Download Is Uganda`s Growth Profile Jobless? Edward Bbaale School of

Is Uganda’s Growth Profile Jobless?
Edward Bbaale
School of Economics,
Makerere University, Kampala-Uganda
Abstract
This paper set out to establish the relationship economic growth and employment in Uganda for
the period 2006-2011. We obtained data from World Development Indicators, Uganda National
Household Panel Survey (2011) and United Nations Statistical Data Base and we adopted the Job
Generation and Decomposition (JoGGs) Tool of the World Bank for the analysis. Our findings
reveal interesting insights into the relationship. The growth profile of Uganda for the period
2006-2011 was jobless as evidenced by 36% change in per capita value added emerging from a
decrease in the employment rate. Agricultural sector contributed the most to the decline in the
percentage change in per capita GDP and in the employment rate by 31% and 6.5%, respectively.
Manufacturing sector contributed positively to the percentage change in per capita GDP by 8%
but negatively to change in total employment rate by 0.2%. Positive contributions to the
employment rate and the percent of total change in per capita GDP were observed in the services
and industrial sectors. The services sector contributed 83% and 1.7% to the total change in per
capita GDP and in the employment rate, respectively. The industrial sector contributed 35% and
0.5% to the percent of total change in per capita GDP and in the employment rate, respectively.
Therefore, by sectors, the growth profile in agriculture and manufacturing was jobless while the
growth profile in services and industry was job-creating. It is further noted that productivity or
output per worker contributed over 100% to total change in per capita GDP growth. By sectors,
output per worker was lowest in the agricultural sector and highest in the industry followed by
the services sector. Hence the industrial and services sectors have higher prospects in an effort by
the country to alleviate poverty via employment creation.
The inter-sectoral shifts had a positive contribution to the output per worker meaning that, on
average, labor moved from lower than average productivity sectors to above average
productivity sectors. All sectors, apart from manufacturing, contributed positively to the intersectoral shift component. Given that the employment rate in agriculture was negative and that it
is a low productivity sector implies that the relocation of workers from agriculture to other
sectors had a positive effect on per capita growth via the inter-sectoral shift component. On the
other hand, the employment rate in the services and industry was positive and that they are high
productivity sectors means that the relocation of workers to these sectors had a positive effect on
the per capita growth via the inter-sectoral shift component. The change in the employment rate
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in manufacturing was zero and that this sector having the lowest productivity after agriculture,
means that the relocation of workers to this sector had a negative but modest effect on per capita
growth via the inter-sectoral shift component. Overall, the promising sectors for poverty
reduction through productivity and employment generation in Uganda by order of importance;
services sector, industrial sector and manufacturing. However, this is not to say that agriculture is
not important, it’s still the major employer in the country (employing almost 70% of the labor
force). This notwithstanding, it is a low productivity sector yielding very low returns to its
participants and hence having limited ability to alleviate poverty. The demographic changes of
the population are generating a window of opportunity for raising per capita incomes if well
exploited. There are now fewer dependents per working age adults and if these engage in
productive activities, this has clear dampening effect on poverty.
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1
Introduction and study concern
The role of employment in poverty reduction and welfare improvements cannot be underscored
given that unemployment and underemployment are major causes and consequences of
widespread poverty (Sodipe et al., 2011). Full, productive and decent employment is the most
important source of income security and paves the way for broader social and economic
advancement, strengthening individuals, their families and communities (ILO et al., 2012). In
developing countries such as Uganda, most of the poor households earn a living by selling their
labor either to themselves or to others; making employment an important channel through which
economic growth impacts poverty (Fox and Gaal, 2008). Indeed, the impact of growth on
poverty is seen as depending on the extent to which growth generates employment and good
earning opportunities. Therefore, there is a great need for a strong employment component in the
sustainable development strategies which aim at raising the productivity of the poorest workers,
and ensuring that they get to keep most of their increased earning power by progressively
strengthening labour market institutions (ILO et al., 2012). In line with these arguments,
employment is an important conduit for the attainment poverty reduction, accelerated sustainable
growth that is inclusive and equitable. According ILO et al. (2012) as work becomes decent in
income terms, people’s concern over corruption and interest in democratic governance increases
and are also more likely to invest in health and education of their children and themselves. There
is little wonder then that many countries around the world are taking purposeful efforts of
attaining economic growth that is employment enhancing particularly for the vulnerable groups;
the youth and women.
It is noteworthy that the Ugandan economy has exhibited a strong growth stance since the
nineties. The GDP average yearly growth was 6.86% (1990-1999), 5.52% (2000-2005), and
7.7% (2006-2010) (UBOS, Statistical Abstract 2010). This may be attributed to a number of
fundamental changes that the government has implemented over the period. These include
among others; improving the investment climate and attracting foreign direct investment,
improving the quality of labor force through universal primary and secondary education and
completely eliminating the red tape in the business environment in order to reduce the cost of
doing business. These reforms have generated major changes in the drivers of the economy with
the services sector (over 52% of GDP) and the industrial sector (over 25% of GDP) playing a
more dominant role than the agricultural sector (less than 14% of GDP). It is noteworthy that the
economy has experienced sectoral shifts in GDP composition but with no sectoral shifts in
employment. For example, the agricultural sector still employs approximately 70% of the labour
force in the country. On the other hand, the services and manufacturing sectors employ 24% and
6%, respectively (UNHS 2009/10). This pattern implies that the participants in the agricultural
sector get meager returns to their labour and hence suffer from biting poverty. On the other hand
it might be the case that there are intra-sectoral shifts within the agricultural sector such that
workers keep moving from less profitable activities to more profitable ones. Therefore,
understanding the dynamics within (intra-sectoral shifts) and between (inter-sectoral) sectors
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shall enable government to design appropriate policies that can uplift workers from poverty
through productivity enhancement.
