metadata: authors: 0: author: Paul,Saumik
1: author: Raju,Dhushyanth
count: Ghana
docna: 0: docna: Sectoral Productivity Shock, Regional
            Differences in Intersectoral Linkages, and Structural
            Transformation in Ghana
docty: Policy Research Working Paper
abstracts: cdata!: This paper studies the effect of a local
            sectoral productivity shock on subnational structural
            transformation. The analysis is based on regional
            input-output tables constructed for 2004 and 2013 and
            available censuses of firms in 2003 and 2013 for Ghana.
            Based on the data, the analysis confirms the occurrence of a
            mining productivity shock. Between 2004 and 2013, mining
            grew dramatically as a share of gross domestic product. The
            mining shock occurred primarily in the south of Ghana with
            much larger increases in mining&#x2019;s share in
            regional output, the number of mining firms, and mining
            employment than in the north of the country. The findings
            show that the mining productivity shock led to growing
            regional (north-south) differences in intersectoral
            linkages, with greater intermediate use of mining output and
            a larger sectoral total factor productivity ratio between
            mining and manufacturing in the south than in the north.
            Informed by international evidence of strong intersectoral
            linkages between mining and heavy manufacturing industries,
            the paper examines the performance of heavy manufacturing in
            response to the mining productivity shock. The elasticity of
            heavy manufacturing to mining employment growth is 50
            percent larger in the south than in the north, generated by
            an increase in both average firm employment and the entry of
            new firms. These north-south differences are interpreted as
            possibly due to weak interregional production linkages.
display_title: Sectoral Productivity Shock,
            Regional Differences in Intersectoral Linkages, and
            Structural Transformation in Ghana
listing_relative_url: /research/2023/05/34063503/sectoral-productivity-shock-regional-differences-intersectoral-linkages-structural-transformation-ghana
topicv3: Structural Transformation for Growth
            m1326245 1089
publishtoextweb_dt: 2023-05-16T00:00:00Z
dois: 10.1596/1813-9450-10446
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 923431
1: author_key: 237905
model_extractions: Policy Research Working Paper 10446 Sectoral Productivity Shock, Regional Differences in Intersectoral Linkages, and Structural Transformation in Ghana _Saumik Paul Dhushyanth Raju_ Social Protection and Jobs Global Practice May 2023
Policy Research Working Paper 10446

# **Abstract**

This paper studies the effect of a local sectoral productivity shock on subnational structural transformation. The analysis is based on regional input-output tables constructed for 2004 and 2013 and available censuses of firms in 2003 and 2013 for Ghana. Based on the data, the analysis confirms the occurrence of a mining productivity shock. Between 2004 and 2013, mining grew dramatically as a share of gross domestic product. The mining shock occurred primarily in the south of Ghana with much larger increases in mining’s share in regional output, the number of mining firms, and mining employment than in the north of the country. The findings show that the mining productivity shock led to growing regional (north-south) differences in intersectoral linkages, with greater intermediate use of mining output and a larger sectoral total factor productivity ratio between mining and manufacturing in the south than in the north. Informed by international evidence of strong intersectoral linkages between mining and heavy manufacturing industries, the paper examines the performance of heavy manufacturing in response to the mining productivity shock. The elasticity of heavy manufacturing to mining employment growth is 50 percent larger in the south than in the north, generated by an increase in both average firm employment and the entry of new firms. These north-south differences are interpreted as possibly due to weak interregional production linkages.

This paper is a product of the Social Protection and Jobs Global Practice. 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 at saumik.paul@newcastle.ac.uk and draju2@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
Sectoral Productivity Shock, Regional Differences in Intersectoral Linkages, and Structural Transformation in Ghana* Saumik Paul<sup>1</sup> Dhushyanth Raju<sup>2</sup> _Keywords_ : Structural transformation, intersectoral linkages, propagation of productivity shock, subnational areas, mining, Ghana _JEL codes_ : L14, L22, O11, O14, R11, R15 1 Newcastle University and IZA, saumik.paul@newcastle.ac.uk. 2

> World Bank, draju2@worldbank.org.

> * This paper is a substantially revised version of a previous paper titled “Regional Differences in Intersectoral Linkages and Diverse Patterns of Structural Transformation,” published as IZA Discussion Paper 15461. We thank Emanuele Ferrari for guidance on the construction of input-output tables from supply and use tables for Ghana. We also thank Al-Mouksit Akim, Andrew Dabalen, Giorgio Fazio, K. N. Harilal, Marco Lorusso, Sunil Mani, Rayaprolu Nagraj, M. Parameswaran, and seminar participants at the Centre for Development Studies (Trivandrum, India), Newcastle University, and the Centre for the Study of African Economies (CSAE) Conference 2023 on Economic Development in Africa (Oxford University, UK) for helpful comments.
# **1. Introduction**

Local sectoral productivity shocks can lead to fluctuations in economic activity at various levels. The literature on production networks describes conditions by which intersectoral and interregional linkages can help to propagate a local sectoral productivity shock to the aggregate level (Acemoglu et al. 2012; Oberfield 2018; Carvalho et al. 2021; Caliendo et al. 2018; among others).<sup>1</sup> Many low-income countries have weak linkages across subnational regions, presumably due to the high cost of doing trade (WTO 2018; World Bank 2022).<sup>2</sup> As the existing literature on production networks is dominated by research on high-income countries, it offers little insight into the propagation of local sectoral productivity shocks in the presence of weak interregional linkages. Weak interregional linkages could restrict the propagation of a local sectoral productivity shock in one region to other regions, and thus to the aggregate level. In line with this thinking, we seek to understand if a sector- and regionspecific productivity shock produces regional differences in intersectoral linkages, and hence leads to regional differences in the sectoral composition of economic activities. In this study, we examine how a positive mining shock alters the course of regional structural transformation in Ghana.

One indication of the mining shock can be seen in the dramatic increase in the sector’s contribution to the country’s export earnings: In Ghana, mining products as a share of export earnings rocketed from 10 percent in 2006 to 70 percent in 2018 (The Growth Lab at Harvard University). Among the country’s diverse mineral resources, gold and more recently, oil and natural gas, have accounted for the bulk of output. As Ghana’s oldest extraction industry, gold mining dates back to the mid-15<sup>th</sup> century, but commercial production started only after 2003. By 2017, Ghana had become the second-largest producer of gold in Sub-Saharan Africa, accounting for 4 percent of global production (Corathers 2019). Gold has also made up a growing share of the country’s export earnings, increasing from 4.3 percent in 2006 to 40 percent in 2018. In 2010, Ghana emerged as a new oil and gas producer in Sub-Saharan Africa (OEF 2019), with cumulative oil production surging from 1.2 million barrels in 2010 to 508.4 million barrels in 2021 (PIAC 2021). Petroleum products have steadily increased as a share of Ghana’s export earnings, rising from 17 percent in 2012 to nearly 25 percent in 2018.

> 1 A growing number of studies also examines the effect of sectoral productivity shock on economic outcomes at the local level (for example, see Kline and Moretti 2014; Feyrer et al. 2017; Toews and Vezina 2020).

> 2 Trade costs (due to tariffs, transport, infrastructure, and logistic hurdles) are five times higher in Sub-Saharan Africa than in high-income countries (WTO 2018).

