Sources of Economic Growth in the Southern African Development Community: Its Likely Impact on Povery and Employment

SOURCES OF ECONOMIC GROWTH IN THE SOUTHERN AFRICAN DEVELOPMENT COMMUNITY: ITS LIKELY IMPACT ON POVERY AND EMPLOYMENT

Monaheng Seleteng and Sephooko Motelle Central Bank of Lesotho

Abstract

As a means to combat poverty, many countries still pursue high and stable rates of economic growth. There are several sources of growth such as physical capital accumulation, human capital development and technological progress. In order to attain sustained economic growth, it is crucial that countries do not only accumulate a certain stock of factors of production, but demonstrate the ability to combine such factors in a manner that is efficient. The Southern African Development Community (SADC) continues to pursue high and stable economic growth as a way of fighting poverty and inequality. This study attempts to investigate the key sources of economic growth in the region using different panel data techniques and make inference on poverty and employment. The findings reveal that the factors affecting economic growth in the region are: inflation, government expenditures, openness to trade, human capital, level of financial development, and political stability. Furthermore, it can be inferred from the analysis that higher growth rate has a positive impact on employment and hence may lead to poverty reduction.

Key words: Convergence, Economic Growth, Fixed Effects, Difference GMM, System GMM, Seemingly Unrelated Regressions, Inequality, Unemployment, Poverty

JEL Classification: C33, E31, O43, O47, O55, J640.

Corresponding author: Central Bank of Lesotho, P. O. Box 1184, Maseru 100, Lesotho; email: [email protected]

The views expressed are those of the authors and do not necessarily represent those of the members of the Committee of Central Bank Governors (CCBG) in the Southern African Development Community (SADC). While every precaution is taken to ensure the accuracy of information, the CCBG shall not be liable to any person for inaccurate information or opinions contained herein.

Any queries should be directed to: Dr M Seleteng ([email protected]) 1. INTRODUCTION

Economic growth is generally a process through which economic inputs and resources, such as skilled labour, capital, and funding for new businesses, are converted into economic outcomes such as wage growth, job creation, or new businesses (Hall and Sobel, 2006). The economic outcome generated from any specific set of economic inputs depends on the institutions (political and economic rules of the game) under which an economy operates.The growth performance during the last four decades has been diverse among countries around the world. On the one hand, rapid growth rates were experienced by the Asian tigers between the 1965 and 1995 (De Gregorio and Lee, 1999). These Asian tigers experienced growth rates of around 6.0 per cent per year in per capita terms. On the other hand, many countries in Sub-Saharan Africa (SSA) and Latin America registered less than 1.0 per cent average growth rates in per capita income during the same period.

In the context of the Southern African Development Community (SADC), the patterns of growth can be examined along three regional groups, namely, the common monetary area (CMA), the Southern African Customs Union (SACU) and what can be called other- SADC1. SADC consists of fifteen member states, namely, Angola, Botswana, Democratic Republic of Congo (DRC), Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa (SA), Swaziland, Tanzania, Zambia and Zimbabwe.2 The member states have differing levels of education, health provisions and other socio- economic development. However, according to Nel (2004) members of SADC are characterised by similar trade patterns. There is also evidence of intra-SADC trade albeit very shallow. The Article 5 of the SADC Treaty highlights that the overall objectives of SADC include the promotion of economic growth and socio-economic development that will eventually eradicate poverty, and promote and maintain peace, security and democracy, through regional cooperation and integration (SADC, 2011).

The macroeconomic convergence target for real GDP growth is 7 per cent. Figure 1 shows that on average the CMA saw a steady upward trend in real GDP growth rate which recorded 2.23, 4.78 and 5.67 per cent for the periods 1981-90, 1991-00 and 2001- 10, respectively. The average trend reflects the patterns of economic growth in Lesotho,

1CMA comprises South Africa, Lesotho, Swaziland and Namibia while SACU adds Botswana to the CMA group. 2Madagascar was suspended in 2009 and has since been reinstated in January 2014

South Africa and Namibia which dampened the decadal decline registered in Swaziland. Similarly, SACU realised an increasing average decadal growth rate during the same period. For this bloc of countries, the average real GDP growth rate was 2.05, 4.53, and 4.99 per cent in 1981-90, 1991-00 and 2001-10, respectively. Even though Botswana and Swaziland realised average growth rates that hovered above the SADC convergence target during the 1981-90 decade, the two countries recorded steady declines in the growth rate of GDP from decade to decade as shown in Figure 1. The average GDP growth rate for the group Other-SADC plummeted from 3.89 per cent recorded for the 1981-90 decade to 3.58 per cent during the 1991-00 decade. This decline was attributable to a recession that was realised in DRC during the 1991-00 decade. However, the average GDP growth rate accelerated to 4.53 per cent during the 2001-10 decade. This recovery was driven by average growth rates registered in Angola, Mozambique and Tanzania which surpassed the SADC convergence target. The Zimbabwean recession was overshadowed by the average growth rates realised in other countries in this grouping. The average growth rate for SADC as a whole followed an upward trend mimicking the trends observed in the CMA and SACU.