The informal sector is by far the greatest employer in Uganda. For example 3.5 million persons
were engaged in the informal sector including non-crop agriculture out of which 2.1 million
persons were engaged in non-agricultural businesses (UNHS, 2009/10). Consequently,
unemployment is seemingly no longer the problem in Uganda as evidenced by the low
unemployment rates recorded recently; 3.5% (UNHS, 2002/03, 1.9% (UNHS, 2005/06) and
3.5% (UNHS, 2009/10). As per the ILO definition of unemployment, the total number of
unemployed population increased from 0.3 million in 2002-03 to 0.5 million in 2009-10.1 In
view of the existing realities, such low rates of open unemployment are expected in a country
such as Uganda since the poor cannot afford to be unemployed, they are compelled to engage in
some activity—even for few hours and at low wages in the informal sector— in order to make
ends meet. Consequently, the informal sector has become more assertive, with self-employment,
part-time employment, and unpaid employment in family enterprises being dominant
components of the overall employment (UNHS, 2009/10). As a result, the ‘standard’
unemployment rate does not provide a real picture of the supply-demand balance of the labor
market and inadequately reflects the degree of inefficiency that prevails in the labor market. In a
situation like this, alternative indicators such as underemployment rates and work intensity are
necessary to supplement the standard indicator of unemployment rate in order to reveal the labor
market reality. A person is classified as time-underemployed if she or he has worked less than
40 hours a week and is willing and available to work more hours (UNHS, 2009/10). According
to the UNHS Report (2009/10), underemployment has declined since 2002; 4 percent of workers
were underemployed in 2009/10 compared to 12 percent in 2005/06 and 17% in 2002/203.
Despite these enormous reforms and structural changes in the economy, employment
enhancement has lagged behind the speed at which growth has been taking place indicating a
phenomenon of jobless growth as indicated in the literature of labor economics (see Yeldan,
2006). It is not surprising therefore that Uganda’s impressive growth has reduced headcount
poverty only by slightly more than a quarter of its level for period 2002-2010. Headcount
poverty as a percentage of the total population decreased from 38.8% in 2002/03 to 31.1% in
2005/06 and to 24.5% (2009/10). In absolute terms, this translates to 9.8 million (2002/03), 8.44
million (2005/06) and 7.5 million (2009/10) living below the poverty line (UNHS, 2009/10). It is
the slow pace at which people were being moved out of poverty that led to the realization by
many poor countries and their development partners that employment creation is a crucial aspect
of any growth agenda. In the same line of argument, it has become a critical question how
1
Following the ILO recommendation, in Uganda a person of age 14-64 years is considered as unemployed if he/she
did not work at all during the preceding week of the survey (even an hour in the reference week) and was actively
looking for work or was available for work but did not work due to temporary illness or because there was no work
available.
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employment growth is influenced by economic growth in developing countries such as Uganda.
This is despite the fact that, hitherto, employment-economic growth nexus had not been lent an
empirical regularity for the case of Uganda. This paper is therefore an attempt to fill this gap and
answers the following pertinent questions. i) how is growth reflected in employment generation
and in changes in output per worker, ii) how is growth reflected in the sectoral pattern of growth
and employment generation, iii) What are the sources of changes in output per worker. Answers
to these questions will help to understand whether the pattern or profile of growth observed is
conducive to poverty reduction, by pinpointing the sector and factors that should be further
analyzed. This paper is expected to trigger public policy dialogues and debates which may
provide the government with alternative policy options that can lift the country from poverty
emerging from unemployment and underemployment problems.
This paper is organized as follows: section 2 gives a brief literature survey; section 3 presents the
methodology and data issues; 4 gives the findings and section 5 concludes with implications for
policy.
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Literature review
Quite extensive strand of literature exists on the relationship between employment and economic
growth in both developed and developing countries. Melamed et al., (2011) undertakes a
rigorous survey of the most recent literature highlighting the key insights the different authors
have provided on the relationship between economic growth and employment. This blend of
literature underscores the differential importance of the different sectors of the economy in
generating gainful employment. A substantial strand of literature supports the view that the
services sector has come to the forefront of generating gainful employment in many countries; an
issue that is practically observable in Uganda’s economy. This view is supported by Kapsos
(2005) who contends that the growth in the services sector generates more employment
opportunities per percentage increase in their growth rates than growth taking place in either
agriculture or manufacturing.
Huong et al. (2006) documents that for the case of Vietnam employment opportunities in the
services sector were more income generating than employment opportunities in manufacturing.
Ravallion (2009) also documents that the services played a dominant role in poverty reduction in
Brazil while it was growth in the agricultural sector that was most important in China. The
author also contends that in contrast to China whose poverty reduction benefited more from
agricultural growth, in India the services sector was more powerful. Demeke et al. (2006)
showed that the growth of the overall economy was largely attributed to the growth of the
services sector for the period 1960-2002 and hence absorbed much of the growing labour force
in Ethiopia of the 1990s due to the slow growth of the agricultural sector. Fox and Gaal (2008)
show that export-oriented growth in industrial and service output has the potential to greatly
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expand formal employment Africa. The authors contend that the share of output has increased in
the service sector and the labor force has followed hence it might generate more employment
than agriculture and manufacturing. However, Melamed et al., (2011) notes that many jobs in the
services sector have strong linkages to manufacturing or agriculture and hence a strategy that
focuses entirely on any single sector would be unlikely to succeed over the long term. Loayza et
al. (2010) show that agriculture is the most poverty reducing sector followed by construction and
manufacturing; while mining, utilities and services do seem to dampen poverty. Khan (2008)
documents the case of East Asian pioneers where rapid growth of industries and modern services
increased the proportion of workers employed in activities with higher levels of productivity and
remuneration and helped to increase the productivity of workers who were left behind in
agriculture and other traditional activities.