2
Notably, the intensification of mining activities has primarily occurred in the south of Ghana. Between 2003 and 2013, gold mines spread from a handful of districts in the Ashanti region to many districts in the Ashanti, Western, and Central regions in the south. Ghana’s oil and gas fields are also located off the south coast. Between 2003 and 2013, the increase in the number of mining firms and in mining employment in the south is larger than in the north by 14 and 62 times, respectively.

We test two predictions related to the positive mining shock in Ghana, by combining the literature on production networks (Acemoglu et al. 2012; Carvalho and Tahbaz-Salehi 2019) with that on mining-led local development (Feyrer et al. 2017; Toews and Vezina 2020). First, we examine whether the mining output shock that primarily occurred in the south of Ghana led to a growing regional (north-south) difference in intersectoral linkages between 2003 and 2013. Second, we examine whether regional differences in intersectoral linkages following the mining shock led to regional differences in the sectoral composition of regional output during the same period.

To estimate regional differences in intersectoral linkages and the composition of sectoral outputs, we use statistics at the sector level (output, productivity, and employment elasticities in other sectors with respect to mining) and at the firm level (rate of firm entry and average firm-level employment in heavy manufacturing industries). Region-level sectoral data come from aggregate and regional five-sector input-output (I-O) tables for 2004 and 2013 that we construct from supply and use tables for Ghana. The five sectors are agriculture, mining, “other industry” (that is, industrial subsectors other than mining), wholesale and retail trade (WRT) services, and “other services” (that is, services subsectors other than WRT services). For firm-level data, we use two rounds of censuses of firms for Ghana: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).

We examine the spatial and temporal pattern of the mining shock by exploiting the variation in the intensity of mining activities between the north and the south and between 2003 and 2013. Differences in regional intersectoral linkages are measured using the I-O tables for the north and the south in 2004 and 2013. Between 2004 and 2013, mining’s output as a share of gross domestic product (GDP) or the Domar weight increased by 16 percentage points, and the increase in the Domar weight for mining was 1.7 times larger in the south than in the north. The sectoral total factor productivity (TFP) ratio between mining and “other industry” in the south (0.532) is more than twice the size of that in the north (0.204). We also find larger growth in the share of intermediate use in mining and the downstreamness index 3
for mining in the south than in the north. Input downstreamness is measured as the average distance from primary inputs suppliers (Antras et al. 2012). Overall, we find stronger intersectoral linkages with mining in the south than in the north.

For the second prediction, i.e., whether regional differences in intersectoral linkages led to regional differences in sectoral composition of regional output between 2003 and 2013, to identify the counterfactual, we distinguish between heavy manufacturing industries (chemicals, machinery, metals, and nonmetallic minerals) and light manufacturing industries (such as food, textiles, paper, and wood). Based on world input-output data for 43 countries, we find that heavy manufacturing and mining show strong intersectoral linkages, as more than 85 percent of mining intermediate inputs in manufacturing are used in heavy manufacturing. We test if employment growth in heavy manufacturing industries occurs in mining districts (districts with at least one mining firm). The elasticity of employment for heavy manufacturing with respect to employment in mining (an elasticity of 0.31) is almost 50 percent larger than the corresponding elasticity for light manufacturing (0.21).

In our subsequent empirical model, the intensity of intersectoral networks is identified jointly by the location of a mining firm and the year in which it was established. We interpret the effect of the mining shock on intersectoral linkages as causal if the contemporaneous and lagged effects are positive and strong, and lead effects (placebo) are weak and negative. Constructing a district-year panel with 119 districts and 24 years (1990–2013) based on data on the location and the year of establishment of a firm collected in the 2014 IBES, we find stronger intersectoral linkages between mining and heavy manufacturing in the south than in the north. The relationship between mining and heavy manufacturing firms in the south appears to be causal, based on the placebo tests.

Finally, we test if average firm-level employment (in different sectors) is higher in mining districts in the south and if average employment in a manufacturing firm is higher in 2013 (compared to 2003) if it is located in a mining district in the south. On average, employment size in heavy manufacturing located in a mining district in the south (compared to the rest) is higher by 23.2 percent. We also find that average employment size of heavy manufacturing firms grew by more than three times in mining districts in the south between 2003 and 2013 (compared to light manufacturing firms). Overall, the variation in the mining employment shock across districts explains employment growth particularly in heavy manufacturing in the south. The evidence strongly suggests that this process of structural transformation was facilitated by the entry of new firms in heavy manufacturing in mining 4
districts as well as an increase in average employment in heavy manufacturing firms in mining districts.

This study contributes to three broad strands of the literature. First, we contribute to the literature on the drivers of structural transformation. Structural transformation is broadly understood as a process driven by both demand-side factors (Kongsamut et al. 2001; Comin et al. 2017) and supply-side factors (Baumol 1967; Ngai and Pissarides 2007). A handful of studies examine the spatial disparity in structural transformation and show that it is linked to the geographic mobility of workers (Caselli and Coleman II 2001; Allen and Arkolakis 2014; Desmet and Rossi-Hansberg 2014). At the same time, interest has grown in understanding the role of changes in input-output flows behind the process of structural transformation (Herrendorf and Valentinyi 2012; Herrendorf et al. 2013; Caliendo et al. 2018; Liu 2019; Fadinger et al 2022). We combine these two subfields to show that changes in the pattern of input-output flows resulting from local sectoral productivity shocks could produce a diverging process of structural transformation across regions.

Second, we contribute to a growing body of research on the roles of intersectoral and interregional linkages governing the propagation of sectoral productivity shocks to the aggregate level (Acemoglu et al. 2012; Atalay 2017; Caliendo et al. 2018; Carvalho et al. 2021). As noted earlier, given that the existing literature on production networks tends to be focused on high-income countries, and takes as an analytical starting point the economic structure and performance of these countries, such studies offer little insight into the propagation mechanisms for low-income countries where trade costs can be much higher than in high-income countries. We show that a local sectoral productivity shock could lead to regional differences in intersectoral linkages and in the pattern of structural transformation. Although we do not provide direct evidence, we interpret this result as possibly due to weak interregional linkages limiting the scope of sectoral productivity shocks amplifying from one region to another in the context of low-income countries.

Finally, we contribute to the literature focusing on the role that mining can play in development in Ghana. Like many other countries in Sub-Saharan Africa, Ghana is blessed with diverse natural resources. Following decades of deindustrialization and growthinhibiting structural transformation, the country’s emergence as an oil and gas producer in 2010 coincided with a national industrialization drive (Fosu 2017; Osei et al. 2020; Paul and Raju 2021, PIAC 2021). The prospects of place-based development through mining (Aragon and Rud 2013; Fafchamps et al. 2017) and the socioeconomic benefits arising from stronger intersectoral linkages between mining and other sectors have already been pointed out by 5
Aryeetey et al. (2014). We make further refinements to this line of research by underscoring the need for policies that could propel aggregate productivity from growing north-south differences in intersectoral linkages and regional specialization.

The remainder of the paper is organized as follows. Section 2 provides an overview of the mining productivity shock and national and regional trends in structural transformation for Ghana. Section 3 discusses how the mining productivity shock in the south of Ghana leads to growing regional differences in the pattern of intersectoral linkages based on regional I-O tables. Section 4 shows how spatial differences in intersectoral linkages affect sectoral output and productivity based on census data for firms. Section 5 concludes.

