INFRASTRUCTURAL DEVELOPMENT, INCOME INEQUALITY AND POVERTY ALLEVIATION IN SUB-SAHARAN AFRICA

Abass A. Bello

Sylvanus I. Ikhide

AbstractThe importance of economic growth for poverty alleviation attracts little or no controversy in development literature. This study offers an empirical assessment of the role of infrastructure development in income distribution and poverty alleviation based on a panel data estimate of 40 SSA economies for the period 1981-2015 using a GMM estimation technique. Findings from the study show that overall, infrastructure development impact negatively on income inequality and poverty. In addition, the study revealed that improvements in the quality of infrastructure have greater impact on inequality and poverty compared to increases in infrastructure stocks.

Keywords: Infrastructure, Development, Poverty Alleviation, Income Inequality

1 IntroductionThe importance of economic growth for poverty alleviation attracts little or no controversy in development literature (Lopez, 2005, Dollar and Kraay 2000, Roemer and Gugerty 1997, World Bank 1990, Fields 1989). Within the past half century, considerable attention has shifted to the contribution of infrastructure development to economic growth. The effects of public infrastructure development on economic growth is now fairly well established in the literature (Aschauer, (1998), (1989a &b), Ahmed and Miller (2000), Alfredo and Jorge (2010), Calderón, Easterly and Servén (2003)). However, the likely effects of infrastructure investment on inequality and poverty remains a question yet to be properly answered. If infrastructure development impact positively on aggregate income, facilitates access of the

poor to productive economic opportunities, helps raise the value of the poor’s assets and enhance their human capital by improving their health and education outcomes as the literature indicates (see Calderon and Serven, (2004), Calderon et. el, (2014), Canning and Pedroni, (2008) Canning, (1999), Chatterjee and Tumvosky, (2012)); could there be any link between physical infrastructure development and inequality? Can infrastructure development be a tool for poverty alleviation? These are questions yet to be properly answered.

This study attempts to bring together the two strands of literature on infrastructure development and poverty alleviation to enhance our understanding of the linkages between the two and further assist policy makers in their quest for optimal policy to resolve the problems of poverty. The next section of this study provides a brief stylized fact on infrastructure development, growth and poverty alleviation in Sub – Saharan Africa (SSA) followed by the literature review in section 3. The theoretical framework, methodology and data are provided in section 4, followed by the analysis of the results in section 5. The last section of the paper provides the summary and conclusion.

2. Stylized Facts on Infrastructure Development, Growth and Poverty in SSAUnlike the economic growth experiences of the lost decade (1970-1990), the average economic growth rate in SSA between the year 2000 and 2015 was as high as 5% per annum (World Bank, 2016). As a result of this development, GNI per capital grew from an average of about $551 in 1995 to $1720 by the end of 2013 (World Bank, 2014). Despite the change in economic narratives, the region still accounts for about half of the world’s poor population. Regional poverty rate in SSA is as high as 46.3 per cent while about 400 million of the estimated 936 million population of the region lives below $1.25/ day poverty line (AfDB, 2012). Based on the World Bank development reports of 2012, 10 of the world’s most unequal nations are in SSA. A number of recent studies in growth performance attribute high poverty rates in SSA to the high level of inequality in the region. Although conventional empirical studies have consistently failed to find any enduring relationship between economic growth and income inequality, findings from recent studies (Ferriera and Ravallion, 2008; Bourguignon, 2004; Clark, 1999; Ravallion and Chen, 1997) suggest that high initial inequality impact negatively on growth elasticity of poverty, especially if inequality increases during the growth process. Thus, the twin problem of poverty and inequality in SSA despite the recent growth experience is better considered a simultaneous occurrence.

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In addition to the problem of poverty and inequality, SSA economies also have a severe unemployment problem. The region’s unemployment rate of 7.5 per cent stands above the world average of 5.7 per cent (Brookings, 2017). The region’s youth population (15-24 years) constitutes about 72 percent of the entire population. Unfortunately, about 75 per cent of this population are unemployed or underemployed, with countries like Burundi, Ethiopia, Nigeria and Zambia having as high as 80 percent youth unemployment rate (AfDB,2012).

The incidence of poverty is inversely related to the level of income or pattern of resource control within a society. Thus, an individual is considered poor if his/her income is low and cannot afford him/her the basic needs of life for a meaningful standard of living. Poverty in SSA is essentially a rural phenomenon. Income within the rural agrarian community is a linear function of the size of landholdings or wage income from farm labor while incidence of poverty in these communities exhibit an inverse relationship with the size of landholding in the community (Ali and Pernia, 2003).

It is implicit that, proceeds from farm produce sales is the primary source of income for landholding rural dwellers while agricultural wage income remains the only source of income for a landless rural agrarian community dweller who surely exchange wages from his agricultural labor for crops. For the former group a bad season implies bad investment and loss of income, for the latter group of rural dwellers a bad season or increase in prices imply low wage income or decline in real income either of which leads to increased poverty. Investments in infrastructural facilities such as electricity, roads and irrigation systems in such communities will increase both agricultural and non-agricultural productivities and employments in the rural community, stimulate the local economy and enhance the real income and standard of living of the poor thus ensuring poverty reduction.

Furthermore, increased industrialization and the prospect for wage income in urban centers increases the migration of population to industrial towns and cities in search of employment. This trend increases the urban poor population and exert pressure on the limited infrastructural facilities available in the urban areas. Most often such rural urban migrants settle in the poor neighborhood of urban cities with less than ideal living conditions. Investment in public infrastructure such as electricity, public water, waste treatment and disposal facilities in such areas will go a long way to increase their wellness and productivity, reduce their illness and medical expenditure and thus increase their disposable income and minimize income disparities in the cities. Investments in public transportation in the urban areas have the potential to reduce transportation cost, increase distance traveled, and

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enhance the prospect for employment and connectivity between potential employer and employee, thus increasing income and bridging income gap in the city. The availability of productive economic opportunities does not enhance income or alleviate poverty in itself except those opportunities are harnessed. However, the ability to harness productive economic opportunities such as employment and training to a large extent depends on the available information on such opportunities and the ease of obtaining such information. In view of this, public investment in telecommunication and internet broadband facilities can bring valuable information about economic opportunities closer to the people and enhance people’s assessment of such opportunities. Such facilities will go a long way to reduce commute time, transportation and transaction cost. It will increase access to information about job opportunities, increase employment and income and reduce income inequality. Furthermore, such facility can also help in manpower training through online classes and improve the technical knowhow of job seekers thus increasing their market worth.

The discussion above brings to focus the role of infrastructure development in SSA region. Governments and government agencies are the largest providers of infrastructural services in SSA. However, increasing demand and budget constraints has made such provision grossly inadequate. A recent estimate shows that the region currently has infrastructural deficit of about $31 billion (AICD, 2010) and requires approximately $93 billion worth of investment annually to meet demand in the region (AU, NEPAD, 2011). Lack of infrastructure to support core economic activities and provide access to economic opportunities is often given as one of the reasons for inequality and high poverty rates in the region. Based on a recent survey by the African Development Bank (AfDB, 2009), less than 30% of the rural population in SSA have access to all season roads while less than 50 percent of such roads are paved. Only about one – third of the population in the region live within two kilometers of all season roads (Gwilliam, et. el, 2008)

Water for consumption, sanitation, industrial and agricultural production is a basic need of life critical to human survival. As important as it is, as many as 40% of the population in SSA lack access to clean portable water. Consequently about 60% of the population lack basic sanitation while most farmers are seasonal farmers due to lack of irrigation facilities. In view of this situation one cannot be surprised that deaths resulting from water related diseases in the region is among the highest in the world and despite having the largest proportion of its labor force in agricultural sector, the region still lacks self-sufficiency in food production. The lack of irrigation facilities makes farmers in the region depend on rainfed agriculture, making the farmers seasonal farmers that mostly plant crops once in a year. However, irrigation facilities help to increase crop planting cycle, improve the quantity and quality of

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farm outputs, create jobs for farm workers and increase their income and that of the farm owners. This goes a long way to reduce poverty and income disparities in the rural communities, stabilize the prices of food and the real income of urban dwellers who depends on farmers outputs for their food needs. Generally, investments in irrigation facilities generates multiplier effects in output, income and employment in the rural economy and a spillover effects in terms of price and real income stability in the urban sector.

