liberalization, inconsistent donor inflows and economic stagnation from 1990 to 2002 leading to recovery from 2003. The economy experienced rapid growth between 1964 and 1973 with an annual average real GDP growth rate of 6.6percent. This was mainly promoted through public investment, encouragement of smallholder agricultural production, and incentives for private industrial investment under the import substitution industrialization strategy. The economy was mainly agriculture based, with agriculture contributing about 37percent to GDP. However, the oil crises of the 1970s led to severe balance of payments problems, exposing the country’s 5 vulnerability to external shocks and the inefficiency and inadequacy of the importsubstitution policy. Economic growth declined between 1974 and 1979 hitting a period low in 1979. The temporary recovery in 1977 was mainly due to sharp increases in international prices of tea and coffee (the coffee boom). Growth in agriculture declined, even though the most notable decline was experienced in the manufacturing sector. This was the advent of manufacturing sector’s poor growth performance, which has persisted to date. The manufacturing sector’s poor performance was mainly attributed to a weak incentive system, policy structure that was heavily biased against exports, and an inefficient import substitution strategy (Government of Kenya 1997; Maureen Were 2007). The situation was further exacerbated by the collapse of the East African Community in 1977 that served as the traditional market outlet for Kenya’s industry, and the growing inefficiency of public industrial investments. By early 1980s, economic management had begun to weaken, fiscal discipline was rising and the public sector was over-extended (Maureen Were, Rose Ngugi and Phyllis Makau 2006). The first half of the 1980s performed poorly, with an average real GDP growth rate of 3.4 percent for 1980-4 period. Although the economic performance in the second half of the 1980s was relatively better, economic growth started declining continuously from the early 1990s. From 1991 to 1993, Kenya had its worst economic performance since independence in 1963. Growth in GDP stagnated, and agricultural production shrank at an annual rate of 3.9percent. The government's budget deficit was over 10percent of GDP. As a result of these problems, bilateral and multilateral donors suspended program aid to Kenya in 1991. 6 As the economic crisis deepened, the government, under pressure from the Bretton Wood financial institutions began implementing a major program of market-oriented reforms and economic liberalization in 1993. Price controls, import licensing and foreign exchange controls were removed and, a range of publicly owned companies were privatized. A number of civil servants were retrenched under the civil service reform, while conservative fiscal and monetary policies were introduced. Unlike the 1960s and 1970s, the role of state in the economy was greatly reduced, with greater emphasis on the role of private sector. Suspension of funding in 1997 due to government’s failure to make significant moves on governance reforms was a major blow to the economy, which was already experiencing hard economic times. Coupled with the effects of adverse weather conditions in 1997, economic growth further stagnated in 1997. By 2000, the economy was in doldrums, recording a negative real GDP growth rate. There was slight improvement in 2001 as weather patterns became more favorable, with real GDP growth rate reaching 2.9percent in 2003. From 2004 the economy has shown remarkable recovery until the recent post-election violence experienced after the December 2007 elections threatened to reverse the economic gains made. Average real GDP growth rate for the period 2004-6 was 5.6 percent. In terms of sectoral priorities, Kenya’s development agenda since independence has emphasized on the role of agriculture and manufacturing as twin engines of economic growth. Agriculture contributes 23 percent of the GDP on average (2004-6) while manufacturing sector’s contribution has stagnated at around 10 percent. It consists of both large-scale and small-scale farming. Agriculture, particularly small-scale agriculture has continued to be the main source of employment and livelihood for most of Kenya’s population. Data from the Labour force Survey of 1998/99 shows that rural areas absorbed 7 70.1percent of the employed persons, where the majority are engaged in farm-related activities. Overall, the economy has undergone an appreciable process of diversification, moving from broadly agriculture-based economy to a service-based economy since 2000. 2.2 General Employment Structure and Gender Composition Kenya has a fragmented labour market where the formal sector co-exists with the informal. The formal sector employment consists of employment in both the private and public sectors. There has been a general decline in the growth of formal sector employment over time, particularly in the public sector. Public sector employment increased disproportionately after independence in 1963, but there was a turn-around in the 1990s. Employment growth in the public sector declined from an annual average of 6.2 percent during the first decade of independence (1964-73) to -0.5 percent and -1.1 percent for the periods 1990-2000 and 2001-5, respectively. This was largely as a result of the 1990s public sector reforms that led to retrenchment and a freeze on employment for some categories of professionals such as teachers. In the formal private sector, employment growth had increased from an annual average