Another strand of literature shows that the phenomenon of jobless growth is persistent in many
countries (Melamed et al., 2011). Aryeetey et al. (2007) contend that for the case of Ghana
policies narrowly focused on achieving macroeconomic stability and accelerated growth without
adequate employment consideration leading to a jobless growth. Jobless growth is also visible in
the recent times of India (Mehta et al., 2011), and is also existent in Latin America (Jemio et al.,
2006). Mehta et al. (2011) observes dependence on agriculture seems to have become a poverty
trap for many, especially in the wake of the jobless growth since the mid1990s. The authors
further noted, in rural India, households whose chief earners were landless wage earners
constituted 30% of all poor households. In the same line of argument ILO estimates that around
40% of workers worldwide are still poor, precisely below the poverty line (ILO, 2011). Melamed
et al., (2011) presents two channels through which growth can be poverty dampening via the
labour market. According to these authors the changes in economic growth and the associated
shifts in the structure of the economy have to generate increased demand for labour and an
increase in the productivity of each worker. The authors further contend that an increase in the
quantity of employment without simultaneous increase in productivity increases the number of
working poor due to the fact that wages stagnate with low productivity. Gutierrez et al. (2009)
supports this argument by showing that the sectoral productivity and employment pattern of
growth may have important implications for poverty alleviation while Kapsos (2005) shows that
in East Asia, rapid economic growth has resulted in large gains in labour productivity,
contributing to rising living standards while also fostering robust mployment growth.
Therefore, decent employment should be the key to labour market policy than generating
employment per se (Fields, 2007).
Recent literature also highlights the important inequalities in the labour market by age and
gender of the players (Melamed et al., 2011). It has been documented by Kapsos (2005) that the
youth cohort (aged 15-24) has experienced low and stagnant employment elasticities. This view
is supported by Ernst (2008) who shows that the youth (15-24 years) comprise of 25% of the
labour force but account for 44% of the unemployed and 20% of those working live below the
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one dollar a day poverty line. Other authors document the fact that youth unemployment is many
times higher than that of the adult counterparts (ILO, 2011). Zependa (2007) contends that
unemployment is highest particularly among the youth (15-24 years) and mature educated
workers (55-64 years). Messkoub (2008) shows that poverty and unemployment, especially
among the young, are widespread in the Middle East and North Africa region There is need to
exploit the opportunities presented by the demographic transition in order to avert the
demographic disaster. Countries need to look at the youth as a strong force of men and women
that are dynamic, energetic and able to work for long hours and hence leading to a quicker
economic transformation. The inequality along the gender dimension also stands out in the
literature. It has been documented by several authors that men are more likely than women to be
employed in formal private and public sectors and to earn a higher wage (ILO, 2011; Rahman et
al., 2006). Zependa (2007) also documents that women are more likely to be unemployed than
their male counterparts in Kenya. Heintz, (2005) indicates substantial labor force segmentation in
Ghana where women are disproportionately represented in more precarious forms of
employment. According to Melamed et al. (2011), gender inequality in the labour market has
been more researched than the inequality between the young and old workers. Against this
backdrop therefore, it is much more important than ever before to critically examine the labor
market dynamics and outcomes for the youth. The big number of the youth in many economies
should be viewed more of a blessing than a curse for the future development of any one country
provided ample employment opportunities for the youth are available.
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3
Methodology and Data Requirements
We adopted the Job Generation and Growth Decomposition Tool (JoGGs) of the World Bank to
analyze the impact of growth on poverty through employment generation and productivity. The
aim of this methodology is to understand how growth is linked to changes in employment, output
per worker and population structure at the aggregate level and by sectors. Additionally, we also
decompose the sources of output per worker growth into total factor productivity (TFP) growth,
movements of employment from one sector to another, or changes in the capital-labor ratio.
To perform the decomposition we needed data on output (aggregate and by sectors), employment
by sectors, population by ages, capital stock and the share of capital stock in total income for two
periods and in our case we considered 2006-2011. Data on output, capital stock and the share of
capital stock in income was obtained from the World Development Indicators (WDI) online
facility of the World Bank. Growth can be measured either by GDP or by Value Added. Value
Added, is roughly the same as GDP but excludes subsidies and taxes. National Accounts
disaggregated by sectors refers to Value Added rather than GDP, so using Value Added rather
than GDP ensures that sectoral disaggregations are consistent with the aggregate ones. Data on
employment by sectors was obtained from Uganda National Household Panel Survey 2009/10.
Data on population by ages was obtained from United Nations online Statistical Data base. All
our monetary data are in constant dollars of 2000. Additionally, in order to perform other
descriptive analysis, we used data from earlier Uganda National Household Survey, especially
that of 2002/03 and 2005/06. These data supported analysis on a wide range of issues including,
but not limited to, the job creation and employment trends by sector, geographical location, sex
and age groups of the population and factors influencing observed trends-determinants of job
creation and employment.
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Findings and their discussion
4.1
Understanding the aggregate employment and productivity profile of growth
We sought to understand how economic growth generates increases in employment and
productivity at the aggregate level and by sectors. As is conventionally known from standard
economic theory, GDP per capita, Y/N=y can be expressed as
Y Y E A

N E AN
Or:
y   *e*a
(1)
(2)
Y is total value added, E is total employment, A is the total population of working age and N is
total population. By extension it implies that Y/E=ω is a productivity measure (total output per
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worker), E/A is a measure of the employment rate (i.e. share of working age population that is
employed) and A/N is the share of population of the working age in the total population.
Therefore, ω, e, and a in the equation above refer to labour productivity, employment rate and
share of labor force in the total population. The summation of the growth in  , e and a refers to
the total change in GDP per capita. Thus if we let  , e and a denote the fraction of growth
linked to each component then the growth rate of an economy can be expressed as:
y
y
y
y

e
a
y
y
y
y
(3)
And total growth as:
y   * y  e * y  a * y
(4)
 * y refers to the contribution of output per worker to the growth in output per capita while
the employment rate e and the share of population of working age a remain fixed. There are in
fact several ways in which these two components can stay unchanged. They can both remain in
the level observed in the initial year, they can both stay at the level observed in the final year, or
one of them can stay in the level observed in the initial year and the other stay in the level
observed in the final year. Some decompositions only consider the case where both components
stay in the level observed in the initial or final year, and thus end up with a residual. However,
the Shapley decomposition considers all possible alternatives, and then makes a weighted
average of each (with the weights reflecting the number of ways each component can remain
unchanged), eliminating in this way the residual, which can be very large. Each component thus
has the interpretation of a counterfactual scenario.