Furthermore, an efficient means of communication is a necessity for modern day trade and commerce, hence a good telephone service is a necessity for economic growth and human development. However, telephone penetration in SSA is just about 14 percent compared to 69 percent in the United States, Canada, Germany and the United Kingdom, 40percent in Mexico and Thailand, and a minimum of 20% in India, Philippines, Indonesia, Iraq and Ukraine. The digital revolution of the 20th century has made e-commerce a major driver of trade and commerce. This makes functional and efficient internet service a necessity for any nations to be part of the new trade order. By the end of 2016, the SSA region has recorded about 420 million unique mobile subscribers which is equivalent to about 43 percent penetration rate. This is expected to increase to about 50 percent by the year 2020 (GSMA, 2017). This is relatively high compared to other regions of the word. However, this encouraging development is mainly accounted for by four countries Democratic Republic of Congo, Ethiopia, Nigeria and Tanzania. In spite of this development, women are 17percentless likely than men to own a mobile phone (GSMA, 2017).

SSA is considered the most electricity – poor of all the regions of the world (IEA, 2016). Two out of three people in the region lack access to electricity, thus, about 600 million people in the region do not have access to modern energy. Where such access exists, the access rate is as low as 20 percent. Although the total installed electricity generating capacity of the region as of 2012 was 90GW which translates to about 0.1 Kw per capital, the combined electricity production of the countries of the region with a total population of about one billion was as low as 158 gw. This is less than the total power generation of the state of California (39.5 million population) which stands at 290.567gw for the same period (CEC, 2017). Based on the most recent estimates of the International Energy Agency (IEA), the demand for electricity in SSA currently stands at 352 TWh, this is expected to increase at the rate of 4percent per annum through 2040. Unfortunately, 40 percent of those projected increase will be accounted for by only Nigeria and South Africa which are the two largest economies in the region (Castellano et al, 2015).

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Infrastructure services in SSA are not only inadequate and grossly inefficient but they are also expensive compared to other regions of the world. The price of a kilowatt of electricity in SSA range between $0.7 and $0.45 per hour, however, the same quantity only cost between $0.05 and $0.22 in other regions of the world. One cubic meter of water cost between $0.03 and $3.8 in other region of the world, however the same quantity of water cost between $0.86 and $8.00 in SSA. Road freight tariff in other regions of the world range between $0.01 and $0.14 in other parts of the world, however, the same freight attracts between $0.04 and $0.14 in SSA.

By facilitating access of the poor to productive economic opportunities, infrastructure development helps to raise the value of their assets, increase their income and their personal development through better education and health services (Calderon and Serven, 2014). It acts as a catalyst to development and stimulates the effects of poverty intervention policies by ensuring the access of the less privileged to economic opportunities. According to Calderon and Serven, (2014), good road network coupled with an efficient communication system expands geographical access; it eases the movement of goods and services, helps to reduce transaction costs and improves profit margin on both farm produce and industrial outputs. It ensures prompt and efficient information dissemination, enhance factor mobility and helps to connect the less privileged to economic opportunities that improves their income and brings them into the core economic activity of the society

Adequate provision of infrastructure does not improve the well-being of the less privileged in the society except the services of such infrastructures are readily available and accessible. For instance, an efficient power supply is of no use to the poor if they cannot afford to connect to its services, just as a good road network without a complimenting affordable transportation system is of no use to the poor. In view of this, if the provision of infrastructure is considered a necessity for the welfare of the less privileged, the accessibility of the services of such infrastructures must be considered a priority. According to Pouliquen (2000), lack of access to infrastructural services results in exclusion from productive economic opportunities and this exclusion is reinforced by three main factors: (1) lack of provision. (2) pricing (3) social-political factors. Lack of provision of infrastructure services deprives the less privileged and excludes them from the mainstream economic development activities going on around them and such exclusions could restrict their potentials and deprive them productive economic opportunities. High prices of infrastructural facilities where they are readily available price such services beyond the reach of the poor and thus excludes them from the benefits of such services. According to

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Pouliquen (2000), the cost of unaffordable infrastructural services to the poor includes both out of pockets and time costs. Generally, lack of access to infrastructural facilities results in deprivation and exclusion which adds to the precarious economic situation of the less privileged in the society. However, this only reflect the “static” aspect of poverty that specifically relates to the income group defined as poor based on some pre-specified criteria. Given that income distribution is hierarchical in nature, it is possible to have some people just on the border line of poverty. Such people are vulnerable to falling below the poverty line the moment their living condition depreciates. Also, some people are so poor that any further negative development in their standard of living puts them into destitution. These two situations reflect the “dynamic” aspects of poverty which relates to the prospect of getting poor or being destitute; the vulnerable in society (Pouliquen, 2000).

While provision of infrastructural facilities and access to such facilities constitutes the primary solution to the case of the static poor, the dynamic poor requires provision, access and protection from depreciation to alleviate their living condition. For the dynamic poor, protection might require special provisions to mitigate risks specific to this group, ensure sustenance, participation in and profit from the national economy. The case of dynamic poverty clearly shows that the impact of infrastructural development on poverty alleviation depends primarily on the needs of the poor as determined by their poverty levels. Thus, to be successful infrastructural development policies must recognize the variations in poverty and the specific needs of the less privileged in society.

3. The Theoretical FrameworkThe relationship between infrastructure development, inequality and poverty alleviation can be viewed from both macro and micro perspectives. Following Anderson et. el (2006), we assume a theoretical one sector, one representative individual market clearing model, where factor prices adjust automatically based on the assumption of full employment. Given this background and results from empirical studies (Calderon and Serven, 2014: 2004; Canning, 1999; Aschauer, 2000: 1998), we assume that infrastructure services and private capital are complements in the production process. Thus, given an aggregate production function of the form:

……………. (1)

Where is defined as the aggregate output, private capital, labour force (both mental

and physical), public capital (infrastructure), natural resources endowment and the total factor output. From this specification one can safely conclude that an increase in

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public capital will increase the aggregate output as well as the productivity of other factor inputs. Infrastructure investments complements other factor inputs to raise aggregate output of goods and services. Assuming a competitive but inelastic labour market (because it takes time to prepare labour for production), increases in aggregate output increases the marginal product of labour as well as the real wage and purchasing power that improves wellbeing. This development creates three distinct effects on individuals and firms as follows:

Quantity Effect: Public infrastructure cannot be adequately priced by private firms due to spillover or externality effects; it is assumed that they are solely provided by the government through the government power of taxation. However, initial supply is limited by government budget constraints, thus, the quantity of infrastructure consumed by an individual or a single firm at a given time is limited and constrained by the initial supply except government increases the supply by increasing its investments in infrastructure services. Subsequent increase in investment by the government increases the supply of infrastructure and relaxes the consumption constraints imposed by the initial supply level. The effect of increase on household’s welfare reflects in the increase in quantity and or quality of services which can be expressed in the household’s utility function as:

