Download Impact of Investment in Kenya`s Priority Sectors

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.
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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
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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
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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).
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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
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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.
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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
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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
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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.
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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
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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;
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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).
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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
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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
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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).
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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
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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).
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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.
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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
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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