of 1.2 percent to 4 percent for periods 1964-73 and 1990-5 before declining to 2.8 percent in 2001-5. The decline in formal private sector employment was attributed to various factors such as the collapse of private firms and retrenchment due to stiff competition from imports following trade liberalization (Damiano Manda 2002), and the poor economic performance of the economy in the 1990s. Figure 1 shows that growth in formal sector employment seems to have largely followed the decline in economic growth, particularly since around 1983. Insert Figure 1 here 8 While the formal sector employment has been on the decline, the informal sector employment has grown rapidly, particularly in the last two decades, mainly as a counter weight to the failure of the formal sector to create sufficient jobs. There was a surge in the informal sector employment in the 1990s, with an estimated average growth of 26 percent during 1990-2000 and 8.8 percent in the period 2001-5. Table 1 shows total employment excluding employment in small-scale agriculture. Whereas the share of formal wage employment in total employment outside the small-scale agriculture declined from 76.2 percent in 1989 to 21.9 percent in 2005, the share of the informal sector in total employment increased from 21.3 percent to 77.3 percent over the same period— an indication that employment is increasingly becoming informalised. The emergency of the informal sector as a key source of employment can be attributed to the declining absorptive capacity of the formal sector vis-à-vis the increasing labour force, retrenchment and down-sizing of employment in both the public and private sector following the market-based reforms of the 1990s, economic stagnation prior to 2003, as well the rapidly changing forms of employment arising from the effects of trade liberalization and globalization. Most of the retrenches from the formal sector ended up in the informal sector, leading to a further surge. The informal sector has, thus, become the country’s labour sponge. It is should be noted however, that informal sector jobs are precarious in nature, offer little or no security, have no statutory entitlements, and have high risk of occupational accidents (Republic of Kenya 2008). Hence, the sector cannot be relied upon to effectively sustain the livelihoods of the majority of Kenyans that depend on it. Consequently, concerted efforts need to be directed at expanding employment creation within the formal sector and at the same time increasing the opportunities of the informal sector to create productive and durable employment opportunities. 9 Insert Table 1 here In terms of sectoral contribution to employment, the service sub-sector is the main source of employment in the formal sector, accounting for over 50 percent of the total formal wage employment. In the informal sector, wholesale and retail trade, hotels and restaurants accounted for 58.6 percent of informal sector employment in 2006 followed by the manufacturing sector at 22 percent in the same year. Insert Figure 2 here In terms of gender, formal sector employment is male-dominated, and women constitute only slightly less than a third of formal sector employment (see Figure 2). Between 2002 and 2006 for instance, the proportion of women employed in the formal sector increased marginally from 29.6 percent to 30.3 percent. Furthermore, approximately 70percent of these women are in the lower income bracket of Ksh.8, 000 to Ksh 25,000 (US $ 103-321) per month. Women tend to be concentrated in lower status employment and informal sector. In the formal sector, education services, followed by agricultural and forestry industries have been the major female employers. In general, women are mostly engaged in activities traditionally dominated by females while men are relatively evenly distributed across sectors. Insert Table 2 here 10 Table 2 shows that the majority of women employed in the formal sector (about 58 percent on average) work in the service industry (community, social and personal services)—the gender composition of employment by occupational categories has remained fairly static over time. Further analysis also shows that even in the service industry especially in the public sector it is 'men heavy' at the top whereas women occupy the lower cadres. Female civil servant employees constitute only about 24percent compared to 76percent men (Were and Kiringai 2004). Though there’s a remarkable improvement in the women’s labourforce participation rates over time, their limited access to formal employment is still reflected in the relatively higher unemployment rates and lower earnings for women. 3. THE SOCIAL ACCOUNTING MODEL FRAMEWORK The Social Accounting Matrix (SAM) is a particular representation of the macro and meso economic accounts of a socio-economic system, which capture the transactions and transfers between all economic agents in the system (Graham Pyatt and Jeffery Round, 1985; Kenneth Reinert and David Roland-Holst, 1997, as quoted by Jeffrey Round 2003). The SAM captures the circular interdependence characteristic of any economic system among production, distribution of the value added to various factors, income distribution among institutions, particularly among different socio-economic household groups (Jacques Defourny and Erik Thorbecke 1984). The SAM has increasingly become a basis for simple modeling, more specifically, through multiplier analysis. The linkages are shown in the form of increase or decrease in the incomes of different accounts as one shilling is injected into an account.2 The SAM multipliers reflect the strength of the linkages among the different 2 For a detailed discussion of SAM methodology and multiplier analysis see Pyatt and Thorbecke 1976; 11 sectors of the economy (Alka Parikh and Erik Thorbecke 1996). This study adopts a simple Social Accounting Matrix multiplier analysis to analyze the impact of exogenous injections in Kenya’s key priority sectors on gendered employment. Given that a SAM represents an interaction between economic processes within a specific year, a SAM for 2003 is used and simulations on growth options and employment outcomes are carried out for 2004. 