…………… (2).

the utility function of a household , is the prices of goods consumed by the

household, represents the fixed quantities of public goods consumed by the

household, in the specification represents the time dimension. This is important because some infrastructure services once provided cannot be unprovided, however, the quality of the services depreciates with time. The welfare effects of government investments in

infrastructure is given by Because of the diminishing marginal utility of the household, the welfare effects of public infrastructure on households depend on the amount and the quality of the initial provision, and this effect declines over time. The higher the initial supply of infrastructural services in the economy, the lower the welfare effect of subsequent investment is expected to be (Anderson et. el. 2006). This relationship is demonstrated graphically as follows:

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I(a) I(a)

C B B’ I(b’)

I(b) 0 A A’In the above diagram rectangle ABC0 represents the initial supply of infrastructural services while household’s consumption is restricted to quantity 0A, subsequent increase in government investment in infrastructure increased supply to A’B’C0. If government imposed no taxes or user charges on the new quantity AA’ supplied, the total infrastructure consumption of households increased to 0A’ and the households now operates on indifference curve I(b’) provided the household placed a high premium on the new supply and attach a high monetary value to it. However, if the household attach low monetary value to the quantity supply, the household still enjoys the quality effect of the new increments as the household operates on a higher indifference curve I(b) as the quality of services provided by the additional supply increase.From the above, the effect of public infrastructure provision on firm’s activities can be projected based on the profit motive of the firm. Giving that public and private capitals are complements in a production process (van de Walle and Nead, 1995; Anderson et. el. 2006; Calderon and Serven, 2014), and that public goods are in fixed supply while the supply of labor and private capital is not constrained; the profit function of an individual firm can be

expressed as: ……… (3)

Here is the profit of firm , is the prices of goods and services produced by the firm.

represents the various amount of public infrastructure accessed by the firm. is a

set of other factors that affect firms profit margin. Like in the case of households, in the specification represents the time dimension, this is important because it changes the profit function of the firm. The profit effect of infrastructure provision on firms’ operation is given

by . This profit effects also depends on the level and quantity of the initial supply of

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infrastructure services. The higher the initial supply of infrastructure the lower the impact of additional supply will be on firm’s activities due to diminishing marginal returns.

The Price effects: By varying the assumption of a strict complement to private goods one can derive the price effects of infrastructure provision on households and firms in the following manner: By serving as a complement to private capital, infrastructure enhances their productivity. On the other hand, by serving as substitute, it increases the variety of goods and services available and enhances the choice of consumers. In these two cases, it helps to drive down the prices of goods and increase the profit margin of the firm. From

equation (1) the price effect on consumer’s utility function can be derived as . However, this effect can either be positive or negative depending on the direction of movement of prices. A reduction in price creates positive effects while increase in price creates negative effects. Moreover, the magnitude of these effects will depend on the quantity of the goods consumed as well as the feature of the commodity in question. From equation (2) the effect

of price change on firm’s profit function is given as . The sign and the magnitude of this effect depend on whether the firm produces the commodity or consumes it, the ease of change in supply as well as the quantity produced or consumed of the commodity (for more detail see Anderson et. el. 2006).

Inequality and Poverty Effects: The quantity effect above creates change in the accessibility which effects go a long way to impact on the welfare of poor consumers in the economy. The quantity effect creates higher accessibility to the poor, the more the government invests in infrastructure services, the higher the accessibility of such services to consumers. This enhances the connection between the less privileged and economic opportunities which initially exist beyond their reach. This also helps to increase the value of their services and products as well as ensure better outcome thus increasing their share of national income. Mosley et. al. (2004) documented that increase in government infrastructure spending had a negative and statistically significant impact on the $1,25 per day Poverty headcount even when the level of GDP is held constant. The distribution effect can be demonstrated graphically as follows

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a

b c

0 In the graph above inequality is measured on the vertical axis while infrastructure provision is measured on the horizontal axis. Increase in government investments in infrastructure

increased the access of the poor from to and increased the well-being of the poor

because it placed them on a higher indifference cure even at their level of income, at

the same time the level of inequality fell from to . Rectangle a b represents the

reduction in inequality while rectangle , , c b represents the increase in wellbeing from

the increase in infrastructure supply. Alternatively, if and are considered to be price

levels, the increase in infrastructure investment caused reduction in price from to the

rectangle , , a b could then be considered to be the reduction in poverty as a result of the

increase in infrastructure, while rectangle , , c b is representing increase in the value of income as a result of the increase in the purchasing power of the income of the poor.

From the above one can conclude that increase in infrastructure investment increases access of the less privileged in the society to economic opportunities through quantity effect while the reduction in prices resulting from the price effect increases the monetary value of income. In addition to the micro-level considerations above, infrastructure investments also affect economy-wide variables such as aggregate demand, national savings, employment and real exchange rates. Based on the Keynesian assumption of flexible wages and prices, infrastructure investments affect national income through its incremental effects on aggregate demand, increase in public investment leads to increases in national income which helps to stimulate aggregate demand and accelerate economic growth at least in the short-run (Hausmann et al. 2004)

4. Empirical Analysis

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Based on evidence from the literature (Fosu, 2009; Foster, Greer and Thorbecke 1984 Hicks and Streeten, 1979; Streeten, 1977 and Adelman, 1975), an individual is considered ‘poor’ if his/her income cannot adequately provide for him/her the basic needs of food, clothing and shelter in a minimal convenient way within his/her immediate environments. Based on Fosu (2009), this basic need assertion can be represented in a Cobb-Douglas function as follows:

………………………………… (1)

In the above specification represents the poverty level defined as the headcount ratio which is measured as the relative frequency of income below the poverty line while the

squared gap represents the severity of poverty. Thus, it follows that the shorter the distance between income level and poverty line, the higher the level of income and the lower the severity of poverty. is the income level, while represents the autonomous

poverty level which is independent of the level of income and is the income-elasticity of poverty. Following Foster, et. el (1984), we differentiate and linearize equation (1) and rewrite it in growth level measures as follows:

……………………………………… (2)

Equation (2) now represents the growth rate of poverty where is the growth rate of

poverty at a constant income level, is the income elasticity of poverty while and are the growth rates of poverty and income respectively. Economic growth that results in higher level of income results in a lower level of poverty. However, this will depend on the existing distribution pattern (Son and Kakwani, 2004). Income-elasticity of poverty is higher for countries with equitable income distribution; thus, such countries exhibit a higher rate of transformation of income growth to poverty reduction. On the other hand, income growth accompanied by high inequality redistributes income in favor of the income group above the poverty line and increases poverty below the poverty line as prices responds to a demand pattern largely determined by the income group above the poverty line. Moreover, poverty level grows faster when the initial income is higher for the income groups above the poverty line. Taking the above factors into consideration (distribution pattern and initial income

level), and in equation (2) can be expressed in terms of their respective determinants as follows:

……………..…………….… (3)

..…………………………....…… (4)

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In equations (3) and (4) above, , and are the coefficients of the determinants of

growth in poverty level that are all independent of the changes in income level. and

are the coefficients associated with changes in income levels. and in equations (3) and (4) are the initial levels of income and the initial distribution of income respectively. Equations (3) and (4) are incorporated into equation (2) to yield equation (5) which is

considered a full specification of the determinant of poverty level ( ) as follows:

….………. (5)The specification above satisfies the two main propositions concerning the relationship between economic growth and poverty alleviation in the literature. The limited equation (2) justifies Dollar and Kraay (2002) proposition that economic growth benefits all income groups without disproportionately affecting the poor. On the other hand, a more elaborate equation (5) clearly suggests that income inequality has implications for poverty as well as the growth-poverty relationships (Ostry, Berg and Tsangarides, 2014; Stiglitz, 2012; Berg and Ostry, 2011; Fosu, 2010, 2009, 2008; Easterly, 2007; Son and Kakwani, 2004).

