metadata: authors: 0: author: Jones,Patricia
1: author: Lartey,Emmanuel Kwasi Koranteng
2: author: Mengistae,Taye Alemu
3: author: Zeufack,Albert G.
count: Africa
docna: 0: docna: Sources of Manufacturing Productivity Growth
            in Africa
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Business Cycles and Stabilization Policies,General Manufacturing,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,International Trade and Trade Rules,Transport Services,Oil Refining & Gas Industry,Labor Markets
teratopic: Industry,Social Protections and Labor,International Economics and Trade,Macroeconomics and Economic Growth,Transport
abstracts: cdata!: This paper investigates the sources of
            growth in manufacturing productivity in Cote D’Ivoire,
            Ethiopia and Tanzania in comparison with the case of
            Bangladesh. Based on the analysis of establishment census
            data since the mid-1990s, it finds that reallocation of
            market share between firms contributed substantially to
            productivity growth in each of the four countries, although
            to a varying extent. In Ethiopia, the impact of market share
            reallocations among survivors tended to be larger than those
            associated with increases in within-plant productivity. In
            addition, plant closure (or exit) boosted productivity more
            than new plant openings (or entry) did in the sense that the
            relative productivity of survivors (or continuing plants)
            was higher relative to that of closing plants (or exit
            cases) than it was relative to the productivity of newly
            opening plants (or new entrants). Reallocation of market
            share plays an important role in raising aggregate
            productivity in Côte d’Ivoire as well. But the pattern here
            is opposite to that in Ethiopia in that in Côte d’Ivoire
            entering (or newly opening) plants have larger impact on
            aggregate productivity growth than closing (or exiting)
            plants. Unlike the case with Cote D’Ivoire and of Ethiopia,
            the reallocation of market share among surviving plants is a
            smaller source of manufacturing productivity growth in
            Tanzania than the new plant openings and plant closure. The
            data suggest that the reallocation of market share among
            surviving plants and exiting plants has larger impact on
            productivity growth in Bangladesh than the productivity gap
            between new plants and survivors, as in the case of Ethiopia.
repnme: repnme: Sources of Manufacturing Productivity
            Growth in Africa
display_title: Sources of Manufacturing
            Productivity Growth in Africa
listing_relative_url: /research/2019/08/31345697/sources-manufacturing-productivity-growth-africa
topicv3: Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Jobs m1503931 2670,Petroleum Industry m1583790 4710
publishtoextweb_dt: 2019-08-19T00:00:00Z
historic_topic: Industry,Social Protections and Labor,International Economics and Trade,Macroeconomics and Economic Growth,Transport
no_of_pages: 48
author_keys: 0: author_key: 1407197
1: author_key: 427631
2: author_key: 142139
model_extractions: Policy Research Working Paper 8980 Sources of Manufacturing Productivity Growth in Africa _Patricia Jones_ _Emmanuel K.K. Lartey Taye Mengistae Albert Zeufack_ Africa Region Office of the Chief Economist August 2019
Policy Research Working Paper 8980

# **Abstract**

This paper investigates the sources of growth in manufacturing productivity in Cote D’Ivoire, Ethiopia and Tanzania in comparison with the case of Bangladesh. Based on the analysis of establishment census data since the mid-1990s, it finds that reallocation of market share between firms contributed substantially to productivity growth in each of the four countries, although to a varying extent. In Ethiopia, the impact of market share reallocations among survivors tended to be larger than those associated with increases in within-plant productivity. In addition, plant closure (or exit) boosted productivity more than new plant openings (or entry) did in the sense that the relative productivity of survivors (or continuing plants) was higher relative to that of closing plants (or exit cases) than it was relative to the productivity of newly opening plants (or new entrants).

Reallocation of market share plays an important role in raising aggregate productivity in Côte d’Ivoire as well. But the pattern here is opposite to that in Ethiopia in that in Côte d’Ivoire entering (or newly opening) plants have larger impact on aggregate productivity growth than closing (or exiting) plants. Unlike the case with Cote D’Ivoire and of Ethiopia, the reallocation of market share among surviving plants is a smaller source of manufacturing productivity growth in Tanzania than the new plant openings and plant closure. The data suggest that the reallocation of market share among surviving plants and exiting plants has larger impact on productivity growth in Bangladesh than the productivity gap between new plants and survivors, as in the case of Ethiopia.

This paper is a product of the Office of the Chief Economist, Africa Region, commissioned as part of the _‘Industrialization for Jobs in Africa’_ regional study. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at jonesp@newpaltz.edu, elartey@worldbank.org, tmengistae@worldbank.org, and azeufack@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Sources of Manufacturing Productivity Growth in Africa**

Patricia Jones SUNY New Paltz Emmanuel K.K. Lartey World Bank Taye Mengistae World Bank Albert Zeufack World Bank August 2019 JEL codes: L6, 014, 012 Keywords: Industry Studies-Manufacturing; Industrialization, Productivity, Growth, Microeconomic Analysis of Development
# **I. Introduction**

In this paper, we focus on three African countries at different rates of industrialization of their economies and assess the different sources of productivity growth in each country. Specifically, we estimate the size and sources of productivity growth for Ethiopia, Côte d’Ivoire, and Tanzania—and then compare these productivity gains to that in an external comparator— Bangladesh. To do this, we compiled establishment-level data from a wide range of sources including manufacturing censuses, business registrars, and economic censuses.

Using these data, we investigate whether differences in the performance of manufacturing establishments can be accounted for by differences in the efficiency with which resources are allocated to their most productive use. We carry out both the static Olley-Pakes (1996) decomposition and the dynamic Olley-Pakes decomposition (DOPD) proposed by Melitz and Polanec (2015). These decomposition methods break down aggregate productivity changes into two components: one component that captures shifts in the productivity distribution (via changes in the unweighted mean of establishment-level productivity); and another component that captures market share reallocations (via changes in the covariance between market shares and establishment-level productivity). This allows us to identify patterns of productivity growth across different types of firms (e.g., by firm size and industry) as well as the efficiency of the factor reallocation in allocating resources to a sector’s most productive firms.

A growing literature has begun to examine the extent of factor misallocation in developing countries (Alfaro, Charlton, and Kanczuk, 2008; Alfaro and Chari, 2014; Barltesman, Haltiwanger, 2
and Scarpetta, 2009, 2013; Hsieh and Klenow, 2009; Restuccia and Rogerson, 2008, 2017). This paper contributes to this literature in two primary ways. First, it is the first paper to our knowledge to apply the Olley-Pakes decomposition methods to African data. To carry out this decomposition, we have generated new longitudinal data for Ethiopia (2012-2014) and Tanzania (2008-2012) and gained access to new panel data from Côte d’Ivoire.<sup>1</sup> In the case of Ethiopia, our dataset adds to the existing panel which covers the period 1996 to 2009.<sup>2</sup> Plus, we have generated a short panel for Bangladesh that covers every other year between 1995 and 2001. Using these data, we examine the sources of manufacturing productivity growth including the relative importance of factor reallocation for African countries.

Several interesting findings emerge from this analysis. First, there are clear differences in the size and sources of productivity growth across both countries and plants within countries. In terms of country performance, Ethiopia outperformed both Côte d’Ivoire and Tanzania on nearly every metric that we estimated. For example, aggregate manufacturing productivity increased by 75 percent in Ethiopia between 2006 to 2016 while it increased by 6 percent in Côte d’Ivoire over a similar period (2004 to 2016). Aggregate manufacturing productivity fell in Tanzania over the period 2008 to 2012. Second, we find several similarities between Ethiopia’s pattern of productivity growth in recent years and that of Bangladesh during its early years of industrialization. For example, Bangladesh’s aggregate manufacturing productivity increased by 52 percent between 2001 and 2012 which is comparable to that achieved by Ethiopia between

> 1 We’d like to thank Nouhoum Traore for sharing the Ivorian panel data with us.

> 2 In addition, we have establishment-level data for 2010, 2011, 2015, and 2016.  We hope to match the establishments in those years to our existing dataset so that we have a panel covering the period 1996 to 2016.  Similarly, we have one additional year of data for Tanzania (2013) and hope to extend the Tanzanian panel from 2008 to 2013.

3
2006 and 2016. Also, Ethiopia’s garment and textile sector achieved comparable productivity growth rates to that achieved by Bangladesh’s garment and textile industry.

Perhaps most interesting are the results that emerge when we estimate the sources of manufacturing productivity growth by firm size and industry. We find strong evidence, for example, that entrants are the main driver of manufacturing productivity growth among firms with less than 200 workers but not among larger firms. This finding raises the possibility that selection mechanisms—particularly the role of entry—may differ by firm size. In addition, we reveal that market share reallocations play an important role in driving productivity growth among garment and textile producers in both Ethiopia and Bangladesh.

Since garment and textile producers often face significant competition from abroad, we estimate the impact of trade exposure on plant-level total factor productivity (TFP). This is accomplished by employing semi-parametric estimation techniques to estimate plant-level total TFP and then use these TFP measures in a second stage regression (following Pavcnik, 2002) to estimate the effects of increased trade exposure on plant-level productivity. Importantly, our identification method separates the variation in productivity that arises due to changes in trade exposure from those emanating from other sources. Two important results stand out from this analysis. First, we find strong evidence that increased trade exposure significantly raises plantlevel TFP in all three African countries. However, we find no evidence that increased trade exposure results in stronger selection mechanisms that weed out less efficient firms.

The rest of the paper is organized as follows. Section II highlights the important role that industrialization plays in the development process. In addition, this section discusses how productivity gaps between plants might persist in long run equilibrium. Section III describes the decomposition methods that we use—both the static Olley-Pakes productivity decomposition and 4
the dynamic Olley-Pakes productivity decomposition. Section IV describes our data and their sources. Section V highlights our main results. Finally, Section VI concludes the paper.

# **II. Conceptual Background**

An economy’s aggregate productivity is the weighted sum of plant-level productivity where the weights are the market shares of individual plants. Economic growth occurs when individual plants become more productive but also when plants with above average productivity expand their market shares. This second component was well understood by the first generation of development economists who built multi-sector growth models based on this stylized fact (Lewis, 1956; Kuznets, 1961; Fei and Ranis, 1964). But, despite the passage of more than 50 years, empirical support of the underlying mechanisms that accelerate factor reallocation remains limited. Instead, much of the empirical growth literature during the past two decades has been based on one-sector models that seek to identify the “best” set of variables that are correlated with cross-country variations in income and growth. The weaknesses of such growth regressions are well known (see, for example, the critique by Durlauf, Johnson, and Temple, 2005).

