Job Flows-130410 adm - IZA Institute of Labor Economics · above average job reallocation rate...

The Dynamics of Job Creation and Job Destruction: Is Sub-Saharan Africa Different?

Admasu Shiferaw University of Göttingen, Germany

Arjun S. Bedi International Institute of Social Studies, Erasmus University Rotterdam, The

Netherlands

April 2010

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Abstract This paper analyzes the creation, destruction and reallocation of jobs in order to understand the micro-dynamics of aggregate employment change in African manufacturing. The nature and magnitude of gross job flows are examined using a unique panel data of Ethiopian manufacturing establishments covering the period 1996-2007. The paper also assesses the relative importance of firm demographics, size, capital intensity and the relative importance of within and across industry effects for job flows. The analysis shows that the rates and patterns of job creation and destruction in Ethiopia are comparable to patterns observed in other developed and emerging economies suggesting that African firms adjust their labor force in a manner broadly similar to firms elsewhere and that African labor markets are not uniquely restrictive in terms of undermining job reallocation across firms. We also find, as in other countries, that job reallocation is predominantly an inter-industry phenomenon and is relatively higher in industries dominated by smaller and younger establishments. The analysis shows that while there is no shortage of new and small enterprises that create jobs at the moment of entry, such establishments contribute less to job creation through post-entry expansion. The similarity in the micro-dynamics of employment generation in an African setting as compared to other countries suggests that the poor employment generation observed in African manufacturing may not be attributed to any peculiar features of African labor markets.

JEL Classification: J20, J23, J49 Key Words: Job Creation, Job Destruction, Job Reallocation, Firm Dynamics, Sub- Saharan-Africa, Ethiopia

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1. Introduction

A growing share of manufacturing in GDP and in employment is a common feature

observed in successful developing countries. Manufacturing, however, has not been a

major source of gainful employment for the African labor force. The sector accounts for

less than 10 percent of total employment in the region except for the island economy of

Mauritius where it accounts for about 25 percent of employment.1 From a macroeconomic

perspective, among others, the lack of growth in manufacturing has been attributed to the

low rates of investment (Collier and Gunning, 1999), low level of domestic demand for

manufactures in Sub-Saharan Africa and the lack of export orientation of its manufacturing

sector. Notwithstanding the relative merits of the various aggregate level explanations, the

micro-dynamics underlying the lackluster aggregate employment performance of African

manufacturing and whether or not the underlying firm level processes of job creation and

job destruction which shape aggregate employment outcomes are different from the rest of

the world is not yet known.

Recent micro level studies indicate that the behavior of African manufacturing firms is not

very different from their counterparts in other developing and advanced economies despite

the fact that African manufacturing is still at an incipient stage. For instance, small firms in

African manufacturing grow faster than large firms (Bigsten and Gebreeyesus, 2007;

Gunning and Mengistae, 2001; Van Biesebroeck, 2005) while relatively efficient firms

stand better chances of survival as in other parts of the world (Frazer, 2005; Shiferaw,

2007, 2009; Söderbom et al., 2006). While some of these studies (Bigsten and

Gebreeyesus, 2007; Gunning & Mengistae, 2001; Van Biesebroeck, 2005) have examined

age and size effects on firm level net employment growth they do not examine the

dynamics of gross job flows - job creation, job destruction and job reallocation - which

underlie aggregate net employment growth. The aim of this article, which is based on data

1 This is far less than the nearly 30 percent share of manufacturing in total employment in East Asia, and the approximately 20 percent share in developed countries (ILO, 2009). In Ethiopia, Denu et al. (2005) note that in 1999, manufacturing employment accounted for 4.45 per cent of total employment. This figure includes employment in small‐scale and cottage/handicraft manufacturing establishments. Employment in medium and small scale manufacturing (more than 10 workers) accounts for about 10 percent of total manufacturing employment, and as shown in Figure 1 rose from about 80,000 workers in 1996 to about 115,000 workers in 2007.

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from a Sub-Saharan African country, is to identify the underlying firm level patterns in

gross job flows and to provide a comparative analysis of the patterns observed in an

African labor market with those observed in other settings.

As reviewed in some more detail in the next section, the growing body of firm-level

analyses of gross job flows in manufacturing yields some clear stylized facts. For example,

in the context of developed countries, Davis and Haltiwanger (1992) and Baldwin et al.

(1998), show that there are high rates of job creation and destruction, in excess of 10

percent a year even within narrowly defined industries, reflecting the simultaneous creation

and destruction of jobs and the substantial firm-level heterogeneity in employment. Firm

level employment adjustments are mostly persistent rather than transitory, and adjustment

rates tend to be higher among smaller and younger firms as compared to larger and older

firms. Especially in the case of developed countries, job destruction is more volatile than

job creation which implies that the reshuffling of jobs across firms is countercyclical.

Researchers have also associated gross job flows with firm demographics to show the

relative contributions of the birth, expansion, contraction and death of firms. The growing

number of firm level studies for different countries, and the cross-country variation in the

rate of job flows observed in these studies serves as an indicator of the degree of labor

market flexibility and the efficiency of resource allocation. While Haltiwanger et al. (2008)

provide the most recent cross-country analysis of job flows using harmonized firm level

data for 16 countries, no African country features in their sample. In fact there are no

comparable studies on the nature and magnitude of job creation and destruction in Sub

Saharan African using establishment level data.

The current paper contributes by providing a firm level analysis of job flows in the context

of Sub-Saharan Africa. Apart from offering a rare description of job flows in this region, the

analysis is motivated by the following inter-related and policy relevant questions: Is the

lackluster aggregate employment performance of African manufacturing a result of limited

job creation, relative to other countries, or is it a result of simultaneous processes of job

destruction and job creation offsetting each other? More broadly, are African labor

markets flexible enough to accommodate a smooth reallocation of labor to its best use?

How distinct are firm demographics in this region and their relative importance for job

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flows? Do business cycles play an important role in driving observed patterns of job flows

as compared to industry and firm-specific characteristics? These questions cannot be

answered by examining net employment change at higher levels of aggregation as the

latter could be consistent with any underlying rates of job creation and destruction. In this

paper we address these questions by analyzing job creation and destruction rates in

Ethiopian manufacturing using a unique census based establishment level panel data

covering the period 1996-2007. The nature of the data also allows us to measure the

relative importance of establishment entry and exit as well as establishment expansion and

contraction for job flows so that we can draw a more complete picture of employment

dynamics. Moreover, the relatively long span of the data and the distinct business cycles

that it captures allows us to determine whether observed patterns of job creation and job

destruction are primarily driven by business cycles or by technological factors and

employer specific characteristics.

The remainder of the paper is organized as follows. The next section provides a review of

job flows, highlighting the key stylized facts. Section 3 provides a description of the data

and briefly discusses the business environment and Ethiopia’s manufacturing sector.

Section 4 introduces the measurement framework. Section 5 provides a temporal and

cross-industry analysis of variations in job flows and decomposes job flows along various

dimensions. An econometric analysis of job flows using industry level characteristics is

provided in section 6 while section 7 contains concluding remarks and policy reflections.

2. Job flows – A review

Firm heterogeneity in employment is a prominent feature of gross job flows in developed

and emerging economies. Firms producing similar products experience a simultaneous

process of job creation and destruction and exhibit large variations in the rates of job

creation and destruction. For instance, during the 1970s and 1980s, new jobs were

created at the rate of 10 percent per annum in the manufacturing sectors of the USA and

Canada while job destruction occurred simultaneously at a comparable rate. In a recent

paper, Haltiwanger et al. (2008) study job flows in 16 developed and emerging economies

using harmonized firm level data sets from the 1990s. Their work goes beyond the results

obtained from a number of country specific studies and provides interesting insights on the

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distribution of job flows across countries. They find job creation rates of about 12.7, 14.8

and 17.4 percent for OECD, Latin American and transition economies, respectively, with

corresponding job destruction rates of 12.7, 14 and 12.8 percent. When economic reforms

began in transition economies in the early 1990s, job destruction rates were much higher

than job creation rates before coming closer to that of OECD countries in the late 1990s

(Faggio et al., 2003).