To examine the effect of infrastructure development on poverty alleviation, an infrastructure variable (z) is added to equation (5) above. Theoretically infrastructure development and economic growth may be highly correlated; in view of this a multicollinearity problem is envisaged, however the application of principal component analysis (PCA) to our infrastructure variable makes it a synthetic variable free of multicollinearity. By introducing infrastructure variable into equation 5 it becomes:

…….………. (6)

In the above specification is the poverty level, is the growth or change in income level, g is the change in income distribution (Gini coefficient) which could be positive or

negative depending on the change in distribution of income. is the initial income

distribution (Gini Coefficient), is the initial income level and is a synthetic measure of

infrastructure investments? is the intercept while is the coefficient of the independent impact of change in income on poverty; due to the inverse relationship between income and

poverty (Fosu, 2010) is expected to have a negative sign. is the independent impact of income distribution (Gini coefficient) on poverty. The higher is this coefficient, the larger

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poverty is expected to be, hence the sign of the coefficient is expected to be positive. is the coefficient of the impact of the interaction of income growth and the initial level of income distribution. Higher level of initial inequality reduces the impact of economic growth

on poverty alleviation (Shimeles 2014), hence is expected to have a positive sign. is the coefficients of the interaction of change in income distribution and the initial level of income on poverty. Increasing income gap between the rich and the poor increases poverty

(Mckay, 2013), hence we expect to have a positive sign. Lastly, is the coefficient of the synthetic infrastructure variable; infrastructure development increases the access to economic opportunities and help increase the value of the assets of the poor as well as their income (Bajar and Rajeev 2015) hence, this coefficient is expected to have a positive sign.

The distributional effect of infrastructure investments at best remains ambiguous (Gibson and Rioja, 2014; Calderon and Serven, 2014). Theoretically, the marginal product of additional investments in infrastructure in poor areas is expected to be higher than the marginal products of such similar investments in affluence areas because of the poor level of existing infrastructure investments in the poor communities (Calderon and Chong, 2004). However, whether this differential marginal product comes with a distribution effect is yet to be determined. Infrastructure investments may serve as a linkage between poor communities and core industrial areas thus helping to improve the access of the poor to productive economic opportunities and increase their income. Such investments could further foster trade, division of labor, specialization and market expansion as production and transaction cost decline overtime and may raise the income of the poor and the value of their assets over and above the average national levels. On the other hand, infrastructure investments may serve as complement to other factors of production, thus yielding higher marginal product in areas with existing substantial investments. Such situations may result in increase in income inequality as infrastructure investments leads to higher returns in richer areas that are relatively abundant in private capital

To examine the impact of infrastructure development on poverty along with other macroeconomic variables we expand equation (6) above to include the lag of Gini coefficient which is our proxy for initial condition based on Son and Kakwani. (2004), Inflation, Trade Openness, Government expenditure and Employment (all based on Sami and Zhang, 2016); credit availability based on Khandker and Koolwal, (2010) and Deininger and Squire (2006).

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We introduce a poverty severity index (PSI) to take account of the severity of poverty in SSA1. Such index will not only help to appreciate the gravity of the poverty situation in SSA better, the effects of the interaction of such index with growth on poverty rates could possibly give an insight into why economic growth in SSA has failed to reduce poverty in the region. Based on the intuition of severity of poverty from (Fosu, 2010) we define our PSI for

the purpose of this study as the square of the poverty rate ( ). Thus, expanded equation (6) becomes equation (7) as follows:

….….(7)

In equation (7) above, is poverty level defined as the income of the lower 20 percent earners of the income distribution. Poverty rate in an economy depends on two basic factors, the average rate of income and the distribution of such income (Son and Kakwani, 2004). Thus, the initial level of economic growth and the prevailing pattern of income

distribution are important factors in subsequent income levels, hence we added

and which are the lags of the distribution and per capital GDP to our explanatory

variable. is the log of (per capita GDP) income level. represents our synthetic

measure of infrastructure. represents inflation is our proxy for trade openness,

private sector credit as a percentage of GDP which is our proxy for credit accessibility.

is government expenditure. is employment share of industry, PSI is poverty severity

index, PSIY measures the interaction of poverty severity index and the GDP, is country

specific effect and is the error term. Equations (7) as specified above does not give any indication of the distributional implication of infrastructure development or the effects of economic growth on income distribution. In order to examine probable distributional effect that infrastructure investment may generate we specify a distribution equation (9) as follows:

.... (8)

1 Unlike many other parts of the world where poverty rates declined significantly within the past three decades, on average the SSA region recorded less than 10 per cent decline in poverty rates

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In equation (8) above, is our inequality variable measured by Gini coefficient, is

lag of Gini coefficient, is inflation rate, is Gross Domestic Product, is the

growth rate of gross domestic product is secondary school enrolment rate, trade

openness, is employment rate, is financial sector development, this is

share of manufacturing sector in gross domestic product, this represent institutional

quality, is unobserved common factor, this is unobserved country specific effects,

is error term. are the coefficients of the respective explanatory variables.

The models presented in equations (7) and (8) as specified above makes use of Principal Component Analysis (PCA) in some of the component variables in order to avoid the problem of multicollinearity. However, as lofty and useful as the PCA is, it deprives us of the advantage of examining the individual impact of the components of our infrastructure variable. In order to be able to examine the impact of the individual infrastructure components we substitute the synthetic infrastructure variable index in equations (7) and (8) with data on electricity installed capacity to examine the impact of power generation capacity in our model, thus we have equations (9) and (10) as follows:

..........… (9)

…. (10)All variables specified in the above equations are in natural log forms.

The study extends further by dividing the sample countries into three sub-groups namely upper middle, lower middle and low per capita income based on the World Bank Atlas classification approach.

5. ResultsIn order to investigate the long run behavior of our variables and determine their stationarity, we conducted a unit root test of our regression variables and the results are presented below:

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Table 1a: Result of the Panel Unit Root Test (Individual Effects and Trends)

Variables LLC Breitung IPS ADF-Fisher PP-Fisher RemarkGINI -7.9901*** 9.1377 -2.2666** 77.2194 93.5650**∆GINI -19.9515*** -2.8675*** -5.7846*** 136.600*** 135.276*** I(1)POV 2.64860 6.22774 3.58434 26.5592 32.7983∆POV -14.7764*** -3.49845*** -10.5202*** 306.166*** 313.287*** I(1)INF -14.1946*** -10.2977*** -10.2826*** 368.708*** 829.359*** I(0)GDPGR -21.8616*** -14.7881*** -22.4847*** 574.770*** 835.168*** I(0)SCHENROL -3.85153*** 9.35678 4.45209 56.7901 58.2738∆SCHENROL -350.676*** 2.7E-12 -7.10831*** 212.401*** 230.102*** 1(1)TRADEOP -3.40603*** -2.02710** -1.93356** 120.091*** 121.259*** 1(0)EMPLM 2.65097 8.67294 5.93096 36.5605 42.5888∆EMPLM -6.54166*** -4.15582*** -8.34636*** 222.579*** 247.102*** 1(1)FSD -0.54299 4.01125 3.02631 60.4810 56.3786∆ FSD -29.7334*** -15.5770*** -28.0922*** 906.633*** 1857.86*** 1(1)SMS -3.8907*** -0.63363 -2.25391** 102.814** 115.998*** 1(0)INST 1.27823 0.23729 2.02666 31.2044 33.2329∆INST -13.7339*** -7.83492*** -12.4253*** 232.542*** 254.150*** 1(1)GOV -6.45098*** -3.61376*** -4.45929*** 157.502*** 168.910*** 1(0)

Note: ***, ** and * denote 1%, 5% and 10% levels of significance respectively. ∆ denotes first difference operator.