More recently, increased attention has been given to identifying the mechanisms that facilitate and constrain the efficient allocation of resources across heterogenous production units (see Hopenhayn, 2014 and Restuccia and Rogerson, 2017 for good reviews of this literature). Recent estimates suggest that such misallocation can explain up to 60 percent of the aggregate TFP differences between rich and poor countries (Kalemli-Ozcan and Sorensen, 2012). The magnitude of this dispersion and its persistence over time raises the question of how low-productivity 5
producers survive in the same industry as high-productivity producers, particularly in the long run. One view is that “the observed dispersion reflects the frictions and perhaps distortions that prevent resources from being immediately reallocated to the most productive firms” (Haltiwanger, 2015, p. 343). Examples include regulations that levy higher taxes on producers that are larger in terms of employment, sales or capital (Hopenhayn and Rogerson, 1993; Guner, Ventura, and Xu, 2008); financial frictions that distort the allocation of capital across producers making the same good (Buera et al, 2011); and trade policies that generate a wedge that prevents the equalization of marginal products across heterogenous producers (Eaton and Kortum, 2002; Melitz, 2003; Eaton, Kortum, and Kramarz, 2011).

Another view is that “there may be sources of curvature in the profit function, so the most productive firms do not take over the market” (Haltiwanger, 2015, p. 343). For example, heterogeneous firms may face downward sloping demand curves due to product differentiation or high transport costs that reduce the scale of market selection via firm entry and exit (Melitz, 2003; Syverson, 2004a, 2004b; Melitiz and Ottaviano, Jones et al, 2018). Such models have become increasingly popular over the last decade as new studies find evidence documenting the existence of substantial price dispersion across producers, even those operaing within the same narrowly defined industry.

In addition, productivity gaps might arise due to selection and learning dynamics among young firms. According to Jovanovic (1982), new firms do not fully know their own productivity but instead enter the market with some prior belief about their relative position in their sector’s productivity distribution. Each period, they update their beliefs as new information becomes available. Efficient firms grow and survive while inefficient firms exit the market. In this model, “firms differ in size not because of the fixity of capital, but because some learn they are more 6
efficient than others” (Jovanovic, 1982, p. 649). As a result, learning dynamics and selection mechanisms that weed out inefficient firms play a key role in explaining industry dynamics. The Jovanovic model is consistent with several empirical findings including the observation that young firms have higher and more variable growth rates than older, more established firms.

Extensions of the Jovanovic model posit that learning dynamics are important not only to young firms but to older firms as well. Hopenhayn (1992), for example, argues that firms are subject to idiosyncratic shocks over their lifespan and must adapt to changing economic circumstances to survive. While a firm’s past successes are correlated to its future successes, the ability to adapt to changing market conditions is what separates successful firms from unsuccessful firms. This combination of idiosyncratic firm shocks, learning dynamics, and firm selection results in resources being constantly reallocated across firms. Over time, new firms enter the market, surviving firms expand or contract, and inefficient firms exit the market. Such reallocation results in high turnover rates across both firms and jobs. For instance, in the United States during the 1990s, about one-third of the stock of jobs and over forty percent of manufacturing firms exited the market and then were replaced by new entrants during each five-year period (Hopenhayn, 1992). Such “churning” in the market results from productivity changes at the establishment level and lies at the heart of all firm dynamics.

# **III. Productivity Decomposition**

In this paper, we use both the static and dynamic Olley-Pakes decomposition (Melitz and Polanec 2015) to examine the size and sources of productivity changes in four developing countries: Bangladesh, Côte d’Ivoire, Ethiopia, and Tanzania. These decompositions breakdown aggregate 7
<!-- Start of picture text -->
-, .<br>P= >.U" 5 t = Bet > U(Sin 5) Vie - Pt),<br>Ab = (Ps2_ 51) + Se2(Pe2- 52) + Sxi(Psi- x1)<br>~, + Acovs + Sg2(Pg2 — Ps2) + Sx1(Ps1 — x1)<br><!-- End of picture text -->
productivity rises when surviving firms become more productive but also when new firms with higher productivity _enter_ the market and when inefficient firms with lower productivity _exit_ the market.

The second line further breaks down each group’s contribution. Specifically, the contribution of _survivors_ can be decomposed into a component that captures the change in average productivity among survivors, Δφ��, and another component that captures the change in the covariance between the productivity of survivors and their market shares, cov�. Similarly, the contribution of _entrants_ can be decomposed into a component that captures the relative difference in the productivity of entrants and survivors, S���φ����φ����, and another component that captures the relative difference in the covariance between market shares and productivity across the two groups (S���cov���cov���. The contribution of _exiting firms_ is analogous to that of entrants: one component that captures the relative difference in the productivity of exiting firms and survivors, S���φ����φ����, and another component that captures the relative difference in the covariance between market shares and productivity across the two groups, S���cov���cov���.

The intuition is simple: the contribution of entrants (or exiting firms) to aggregate productivity is the change in aggregate productivity that would have been generated if we were to add entrants (or remove exiting firms) to some initial distribution of firms. Since we cannot observe entrants in period 1 nor exiting firms in period 2, we cannot apply the same counterfactual in both periods. Instead, the DOPD method uses “the set of surviving firms as a benchmark and [then] asks how adding the group of entrants (or exiters) affects the aggregate productivity change” (Melitz and Polanec, 2014). Using a different reference period for each group is critical because of the timing of entry and exit. In the DOPD method, entrants only contribute to productivity growth if their productivity is higher than survivors when entry occurs (period 2). Likewise, exiting 9
firms only contribute to productivity growth if their productivity is higher than survivors when exit occurs (period 1). The fact that the DOPD specification uses different reference periods when estimating the impact of entry and exit is a clear advantage over other decomposition methods that use the same reference period (e.g., Griliches and Regev, 1995; Foster, Haltiwanger, and Krizan, 2001). These alternative methods are likely to result in measurement bias when measuring the contribution of one group or the other.

While the dynamic decomposition has clear advantages over the static decomposition, we use both methods to take advantage of the cross-sectional data that we have for the period since 2016. These data are included in our analysis to highlight current trends as well as the different sources of manufacturing growth over the past decade.

# **IV. Data**

The analysis of business dynamics requires data that both tracks individual establishments over time and is representative of different firm types (e.g., survivors, entrants, and exiting plants). Typically, these criteria are met only by manufacturing censuses and business registers as surveybased data are unlikely to be representative of entrants and exiters as well as firms at different stages of their life-cycle (e.g, young and mature plants). Table 1 lists the data sources that we use to conduct our analysis. In total, we have data on more than 67,000 establishments from 45 country-year samples. Its breakdown is as follows: data covering 21 years for Ethiopia (19962016), 12 years for Côte d’Ivoire (2003-2014), six years for Tanzania (2008-2013), and six years for Bangladesh (1995, 1997, 1999, 2001, 2005, and 2012).

10
Our first step in the productivity decomposition is to classify each establishment as either an entrant, exiter, or a survivor. Following Bartelsman, Haltiwanger, and Scarpetta (2013), we adopt the following definitions: _Entrant (E)_ : Entrants and their employees are defined by the _first year_ they are observed in the registry. Entrants are those observed as (out, in) in the registry at time (t-1, t).

_Exiter (X)_ : Exiters and their employees are defined by the _last year_ they are observed in the registry. Exiters are those observed as (in, out) in the registry at time (t-1, t).

_Survivor (S)_ : Survivors and their employees are those establishment that were in the registry for two consecutive years. Survivors are those observed as (in, in) at time (t-1, t).

Given these definitions, there is a clear link between the change in the stock of survivors and the number of entrants and exits in any two-year period. This relationship can be expressed as: Given that survivors and entrants both exist in time t, the total number of establishments in any year t is defined as the sum of survivors and entrants. That is, This implies that the change in the total number of establishments between year t and t�1 can be defined as the difference between the number of entrants and exits. We write this relationship as: 11
In other words, the total change in the number of establishments, ∆T, between period t and t�1 is simply the difference between the cumulative sum of entrants and the cumulative sum of exits over the period. That is, Using these definitions, we can classify each establishment into one of three categories: survivors (S), entrants (E), and exiting plants (X). Several patterns are worth noting. First, all four countries exhibit considerable firm turnover. About 20% of operating plants “enter” the market each year while another 18% of plants “exit.” Given the fact that entry rates exceed exit rates, the number of plants in each country rises over time. While these increases are relatively modest in the African countries, Bangladesh ends the period with more than 10,000 new manufacturing plants.

Table 2 provides some descriptive statistics on the manufacturing sector in each country. The first pattern to notice is that African establishments are smaller than those in Bangladesh. In 2012, the median establishment in 2012 employed 33 workers in Côte d’Ivoire, 20 workers in Ethiopia, 20 workers in Tanzania, and 119 workers in Bangladesh.<sup>3</sup> Perhaps more important, the average scale of plants in Africa does not appear to be rising whereas it is clearly rising in Bangladesh. A quick look at the size distribution of African manufacturing establishments (Figures 1a-1d) reveals that the largest share of establishments employs less than 50 workers and increases in size over time. For example, the share of establishments employing less than 50 workers increases from 69 percent to 76 percent in Ethiopia (between 1996 and 2016); from 47 percent to 60 percent in Côte d’Ivoire (between 2003 and 2014); and from 62 percent to 68 percent in

> 3 In each country, these numbers are based on establishments with 10+ workers.

12
Tanzania (between 2008 and 2013). In addition, all African countries experience a monotonic decline in the share of larger establishments over these time periods. The opposite trend occurs in Bangladesh where the share of establishments employing less than 50 workers _falls_ from 79 percent to 62 percent and the share of larger establishments (e.g., those employing 50 to 199 workers) _rises_ by 12 percent 25 percent over the period 2001 to 2012. The failure of the African manufacturing sector to generate scale should concern policy makers, particularly given recent evidence that larger establishments are highly correlated with both increased industrialization and higher income per capita (Buera and Kaboski, 2011; Buera et al, 2012; Bento and Restuccia, 2017).