Similarly, e * y refers to the contribution of the employment to the growth in output per capita
while the productivity parameter  , and the share of population of working age a remain fixed.
Finally, a * y refers to the contribution of the demographic changes to the growth in output per
capita while the first two components remain fixed.
In Table 1 we present the main data used for the aggregate decomposition: Output, employment
and populations, as well as employment shares, output per worker, and share of population of
working age. Uganda registered a growth rate of 17% in per capital Value Added, for the whole
period. This growth was accompanied by decreases in employment rate (-5.41%), increases in
labor productivity (22.37%) and increases in the share of population of working age (0.41%).
The results from this data description already points to a profile of growth that is jobless since its
associated with a reduction in the share of the employed in the total population of working age.
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Table 1: Employment, Output, Productivity and Population. Uganda 2006-2011
2006
GDP (value added) (in Ugandan
Shillings)
Total population
2011
9,395,979, 621
% change
12, 895,873,648
37.2
28,431,204
33,424,683
17.6
Total population of working age
13,438,875
15,935,601
18.6
Total number of employed
11,113,222
12,464,780
12.2
GDP (value added) per capita
330
386
16.74
Output per worker
845
1,035
22.37
Employment rate
82.69
78.22
-5.41
Share of population of working age
47.27
47.68
0.41
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.1.1 Results and interpretation
Using equation (4) we decompose the aggregate per capita growth into its main components and
results are reported in Table 2 and figure 1 below. The table shows the contribution in US dollars
of 2000 to absolute observed growth in per capita GDP as well as the percent contribution. The
figure illustrates the results in US dollars of 2000.
Table 2: Decomposition of Growth in per capita Value Added, Uganda 2006-2011
2000
USD
Percent of total change
in per capita value
added growth
Total Growth in per capita GDP (value added)
55.34
100
Growth linked to output per worker
72.23
19.97
3.08
130.52
Growth linked to changes employment rate
Growth linked to changes in the share of population of working Age
-36.09
5.57
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
The results suggest that 6 percent of the change in per capita Value Added can be linked to
changes in the structure of the population. In other words, had everything else stayed the same,
the sole change in the age structure of the population would have generated a growth equivalent
to 6 percent of the actual observed growth. In other words, there were fewer dependants (minors
and elderly) per each working age adult. Changes in productivity or output per worker were the
most important, accounting for some 131 percent of observed growth. Unfortunately, changes in
employment acted in the opposite direction. This suggests that 36% of the change in per capita
value added can be linked to a decrease in the employment rate. This implies that the growth
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profile in Uganda for the period 2006-2011 was a job-less growth. Figure 1 below shows the
contribution to growth in value added per capita, 2000 US dollars. It shows that the demographic
change and productivity contributed USD 3.1 and 72.2 per capita, respectively. Yet, the
employment rate contributed negatively USD 20 per capita.
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Having decomposed aggregate per capita growth we can go further and undertake a
decomposition of its key components as follows: i) we decompose changes in the overall
employment rate e , to understand the role played by different sectors and ii) decompose total
output per worker  , to understand the role of capital, total factor productivity and inter-sectoral
employment shifts. This last step can be undertaken both at the aggregate level and by sectors.
This amounts to doing a step-wise decomposition: first decomposing aggregate growth into
employment and productivity changes and then decomposing employment and productivity
changes, into other sub-components. Figures 2a and 2b illustrate employment by sectors and
output per worker by sectors, respectively.
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Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Figure 2a shows that agriculture is the leading employer in Uganda employer well over 8 million
Ugandans (translating to over 70% of the entire labor force) in both periods. This is followed by
the services sector that is employing over 2 million Ugandans (19% in 2006 and 22% in 2011).
Both manufacturing and industry (comprises other components apart from manufacturing e.g.
construction, mining and utilities etc.) employ well below 1 million Ugandan but manufacturing
is doing better than the other industry. As expected, in terms of productivity, we observe the
opposite in figure 2b. Agriculture has the lowest output per worker and industry has the highest
in both 2006 and 2011. The industrial sector is followed by the services sector and
manufacturing. This puts a policy question on the productivity of jobs held in the agricultural
sector in terms of income that workers can generate. Lower productivity implies lower earnings
for workers involved in that type of economic activity and, by extension, it implies low
prospects of overcoming the problem of poverty by those employed in that sector. Agriculture
being the major employer in Uganda, it implies that the majority of workers in Uganda are
holder low paying jobs an issue which has a great bearing on poverty alleviation.
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Figure 2b: Output per Worker by Sectors, Uganda 2006-2011
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.2 Understanding the role of each sector in employment generation
To understand the way in which sectors contributed to employment generation and to total per
capita growth we can further decompose employment (rate) growth ( e ) by sectors. The easiest
is of course to express the total growth in employment as the sum of employment generation in
each sector.
s
e   ei
(5)
i 1
13
Ei
is just the change in employment in sector i as a share of total working age
A
population. This gives a simple measure of which sector contributed more to changes in the
employment rate.
Where ei  
Once we understand the sources of growth in the employment rate, we can understand the link of
employment growth in sector i , to the observed change in per capita output by combining the
results in steps 1 and 2. The total contribution of sector i will be its contribution to changes in
total employment times the contribution of employment rate changes to total growth calculated
in step 1. As before this can be interpreted as the per capita growth consistent with a
counterfactual scenario, in which all else (productivity, demographics, and employment in the
remaining sectors) had all remained unchanged, and the only change had been the observed
employment growth in sector i .