# **2. Overview of Mining Shock and Structural Transformation in Ghana**

We begin with a brief account of the recent mining productivity shock in Ghana, followed by an overview of the annual and spatial trends in mining and manufacturing based on statistics from the GGDC-UNU-WIDER economic transformation database (ETD)<sup>3</sup> and district-level data from two censuses on Ghana firms: the 2003 National Industrial Census (NIC) and the 2014 Integrated Business Establishment Survey (IBES).<sup>4</sup> The 2003 NIC and 2014 IBES were administered by the Ghana Statistical Service (GSS). The 2003 NIC covered only industrial sectors (mining, manufacturing, public utilities, and construction), whereas 2014 IBES covered industrial as well as all services sectors (wholesale and retail trade, transport, communications, finance, real estate, government services, and private services). Both censuses were conducted in two phases. Phase II involved a detailed questionnaire, capturing workforce, wages and salaries, stocks, value of fixed assets, quantity and cost of inputs purchased, other operating costs, and sales and other income. We use the data from phase II for both surveys. The reference year for phase II of the 2003 NIC is the calendar year 2003 and the reference year for phase II of the 2014 IBES is the calendar year 2013.<sup>5</sup>

> 3 The ETD is a joint initiative of the Groningen Growth and Development Centre (GGDC) and United Nations University-World Institute for Development Economics Research (UNU-WIDER). It is publicly available at <u>https://www.wider.unu.edu/project/etd-economic-transformation-database, along with documentation on its</u> contents and construction.

> 4 For survey design and implementation details, see GSS (2006) and (2018).

> 5 Phase II of 2003 NIC was fielded between December 2004 and February 2005, and phase II of 2014 IBES was fielded between November 2015 and April 2016.

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# **Growth of Mining Activities in Ghana**

Ghana boasts a wide range of mineral resources including gold, manganese, bauxite, diamonds, and oil and gas. Gold is its oldest extraction industry, starting in the mid-15<sup>th</sup> century during the country’s colonial era. In 2017, Ghana became the world’s second-largest producer of gold, accounting for 4 percent of global production (Corathers 2019). Even as overall production has increased steadily, short-term output often fluctuated, sometimes dramatically, between 1995 and 2003 (figure 1, panel a). Production initially peaked in the late 1990s, before dropping in the early 2000s. Output picked up again in 2003 and continued to increase since then. Since 2003, gold mining activities have shifted from the Ashanti region, as many mines there closed, primarily to the Central, Eastern, and Western regions (Fafchamps et al. 2017).

The first discovery of oil and gas in Ghana dates back to the 1970s. As the volume of extraction was modest until the turn of the 21<sup>st</sup> century, the production of oil and gas during this period was classified as noncommercial. After decades of more comprehensive exploration, Ghana discovered oil in large quantities in the oil fields in the Deep Water Tano and West Cape Three Points blocks in 2007. In November 2010, the Jubilee partners (comprising Tullow Oil, Kosmos Energy, Anardako Petroleum Corporation, Sabre Oil and Gas, E.O. Group, and Ghana National Petroleum Company) started extracting and producing oil in commercial quantities. Several other oil fields were discovered between 2010 and 2020. Ghana started drilling in TweneboaEnyenra Ntomme in 2016 and in Sankofa Gye Nyame the following year. The country’s volume of cumulative oil production increased from 1.2 million barrels in 2010 to 508.4 million barrels in 2021 (PIAC 2021) (figure 1, panel b). With offshore mining continuing to thrive in the south, Ghana is now aiming to explore onshore mines across several locations in the Voltaian basin in the north (Skaten 2019).

The growing importance of minerals is reflected in Ghana’s export basket (appendix figure A1). In 2006, gold accounted for a mere 4.3 percent of export earnings. In 2012, after the country started producing oil and gas on a commercial basis, that sector constituted almost 17 percent of total export earnings. The same year, the contribution of gold to total export earnings rose to 25 percent. Within the next six years, the composition of Ghana’s export earnings changed dramatically, with 70 percent of export earnings deriving from minerals in 2018, including 40 percent from gold and 25 percent from crude petroleum.

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# **National Trends in Sectoral Employment and Value-Added Shares**

Figure 2 depicts trends in each sector’s share of employment and value-added, which reflect structural transformation, based on ETD statistics. Ghana did not exhibit any strong signs of structural transformation until 2000. Between 2000 and 2010, structural transformation gained momentum, but the changes were mostly limited to agriculture’s share of employment declining and services’ share of employment increasing. Industry’s share of employment grew from 16 percent in 2010 to 20 percent in 2018, driven primarily by manufacturing. Meanwhile, agriculture’s share of employment declined from 55 percent to 33 percent, and services’ share of employment grew from 30 percent to 47 percent over the same period.

The sectoral shares of value-added followed somewhat different trends. Between 1990 and 2018, services’ share of value-added hovered at about 50 percent. Both agriculture’s and industry’s shares of value-added remained close to 25 percent until 2010. Since then, the two sectors’ shares of value-added diverged, with industry’s share rising above that of agriculture. Within industry, mining’s share of value-added increased from 3 percent in 2000 to 12 percent in 2018 (9 percentage points). Construction’s share of industry value-added has increased moderately since 2005, whereas manufacturing’s share has been declining since the mid-2000s.

# **Regional Trends in Industry and Services in the North and the South**

Trends in employment and value-added shares at a more detailed sectoral level are not available in the ETD. As an alternative, we use 2014 IBES summary statistics from GSS (2016) on the number of firms in industry and services, categorized by district and year of establishment, to describe the evolution of sectoral activities at the regional level. We follow the classification of Ghanaian regions prior to the 2018 referendum, which expanded the number of regions from 10 to the 16 in effect today. We define the north as comprising the Brong Ahafo, Northern, Upper East, Upper West, and Volta regions, and the south as comprising the Ashanti, Central, Eastern, Greater Accra, and Western regions (figure 3, panel a). We retain this definition of the north and south of Ghana throughout the paper.

From approximately 1975 to the early 2010s, the numbers of firms in industry and in services increased steadily in both the north and the south, but the total number of firms (for both sectors) in the south has far exceeded that in the north (appendix figure A2, panel a). The growth rate in terms of the number of firms (referred to herein as “growth in firms”) in industry has always been higher in the north than in the south (appendix figure A2, panel b).

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A similar trend is observed for firms in services, with the number of such firms in the north gradually converging with that in the south except between 2005 and 2014.

# **Regional Trends in Mining and Manufacturing in the North and the South**

We look at regional trends for subsectors within industry, specifically mining and manufacturing. For this, we use data from 2003 NIC and 2014 IBES. The 2003 NIC only surveyed industrial firms. Given this, it is not feasible to examine the number of firms and employment in agriculture or services over time.