The pooled results of the panel root individual effects and trends tests as presented in table 1a shows that, inflation, GDP growth rate, trade openness, government expenditure and SMS are all stationary at levels with significance ranging from 1% to 5%. On the other hand, other variables such as Gini coefficient, poverty, school enrolment rates, employment rates, Financial sector development (FSD) and quality of institutions were all stationary after first differencing at significant levels ranging from 1% to 5%. In order to test the stationarity of the variables employed in our Principal Component Analysis (PCA) we also carried out Unit Root tests on our PCA variables and the pooled results are presented in table 1b

Table 1b: Results of Unit Root tests for PCA Variables

Variables LLC Breitung IPS ADF-Fisher PP-Fisher Remark

TELESUB 16.2081 29.0402 25.9601 0.94959 1.53674∆TELESUB -7.71194*** 2.10915 -9.00352*** 238.218*** 240.099*** 1(1)HEALTHEXP 0.06602 4.24331 3.27742 53.2546 70.0363∆HEALTHEXP -19.9079*** -14.9216*** -16.0599*** 466.122*** 596.278*** 1(1)ELECTPROD -1.81224** 1.44865 -1.85531** 66.1190** 63.0534** 1(0)ELECTLOSS -3.9422*** -1.45357* -2.35275*** 83.8169*** 87.4816*** 1(0)TELEPLOSS -3.30273*** 1.84791 -0.85153 110.272*** 123.536***∆TELEPLOSS -28.8869*** -15.0053*** -14.0327*** 532.801*** 1615.22*** 1(1)LIFEXP 1.19776 16.5402 6.48797 105.503 58.2801∆LIFEXP 1.33716** 108.4375** 0.00974 109.565** 109.659** 1(0)

Note: ***, ** and * denote 1%, 5% and 10% levels of significance respectively. ∆ denotes first difference operator.

Table 1b shows that our PCA variables exhibits mixed levels of stationarity and varying degree of significance. While telephone subscription, health expenditures and telephone loss

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and life expectancy all exhibit stationarities in different degrees of significance ranging from 1% to 5% at first differencing, only electricity production and electricity loss have stationarities at levels.

Table 1c presents the descriptive statistics and the pairwise correlations of the main variables in the model

Table 1c: Descriptive Statistics of Poverty, Inequality and Infrastructural Variables in Africa

POV GINI ELECTPRO ELECTLOSS TELELOSS TELESUB HEALTHEXP LIFEXP Mean  5.888405  48.59235  1.41E+10  1.50E+09  60391.67  15.49082  66.40827  52.34223 Median  5.851200  46.70000  4.16E+09  4.86E+08  25801.00  2.301566  29.07826  51.23644 Maximum  11.83000  66.30000  2.08E+11  1.71E+10  557449.0  125.6818  465.5407  64.22432 Minimum  0.830000  29.50229  4.07E+08  0.000000  24.00000  0.181668  3.805632  41.92988 Std. Dev.  2.232753  9.214153  3.73E+10  2.85E+09  70300.51  25.78046  86.81680  5.657818 Skewness  0.129684  0.387197  3.988589  3.284667  1.888823  2.106170  2.090286  0.217210 Kurtosis  2.814589  2.233534  17.80882  14.50469  9.430907  6.925679  6.687118  2.051894 Jarque-Bera  1.571325  18.35146  4373.728  2713.154  859.9034  512.5173  480.3218  16.81288

From table 1c, the mean and standard deviation of poverty rates is 5.888405 and 2.232753 respectively showing that poverty rates in SSA countries exhibits a very low dispersion rate., suggesting that poverty rates across the region display quite similar characteristics. This low dispersion is further confirmed by the low values of Skewness, Kurtosis and Jarque -Bera measures which are all measures of central tendencies. The mean of GINI in table 1c is given as 48.59235 while the standard deviation is 9.214153. The high standard deviation observed in the distribution of GINI confirms the wide dispersion in the distribution of income across the region. Generally, the individual measures of infrastructure services and quality as well as their aggregate measures exhibit high degree of dispersion which reflects the varying levels of provision and quality of these services in the SSA region.

The pairwise correlation analysis of the two major dependent variables and determinants of primary interest is presented in table 1d. Electricity provision (ELECTP) and Electricity loss (ELECTLOSS) shows high degree of correlation reflecting the low transmission capacity and high rates of power outage in the region. However, this high-level correlation is not expected to cause multicollinearity problem in the regression results since the two variables are neither joint determinants in any of our models nor have any causal relationships within the model.

Table 1d: Correlation Matrix of Poverty, Inequality and Infrastructural Variables in Africa

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POV GINI ELECTPRO ELECTLOSS TELELOSS TELESUB HEALTHEX LIFEXP

POV 1

GINI -0.2868 1

ELECTPRO -0.38995 0.4027 1

ELECTLOS -0.41014 0.3113 0.9036 1

TELELOSS -0.1555 -0.1047 0.3012 0.38298 1

TELESUB -0.1062 0.2399 -0.0357 -0.03265 -0.17992 1

HEALTHEX -0.14173 0.27347 0.54628 0.45350 0.02541 0.44869 1

LIFEXP -0.00586 0.250006 0.247558 0.11789 -0.19764 0.25046 0.39223 1

Infrastructure and Poverty Alleviation

The regression results of the empirical relationship between aggregate infrastructure development (quantity and, quality) and poverty rates are presented in tables 2a and 2b. Here poverty rate which is the main dependent variable is defined as the income of the bottom 20 percent quantile of the income distribution. Among regressors included in the study are: income of the bottom 20 percentile of income distribution which serves as our proxy for poverty rate, inflation rate, GDP per capital, and one indicator of human capital development (secondary school enrolment). Other regressors are financial development, trade openness, institutional quality and government expenditure. The square of poverty rates and the lag of poverty are included as proxies for severity of poverty and the prevailing poverty situation. Finally, aggregate measures of infrastructure stocks and quality are added as measures of infrastructure. The results of the aggregate model a (column 2) and the country classifications (columns 3-5) are summarized in this section.

In table 2a we present the results of our basic model where poverty is strictly a function of the individual measures of infrastructure quality, quantity and their synthetic measures. The table also includes results of sub-regional groupings of income groups in the region, thus the table gives more detail on the relationship under consideration. The results from table 2a shows that quantity and quality of infrastructure are generally associated with poverty alleviation. While the coefficients of the aggregate measures and their signs conform with a priori expectations, the signs of the coefficients of individual measures of infrastructure did not perform well. This may be due to the high degree of correlation between these variables. We report the results of the aggregated infrastructure variable in table 2b.