These cross-country differences in scale may be partially driven by differences in the sectoral composition of the manufacturing sector in each country. As revealed in Figures 3a-3d, there are clear differences between manufacturing activities pursued by African and Asian establishments. In Bangladesh, 43 percent of all manufacturing establishments produce either textiles or garments (in 2012) whereas this share is just 13 percent in Ethiopia (in 2016), 5 percent in Tanzania (in 2013) and 2 percent in Côte d’Ivoire (2014). Since garments and textiles are often produced for global markets, this suggests that African establishments may not have a comparative advantage in these sectors. By contrast, the share of African establishments engaged in manufacturing activities that cater to domestic markets (e.g., food and beverages, furniture) remains high. In all three African countries, establishments that produce food and beverages comprise the largest share of manufacturing firms and the largest share of manufacturing employment (see Figures 5a-5d). Forty-three percent of manufacturing workers are employed by the food and beverage industry in Tanzania (in 2013) while 23 percent are employed in Ethiopia (in 2016) and 22 percent are employed in Côte d’Ivoire (in 2014). In Bangladesh, this percentage is only 6 percent.

13
Are these differences in sectoral composition and scale being driven by differences in plant-level productivity across establishments? We next turn to this issue by examining the sources of productivity growth among different categories of plants. Using both the static Olley-Pakes (1996) decomposition and the dynamic Olley-Pakes decomposition (DOPD) developed by Melitz and Polanec (2015). Our preferred method is the Melitz and Polanec (2015) decomposition but utilize the static decomposition method when panel data are not available. As discussed by OlleyPakes (1996), this method is more directly linked to theoretical models of firm dynamics developed to analyze the sources of market share reallocations across heterogenous firms (see, for example, Hsieh and Klenow, 2009; Collard-Wexler, Asker and de Loecker, Bartlesman, Haltiwanger, and Scarpetta, 2011; and Restuccia and Rogerson, 2017).

# **V. Results**

Tables 3 and 4 report the results of the Olley-Pakes decomposition methods. Several patterns are worth noting. First, aggregate productivity growth in Ethiopia (Table 3) outstripped that of both Côte d’Ivoire and Tanzania (Table 4). In Ethiopia, aggregate productivity increased by 76 percent between 2006 and 2016 whereas it increased by only 6 percent in Côte d’Ivoire over a similar period (2004 to 2014).<sup>4</sup> The weak performance of the Ivorian manufacturing sector is not surprising given the country’s two civil wars and extreme political instability during this period. However, growth rates rebounded following the peace agreement that ended the Second Ivorian Civil War (2010-2011). As reported in Table 4, aggregate productivity increased by 30 percent in

> 4 Table 5 reports that aggregate productivity growth increased by 47 percent between 1996 and 2009.  Note our estimates contradict earlier findings by Newman et al (2016) drawn from the Groningen Africa Database who estimate manufacturing productivity changes based on aggregate (sectoral) data.

14
2012 and by 27 percent in 2013. Somewhat surprisingly, we find that aggregate manufacturing productivity in Tanzania fell each year between 2009 and 2012.

Second, we find evidence that market share reallocations contributed substantially to productivity growth in each country, although with vary magnitudes. In Ethiopia, the impact of market share reallocations among survivors (represented by Δcov) tended to be larger than those associated with increases in within-plant productivity (represented by the Δ in unweighted productivity). In addition, we find evidence that exiting plants boost productivity more than entering plants. The relative productivity of survivors is higher than that of exiting plants, suggesting that selection mechanisms are weeding out the least productive plants. In a related paper, Jones et al (2018) find that exiting plants in Ethiopia have lower physical total factor productivity (TFPQ) than surviving plants, but only when after controlling for producers’ transport costs.

In Côte d’Ivoire, market share reallocations also play an important role in raising aggregate productivity. But, the pattern is opposite to what we find for Ethiopia. In Côte d’Ivoire, entering plants—not exiting plants—tend to have a larger impact on aggregate productivity growth. The relative productivity of entering plants is higher than that of surviving plants in 8 out of the 10 years between 2004 and 2014. By contrast, exiting plants reduce productivity in 7 out of 10 years during this period. Once again, a different pattern emerges in Tanzania. In Tanzania, we find that market share reallocations among surviving plants appear to have a smaller impact on aggregate productivity growth than both entry or exit. So how do these patterns compare to those in Bangladesh? Table 5 presents the decomposition of aggregate productivity growth for Bangladesh. While our data are limited, the evidence suggests that market share reallocations among surviving 15
firms and exiting firms have the largest impact on productivity growth in Bangladesh. This pattern is similar to what we find for Ethiopia.

Next, we examine how the sources of productivity growth differ by firm size and industry. Given the large number of years covered, we present the results in roughly five-year intervals for each country. The results are reported in Tables 6 and 7. Several interesting findings emerge. First, the smallest plants (those with less than 50 workers) experienced the largest productivity growth in three out of the four countries (Ethiopia, Tanzania, and Bangladesh). This is important as plants in this size category comprise the majority of formal sector plants with at least 10 workers in each country. Côte d’Ivoire is the exception with slightly larger plants (those with 50 to 199 workers) experiencing the fastest productivity growth. Another key finding is that entrants are the main source productivity growth among plants with less than 200 workers and exiting plants have depress productivity in plants w200 to 499 workers. This raises the possibility that selection mechanisms may have different effects on plants that vary in size.

Finally, we conduct the productivity decomposition for plants in different industries. Initially, we focus on three industries: 1) garments and textiles; 2) food and beverages; and 3) furniture. While garments and textiles tend to be export-intensive industries, firms in the food and furniture industries tend to sell their products locally. Among garment and textile producers, Ethiopian plants achieved the highest productivity growth of the four countries. Aggregate productivity in Ethiopia rose by 24 percent between 1996 and 2001 and by 30 percent between 1996 and 2006. In Bangladesh, aggregate productivity increased by 33 percent between 1995 and 2001. Interestingly, this productivity growth was largely driven mainly by more efficient survivors gaining market shares and by less efficient plants exiting the market. A similar pattern emerges among garment and textile plants in Ethiopia. During the first period, market share reallocations 16
were the primary source of productivity growth whereas, during the second period, the entry of more efficient plants was the driving force. Neither Côte d’Ivoire nor Tanzania experienced positive productivity growth in their garments and textile industries over the periods for which we have data.

Mixed results are found for firms in the food and beverage industry as well as for firms in the furniture industry. Overall aggregate productivity in the food and beverage industry grew by 11 percent in Ethiopia between 1996 and 2006 and by 33 percent in Bangladesh between 1995 and 2001. This productivity growth was driven by market share reallocations among survivors in Ethiopia and by selection mechanisms in Bangladesh. By contrast, aggregate productivity fell for plants producing food and beverages in both Côte d’Ivoire and Tanzania. For Côte d’Ivoire, this fall in productivity was due to surviving firms becoming less productive, a likely result of the political unrest in the country. In Tanzania, however, productivity fell as an increasing share of output was shifted to less productive plants in the industry.

Lastly, we examine the sources of productivity in the furniture industry in each country. Both Ethiopia and Côte d’Ivoire experienced a rise in aggregate productivity among furniture producers. Aggregate productivity rose by 36 percent between 1996 and 2006 in Ethiopia and by 19 percent in Côte d’Ivoire between 2004 and 2014. The main source of productivity growth in Ethiopia was due to surviving plants becoming more productive and gaining market share. By contrast, the main source of productivity growth in Côte d’Ivoire was due to more productive survivors gaining market share and less efficient plants exiting the market.

It is often argued that increased trade exposure in a market strengthens the selection mechanisms that weed out less efficient firms. Is there any evidence that increased trade exposure has this effect on African manufacturing firms? We test this hypothesis using a two-stage 17
estimation technique similar to that developed by Pavnik (2002). In the first stage, we estimate plant-level TFP using the Levinsohn-Petrin (2003) technique for estimating production functions. This estimation technique controls for the possible correlation between a plant’s chosen input levels and its unobserved plant-level productivity shocks by using intermediate inputs as a proxy for the unobserved productivity shock. Once we have estimated plant-specific TFP, we use these TFP measures in a second stage regression (following Pavcnik) to estimate the effects of increased trade exposure on plant-level productivity. Importantly, our identification method separates the variation in productivity that arises due to changes in trade exposure from those emanating from other sources. We do this by exploiting both the variation in productivity over time and across plants with different exposures to trade. We estimate the following equation: where TFP�� is the unweighted productivity estimate for plant i in year t, Year is a vector of year indicators, Trade is a dummy variable that indicates whether plant i has positive foreign sales, and Z�� is a vector of plant characteristics including industry fixed effects and whether the plant exits the market in any given year. In this specification, the direct effects of trade are represented by the coefficient α� and the indirect effects of trade (via exit) are represented by the coefficient α�. Tables 8, 9 and 10 report the results of the second-stage regression. Two important results stand out from this analysis. First, we find strong evidence that increased trade exposure significantly raises plant-level TFP. For each country, the coefficient on exporter is both positive and significant at the 1% level. However, we find no evidence that increased trade exposure results in stronger selection mechanisms that weed out less efficient firms.

18
# **VI.    Conclusion**

This paper examines the size and sources of productivity growth for three African countries— Ethiopia, Côte d’Ivoire, and Tanzania—and then compares these productivity gains to those of an external comparator: Bangladesh. We utilize static and dynamic Olley-Pakes decomposition and to obtain several interesting findings emerge from this analysis.

First, there are clear differences in the size and sources of productivity growth across both countries and plants within countries. In terms of country performance, Ethiopia outperformed both Côte d’Ivoire and Tanzania on nearly every metric that we estimated. Second, we find strong evidence that entrants are the main driver of productivity growth among small and medium-sized firms (those with less than 200 workers) but not among the countries’ largest firms (those with 200+ employees). This finding raises the possibility that selection mechanisms—particularly the role of entry—may differ by firm size. In addition, the results reveal that market share reallocations play an important role in driving productivity growth among garment and textile producers in both Ethiopia and Bangladesh. Furthermore, the findings provide strong evidence that increased trade exposure significantly raises plant-level TFP but do not show that increased trade exposure in a market results in stronger selection mechanisms that weed out less efficient firms.

19
# References

- Alfaro, Laura, Andrew Charlton, and Fabio Kanczuk. 2008. “Plant Size Distribution and CrossCountry Income Differences.” NBER Working Paper 14060.

- Alfaro, Laura and Anusha Chari. 2014. “Deregulation, Misallocation and Size: Evidence from India,” _Journal of Law & Economics_ 57(4): 897-936.