Table 1 reports the numbers of firms and employees in 2003 and 2013 at the national level, in the north, and in the south. At the national level, the number of firms engaged in mining increased from 125 in 2003 to 294 in 2013 and the number of firms engaged in manufacturing increased from 23,797 in 2003 to 101,789 in 2013. The numbers of firms and employees remain overwhelmingly large in the south in both manufacturing and mining, compared to numbers for the north. The change in the number of mining firms and of employment in the south is higher than in the north by 14 times and 62 times, respectively. The south’s share of mining firms increased from 82 percent (103 out of 125) in 2003 to 89 percent (261 out of 294) in 2013, and its share of mining employment continued to dominate, totalling an overwhelming 98 percent in both years. This is also evident from figure 3 (panels b and c). On the other hand, the north’s share of manufacturing firms grew from 19 percent (4,623 out of 23,797) in 2003 to 31 percent (31,281 out of 101,789) in 2013. Similarly, its share of manufacturing employment grew from 9 percent (10,386 out of 116,774) in 2003 to 16 percent (44,507 out of 271,863) in 2013. The evidence suggests manufacturing employment in the north has partially converged with that in the south over time.<sup>6</sup> We next look at regional trends in manufacturing subsectors. We distinguish between heavy manufacturing and light manufacturing.<sup>7</sup> Given the importance of food and clothing, we also examine them separately in selected analyses. Table A1 compares growth of firms and growth in the number of employees (“growth of employees”) at the district level between

> 6 Data are not available for nonindustrial sectors in NIC 2003. However, based on comparable national household sample surveys for 2005/06, 2012/13, and 2016/17 (rounds of the Ghana Living Standards Survey), services employment in the north grew faster than that in the south, with the north’s share of services employment increasing from 43 percent to 45 percent; in comparison, manufacturing-employment growth was much slower in the north (Paul and Raju 2021).

> 7 Heavy manufacturing refers to manufacturing of coke and refined petroleum, chemicals, basic metals and fabricated metal, machinery and equipment, motor vehicles, and other transport vehicles, among others. Light manufacturing includes manufacturing of wood, paper, printing and reproduction of recording, pharmaceuticals, rubber and plastic, other nonmetallic items, computers, electronics, electrical equipment, furniture, food, and clothing.

9
2003 and 2013 in manufacturing as a whole and in four subcategories of manufacturing, namely heavy manufacturing, light manufacturing, clothing, and food.<sup>8</sup> The rate of growth of firms and employees in the clothing industry is significantly higher in the north than in the south. Figure 3 (panels d, e, and f) provides corroboratory evidence based on district maps. District hubs for heavy and light manufacturing activities are scattered evenly between the north and the south. However, the top one-third of districts experiencing employment growth in the clothing sector between 2003 and 2013 are predominantly located in the north. Overall, the growth of manufacturing employment in the north is predominantly led by the clothing sector.

# **3. Spatial Differences in Intersectoral Linkages**

To estimate spatial differences in intersectoral linkages, we construct five-sector national I-O tables based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021). The five sectors are agriculture; mining; “other industry,” which includes industrial subsectors other than mining; wholesale and retail trade (WRT) services; and “other services,” comprising services subsectors other than WRT services.<sup>9</sup> This five-sector classification allows us to examine the changing patterns of intersectoral linkages between mining and other sectors. Table 2 reports five-sector national I-O tables for 2004 and 2013. The unit for inputs and outputs are in billion 2004 cedis for 2004 and million 2013 cedis for 2013. In 2007, due to inflation, the Ghana cedi was devalued. The current cedi is 10,000 times the old cedi (before 2007). We converted the figures to million cedis in both years, and then applied the sectorlevel deflators from the ETD to have them in 2004 constant prices. In addition, GSS rebased Ghana’s national accounts series from the 1993 base year to 2006. In our analysis, both 2004 and 2013 I-O tables use the rebased figures. For robustness, we compare the value-added shares from our I-O tables against the ones available from the ETD. The values closely match for “other industry” and WRT services but appear somewhat different for the rest of the sectors. This is mainly because several adjustments have been made to ensure consistency over time of sectoral value-added and employment shares in the ETD.

Appendix B discusses how we constructed national I-O tables from the supply and use tables for Ghana. To construct I-O tables for the north and the south of Ghana for 2004

> 8 Since independence in 1960, Ghana’s district boundaries have changed multiple times. NIC 2003 follows a classification of 138 districts, and IBES 2014 follows a classification of 216 districts.

> 9 In our five-sector classification, “other industry” encompasses manufacturing, construction, and utilities, and “other services” include transport, communications, finance, commerce, government services, and private services.

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and 2013 from the national I-O tables, we applied a nonsurvey-based method (Miller and Blair 2009; Flegg and Tohmo 2011; Kowalewski 2013). Appendix C discusses how we constructed the regional I-O tables.

# **Sectoral Domar Weights and Value-Added Shares**

Appendix figure A3 (panel a) shows changes in Domar weights, which is the ratio of sectoral output to GDP, between 2004 and 2013. In the south, production activities primarily shifted from agriculture and “other services” to mining and “other industry.” The Domar weight increased the most for mining (17 percentage points), followed by “other industry” (6 percentage points). In the north, the Domar weight for agriculture decreased by 36 percentage points. “Other industry” experienced the largest increase in the Domar weight (by 16 percentage points), followed by mining (10 percentage points) and WRT services (10 percentage points).

Appendix figure A3 (panel b) shows the change in value-added shares for each sector between 2004 and 2013. The regional pattern of the change in sectoral value-added shares is comparable to the regional pattern of the change in Domar weights. In the north, the gain in the value-added share in “other industry” (23 percentage points) is larger than the change in the Domar weight in “other industry” (16 percentage points). At the same time, the gain in the value-added share in mining in the south (13 percentage points) is lower than the Domar weight in mining (17 percentage points). The implications of changes in the Domar weight and the sectoral value-added share for intersectoral linkages are twofold. First, “other industry” continues to be the most important sector in the production network of the north. Second, mining plays a dominant role in the production network, especially in the south.

# **Intermediate Input Use and Input Downstreamness Index**

We compute the intermediate use share as the percentage of sectoral output used as intermediate inputs for other sectors, and its change between 2004 and 2013. Appendix figure A4 (panel a) compares the change in the intermediate share across sectors. In both the north and the south, the share of intermediate use in mining has increased by 11 and 13 percentage points, respectively. In the south, the change in the intermediate use in WRT services (by 32 percentage points) is more the four times the same in the north (by 7 percentage points).

We next compute downstreamness (Miller and Blair 2009; Antras et al. 2012; Antras and Chor 2013) at the sector level for 2004 and 2013 using the national and regional I-O tables. The downstreamness index measures the average distance from primary input 11
suppliers (Antras et al. 2012). Appendix C discusses the derivation of these indices and the procedure for calculating them. Appendix figure A4 (panel b) shows the change in sectoral input downstreamness between 2004 and 2013. The change in the downstreamness for mining is higher in the south (18 percentage points) than in the north (15 percentage points). Overall, regional variation in the change in downstreamness and the intermediate input use share point to mining’s stronger role as a downstream industry in the south than in the north. The relevance of mining in the supply chain has gradually increased over time. While the large-scale production of oil and gas in Ghana since 2010 has directly contributed to it, it is undeniable that a stronger intersectoral linkage between “other industry” and mining has also played a major role in growing importance of mining in the supply chain.

# **Final Remarks on Intersectoral Linkages and Structural Transformation**

As a final step, we estimate sectoral TFP using the I-O tables. This technique is based on Jorgenson et al. (1987), a standard reference in the literature for the estimation of TFPs. Since we only have I-O tables for two points in time, 2004 and 2013, we can only calculate TFP for one period (2004 to 2013). As reported in appendix table A2, the TFP ratio between mining and agriculture is much larger in the south (12.088) than in the north (–9.594). The south experienced a much larger productivity shock in mining than in the north—the TFP ratio between mining and “other industry” is larger in the south (0.532) than in the north (0.204). On the other hand, the TFP ratio between mining and WRT services in the south (–1.785) is much smaller than that in the north (21.434).