3.1 The 2003 Social Accounting Matrix for Kenya The 2003 Kenya SAM3 includes the six standard accounts: the production account (activities and commodities), the factors of production (land, labour and capital), institutions (households and enterprises), the Government (also includes taxes), the capital account (savings – investments) and the rest of the world account. The SAM distinguishes between ‘activities’ (the entities that carry out production) and ‘commodities’ (representing markets for goods and non-factor services). SAM flows are valued at producers’ prices in the activity accounts and at market prices (including indirect commodity taxes and transactions costs) in the commodity accounts. Similarly, there is a distinction between own-production ownconsumption output and marketed consumption, because own-production ownconsumption output is valued at producer prices while marketed consumption is valued at market prices. The government is disaggregated into a core government account and different tax collection accounts, one for each tax type. Sadoulet and Janvry 1995; and Defourny and Thorbecke 1984. 3 For details on the SAM construction, see Jane Kiringai, Bernadette Wanjala, James Thurlow, Nicholas Waiyaki, Clive Mutunga, Moses Njenga, Nancy Nafula and John Mutua (forthcoming), ‘A 2003 Social Accounting Matrix (SAM) for Kenya’, Kenya Institute for Public Policy Research and Analysis (KIPPRA) and International Food Policy Research Institute (IFPRI). 12 For the purpose of this particular analysis, the micro SAM is further disaggregated4 as follows: The production account (activities and commodities) was disaggregated into 27 sectors each as defined in national accounts. They include: Agriculture5, Fishing, Forestry, Mining and Quarrying, Meat and dairy processing, Milling, Bakery and confectionary, Beverages and tobacco, Other food manufactures, Textiles and Footwear, Wood and Paper, Printing and Publishing, Petroleum, Chemicals, Metals and Machinery, Non-metallic manufactures, Other manufactures, Electricity and Water; Building and construction; Trade; Hotels and restaurants; Transport and communication; Financial services; Other services; Education; Health and; Public Administration. Factors of production: there was no disaggregation for land and gross operating surplus. Compensation of employees was disaggregated into 16 categories according to gender (male/female), region (rural/urban), sector (formal/informal) and skill level (skilled/unskilled). It was important to factor in the informal sector because the sector has been growing faster than the formal sector in Kenya, and as earlier indicated, accounts for 76% of Kenya’s total employment. Skill levels were not indicated in the labour force survey questionnaire, so they were derived using education qualifications. A more highly disaggregated Micro SAM is available, with 50 activities and 50 commodities (22 agriculture, 18 industry, and 10 services), 3 transaction costs (domestic, import and export), 12 labour categories (gender and skill level), 20 household categories (region and per capita expenditure), capital (region and formal/informal), enterprises (region and formal/informal) and 3 taxes (direct, commodity and trade taxes). 4 Agriculture sector was not disaggregated further because of lack of disaggregated employment data for the different agricultural sectors. 5 13 Institutions: there was no disaggregation for enterprises, but households were disaggregated according to region (rural/urban) and expenditure deciles (lower/upper)6. The Government account: taxes were disaggregated into commodity, direct and trade taxes, plus a core government account. There was no disaggregation for capital (savings-investment) and the rest of the world accounts. 3.2 The Employment Satellite account The construction of an employment satellite account was mainly motivated by the need to analyze employment outcomes (in numbers) in a SAM framework, given that labour in a conventional SAM framework is in monetary terms (Jorge Alarcon, Jan Van Heemst and Nick de Jong, 1997). Given the different nature of such an extension, the interpretation of multipliers will not refer to average propensities, but will be employment ratios. The 2003 Kenya SAM employment satellite account was developed using national accounts employment data, supplemented with national household survey data. The former provided national aggregates (disaggregated into formal (public and private) and informal), with a further disaggregation of the formal employment by sector and gender. From the statistics, total employment in Kenya in 2003 was 7,325,700; of which 1,727,300 was formal employment while 5,598,400 was informal employment. Thus, the informal sector employment accounted for about 76 percent of Kenya’s total employment in 2003. The 6 It was not possible to disaggregate households by gender because of complications related to the definition of gender and household head in the household survey. 