metadata: authors: 0: author: Ofori Adofo,Josephine
1: author: Tarui,Nori
2: author: Tanaka,Tomomi
count: Ghana
docna: 0: docna: The Local Economic Effects of Natural
            Resources : Evidence from Ghana
docty: Policy Research Working Paper
abstracts: cdata!: This paper estimates the welfare impacts
            of natural resources by analyzing Ghana&#x2019;s
            offshore oil discovery and subsequent production. It finds
            substantial increases in real income, but no effect on
            consumption and poverty.  The income effects are stronger
            for skilled workers. Estimates of the effects of oil
            discovery on employment show that employment in general
            increased by 4 percentage points. The positive employment
            effects are largely concentrated in non-oil local sectors:
            manufacturing and construction. The findings do not show
            significant impacts on employment in the agriculture and
            service sectors where a large proportion of individuals
            below the poverty line are engaged. This largely explains
            why the oil discovery had no effect on poverty reduction, as
            it benefited the non-poor rather than the poor.
display_title: The Local Economic Effects of
            Natural Resources : Evidence from Ghana
listing_relative_url: /research/2022/08/33890382/local-economic-effects-natural-resources-evidence-ghana
topicv3: Offshore Oil and Gas Production m1325953 2304,Natural Resources m1326446 1979,Labor Markets m1326488 855,Poverty m1326687 695,Jobs m1503931 2670
publishtoextweb_dt: 2022-08-30T00:00:00Z
dois: 10.1596/1813-9450-10154
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 1589389
1: author_key: NA
2: author_key: NA
model_extractions: Policy Research Working Paper 10154 The Local Economic Effects of Natural Resources

# Evidence from Ghana

_Josephine Ofori Adofo Nori Tarui Tomomi Tanaka_ Africa Region Office of the Chief Economist August 2022
Policy Research Working Paper 10154

# **Abstract**

This paper estimates the welfare impacts of natural resources by analyzing Ghana’s offshore oil discovery and subsequent production. It finds substantial increases in real income, but no effect on consumption and poverty. The income effects are stronger for skilled workers. Estimates of the effects of oil discovery on employment show that employment in general increased by 4 percentage points. The positive employment effects are largely concentrated in non-oil local sectors: manufacturing and construction. The findings do not show significant impacts on employment in the agriculture and service sectors where a large proportion of individuals below the poverty line are engaged. This largely explains why the oil discovery had no effect on poverty reduction, as it benefited the non-poor rather than the poor.

This paper is a product of the Office of the Chief Economist, Africa Region. 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 joa94@hawaii.edu, nori@hawai.edu, and tomomit@gmail.com.

_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
# The Local Economic Effects of Natural Resources: Evidence from Ghana

By Josephine Ofori Adofo, Nori Tarui, and Tomomi Tanaka<sup>*</sup>

**_Keywords_** _— Natural resources, local labor markets ,jobs, poverty, offshore oil JEL: J21, O13, O15, R20, Q32_

> *Ofori Adofo: Department of Economics, University of Hawaii at Manoa, Honolulu, HI 96822, USA,  and the World Bank, (Email: joa94@hawaii.edu). Tarui: Department of Economics, University of Hawaii at Manoa, Honolulu, HI 96822, USA, (Email: nori@hawii.edu). Tanaka: Poverty and Equity Global Practice, The World Bank, Washington D.C, 20006, USA, (Email: tomomit@gmail.com). We thank Inessa Love and Teresa Molina for their comments and suggestions. We are grateful to Dan Pavelesku for his support.
# **1. Introduction**

Does the discovery of a new resource exacerbate the resource curse? It has been long debated whether natural resource endowments promote economic development (Van der Ploeg, 2011b). Previous research indicates mixed findings (Alexeev and Conrad, 2009; van der Ploeg, 2011a; Smith, 2015). Cross-country studies based on aggregate data suggest that resource abundance could have negative effects on economic growth (Sachs, 1995; Sachs and Warner, 2001).<sup>1</sup> The literature on the “natural resource curse” has identified several factors that influence the relationship between resource abundance and economic growth. These factors include conflicts (Angrist and Kugler, 2008), the presence of weak institutions, and the extent of democracy (Aragón, ChuhanPole, and Land, 2015).<sup>2</sup> The primary objective of this paper is to examine the impacts of natural resource discovery on welfare and employment at the sub-national level. We study Ghana’s offshore oil discovery in 2007, which is the largest and most significant oil discovery in the country’s history. Have the discovery of oil and subsequent oil production increased local employment and contributed to poverty reduction? Economic theory suggests that local economies benefit from natural resource discovery through increased local demand for labor not only in the mining sector but also in non-mining sectors due to spillover effects (Black, McKinnish, and Sanders, 2005; Marchand, 2011). In addition, households can benefit from windfall revenues if the government redistributes the revenues in the form of transfers or provision of public goods. Whether and how households are ultimately affected by a shock to natural resource availability is an empirical question, which we address in this paper.

We use three waves of household survey data covering the period 1998 to 2013 and information on oil fields to estimate the economic impacts of the oil discovery. We exploit the quasi-experimental nature of the discovery by comparing districts in regions associated with oil (treatment group) and districts in regions without oil (control group).

> 1  Cotet and Tsui (2013) show that oil discoveries increase military spending.

> 2 Aragón, Chuhan-Pole, and Land (2015) provide  a literature review of both cross-country and subnational level impacts of natural resources.