Table 3 presents the data on employment by sector. All sectors registered absolute growth in the
number of employed, with the industrial sector leading followed by the services, manufacturing
and agriculture came last. It is worth noting that only services and industry gained in the share of
employment in the working age population. Figure 2a illustrates the same data. Total
employment grew by 12%, but as a result of the simultaneous growth in the working age
population, the employment rate declined by only 5.41%.
Table 3: Employment by Sectors of Economic Activity, Uganda 2006-2011
Total employment
Agriculture
services
Manufacturing
Industry
Total
2006
2011
8,389,075
2,095,951
444,565
183,631
11,113,222
8,913,741
2,760,421
498,418
292,200
12,464,780
Employment/pop. of working age
% change
6.25
31.70
12.11
59.12
12.16
2006
62.42
15.60
3.31
1.37
82.69
2011
% change
55.94
17.32
3.13
1.83
78.22
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.2.1 Results and Interpretation
Table 4a and Figure 3a show the results of the decomposition of the employment growth by
sectors of the economy. This enables us to tell a sector whose growth was jobless or job-creating.
The first column of the table shows how the -4.47 percentage points of decline in the
employment rate were distributed among the different sectors. Agriculture is responsible for
most of the decline and then the manufacturing sector. Agriculture and manufacturing sectors
contributed to the decline in the employment rate by 6.5 and 0.2 percentage points respectively.
14
-10.39
11.07
-5.45
34.19
-5.41
Positive increments are noted in the services and industrial sector with 1.73 and 0.5 percentage
points, respectively. Figure 3a illustrates how the decline of the employment rate of 4.47 percent
points was distributed among the different sectors. These results imply that growth in the
agricultural and manufacturing sectors was jobless while growth in the services and industrial
sectors was job generating. These findings combined with high productivity in services and
industrial sectors give a great prospect to these sectors in an effort to reduce poverty via
employment creation.
Table 4a: Contribution of employment changes to overall change in
employment rate, Uganda 2006-2011
Agriculture
services
Manufacturing
Industry
Total employment rate
Contribution to
change in total
employment rate
(percent points)
Percent
contribution of the
sector to total
employment rate
growth
-6.49
1.73
-0.18
0.47
-4.47
145.0
-38.6
4.0
-10.4
100.0
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
15
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Tables 4b and Figure 3b, show the contribution of sectoral employment changes to growth in
total per capita output. Of the -20 US dollars registered in per capita output, agriculture and
manufacturing contributed negatively with -29 and -0.8 US dollars, respectively. Services and
industry contributed positively 7.7 and 2.1 US dollars, respectively. Agriculture being the major
sector in Uganda, a small contraction in employment led to a very big negative effect on growth
such that the positive effect in services and industry could not offset it.
Table 4b: Contribution of employment changes to overall change in per
capita GDP (value added), Uganda 2006-2011
Agriculture
services
Manufacturing
Industry
Total contribution
Contribution to change
in per capita GDP
(value added)
Percent of total change
in per capita GDP
(value added)
-29.0
7.7
-0.8
2.1
-20.0
-52.3
13.9
-1.5
3.8
-36.1
16
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.3
Decomposing output per worker growth in within sectoral changes and between
sectoral changes
We further decompose output per worker into sectoral employment shifts and changes in output
per worker within sectors by noting that:
Y E
Y
 i i
E
S Ei E
(6)
Or equivalently:
S
    i si
(7)
i 1
Where Yi is value added of sector i  1,...S ; E i is employment in sector i ; and E is total
employment. This means that  i 
Yi
will correspond to output per worker in sector i ,
Ei
17
Ei
is the share of sector i in total employment. This equation just states that total output
E
per worker is the weighted sum of output per worker in all sectors, where the weights are simply
the employment share of each sector.
si 
Using the Shapley approach, changes in aggregate output per worker can be decomposed into
changes in output per worker within sectors (also known as within component), and movements
of labor between sectors (also referred to as between component). Increases in output per worker
within a sector will increase average output per worker. How big the effect is, will depend on the
size of each sector (i.e. its share in total employment). On the other hand, relocation of workers
across sectors of different productivity levels can increase average output per worker if the final
relocation implies that a larger share of workers becomes employed in higher productivity
sectors.
Once we can trace the growth in output per worker back to its within and between components,
we can then calculate the amount of growth in total output per capita that can be linked to
changes in output per worker in sector i, and to inter-sectoral relocation of labor by combining
the contribution of each of these components to changes in output per worker, with the
contribution of changes in output per worker to total per capita growth (obtained in step 1). As
before, the contribution of changes in output per worker within a sector can be interpreted as the
total per capita growth consistent with a counterfactual scenario, in which all else (employment
rate, demographics, and output per worker in the remaining sectors) had all remained unchanged,
and the only change had been the observed change in output per worker in sector i. The
contribution of the inter-sectoral shift component can be interpreted as a counterfactual scenario
in which the employment rate, the demographic structure of the population and output per
worker in each sector had remained unchanged, and labor had reallocated across sectors as
observed.
Table 5 shows output per worker for the two years under consideration as well as the percent
growth for the period. The industrial sector saw a sharp decline in the output per worker (11%)
while the agricultural sector saw a small positive growth of 2% compared to manufacturing
(24%) and services sector (13%).
Table 5: Changes in Output per Worker by Sectors. Uganda 2006-2011
2006
Agriculture
services
Manufacturing
Industry
249
2,118
1,427
12,13
2011
254
2,392
1,764
10,77
18
% change
1.84
12.94
23.57
-11.23
Total output per worker
8
845
5
1,035
22.37
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Table 6a and Figure 4a below; show the results of applying the above formula to Ugandan data.