- Asker, John, Collard-Wexler, Alan, and Jan de Loecker. 2014. “Dynamic Inputs and Resource (Mis)Allocation,” _Journal of Political Economy_ 122(5): 1013-1063.

- Banerjee and Duflo. 2004. “Growth Theory through the Lens of Development Economics,” in Aghion Philippe and Steven Durlauf (Eds.) _Handbook of Economic Growth_ , Vol. 1. Amsterdam: Elseveier.

- Bartelsman, Eric, John Haltiwanger, and Stefano Scarpetta. 2009. “Measuring and Analyzing Cross-country Differences in Firm Dynamics,” in Dunne, Timothy, J. Jensen, and Mark J. Roberts (Eds.) _Producer Dynamics: New Evidence from Micro Data_ . Chicago: University of Chicago.

- Bartelsman, Eric, John Haltiwanger, and Stefano Scarpetta. 2013. “Cross-Country Differences in Productivity: The Role of Allocation and Selection,” _American Economic Review_ 103(1): 305-334.

- Bento, Pedro, and Diego Restuccia. 2017. “Misallocation, Establishment Size, and Productivity,” _American Economic Journal: Macroeconomics_ 9(3): 267-303.

- Buera, Francisco J., Joseph P. Kaboski, and Yongseok Shin. 2011. “Finance and Development: A Tale of Two Sectors.” _American Economic Review_ 101(5): 1964-2002.

- Buera, Francisco J. and Joseph P. Kaboski. 2012. “Scale and the Origins of Structural Change,” _Journal of Economic Theory_ 147:684-712.

- Durlauf, Steven D, Paul A. Johnson, and Jonathan R.W. Temple. 2005. “Growth Econometrics,” in Aghion Philippe and Steven N. Durlauf (Eds). _Handbook of Economic Growth,_ Vol. 1. Amsterdam: Elsevier.

- Eaton, Jonathan and Samuel Kortum. 2002. “Technology, Geography, and Trade,” _Econometrica_ 70(5): 1741-1779.

- Eaton, Jonathan, Samuel Kortum, and Francis Kramarz. 2011. “An Anatomy of International Trade: Evidence from French Firms,” _Econometrica_ 79(5): 1453-1498.

- Foster, Lucia, John C. Haltiwanger, and C.J. Krizan. 2001. “Aggregate Productivity Growth: Lessons from Microeconomic Evidence,” in Hulten Charles R., Edwin R. Dean, and

20
Michael J. Harper (Eds), _New Developments in Productivity Analysis_ . Chicago: University of Chicago Press.

- Gollin, Douglas, Stephen L. Parente, and Richard Rogerson. 2004. “Farm Work, Home Work, and International Productivity Differences,” _Review of Economic Dynamics_ (7): 827-850.

- Griliches, Zvi and Haim Regev. 1995. “Firm Productivity in Israeli Industry: 1979-1988 _.” Journal of Econometrics_ 65: 175-203.

- Gunner, Nezih, Gustavo, Ventura, and Yi Xu. 2008. “Macroeconomic Implications of SizeDependent Policies,” Review of Economic Dynamics 11(4): 721-744.

- Haltiwanger, John. 2015. “Job Creation, Job Destruction, and Productivity Growth: The Role of Young Businesses,” _Annual Review of Economics_ 7: 341-358.

- Hopenhayn, Hugo. 1992. “Entry, Exit, and Firm Dynamics in Long run Equilibrium,” _Econometrica_ 60(5): 1127-1150.

- Hopenhayn, Hugo. 2014. “Firms, Misallocation, and Aggregate Productivity: A Review,” Annual Review of Economics 6: 735-770.

- Hopenhayn, Hugo and Richard Rogerson. 1993. “Job Turnover and Policy Evaluation: A General Equilibrium Analysis,” _Journal of Political Economy_ 101(5): 915-938.

- Hsieh, Chang-Tai and Peter J. Klenow. 2014. “The Life Cycle of Plants in India and Mexico,” _Quarterly Journal of Economics_ 129(3): 1035-1084.

- Jovanovic, Boyan. 1982. “Selection and the Evolution of Industry,” _Econometrica_ 50(3):649-670.

- Jones, Patricia, Taye Mengistae, and Albert Zeufack. 2018. “Selection, Firm Turnover, and Productivity Growth,” World Bank Policy Research Working Paper 8291.

- Kalemli-Ozcan, Sebnem and Bent E. Sorenson. 2012. “Misallocation, Property Rights, and Access to Finance: Evidence from Within and Across Africa.” NBER Working Paper 18030.

- Levinsohn, James and Amil Petrin. 2003. “Estimating Production Functions using Inputs to Control for Unobservables,” _Review of Economic Studies_ 70(2): 317-341.

- Melitz, Marc J. “The Impact of Trade on Intra-industry Reallocations and Aggregate Industry Productivity,” _Econometrica_ 70(6): 1695-1725.

- Melitz, Marc J. and G.I.P. Ottaviano. 2008. “Market Size, Trade and Productivity”, _Review of Economic Studies_ 75: 295-316.

- Melitz, Marc J. and G.I.P. Ottaviano. 2008. “Market Size, Trade and Productivity”, _Review of Economic Studies_ 75: 295-316.

21
Newman, Carol, John Page, John Rand, Abebe Shimeles, Måns Söderbom, and Finn Tarp. 2016. _Made in Africa: Learning to Compete in Industry_ . Washington, DC: Brookings Institution Press.

- Page, John. 2012. “Can Africa Industrialize?” _Journal of African Economies_ , Vol. 21. AERC Supplement 2, pp. ii86-ii125.

- Page, John. 2016. “Are Certain Countries Doomed to Remain Emerging? Globalization, Structural Change, and the Uneven Geography of Convergence.” _Report #6 in 11 Global Debates_ . Brookings Institute.

- Page, John. 2018. “Rethinking Africa’s Structural Transformation: The Rise of New Industries.” In Brahima S. Couliby (Ed.) _Foresight Africa: Top Priorities for the Continent in 2018_ (pp. 64-80). Washington, DC: Brookings Institute.

- Olley, G. Steven and Ariel Pakes. 1996. “The Dynamics of Productivity in the Telecommunications Equipment Industry.” _Econometrica_ 64: 1263–1297.

- Pavcnik, Nina. 2002. “Trade Liberalization, Exit, and Productivity Improvements: Evidence from Chilean Plants,” _Review of Economic Studies_ 69(1): 245-276.

- Melitz, Marc and Sašo Polanec. 2015. “Dynamic Olley-Pakes Productivity Decomposition with Entry and Exit.” _Rand Journal of Economics_ 46(2): 362-375.

- Restuccia, Diego and Richard Rogerson. 2008. “Policy Distortions and Aggregate Productivity with Heterogenous Establishments,” Review of Economic Dynamics 11(4): 707-720.

- Restuccia, Diego and Richard Rogerson. 2017. “The Causes and Costs of Misallocation _,” Journal of Economic Perspectives_ 31(3): 151-174.

Rodrik, Dani. 2015. “Premature Deindustrization.” NBER Working Paper 20935.

Rodrik, Dani. 2017. “Growth without Industrialization?” _Project Syndicate_ (October 10, 2017).

- Syverson, C. 2004a. “Market Structure and Productivity: A Concrete Example,” _Journal of Political Economy_ 112(6) : 1181-1222.

- Syverson, C. 2004b. “Product Substitutability and Productivity Dispersion,” _Review of Economics and Statistics_ 86(2): 534-550.

- Syverson, C. 2011. “What Determines Productivity?” _Journal of Economic Literatur_ **_e_** 49(2): 326365.

- United Nationas Industrial Development Organization (UNIDO). 2009. _Industrial Development Report. Breaking In and Moving Up: New Industrial Challenges for the Bottom Billion and the Middle Income Countries_ . Vienna, Austria.

22
23
||Data<br>|TABL<br>Sources Used for Pro<br>|E 1:<br>ductivity Decomp<br>|osition<br>|||
|---|---|---|---|---|---|---|
|Country|Name of Survey/Census|Source|Period|Sectors|Panel|Threshold|
|Côte d’Ivoire|Registrar of Companies for the<br>Modern Enterprise Sectors|Business<br>Registrar|2003-2014|All<br>sectors|yes|None|
|Ethiopia|Survey of Large & Medium Scale<br>Enterprises|Manufacturing<br>Census|1996-2009<br>2012-2014|Manufacturing<br>only|yes|Employment ≥ 10|
||Survey of Large & Medium Scale<br>Enterprises|Manufacturing<br>Census|2010-2016|Manufacturing<br>only|no|Employment ≥ 10|
|Tanzania|Annual Survey of Industrial<br>Production (ASIP)|Manufacturing<br>Census|2008-2012|Industrial sectors<br>only|yes|Employment ≥ 10|
||Census of Industrial Production<br>(CIP)|Industrial<br>Census|2013|All<br>sectors|no|None|
|Bangladesh|Census of Manufacturing Industries<br>(CMI)|Manufacturing<br>Census|1995, 1997,<br>1999, 2001|Manufacturing<br>only|yes|Employment ≥ 10|
||Census of Manufacturing Industries<br>(CMI)|Manufacturing<br>Census|2005, 2012|Manufacturing<br>only|no|Employment ≥ 10|

24
<u>TABLE 2: Descriptive Statistics</u>

|||Ethiopia:<br>1996-2016|||Côte d’Ivoir<br>2003-2014|e:<br>||Tanzania:<br>2008-2013|||Bangladesh<br>1995-2012|:<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Year|# of<br>firms|# of<br>workers|Median<br>firm size|# of<br>firms|# of<br>workers|Median<br>firm size|# of<br>firms|# of<br>workers|Median<br>firm size|# of<br>firms|# of<br>workers|Median<br>firm<br>size|
|1995||||||||||28,769|2,016,638|70|
|1996|623|82,079|21||||||||||
|1997|697|83,138|20|||||||28,297|2,175,025|82|
|1998|725|81,764|20||||||||||
|1999|725|89,508|21|||||||26,969|2,230,087|103|
|2000|739|79,839|21||||||||||
|2001|722|73,568|22|||||||27, 276|<br>2,801,441|86|
|2002|883|80,126|19||||||||||
|2003|939|83,655|20|332|69,306|56|||||||
|2004|997|86,916|22|342|65,729|51|||||||
|2005*|763|86,161|35|339|60,596|54||||27,461|2,347,769|90|
|2006|1,153|102,655|24|328|62,872|54|||||||
|2007|1,339|111,763|20|335|62,110|54|||||||
|2008|1,734|112,997|17|366|74,878|50|649|107,388|29||||
|2009|1,948|126,837|16|398|63,130|42|659|93,573|30||||
|2010|1,958|157,817|21|413|75,043|41|1,079|105,752|18||||
|2011|1,934|123,997|16|468|74,345|34|1,087|109,957|18||||
|2012|2,129|178,708|20|526|81,230|33|1,091|117,562|20|41,716|4,982,446|119|
|2013|2,388|202,347|21|569|74,820|33|998|107,732|22||||
|2014|2,405|196,637|20|591|91,373|32|||||||
|2015|2,718|200,390|15||||||||||
|2016|2,719|227,971|17||||||||||