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The identification strategy assumes that the trends in outcomes for households in the treatment and the comparison groups would have been similar in the absence of the discovery of oil. Although this assumption is untestable, we demonstrate that households in the treatment group were not significantly different from those in the comparison group prior to the oil discovery.

Our results indicate that the oil discovery increased real household income but had no effect on consumption and poverty. The probability of being employed increased by 4 percentage points. The effects on employment in non-oil local sectors are concentrated in manufacturing and construction. The increase in manufacturing employment indicates that the manufacturing sector may have benefited from agglomeration effects (Greenstone, Hornbeck, and Moretti, 2010) due to offshore oil operations (Aragón, Chuhan-Pole, and Land, 2015). However, the positive employment impacts are not observed beyond these sectors: we do not find significant impacts on employment in the agriculture and service sectors where a large proportion of individuals below the poverty line are engaged. In order to explore the heterogeneous impacts on employment, we investigate how the effects differ between educated and less educated workers, and between men and women. We find that the positive effects are more concentrated on educated workers compared to less educated workers. The impacts are similar for both men and women.

How do these findings compare with what we have learned from the existing studies? Several studies have examined local economic impacts of resource discovery on wealth, income, employment, inequality, and standard of living (Loayza, Teran, and Rigolini, 2013; Berman et al., 2017; Aragón and Rud, 2013; Douglas and Walker, 2017; Chuhan-Pole et al., 2015; Kotsadam and Tolonen, 2016). Their findings suggest that oil discovery increases employment in the oil extraction and manufacturing sectors (Michaels, 2011), and leads to small increases in household income when windfall revenues are distributed through transfers. Similarly, Fleming and Measham (2015) find an increase in income among families living in regions with natural gas compared to families in other regions in Australia. Aragón and Rud (2013) find that the expansion of 3
Peru’s gold mine had a positive effect on income, household consumption and poverty reduction. In the case of mining in Africa, the expansion of mining activities increased women’s employment in the services sector and shifted employment away from the agricultural sector (Kotsadam and Tolonen, 2016). Chuhan-Pole et al. (2015) demonstrate that infant mortality considerably decreased in mining communities in Ghana.

This paper contributes to the existing literature in three important ways. First, our paper complements the literature on local impacts of natural resources by providing new evidence. Most of the studies on the local impacts of resources, as summarized above, focus on developed countries (Black, McKinnish, and Sanders, 2005; Michaels, 2011; Allcott and Keniston, 2017) while a few others focus on developing countries mainly in Latin America such as Peru and Brazil (Aragón and Rud, 2013; Caselli and Michaels, 2013). Their findings may not be applicable to other developing countries, especially in Africa because the socioeconomic impacts may be different under different economic structures and development stages. Our findings about Ghana’s offshore oil discovery have direct implications for other low-income countries, including those in West Africa with large prospects on offshore oil development. Second, this paper departs from the literature, which focuses on traditional onshore discovery of oil and mineral resources, by evaluating the impacts of offshore oil on local employment and standard of living in Africa. Research on theses impacts is scarce (Hilson, 2009; Aragón, Chuhan-Pole, and Land, 2015; Kotsadam and Tolonen, 2016; Berman et al., 2017). Out of the available research, only a few have investigated the impacts of offshore oil discoveries. The local economic impacts of offshore oil development may be different from those of conventional onshore oil and other minerals in an important way. Offshore oil development is more capital-intensive than other conventional oil and gas explorations because the facilities required for extraction and delivery of products are typically more complex with the offshore development. Because of its capital-intensive nature, offshore oil development requires relatively more skilled labor workforce than the inland counterpart. These feastures of offshore oil indicate that the positive direct 4
economic impacts, if they exist, may be concentrated on those workers with high skills. In fact, our empirical finding is consistent with this conjecture. To the extent that the future resource exploration involves capital-intensive development, our findings indicate possibly limited roles of offshore oil development on poverty reduction.

Finally, we evaluate the effects of oil discovery at the household and individual level. We apply a difference-in-differences approach to a newly constructed household income data combined with location information of households to analyze the impact of oil discovery on households’ income. This approach allows us to address individual-level impacts of offshore oil discovery, which tend to be masked in most previous studies based on municipality, county or province level data.

The remainder of the paper is organized as follows: Section 2 provides a brief overview of oil in Ghana, Section 3 discusses related theories and channels through which natural resources affect the local economy, Section 4 describes the dataset used for analysis, summary statistics, empirical strategy and a discussion on the validity of the identification strategy. Section 5 reports empirical results, Section 6 discusses alternative mechanisms, and Section 7 concludes.

# **2. An Overview of Oil in Ghana**

Oil exploration in Ghana began in the 19<sup>th</sup> century. The first onshore oil was discovered and drilled in 1896. Following this discovery, exploration activities continued between 1957 and 1966. Offshore oil is more recent, with discovery and drilling taking place in the 1970’s.<sup>3</sup> By 1983, the country had 54 well counts of both onshore and offshore oil, yet production and commercialization were not on a large scale. Crude oil was discovered in the Western region 2007, which is the most significant discovery among all discoveries because it puts Ghana on the forefront of exporting oil in much larger quantities.