In terms of patterns across industries, Haltiwanger et al. (2008) find a positive rank

correlation of job reallocation rates (the sum of job destruction and job creation rates)

across industries in their sample of 16 countries suggesting that some industries have

above average job reallocation rate across all countries. More specifically, Baldwin et al.

(1998) show that industry job-flow patterns are very similar across the United States and

Canada and that sectors which have a high job reallocation rate in Canada also have a

high job reallocation rate in the United States (a correlation of 0.83). These patterns

suggest that there may be common industry level characteristics such as technology, cost

and demand factors that drive job reallocation patterns across different countries. Two

other points which emerge are that the overwhelming fraction of job reallocation occurs

within industries rather than across industries and that firm decisions to create and destroy

jobs tend to be persistent, reflecting adjustments toward desired firm size rather than

temporary layoffs and rehires (Davis and Haltiwanger, 1990, 1992).2

Beyond these basic patterns an interesting aspect of the literature on job flows is the

cyclical nature of job reallocation across producers. In developed countries, the job

reallocation rate is countercyclical, that is, it intensifies during recessions or periods of net

employment loss. Baldwin et al. (1998) show that net employment growth in the

manufacturing sectors of the US and Canada is accompanied by a reduction in job

destruction rate without significant improvement in job creation rate. Similarly, net

employment loss at the aggregate level is mainly associated with a rapid increase in job

destruction with only a slight decrease in job creation. In other words, the variance of job

2 For instance, Baldwin et al. (1998) find that only 2 to 4 percent of excess job reallocation in Canada and the United States may be attributed to shifts across industries. Davis and Haltiwanger (1992) report that the average one‐year persistence rates for annual job creation and destruction lie between 68 and 81 percent.

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destruction is higher than that of job creation leading to the countercyclical movement of

job reallocation. Campbell and Fisher (2000) argue that this is partly because of

asymmetric adjustment costs. Job creation not only involves the actual adjustment cost of

hiring new workers but also the expected cost of future separation, making job creation

less responsive to business cycles than job destruction. It should be noted that the

countercyclical nature of job reallocation is not universal. Even for the US, the

countercyclical pattern is observed mainly among larger and older firms while firms in

transition economies, where average firm size is smaller than that of the US, exhibit pro-

cyclical movements in job reallocation (Haltiwanger et al. 2008).

Turning to correlates of firm heterogeneity in job flows, various authors have examined the

role of characteristics such as firm size and age, and technological characteristics such as

industrial affiliation and capital intensity. In general, job creation and destruction rates

decline with firm size and age although at the aggregate level the bulk of (or size weighted)

gross job flows is accounted for by larger and older firms (Davis and Haltiwanger, 1990,

1992; Haltiwanger et al., 2008). Similarly, explicit decomposition analyses of firm

demographics and job flows have shown that during the 1970s and 1980s, new

establishments accounted for 20 percent of job creation in US manufacturing while firm

closures accounted for 25 percent of job destruction (Davis and Haltiwanger, 1990). The

bulk of labor adjustment therefore takes place among continuing firms. Comparable

numbers are not available for transition economies as the existing firm level data for these

countries does not capture firm entry and exit.

With empirical evidence on job flows emerging for a growing number of countries, the

cross country variation in job reallocation across firms has become an important indicator

of labor market flexibility. In this regard the experience of developed countries, particularly

the US, serves as a benchmark to gauge the efficiency of labor allocation in emerging

economies. Haltiwanger et al. (2008) show that while a large share (close to 60 percent) of

the cross country variation in job flows can be explained by industry and firm size effects

(the firm size effect being dominant), a significant proportion of cross country variation

maybe linked to differences in labor market regulations and differences in business

environment across countries. Thus, countries with restrictive labor laws exhibit relatively

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less reallocation of jobs across firms, the reductions being stronger in those industries with

inherently high job reallocation rates.

3. Data and background

This paper uses establishment level panel data from Ethiopian manufacturing covering the

period 1996-2007. The data come from the annual manufacturing census carried out by

the Central Statistical Authority (CSA) of Ethiopia. The census covers all establishments

with at least 10 workers each of which are identified by unique identification numbers. The

number of establishments increases from 623 in 1996 to 1339 in 2007 and contains a total

of 10,305 observations (establishment-years). About two-thirds of the manufacturing

establishments are located in and around the capital city, Addis Ababa, and about 70

percent are small producers with less than 50 employees.

In 1992, Ethiopia launched a comprehensive set of economic reforms marking the

country’s transition to a market based economy after 17 years of socialism and military

dictatorship. The first few years saw the opening up of the economy to international trade

and witnessed greater participation of the private sector (Shiferaw, 2009). Except for a rise

in inflationary pressure since 2005, macroeconomic conditions have been stable and the

government continues to spend aggressively on physical infrastructure.3 The economy

grew at a respectable average annual rate of 5.6 percent during the 1990s and has

continued to grow at even higher rates (average annual rate of about 8 percent) since the

turn of the century. The business environment has also witnessed substantial

improvements. According to the World Bank’s “Doing Business” report for 2009, it takes 7

procedures to start a new business in Ethiopia as compared to the African average of 10

procedures. The time taken to go through these procedures has declined from about 44

days in 2003 to 16 days in 2009 which is again far less than the 2009 regional average of

about 45 days. However, other aspects of the business climate are not as favorable. The

legal system remains unreliable and it takes an average of 690 days to enforce contracts

3 Between 2002 and 2007, the Ethiopian government’s capital expenditure increased from 31.8 percent to 51.8 percent of total government expenditure within which the share of economic development projects (such as roads, bridges and dams) increased from 20 percent in 2002 to 32 percent in 2007. The remainder is spent on social development projects and multi‐sector development projects (IMF, 2008).

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and the country is ranked very low (below 100) in terms of protecting investors and

registering property. Although labor laws have been adjusted twice (in 1993 and 2003), the

country is ranked 94 in the world in terms of the ease of hiring and firing workers. Since

the independence of Eritrea in 1993, Ethiopia has become landlocked and has shifted its

trade gateway almost entirely to the smaller and more expensive port of Djibouti in the

aftermath of the 1998-2000 border conflict with Eritrea. Political tension and uncertainty

remain high, particularly since the disputed elections in 2005 that seriously damaged the

domestic and international standing of the current government.

Unsurprisingly, the Ethiopian manufacturing sector is dominated by light consumer goods

industries. About 60 percent of total manufacturing employment is located in the textile and

garments (36 percent) and food and beverage (24 percent) industries. These two

industries also account for about 50 percent of total manufacturing sales. In terms of

temporal patterns, Figure 1 plots manufacturing employment and sales in Ethiopia

between 1996 and 2007. The figure reveals very little change in manufacturing

employment during the first six years of the sample period culminating with an absolute

decline in 2001. Since 2002, this trend has reversed and the sector has experienced

strong employment growth. The figure suggests a natural way of dividing the data in order

to examine cyclical patterns in job flows, that is, a period 1996 to 2001 and a period since

2002 and one of the tasks we undertake in this paper is to breakdown this aggregate

picture and investigate the underlying micro-dynamics in line with the literature on gross

job flows.

4. Measuring job flows

This section introduces various concepts and outlines the framework used in this paper

and the existing literature to measure employment changes. We begin by defining a

measure of establishment level employment growth which is in turn linked to industry level

measures of job creation and job destruction. Employment growth, g at the establishment

level, between time t-1 and t is given by,

1

10.5ijt ijt ijt

ijtijt ijt ijt

X X Xg

m X X

, (1)

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where X is the number of employees and, i and j index establishments and industries,

respectively. Equation (1) shows a growth rate calculation in which change in employment

is divided by average establishment size between two periods ijtm rather than by initial

size as conventionally calculated. This approach is widely used in the literature on job

flows and offers a number of advantages. It minimizes measurement problems in growth

rate due to transitory low/high initial and end of period establishment sizes that may lead to

overestimation of the expansion of small establishments or the contraction of large

establishments — a bias that could generate a negative association between

establishment size and growth. The formula yields a symmetric distribution of growth rates

centered about zero and is bounded in the interval [-2, 2] which corresponds, respectively,

to establishment exit and entry. In contrast, growth rates calculated in the traditional

manner range between zero and infinity, and do not capture entry and exit. Thus, a clear

advantage of the measure is that it accommodates a combined treatment of establishment

births (entry), deaths (exit) and continuing establishments on employment growth. At the

same time it is monotonically related to the standard growth calculation up to a second-

order Taylor series expansion (Davis et al., 1996).