Table 3 summarizes the main findings on regional differences in intersectoral linkages between 2004 and 2013 using the I-O tables for the north and the south. Simple difference-indifferences calculations (between 2004 and 2013, between the north and the south) for valueadded share, Domar weights, and the TFP, suggest a larger mining output and productivity shock in the south than in the north. Correspondingly, the TFP in “other industry” is also larger in the south than in the north. In addition, the difference-in-differences outcomes on the intermediate use share, and input downstreamness index point to the prominence of mining in supply chain growing at a faster rate in the south than in the north, which in turn produces differences in intersectoral linkages with mining between the south and the north. To recapitulate, we find support for the link between the mining productivity shock and differences in intersectoral linkages between the south and the north. Several factors could explain this result, including a weak productivity shock in mining and limited intermediate use by other sectors in the north.

12
# **4. Intersectoral Linkages and Sectoral and Firm Performance**

In the previous section, we presented evidence on the role of the mining output shock in shaping regional differences in intersectoral linkages. We now aim to understand the mechanisms that link production networks and structural transformation but at a more granular level. We use 2003 NIC and 2014 IBES data to examine (1) how the positive mining employment shock relates to employment growth in other sectors across districts and (2) how the positive mining employment shock across time and geographic areas affects the entry of new firms and the growth of firm employment in different sectors.

# **District-Level Analysis**

We first examine the sensitivity of changes in employment in other sectors to changes in employment in mining across districts. Propagation of the mining employment shock to other sectors is identified based on the assumption that employment growth in sectors that have stronger linkages to mining (for example, heavy manufacturing industries) predominantly takes place in mining districts, that is, districts with at least one mining firm. The regression model is as follows: s ∆ log(Empd)03 − 13 = α +σ∆ log(MEmpd)03 − 13 +φ∆ log(MEmpr ,d ′≠ d)03 − 13 + β 1 ∆ log(IFirmd)93 − 03 + β 2 ∆ log(IFirmd)83 − 93 + β 3 ∆ log(SFirmd)93 − 03 + (1) β 4 ∆ log(SFirmd)83 − 93 + ε d , where ∆ log( _Empd_ )03<sup>_s_</sup> − 13 and ∆ log( _MEmpd_ )03 − 13 measure changes in log employment in sector _s_ in district _d_ between 2003 and 2013 and changes in the mining sector in district _d_ between 2003 and 2013, respectively. To capture the effect of mining activities in neighboring and other districts within the same region, we control for changes in log mining employment in region _r_ less mining employment in district _d_ denoted as ∆ log( _MEmpr_ , _d_ ′≠ _d_ )03 − 13 . The elasticity of sectoral employment with respect to mining employment at the district level is denoted by σ , and φ is the elasticity of sectoral employment at the district level with respect to mining employment at the region level. Industrial productivity in district over the past decades may have driven employment growth in sector and district between 2003 and 2013. In such a case, any dd systematic variation in past employment growth across districts could confound the effect of ss dd mining employment growth on employment growth in other sectors. To filter out this confounding effect, we control for the growth of firms in industry between 1993 and 2003 13
( ∆ log( _IFirmd_ )93 − 03 ), and between 1983 and 1993 ( ∆ log(IFirmd)83 − 93 ), and the growth of firms in services between 1993 and 2003 ( ∆ log( _SFirmd_ )93 − 03 ) and between 1983 and 1993 ( ∆ log(SFirmd)83 − 93 ). We estimate equation 1 using district-level employment figures that we construct from firm-level data.

Table 3 reports the regression results for equation 1, separately for light and heavy manufacturing, at the national level, and separately for the north and the south. Overall, the association between changes in employment in both heavy and light manufacturing and changes in employment in mining is positive. The elasticity of heavy manufacturing employment with respect to mining employment (.31) is almost 50 percent larger than the elasticity of light manufacturing employment with respect to mining employment (.21). The difference in the results between the north and the south supports a stronger production network between heavy manufacturing and mining in the south than in the north. The aggregate employment effect at the region level, however, is stronger for light manufacturing than heavy manufacturing in the south. Lastly, industrial development in the previous decades appears to play a less significant role compared to the growth in mining employment in explaining employment growth in manufacturing between 2003 and 2013.

Identification of intersectoral linkages in equation 1 relies only on geographic proximity to a mining firm. As such, it is not sensitive to the time lag between the establishment of a mining firm and a manufacturing firm. In our next model, production networks are identified jointly by the location of a mining firm and the year in which it was established, as follows: _NewFirmds_ , _t_ is an indicator variable, which takes the value of one, if at least one new firm enters in sector _s_ , in district _d_ , and in year _t_ , and zero otherwise. β 1 and β 2 denote the immediate and lagged (by one year) effect of the number of new mining firms established in district _d_ on the probability of having a new firm in sector _s_ and district _d_ . To estimate equation 2, we use a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and the year of establishment of a firm, using 2014 IBES data. Since employment and other firm characteristics are available only for the census year, we use the number of firms to measure the outcome variable. For the same reason, the explanatory 14
variables are also measured using the number of firms established in different years. Similar to equation 1, we control for the aggregate effect at the region level.

Table 4 reports the regression results for equation 2 separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services,” and by north versus south. To establish the causal effect of the growth of firms in mining on the growth of firms in other sectors, we conduct placebo tests. We consider the lead effects of new mining firms for two periods, _NewMFirmd_ , _t_ + 1 and _NewMFirmd_ , _t_ + 2 . In the north (panel a), we find weak evidence of the relationship between the entry of new firms in other sectors and in mining, as the results are mostly statistically insignificant. This is primarily due to the limited expansion of mining in the north. In the south (panel b), both contemporaneous and lagged effects of new mining firms on the entry of heavy manufacturing firms are positive and statistically significant. The entry of mining and heavy manufacturing firms in the south appears causal based on the results of the placebo tests, as the lead effects are statistically insignificant for heavy manufacturing. For other sectors, the placebo test results do not support a causal interpretation for the increase in the number of firms due to an increase in the number of mining firms. Appendix table A4 reports the regression results for equation 2 at the national level. For heavy manufacturing, the results at the national level reflect a combination of the results obtained at the regional level.

To conclude, the district-level results reinforce the results in Section 3 based on regional I-O tables that mining has a stronger effect on heavy manufacturing through production networks in the south than in the north.

# **Firm-Level Analysis**

The district-level evidence suggests that employment in heavy manufacturing has grown in mining districts between 2003 and 2013. An increase in the number of heavy manufacturing firms could generate this result, without an increase in average employment in heavy manufacturing firms in mining districts. We test whether average log employment in different sectors (heavy manufacturing, in particular) is higher in mining districts in the south, based on the following model: ′ log _Empi_ = α +φ _Southi_ +γ _MDisti_ + δ _DDSouthi_ × _MDisti_ +θ _Xi_ + ε _i_ . (3) We estimate the double-difference parameter ( δ _DD_ )in equation 3 using 2014 IBES data, controlling for various firm characteristics (<sup>_X_</sup> _i_ ) including informality status, type of ownership, and legal organization.