The discovery has drawn attention from both within Ghana and international

> 3 Ghana has <mark>3 offshore sedimentary basins and one onshore basin: Tano-Cape Three Point Basin, Saltpond Basin, Accra-Keta Basin and Voltarian Basin.</mark> Current productive oil fields lie within the <mark>Tano-Cape Three</mark> Basin.

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organizations due to the significantly large size of the reserve and possible daily production quantities. Actual production for exporting began in 2010. According to a report by Ghana Extractive Industries Transparency Initiative, an average of 63,932 barrels was produced per day in December 2010 (Ministry of Finance, 2014). This makes it the most important discovery in the country’s history of oil and gas exploration. Export of oil is ranked among the top five commodities of Ghana’s exports (World Trade Integrated Solution 2016) and has substantially contributed to GDP growth. Figure 1 shows a clear increase in the share of oil rents in GDP in 2010, when oil production from the offshore fields started. In 2014, exports of crude oil averaged 101,922 barrels per day.

In terms of control and management, the Petroleum Commission, a state institution, regulates the oil industry. The major function of the Petroleum Commission is to regulate and manage the upstream petroleum operations. Actual exploration, production and the supply of petroleum products are the sole responsibilities of the Ghana National Petroleum Corporation (GNPC), Ghana’s main oil company. With respect to the distribution of royalties and tax revenue from the oil industry, a large part of the revenue is allocated to Ghana National Petroleum Corporation (GNPC), followed by the Annual Budget Funding Amount (ABFA), Stabilization Fund and Heritage Fund. The Annual Budget Funding Amount (ABFA) allocates funds for the provision of infrastructure in sectors such as agriculture, energy, education, housing and health. Local communities are supposed to benefit from the windfall revenue through the provision of infrastructure.

The main focus of the paper is on the direct and indirect impacts of the oil through labor demand and income effects. We do not focus on the impact of the windfall allocation because the oil revenues are not redistributed to households in the form of transfers from the Government in Ghana. For robustness, we address concerns that distribution of government transfers may be driving our results.

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# **3. Local Economic Impacts of Natural Resources**

The impact of natural resources on economic growth at the national level is well documented (Bulte, Damania, and Deacon, 2005; Mehlum, Moene, and Torvik, 2006; Gylfason and Zoega, 2006; van der Ploeg, 2011a). However, empirical evidence on the channels through which extraction of natural resources affects the local economy is still lacking. In this section, we describe the possible channels through which local economies may be affected by natural resources. We do not attempt to evaluate all the channels, but rather provide insights on the overall empirical effects of natural resources on the local economy—specifically, how local demand shocks from the oil discovery may affect the local labor markets and households.<sup>4</sup> Natural resources may generate both positive and negative impacts<sup>5</sup> on the local economy. On the positive side, the discovery of a new resource and subsequent extraction can create new jobs for the local economy where the resource discovery and production occur. In the case of oil extraction, jobs at firms such as oil refinery companies emerge, which increase the demand for labor and in turn increase income. As theory predicts, the increase in income will likely lead to increases in spending on both tradable and non-tradable goods, thereby creating employment in other sectors, particularly, the non-tradable sector. Besides the influx of oil-related jobs, other nonmining sector jobs are more likely to be created through a multiplier effect. This is because a positive shock to the tradable sector has the potential of creating employment opportunities in the non-tradable sector through a local multiplier effect (Moretti, 2010). For example, accommodation business may experience a higher demand due to inflows of workers and other businesses into the local economy.<sup>6</sup> On the negative side, the extraction of oil may crowd out other sectors of the economy. Workers might move from other sectors such as agriculture and manufacturing to the mining sector due to higher wages. Moreover, housing prices,

> 4 Cust and Poelhekke (2015) provide a survey of the literature on the local economic impacts of natural resource extraction.

> 5  Aragón and Rud (2015) demonstrate how the gold mining industry caused pollution and negatively impacted agricultural productivity in Ghana.

> 6 See Morreti (2010) for a detailed discussion on “Local Multipliers”.

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land prices and prices of other local goods may increase in the locations affected by the oil extraction activities. The local economy will not benefit from the resource if higher prices increase the cost of living and discourage businesses from operating in affected districts.

Another channel through which oil extraction affects the local economy is through oil windfalls. Some governments redistribute oil windfalls through income transfers and provision of public goods such as roads, health facilities, classroom blocks and public water supply. The provision of public goods benefits both households and businesses. On the other hand, the presence of oil revenues can create corruption (Ades and Di Tella, 1999), rent seeking behavior (Brollo et al., 2013), and even conflicts (Angrist and Kugler, 2008). Vicente (2010), for example, finds that offshore oil discovery in São Tomé and Príncipe led to perceived increases in corruption.

# **4. Research Design**

In this paper, we examine the impact of natural resources on the local economy by focusing on the effects of oil extraction on the employment and standard of living of households in affected districts. We use consumption and poverty status of the household as a measurement of the standard of living besides income.

# _4.1.  Data_

We use three rounds of the Ghana Living Standards Survey (GLSS) covering the period between 1998 and 2013 (1998/1999-GLSS4, 2005/2006-GLSS 5 and 2012/2013GLSS 6).<sup>7</sup> GLSS is a nationally representative household survey and is collected by the Ghana Statistical Service (GSS). It reports detailed information on demography, income, expenditure and other household and individual characteristics. With the support of the Ghana Statistical Service, we created a complete dataset of location information of enumeration areas of the household surveys. The GLSS 6 sample includes 818

> 7 We use GLSS 5 (2005/2006) and 6 (2012/2013) in the main analysis, and GLSS 4 (1998/1999) in a test for similarity in pre-trends. We exclude GLSS 4 from the main analysis because it contains only 102 districts.