Based on (1), several measures of gross job flows may be defined. Gross job creation is

obtained by summing the jobs created by new establishments and expanding incumbents

within an industry. In turn, the gross job creation rate (GJCR), a size weighted average

growth rate of all establishments in an industry with a positive growth rate, is written as,

ijtjt ijt

i J jt

mG JC R g

M

, (2)

where ijtg is positive employment growth rate, ijtm is average establishment size and jtM

is average industry size. Similarly, gross job destruction is obtained by summing the job

losses in an industry due to the closure and contraction of establishments and the gross

job destruction rate is written as,

ijtjt ijt

i J jt

mGJDR g

M

, (3)

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where ijtg is the absolute value of negative employment growth rates. The weights in

equations (2) and (3) reflect the size of an establishment relative to the size of the industry

to which it belongs and both establishment and industry size are expressed as the average

employment in periods t-1 and t. Net employment growth rate (NEGR) is the difference

between GJCR and GJDR while the sum of GJCR and GJDR is termed the Gross Job

Reallocation Rate (GJRR). To elaborate, the GJRR which is the sum of the total jobs

created and destroyed relative to the size of an industry captures the extent of reshuffling

of jobs across employers within an industry associated with a given net employment

growth rate.4 Finally, the excess job reallocation rate (EJRR) refers to gross job

reallocation rate that is in excess of net employment change. This is calculated as the

difference between GJRR and the absolute value of NEGR. To illustrate, since in principle,

a 5 percent NEGR can be achieved with a 5 percent GJRR (i.e., 5 percent GJCR and 0

percent GJDR), the EJRR is a measure of the depth of adjustment beyond that needed to

accommodate a certain NEGR. To sum up the relationship between the various rates may

be written as follows,

j t j t j t

j t j t j t

j t j t j t

N E G R G J C R G J D R

G J R R G J C R G J D R

E J R R G J R R N E G R

.

5. Patterns of job flows

A. Temporal patterns

Based on the preceding discussion, we begin our empirical analysis by using equation (1)

to calculate establishment level growth rates. Unweighted and size weighted frequency

distributions of these growth rates for the entire period, 1997 to 2007, and for the two sub-

periods, 1997-2001 and 2002 to 2007, are provided in Figures 2a and 2b, respectively.

These figures provide an assessment of the degree of heterogeneity in the data. Figure 2a

exhibits wide variation across manufacturing establishments in terms of employment

growth rates. The bars labeled ‘entry’ and ‘exit’ indicate that Ethiopian manufacturing has 4 GJRR also represents that part of the total movement of workers triggered by employers’ decisions to create and destroy jobs, the other part of worker flows being explained by search and match processes and movements in and out of the labor force.

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an 18-20 percent establishment entry rate and a 14-17 percent exit rate per annum. A

large percentage of the continuing establishments, (about 36-40 percent) experience

employment growth in the neighborhood of zero (±1 percent) growth rates. Figure 2b,

depicts the same distribution weighted by establishment size. The collapse in the mass of

the distribution corresponding to entry and exit reveals that new and dying establishments

are rather small in size and together account for about 10 percent of all size-weighted

growth rate observations. The increased concentration (about 68-70 percent) around zero

(±1 percent) growth rates shows that large incumbents expand or contract rather slowly as

compared to small establishments. Such an inverse relationship between firm size and

employment growth is a widely recognized empirical regularity (Evans, 1987; Bigsten and

Gebreeyesus, 2007; Gunning & Mengistae, 2001; Van Biesebroeck, 2005).

Before discussing and considering industry-level job flows, a concern is whether

establishment level employment changes, which underlie the industry-level job flows

discussed below, represent transitory fluctuations in size or adjustments towards a desired

level of employment. Table 1 offers a one year transition probability in firm growth regimes

as a measure of persistence. It shows that about 54 percent of firms which have created

jobs this year will continue to create jobs next year (41.4 percent) or maintain their current

size (12.5 percent). Similarly, about 55 percent of establishments that shed jobs in the

current period will either continue to cut jobs in the next period (44.5 percent) or maintain

their current size (10.3 percent). These patterns suggest that most of the jobs created or

destroyed are relatively persistent reflecting changes in desired employment rather than

temporary layoffs and rehires. While the degree of persistence in our sample is far less

than that experienced in US manufacturing where Davis and Haltiwanger (1992) report

persistence rates of 67 percent for job creation and 81 percent for job destruction, the point

remains that even in the Ethiopian case the bulk of job flows may be thought of as the

result of firms trying to adjust jobs to achieve a desired employment level.

Based on equations (1), (2) and (3), Table 2 presents annual job flows for the

manufacturing sector over the period 1996-2007. Over the entire period, the average

annual rate of job creation is 17.3 percent while the average annual rate of job destruction

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is 10.3 percent, translating into an annual net employment growth rate of 7 percent. A look

at the year-specific figures shows that every year there is substantial job creation and job

destruction and even during periods of high net employment growth, job destruction never

falls below 7 percent. This observation highlights the point that the weak aggregate

performance of manufacturing employment during 1996-2001, as depicted in Figure 1, was

not the result of a particularly low job creation rate but due to a simultaneous process of

job creation and destruction. The average GJCR during 1997-2001 was 12.4 percent and it

never fell below 10 percent at any point over that period. However, this was matched by a

job destruction rate of about 11.7 percent leading to a low net employment growth (about

0.7 percent) during 1997-2001. The strong expansion of manufacturing employment during

2002-2007 was the result of a 9 percentage point increase in GJCR relative to 1997-2001,

coupled with a modest decline (of 2.5 percentage points) in GJDR, translating into a

remarkable 12.2 percent average annual increase in employment growth between 2002

and 2007.

Turning to the remaining columns in the table, we see that over the entire sample period

the average gross job reallocation rate is very high. The rate ranges from a low of 19.7

percent in 1999 to a high of 42.9 percent in 2007 with an average of 27.6 percent. These

figures imply that more than a quarter of all manufacturing jobs were either created or

destroyed each year to accommodate the 7 percent average net employment growth rate

recorded between 1996 and 2007. This amounts to an excess job reallocation rate of

about 20 percent. The rates of excess job reallocation remain high regardless of net

employment growth. Throughout the period there is greater variation in job creation as

compared to job destruction and gross job reallocation increases from 24 percent during

the first period (1996-2001) to 30.6 percent during the upswing (i.e. 2002-2007) implying a

pro-cyclical nature of job reallocation.

How do these observations compare with job flows in other parts of the world? We provide

a comparison with the cross country evidence in Haltiwanger et al. (2008) for the 1990s.

The simultaneous occurrence of high rates of job creation and destruction in Ethiopian

manufacturing is quite similar to the patterns that have been observed in other developed

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and emerging economies. The 17 percent GJCR in our data is, however, well above the

12.7 percent and 14.8 percent average GJCR in OECD and Latin American countries,

respectively, while being almost equal to the average job creation rate for transition

economies. In terms of job destruction, the 10 percent average for Ethiopian

manufacturing is slightly below the 12.7 percent GJDR for both the OECD and transition

economies, and the 14 percent average for Latin American countries; it is however

comparable to that of the US during the 1970s and 1980s (Davis and Haltiwanger, 1992).

In terms of gross job reallocation rates, for the entire period the figures for Ethiopian

manufacturing (28 percent) are close to the magnitudes observed in other regions. Even

though the reallocation rate accelerated during the upswing (2002-2007), it matches the

job reallocation rates observed in transition economies, a region with the highest job

reallocation rate in the Haltiwager et al. (2008) sample.