15
Table 5 reports the regression results for equation 3, separately for light manufacturing, heavy manufacturing, “other industry,” WRT services, and “other services.” An increase in the size of employment in an average firm in heavy manufacturing if it is located in a mining district in the south (compared to firms in other districts) is higher by 23.2 percent, followed by 13.7 percent higher in “other industry,” 12.1 percent higher in “other services” and 6.2 percent higher in WRT services. The result is statistically significant only for heavy manufacturing and “other services.” Finally, we extend our model in equation 3 to include the effect of changes in the mining employment shock over time, as follows: Equation 4 estimates the difference in average employment of a firm in the manufacturing sector if it is located in a mining district in the south compared to other manufacturing firms between 2003 and 2013. Since we use both 2003 NIC and 2014 IBES data, to estimate δ _DDD_ , the sample is restricted to only manufacturing firms. _Xi_ , _t_ in equation 4 controls for various firm-level characteristics other than employment.

Table 6 reports the results of the regression results for equation 4 separately for light and heavy manufacturing. We do not find a significant difference in employment in light manufacturing firms that are located in mining districts in the south and light manufacturing firms located in other districts between 2003 and 2013. Among light manufacturing industries, average firm employment in the food industry grew by almost 26 percent in mining districts in the south over this period compared to those in other districts. Meanwhile, average firm employment in the paper industry fell by 22 percent in mining districts in the south compared to those in other districts during the same period.

Average employment size in heavy manufacturing firms in mining districts in the south grew by 31 percent compared to firms in other districts between 2003 and 2013. The firm-level evidence suggests that firm growth in heavy manufacturing over this period is primarily driven by chemical firms, registering a growth of 61 percent in average firm employment size, followed by nonmetal (30 percent), machinery (28 percent), and metal (23 percent). Overall, the growth in average employment size in heavy manufacturing firms is more than three times larger than that for light manufacturing firms in mining districts in the south between 2003 and 2013.

16
Summarizing the results from the analysis at the district and firm levels, the variation in the mining employment shock across districts corresponds to employment growth particularly in heavy manufacturing sectors in the south between 2003 and 2013. This process of structural transformation was facilitated by the entry of new firms in the heavy manufacturing sector in the mining districts as well as by an increase in the average size of employment in heavy manufacturing firms in the mining districts over time.

# **5. Conclusion**

Evidence based on regional I-O tables and census data for firms supports the occurrence of a mining shock in the south of Ghana. We find evidence that the mining productivity shock drove the differentiation of production networks and the structural transformation process in the north and the south of the country. We interpret the regional difference in the sectoral composition of economic activities to be possibly due to weak interregional linkages which prevent the propagation of the local sectoral productivity shock from the south to the north.

Regional differences in intersectoral linkages and the pattern of structural transformation have several implications for aggregate productivity growth in Ghana. First, a large mining output shock in the south creates scope for policy interventions to sustain productivity growth by maintaining a strong production network in the region. Industrial policies aimed at fostering stronger linkages between mining and other sectors can help not only to secure greater economic gains from mining in the south but also to redress the lack of access to modern energy services in other sectors, particularly in manufacturing, as suggested by Aryeetey and Ackah (2018). Second, allocating resources to strengthen intersectoral linkages in the north also appears crucial as geological preconditions for oil and gas deposits have been found in the Voltaic basin located in the north of the country (Skaten 2018).

Third, divergent patterns of structural transformation across regions promote regional specialization (for example, clothing industry in the north, heavy manufacturing in the south), which, with lower trade costs, could enhance aggregate productivity growth through stronger interregional linkages (Caliendo et al. 2018). Finally, with Ghana’s upstream mining activities and petroleum revenues starting to recover in the first half of 2021 in the wake of the pandemic, followed by a negative oil price shock, the country is better positioned to pursue placed-based development (Kline and Moretti 2014)—a strategy that has already been emphasized in the context of Ghana (Aragon and Rud 2013; Fafchamps et al. 2017).

Our study suggests two areas for further research. First, similar studies on other emerging oil and gas producers including Kenya, Mozambique, Tanzania, and Uganda could 17
provide insight into the role of mining in structural transformation and productivity growth in the presence of weak interregional linkages. Second, further examination of changes in intersectoral linkages and reallocation of sectoral output arising from productivity shocks in manufacturing and wholesale and retail trade services could help inform policy makers on how to make the ongoing process of industrialization in Sub-Saharan Africa sustainable (McMillan and Zeufack 2022).

18
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|**Table 1: Firms and E**|**mployees, M**|**ining and M**<br>|**anufacturing**<br>|**, 2003 and 201**<br>|**3**<br>|
|---|---|---|---|---|---|
|||2003<br>(number)<br>(1)|2013<br>(number)<br>(2)|Change,<br>2003 to 2013<br>(number)<br>(3)|South/north<br>in (3)<br>(ratio)<br>(4)|
|**a. Firms**||||||
||National|125|294|169||
|Mining|North|22|33|11|144|
||South|103|261|158|.|
||National|23,797|101,789|77,992||
|Manufacturing|North|4,623|31,281|26,658|19|
||South|19,174|70,508|51,334|.|
|**b. Employees**||||||
||National|14,869|40,120|25,251||
|Mining|North|247|645|398|624|
||South|14,622|39,475|24,853|.|
||National|116,774|271,863|155,089||
|Manufacturing|North|10,386|44,507|34,121|35|
||South|106,388|227,356|120,968|.|

_Source_ : Authors’ estimates based on data from the 2003 National Industrial Census and the 2014 Integrated Business Establishment Survey.

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# **<mark>Table 2: Input-Output Tables, 2004 and 2013</mark>**

|**a. 2**|**004 (in bil**|**lion 200**<br>|**4 cedis)**<br>|||||||
|---|---|---|---|---|---|---|---|---|---|
|||AGR|MIN|O-IND|WRT|O-SER|Intermediate<br>share of<br>output|Final<br>demand|Output|
|2004|AGR|0.077|0.000|0.117|0.016|0.000|0.211|32,513|41,195|
|<br>i|MIN|0.000|0.000|0.066|0.000|0.000|0.066|4,472|4,790|
|(n<br>2004|O-IND|0.031|0.036|0.201|0.025|0.070|0.362|14,141|22,174|
|<br>rices)|WRT|0.272|0.010|0.194|0.092|0.000|0.567|5,433|12,562|
|p|O-SER|0.070|0.011|0.017|0.129|0.252|0.479|20,915|40,135|
|**b. 2**|**013 (in bil**|**lion 201**<br>|**3 cedis)**<br>|||||<br>|<br>|
|||AGR|MIN|O-IND|WRT|O-SER|Intermediate<br>share of<br>output|Final<br>demand|Output|
|2013|AGR|0.136|0.000|0.068|0.018|0.041|0.263|30,944|41,959|
|<br>i|MIN|0.000|0.053|0.138|0.001|0.004|0.195|21,913|27,227|
|(n<br>2013|O-IND|0.039|0.026|0.099|0.145|0.080|0.388|45,247|73,906|
|<br>prices)|WRT|0.010|0.090|0.001|0.003|0.180|0.284|38,534|39,654|
||O-SER|0.072|0.019|0.042|0.073|0.119|0.325|38,703|57,364|

_Source_ : Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).

_Note:_ AGR = agriculture. MIN = mining. O-IND = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. O-SER = services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services.