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enumeration areas (EAs), 580 EAs in GLSS 5, and 300 EAs in GLSS 4.<sup>8</sup> Based on the location information, we match households to districts of regions with oil and non-oil regions. The GLSS 5 data includes 110 districts while GLSS 6 includes 170 districts.<sup>9</sup> The number of districts in Ghana has increased over the years from 110 districts in 2000 to 170 districts in 2010. With information on both the original districts that have been split and newly created districts, we are able to match the new districts back to the old districts that existed in 2000.

Data on household income is taken from the Rural Income Generating Activities project (RIGA), which is compiled by the Food and Agriculture Organization of The United Nations.<sup>10</sup> The RIGA income data is constructed using household survey data (GLSS), with the idea of including all sources of household income that are often neglected in the computation of income. Such sources include income from selfemployment and income from own-farm production of crops. Additionally, we use information on the location of oil fields from Petroleum Commission of Ghana to determine the oil and non-oil regions. The data contains information on the number of offshore oil fields, wells, location, year of discovery and status of operation, which is whether the oil field is currently producing, under appraisal or exploration.

The empirical analysis focuses on regions in Southern Ghana by comparing households and individuals in treatment and control areas. We focus on the southern part of Ghana for two reasons. First, we limit our focus on offshore oil. The oil discovered in 2007 as well as the subsequent discoveries are offshore. We define treatment areas as districts in regions that face offshore oil or districts in regions that have oil in its offshore boundaries. Second, districts considered in our analysis are more similar to each other compared to districts in the north. One of the regions in the south that has oil, i.e. the Central region, is excluded, because the oil field in that region was

> 8 GLSS is geocoded at the locality level.  The 6th wave (GLSS 6) contains a total of 1200 localities but our sample includes 818 EAs out of the total, because GPS information on 382 localities was missing. The majority of the missing EAs are located in the Northern regions. These regions are not included in our main analysis since we focus on the south. However, we use EAs in the Northern regions with GPS information to show that excluding these regions does not affect our results.

> 9 Districts are second level sub-national administrative units in Ghana after regions.

> 10 Rural Income Generating Activities (RIGA) project- Food and Agriculture Organization, United Nation. <u>http://www.fao.org/economic/riga/riga-database/en/</u>

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discovered years before the 2007 discovery. Oil production from this field is smaller compared to the production from the oil fields discovered in 2007. Daily production of oil from this oil field was 700 barrels per day in 2006 (Ministry of Finance, 2014). Besides its size, there have been several shutdowns and operation resumptions on the field due to operational issues. By excluding this region, we avoid attributing changes in the outcome of the control group due to a change in the existing oil operation to the 2007 offshore oil discovery. This also ensures that our control group is comparable to the treatment group. Figure 2 shows the districts in our sample: control and treatment districts.

# _<mark>4.2. Summary Statistics</mark>_

<mark>Table 1 reports summary statistics for our household sample. The outcome variables are income, consumption expenditure and an indicator for poverty status.</mark><sup>11</sup> <mark>Majority of household heads have completed secondary school education, and 20 percent of them have completed primary school education. A large share of household members work in the agriculture and services sector compared to the industrial sector. Table A-1 in the Appendix reports summary statistics for the individual sample (employment effects of the oil discovery).</mark>

# _4.3. Empirical Strategy_

We apply a difference in difference estimation strategy by comparing households (and individuals) in districts of oil and non-oil regions before and after the discovery of oil. The identification strategy assumes that the trends in outcomes for households in our treatment group and comparison group would have been similar in the absence of the oil.

We define the treatment group as households who live in districts in a region with oil. Specifically, a treatment district refers to a district in a region whose offshore oil was discovered in 2007 or after 2007 and whose oil was extracted during or after

> 11 The variable poor defines the poverty status of a household based on the poverty line. The poverty line is GH¢1,314 per adult equivalent per year in 2013 prices.

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2010. All households in such districts are assigned to the treatment group. The treatment definition is based on data of all oil fields in Ghana. The data gives information on the status of the oil fields (that is, whether the field is under exploration, appraisal, being drilled, or inactive). We estimate the following regression for household-level outcome variables: where hd is the outcome variable (real income and real consumption of household _h_ in district _d_ at time _t_ . Variable represents a dummy variable that takes the value of 1 YY if the household lives in a region with offshore oil and 0 otherwise; dd _Post_ is a time TT dummy for the year 2012/2013 (post oil discovery); and h a vector of household and individual covariates, including level of education, a dummy for whether the household XX has access to electricity, sex of the household head, share of household members working in agriculture, share of household members working in industry, share of household members working in services, a dummy for whether the household lives in an urban area and geographic controls,<sup>12</sup> λ denotes a district fixed effects and ε hd is the error term. The coefficient of interest is dd σ, which is the DD (difference-in-differences) estimator.

For individual-level outcome variables, we estimate the following regression: where is employment of individual _i_ in district _d_ at time period _t_ . Specifically, employment takes the value of 1 for an employment category and 0 otherwise. iid<sup>13</sup> These YY categories include manufacturing, agriculture, services, construction and retail and wholesale. We estimate all regression models by using sample weights. We control for

> 12 The geographical controls include GPS location (longitude and latitude) of a locality. For robustness, we include ecological zones such as coastal, forest, and savannah as additional geographic controls.