The key difference between the time-series patterns observed in Ethiopia and developed

counties is the cyclical nature of job reallocation. In developed countries such as the

United States and Canada, as shown by Baldwin et al. (1998), the temporal variance of job

destruction is higher than that of job creation which implies that gross job reallocation is

countercyclical. In the case of Ethiopia the pattern is the opposite. Throughout the period,

the variation in the rate of job creation is higher than rates of job destruction indicating that

gross job reallocation is pro-cyclical. The shift from a sluggish performance in

manufacturing employment during 1996-2001 to a strong expansion during 2002-2007 is

characterized by a sharp increase in gross job creation with a modest change in job

destruction. While the Ethiopian case is different from the United States and Canada, it is

not unique. As shown in Table 2, similar patterns are observed in transition economies,

where rates of job creation outstrip rates of job destruction. One explanation for the

different cyclical pattern of job flows in our sample as compared to the US is the incipient

nature of the manufacturing sector where the skill mix of workers is likely to be simple and

less specific to an industry or to a firm. This is more likely to be the case among small firms

that dominate the industrial landscape in developing countries. Assuming that the level and

asymmetry of adjustment costs increases with the skill level of workers, as jobs that

demand specialized skills are harder to fill, small manufacturers in countries like Ethiopia

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may have a relatively high elasticity of job creation with respect to demand than job

destruction – resulting in a different outcome than predicted by Campbell and Fisher

(2000).

B. Job flows across industries

While the manufacturing sector as a whole exhibits high rates of job creation and

destruction, it is likely that there are variations across industries due to differences in

industry specific technologies and market structure. To investigate such variations this

section analyses job-flow rates at the two-digit industry level. Figures 3 and 4 depict

average annual rates of GJCR and GJDR, respectively, for eight two-digit industries. The

industries are sorted in ascending order of average job flows during 1997-2001 for easy

comparison across industries and over business cycles. More information on industry level

annual job flows is provided in Table A1.

As shown in Figure 3, the rapid increase in job creation during the second half of the

sample period is experienced by all industries, albeit at different rates. The food and

beverage industry represents the average job creation rate for the entire manufacturing

sector while the textile, leather and printing industries have below average job creation

rates and the chemical, non-metal, metal and wood industries record above average

performance. The ranking of industries remains essentially the same during periods of

slow and rapid change in aggregate employment, suggesting that cross industry variation

in job creation is not randomly distributed but reflects systematic differences in technology

and market structure. At the same time, there is evidence of convergence in job creation

rates during the upswing as the gain in job creation rate since 2002 has been more

pronounced in industries with below average performance.5

Figure 4 shows that all industries, except food and beverage, have experienced a

reduction in gross job destruction rate in the second sub-period. In comparison with

GJCR, there is less disparity across industries in GJDR; the standard deviations are 8

5 Until 2001, the coefficient of variation for GJCR was about 0.54. This has declined to 0.34 since 2002.

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percent and 4.5 percent, respectively. The spread in job destruction has also narrowed

since 2002 as job destruction rates declined sharply for industries with above average

GJDR. While the ranking of industries is not very distinct particularly for industries with job

destruction rates close to the sector average, there is enough variation to suggest that

certain industries have relatively higher job destruction rates than others irrespective of

business cycles. It is worth noticing that the industries with above average job creation

rates also feature above average job destruction rates, with the exception of the chemical

industry. This implies that job losses are on average higher in industries that created more

job opportunities and net employment growth is associated with sizable readjustment of

employment positions across firms. This point is further supported by Figure 5.

Figure 5 shows an interesting aspect of job flows where net employment growth is

positively correlated with gross job reallocation rate. This suggests that fast growing

industries in Ethiopian manufacturing are characterized by massive reallocation of labor

across establishments. In fact, despite the country’s low ranking in terms of the ease of

hiring and firing of workers, the job reallocation rate in Ethiopia is comparable to that of

developed and emerging economies suggesting that labor market regulations are not

exceptionally restrictive. A likely explanation is that while on-paper labor laws are

restrictive they are not strictly adhered to due to weak law enforcement mechanisms.

Haltiwanger et al. (2008) find a strong rank correlation between industry level job flows in

OECD, Latin American and transition economies and US manufacturing industries –

meaning that industries with higher/lower job creation rates in the US also tend to have

higher/lower job creation rates in comparator countries. Looking at the rank correlation of

GJRR for the eight industries in Ethiopian manufacturing with that of the US and Canada

during 1972-1992 as reported in Baldwin et al. (1998), we find very small positive

correlation coefficients (0.14 with Canada and 0.10 with the US) which are not statistically

significant. This outcome may not be surprising given the differences in the composition

and degree of sophistication of Ethiopian manufacturing as compared to advanced and

emerging economies. We also find that the industries with better than average net

employment growth (chemical and plastic, wood and furniture, metal) in our data are not

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the priority areas indicated in the Ethiopian government’s industrial policy which include

the food processing, textile and leather industries. The latter are given priority mainly

because they fit well with the government’s overall development strategy which is widely

known as ADLI (Agricultural Development Led Industrialization) and seeks to promote

backward linkages of manufacturing with agriculture.

C. Decomposing job flows As discussed above, similar to other parts of the world, Ethiopian manufacturing firms

simultaneously generate and destroy jobs. To characterize these patterns and to obtain

additional insights on the process of job creation and destruction, this section pushes the

analysis in several directions. In turn, we examine the link between job flows and firm

demographics, job flows and firm size, job flows and production technology, and finally the

extent to which job reallocation may be attributed to within sector reallocation as opposed

to across sectors.

To examine the link between the life-cycle of establishments and job creation and

destruction we decompose gross job creation into the fraction of jobs created by the

expansion of incumbents and by the entry of new establishments. Similarly, gross job

destruction is decomposed into jobs lost due to downsizing and bankruptcy of incumbents.

Figure 6 shows that the majority of new jobs, about 55 percent, are created by new

establishments while the expansion of incumbents accounts for the remainder. This

relative importance is not sensitive to business cycles. In terms of international

comparisons, the 55 percent contribution of firm entry to job creation in our data is higher

than the 40 percent contribution of entrants in transition economies which in turn is higher

than the 35 percent contribution in OECD countries as documented in Haltiwanger et al.

(2008). These patterns clearly suggest that in more developed economies new entrants

play a much smaller role in generating new jobs as compared to developing countries.

Since most entrants are likely to be relatively small in size, these figures also corroborate

the well recognized fact that small firms play a disproportionately larger role in gross job

creation (relative to their share in total employment). The figure shows that job destruction

17

occurs mainly through the contraction of incumbents accounting for 60 percent and 52

percent of job losses during 1997-2001 and 2002-2007, respectively. The slowdown in

sector-wide job destruction during the upswing is thus entirely due to a decline in the rate

of contraction of incumbents, as the share of job losses due to firm closure increases from

40 to about 48 percent between the two periods. It is interesting to note that both the rate

of establishment exit (Figure 2a) and its contribution to job destruction (Figure 6) went up

rather than down during the rapid aggregate expansion since 2002 pointing to the

relentless pressure of competitive market selection. Since it is likely that a number of the

exiting establishments are small producers and recent market entrants, the overall effect is

a fall in sector-wide job destruction rate during the upswing, albeit by only 2.5 percentage

points.

A more explicit examination of the link between firm size and job flows is provided in Table

3. Manufacturers that employ at least 50 workers are classified as large establishments.

Although small establishments account for about 15 percent of total employment in our

data, the table shows that they contribute to one-third of new jobs both during periods of

slow and rapid aggregate employment growth. Small producers contribute to job creation

mainly at the point of market entry (22.5 percent), about twice their contribution through

post-entry expansion (10.2 percent). Large establishments account for the remaining two-

thirds of new jobs. The relative importance of entry and expansion for job creation among

large establishments is nearly symmetric with entry having a slight edge. The patterns in

terms of job destruction are similar with small firms accounting for about a third of job loss.

The figures for Ethiopia match the patterns observed in other settings. Haltiwanger et al.

(2008) show that large firms that employ at least 50 workers account for about 60-70

percent of total job creation and destruction in a number of countries in the OECD (Italy,

Portugal, France, UK, US), in Latin American (Argentina, Chile, Columbia and Mexico) as

well as in emerging economies (Latvia, Slovenia and Hungary).