The first column of the table shows the contribution of each sector as well as of inter-sectoral
employment shifts to the observed growth in total output per worker. The $189.1 increase in
output per worker for the period is accounted for by a $27 reduction from industry and an
increase in Agriculture ($3.4), Services ($56.2), Manufacturing ($13.5) and inter-sectoral labor
relocation of $143.3. The fact that inter-sectoral shifts had a positive contribution means that on
average labor moved from lower than average productivity sectors to above average productivity
sectors. As we saw in the previous section all sectors increased their employment shares, but
agriculture had the lowest share of 6.3%. Thus we can conclude that an important share of
growth in output per worker was due to movements of the labor force into other sectors and to a
small extent into agriculture.
The second column of Table 6a shows the results as percentage of total output per worker
growth. Of the total increase in output per worker, agriculture accounted for 2%, services (30%),
manufacturing (7%) and the inter-sectoral shift (76%). The industrial sector accounted for a
decline of 14.4%. In summary, Agriculture, Manufacturing and Services, all saw increases in
output per worker, and given their large share in total employment had important positive effects
on aggregate output per worker. The industry exerted a negative effect on average output per
worker. Inter-sectoral shifts, which capture movement of labor between sectors, exerted a
positive effect on output per worker, which means that on average labor moved from low
productivity sectors to high productivity sectors.
Table 6a: Decomposition of Output per Worker into Within Sector
Changes in Output per Worker and Inter-sectoral Shifts. Uganda 20062011
Contribution to
Change in Total
Output per
Worker
Agriculture
services
Manufacturing
Industry
Inter-sectoral shift
Total change in output per worker
3.4
56.2
13.5
-27.2
143.3
189.1
19
Contribution to
Change in Total
Output per
Worker (%)
1.8
29.7
7.1
-14.4
75.8
100.0
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Table 6b and Figure 4b show the contribution of changes in output per worker and inter-sectoral
shifts to total growth in per capita value added.
Table 6b: Contribution of within Sector Changes in Output per Worker and
Inter-sectoral Shifts to Change in GDP (value added) per capita. Uganda 20062011
Contribution to
change in GDP
(value added)
per capita
Agriculture
services
Manufacturing
Industry
Inter-sectoral shift
Total contribution to change in per
capita GDP (value added)
20
Percent of total
change in GDP (value
added)
per capita
1.3
21.5
5.1
-10.4
54.7
2.3
38.8
9.3
-18.8
98.9
72.2
130.5
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.4 Understanding the sources of changes in total output per worker (net of inter-sectoral
shifts) at the aggregate level
The previous section decomposed changes in output per worker into changes in output per
worker within sectors and changes in output per worker due to movements of labor between
sectors with different levels of productivity.
Aggregate and sectoral changes in output per worker (Δω), will capture changes in output per
worker, but its interpretation is not so straight forward. Increases in output per worker can come
from three different sources: i) increases in capital labor ratio ii) increases in Total Factor
Productivity (TFP) and iii) relocation of jobs between bad jobs sectors (low productivity) to good
21
jobs sector (high productivity), which we described above as the between component of growth
in output per worker or the labor relocation effects.
To see this, note that under constant returns to scale, if Yt  t f ( Et , K t ) where K t is the capital
stock
t
and
a
multiplicative
shift
parameter,
then
output
per
worker
will
be Yt Et  t f (1, K t Et ). Therefore it will capture changes in capital labor ratio K t Et , and
changes in the shift parameter, t . Changes in the shift parameter will be capturing all other
sources of growth not due to changes in capital labor ratio, or in other words the Solow residual.
It will mainly capture changes in technology and relocation of production between sectors with
different productivity levels (inter-sectoral shifts), but it may also capture cyclical behavior of
output: firms operating in economic downturns may have underutilized capital, when the demand
rises again; it will be reflected as rise in output per worker. We can thus decompose within
productivity changes (or productivity changes net of inter-sectoral shifts) into changes due to
increases in the capital-labor ratio and the residual, which can be interpreted (with caution) as
TFP growth.
If data on capital stock is available then we can assume a particular functional form for the
production function and separate the contribution of higher capital-labor ratios and the rest.
For example if we are willing to assume that the production function is Cobb-Douglas then:
1
Y
K
  
E
E
(8)
In competitive markets 1   is the share of payments to capital in total Value Added.
Once we have an estimate of  , we can calculate total factor productivity as a residual: In the
first period it will be:
(1 )
Y   K 
 TFPt 0 .
  / 
 E  t 0  E  t 0
(9)
In the second period we need to take into account that part of the change in output per worker
due to relocation shifts so that:
(1 )
 Y 
 K
 TFPt 1
    B  /  
 E t 1
  E t 1
(10)
The term in square brackets is just output per worker in period two net of relocation effects.
22
In this way we are able to see whether changes in output per worker net of relocation effects,
were due to increases in capital per worker (K/E) or in total factor productivity (net of relocation
effects).
Once we have decomposed changes in output per worker into its two components –TFP and the
capital labor ratio- we can calculate the amount of total per capita growth that can be linked to
each component.
Table 7 illustrates the data used for this decomposition.
Table 7: Data used for Decomposition of Output per Worker, Capital Stocks, Capital Labor
Ratio and Share of Capital in Total Income. Uganda 2006-2011
2006
Share of Capital in Total Income (%)
Capital
Total output per worker
Output per worker net of inter-sectoral shifts
Capital Labor Ratio
TFP residual net of inter-sectoral shifts
21%
2,250,759,969
845
845
203
278
2011
24%
3,073,422,546
1,035
891
247
232
% change
0.00
36.55
22.37
5.41
21.74
-16.51
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.4.1 Results and interpretation
Figure 5 show the decomposition of changes in aggregate output per worker for Uganda between
2006 to 2011. It illustrates the role of capital and TFP using the above formulas, as well as the
inter-sectoral shifts we calculated earlier on.
Total output per worker increased by 22.37 percent (Table 7). Of this increase inter-sectoral
employment shifts exerted a positive effect on output per worker contributing with $143.3 of
total productivity growth. The capital labor ratio also increased, contributing an additional $204.