> 13 All employment variables are estimated using a linear probability model for ease of interpretation.

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individual characteristics such as age, sex and whether an individual lives in an urban area.<sup>14</sup>

# _4.4 Validity of the Identification Assumption_

Our identification strategy requires the treatment group and the control group to have similar trends from the pre-treatment periods. One could argue that members of a local community of a region with oil do not have control over oil exploration and drilling activities, and hence, oil assignment cannot be influenced. In addition, the households did not have prior information on the geology of the offshore region with respect to oil. Therefore, the location of offshore oil fields would not have influenced their settlement decisions. This suggests that there is unlikely to be self-selection as a result of the oil prior to the discovery, which supports our identification assumption. We show trends in the conditional mean of real income for households in the treatment and control groups. As seen in Figure 3, both the control and treatment households had similar trends prior to the discovery.

In Table A-2 in the Appendix, similarity in pre-trends is tested by regressing income and employment on the interaction of time and treatment status ( _T_ ) for periods before the discovery. As seen in columns 1 and 2, the coefficients on income and employment are not significantly different from zero. This indicates that both groups experienced similar trends prior to the discovery.

To further support the validity of the identification, household and individual characteristics are compared across the treatment and control groups before the discovery. While significant differences in characteristics between the groups would not necessarily violate our identification assumption, similarity across the two groups would help support the idea that these two groups would have shown similar trends in characteristics in the absence of oil discovery. In Table 2, we show that households in the treatment and control groups have similar characteristics. There is no significant difference in the education level, age, household size and the share of households with

> 14 We restrict the working-age group to individuals aged between 15 and 65.

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access to electricity except for the percentage of households headed by men. This similarity in characteristics lends support to the idea of the treatment and control groups having similar trends in the absence of the oil.

# **5. Empirical Results**

# _5.1 Effects on Income and Consumption_

The results from estimating the impacts on real income and consumption are presented in Table 3.<sup>15</sup> The coefficient of interest is T*Post interaction, which is the effect of the oil discovery on outcome variables of interest. All specifications include district fixed effects and household controls. The coefficient on the log of real income is positive and significant. Household income increased by 39 log points (or about 47 percent) for the households in the oil region compared to the households in non-oil regions. Given the large magnitude of the effect, we investigate why households in the oil region experienced large changes in income. Our results indicate increases in the average employment income per household<sup>16</sup> at both the extensive and the intensive margin. First, the number of income earners per household increased by about 0.2 for households in the treatment group (in Appendix Table A-6). Given the average number of income earners in the pre-treatment period, this effect translates to about a 10 percent increase in the number of income earners. Second, we find an increase in household employment income by more than 30% (in Appendix Table A-7). This implies that employment income per income earner also increased.

In contrast, the effect on consumption (column 2) is statistically insignificant. We also investigate the effect on household poverty status by using a dummy variable P P , which is equal to one if a household is below the poverty line and zero

> 15 Data on price index used to compute the real income in this study is compiled by Ghana Statistical Service (GSS). This data is available in the 3 rounds of GLSS data.

> 16 Employment income includes wage and self-employment income. We considered other components of household income such as transfers separately. Although not presented in the paper, we do not find significant increases in transfers.

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otherwise<sup>17</sup> (column 3). The coefficient has the right sign, which indicates a reduction in poverty even though it is not statistically significant. Taken together, the results suggest that the increase in real income did not translate into significant increases in consumption. We discuss alternative reasons for this finding in section 5.6.

# _5.2 Effect on Employment_

Oil production can have indirect impacts on other sectors.<sup>18</sup> To determine the indirect effects of the oil discovery on employment in various sectors, we construct indicators of employment in sectors such as manufacturing, retail and wholesale trade, construction, services and agriculture. These indicators are dummy variables for the sector of employment for each individual who falls within the age group under consideration. We control for the individual’s age, gender, whether the individual lives in an urban or rural area and geographic controls. Table 4 reports regression results on employment. Employment in general increased by 4 percentage points. Manufacturing and construction employment increased significantly by 4 and 2 percentage points respectively (Columns 3 and 4). Employment in the agricultural, retail and services sectors increased although not significant. The findings of IFC (2018) indicate that, despite Ghana’s local content regulation that started in 2014,<sup>19</sup> limited opportunities are available to the local workforce, mainly because of the knowledge and technology intensive nature of the industry, and lack of education and vocational training of local labor force. Our results are consistent with these findings.

# 5.3 _Robustness Checks_

> 17  The variable poor defines the poverty status of a household based on the poverty line. The poverty line is GH¢1,314 per adult equivalent per year in 2013 prices.

> 18 Direct local employment in the oil and gas sector constitutes a small percentage (0.039%) of total employment (Ministry of Finance, 2014). Hence, we focus on the indirect impacts of oil discovery on employment in other sectors. 19 In Ghana, the local content regulation requires that, within a decade from the date of effectiveness of the license or the petroleum agreement, over 60 percent of goods and services be sourced from local companies and over 70 percent of technical and management staff be Ghanaians (IFC, 2018).