25
Table 3: Ethiopia <u>Decomposition of Aggregate Productivity Growth</u>

|_Ethiopi_<br>|_a_<br>|<br>|<br>Surviving <br>|<br>Firms<br>|<br>||
|---|---|---|---|---|---|---|
|_Dynami_<br>T=1|_c OP_<br>T=2|Aggregate<br>Productivity|Δ Unweighted<br>Productivity|Δ<br>Covariance|Entering<br>Firms|Exiting<br>Firms|
|1996|1997|0.06|0.02|0.04|-0.02|0.02|
|1996|1998|0.01|-0.12|0.13|-0.03|0.03|
|1996|1999|0.09|-0.15|0.22|-0.03|0.04|
|1996|2000|0.19|-0.05|0.22|0.02|0.00|
|1996|2001|0.20|-0.20|0.21|0.11|0.09|
|1996|2002|0.02|-0.08|0.07|-0.01|0.04|
|1996|2003|0.20|-0.04|0.21|-0.01|0.05|
|1996|2004|0.28|0.06|0.19|-0.03|0.06|
|1996|2005|0.26|0.07|0.07|0.08|0.05|
|1996|2006|0.37|0.03|0.13|0.16|0.04|
|1996|2007|0.55|0.15|0.24|0.09|0.07|
|1996|2008|0.58|0.25|.023|-0.04|0.15|
|1996|2009|0.47|0.07|0.47|-0.17|0.09|
|...<br>2012|2013|0.08|0.02|0.01|0.02|0.02|
|2013|2014|0.22|0.17|0.02|-0.08|0.11|

|_Ethiopia_<br>_Static OP_<br>T=1||Aggregate<br>Productivity|Δ Unweighted<br>Productivity|Δ<br>Covariance|
|---|---|---|---|---|
|T=2|||||
|2006|2007|0.18|-0.05|0.23|
|2006|2008|0.22|-0.07|0.29|
|2006|2009|0.10|-0.19|0.29|
|2006|2010|0.08|0.33|-0.25|
|2006|2011|0.80|0.84|-0.04|
|2006|2012|0.17|0.32|-0.15|
|2006|2013|0.29|0.46|-0.17|
|2006|2014|0.39|0.56|-0.16|
|2006|2015|0.61|0.34|0.27|
|2006|2016|0.76|-0.07|0.76|

26
Table 4: Côte d’Ivoire & Tanzania <u>Decomposition of Aggregate Productivity Growth</u>

|**Côte**<br>**’**||<br>A|<br>Surviving <br>|<br>Firms<br>|<br>||
|---|---|---|---|---|---|---|
|**dIvo**<br>**Dyna**|**re**<br>**mic OP**|ggregate<br>Productivity|Δ Unweighted<br>Productivity|Δ<br>covariance|Entering<br>Firms|Exiting<br>Firms|
|T=1|T=2||||||
|2004|2005|-0.02|-0.10|0.03|0.07|-0.01|
|2004|2006|-0.07|-0.10|0.00|-0.01|0.05|
|2004|2007|0.00|-0.01|-0.03|0.11|-0.07|
|2004|2008|-0.01|-0.05|0.01|0.08|-0.05|
|2004|2009|0.03|-0.11|-0.03|0.19|-0.02|
|2004|2010|0.20|-0.09|0.04|0.26|-0.01|
|2004|2011|-0.03|-0.27|0.12|0.10|0.02|
|2004|2012|0.27|-0.26|0.30|0.20|0.03|
|2004|2013|0.47|-0.20|0.28|0.39|0.00|
|2004|2014|0.06|-0.27|0.40|-0.01|-0.05|
|**Tanza**|**nia**||Surviving|Firms|||
|**Dyna**|**mic OP**|Aggregate<br>|Δ Unweighted|Δ|Entering|Exiting|
|T=1|T=2|Productivity|Productivity|covariance|Firms|Firms|
|2008|2009|0.22|0.22|-0.05|0.02|0.06|
|2008|2012|-0.14|-0.14|0.14|0.10|0.03|
|2008|2013|-0.08|-0.08|-0.02|0.25|0.01|
|2008|2014|-0.16|-0.16|0.05|0.17|-0.03|

27
Table 5: Bangladesh <u>Decomposition of Aggregate Productivity Growth</u>

|**Bangl**|**adesh**||<br>SurvivingFirms|||
|---|---|---|---|---|---|
|**Dyna**<br>T=1|**mic OP**<br>T=2|Aggregate<br>Productivity|Δ Unweighted<br>Productivity<br>Δ<br>covariance|Entering<br>Firms|Exiting<br>Firms|
|1995|1997|0.13|-0.04<br>0.30|-0.35|0.21|
|1995|1999|0.01|-0.12<br>0.28|-0.21|0.06|
|1995|2001|0.33|-0.44<br>0.81|-0.19|0.16|
|**Bangl**|**adesh**|||||
|**Static**|**OP**|Aggregate|Unweighted|||
|T=1|T=2|Productivity|Productivity|Cov|ariance|
|2001|2005|0.46|0.22|0|.24|
|2001|2012|0.52|0.57|-0|.05|

28
# Table 6: <u>Decomposition of Aggregate Productivity Growth by Firm Size</u>

|||A|<br>**Plants wit**<br>Δ Uihd|<br>**h 10-49 Wor**|<br>**kers**<br>Ei|<br>Eii|<br>A|<br>**Plants with**<br>Δ Uihd|**50-199 Wo**|**rkers**<br>Ei|Eii|
|---|---|---|---|---|---|---|---|---|---|---|---|
|||ggregate|nwegte||nterng|xtng|ggregate|nwegte||nterng|xtng|
|Country|Year|Productivity|Productivity|Δ Cov|Firms|Firms|Productivity|Productivity|Δ Cov|Firms|Firms|
|Ethiopia|1996-2001|1.16|-0.20|0.29|1.18|-0.12|0.11|-0.34|0.02|0.34|0.09|
||1996-2006|0.48|0.35|-0.03|0.21|-0.05|0.44|-0.04|0.07|0.29|0.12|
|Côte d’Ivoire|2004-2009|-0.16|-0.13|0.08|0.41|-0.52|0.22|0.14|-0.27|0.48|-0.13|
||2004-2014|-0.65|-0.20|0.10|0.15|-0.70|0.21|-0.17|0.18|0.43|-0.23|
|Tanzania|2008-2012|0.47|0.23|0.27|0.27|-0.31|-1.43|-0.59|-1.37|0.15|0.37|
|Bangladesh|1995-2001|0.13|-0.29|-0.21|0.32|0.31|0.02|-0.37|-0.09|0.27|0.20|
||||**Plants with**<br>|**200-499 Wo**|**rkers**<br>|||**Plants with**<br>|**500+ Wor**|**kers**<br>||
|Country|Year|Aggregate<br>Productivity|Δ Unweighted<br>Productivity|Δ Cov|Entering<br>Firms|Exiting<br>Firms|Aggregate<br>Productivity|Δ Unweighted<br>Productivity|Δ Cov|Entering<br>Firms|Exiting<br>Firms|
|Ethiopia|1996-2001|-0.22|-0.14|-0.08|-0.13|0.013|0.30|-0.07|0.25|0.07|0.06|
||1996-2006|0.02|0.13|-0.14|0.23|-0.20|0.55|-0.02|0.43|0.13|-0.01|
|Côte d’Ivoire|2004-2009|-0.06|-0.06|-0.35|0.28|0.07|-0.18|-0.10|-0.17|-0.03|0.12|
||2004-2014|-0.48|.024|-0.08|-0.20|0.03|-1.47|-0.29|-0.14|-0.13|-0.91|
|Tanzania|2008-2012|-0.54|0.05|-0.73|-0.13|0.28|-0.34|0.19|-0.25|0.05|-0.33|
|Bangladesh|1995-2001|0.12|-0.04|-0.15|0.29|0.01|-0.06|0.06|0.27|-0.36|-0.03|

29
# Table 7: <u>Decomposition of Aggregate Productivity Growth by Industry</u>

||||<br>**Garme**<br>|<br>**nts & Textil**|<br>**es**<br>|<br>|<br>|<br>**Food &**<br>|**Beverage**<br>|**s**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||reate|nwete||ntern|xtn|reate|nwete|ov|ntern|xtn|
|Country|Year|<br>Productivity|<br>Productivity|Δ Cov|<br>Firms|<br>Firms|<br>Productivity|<br>Productivity||<br>Firms|<br>Firms|
|Ethiopia|1996-2001|0.25|-0.27|0.35|0.19|-0.03|0.01|-0.32|0.04|0.16|0.12|
||1996-2006|0.54|0.05|-0.07|0.56|-0.02|0.11|-0.17|0.26|-0.04|0.07|
|Côte d’Ivoire|2004-2009|-0.40|-0.52|0.04|0.09|-0.01|-0.24|-0.13|-0.32|0.05|0.16|
||2004-2014|-1.03|-1.26|0.00|-0.82|1.05|-1.25|-0.36|0.10|-0.42|0.56|
|Tanzania|2008-2012|-0.50|-0.14|-0.36|0.02|-0.03|-0.70|0.16|-0.93|0.05|0.03|
|Bangladesh|1995-2001|0.33|-0.44|0.81|-0.19|0.16|0.47|0.01|-0.28|0.64|0.10|
||||**F**<br>|**urniture**||||||||
|Country|Year|Aggregate<br>Productivity|Δ Unweighted<br>Productivity|ΔCov|Entering<br>Firms|Exiting<br>Firms||||||
|Ethiopia|1996-2001|-0.02|-0.47|0.72|-0.25|-0.02||||||
||1996-2006|0.36|0.59|0.55|-0.59|-0.20||||||
|Côte d’Ivoire|2004-2009|-0.24|-0.05|0.07|-0.31|0.05||||||
||2004-2014|0.19|-0.27|0.39|-0.25|0.32||||||
|Tanzania|2008-2012|-0.47|0.34|-0.36|0.00|-0.45||||||
|Bangladesh|1995-2001|-2.34|0.06|-2.25|0.58|-0.72||||||