Figure 6 has shown that the decline in GJDR is due to a slowdown in the degree of

contraction of continuing establishments rather than a reduction in the rate of exit. The

lower panel of Table 3 reveals that the slowdown in job losses due to establishment

18

contraction is evident only among large producers where its contribution has dropped from

47 percent of all job losses during 1997-2001 to 41.4 percent during 2002-2007. At the

same time, job losses by small producers have gone up from about 27 percent of total job

losses during 1997-2001 to nearly one-third during 2002-2007, an increase both in terms

of contraction as well as exit. The expansion of manufacturing employment in the second

sub-period is therefore accompanied by a reallocation of labor from small to large

establishments. Since large firms typically offer better working conditions than small firms,

other things being equal, the upswing may have led to a tight labor market and made it

difficult for small firms to retain their trained and experienced workers.6

Heterogeneity in job flows across establishments may partly be traced to the choice of

production technology, an important aspect of which is the choice of input proportions.

Accordingly, Table 4 presents the results of a decomposition of job flows conditional on

capital intensity. Capital intensity is defined in terms of the capital-labor ratio and

establishments with above average capital-labor ratio are treated as capital intensive. The

analysis shows that capital intensive establishments account for nearly 60 percent of job

creation and that the rise in gross job creation during 2002-2007 was driven mainly by the

expansion of capital intensive establishments. While the latter created most of the new

jobs, they also account for the bulk of job destruction mainly through contraction. The

increase in net employment growth since 2002 is therefore the result of a higher rate of job

creation among capital intensive establishments – through expansion, coupled with a lower

rate of job destruction among labor intensive establishments. However, since the reduction

in overall GJDR during 2002-2007 (2 percentage points) is much less than the gain in

GJCR (9 percentage points), there has been a reallocation of jobs in favor of more capital

intensive firms in the Ethiopian manufacturing sector.

The sizable excess reallocation of jobs raises the issue as to whether excess churning is

mainly due to an intra- or inter-industry reallocation of jobs. In the latter case jobs will be

6 The average wage rate among small Ethiopian firms, i.e., total wage bill as a ratio of the number of employees, is

about 50% of the average wage rate among large firms. This figure does not take into account differences in the

human capital of the work force of large and small firms and hence is likely to overestimate the wage gap.

19

reallocated from shrinking to expanding industries while in the former the adjustment is

within industries. A formulation to decompose excess job reallocation into these

constituents, as suggested by Davis and Haltiwager (1992) is,

St St St jt jt St jt St Stj S j S

GJRR NEGR M GJRR NEGR M NEGR NEGR M

, (4)

where S stands for the manufacturing sector, M is average size and, while j and t index

industry and time. The left hand side of equation (4) represents the volume of excess job

reallocation for the manufacturing sector expressed as the product of average

manufacturing sector employment and the excess job reallocation rate (the difference

between gross job reallocation rate and the absolute value of net employment growth

rate). The first term on the right hand side captures intra-industry excess job reallocation

measured as the sum over all industries of the product of excess job reallocation rate and

average size of the manufacturing sector at time t. The second term on the right hand side

is the proportion of excess job reallocation which may be attributed to inter-industry

reallocation of jobs and is the sum of the weighted product of the deviation of absolute net

employment growth for the industry level from the absolute net employment growth rate for

the manufacturing sector as a whole.

On average, over time, we find that 86 percent of excess job reallocation takes place

within industries. Overwhelmingly, excess job reallocation is an intra-industry phenomenon

reflecting the reshuffling of jobs across establishments producing broadly similar products.

There is some variation over time with inter-industry reallocation of jobs accounting for

about 20 percent of excess job reallocation during 1997-2001 which falls to 10 percent

over the period 2002-2007.7 The dominant role of intra-industry movement is consistent

with the patterns found in US manufacturing (Davis and Halitwanger, 1992) and in

transition economies (Faggio and Konings, 2003) where the share of between-industry

movements is even less than in the Ethiopian case.

7 This is mainly the result of a sharp decline in the employment share of the textile sector during the late 1990s, a decline which has abated since 2004. The textile industry is dominated by public enterprises and was until recently the single most important employer in the manufacturing sector.

20

6. Econometric analysis of job flows

The preceding section has shown that job flows vary over time and across groups of

establishments defined in terms of industries, and are sensitive to firm age, size and

capital intensity. The objective of this section is to consolidate the analysis and to assess

the relative importance of these sources of variation by simultaneously analyzing their

effects on industry-level job flows. We estimate a set of econometric models with gross job

reallocation as a dependent variable and time varying industry level characteristics, and

industry and time fixed effects as regressors. The time varying covariates include average

age of industries in a firm, the share of small establishments (less than 50 employees) to

capture size effects, and capital intensity. While this section focuses on job reallocation,

since it encompasses job creation and destruction, as discussed below we also estimate

models with job creation, job destruction and net job growth as dependent variables.

The choice of covariates is motivated by theory and existing empirical evidence. Models of

passive learning in the industrial evolution literature suggest that as compared to young

and small firms, older and larger firms grow at a slower pace as they are more likely to

have approached the scale of operation dictated by their innate relative efficiency

(Jovanovic, 1982; Lippman and Rumelt, 1982). This is essentially the result of market

selection based on time invariant initial conditions generating simultaneous job creation

and destruction within narrowly defined industries, an effect that is expected to taper off as

the industry matures. One would therefore expect a negative firm size and age effect in a

model of job reallocation. Theories of active learning however suggest that firms can

change their fate by engaging in productivity enhancing activities. In this case the basis for

selection and job reallocation is the degree of success in productivity enhancing activities,

i.e., establishments that succeed in improving productivity expand while establishments

that fail to do so lose jobs (Ericson and Pakes, 1995; Pakes and Ericson, 1998). We intend

to capture the latter effect using the capital-labor ratio.8 The model has the following

structure:

8 Given the interest in the literature about the cyclical nature of job reallocation, we include net employment growth

as an additional regressor in one of the specifications to capture expansion or contraction at the industry level.

21

1j t j t j t

j t j t

G J R R X u

u j t e

, (5)

where jtGJRR is gross job reallocation rate in industry j at time t, 1jtX stands for industry

level covariates lagged by one period, and jtu is a composite error term with industry and

time fixed effects as well as a time-varying error term ( jte ).

To isolate the sources of variation in GJRR, we start the analysis by controlling only for

industry fixed effects, this is followed by the inclusion of time fixed effects and finally the

model is expanded to include the time-varying industry specific factors discussed above.

We estimate these specifications using OLS. Since industry level job flows tend to be

persistent, we also estimate (5) using a feasible generalized least squares (FGLS)

technique which provides efficient estimates in the presence of autocorrelated and

heteroscedastic errors ( jte ). All variables enter the estimation models with a one period

lag as the contemporaneous values will obviously be influenced by current job flows.

Estimates of these various specifications are presented in Table 5.

The first column of Table 5 shows that industry specific effects play a large role (57

percent) in accounting for variations in gross job reallocation. As shown in column 2, the

inclusion of time fixed effects raises the proportion of the explained variation to 79 percent.

Consistent with the descriptive statistics presented earlier (Figures 3, 4 and 5), industries

like metal and, wood and furniture feature very high rates of job reallocation relative to the

food industry (omitted industry) which represents the average job reallocation rate for the

manufacturing sector.

Column 3 displays estimates which include the time varying covariates. The inclusion of

these variables leads to a small (4 percentage point) increase in the proportion of

explained variation in GJRR. Turning to the individual effects, the results indicate that job

reallocation increases with the fraction of small firms in an industry confirming the patterns

observed in the descriptive analysis. This result is also consistent with models of passive

learning. While the linear and quadratic terms of age have the expected signs, where job

22

reallocation decreases with age in a non-linear fashion, they are statistically insignificant.