84% of the respondents were in the rural areas, which could impact on the definition of male vs. female-headed households. For instance, some households have the male head residing in the urban areas, but transfer income to their rural homes headed by the wife. Such households were most probably termed as female-headed. Thus, classifying households by gender could result 14 females accounted for 30 percent of total formal employment, and were predominantly in education, agriculture and public administration sectors, while the males were predominantly in agriculture, manufacturing and education, respectively. To further disaggregate formal employment into the various labour categories according to gender (male/female), region (rural/urban), sector (formal/informal) and skill level (skilled/unskilled), the respective (employment) ratios were derived from the Integrated Household Labourforce Survey (1998/99). Only individuals earning an income were included in the sample (same case as the derivation of wage categories under compensation of employees). Unfortunately, income was only reported by paid employees, who only represented about 25 percent of the sample. Given that a large percentage of women in Kenya engage in unpaid activities, such as unpaid work, the proportion of women in the selected sample was reduced. Statistics from the labour force survey revealed that only 13.3percent of females were paid employees, while 62.7percent were unpaid family workers with no income, as compared to 35.9percent of males as paid employees and 35.9percent as unpaid family workers. To increase the sample size, the data set was merged with the Welfare Monitoring Survey 1997, which contained information on non-wage income.7 The resultant sample size contained 66 percent males and 34 percent females, which is a close approximation of the national employment statistics. Highly disaggregated sectoral employment data for the informal sector was not available, so ratios of the different labour categories were derived from Labourforce survey e.g. if male rural skilled labour in the informal sector accounted for one percent of the total employment into biased estimates, e.g. over-estimation of the proportion of female-headed households. 7 Additional data was obtained only where the same households could be identified. Given data requirements, 15 as per the Labourforce survey, then it would similarly account for one percent of total informal sector employment in total informal sector employment in national accounts. Thus, for consistency, the disaggregation ensured that the aggregate figure from national accounts was maintained. 3.3 The SAM Model In developing a simple multiplier model, the first step is to decide which accounts should be exogenous and which ones should be endogenous. It is customary to regard transactions in the government account, the capital account and the rest of the world account to be exogenous (Round 2003; Defourny and Thorbecke 1984). This is because government outlays are essentially policy-determined, the external sector is outside domestic control, and as the model has no dynamic features investment is exogenously determined. The corporate enterprise outlays (e.g. distributed profits and property incomes) are treated as either exogenously or endogenously determined. The endogenous accounts are therefore usually limited to those of production (activities and commodities), factors and households (private institutions). Defining the endogenous transactions in this way helps to focus on the interaction between two sets of agents (production activities and households) interacting through two sets of markets (factors and commodities). The endogenous and exogenous accounts for this particular analysis are in table 3. The production account, factors of production, and households are endogenous, while other institutions, the government, savings-investment and rest of the world are exogenous. Insert Table 3 here merging of data sets or survey data is common in SAMs (e.g. see Parikh and Thorbecke 1996). 16 4. MULTIPLIER ANALYSIS AND SIMULATION RESULTS In most cases, the basic aim of multiplier analysis has been to examine the nature of the multiplier effects of an income injection in one part of an economic system on the functional and institutional distribution in general and on the incomes of socio-economic groups of households in particular (Round, 2003, Pyatt, G and J I Round 1979). In this paper, the multiplier analysis is also used to analyze the effect of external injections into the key growth- priority sectors on gendered employment outcomes. The government of Kenya’s blue print for economic recovery, the Economic Recovery Strategy for Wealth and Employment Creation (2003) and vision 2030 recognize productive sectors as agriculture, industry and tourism. Since it has been envisaged that investing in these sectors not only spur growth but also generate gainful employment opportunities given the growing unemployment, it is important to analyse the gender disparities in the employment creation. The level of investment (which is an exogenous injection) was equated to the level of injection that was required to achieve the actual growth in GDP between 2003 and 2004 (in nominal terms). The priority sectors used were agriculture, manufacturing and services. Within manufacturing and services, the sectors with the highest level of linkages (both backward and forward) were selected. For manufacturing, the sectors included meat and dairy, grain milling, beverages & tobacco and textiles and footwear. Trade, hotels & restaurants, transport & communication and financial services were selected under services. Insert Table 4 here 17 Five different types of simulations targeting different combinations of the sectors were chosen as follows: (1) agriculture (2) manufacturing (3) private services (4) agriculture and manufacturing (5) agriculture, manufacturing and private services. It is expected that more jobs will be created in the informal sector as compared to the formal sector. Also, female job creation is expected to be more predominant in the informal sector than the formal sector, especially in some sectors in manufacturing and services. For instance, the proportion of women in paid employment is higher in manufacturing as compared to agriculture. The impact of the injections on compensation of employees, distribution of factor incomes across households and employment creation are as discussed below. 