30
<u>Table 8: Impact of Trade Exposure on Productivity: Ethiopia</u>

||<br>Plant-Level TFP<br>|<br>Plant-Level TFP<br>|
|---|---|---|
|Exporter|0.631<sup>***</sup><br>(0.134)|0.709<sup>***</sup><br>(0.140)|
|Exporter*2013|-0.231<br>(0.182)|-0.167<br>(0.185)|
|Exporter*2014|-0.0328<br>(0.178)|-0.111<br>(0.183)|
|Exiting firm|-0.215<sup>***</sup><br>(0.0438)|-0.197<sup>***</sup><br>(0.0448)|
|Exporter*Exiting Firm||-0.376<br>(0.203)|
|Year Fixed Effects|Yes|Yes|
|Industry Fixed Effects|Yes|Yes|
|Observations<br>|6,004|6,004|
|_R_<sup>2</sup>|0.113|0.114|

Standard errors in parentheses * _p_ < 0.05, ** _p_ < 0.01, *** _p_ < 0.001 31
|Table 9: Impact of Trade Exposure on Producti<br>|vity: Cote d'Ivoire<br>|
|---|---|
|Plant-Level TFP<br>|Plant-Level TFP<br>|
|Exporter<br>0.514<sup>***</sup>|0.489<sup>***</sup>|
|(0.0549)|(0.0603)|
|Exporter*2011<br>-0.0696|-0.0646|
|(0.218)|(0.218)|
|Exporter*2012<br>0.107|0.0962|
|(0.215)|(0.215)|
|Exporter*2013<br>0.109|0.0994|
|(0.220)|(0.220)|
|Exporter*2014<br>0.0635|0.0864|
|(0.228)|(0.229)|
|Exiting firm<br>-0.0246|-0.0409|
|(0.0397)|(0.0431)|
|Exporter*Exiting Firm|0.105<br>(0.109)|
|Year Fixed Effects<br>Yes|Yes|
|Industry Fixed Effects<br>Yes|Yes|
|Observations<br>6,515<br>|6,515|
|_R_<sup>2</sup><br>0.161|0.161|

Standard errors in parentheses * _p_ < 0.05, ** _p_ < 0.01, *** _p_ < 0.001 32
|Table 10: Impact of Trade Exposure on Produc|tivity: Tanzania|
|---|---|
|Plant-Level TFP<br>|Plant-Level TFP<br>|
|Exporter<br>0.539<sup>*</sup>|0.526<sup>*</sup>|
|(0.253)|(0.262)|
|Exporter*2009<br>-0.489|-0.497|
|(0.394)|(0.397)|
|Exporter*2010<br>-0.197|-0.192|
|(0.398)|(0.399)|
|Exporter*2011<br>0.252|0.251|
|(0.381)|(0.381)|
|Exporter*2012<br>-0.299|-0.286|
|(0.384)|(0.390)|
|Exiting firm<br>-0.0891|-0.101|
|(0.136)|(0.150)|
|Exporter*Exiting Firm|0.0699<br>(0.362)|
|Year Fixed Effects<br>Yes|Yes|
|Industry Fixed Effects<br>Yes|Yes|
|Observations<br>789<br>|789|
|_R_<sup>2</sup><br>0.095|0.095|

Standard errors in parentheses * _p_ < 0.05, ** _p_ < 0.01, *** _p_ < 0.001 33
34
<!-- Start of picture text --> Figure 1a: Ethiopia:<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>1996 432 1996 0.69<br>10-49 Workers 2006 795 10-49 Workers 2006 0.69<br>2016 2,131 2016 0.76<br>1996 109 1996 0.17<br>50-199 Workers 2006 250 50-199 Workers 2006 0.22<br>2016 434 2016 0.16<br>1996 42 1996 0.07<br>200-499 Workers 2006 65 200-499 Workers 2006 0.06<br>2016 150 2016 0.05<br>1996 40 1996 0.06<br>> 500 Workers 2006 43 > 500 Workers 2006 0.04<br>2016 76 2016 0.03<br>Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries, Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries,<br>1996, 2006, & 2016. 1996, 2006, & 2016. Shares may not add to one due to rounding errors.<br>Total Employment Employment Share<br>1996 7,461 1996 0.09<br>10-49 Workers 2006 14,809 10-49 Workers 2006 0.14<br>2016 36,820 2016 0.16<br>1996 11,973 1996 0.15<br>50-199 Workers 2006 23,431 50-199 Workers 2006 0.23<br>2016 43,789 2016 0.19<br>1996 13,808 1996 0.17<br>200-499 Workers 2006 20,650 200-499 Workers 2006 0.20<br>2016 44,957 2016 0.20<br>1996 48,838 1996 0.60<br>> 500 Workers 2006 43,765 > 500 Workers 2006 0.43<br>2016 102,405 2016 0.45<br>Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries, Source: Ethiopia Survey of Large & Medium Scale Manufacturing Industries,<br>1996, 2006, & 2016. 1996, 2006, & 2016. Shares may not add to one due to rounding errors<br><!-- End of picture text --> 35
<!-- Start of picture text --> Figure 1b: Côte d'Ivoire<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>2003 160 2003 0.47<br>10-49 Workers 2008 190 10-49 Workers 2008 0.50<br>2014 374 2014 0.60<br>2003 107 2003 0.31<br>50-199 Workers 2008 108 50-199 Workers 2008 0.28<br>2014 151 2014 0.24<br>2003 44 2003 0.13<br>200-499 Workers 20082014 4961 200-499 Workers 20082014 0.100.13<br>> 500 Workers 200320082014 323333 > 500 Workers 200320082014 0.050.090.09<br>Source: Côte d'Ivoire Registrar of Companies for the Modern Enterprise<br>Source: Côte d'Ivoire Registrar of Companies for the Modern EnterpriseSectors, 2003, 2008, & 2014. Sectors, 2003, 2008, & 2014. Shares may not sum to one due to roundingerrors<br>Total Employment Employment Share<br>2003 3,662 2003 0.05<br>10-49 Workers 2008 4,503 10-49 Workers 2008 0.06<br>2014 7,943 2014 0.09<br>2003 11,114 2003 0.16<br>50-199 Workers 2008 10,099 50-199 Workers 2008 0.13<br>2014 14,276 2014 0.16<br>2003 13,466 2003 0.19<br>200-499 Workers 20082014 14,07818,472 200-499 Workers 20082014 0.190.20<br>> 500 Workers 20032008 41,06446,198 > 500 Workers 200320082014 0.550.590.62<br>2014 50,682<br>Source: Côte d'Ivoire Registrar of Companies for the Modern Enterprise<br>Source: Côte d'Ivoire Registrar of Companies for the Modern EnterpriseSectors, 2003, 2008, & 2014. Sectors, 2003, 2008, & 2014. Shares may not sum to one due to roundingerrors<br><!-- End of picture text --> 36
<!-- Start of picture text --> Figure 1c: Tanzania<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>2008 422 2008 0.62<br>10-49 Workers 2013 682 10-49 Workers 2013 0.68<br>50-199 Workers 20082013 163 212 50-199 Workers 20082013 0.210.24<br>200-499 Workers 20082013 5467 200-499 Workers 20082013 0.070.08<br>> 500 Workers 20082013 3841 > 500 Workers 20082013 0.040.06<br>Source: Tanzania Annual Survey of Industrial Production, 2008 & Census<br>Source: Tanzania Annual Survey of Industrial Production, 2008 of Industrial Establishments, 2013. Shares may not sum to one due to<br>& Census of Industrial Establishments, 2013. rounding errors.<br>Total Employment Employment Share<br>2008 8,477 2008 0.08<br>10-49 Workers 2013 13,863 10-49 Workers 2013 0.13<br>50-199 Workers 20082013 15,94621,110 50-199 Workers 20082013 0.150.20<br>200-499 Workers 20082013 17,32221,386 200-499 Workers 20082013 0.160.20<br>> 500 Workers 20082013 51,374 65,643 > 500 Workers 20082013 0.48 0.61<br>Source: Tanzania Annual Survey of Industrial Production, 2008 & Census<br>Source: Tanzania Annual Survey of Industrial Production, 2008 of Industrial Establishments, 2013. Shares may not sum to one due to<br>& Census of Industrial Establishments, 2013. rounding errors.<br><!-- End of picture text --> 37
<!-- Start of picture text --> Figure 1e: Bangladesh<br>Firm Count & Employment by Firm Size<br>Number of Firms Share of Firms<br>1995 22,830 1995 0.79<br>10-49 Workers 2005 20,444 10-49 Workers 2005 0.74<br>2012 25,974 2012 0.62<br>1995 3,532 1995 0.12<br>50-199 Workers 2005 3,270 50-199 Workers 2005 0.12<br>2012 10,251 2012 0.25<br>1995 1,640 1995 0.06<br>200-499 Workers 2005 1,416 200-499 Workers 2005 0.05<br>2012 3,791 2012 0.09<br>1995 767 1995 0.03<br>> 500 Workers 2005 2,333 > 500 Workers 2005 0.08<br>2012 1,700 2012 0.04<br>Source: Census of Manufacturing Industries, Source: Census of Manufacturing Industries, 1995, 2005, & 2012. Shares may not<br>1995, 2005, & 2012. sum to one due to rounding errors.<br>Total Employment Employment Share<br>1995 384,460 1995 0.19<br>10-49 Workers 2005 419,730 10-49 Workers 2005 0.18<br>2012 573,713 2012 0.12<br>1995 361,069 1995 0.18<br>50-199 Workers 2005 297,136 50-199 Workers 2005 0.13<br>2012 1,043,740 2012 0.21<br>1995 520,058 1995 0.26<br>200-499 Workers 2005 462,905 200-499 Workers 2005 0.20<br>2012 1,100,902 2012 0.22<br>1995 751,051 1995 0.37<br>> 500 Workers 2005 1,168,044 > 500 Workers 2005 0.50<br>2012 2,264,091 2012 0.45<br>Source: Census of Manufacturing Industries, Source: Census of Manufacturing Industries, 1995, 2005, & 2012. Shares may not<br>1995, 2005, & 2012. sum to one due to rounding errors.<br><!-- End of picture text --> 38
# Table 2a: Ethiopia:

Firm Count by Industry <!-- Start of picture text --> Number of Firms Number of Firms<br>1996 2016<br>Tobacco 1 Petroleum 1<br>Elec. Machinery 1 Recycling 1<br>Tobacco 2<br>Basic Metal 3 Precision Instr. 3<br>Paper 5 Media Equip. 10<br>Vehicles 11 Vehicles 10<br>Machinery 14 Elec. Machinery 12<br>Rubber & Plastics 15 MachineryPaper 2024<br>Garments 23 Basic Metal 63<br>Wood, not furn 26 Wood, not furn 64<br>Textiles 32 Leather 66<br>Chemicals 89<br>Chemicals 35<br>Garments 105<br>Printing 38 Printing 107<br>Fab. metal 39 Fab. metal 174<br>Leather 63 Rubber & Plastics 186<br>Furniture 75 Textiles 258<br>Furniture 395<br>Non-metallic metal 82 Non-metallic metal 533<br>Food & Bev 160 Food & Bev 668<br>0 100 200 300 400 500 600 700 0 100 200 300 400 500 600 700<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> Table 3a: Ethiopia: <!-- Start of picture text --> Firm Shares by Industry<br>Share of Firms Share of Firms<br>1996 2016<br>Tobacco 0.2 Petroleum 0.0<br>Elec. Machinery 0.2 Recycling 0.0<br>Tobacco 0.1<br>Basic Metal 0.5 Precision Instr. 0.1<br>Paper 0.8 Media Equip. 0.4<br>Vehicles 1.8 Vehicles 0.4<br>Machinery 2.2 Elec. Machinery 0.4<br>Rubber & Plastics 2.4 Machinery 0.7<br>Paper 0.9<br>Garments 3.7 Basic Metal 2.3<br>Wood, not furn 4.2 Wood, not furn 2.3<br>Textiles 5.1 Leather 2.4<br>Chemicals 3.2<br>Chemicals 5.6<br>Garments 3.8<br>Printing 6.1 Printing 3.8<br>Fab. metal 6.3 Fab. metal 6.2<br>Leather 10.1 Rubber & Plastics 6.7<br>Furniture 12.0 Textiles 9.2<br>Furniture 14.2<br>Non-metallic metal 13.2 Non-metallic metal 19.1<br>Food & Bev 25.7 Food & Bev 23.9<br>0 5 10 15 20 25 30 0 5 10 15 20 25 30<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> 39
# Table 2b: Côte d'Ivoire

Firm Count by Industry <!-- Start of picture text --> Number of Firms Number of Firms<br>2003 2014<br>Tobacco 2 Tobacco 2<br>Paper 5 Paper 4<br>Other Transport 7 Other Transport 5<br>Textiles 9 Furniture 7<br>Non-metallic metal 10 Non-metallic metal 10<br>Leather 12 Textiles 12<br>Petroleum 12 Petroleum 26<br>Electrical Machinery 12 Leather 35<br>Furniture 13 Electrical Machinery 45<br>Fabricated metal 31 Chemicals 54<br>Chemicals 35 Rubber & Plastics 55<br>Printing 37 Fabricated metal 57<br>Rubber & Plastics 41 Wood, not Furniture 69<br>Wood, not Furniture 54 Printing 70<br>Food & Bev 63 Food & Bev 168<br>0 50 100 150 200 0 50 100 150 200<br>Graphs by year Graphs by year<br><!-- End of picture text -->

# Table 3b: Côte d'Ivoire

Firm Shares by Industry <!-- Start of picture text --> Share of Firms Share of Firms<br>2003 2014<br>Tobacco 0.6 Tobacco 0.3<br>Paper 1.5 Paper 0.6<br>Other Transport 2.0 Other Transport 0.8<br>Textiles 2.6 Furniture 1.1<br>Non-metallic metal 2.9 Non-metallic metal 1.6<br>Leather 3.5 Textiles 1.9<br>Petroleum 3.5 Petroleum 4.2<br>Electrical Machinery 3.5 Leather 5.7<br>Furniture 3.8 Electrical Machinery 7.3<br>Fabricated metal 9.0 Chemicals 8.7<br>Chemicals 10.2 Rubber & Plastics 8.9<br>Printing 10.8 Fabricated metal 9.2<br>Rubber & Plastics 12.0 Wood, not Furniture 11.1<br>Wood, not Furniture 15.7 Printing 11.3<br>Food & Bev 18.4 Food & Bev 27.1<br>0 5 10 15 20 0 10 20 30<br>Graphs by year Graphs by year<br><!-- End of picture text --> 40
<!-- Start of picture text --> Table 2c: Tanzania<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count by Industry<br>Number of Firms Number of Firms<br>2008 2013<br>Garments 1 Precision Instr. 1<br>Elec. Machinery 1 Leather 2<br>Vehicles 1 Oth. Transport 2<br>Leather 3 Tobacco 4<br>Oth. Transport 3 Garments 4<br>Tobacco 4 Elec. Machinery 7<br>Media Equip. 5 Basic Metal 14<br>Precision Instr.Paper 57 PetroleumPaper 1415<br>Basic MetalPetroleum 77 Media Equip. 21<br>Rubber & PlasticsMachinery 1315 ChemicalsMachinery 3233<br>Wood, not furn 17 Rubber & Plastics 43<br>Non-metallic metal 26 Textiles 45<br>Chemicals 30 Printing 46<br>Fab. metal 31 Fab. metal 54<br>Textiles 40 Wood, not furn 68<br>Printing 51 Furniture 76<br>Furniture 177 Non-metallic metal 88<br>Food & Bev 236 Food & Bev 430<br>0 100 200 300 400 500 0 100 200 300 400 500<br>Graphs by year Graphs by year<br><!-- End of picture text --> Table 3c: Tanzania Firm Shares by Industry <!-- Start of picture text --> Share of Firms Share of Firms<br>2008 2013<br>Garments 0.1 Precision Instr. 0.1<br>Elec. Machinery 0.1 Leather 0.2<br>Vehicles 0.1 Oth. Transport 0.2<br>Leather 0.4 Tobacco 0.4<br>Oth. Transport 0.4 Garments 0.4<br>Tobacco 0.6 Elec. Machinery 0.7<br>Media Equip. 0.7 Basic Metal 1.4<br>Precision Instr.Paper 0.71.0 PetroleumPaper 1.41.5<br>Basic MetalPetroleum 1.01.0 Media Equip. 2.1<br>Chemicals 3.2<br>Rubber & PlasticsMachinery 1.92.2 Machinery 3.3<br>Wood, not furn 2.5 Rubber & Plastics 4.3<br>Non-metallic metal 3.8 Textiles 4.5<br>Chemicals 4.4 Printing 4.7<br>Fab. metal 4.6 Fab. metal 5.4<br>Textiles 5.9 Wood, not furn 6.8<br>Printing 7.5 Furniture 7.6<br>Furniture 26.0 Non-metallic metal 8.8<br>Food & Bev 34.7 Food & Bev 43.1<br>0 5 10 15 20 25 30 35 40 45 50 0 5 10 15 20 25 30 35 40 45 50<br>Graphs by year Graphs by year<br><!-- End of picture text --> 41
<!-- Start of picture text --> Table 2e: Bangladesh<br><!-- End of picture text --> <!-- Start of picture text --> Firm Count by Industry<br>Number of Firms Number of Firms<br>2001 2012<br>Petroleum 10 Office Equipment 13<br>Precision Instruments 32 Petroleum 19<br>Media Equipments 40 Media Equipments 136<br>Vehicles 63 Vehicles 137<br>Other Transport 110 Furniture 235<br>Electrical Machinery 115 Other Transport 276<br>Paper 127 Wood, not Furniture 298<br>Machinery 162 Machinery 461<br>Basic Metal 189 Tobacco 487<br>Tobacco 206 Electrical Machinery 738<br>Wood, not Furniture 250 Paper 902<br>Rubber & Plastics 355 Printing 904<br>Leather 368 Leather 930<br>Fabricated metal 531 Rubber & Plastics 1,036<br>Chemicals 596 Chemicals 1,057<br>Printing 848 Basic Metal 1,205<br>Furniture 1,255 Fabricated metal 1,449<br>Non-metallic metal 2,271 Non-metallic metal 4,652<br>Garments 3,654 Garments 4,670<br>Food & Bev 5,416 Food & Bev 8,805<br>Textiles 6,841 Textiles 13,297<br>0 5,000 10,000 15,000 0 5,000 10,000 15,000<br>Graphs by year Graphs by year<br><!-- End of picture text --> Table 3e: Bangladesh <!-- Start of picture text --> Firm Shares by Industry<br>Share of Firms Share of Firms<br>2001 2012<br>Petroleum 0.0 Office Equipment 0.0<br>Precision Instruments 0.1 Petroleum 0.0<br>Media Equipments 0.2 Media Equipments 0.3<br>Vehicles 0.3 Vehicles 0.3<br>Other Transport 0.5 Furniture 0.6<br>Electrical Machinery 0.5 Other Transport 0.7<br>Paper 0.5 Wood, not Furniture 0.7<br>Machinery 0.7 Machinery 1.1<br>Basic Metal 0.8 Tobacco 1.2<br>Tobacco 0.9 Electrical Machinery 1.8<br>Wood, not Furniture 1.1 Paper 2.2<br>Rubber & Plastics 1.5 Printing 2.2<br>Leather 1.6 Leather 2.2<br>Fabricated metal 2.3 Rubber & Plastics 2.5<br>Chemicals 2.5 Chemicals 2.5<br>Printing 3.6 Basic Metal 2.9<br>Furniture 5.4 Fabricated metal 3.5<br>Non-metallic metal 9.7 Non-metallic metal 11.2<br>Garments 15.6 Garments 11.2<br>Food & Bev 23.1 Food & Bev 21.1<br>Textiles 29.2 Textiles 31.9<br>0 10 20 30 0 10 20 30<br>Graphs by year Graphs by year<br><!-- End of picture text --> 42
Table 4a: Ethiopia: Employment by Industry <!-- Start of picture text --> Industry Employment Industry Employment<br>1996 2016<br>Elec. Machinery 88 Petroleum 0<br>Machinery 371 Recycling 0<br>Precision Instr. 60<br>Vehicles 546 Machinery 552<br>Tobacco 965 Tobacco 570<br>Basic Metal 1,082 Media Equip. 805<br>Paper 1,204 Paper 1,969<br>Rubber & Plastics 1,853 Elec. Machinery 2,193<br>Wood, not furn 2,479<br>Fab. metal 1,902 Basic Metal 5,390<br>Wood, not furn 2,217 Printing 6,777<br>Furniture 2,240 Vehicles 6,791<br>Fab. metal 9,046<br>Chemicals 2,403<br>Garments 12,134<br>Printing 3,875 Furniture 12,388<br>Garments 3,959 Chemicals 12,434<br>Non-metallic metal 4,345 Leather 13,361<br>Leather 7,285 Rubber & Plastics 20,481<br>Textiles 26,085<br>Food & Bev 20,563 Non-metallic metal 42,942<br>Textiles 27,181 Food & Bev 51,514<br>0 20,000 40,000 60,000 0 20,000 40,000 60,000<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> Table 5a: Ethiopia: Employment Shares by Industry <!-- Start of picture text --> Employment Share Employment Share<br>1996 2016<br>Elec. Machinery 0.1 Petroleum 0.0<br>Machinery 0.5 Recycling 0.0<br>Precision Instr. 0.0<br>Vehicles 0.7 Machinery 0.2<br>Tobacco 1.2 Tobacco 0.3<br>Basic Metal 1.3 Media Equip. 0.4<br>Paper 1.5 Paper 0.9<br>Rubber & Plastics 2.3 Elec. MachineryWood, not furn 1.01.1<br>Fab. metal 2.3 Basic Metal 2.4<br>Wood, not furn 2.7 Printing 3.0<br>Furniture 2.7 Vehicles 3.0<br>Fab. metal 4.0<br>Chemicals 2.9<br>Garments 5.3<br>Printing 4.7 Furniture 5.4<br>Garments 4.8 Chemicals 5.5<br>Non-metallic metal 5.3 Leather 5.9<br>Leather 8.9 Rubber & Plastics 9.0<br>Textiles 11.4<br>Food & Bev 25.1 Non-metallic metal 18.8<br>Textiles 33.1 Food & Bev 22.6<br>0 10 20 30 40 0 5 10 15 20 25<br>Graphs by Year Graphs by Year<br><!-- End of picture text --> 43
<!-- Start of picture text --> Table 4b: Côte d'Ivoire<br><!-- End of picture text --> <!-- Start of picture text --> Employment by Industry<br>Industry Employment Industry Employment<br>2003 2014<br>Tobacco 313 Tobacco 295<br>Machinery 576 Furniture 432<br>Non-metallic metal 705 Paper 488<br>Other Transport 719 Other Transport 584<br>Paper 1,162 Non-metallic metal 1,522<br>Furniture 1,355 Machinery 2,557<br>Printing 1,936 Leather 3,249<br>Leather 2,347 Printing 3,403<br>Fabricated metal 2,805 Textiles 3,933<br>Rubber & Plastics 4,264 Fabricated metal 5,691<br>Textiles 4,918 Wood, not Furniture 10,118<br>Chemicals 5,105 Chemicals 12,204<br>Wood, not Furniture 13,140 Rubber & Plastics 13,257<br>Petroleum 14,329 Petroleum 13,324<br>Food & Bev 15,632 Food & Bev 20,316<br>0 5,000 10,000 15,000 20,000 25,000 30,000 0 5,000 10,00015,000 20,000 25,000 30,000<br>Graphs by AN_EXO Graphs by AN_EXO<br><!-- End of picture text --> Table 5b: Côte d'Ivoire <!-- Start of picture text --> Employment Shares by Industry<br>Employment Share Employment Share<br>2003 2014<br>Tobacco 0.5 Tobacco 0.3<br>Machinery 0.8 Furniture 0.5<br>Non-metallic metal 1.0 Paper 0.5<br>Other Transport 1.0 Other Transport 0.6<br>Paper 1.7 Non-metallic metal 1.7<br>Furniture 2.0 Machinery 2.8<br>Printing 2.8 Leather 3.6<br>Leather 3.4 Printing 3.7<br>Fabricated metal 4.0 Textiles 4.3<br>Rubber & Plastics 6.2 Fabricated metal 6.2<br>Textiles 7.1 Wood, not Furniture 11.1<br>Chemicals 7.4 Chemicals 13.4<br>Wood, not Furniture 19.0 Rubber & Plastics 14.5<br>Petroleum 20.7 Petroleum 14.6<br>Food & Bev 22.6 Food & Bev 22.2<br>0 5 10 15 20 25 0 5 10 15 20 25<br>Graphs by AN_EXO Graphs by AN_EXO<br><!-- End of picture text --> 44
<!-- Start of picture text --> Table 4c: Tanzania<br><!-- End of picture text --> <!-- Start of picture text --> Employment by Industry<br>Industry Employment Industry Employment<br>2008 2013<br>Elec. Machinery 39 Precision Instr. 13<br>Oth. Transport 44 Leather 33<br>Garments 46 Garments 193<br>Vehicles 65 Oth. Transport 199<br>Precision Instr. 89 Elec. Machinery 900<br>Media Equip. 367 Media Equip. 1,064<br>Leather 593 Petroleum 1,376<br>Wood, not furnBasic Metal 779818 Machinery 1,720<br>Basic Metal 2,174<br>Petroleum 1,140<br>Fab. metal 2,189<br>MachineryPaper 1,2332,026 Paper 2,303<br>Fab. metal 2,395 Printing 2,773<br>Non-metallic metal 2,560 Wood, not furn 3,769<br>Printing 3,531 Rubber & Plastics 4,243<br>Chemicals 3,547 Furniture 4,447<br>Rubber & Plastics 4,125 Non-metallic metal 4,548<br>Tobacco 6,747 Tobacco 5,061<br>Textiles 11,983 Chemicals 6,026<br>Furniture 15,623 Textiles 17,810<br>Food & Bev 49,638 Food & Bev 46,889<br>0 20,000 40,000 60,000 0 20,000 40,000 60,000<br>Graphs by year Graphs by year<br><!-- End of picture text -->