Firms in capital intensive industries tend to have higher rates of labor adjustment although

the size of the coefficient is small. In column 4 we take into account heteroscedasticity and

panel specific autocorrelation using the FGLS estimator. These results show that the OLS

estimates are largely immune to potential problems which may be associated with the error

term. Finally, in the spirit of establishing the story revealed in Figure 5 we include NEGR

as a regressor to capture the correlation between job reallocation and net employment

expansion. The positive and statistically significant link between NEGR and GJRR

underscores the previous finding that, in our data, job reallocation is pro-cyclical and that

fast growing industries in Ethiopia manufacturing are characterized by high rates of intra-

industry labor reallocation. While the results in this section confirm a number of the

bivariate patterns identified earlier, the main insight which emerges is that similar to the

patterns observed in other countries (for example, see Baldwin, 1998) industry specific

factors account for the bulk of cross- industry differences in job reallocation rates. While

we do not explore the underlying sources of these industry-specific differences these

probably include differences in technology, the skill-mix required in such industries, the

costs associated with creating a new job, and demand conditions.

To complete the analysis, Tables A2 to A4 provide estimates of job creation, job

destruction and net employment growth. Similar to the patterns observed for job

reallocation, Tables A2 and A3 show that industry and time fixed effects explain large

shares of the variation in job creation (industry effect - 44 percent and time effect - 30

percent) and job destruction (industry – 32 percent and time effect – 31 percent). Thus,

industry-specific technology and market structure is relatively less important in explaining

job destruction than job creation, a feature already indicated in Figures 3 & 4. With regard

to net job growth the estimates show that there is very little cross-industry variation and

only 12 percent of the variation in job growth is explained by industry fixed effects (Table

A4). This reaffirms the previous observation that industries with high job creation rates also

tend to have high job destruction rates. As shown in Figures 3 and 4 all industries have

enjoyed an increase in job creation in the second sub-period while all except the food

industry have experienced a reduction in the job destruction rate. Since there has been a

convergence among industries in terms of job creation and job destruction rates during the

23

upswing, the overall difference in net employment changes across industries tends to be

lower. Most of the variation in net employment growth is therefore associated with

business cycles as time fixed effects explain over 40 percent of the variation.9

Turning to other regressors, according to the FGLS estimates, job creation increases with

the share of small firms in an industry. Similarly, job destruction decreases with the

dominance of large firms in an industry as most of the ‘creative destruction’ seems to take

place at the lower end of the firm size distribution. Capital intensity has a statistically

significant but weak positive association with job destruction. It is notable that both the

fraction of small firms in an industry and capital intensity do not exert a significant effect on

net employment change although they exert an influence on job creation, job destruction

and job reallocation rates. This raises an important question about the relative importance

of ‘creative destruction’ and resource reallocation for overall employment and economic

growth. While most of the firm churning and job reallocation takes place at the lower tail of

the firm size distribution, industries dominated by small firms do not seem to show a

significantly higher net employment growth rate.

7. Conclusion

This paper represents the first attempt at a detailed analysis of gross job flows in Sub-

Saharan Africa using establishment level data. Key findings include the existence of high

rates of simultaneous job creation and destruction behind the seemingly unimpressive

contribution of manufacturing to overall employment in the region. Furthermore, the rates

and patterns of job flows reported in this paper are very similar to the findings from studies

based on developed and emerging economies. As in other countries we find that job

reallocation is a predominantly intra-industry phenomenon and is relatively higher in

industries dominated by smaller and younger establishments. Somewhat different from the

existing work is that while there is strong rank correlation between industry level job

creation and job destruction rates between the US and other developed and emerging

economies, such a correlation does not exist between US and Ethiopian manufacturing. 9 The importance of industry fixed effects in explaining job creation, destruction and reallocation while playing a

minimal role in explaining net job growth is similar to patterns observed in Canada and the US (Baldwin et al. 1998).

24

Notwithstanding this difference, the upshot of the empirical work reported here is that

African firms adjust their work force in a manner broadly similar to firms elsewhere and

African labor markets are not uniquely restrictive in terms of undermining job reallocation

across firms. While in general, labor laws that make it difficult to hire and fire workers may

be expected to reduce labor market flexibility, there is little evidence that such restrictions

have a bearing on job flows in the current case. This might however be the result of

inadequate law enforcement rather than a reflection of labor market reforms as seems to

be the case in Ethiopia.

Some of the other findings are also quite informative. The growth of manufacturing

employment in Ethiopia during the second half of the study period was driven mainly by

new establishments joining the sector - a degree of contribution well above that of new

firms in transition economies which in turn is higher than that observed in OECD countries.

Thus, while there is no shortage of business startups which create jobs at the moment of

entry, such small establishments contribute less to job creation through subsequent

expansion. A similar observation has been made by other firm level studies in Africa which

show a lack of graduation of small firms into medium and large size categories. While it is

true that small firms grow faster than large firms, as documented in a number of firm level

studies, the growth rate does not seem to be strong enough to catapult them into a

different size category at least in the African context.

The similarity in the micro-dynamics of employment generation in an African setting as

compared to other countries suggests that poor employment generation in African

manufacturing may not be attributed to any peculiar features of African labor markets. This

is similar to the finding from recent firm-level studies (Frazer, 2005; Shiferaw, 2007) which

show that African manufacturing firms are subjected to the same market scrutiny and

competition as firms in other parts of the worlds. Thus, answers to the inability of African

firms in terms of creating and sustaining jobs probably lies in the broader political and

economic risk that they face rather than labor market regulations and restrictions.

25

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28

Table 1: One period transition probabilities in firm growth regimes (%) Growth Regimes

Positive Zero Negative

Positive 41.4 12.5 46.1

Zero 38.9 28.9 32.2

Negative 45.2 10.3 44.5

Source: Authors’ calculation based on CSA’s manufacturing census. Figures exclude entry and exit.

Table 2: Job Flows in Ethiopian Manufacturing GJCR GJDR NEGR GJRR EJRR

1997 0.1342 0.1861 ‐0.0519 0.3202 0.2683

1998 0.1248 0.1049 0.0199 0.2297 0.2098

1999 0.1042 0.0929 0.0112 0.1971 0.1859

2000 0.1335 0.1019 0.0316 0.2354 0.2038

2001 0.1230 0.0973 0.0256 0.2203 0.1946

2002 0.2238 0.0849 0.1389 0.3088 0.1698

2003 0.1306 0.0834 0.0473 0.2140 0.1667

2004 0.1463 0.0736 0.0727 0.2199 0.1472

2005 0.1966 0.0882 0.1085 0.2848 0.1763

2006 0.2980 0.0789 0.2192 0.3769 0.1577

2007 0.2871 0.1418 0.1453 0.4289 0.2836

Standard Deviation 0.1146 0.0827 0.1213 0.1589 0.1323

Period Averages

1997‐2001 0.1239 0.1166 0.0073 0.2406 0.2125

2002‐2007 0.2138 0.0918 0.1220 0.3055 0.1836

1997‐2007 0.1729 0.1031 0.0698 0.2760 0.1967

Other Regions (1990s)

OECD 0.127 0.127 0.000 0.254 0.223

Latin America 0.148 0.140 0.008 0.288 0.248

Transition Economies 0.174 0.128 0.046 0.303 0.227

Source: Authors’ calculations based on CSA’s manufacturing census data for Ethiopia and Table 3 of Haltiwanger et al. (2008) for other regions.

29

Table 3: Decomposition of Job Creation and Destruction by Firm Size Small Firms Large Firms

Job Creation Expansion Entry Total Expansion Entry Total

1997‐2001 0.0980 0.2353 0.3332 0.2773 0.3894 0.6668

2002‐2007 0.1052 0.2157 0.3209 0.3484 0.3306 0.6791

1997‐2007 0.1019 0.2246 0.3265 0.3161 0.3574 0.6735

Job Destruction Contraction Exit Total Contraction Exit Total

1997‐2001 0.0941 0.1747 0.2688 0.4701 0.2611 0.7312

2002‐2007 0.1097 0.2140 0.3237 0.4144 0.2619 0.6763

1997‐2007 0.1026 0.1961 0.2987 0.4398 0.2615 0.7013

Source: Authors’ computations based on CSA’s manufacturing census

Note: Job creation and destruction rates in the two size groups add up to 1.