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Infrastructure quality is significantly negatively related to poverty rate; a one standard deviation increase in the quality of infrastructure stock is likely to reduce poverty rate by 0.20 percentage point within the next five years among the whole sample group and between 0.20 and 1.14 percentage point within the income sub-groups. The regression result shows a significantly negative relationship between aggregate measure of infrastructure stock and poverty rates. Based on the regression result, a one standard deviation increase in the aggregate measure of infrastructure stock will reduce poverty rate by 0.06 in the whole sample group, 0.23 within the upper middle income sub-group, 0.12 in the lower middle income sub-group and 0.22 for the low income sub-group. A critical look at the coefficients of aggregate infrastructure stock and quality reveals that although availability of infrastructure services is negatively related to poverty alleviation in the sampled countries, however, the quality of such service is of more importance for poverty alleviation as reflected in the higher coefficients exhibited by aggregate infrastructure quality.

Based on the results from table 2b, an increase of one standard deviation in lagged poverty rate is likely to reduce earned income within the bottom 20 percentile income group by between 0.08 and 0.35 percentage points. A one standard deviation increase in inequality will reduce income within the bottom 20 percentile income distribution and increase poverty in the group by as much as 0.0037 to 0.04 percent within the subsequent 5 years. The result confirms the findings by Weide and Milanovic, (2015), Fosu, (2008), Kalwij and Verschoor, (2007); Ghura, Leite and Tsangarides, (2002); Datt and Ravallion, (1992). The results specifically confirms the findings in Ravallion, (1997) which concluded that high-inequality developing countries might find it difficult to escape absolute poverty. A one standard deviation increase in inflation is shown to be associated with between 0.00024 and 0.059 percentage increase in poverty rate in the bottom 20 percentile income distribution. This result conforms with earlier studies such as Narob, (2015) and Monnin, (2014). A one standard deviation increase in GDP per capital will reduce poverty within the bottom 20 percentile income group by about 0.0378 thus underlining the importance of GDP growth in poverty alleviation (Dollar and Kraay, 2002). However, the results of the sub-groups shows some interesting dimensions; while the result of the lower income economies conform with a priori expectations, the results for upper middle income and lower middle income groups shows a positive and statistically significant relationship with poverty rate within the lower 20 percentile income groups.

According to Roemer and Gugerty (1997) the effects of economic growth on poverty alleviation depends largely on the structure of the economy, the initial income distribution

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and the quality of the available market mechanism that helps to transmit economic growth to a broad based income effect. Thus, an extraction based economic growth without adequate policies to involve lower income groups increases income gap and heighten the scrunching effects of poverty among the lower income groups as commodity prices respond to demands from the upper income group. This might possibly be the explanation for the positive correlation observed between GDP growth and poverty among the upper middle income and the lower middle income sub-groups in our sample as majority of the countries in these group are predominantly commodity based economies.

Our result shows that a one standard deviation increase in secondary school enrolment rate is likely to increase income and reduce poverty rate within the bottom 20 percentile of income distribution by about 0.0002 and 0.023 percent within the subsequent 5 years. This result conforms with earlier studies (Calderon and Serven, 2004, Calderon and Chong, 2004). The result on trade openness and poverty rate shows that a one standard deviation increase in trade openness will reduce poverty rate within the bottom 20 percentile income distribution by as little as 0.001 percentage point and is statistically insignificant. However, the results for the various income classifications is significant and rightly signed for the lower middle and low income countries.

A one standard deviation increase in financial development will likely increase poverty within the bottom 20 percentile by 0.026 for the whole sample and by between 0.015 and 0.033 percentage point in the sub grouping samples. This should not come to us as a surprise. Our proxy for financial sector development is credit to the private sector as a ratio of GDP which is barely 30 percent in the region (IMF, 2017). Mihci (2006) indicates a threshold level of financial development, with the implication that positive effects fail to materialize at relatively lower stages of financial development (see also, Yilmazkuday, Hakan. 2011).

Institutional quality is significantly negatively related to poverty. Within the overall model, a one standard deviation increase in the quality of broad institutions will reduce poverty rates by about 0.006 percentage point in the next five years. This negative relationship also holds for all sub regional income groups except for the low income sub regional group where the relationship is positive but not significant.

The result shows that a one standard deviation increase in government expenditure will cause a decline of about 0.020 in poverty levels within the next five years. While the results

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of the upper middle income and lower middle income sub-regional groups, are also negative and statistically significant, the results of the low income sub-group shows a positive but statistically insignificant relationship between government spending and income inequality. The results for the whole sample and the sub regional groupings shows a significant positive relationship between poverty severity and poverty rates.

Infrastructure and Income Distribution

In this section we shift attention of our study to the relationship between infrastructure development and income distribution. Following the format above, we summarize the results of the link between income inequality and the components of infrastructure as well as the quality and quantity of infrastructure in Table 3a. The coefficients of individual and aggregate measures of infrastructure stock and quality are negatively correlated with income distribution (GINI) at different levels of significance with correlation ranging from 0.07-0.18 (telecommunication stock), 0.03 -0.20 (health stock), 1.07 – 4.90 (electricity stock), 0.83 – 9.80 (telecommunication quality), 0.6- 1.11 (health quality), 0.0001– 6.25 (electricity quality), 0.07- 3.59 (aggregate infrastructure stock) and 1.11 – 3.22 (aggregate infrastructure quality).

The regression result for the full model on the relationship between infrastructural development and income distribution is presented in table 3b. The aggregate measures of infrastructural stock and quality were found to be significantly negatively correlated with income inequality both in the whole sample case and the sub-regional income groups A one standard deviation increase in infrastructural stock and quality was revealed to reduce income inequality in the sampled countries by between 1.040 – 7.444 (infrastructure quality) and 0.242 – 0.842 (infrastructural stock) percentage points respectively within the period. Increases in the quality of infrastructure has a far greater impact on income inequality compared to the impact of increases in the stock of infrastructure. A similar observation was made by Calderon and Chong, (2004) and Calderon and Serven, (2004).

Based on the result on Table 3b, the coefficient of lagged inequality is shown to be significant and positively correlated with income inequality. A one standard deviation increase in lagged inequality will increase income inequality within the next 5 years by between 0.32 – 1.00 (see also Fosu, 2010). A one standard increase in inflation rates within the whole sample will increase income inequality by 0.019 on the aggregate and by between 0.018 and 0.607 for the country groups. The results support findings by Narob, (2015),

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Stefania, (2002) and Bulif, (1998) who have all found positive correlation between inflation and income inequality especially in the early stage of development.

The regression result shows that GDP per capital is negatively correlated with income inequality. A one standard deviation increase in GDP per capita is likely to reduce inequality by 0.11 percentage point in the whole sample. For the sub-regional income groups, the result shows that a one standard deviation increase in GDP per capital will reduce income inequality by between 0.31 and 3.21 percentage point in the next five years

The regression result shows a negative correlation between economic growth and income inequality, the result indicate that a one standard deviation increase in economic growth will reduce inequality by between 0.13 – 3.34 percentage points within the next five years. With respect to trade openness, the result for the whole sample shows a positive and substantial significant correlation between trade openness and income inequality; a one standard deviation increase in trade openness is shown to increase income inequality by 0.068 percentage points. Whereas, the upper and lower middle income countries exhibit a negative and statistical significance negative correlation between trade openness and income distribution, the lower middle and low income economies show a positive and statistically significant positive correlation between trade openness and income distribution which range between 0.077 and 0.159 percentage points.