# Table 5c: Tanzania

<!-- Start of picture text --> Employment Shares by Industry<br>Employment Share Employment Share<br>2008 2013<br>Elec. Machinery 0.0 Precision Instr. 0.0<br>Oth. Transport 0.0 Leather 0.0<br>Garments 0.0 Garments 0.2<br>Vehicles 0.1 Oth. Transport 0.2<br>Precision Instr. 0.1 Elec. Machinery 0.8<br>Media Equip. 0.3 Media Equip. 1.0<br>Leather 0.6 Petroleum 1.3<br>Wood, not furnBasic Metal 0.70.8 Basic MetalMachinery 1.62.0<br>Petroleum 1.1 Fab. metal 2.0<br>Fab. metalMachineryPaper 1.11.92.2 PrintingPaper 2.12.6<br>Non-metallic metal 2.4 Wood, not furn 3.5<br>Printing 3.3 Rubber & Plastics 3.9<br>Chemicals 3.3 Furniture 4.1<br>Rubber & Plastics 3.8 Non-metallic metal 4.2<br>Tobacco 6.3 Tobacco 4.7<br>Textiles 11.2 Chemicals 5.6<br>Furniture 14.5 Textiles 16.5<br>Food & Bev 46.2 Food & Bev 43.5<br>0 10 20 30 40 50 0 10 20 30 40 50<br>Graphs by year Graphs by year<br><!-- End of picture text --> 45
<!-- Start of picture text --> Table 4e: Bangladesh<br>Employment by Industry<br>Industry Employment Industry Employment<br>2001 2012<br>Precision Instruments 469 Office Equipment 1,027<br>Petroleum 557 Petroleum 1,417<br>Media Equipments 1,826 Vehicles 4,906<br>Machinery 5,535 Wood, not Furniture 8,495<br>Wood, not Furniture 5,957 Furniture 9,471<br>Vehicles 6,473 Media Equipments 15,363<br>Other Transport 10,010 Other Transport 17,921<br>Paper 11,288 Machinery 18,620<br>Electrical Machinery 12,281 Printing 26,667<br>Rubber & Plastics 12,428 Electrical Machinery 37,495<br>Basic Metal 13,119 Rubber & Plastics 41,139<br>Fabricated metal 15,562 Paper 42,376<br>Leather 29,474 Fabricated metal 44,462<br>Furniture 45,810 Tobacco 52,204<br>Tobacco 54,596 Leather 75,524<br>Printing 88,377 Basic Metal 120,965<br>Chemicals 152,493 Chemicals 123,978<br>Food & Bev 211,746 Food & Bev 300,675<br>Non-metallic metal 230,807 Non-metallic metal 471,816<br>Textiles 511,591 Textiles 1,715,008<br>Garments 1,352,008 Garments 1,852,834<br>0 750,000 1,500,000 2,250,000 0 750,000 1,500,000 2,250,000<br>Graphs by year Graphs by year<br><!-- End of picture text --> Table 5e: Bangladesh Employment Shares by Industry <!-- Start of picture text --> Employment Share Employment Share<br>2001 2012<br>Precision Instruments 0.0 Office Equipment 0.0<br>Petroleum 0.0 Petroleum 0.0<br>Media Equipments 0.1 Vehicles 0.1<br>Machinery 0.2 Wood, not Furniture 0.2<br>Wood, not Furniture 0.2 Furniture 0.2<br>Vehicles 0.2 Media Equipments 0.3<br>Other Transport 0.4 Other Transport 0.4<br>Paper 0.4 Machinery 0.4<br>Electrical Machinery 0.4 Printing 0.5<br>Rubber & Plastics 0.4 Electrical Machinery 0.8<br>Basic Metal 0.5 Rubber & Plastics 0.8<br>Fabricated metal 0.6 Paper 0.9<br>Leather 1.1 Fabricated metal 0.9<br>Furniture 1.7 Tobacco 1.0<br>Tobacco 2.0 Leather 1.5<br>Printing 3.2 Basic Metal 2.4<br>Chemicals 5.5 Chemicals 2.5<br>Food & Bev 7.6 Food & Bev 6.0<br>Non-metallic metal 8.3 Non-metallic metal 9.5<br>Textiles 18.5 Textiles 34.4<br>Garments 48.8 Garments 37.2<br>0 10 20 30 40 50 0 10 20 30 40<br>Graphs by year Graphs by year<br><!-- End of picture text --> 46