Figure 1: Trends in Real GDP Growth in the SADC

14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 -2.00 -4.00 -6.00 -8.00

1981-90 1991-00 2001-10 SADC Target

Source: IMF, 2014

Figure 1 shows that disparities in the rate of growth is not unique to regional comparisons as discussed at the outset, but are evident even within SADC. This disparity in growth rates conjures a key issue on factors that drive economic growth. In fact, the key question is: what are the sources of economic growth? The paper investigates the

3 sources of economic growth in SADC and makes some important inferences on how economic growth may affect employment and poverty in the region. The rest of the paper is structured as follows: after this introduction, section 2 provides stylized facts about the main drivers of economic growth in each respective member country. Section 3 synthesises both theoretical and empirical literature on the sources of economic growth. Section 4 outlines the methodological tools employed in the study, namely; fixed effects (FE) estimator, difference and system generalised method of moments (GMM) estimators and seemingly unrelated regression estimators (SURE). Section 5 provides the data analysis, focusing first on the entire SADC and then on the members of SADC without countries that are outliers, while section 6 makes inference on employment and poverty. Section 6 concludes the paper and offers some policy recommendations.

2. MAIN DRIVERS OF ECONOMIC GROWTH IN THE REGION

This section provides a summary of the main drivers of economic growth over the years, for each respective member countries.

Angola

Angola is a country rich in natural resources, namely; oil and diamond that are not yet being exploited in their full potential. Since the end of the civil war, in 2002, the Government has been putting efforts to elaborate and implement projects that will allow a better exploration of the natural resources and a much more diversified economy. Angola has million hectares of potentially arable land and favorable climate and rich water resources what favors the Agriculture sector.

Since 2002, the Angolan economy has entered in a rapidly growing phase, sustained, mainly, by revenues from the oil sector. In 2007 there was a boom in the economy, leading to a real growth rate of 23.2 per cent attributable to the oil production and international prices that were in good levels. At that time, the natural resources, in particular, oil, represented 56.0 per cent of GDP and approximately 80.0 per cent of the government revenues. Up until 2008, the economy grew at high rates, both oil and non- oil GDP, as a result of the input and output of major fields and a significant increase in activity levels of construction, agriculture and trade related services.

The years 2009-2010, were characterized by a decrease in oil production combined with a reduction in other important sectors in the GDP structure growth rates such as agriculture and trade related services. The main reasons for the decline in oil production during that period were the output restrictions due to maintenance and mechanical problems and poor performance of gas lift in some fields. Coupling the drop in production with the reduction in oil prices (according to British Petroleum, the average price of Brent crude oil fell from US$121/barrel in the second quarter of 2008 to US$45/barrel, in the similar quarter of 2009; therefore, government tax revenues declined significantly. Hence, leading to aggregate demand contraction and consequently declining in activity levels of key sectors such as agriculture, trade related services and manufacturing sector.

From 2011 the economy started to recover. Furthermore, due to the 2008 crisis more pressure was put to the Government that started to intensify actions to finalise the projects linked to the diversification of the economy. In 2012 the oil sector represented only 47.0 per cent of GDP giving space to other sectors as Market Services, Construction, Agriculture and Manufacturing that represented 22.1; 8.6; 7.0 and 6.8 per cent, respectively.

For instance, the construction sector is going through one of its best moments, succeeding the construction projects in various areas such as: Rehabilitation of infrastructure (railways, roads, ports, airports, communications, access to water, sanitation basic, etc.); the agricultural production has improved after the introduction of irrigation system, and the social reintegration of displaced populations. This is a key area, especially for job creation and settlement of populations outside Luanda.

Botswana

Botswana has transformed itself from one of the poorest countries in the world to a middle-income country. Debswana, the largest diamond mining company operating in Botswana, is 50 per cent owned by the government. The mineral industry provides about 40 per cent of all government revenues. In 2007, significant quantities of uranium were discovered, and mining was projected to begin by 2010. Several international mining corporations have established regional headquarters in Botswana, and prospected for

5 diamonds, gold, uranium, copper, and even oil, many coming back with positive results. Government announced in early 2009 that they would try to shift their economic dependence on diamonds, over serious concern that diamonds are predicted to dry out in Botswana over the next twenty years.

Botswana’s Orapa mine is the largest diamond mine in the world in terms of value and quantity of carats produced annually. It is estimated to produce over 11 million carats in 2013, with an average price of US$145/carat, the Orapa mine is estimated to produce over US$1.6 billion worth of diamonds in 2013.