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We conduct a number of robustness checks. First, as discussed in section 4, we excluded the Central region due to an existing onshore oil field.<sup>20</sup> We examine the sensitivity of the results to the inclusion of districts in this region. The results are presented in Table 5a. The sample includes the excluded region and all regions in the main results. The results are robust to the inclusion of districts in this region, indicating that our main results are not driven by the exclusion of these districts. We also examine whether the findings on employment are sensitive to the inclusion of these districts. In Table 5b, we show that manufacturing and construction employment remain significant. The coefficients on retail and services remain positive and insignificant.

Secondly, in order to ensure that the control group is similar to the treatment group, we use non-oil regions that are within 200 miles of the oil region. Unlike the main results that include all districts in non-oil regions, this specification restricts the control group to only districts in non-oil regions within 200 miles. The results remain unchanged for income, consumption and employment. There is an increase in income for households in the oil region compared to those in the non-oil region. Employment increased in both manufacturing and construction sectors. The results are robust to different thresholds of distance. These results are presented in Tables A-3 and A-4 in the Appendix.

Third, in a separate regression, we look at public transfers received by households. According to official documents, the government does not redistribute oil revenues in the form of transfers to households. Consistently, our result indicates that the government transfers do not drive the observed results of income increases.<sup>21</sup> Finally, the interaction of a time trend and an indicator variable for households that live in coastal areas was included as a control. This addresses concerns that coastal areas might be seeing an improvement in income and employment over the time period under consideration due to reasons other than the oil. As an alternative to the indicator

> 20 In addition to the central region, we show that including regions in the north as control regions does not affect the main results.

> 21 These results are not included in the main paper due to small sample size. Public transfer is defined as all income transfers from the government, including income from Ghana’s conditional transfer program (LEAP). Also, the results lend support to the argument that increases in income are as a result of the oil through the employment channel.

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for coastal areas, we include distance to the port city. These results are presented in Tables 6 and A-5 in the Appendix.

# 5.4 _Heterogeneous Effects_

Our results indicate that the oil discovery increased household income and likelihood of gaining employment. We examine heterogeneous impacts by gender and education of workers. Offshore oil development is more capital-intensive than other conventional oil and gas explorations because the facilities required for extraction and delivery of products are typically more elaborate. As a result of its capital-intensive nature, offshore oil development may require relatively more educated labor workforce than the inland counterpart. This observation indicates that the local labor workforce with high level of education is more likely to benefit from the positive impacts.

To determine whether the impacts are different for educated and uneducated workers, we use the education level of household heads (in the household sample) and individuals (in the individual sample). Skilled workers are defined as those who have completed middle school education or higher. The results are reported in Table 7. The positive effect on income is mainly driven by educated workers (panel A, column 1). This is consistent with the findings by IFC (2018). Additionally, to determine the gender differences, we run regressions for men and women separately. The effects are similar for men and women (columns 3 and 4).

# 5.5. _Why did the increase in income not translate into increases in consumption expenditure?_

Our results show that though the oil discovery increased households’ income this did not translate into an increase in consumption. The coefficient on consumption is positive but not statistically significant. We examine possible explanations for this result. First, the oil discovery increased employment in manufacturing and construction sectors where only a small percentage of the poor are engaged. It had no impacts on retail, 16
services and agricultural employment in which a large proportion of the poor are engaged. Second, changes in the composition of household spending can result in reduced spending.<sup>22</sup> Households can shift expenditure on food, for example, to more durable goods (Banerjee et al., 2015).<sup>23</sup> Lastly, since the consumption and poverty variables have the right signs, the insignificant results suggest that it may take some time for the effects to become visible. Note that our results are consistent with previous studies. Chuhan-Pole et al. (2015) find that total expenditure for households in gold mining communities in Ghana did not increase though wages increased.

# **6. Alternative Mechanisms**

# _Are the results driven by selective migration?_

Migration is likely to bias our results if households moved into districts of the oil region in pursuit of better opportunities as a result of the oil. There is a possibility of households self-selecting into the region after the discovery. Additionally, given that the oil region has mining sites, there is a likelihood that some households migrated into the region because of mining. For example, there will be an upward bias in our results if people who already have jobs or earn more income move into the oil region. Thus, it is important to ensure that our results are not driven by such compositional changes.

To alleviate this concern, we restrict the analysis to the non-migrant subpopulation. We define non-migrants as households (or individuals) who lived in the same region before and after the oil discovery.<sup>24</sup> The results are presented in Tables 8a and 8b. Estimates on the effect of oil discovery on income and employment remain positive and significant, indicating that our results are not necessarily driven by selective migration into the region. This result is consistent with an earlier study on an oil boom in

> 22 Banerjee et al. (2015) find that households’ access to microcredit did not lead to significant increases in total consumption, but a shift in the composition of consumption goods from non-durable goods to durable goods such as refrigerators and motorcycles.

> 23 Our measure of total expenditure includes both food and non-food expenditures.

> 24 Our data allows us to determine individuals who are migrants and when they migrated.

17
Brazil, which finds no effects of oil discoveries on migratory flows (Caselli and Michaels 2013).

# **7. Conclusion**

This paper examined the local impacts of Ghana’s offshore oil discovery in 2007. We find that the oil discovery increased real income and employment for households and individuals in districts associated with oil discovery compared to those without such discovery. Income increased by 4 percent for households living in the oil region compared to households in non-oil regions. The increase in income is stronger for workers with higher levels of education relative to less educated workers. We find similar effects when studying employment outcomes. Local employment in general increased by 4.5 percent, with the discovery having an indirect impact on other non-oil local sectors such as manufacturing and construction. There were no significant impacts on agriculture and service employment.