But TFP suffered an important reduction of $158, however, did not offset the positive effects
(Figure 5). From this data we can clearly say that TFP changes are responsible for the lower
output per worker. A natural next step would be to try to understand what the forces behind TFP
decreases were.
23
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
4.5 Understanding the role of inter-sectoral employment shifts
It is possible to understand further how changes in the share of employment in the different
sectors help explain the overall contribution of inter-sectoral shifts to per capita growth or output
per worker. A large number of studies have found that structural change, which is movements of
labor force shares from low productivity sectors to high productivity sectors, is an important
factor behind growth. Increases in the share of employment in sectors with above average
productivity will increase overall productivity and contribute positively to the inter-sectoral shift
term. On the contrary, movements out of sectors with above average productivity will have the
opposite effect. By the same token, increases in the share employment in sectors with below
average productivity should reduce growth, while reductions in their share should contribute
positively to growth.
Thus if sector i has productivity below the average productivity, and decreases its share si its
contribution will be positive, that is outflows from this low productivity sector have contributed
to the increase in output per worker. If on the other hand, the sector sees an increase in its share,
these inflows into this low productivity sector will decrease output per worker and thus have a
24
negative effect on the inter-sectoral shift term. The magnitude of the effect will be proportional
to: i) the difference in the sector’s productivity with respect to the average and ii) the magnitude
of the employment shift.
Once we have calculated the contribution of each sector to changes in output per worker linked
to employment relocation effects, we can proceed further and calculate the amount of total per
capita growth that can be liked to relocation effects in each sector.
4.5.1 Results and interpretation
Table 8 illustrates the results of this exercise. The first column shows average output per worker
between 2006 and 2011, with the last row of the column showing average output per worker for
the whole economy. The second column shows the change in employment shares in each sector.
The final column shows the contribution of each sector to the $143.3 contributed by intersectoral employment shifts to total growth in output per worker. The change in the employment
share in agriculture is negative and agriculture being a low productivity sector, contributed
positively to the inter-sectoral shift component. The change in employment share in services and
industry is positive and these sectors being high productivity sectors, they contributed positively
to the inter-sectoral shift component. The change in the employment share in the manufacturing
sector was neutral and hence had a minor negative contribution to the inter-sectoral shift
component. Of the three positively contributing sectors, agriculture had the lowest contribution
because it’s a low productivity sector while industry and services are high productivity sectors
and hence contributed most to the inter-sectoral shift component. Hence, movements of labor to
industry and services sectors are associated with increases in per capita growth or productivity
growth.
Table 8: Understanding the Role of Inter-Sectoral Employment Shifts. Uganda
2006-2011
Change in
employment
share (percent
points)
Average
Output per
Worker
Agriculture
services
Manufacturing
Industry
Aggregate
252
2,255
1,595
11,457
940
-0.040
0.033
0.000
0.007
Sectoral
contribution to
inter-sectoral
shift component
27.37
43.22
-0.01
72.76
143.33
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Table 9 shows the percent contribution of each term to the inter-sectoral shift or between
components of productivity changes. Whereas agricultural had a negative employment share
25
shift, it contributed positively (by 19%) to inter-sectoral shifts due to the fact that it’s a low
productivity sector and hence any shifts of workers from this sector positively influences per
capita growth. Services and industry had a positive employment share and being high
productivity sectors, contributed positively (by 30% and 51%, respectively) to inter-sectoral
shifts. Hence any shift of labor to these sectors positively influences per capita growth.
Manufacturing had a negative employment shift share and being a high productivity sector,
contributed negatively (by 0.01%) to inter-sectoral shifts. This implies that shifts of labour from
the manufacturing sector somewhat hurts per capital growth.
Table 9: Decomposition of Inter-sectoral Shifts. Uganda
2006-2011
Direction of
Employment Share
shift
Sectoral contributions
Agriculture
services
Manufacturing
Industry
Contribution to
Inter-sectoral Shifts
(%)
+
+
19.09
30.15
-0.01
50.76
Total Contribution of inter-sectoral shifts
100
4.6 Putting everything together
Once the above steps are completed the percent contribution of each factor to total changes in
GDP per capita can be obtained. A simple example will illustrate how this is done. Assume we
want to know what the contribution of TFP changes to per capita GDP growth was. Using step 3
we calculate the percent contribution of TFP growth to total output per worker growth, denote
this by TFP  , and from step 2 we calculated the share of growth of GDP per capita that was
linked to changes output per worker, denote this by  . Thus the share of growth in per capita
GDP that can be attributed to TFP changes will be TFP  TFP  *  . And the amount of growth
that can be linked to changes in TFP will be TFP * y. 
4.6.1 Results and interpretation
Table 10a and Table 10b below illustrate the results for Uganda, in percentage contribution and
in US dollars of 2000, respectively. The demographic component accounts for 6% of all the
change. The other 94% is explained by an increase in output per worker within sectors (32%), a
decrease in the share of working age population employed (36%), and a positive effect of labor
relocation (99%), which on average moved from low productivity sectors to high productivity
sectors. When looking across sectors the biggest role was played by the Services sector (83%),
26
industrial sector (35%) and manufacturing sector (8%). Agriculture contributed negatively by
31%.
Sectoral contributions are decomposed into: i) contribution of within changes in output per
worker (first column), ii) contribution of changes in employment (second column), and iii)
contributions of the sector to the inter-sectoral employment shifts. The final column is the total
effect of the sector. Overall, services, industry and manufacturing had a positive contribution
while the agricultural sector had a negative contribution.