The result further show that a one standard deviation increase in employment will reduce income inequality by between 0.062 – 0.441 within the subsequent five years.From the regression result, a one standard deviation increase in financial sector development is likely to reduce income inequality by between 0.009-0.061 percentage points. However, for the upper middle income group a one percentage point increase in financial sector development is likely to increase income inequality by about 0.190 percentage point within the subsequent five years. This result should not come as a surprise. In highly unequal societies such as Botswana, Namibia, South Africa and other upper middle income economies in SSA, it is doubtful if financial sector development proxied by credit to GDP ratio would reduce income inequality given that only the well-placed have access to credit (Honohan, 2007). Tita and Aziakpono, (2016) had earlier found a weak evidence of positive correlation between financial development and income inequality in a sample of SSA economies.

With respect to the impact of manufacturing sector performance, our results show that a one percentage point deviation increase in manufacturing sector performance will reduce

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income inequality by between 0.184- 0.728 percentage points within the next 5 years. The result shows that a one standard deviation increase in the quality of institutions is likely to reduce income inequality by between 0.123 – 0.157 percentage points within the subsequent five years, except in the case of upper middle income countries where the relationship is negative and statistically significant.; Government expenditure is significantly negatively related to income inequality in the sampled countries over the sampled period. A one standard deviation increase in government expenditure will reduce inequality by 0.042 percentage points in the whole sample reduce it by 0.231 percentage points in the middle upper income group, by 0.612 percentage points in the lower middle income countries and by 0.039 percentage points in the low income countries With regards to poverty severity, the regression results shows a significant positive correlation between poverty severity and income inequality., We interacted poverty severity index on income growth variable (GDP) and regressed the outcome on our inequality variable. The result of the regression shows that the interacted variable is negatively correlated with income inequality with varying degrees of statistical significance across the sub-regional income groups. The result shows that a one standard deviation increase in the interaction variable is likely to reduce income inequality by between a range of 0.008 – 0.422 within the subsequent 5 years. Just like the case of the relationship between poverty severity and poverty the presence of severe poverty drastically reduces the impact of income growth on income inequality thus suggesting that economic growth might be a weak tool for the reduction of income inequality in a severe poverty situation as earlier implied by Ghura et.el, (2002) and Moser and Ichida, (2001) .

Finally, in order to test for the robustness of the regression results, we carried out autocorrelation test; the result of the test confirmed that the regression model do not suffer from first order AR [1] or second order AR [2] correlation problem. Thus, based on this result and Hausman test result we can confirm that the empirical results of this study are robust, reliable and unbiased. Generally, all the coefficients emanating from the regression result with the exception of trade openness are statistically significant.6 Conclusion

This study investigated two questions that bothers directly on the effects of infrastructural development on human welfare outside the conventional infrastructure-growth nexus: (1) the effects of infrastructural development on income distribution, and (2) the effects of infrastructural development on poverty alleviation. Aggregate infrastructure stock and the quality of the available infrastructure services have significant positive effects on poverty alleviation and the intensity of the effect depends on the amount and the quality of the available services. Regions with higher quantity and

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better quality infrastructure experience lower poverty rates compared to regions with lower infrastructure stocks and quality. However better infrastructure quality impact stronger on poverty rates compared to increase in infrastructure stock. The study shows that increase in aggregate infrastructure development measured by increased stock and improved quality have significant negative effects on income distribution. Thus, suggesting that, the higher and better the quality of infrastructure services, the easier policies targeted at reducing income inequality might achieve their goals.

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EXPLANATION OF VARIABLESThe data for this study contains samples from 40 countries drawn from the four regions of Sub Saharan Africa over the period 1980-2015. The variables used in the study, their definition and sources are presented in the table below:

VARIABLE DEFINITION SOURCEEducation Ratio of total secondary school

enrollment regardless of age to the population of age group that officially correspond to that level of education

The World Bank World Development Indicators

Employment Rate Ratio of the employed to total working age population.

International Labour Organization (ILO)

Electricity Electricity generating capacity in MW per 1000 workers expressed in log

Calculated from United Nations Energy Statistic Yearbook

Electricity Quality Electricity power transmission loses as a percentage of electricity output ranked between 0 and 1

Calculated from World Development Indicators (WDI) and individual country sources

Financial Sector Development

Ratio of total domestic credit to private sector as a percentage of GDP

Calculated from World Development Indicators

Health (quantity) Total government expenditure of health

World Development Indicator

Health (quality) Life expectancy at birth World Development IndicatorGovernment Expenditure Gross non-capital expenditure as a

percentage of GDP.World Bank World Development Indicators and IMF government financial statistics

Gross Domestic Product (GDP) per capita

Gross domestic product (constant 2010 US$) divided by mid-year population

World Development Indicators various years

GDP Growth Rate (annual)

Aggregate value added by all resident in the economy plus any product taxes, minus any subsidies not included in the value of the products at annual market prices based on constant local currency.

World Bank World Development Indicators and IMF Financial statistics

Inequality Measured by Gini coefficient which is defined as the area between the Lorenz curve and the line of absolute equality, expressed as a percentage of

Calculated from World Bank World Development Indicators and World Bank Povnet data base and SWIID GINI data

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the maximum area under the line.

Inflation Rate Consumer price index serves as our inflation variable

Calculated from IPS and Central Banks publications

Infrastructure Quantity(synthetic Index)

First principal component (PCA) of the quantity of Energy (electricity generating capacity in Kilowatts per thousand of the population); density, and Telecommunication (defined as cell phone subscription and internet penetration per 100 people).

International Telecommunication Union (ITU)United Nations Energy Statistics Yearbooks, World Bank. World Development Indicators (WDI)

Institutional Quality ICRG Political Risk Index quality range between 1 – 6, interpreted as 1 for the lowest and 6 for highest.

International Country risk guide

Manufacturing, value added (% of GDP)

industries belonging to ISIC divisions 15-37

World Bank national accounts data.

Poverty Rate income of the bottom 20 percentile of income distribution

World Bank Development Indicator

Telecommunication(quantity)

Telephone access per 100 of population

ITU Telecommunication Reports

Telecommunication(Quality)

Dropped call per day International telecommunication union

Terms of trade Net barter terms of trade index Calculated from World Development Indicators

Trade Openness Total Export as a % of GDP "World Bank national accounts data and World Economic Outlook data.

Table 2a: Infrastructure and Poverty across African Countries, Panel Regression, 1981-2015. Dependent Variable: Poverty.

All SSA Countries

Upper Income Countries

Lower Middle Income Countries

Lower Income Countries

I. Infrastructural Stocks Telecommunications -0.2876*** -1.2278** -0.0656*** -0.074589***

(0.0784) (0.4464) (0.0239) (0.0243) Health 0.1219*** 0.3702** 0.0508*** 0.0531**

(0.0363) (0.1524) (0.0121) (0.0393) Electricity -4.1889 -20.3051 -0.1216 -0.4645

(3.3030) (59.3273) (0.7962) (0.7903)II. Infrastructural Quality Telecommunication -2.4061*** -5.8706** -0.6038 -0.6157**

(0.8256) (2.3625) (0.5573) (0.4572)

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Health -1.1557*** -25.2760 -1.2561 -1.2583(6.8174) (17.7176) (5.3792) (3.5943)

Electricity -12.9912*** -1.5435*** -0.4182 -0.8286***(0.5186) (0.3980) (0.6849) (0.7356)

III. Aggregate MeasuresInfrastructural Stock -0.6594 -6.2676*** -0.2906 -0.769464**

(0.6975) (1.8495) (0.2668) (0.3073)Infrastructural Quality -1.744436*** -32.0957*** -0.1909*** -0.1001***

(0.7791) (5.1483) (0.3010) (0.2080)

Hausman Test 5.048837 5.2035 1.796 1.3718Prob. (0.0801) (0.1575) (0.6158) (0.7122)Random/Fixed Random Random Random Random

Notes: ***, ** and * indicates 1%, 5% and 10% respectively. Standard error is noted in parenthesis. SSA means Sub-Saharan Africa

Table 2b: Variable Effect of Infrastructure on Poverty Across African Countries, GMM Regression, 1981-2015.