Though we find the effect of offshore oil production on income to be significant, we do not find a statistically significant impact on consumption. This may be because poor households that are mainly involved in the agriculture and service sectors did not benefit from the increase in employment or because of shifts in consumption from nondurable to durable goods.

We note that these results should be viewed as short-run effects because offshore oil discovery in Ghana is relatively recent, and thus we examine the impacts only a few years after the discovery.<sup>25</sup> In order to explore the longer-term effects of oil on local communities, an extension of the time period with a more recent wave of household survey data (GLSS 7) will be carried out. Later waves of household survey will provide in-depth insights on the long-term impacts of oil on local communities.

Overall, the findings suggest that the offshore oil discovery and production can create opportunities for local firms and workers in other sectors. They also indicate that

> 25 The findings are consistent with  Arezki, Ramey, and Sheng (2017) who argue that the news of large-scale oil resource discovery affects macroeconomic outcomes after 5 years due to the lag between discovery and production. However, our approach allows us to look at the distributional impacts at the household level.

18
the positive economic impacts may be limited to those households with educated workers. The fact that the benefits reached the more educated workforce implies that investment in education and skills training is important for the local labor force and could serve as a poverty reduction tool. Investigating whether such distributional impacts can be resolved over time requires further studies with longer-term observations upon offshore oil discovery.

There is large potential to create economic benefits from the oil development locally through transfers of oil revenue from the central government to the district governments. It can support investment in education and infrastructure. Such investment can strengthen the positive impacts of oil development and reduce poverty. Thus, efficient public spending needs to be given a high priority on the policy agenda.

The quality of governance and institutions has tremendous impacts on the allocation of revenue (Anderson, Francois, and Kotwal, 2015; Besley and Burgess, 2002; Bhattacharyya and Hodler, 2010; Brollo et al., 2013; Lizzeri and Persico, 2001). Empirical evidence from other countries suggests that oil revenue can bring about corruption and rent seeking behavior. Transparency of revenue collection and allocation is critical to prevent the misuse of the revenue.

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Table 1: Summary statistics – household sample

|Variables|Mean<br>|SD|
|---|---|---|
|Income Per capita|5361.04|19595.94|
|Consumption Per capita|1575.27|2276.88|
|Household Size|5.44|2.73|
|Migrant|0.51|0.50|
|Male|0.74|0.44|
|Age|45.53|13.35|
|Urban|0.54|0.50|
|Electricity|0.65|0.48|
|No Education|0.18|0.38|
|Primary Education|0.20|0.40|
|Secondary Education|0.54|0.50|
|Tertiary Education|0.08|0.28|
|HH. members in Industry|0.28|0.45|
|HH. members in Services|0.56|0.50|
|HH. members in Agriculture|0.48|0.50|
|Poor|0.19|0.39|
|Number of Observations|5914||

Notes: Means are estimated using sample weights. HH. members in (Industry, Service and Agriculture) refers to the share of household members in each sector. Income and consumption are measured in Ghana Cedi. Poor refers to percentage of households below the poverty line in the sample.

23
Table 2: Baseline household characteristics between treatment and control groups

||Con|trol Grou|p|Treat|ment Gro|up||
|---|---|---|---|---|---|---|---|
|Variable|Mean|SD|N|Mean|SD|N|Difference|
|Household Size|5.272|2.735|4465|5.632|2.854|834|0.360|
|Male|0.733|0.443|4465|0.770|0.421|834|0.037*|
|Age|46.095|13.821|4465|45.400|13.466|834|-0.695|
|Urban|0.516|0.500|4465|0.702|0.458|834|0.186|
|Electricity|0.553|0.497|4465|0.533|0.499|834|-0.019|
|No Education|0.205|0.404|4129|0.207|0.405|760|0.002|
|Primary Education|0.154|0.361|4129|0.171|0.377|760|0.016|
|Secondary Education|0.566|0.496|4129|0.564|0.496|760|-0.002|
|Tertiary Education|0.075|0.263|4129|0.058|0.235|760|-0.016|
|HH. members in Industry|0.277|0.447|4029|0.202|0.401|747|-0.075|
|HH. members in Services|0.522|0.500|4029|0.383|0.486|747|-0.139|
|HH. members in Agriculture|0.502|0.500|4029|0.664|0.473|747|0.162|
|Numberof income earners|2.102|1.171|3647|2.070|0.910|704|-0.032|

Notes: This table reports average characteristics of households in the control and treated groups using data from GLSS 5. Standard errors of the differences in average characteristics are in parentheses *** p<0.01, ** p<0.05, * p<0.1 24
Table 3: Effects of oil discovery on household income and consumption

|Variables|Ln (Real Income)|Ln (Real Expenditure)|Poor|
|---|---|---|---|
||(1)|(2)|(3)|
|_T*Post_|0.391***|0.039|-0.006|
||(0.129)|(0.064)|(0.045)|
|Observations|5,914|5,914|5,914|
|R-squared|0.635|0.890|0.240|