4.1 Impact of Exogenous Injections on compensation of employees The analysis of the impact of injections on compensation of employees reveals that investing in agriculture results in the highest increase in the cost of labour (14.1 percent as compared to an 8.5 percent increase when the exogenous injection is put into services). This is mainly due to the fact that agriculture is more labour-intensive as compared to private services, which are more capital intensive. It can be seen from the 2003 SAM that while labour costs accounted for about 47percent of total production costs in 2003, they only accounted for 37 percent of production costs in private services. Private services have become more capital intensive over time, with increased automation especially in financial services and, the rapid growth in the information and communication technologies. Additionally, the agriculture sector has low labour ratios, which have mainly been attributed to the low wages prevailing in the sector, as well as the dominance of self-employment and unpaid family workers (Republic of Kenya, 1996). 18 Insert Table 5 here In general, the increase in labour costs is higher in the informal sector as compared to the formal sector. This is anticipated because employment trends reveal that employment creation has largely been in the informal sector. The employment in the formal sector shrunk over time, mainly due to some of the factors mentioned earlier, i.e. retrenchment and staff rationalization under public sector reforms of the 1990s, economic stagnation, increased competition following trade liberalization. The sectoral analysis reveals that private services accounted for the highest increase in the proportion of the formal sector, that is, 61 percent of the increase in labour costs in the formal sector, as compared to 50 percent in manufacturing and 42 percent in agriculture. This is expected given that of the financial services, transport and communication and hotels and restaurants are largely formal, while agriculture is largely informal, characterized by small holder subsistence farming and unpaid family labour. The private services sector is likely to employ high-skilled labour. An analysis of the increase in costs by gender reveals that the proportion for females is relatively higher in the informal sector as compared to the formal sector, which is in line with the fact that women constitute slightly less than a third of formal sector employment in Kenya. Males accounted for 54 percent of total increase in compensation of employees in services (formal sector), as compared to only 8percent for females. Males dominate formal sector employment and have also been shown to earn higher incomes as compared to the females (Mariara, 2003). Overall, male accounted for relatively higher proportion of labour costs in both formal and informal sector. 19 In terms of skill, skilled labour accounted for over 70 percent of the labour costs in all sectors. This finding supports evidence of earning disparities between skilled and unskilled workers, with higher returns to higher education (see Were 2007). The analysis further shows that males are more trained and skilled than their female counterparts. This is evidenced by the relatively higher proportion of men in higher education institutions. They therefore, account for a larger proportion of skilled manpower in Kenya as compared to the females. From the sample, about 65.9 percent of the Labourforce were skilled, 55 percent of which were males while 11 percent were females. The analysis reveals that the increase in labour costs is higher for skilled labour than unskilled, and consequently higher for males as compared to the females. While investing in agriculture results into an increase in the cost of skilled labour by 86 percent, it only results into only 14 percent increase in the unskilled labour cost. The same trend applies for the other sectors. The increase from the baseline is higher for males than females, which implies that males would benefit more than females, in terms of increased wages. This can be attributed to the existing socio-economic structure and employment characteristics. 