23
|**Table 3: **<br>|**Change in Inters**<br>Change in X [201<br>|**ectoral Link**<br>3–2004] in th|**ages, 2004–2013**<br>e south–change i<br>|n X [2013–2004] in the north<br>|
|---|---|---|---|---|
|X =|Value-added<br>share|Domar<br>weight|Total factor<br>productivity|Intermediate use<br>share<br>Input<br>downstreamness<br>index|
||(1)|(2)|(3)|(4)<br>(5)|
|AGR|0.181|0.162|–0.021|0.005<br>0.005|
|MIN|**0.067**|**0.070**|**0.004**|**0.019**<br>**0.033**|
|O-IND|–0.118|–0.092|0.355|–0.007<br>–0.025|
|WRT|0.026|–0.058|0.067|–0.248<br>–0.308|
|O-SER|–0.155|–0.253|–0.065|–0.023<br>–0.116|

_<mark>Source</mark>_ <mark>: Authors’ elaboration based on 2004 and 2013 supply and use tables for Ghana (GSS 2006, 2021).</mark> _Note:_ TFP = total factor productivity. AGR = agriculture. MIN = mining; O-IND = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. O-SER = services other than wholesale and retail trade; they include transport, communication, finance, commerce, government services, and private services. 5 Following Antras et al. (2012), input downstreamness is measured as = 1 × <u>i</u> + 2 × <u>j j j</u> + 5 5 5 5 VV ∑VV AA jii 3 × <u>j j j</u> + 4 × <u>j j j</u> + ⋯. DDii XXii XXii ∑∑VVjj AA AAjjii ∑∑∑VVjj 5ss AA AAjjssAAssii XXii XXii 24
|**Table 4: Sectoral Emp**|**loyment Elasticitie**<br>Nati|**s**<br>onal|No<br>∆<br>log(<br>Em|rth<br>−<br>03 13<br>)<sup>s</sup><br>d<br>p|So|uth|
|---|---|---|---|---|---|---|
||Light<br>manufacturing<br>(1)|Heavy<br>manufacturing<br>(2)|Light<br>manufacturing<br>(3)|Heavy<br>manufacturing<br>(4)|Light<br>manufacturing<br>(5)|Heavy<br>manufacturing<br>(6)|
|−<br>∆<br>03 13<br>log(<br>)<br>d<br>MEmp|0.210**|0.312***|0.558***|0.259|0.164|0.355***|
||(0.087)|(0.105)|(0.156)|(0.323)|(0.106)|(0.111)|
|′≠<br>−<br>∆<br>,<br>03 13<br>log(<br>)<br>r d<br>d<br>MEmp|0.451***|0.254*|–0.219|–0.172|1.109***|0.554*|
||(0.147)|(0.132)|(0.272)|(0.360)|(0.326)|(0.282)|
|−<br>∆<br>93 03<br>log(<br>)<br>d<br>IFirm|–0.403|–0.032|–0.353|–0.086|–0.891|0.204|
||(0.625)|(0.966)|(0.966)|(1.411)|(1.180)|(1.045)|
|−<br>∆<br>83 93<br>log(<br>)<br>d<br>IFirm|–0.225|–0.970|–0.237|–1.006|0.282|–0.936|
||(0.706)|(0.843)|(0.780)|(1.206)|(1.269)|(1.245)|
|−<br>∆<br>93 03<br>log(<br>)<br>d<br>SFirm|–1.214|1.945|–3.546|–0.173|–0.932|2.822|
||(1.679)|(1.688)|(3.058)|(3.468)|(2.698)|(2.933)|
|−<br>∆<br>83 93<br>log(<br>)<br>d<br>SFirm|3.569*|3.240*|0.565|2.313|6.719*|3.760|
||(1.933)|(1.855)|(2.433)|(2.888)|(3.977)|(4.078)|
|Constant|–1.853|–2.792**|3.197|0.489|–6.849***|–5.569***|
||(1.261)|(1.135)|(2.209)|(2.320)|(1.617)|(1.676)|
|_N_|136|136|67|67|69|69|
|_R_<sup>_2_</sup>-statistic|0.167|0.175|0.188|0.052|0.400|0.331|

_<mark>Source:</mark>_ <mark>Authors’ estimates based on data from the 2003 National Industrial Census, the 2014 Integrated Business Establishment Survey, and GSS (2016).</mark> _Note:_ This table reports estimates for equation 1. Dependent variable = district-level employment in the given sector. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

25
|**Table 5: FirmCrea**|**tion, North and Sout**|**h**<br>_New_|,<br>_s_<br>_d t_<br>_Firm_|||
|---|---|---|---|---|---|
||Light<br>manufacturing<br>(1)|Heavy<br>manufacturing<br>(2)|Other<br>industry<br>(3)|WRT<br>services<br>(4)|Other<br>services<br>(5)|
|**a. North**||||||
|−<br>,<br>1<br>d t<br>NewMFirm|0.111|0.059|0.143|0.242|0.633***|
||(0.206)|(0.131)|(0.148)|(0.221)|(0.100)|
|,<br>d t<br>NewMFirm|0.679*|0.046|0.130|–0.001|0.034|
||(0.359)|(0.095)|(0.104)|(0.112)|(0.267)|
|+<br>,<br>1<br>d t<br>NewMFirm|0.419*|0.406|0.032|0.058|–0.263|
||(0.226)|(0.293)|(0.046)|(0.249)|(0.161)|
|+<br>,<br>2<br>d t<br>NewMFirm|–0.429***|–0.128|–0.050|0.322|0.007|
||(0.106)|(0.085)|(0.049)|(0.213)|(0.232)|
|Constant|0.366***|0.153***|0.110***|0.425***|0.538***|
||(0.028)|(0.022)|(0.016)|(0.026)|(0.029)|
|_N_|<br>1,407|<br>1,407|<br>1,407|<br>1,407|<br>1,407|
|_R_<sup>_2_</sup>-statistic|<br>0.036|<br>0.018|<br>0.007|<br>0.019|<br>0.027|
|**b. South**||||||
|−<br>,<br>1<br>d t<br>NewMFirm|0.012|0.123***|0.239***|0.117*|0.058|
||(0.053)|(0.044)|(0.067)|(0.064)|(0.071)|
|,<br>d t<br>NewMFirm|-0.042|0.163***|-0.003|0.123*|0.049|
||(0.047)|(0.047)|(0.045)|(0.073)|(0.056)|
||(0.030)|(0.025)|(0.014)|(0.032)|(0.025)|
|+<br>,<br>1<br>d t<br>NewMFirm|0.167***|0.067|0.105***|0.213***|0.109***|
||(0.051)|(0.044)|(0.032)|(0.045)|(0.036)|
|+<br>,<br>2<br>d t<br>NewMFirm|0.060|0.080|-0.030|0.189***|0.046|
||(0.041)|(0.051)|(0.038)|(0.056)|(0.053)|
|Constant|<br>0.409***|<br>0.211***|<br>0.116***|<br>0.384***|<br>0.583***|
||(0.030)|(0.028)|(0.023)|(0.029)|(0.032)|
|_N_|<br>1,449|<br>1,449|<br>1,449|<br>1,449|<br>1,449|
|_R_<sup>_2_</sup>-statistic<br>_Source:_Authors’ estimat|<br>0.027<br>es based on data from the|<br>0.019<br>2014 Integrated Bus|<br>0.027<br>iness Establish|<br>0.050<br>ment Survey.|<br>0.028|