Notes: This table presents results from estimating the effect of oil discovery on income, consumption, and poverty. T is a dummy equal to 1 for households in the treatment group. Poor is a dummy equal to 1 for households below the poverty line. All regressions include household controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 25
Table 4: Effects of oil discovery on employment

|Variables|Employment|Agriculture|Manufacturing|Construction|Retail|Services|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|_T*Post_|0.040**|-0.030|0.039*|0.021**|0.027|0.003|
||(0.017)|(0.046)|(0.020)|(0.010)|(0.027)|(0.035)|
|Observations|17,672|12,794|12,794|12,794|12,794|12,794|
|R-squared|0.768|0.372|0.040|0.063|0.153|0.225|

Notes: All outcome variables are indicators of employment in a particular sector. Employment refers to whether an individual is employed or not. These are estimated using a linear probability model. T is a dummy equal to 1 for individuals in the treatment group. All regressions include individual controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level.*** p<0.01, ** p<0.05, * p<0.1 26
Table 5a: Robustness to inclusion of excluded region - Real income and Consumption

|Variables|Ln (Real Income)|Ln (Real Expenditure)|Poor|
|---|---|---|---|
||(1)|(2)|(3)|
|_T*Post_|0.479***|0.065|-0.033|
||(0.130)|(0.061)|(0.042)|
|Observations|6,455|7,532|7,532|
|R-squared|0.637|0.886|0.235|

Notes: All regressions include a non-oil region excluded from the control group in the main results. T is a dummy equal to 1 for households in the treatment group. Poor is a dummy equal to 1 for households below the poverty line. Each column includes household controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 27
Table 5b: Robustness to inclusion of excluded region - Employment

|Variables|Employment|Agriculture|Manufacturing|Construction|Retail|Services|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T*Post|0.046***|-0.042|0.038*|0.020**|0.032|0.013|
||(0.016)|(0.045)|(0.020)|(0.010)|(0.026)|(0.035)|
|Observations|19,327|13,895|13,895|13,895|13,895|13,895|
|R-squared|0.776|0.372|0.044|0.062|0.152|0.222|

Notes: All outcome variables are indicators of employment in a particular sector. Employment refers to whether an individual is employed or not. T is a dummy equal to 1 for individuals in the treatment group. These are estimated using a linear probability model. All regressions include a non-oil region excluded from the control group in the main results. Each column includes individual controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 28
Table 6: Robustness to inclusion of coastal indicators and distance to port city – Real income and Consumption

|Variables|Ln (Real<br>Income)|Ln (Real<br>Expenditure)|Poor|Ln (Real<br>Income)|Ln (Real<br>Expenditure)|Poor|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T*Post|0.398***|0.073|-0.006|0.383***|0.039|-0.004|
||(0.129)|(0.067)|(0.047)|(0.130)|(0.064)|(0.045)|
|Observations|5,914|5,914|5,914|5,914|5,914|5,914|
|R-squared|0.636|0.892|0.240|0.635|0.890|0.240|

Notes: Columns 1-3 include an interaction of a time trend and an indicator for coastal areas. Columns 4-6 include distance to the port city. Poor is a dummy equal to 1 for households below the poverty line. All regressions include household controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 29
Table 7: Heterogeneous impacts

|Variables|Skilled|Unskilled|Men|Women|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|_Panel A: Income_|||||
|T*Post|0.437***|0.264|0.337**|0.612**|
||(0.143)|(0.291)|(0.154)|(0.224)|
|Observations|4,906|1,008|4,216|1,698|
|R-squared|0.636|0.639|0.638|0.655|
|_Panel B: Employment_|||||
|T*Post|0.044**|0.011|0.039**|0.040**|
||(0.017)|(0.021)|(0.018)|(0.020)|
|Observations|15,373|2,299|8,445|9,227|
|R-squared|0.777|0.599|0.792|0.749|

Notes: All regressions include household and individual controls, and district fixed effects. T is a dummy equal to 1 for individuals in the treatment group. Skilled workers are individuals who have completed middle school education or higher. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 30
Table 8a: Selective migration - Real income and Consumption

|Variables|Ln (Real Income)|Ln (Real Expenditure)|Poor|
|---|---|---|---|
||(1)|(2)|(3)|
|T*Post|0.397***|0.039|-0.006|
||(0.129)|(0.064)|(0.045)|
|Observations|5,811|5,811|5,811|
|R-squared|0.636|0.890|0.240|

Notes: All regressions include household controls and district fixed effects. T is a dummy equal to 1 for households in the treatment group. Poor is a dummy equal to 1 for households below the poverty line. Standard errors in parentheses area clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 31
Table 8b: Selective migration - Employment

|Variables|Employment|Agriculture|Manufacturing|Construction|Retail|Services|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|T*Post|0.040**|-0.032|0.039*|0.022**|0.030|0.006|
||(0.017)|(0.046)|(0.020)|(0.010)|(0.027)|(0.035)|
|Observations|17,411|12,609|12,609|12,609|12,609|12,609|
|R-squared|0.771|0.375|0.041|0.063|0.153|0.226|

Notes: All outcome variables are indicators of employment in a particular sector. Employment refers to whether an individual is employed or not. T is a dummy equal to 1 for individuals in the treatment group. All regressions include individual controls and district fixed effects. Standard errors in parentheses are clustered at the enumeration area level. *** p<0.01, ** p<0.05, * p<0.1 32
- Oil and n on-oil Di

- 5 eme

- A 24 tales j ee
Figure 3: Conditional mean of real income.

Notes: This figure plots the trends in real income of households in the treatment and control groups.

35