Dependent: Poverty (Aggregate Infrastructure).

All SSA Countries

Upper Income Countries

Lower Middle Income Countries

Lower Income Countries

Poverty Lagged -0.22896*** -0.20155*** -0.08889** -0.3535*** (0.07196) (0.2052) (0.0643) (0.1110)

Inequality -0.01289*** -0.00375** -0.0121 -0.0434*** (0.010097) (0.0242) (0.0131) (0.0308)

Inflation 0.00052*** 0.05918** 0.00024 0.00373**(0.000169) (0.2263) (0.00028) (0.00188)

GDP Growth -0.0378** 0.5493** 0.1434*** -0.2439**(0.0464) (0.4568) (0.0554) (0.0114)

School Enrolment -0.00367 0.03949 -0.00018 -0.0232**(0.00312) (0.0315) (0.00479) (0.0116)

Trade Openness -0.00101 0.00483** -0.00947*** -0.01575**(0.00559) (0.0058) (0.00318) (0.01570)

Financial Development 0.000261*** 0.0326** 0.01475* 0.0176**(0.00559) (0.03128) (0.00809) (0.0235)

Institutional Quality -0.00582** -0.14396** -0.1371** 0.46108(0.1049) (0.1318) (0.0691) (0.2582)

Government Expenditure -0.0201*** -0.0165** -0.01595*** 0.01187**(0.0022) (0.0145) (0.0061) (0.01137)

Poverty Severity 0.0479*** 0.0436*** 0.08168*** 0.0544***(0.0043) (0.0378) (0.00671) (0.00577)

Poverty Severity*Income 0.00459** 0.0049*** 0.01497** -0.0129***(0.00549) (0.0029) (0.00701) (0.00403)

III. Aggregate MeasuresInfrastructural Quality -0.1979*** -0.21041** -0.19877** -1.1446***

(0.0008) (0.1282) (0.1129) (0.4178)Infrastructural Stock -0.05516 -0.22719** -0.12418** -0.2195***

(0.0471) (0.0369) (0.06826) (0.20236)

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No of Countries 40 07 13 20Sargan Test of Overid. (Prob.)

8.4890(04856) 4.3370(0.3218) 4.4073(008826) 16.4156(0.0587)

Serial Autocorrelation Test AR (1) (p-val.) -0.6383(0.5233) -0.4352(0.2344) -0.6821(0.7134) -1.2742(0.2312) AR (2) (p-val.) -1.3880(0.1651) -2.4542(0.2060) -2.2156(0.3658) 0.8683(0.2895)F-Stat (Prob.) 10748(0.0000) 1255.47(0.0000) 8921.95(0.0000) 15611(0.0000)

Notes: ***, ** and * indicates 1%, 5% and 10% respectively. Standard error is noted in parenthesis.

Table 3a: Infrastructure and Income Inequality Across African Countries, Panel Regression, 1981-2015. Dependent Variable: GINI.

All African Countries

High Income Countries

Middle Lower Income Countries

Lower Income Countries

I. Infrastructural Stocks Telecommunications -0.1208** -0.1451 -0.0738 -0.1876***

(0.1208) (0.09002) (0.1152) (0.0841) Health -0.05999*** -0.06542* 0.0328** 0.2094

(0.0207) (0.03297) (0.0703) (0.1335) Electricity -4.8703*** -2.5368*** -1.0685*** -3.6966***

(1.3622) (1.5992) (4.5530) (3.7611)II. Infrastructural Quality Telecommunication -1.3032 -9.8350* -0.8309*** -1.0435***

(1.0076) (5.1891) (1.9305) (1.3056) Health -1.1118* -0.6907 -1.0777** -0.3839**

(8.1846) (29.6173) (19.3275) (12.9858) Electricity -6.2512*** -0.00011*** -0.57158*** -1.7274**

(0.5517) (3.83E-05) (3.2832) (3.7543)

III. Aggregate MeasuresInfrastructural Stock -1.4253*** -3.5991*** -0.0722 -1.4663**

(0.5435) (1.0022) (1.6681) (0.9858)Infrastructural Quality -1.6451*** -1.1527*** -3.2153*** -1.1076**

(0.5356) (2.954197) (1.7696) (0.5637)

Hausman Test 2.8067 2.2592 6.4856 1.8465Prob. 0.8327 0.3232 0.3710 0.9333Random/Fixed Random Random Random Random

Notes: ***, ** and * indicates 1%, 5% and 10% respectively. Standard error is noted in parenthesis.

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Table 3b: Effect of Infrastructure on Income Inequality Across African Countries, GMM Regression,

1981-2015. Dependent Variable: GINI (Aggregate Infrastructure).

All African Countries

High Income Countries

Lower Middle Income Countries

Lower Income Countries

Inequality Lagged 1.0071*** 0.3236***5 0.4849*** 0.5613***(0.0864) (0.3026) (1.2706) (0.7310)

Inflation 0.0193 0.6077*** 0.1747** 0.0189**(0.150) (0.4138) (0.1636) (0.00754)

Economic Growth -0.11068** -3.2107*** -1.1813** -0.3106*(0.1824) (3.2304) (1.8049) (0.1606)

Trade Openness 0.0684** -0.0599** 0.1586* 0.0766***(0.0326) (0.0535) (0.4726) (0.0248)

Employment -0.0623** -0.3806** -0.4406** -0.2411***(0.0635) (0.3276) (0.4726) (0.0355)

Financial Development -0.0085*** 0.1902*** -0.0250** -0.0625***(0.04444) (0.1054) (0.3067) (0.0705)

Manufacturing Sector Perf. -0.1962* -0.4038*** -0.7281** -0.1840*(0.1321) (0.2673) (0.8105) (0.1019)

Institutional Quality -0.2225 -0.7368** -1.5712 -0.1230(0.4572) (0.6425) (1.6853) (0.3579)

Government Expenditure -0.0424*** -0.2314*** -0.6120** -0.0392*(0.0382) (0.2021) (0.6075) (0.0221)

Poverty Severity 0.1656** 0.1272*** 0.1035*** 0.0047**(0.4609) (0.2834) (0.2477) (0.0269)

Poverty Severity*Income -0.0018* -0.4219*** -0.0244** -0.00751**(0.0034) (0.3523) (0.0571) (0.0233)

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III. Aggregate MeasuresInfrastructural Quality -1.3583** -7.4439** -2.1384** -1.0402**

(0.7970) (6.43038) (2.9971) (0.5403)Infrastructural Stock -0.2421** -0.8426** -0.7607 -0.5253**

(0.3769) (2.3036) (1.9080) (0.4665)

No of Countries 40 07 13 20Sargan Test of Overid. (Prob.) 5.5244(0.7003) 2.1304(0.6212) 3.00463(0.9641) 7.7319(0.5614)Serial Autocorrelation Test AR (1) (p-val.) -0.5634(0.4452) -1.4334(0.2484) -1.3355(0.1817) -0.4245(0.6712) AR (2) (p-val.) -1.2907(0.1968) -2.0895(0.2958) -0.6589(0.5099) -0.9325(0.3511)F-Stat (Prob.) 481.46(0.0000) 537.57(0.0000) 21.71(0.0600) 12108(0.0000)

Notes: ***, ** and * indicates 1%, 5% and 10% respectively. Standard error is noted in parenthesis.

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