Table 10a: Growth Decomposition. Percent Contribution to Total Growth in GDP (value added)
per capita, Uganda 2006-2011
Contribution of
within sector
changes in output
per worker (%)
Contribution of
changes in
Employment (%)
Sectoral contributions
Agriculture
2.32
services
38.79
Manufacturing
9.28
Industry
-18.80
Subtotals
31.60
Demographic
component
Total
Total % change in value added per capita 2006-2011
-52.33
13.92
-1.45
3.77
-36.09
-
Contributions of
Inter-sectoral
Shifts (%)
18.89
29.83
-0.01
50.22
98.92
Total (%)
-31.12
82.54
7.82
35.18
94.43
5.57
100.00
16.74
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
Despite the positive output per worker contribution of the agricultural sector, the decrease in its
employment rate was so large that it more than offset the output per worker growth. Even, the
positive effect of shifts of labor away from agriculture to other sectors could not offset this
effect, so that in the aggregate agriculture reduced total per capita growth by 31%. The services
sector had all its contribution positive; contributed 39% to output per worker, 14% to the growth
in employment, 30% to the inter-sectoral shifts and overall contributed 83% to total per capital
growth. In all three sectors the effect was mostly due to increases in inter-sectoral shifts. The
industrial sector on the other hand contributed negatively to output per worker by 19%, but
contributed positively and strongly via inter-sectoral shifts by 50% and a positive but modest
contribution to the employment rate by 4% leading to an overall contribution of per capita
growth of 35%. The manufacturing sector contributed positively only to output per worker by
9% but contributed negatively by 1.5% and 0.1% to employment rate and inter-sectoral shifts
respectively. However, the positive effects more than offset the negative contribution leading to
an overall positive contribution of 8% to overall per capital growth.
27
Table 10b: Growth Decomposition. Contribution to Total Growth in GDP (value added) per capita,
Uganda 2006-2011
Contribution of
within sector
changes in output
per worker
Sectoral contributions
Agriculture
services
Manufacturing
Industry
Subtotals
Demographic component
Total change in value added per capita
1.29
21.47
5.14
-10.41
17.48
-
Contribution of
changes in
Employment
Contributions of
Inter-sectoral
Shifts
-28.96
7.70
-0.80
2.09
-19.97
-
10.45
16.51
0.00
27.79
54.74
Total
Monetary values are 2000 USD
Source: Table obtained using JoGGs Decomposition tool with data from World Development
Indicators, National Household Panel Survey (2009/10) and United Nations Statistical Data Base.
28
-17.22
45.68
4.33
19.47
52.25
3.08
55.34
Conclusions and implications
This paper set out to establish the link economic growth and employment in Uganda for the
period 2006-2011. We obtained data from World Development Indicators, Uganda National
Household Panel Survey (2011) and United Nations Statistical Data Base and we adopted the Job
Generation and Decomposition (JoGGs) Tool of the World Bank during the analysis. Our
findings reveal interesting insights into the link. The growth profile of Uganda for the period
2006-2011 was jobless as evidenced by 36% change in per capita value added emerging from a
decrease in the employment rate. Agricultural sector contributed the most to the decline in the
percent of total change in capita GDP and in the employment rate by 31% and 6.5%,
respectively. Manufacturing sector contributed positively to the percent of total change in per
capita GDP by 8% but negatively to change in total employment rate by 0.2%. Positive
contributions to the employment rate and the percent of total change in per capita GDP were
observed in the services and industrial sectors. The services sector contributed 83% and 1.7% to
the percent of total change in per capita GDP and in the employment rate, respectively. The
industrial sector contributed 35% and 0.5% to the percent of total change in per capita GDP and
in the employment rate, respectively. Therefore, by sectors, the growth profile in agriculture and
manufacturing was jobless while the growth profile in services and industry was job-creating. It
is further noted that productivity or output per worker contributed over 100% to total change in
per capita GDP growth. By sectors, output per worker was lowest in the agricultural sector and
highest in the industry followed by the services sector. Hence the industrial and services sectors
have higher prospects in an effort by the country to alleviate poverty via employment creation.
The inter-sectoral shifts had a positive contribution to the output per worker meaning that, on
average, labor moved from lower than average productivity sectors to above average
productivity sectors. All sectors, apart from manufacturing, contributed positively to the intersectoral shift component. Given that the employment rate in agriculture was negative and that it
is a low productivity sector implies that the relocation of workers from agriculture to other
sectors had a positive effect on per capita growth via the inter-sectoral shift component. On the
other hand, the employment rate in the services and industry was positive and that they are high
productivity sectors means that the relocation of workers to these sectors had a positive effect on
the per capita growth via the inter-sectoral shift component. The change in the employment rate
in manufacturing was zero and that this sector having the lowest productivity after agriculture,
means that the relocation of workers to this sector had a negative but modest effect on per capita
growth via the inter-sectoral shift component.
The key messages from our analysis are: First, the results show the importance of the Services
sector with a positive and strong effect on the output per worker, employment rate, inter-sectoral
shifts and hence on the overall per capita value added growth. Second, despite the positive output
per worker and inter-sectoral shifts contribution of the agricultural sector, the decrease in its
employment rate was so large that it more than offset the positive effects so that in the aggregate
29
agriculture reduced total per capita growth. Third, the industrial sector contributed negatively to
output per worker but contributed positively and strongly via inter-sectoral shifts and positively
but modestly via the employment rate leading to an overall positive contribution to per capita
growth. Fourth, the manufacturing sector contributed positively only to output per worker by 9%
but contributed negatively to employment rate and inter-sectoral shifts respectively. However,
the positive effects more than offset the negative contribution leading to an overall positive
contribution to overall per capital growth. Fifth, the demographic changes of the population are
generating a window of opportunity to raise per capita income and thus reduce poverty: there are
now fewer dependents per working age adults and this trend is likely to continue. If adults
manage to engage in productive activities and sectors, this demographic shift will have an
important poverty reducing impact. Overall, the promising sectors for poverty reduction through
productivity and employment generation in Uganda by order of importance; services sector,
industrial sector and manufacturing. However, this is not to say that agriculture is not important,
it’s still the major employer in the country (employing almost 70% of the labor force according
the UNHS 2009/10). This notwithstanding, it is a low productivity sector yielding very low
returns to its participants and hence having limited ability to alleviate poverty.
30
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