Table 4: Decomposition of Job Creation by Factor Intensity of Firms Labor Intensive Capital Intensive

Job Creation

Expansion Entry Total Expansion Entry Total

1997‐2001 0.2558 0.1780 0.4339 0.1882 0.3689 0.5571

2002‐2007 0.1676 0.2239 0.3915 0.2855 0.3120 0.5976

1997‐2007 0.2077 0.2030 0.4107 0.2413 0.3379 0.5792

Job Destruction

Contraction Exit Total Contraction Exit Total

1997‐2001 0.2489 0.2376 0.4865 0.3436 0.1608 0.5045

2002‐2007 0.1926 0.1985 0.3912 0.3267 0.2686 0.5953

1997‐2007 0.2182 0.2163 0.4345 0.3344 0.2196 0.5540


Note: Job creation and destruction rates in the two groups add up to 1.

30

Table 5: Gross Job Reallocation OLS

(1) OLS (2)

OLS (3)

FGLS (4)

FGLS (5)

Textile & Garments ‐0.0992**(0.0466)

‐0.0992***(0.0345)

‐0.0342(0.0787)

0.0040(0.0595)

0.0552 (0.0476)

Leather & Footwear ‐0.1058**(0.0466)

‐0.1058***(0.0345)

‐0.0929**(0.0455)

‐0.0801*** (0.0279)

‐0.0616*** (0.0219)

Printing & Paper ‐0.0901*(0.0466)

‐0.0901**(0.0345)

‐0.0902**(0.0378)

‐0.0888*** (0.0254)

‐0.0751*** (0.0200)

Chemical & Plastic ‐0.0320 (0.0466)

‐0.0320(0.0345)

0.0349(0.0663)

0.0492(0.0456)

0.0303 (0.0381)

Non‐metal 0.0324 (0.0466)

0.0324(0.0345)

‐0.0031(0.0434)

‐0.0171(0.0308)

‐0.0376 (0.0248)

Metal 0.1191**(0.0466)

0.1191***(0.0345)

0.0875**(0.0403)

0.0790** (0.0335)

0.0508* (0.0304)

Wood & Furniture 0.2617***(0.0466)

0.2617***(0.0345)

0.1953***(0.0527)

0.1746*** (0.0387)

0.1375*** (0.0336)

Time Dummies No Yes Yes Yes Yes

Aget‐1 ‐0.0217(0.0421)

‐0.0161(0.0324)

‐0.0208 (0.0270)

Age2t‐1 0.0009(0.0012)

0.0007(0.0009)

0.0007 (0.0008)

Small Firmst‐1 0.4917*(0.2932)

0.5775*** (0.2139)

0.5766*** (0.1720)

Capital Intensityt‐1 0.0009**(0.0004)

0.0008*** (0.0002)

0.0008*** (0.0002)

NEGR 0.4633*** (0.0688)

Constant 0.3000***(0.0329)

0.4063***(0.0366)

0.0206(0.4671)

‐0.0746(0.3523)

‐0.0389 (0.2963)

Observations 88 88 80 80 80 R‐squared 0.57 0.79 0.83


Notes: Standard errors in parentheses. *, ** and *** represent significance at 10%, 5% and 1%,

respectively.

31

Figure 1: Manufacturing Employment and Sales in Ethiopia

Note: Sales values are on the right‐hand‐side y‐axis in millions of Ethiopian Birr and employment is in terms of the number of employees in the manufacturing sector.

32

Exit

Entry

0.05

.1.15

.2

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

1997-2001

Exit

Entry

0.05

.1.15

.2

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

2002-2007

Exit

Entry

0.05

.1.15

.2

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

1997-2007

Figure 2a: Distribution of Unweighted Establishment Level Employment Growth Rates

33

ExitEntry

0.1

.2.3

.4

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

1997-2001

ExitEntry

0.1

.2.3

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

2002-2007

ExitEntry

0.1

.2.3

Fra

ction

-2-1.5-1 -.5 0 .5 1 1.5 2

1997-2007

Figure 2b: Distribution of Size Weighted Establishment Level Employment Growth Rates

34

Figure 3: Gross Job Creation Rate by Industry

Figure 4: Gross Job Destruction Rate by Industry

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Figure 5: Net Employment Growth and Gross Job Reallocation (1997‐2007)

Figure 6: Decomposition of Gross Job Creation and Destruction Rates

36

Table A1: Job Flows by Industry

year Flows Food &

Beverage

Textile &

Garments

Leather &

Footwear

Wood &

Furniture

Printing &

Paper

Chemical &

PlasticNon‐Metal Metal

1997 GJCR 0.1797 0.0413 0.0702 0.4238 0.0661 0.2890 0.1581 0.2381

GJDR 0.1271 0.1632 0.1124 0.5722 0.2602 0.0720 0.2589 0.3043

GJRR 0.3068 0.2045 0.1826 0.9960 0.3263 0.3610 0.4170 0.5424

NEGR 0.0526 ‐0.1219 ‐0.0422 ‐0.1484 ‐0.1941 0.2170 ‐0.1008 ‐0.0662

EJRR 0.2542 0.0826 0.1404 0.8476 0.1322 0.1440 0.3162 0.4762

1998 GJCR 0.1066 0.0541 0.0717 0.3866 0.1387 0.2401 0.2481 0.1605

GJDR 0.0739 0.1157 0.0790 0.2910 0.0292 0.0586 0.1079 0.1669

GJRR 0.1805 0.1698 0.1507 0.6776 0.1679 0.2987 0.3560 0.3274

NEGR 0.0327 ‐0.0616 ‐0.0073 0.0956 0.1095 0.1815 0.1402 ‐0.0064

EJRR 0.1478 0.1082 0.1434 0.5820 0.0584 0.1172 0.2158 0.3210

1999 GJCR 0.1844 0.0549 0.0635 0.1939 0.0628 0.0618 0.0921 0.1499

GJDR 0.1478 0.0570 0.0715 0.1199 0.0751 0.0915 0.1017 0.0948

GJRR 0.3322 0.1119 0.1350 0.3138 0.1379 0.1533 0.1938 0.2447

NEGR 0.0366 ‐0.0021 ‐0.0080 0.0740 ‐0.0123 ‐0.0297 ‐0.0096 0.0551

EJRR 0.2956 0.1098 0.1270 0.2398 0.1256 0.1236 0.1842 0.1896

2000 GJCR 0.1663 0.0510 0.0402 0.2797 0.1220 0.1137 0.2567 0.3337

GJDR 0.0724 0.1630 0.0612 0.1220 0.0639 0.0525 0.0711 0.0777

GJRR 0.2387 0.2140 0.1014 0.4017 0.1859 0.1662 0.3278 0.4114

NEGR 0.0939 ‐0.1120 ‐0.0210 0.1577 0.0581 0.0612 0.1856 0.2560

EJRR 0.1448 0.1020 0.0804 0.2440 0.1278 0.1050 0.1422 0.1554

2001 GJCR 0.1214 0.1068 0.0850 0.2668 0.0543 0.1528 0.0924 0.2031

GJDR 0.0865 0.0534 0.0776 0.2283 0.0812 0.0708 0.1378 0.3333

GJRR 0.2079 0.1602 0.1626 0.4951 0.1355 0.2236 0.2302 0.5364

NEGR 0.0349 0.0534 0.0074 0.0385 ‐0.0269 0.0820 ‐0.0454 ‐0.1302

EJRR 0.1730 0.1068 0.1552 0.4566 0.1086 0.1416 0.1848 0.4062

2002 GJCR 0.2492 0.0770 0.2245 0.4919 0.3153 0.3214 0.1848 0.3855

GJDR 0.1228 0.0606 0.1098 0.1524 0.0285 0.0503 0.0803 0.0710

GJRR 0.3720 0.1376 0.3343 0.6443 0.3438 0.3717 0.2651 0.4565

NEGR 0.1264 0.0164 0.1147 0.3395 0.2868 0.2711 0.1045 0.3145

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EJRR 0.2456 0.1212 0.2196 0.3048 0.0570 0.1006 0.1606 0.1420