_Note:_ This table presents estimates for equation 2. The dependent variable is an indicator variable indicating entry of at least one new firm in year _t_ in district _d_ in the given sector. The database used consists of a district-year panel comprising 119 districts and 24 years (1990–2013) based on the location and year of entry of the firm. Other industry = industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

26
**Table** **<u>6: Changes in Average Firm-Level Employment across Districts</u>**

|||<br>lo|<br>g<br>i<br>Emp|||
|---|---|---|---|---|---|
||Light<br>manufacturing|Heavy<br>manufacturing|Other<br>industry|WRT<br>services|Other<br>services|
||(1)|(2)|(3)|(4)|(5)|
|×<br>MDist<br>South|–0.007|0.209**|0.128|0.060|0.114***|
||(0.059)|(0.086)|(0.198)|(0.042)|(0.042)|
|MDist|0.019|0.033|0.130|0.040|–0.027|
||(0.041)|(0.066)|(0.129)|(0.026)|(0.031)|
|South|0.333***|0.308***|0.112|0.453***|0.349***|
||(0.070)|(0.115)|(0.271)|(0.052)|(0.055)|
|Constant|4.053***|3.498***|3.117***|2.598***|2.689***|
||(0.197)|(0.330)|(0.249)|(0.226)|(0.053)|
|_N_|3,851|1,560|814|5,618|10,903|
|_R_<sup>_2_</sup>-statistic|0.356|0.484|0.264|0.345|0.290|

_Source:_ Authors’ estimates based on data from the 2014 Integrated Business Establishment Survey.

_Note:_ This table presents estimates for equation 3. Dependent variable = log employment in manufacturing. Other industry = those industries other than mining; they include manufacturing, construction, and utilities. WRT = wholesale and retail trade. Other services = those services other than wholesale and retail trade; they include transport, communications, finance, commerce, government services, and private services. All regressions control for the informality status of a firm, the type of ownership, the type of legal organization, and region. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

27
# **<mark>Table</mark>** **<u><mark>7: Changes in Firm-Level Employment, 2003–2013</mark></u>**

# **<mark>a. Light manufacturing</mark>**

|||l|,<br>og<br>_i t_<br>_ManEmp_|||
|---|---|---|---|---|---|
||Light<br>(1)|Food<br>(2)|Clothing<br>(3)|Paper<br>(4)|Other<br>(5)|
|×<br>×<br>MDist<br>Year<br>South|0.089|0.229**|0.062|–0.224|0.159*|
||(0.080)|(0.116)|(0.105)|(0.183)|(0.082)|
|MDist|0.076|–0.054|0.085|0.254*|0.070|
||(0.057)|(0.079)|(0.068)|(0.132)|(0.044)|
|Year|–0.644***|–0.494***|–0.712***|–0.421***|–0.756***|
||(0.043)|(0.080)|(0.060)|(0.154)|(0.051)|
|South|–0.035|0.181**|–0.141**|0.056|–0.125***|
||(0.039)|(0.085)|(0.056)|(0.123)|(0.042)|
|Constant|4.695***|4.915***|4.293***|4.427***|3.497***|
||(0.176)|(0.311)|(0.228)|(0.342)|(0.280)|
|_N_|5,895|1,398|2,224|874|1,399|
|_R_<sup>_2_</sup>-statistic|0.358|0.457|0.250|0.392|0.318|

# **<mark>b. Heavy manufacturing</mark>**

|||<br>l|<br>,<br>og<br>_i t_<br>_ManEmp_|||
|---|---|---|---|---|---|
||Heavy|Chemical|Nonmetallic<br>mineral|Metal|Machinery|
|×<br>×<br>MDist<br>Year<br>South|0.267***|0.482*|0.257|0.209*|0.247|
||(0.076)|(0.270)|(0.172)|(0.116)|(0.156)|
|MDist|0.047|–0.425***|–0.001|0.185***|0.114|
||(0.053)|(0.146)|(0.109)|(0.064)|(0.110)|
|Year|–0.564***|–0.530**|–0.388***|–0.686***|–0.465***|
||(0.055)|(0.212)|(0.109)|(0.093)|(0.106)|
|South|–0.078|–0.041|–0.023|–0.124|–0.145|
||(0.048)|(0.205)|(0.113)|(0.082)|(0.101)|
|Constant|4.258***|5.236***|4.474***|4.199***|3.385***|
||(0.283)|(0.446)|(0.452)|(0.373)|(0.258)|
|_N_|2,206|378|400|1,050|378|
|_R_<sup>_2_</sup>-statistic|0.454|0.546|0.301|0.419|0.433|

_Source:_ Authors’ estimates based on data from the 2003 National Industrial Census and the 2014 Integrated Business Establishment Survey.

_Note:_ This table reports estimates for equation 4. Dependent variable = log employment in firm _i_ at time _t_ . All regressions control for the informality status of a firm, the type of ownership, the type of legal organization, and region. Standard errors are reported in parentheses. Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.

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# **Figure 1: Gold and Oil Production in Ghana**

# **a. Gold production, 1990–2018**

<!-- Start of picture text --> 150<br>125<br>100<br>75<br>50<br>25<br>0<br>Year<br>Natural oil and gas production, 2010–2021<br>80 500<br>400<br>60<br>300<br>40<br>200<br>20<br>100<br>0 0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Year<br>Annual oil production Cumulative oil production<br>Gold production (unit: 1000 Kg)<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018<br>Annual oil production (billion barrels)<br>Cumulative oil production (billion barrels)<br><!-- End of picture text -->

# **b. Natural oil and gas production, 2010–2021**

_Source_ : Authors’ estimates based on information obtained from PIAC (2021) and the United States Geological Survey (USGS) Database. https://www.usgs.gov/.

_Note_ : Gold production figures include reported artisanal and small-scale output.

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# **Figure 2: Trends in Sectoral Shares, for Employment and Value-Added, 1990–2018**

# **Sectoral share of employment**

# **b. Subsectors within industry**

# **a. Broad sectors**

<!-- Start of picture text --> 60 20<br>50 15<br>40<br>10<br>30<br>5<br>20<br>10 0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>0 Year<br>1990 1994 1998 2002 2006 2010 2014 2018 Industry Manufacturing Mining Public Utility Construction<br>Year<br>Agriculture Industry Services<br>Broad sectors d. Subsectors within industry<br>40<br>50<br>40 30<br>30 20<br>20 10<br>10 0<br>1990 1994 1998 2002 2006 2010 2014 2018<br>0 Year<br>1990 1994 1998 2002 2006 2010 2014 2018 Industry Manufacturing Mining Public Utility Construction<br>Year<br>Agriculture Industry Services<br>Share (percent)<br>Share (percent)<br>Share (percent)<br>Share (perent)<br><!-- End of picture text -->

# **Sectoral share of value-added**

# **c. Broad sectors**

_Source_ : Authors’ estimates based on statistics for Ghana from the Economic Transformation Database.

_Note_ : Agriculture includes forestry and fisheries; industry includes mining, manufacturing, public utilities, and construction; and services include wholesale and retail trade, transportation and storage, financial and real estate activities, government services, and private services. Almost 95 percent of industrial employment is in mining and manufacturing.

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<!-- Start of picture text --> Cn Wy 3S<br><!-- End of picture text --> <!-- Start of picture text --> 2 BA<br><!-- End of picture text -->