4.2 Impact of Exogenous Injections on distribution of wage incomes across households The analysis reveals that investing in agriculture results into the highest increase in wage incomes for households, which is a 26 percent increase as compared to 19 percent for a combination of agriculture and manufacturing and 18 percent for manufacturing. This increase benefits rural households more than urban households, and benefits households that are in upper deciles more than lower deciles. This is mainly because agriculture is a 20 predominantly rural activity in Kenya, with about 70 percent of the population deriving their livelihood from agriculture. Thus, efforts to reduce poverty and raise rural incomes should take into consideration the role of agriculture. Insert Table 6 here Investing in services results into the lowest increase in wage incomes for households, averaging about 6.2 percent, and mainly benefiting urban households. This outcome is expected because services are more capital intensive and the linkage with households through wage income is weaker as compared to agriculture. Investing in manufacturing also benefits the urban population more than rural population. Due to data limitations as indicated earlier, the income or wage analysis could not incorporate gender characteristics within the household even though that would have been more insightful. Nonetheless, it can be inferred for instance, that women can benefit from investing in agriculture since the majority of women are engaged in the agricultural sector e.g. subsistence or small-scale farming. 4.3 Impact of Exogenous Injections on gendered employment creation Unlike the analysis on compensation of employees, investing in manufacturing results into the highest increase in employment, generating about 17percent additional jobs as compared to 14percent for agriculture and 12percent for services. However, the types of jobs created are largely informal or casual jobs with low earnings and precarious in nature. From the 21 analysis, investing in the manufacturing sector accounts for the highest increase in informal sector jobs (85.8 percent compared to 79.6 percent in the case of agriculture, for instance). Additionally, females account for a larger proportion of growth in informal sector employment as compared to the increase in formal sector employment. Insert Table 7 here In terms of skill level, unskilled labour accounts for the highest proportion of the increase, with agriculture accounting for the highest increase (at 71.1 percent compared to 68 and 64.1 percents for manufacturing and services, respectively). The proportion of employment for skilled labour declines for both male and female while women benefit slightly relatively more from the increase in the unskilled labour. This can be attributed to the fact that the majority of women possess low-level skills, which are cheaper. Manufacturing sector accounts for slightly higher proportion of increase in employment of females (32.9 percent compared to 32.2 percent for services and 30.6 percent for agriculture). Though women still constitute a small proportion of manufacturing sector employment, the numbers are gradually rising, especially casual employment, which show a rising trend (Were 2007). It is acknowledged that SAM-based multiplier models do have a role to play in examining the nature of the socioeconomic structure of an economy. However, SAM-based multipliers rely on some strong assumptions such as the implicit assumption that there is excess capacity in all sectors and unemployed (or underemployed) factors of production and also that prices are fixed, among other limitations. Because of these limitations, it is acknowledged that at best, the SAM multipliers provide us with a first-cut estimate of the effects of a policy or 22 external shock, which only rely on the SAM structure. Therefore, these results should be interpreted with great caution. 5. CONCLUSION The paper sought to analyze the impact of investing in Kenya’s key sectoral priorities on gendered employment outcomes. An employment satellite account, disaggregated according to gender, skill level and sector (formal/informal) was generated using both national accounts and household survey data. From the data, it is shown the majority of women work in the informal sector and in rural areas where they are actively involved in subsistence activities and agricultural production besides unpaid domestic work. Since most of the activities women are engaged in are non-wage or not paid for, the proportion of females captured in the data set is relatively small (34 percent) compared to males (66 percent), which however is a close approximation of the proportion of female wage employment in the national employment statistics. Using the actual growth rate in Gross Domestic Product at factor cost in 2004, simulations were carried out on a combination of sectors with the highest level of linkages within the economy, i.e. agriculture, manufacturing (meat and dairy, grain milling, beverages & tobacco and textiles and footwear) and services (Trade, hotels & restaurants, transport & communication and financial services). An analysis of the multiplier effects on compensation of employees revealed that skilled labour accounted for the greatest increments, with the increments for male-skilled labour being relatively higher than female skilled labour. Being 23 more skilled than women, men benefited more than females in terms of increase in wages. There is thus, need for improving skills of women (e.g. through training, higher education etc) to enable to them benefit from wage increases. Investing in agriculture resulted in the highest increase in the labour costs (compensation of employees) and the increase benefited rural households more than urban households. Since women are largely engaged in agricultural activities, it can be inferred that investing in agriculture could help boost rural incomes of women. A gender analysis of the increase in labour costs showed that the proportion for females was relatively high in the informal sector than formal sector. Formal sector is male-dominated as the majority of women work in the informal sector and unpaid family work. On the other hand, investing in