2003 GJCR 0.1506 0.0479 0.1260 0.3127 0.0787 0.1295 0.2287 0.2170

GJDR 0.1305 0.0414 0.0601 0.1525 0.0736 0.0619 0.0939 0.0864

GJRR 0.2811 0.0893 0.1861 0.4652 0.1523 0.1914 0.3226 0.3034

NEGR 0.0201 0.0065 0.0659 0.1602 0.0051 0.0676 0.1348 0.1306

EJRR 0.2610 0.0828 0.1202 0.3050 0.1472 0.1238 0.1878 0.1728

2004 GJCR 0.1574 0.0970 0.1220 0.3085 0.0807 0.1266 0.2419 0.1944

GJDR 0.0855 0.0775 0.0267 0.1340 0.0260 0.0423 0.0984 0.0752

GJRR 0.2429 0.1745 0.1487 0.4425 0.1067 0.1689 0.3403 0.2696

NEGR 0.0719 0.0195 0.0953 0.1745 0.0547 0.0843 0.1435 0.1192

EJRR 0.1710 0.1550 0.0534 0.2680 0.0520 0.0846 0.1968 0.1504

2005 GJCR 0.1477 0.1545 0.2085 0.1935 0.1763 0.2986 0.2406 0.3067

GJDR 0.1051 0.0924 0.0346 0.1999 0.0471 0.0544 0.1164 0.0720

GJRR 0.2528 0.2469 0.2431 0.3934 0.2234 0.3530 0.3570 0.3787

NEGR 0.0426 0.0621 0.1739 ‐0.0064 0.1292 0.2442 0.1242 0.2347

EJRR 0.2102 0.1848 0.0692 0.3870 0.0942 0.1088 0.2328 0.1440

2006 GJCR 0.3157 0.2570 0.1440 0.6382 0.2005 0.1974 0.3028 0.4504

GJDR 0.0829 0.0730 0.0731 0.0990 0.0523 0.0746 0.0900 0.0914

GJRR 0.3986 0.3300 0.2171 0.7372 0.2528 0.2720 0.3928 0.5418

NEGR 0.2328 0.1840 0.0709 0.5392 0.1482 0.1228 0.2128 0.3590

EJRR 0.1658 0.1460 0.1462 0.1980 0.1046 0.1492 0.1800 0.1828

2007 GJCR 0.3440 0.2532 0.1831 0.3267 0.1375 0.3095 0.3399 0.3040

GJDR 0.1429 0.1176 0.0915 0.2861 0.1389 0.0788 0.1146 0.2938

GJRR 0.4869 0.3708 0.2746 0.6128 0.2764 0.3883 0.4545 0.5978

NEGR 0.2011 0.1356 0.0916 0.0406 ‐0.0014 0.2307 0.2253 0.0102

EJRR 0.2858 0.2352 0.1830 0.5722 0.2750 0.1576 0.2292 0.5876

Source: Authors’ computation based on CSA’s manufacturing census for Ethiopia

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Table A2: Gross Job Creation

OLS OLS OLS FGLS

(1) (2) (3) (4)

Textile & Garment ‐0.0844**(0.0381)

‐0.0844***(0.0279)

‐0.1242(0.0774)

‐0.0694 (0.0495)

Leather & Footwear ‐0.0713*(0.0381)

‐0.0713**(0.0279)

‐0.0773*(0.0448)

‐0.0502* (0.0267)

Printing & paper ‐0.0627(0.0381)

‐0.0627**(0.0279)

‐0.0722*(0.0372)

‐0.0713*** (0.0236)

Chemical & Plastic 0.0107(0.0381)

0.0107(0.0279)

0.0188(0.0653)

0.0595 (0.0407)

Non‐Metal 0.0239(0.0381)

0.0239(0.0279)

0.0339(0.0427)

0.0061 (0.0287)

Metal 0.0746*(0.0381)

0.0746***(0.0279)

0.0922**(0.0397)

0.0857*** (0.0239)

Wood & Furniture 0.1545***(0.0381)

0.1545***(0.0279)

0.1490***(0.0519)

0.1161*** (0.0332)

Time Dummies No YES YES YES

Aget‐1 0.0060(0.0414)

0.0088 (0.0266)

Age2t‐1 0.0002(0.0012)

0.0001 (0.0007)

Small Firmst‐1 0.1094(0.2885)

0.3462* (0.1907)

Capital Intensityt‐1 0.0003(0.0004)

0.0003 (0.0002)

Constant 0.1930***(0.0269)

0.1776***(0.0296)

‐0.0368(0.4595)

‐0.2441 (0.3044)

Observations 88 88 80 80

R‐squared 0.44 0.74 0.74


Notes: Standard errors in parentheses. *, ** and *** represent significance at 10%, 5%

and 1%, respectively.

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Table A3: Gross Job Destruction

OLS OLS OLS FGLS

(1) (2) (3) (4)

Textile & Garment ‐0.0148(0.0303)

‐0.0148(0.0239)

0.0900*(0.0470)

0.0608** (0.0249)

Leather & Footwear ‐0.0345(0.0303)

‐0.0345(0.0239)

‐0.0156(0.0272)

‐0.0184 (0.0133)


‐0.0274(0.0239)

‐0.0180(0.0225)

‐0.0315*** (0.0112)

Chemical & Plastic ‐0.0427(0.0303)

‐0.0427*(0.0239)

0.0162(0.0396)

0.0082 (0.0218)

Non‐Metal 0.0085(0.0303)

0.0085(0.0239)

‐0.0370(0.0259)

‐0.0298** (0.0130)

Metal 0.0445(0.0303)

0.0445*(0.0239)

‐0.0047(0.0241)

0.0072 (0.0180)

Wood & Furniture 0.1073***(0.0303)

0.1073***(0.0239)

0.0463(0.0315)

0.0439** (0.0186)

Time Dummies NO YES YES YES

Aget‐1 ‐0.0276(0.0251)

‐0.0088 (0.0141)

Age2t‐1 0.0007(0.0007)

0.0003 (0.0004)

Small Firmst‐1 0.3823**(0.1751)

0.3069*** (0.0976)

Capital Intensityt‐1 0.0006**(0.0002)

0.0004*** (0.0001)

Constant 0.1070***(0.0214)

0.2287***(0.0253)

0.0574(0.2788)

‐0.0591 (0.1603)


R‐squared 0.32 0.63 0.66


Notes: Standard errors in parentheses. *, ** and *** represent significance at 10%, 5%

and 1%, respectively.

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Table A4: Net Employment Growth

OLS OLS OLS FGLS

(1) (2) (3) (4)

Textile & Garment ‐0.0696(0.0506)

‐0.0696*(0.0388)

‐0.2141**(0.1010)

‐0.1158** (0.0526)

Leather & Footwear ‐0.0368(0.0506)

‐0.0368(0.0388)

‐0.0616(0.0584)

‐0.0273 (0.0316)


‐0.0353(0.0388)

‐0.0541(0.0485)

‐0.0377 (0.0276)

Chemical & Plastic 0.0534(0.0506)

0.0534(0.0388)

0.0026(0.0852)

0.0586 (0.0451)

Non‐Metal 0.0154(0.0506)

0.0154(0.0388)

0.0708(0.0558)

0.0297 (0.0314)

Metal 0.0301(0.0506)

0.0301(0.0388)

0.0968*(0.0518)

0.0798*** (0.0298)

Wood & Furniture 0.0472 (0.0506)

0.0472 (0.0388)

0.1026 (0.0677)

0.0715* (0.0367)

Time Dummies NO YES YES YES

Aget‐1 0.0336(0.0541)

0.0158 (0.0288)

Age2t‐1 ‐0.0006(0.0015)

‐0.0001 (0.0007)

Small Firmst‐1 ‐0.2729(0.3765)

0.0871 (0.2065)

Capital Intensityt‐1 ‐0.0004(0.0005)

‐0.0001 (0.0003)

Constant 0.0860**(0.0358)

‐0.0510(0.0412)

‐0.0941(0.5997)

‐0.2034 (0.3394)


R‐squared 0.12 0.55 0.55


Notes: Standard errors in parentheses. *, ** and *** represent significance at 10%, 5% and

1%, respectively.

Job Flows-130410 adm - IZA Institute of Labor Economics · above average job reallocation rate...

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