manufacturing sector resulted in the highest employment increase. However, 86 percent of the employment increase was in the informal sector. Thus, although women befitted relatively more from the employment creation, the concern is the type and nature of jobs—precarious, informal or casual type of jobs with relatively low wages. With increased integration into the global market, the manufacturing sector is increasingly becoming the focus of employment creation e.g. through exportpromotion policies. However, the results of the analysis give further impetus for need to consider the quality of jobs created as a policy priority. Overall, policies aimed at increasing productivity and raising women skills while encouraging women’s participation in the formal labour market are essential. The lack of highly disaggregated data for most of the variables was a major limitation to a more detailed analysis of the gendered employment outcomes. Also, lack of time use data limited the development and use of a gendered Social Accounting Matrix, as documented in Marzia Fontana and Peter Wobst, 2001. This is therefore an area for further research. 24 REFERENCES Atieno, Rosemary and Francis Teal (2006), ‘Gender, Education and Occupational Outcomes: Kenya’s Informal Sector in 1990s’, Global Poverty Research Group, GPRGWPS-050 Alarcon, Jorge, Jan Van Heemst and Nick de Jong (1997), ‘Extending the SAM with Social and Environmental Indicators: an Application to Bolivia’, Working Paper – General series, 256, Institute of Social Studies, The Hague, The Netherlands. Defourny Jacques and Erik Thorbecke. 1984. “Structural Path Analysis and Multiplier Decomposition Within a Social Accounting Matrix Framework.” The Economic Journal 94(373): 111-136. Fontana Marzia and Peter Wobst (2001), ‘ A Gendered 1993-94 Social Accounting Matrix for Bangladesh’, Trade and Macroeconomics Division, IFPRI, TMD Discussion Paper No. 74 Gordon, Robert J. (1995), ‘Is there a trade-off between unemployment and Productivity Growth?’, NBER Working Paper 5081. Government of Kenya (1997), ‘National Development Plan 1997-2001’, Government printers, Nairobi. 25 Government of Kenya, Labourforce Survey of 1998/99, Central Bureau of Statistics, Ministry of Planning and National Development, Nairobi, Kenya. Government of Kenya, Welfare Monitoring Survey 1997, Central Bureau of Statistics, Ministry of Planning and National Development, Nairobi, Kenya. Government of Kenya, Economic Surveys, Central Bureau of Statistics, Ministry of Planning and National Development, Nairobi, Kenya, various issues. Government of Kenya, Statistical Abstracts, Central Bureau of Statistics, Ministry of Planning and National Development, Nairobi, Kenya, various issues. Kiringai Jane, Wanjala Bernadette, Thurlow James, Waiyaki Nicholas, Mutunga Clive, Njenga Moses, Nafula Nancy and Mutua John (forthcoming), ‘A 2003 Social Accounting Matrix (SAM) for Kenya’, Kenya Institute for Public Policy Research and Analysis (KIPPRA) and International Food Policy Research Institute (IFPRI). Manda, Damiano K. (2004). “Globalisation and the Labour Market in Kenya.” KIPPRA Discussion Paper DP/31/2004. Nairobi: Kenya Institute for Public Policy Research and Analysis. Manda, Damiano .K. and Kunal Sen. 2004. “The Labour Market Effects of Globalisation in 26 Kenya,” Journal of International Development, 16, pp 29-43. Mariara, Jane Kabubo (2003), ‘Wage Determination and the Gender Wage Gap in Kenya: Any evidence of Gender Discrimination’, African Economic Research Paper No. 132, May 2003, Nairobi, Kenya. Oiro, Miriam, Germano Mwabu and Damiano Manda. (2004), ‘Poverty and Employment in Kenya.’ Discussion Paper DP/33/ 2004. Nairobi: Kenya Institute for Public Policy Research and Analysis. Parikh, Alka, and Erik Thorbecke. 1996. “Impact of Rural Industrialisation on Village Life and Economy: A Social Accounting Matrix Approach.” Economic Development and Cultural Change 44(2): 351-377. Pyatt Graham and Jeffrey Round (eds) (1985) Social Accounting Matrices: A Basis for Planning, The World Bank, Washington D C. Pyatt, Graham and Jeffrey I Round (1979) 'Accounting and Fixed Price Multipliers in a SAM Framework', Economic Journal, 89: 850-873. Pyatt, Graham and Erik Thorbecke. 1976. Planning Techniques for a Better Future. Geneva: International Labour Office. Reinert, Kenneth. A. and David Roland-Holst (1997) ‘Social Accounting Matrices’, J. F. Francois and K. A. Reinert (eds), Applied Methods for Trade Policy Analysis: A Handbook, 27 Cambridge University Press, Cambridge: 94-121. Round, Jeffrey (2003), ‘Social Accounting Matrices and SAM-Based Multiplier Analysis’, in Bourguignon, F, P. da Silva and A, Luiz, ‘The Impact of Economic Policies on Poverty and Income Distribution: Evaluation Techniques and Tools’, The World Bank, Washington DC. Sadoulet, Elizabeth and Alain D. Janvry (1995), ‘Quantitative Development Policy Analysis’, The John Hopkins University Press, Baltimore and London. Toche, Patrick (2001), ‘Is there a growth-employment trade-off?’, Discussion paper No. 62, Department of Economics, University of Oxford. Were, Maureen (2007). “Employment Outcomes and Earnings during Trade Liberalisation in Kenya: The Case of Manufacturing Sector in Kenya”. PhD thesis. University of Dar es salaam. Were, Maureen, Rose Ngugi, and Phylis Makau 2006, ‘Understanding Reforms in Kenya’, in Mensah J. (eds) 2006, ‘Understanding Reforms in Africa, A Tale of Seven nations’, Palgrave Macmillan. Were, Maureen, Jane Kiringai, (2004) in Wanyeki, L.M, Patel, A., (Eds) in Gender Mainstreaming in Macroeconomic Policies and Poverty Reduction Strategy in Kenya; GTZ / African Women's Development & Communication Network (FEMNET) 28 Zagler, Martin (2000), ‘Economic Growth, Structural Change and Search Unemployment’, European University Institute, Florence. 29 Figure 1: Growth in Real GDP and Formal Employment, 1966-2003 14.0 12.0 % Growth 10.0 8.0 6.0 4.0 2.0 0.0 19 66 19 68 19 70 19 72 19 74 19 76 19 78 19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 -2.0 Real GDP Total formal employment Source: Authors’ computations from various Economic Surveys Figure 2: Distribution of wage employment by sex 1400 number'000s 1200 1000 800 600 400 200 0 1985-90 1991-95 1996-00 Male female Source: Various Economic surveys 30 2001-04 Table 1: Total Employment Excluding Small-Scale Agriculture Year Total Formal wage Self-employed Informal (“000”) employment &Unpaid family sector (percent) workers (percent) (percent) 1989 1796.2 76.2 2.5 21.3 1990 2395.0 58.8 2.0 39.3 1991 2557.1 56.4 2.0 41.6 1992 2753.2 53.1 2.0 44.9 1993 2998.2 49.2 1.9 48.9 1994 3356.2 44.8 1.4 53.4 1995 3858.6 40.4 1.6 58.0 1996 4325.8 37.4 1.5 61.1 1997 4698.4 35.1 1.3 63.6 1998 5083.2 32.8 1.2 66.0 1999 5492.6 30.7 1.2 68.1 2000 5911.6 32.1 1.1 70.2 2001 6366.9 26.3 1.0 72.6 2002 6851.6 24.8 1.0 74.2 2003 7325.7 23.6 0.9 75.5 2004 7800.1 22.6 0.9 76.5 2005 8271.4 21.9 0.8 77.3 Total (percent) 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 Source: Various Economic Surveys Table 2: Sectoral Distribution of Female employment in the Formal Sector (%) Sectors 2003 2004 2005 Agriculture & Forestry 15.4 15.3 15.3 Mining & quarrying 0.2 0.2 0.2 Manufacturing 8.2 8.1 8.1 Electricity & water 0.7 0.7 0.7 Building & construction 1.0 0.9 0.9 Trade, restr & hotels 8.5 8.6 8.8 Transport & communication 3.4 3.9 4.5 Finance, insur, real estate & business services 4.2 4.2 4.2 Community, social &personal services 58.3 58.0 57.3 TOTAL 100.0 100.0 100.0 Source: Economic Survey (various issues) 31 Table 3: The SAM Endogenous and Exogenous Accounts ACCOUNT Products Endogenous Intermediate consumption Exogenous Household consumption expenditures Value added Factors Factor income to households Interhousehold transfers Imports, Indirect taxes Other factor payments Savings, etc Total activity outputs Total factor income payments Total household outlays Households Other accounts (Exogenous) TOTAL Source: Adopted from Round (2003) 32 TOTAL Other final demands Total demands for products Factor income from abroad Total factor income receipts Non-factor income receipts Total household incomes Total exogenous receipts Total exogenous payments Table 4: Summary of Multipliers Agriculture Fishing Forestry Mining Meat and dairy processing Grain Milling Bakery and confectionary Beverages and tobacco Other food manufactures Textiles and Footwear Wood and Paper Printing and Publishing Petroleum Chemicals Metals, Machinery and Equipment Non-mettalic manufactures Other Manufactures Electricity and Water Building and Construction Trade Hotels and Restaurants Transport and Communication Financial services Other Services Administration Health Education Source: Authors’ computation Production backward linkages 3.43 3.15 3.40 3.33 3.45 3.84 2.91 2.64 1.98 2.80 3.20 2.59 1.96 1.84 1.56 2.97 2.45 3.11 3.24 3.64 3.14 3.23 3.33 3.30 3.29 3.91 3.62 33 Production forward linkages 8.42 1.14 1.23 1.07 2.47 2.51 1.99 2.77 2.35 2.73 1.15 2.34 5.21 3.09 2.63 1.61 3.64 2.12 1.19 7.11 3.65 8.01 4.52 3.92 1.19 1.60 1.67 Table 5: Impact of exogenous injections on Compensation of Employees (Percentages) Baseline Total increase in cost Agriculture, manufacturing and services Agriculture Manufacturing Services Agriculture and manufacturing 14.1 9.5 8.5 10.0 6.9 Formal Of which: Male Female 63.4 54.0 9.4 42.2 35.2 7.0 49.6 42.6 7.1 61.2 53.5 7.8 44.3 37.3 7.0 52.0 44.6 7.4 Informal Of which: Male Female 36.6 29.6 7.0 57.8 46.6 11.2 50.4 40.4 10.0 38.8 31.5 7.3 55.7 44.8 10.9 48.0 38.8 9.2 Skilled Of which: Male Female 65.9 55.4 10.5 86.1 70.7 15.4 79.3 66.1 13.2 73.6 63.0 10.6 84.1 69.4 14.7 79.9 67.0 12.9 Unskilled Of which: Male Female 34.1 28.1 5.9 13.9 11.1 2.9 20.7 16.8 3.9 26.4 21.9 4.5 15.9 12.7 3.1 20.1 16.4 3.7 Source: Authors’ computation 34 Table 6: Impact of Exogenous Injections on Distribution of Wage Incomes across Households (percentages) Baseline Total increase Agriculture Manufacturing Services Agriculture and manufacturing Agriculture, manufacturing and services 26.4 18.4 16.2 18.9 13.1 Rural households 43.6 14.8 7.9 5.4 9.8 5.8 Of which: Rural lower decile Rural upper decile 16.1 27.6 6.0 8.8 3.2 4.7 2.2 3.2 4.0 5.8 2.4 3.4 Urban households 56.4 11.6 10.5 10.8 9.0 7.3 Of which: Urban lower decile* 1.4 0.3 0.3 0.3 0.3 0.2 55.0 11.3 10.2 10.5 8.8 7.1 Urban upper decile Source: Authors’ computation *The low percentages for urban lower decile could be attributed to a sampling bias. This category accounted for a very small proportion of the total population. 35 Table 7: Impact of Exogenous Injections on Gendered Employment Creation Baseline Total employment creation Agriculture, manufacturing and services Agriculture Manufacturing Services Agriculture and manufacturing 14.1 17.0 11.6 12.4 8.5 Formal Of which: Male Female 23.6 17.2 6.4 20.4 14.5 5.9 14.2 10.9 3.3 15.6 11.7 3.9 17.6 12.9 4.8 17.5 12.9 4.6 Informal Of which: Male Female 76.4 43.2 33.2 79.6 45.5 34.1 85.8 45.0 40.8 84.4 44.6 39.8 82.4 45.3 37.1 82.5 44.9 37.7 Skilled Of which: Male Female 37.6 25.9 11.7 28.9 19.5 9.4 32.0 20.7 11.2 35.9 24.3 11.6 30.1 19.9 10.1 32.7 22.0 10.7 Unskilled Of which: Male Female 62.4 34.5 27.9 71.1 40.5 30.6 68.0 35.1 32.9 64.1 31.9 32.2 69.9 38.3 31.7 67.3 35.8 31.5 Source: Authors’ computation 36