Democratic Republic of Congo

The DRC) is widely considered to be the richest country in the world regarding natural resources; its untapped deposits of raw minerals are estimated to be worth in excess of US$24 trillion. The Congo has 70 per cent of the world's coltan, a third of its cobalt, more than 30 per cent of its diamond reserves, and a tenth of its copper.

Despite such vast mineral wealth, the economy of the DRC has declined drastically since the mid-1980s. The country generated up to 70 per cent of its export revenue from minerals in the 1970s and 1980s, and was particularly hit when resource prices deteriorated at that time. By 2005, 90 per cent of the DRC's revenues derived from its minerals (Exenberger and Hartmann 2007:10).

Lesotho

The economy of Lesotho is based on agriculture, livestock, manufacturing and mining, and depends heavily on inflows of workers’ remittances and receipts from the Southern African Customs Union (SACU). The majority of households subsist on farming. The formal sector employment consists of mainly the female workers in the apparel sector, the male migrant labour, primarily miners in South Africa for 3 to 9 months and employment in the Government of Lesotho (GOL). The western lowlands form the main agricultural zone. Almost 50 per cent of the population earn income through informal crop cultivation or animal husbandry with nearly two-thirds of the country's income coming from the agricultural sector. Lesotho has taken advantage of the African

Growth and Opportunity Act (AGOA) to become the largest exporter of garments to the US from sub-Saharan Africa

In mid-2004 employment in the textile and clothing sub-sector reached over 50,000 mainly female workers, marking the first time that manufacturing sector workers outnumbered government employees. Water and diamonds are Lesotho's significant natural resources. Water is utilized through the 21-year, multi-billion-dollar Lesotho Highlands Water Project (LHWP), under the authority of the Lesotho Highlands Development Authority. The LHWP is designed to capture, store, and transfer water from the Orange River system to South Africa's Free State and greater Johannesburg area, which features a large concentration of South African industry, population, and agriculture. Completion of the first phase of the project has made Lesotho almost completely self-sufficient in the production of electricity and generated approximately US$70 million in 2010 from the sale of electricity and water to South Africa.

Diamonds production in 2014 was estimated to have generated an amount to a tune of US$300 million. One of the mines, namely, Letseng diamond mine is estimated to produce diamonds with an average value of US$2172/carat, making it the world’s richest mine on an average price per-carat basis. However, the sector suffered a setback in 2008 as the result of the world recession but rebounded in 2010 and 2011.

Lesotho has progressed in moving from a predominantly subsistence-oriented economy to a lower middle income economy exporting natural resources and manufacturing goods. The exporting sectors have brought higher and more secure incomes to a significant portion of the population.

Madagascar

Madagascar growth is essentially driven by agriculture, fish-related farming, mining sector, manufacturing with the Export Processing Zones, construction and tourism. Agriculture still occupies the biggest part of the GDP which explains its role on growth. Since 2012, new mining companies exporting nickel, cobalt, zirconium and titanium have contributed significantly to the growth. EPZs also remain a significant source of growth though they have lost their momentum this last decade due to lack of innovation and

7 erosion of US market access. The newly adopted National Development Program puts efforts on promoting Tourism sector since the country has competitive destination.

Malawi

The major driver for economic growth in Malawi is agriculture. It has accounted for about 30.0 per cent of the real GDP in the last five years. This is followed by wholesale and distribution which has contributed by about 15.0 per cent of GDP growth in the last five years. In 2010, Malawi commenced mining of uranium in 2010 and the contribution of mining activities rose to as high as 7.6 per cent of GDP in 2013. Previously, mining activities accounted for less than 1 per cent on GDP. Production of the yellow cake (uranium) was halted in 2014 due to rock bottom prices which were fetched after the nuclear plant fall out in Fukushima.

Furthermore, between 2010 and 2012 Malawi experienced acute foreign exchange shortages which resulted in freezing of international lines of credit and strategic commodities such as fuel. This resulted in 49 per cent devaluation of the exchange rate and abandonment of a de facto fixed exchange rate in favour of a more flexible regime. Consequently, growth in 2012 shrunk to 1.9 per cent and capacity utilization stood at around 40.0 per cent. Meanwhile, capacity utilization has been restored and currently hovers above 75.0 per cent in most industries. Economic growth has also rebound to above 6.0 per cent since then. It is currently projected that the economy will grow by 5.8 per cent in 2015 from steady growth rates of 6.3 per cent in 6.1 per cent in 2013 and 2014, respectively.

Mauritius

Growth over the years 1996 through 2005 was driven by a practical industrial policy geared towards economic diversification and employment creation and development of the services sector. The main drivers of growth were manufacturing, mainly the textile sector, and tourism and financial services, while the importance of the agricultural sector declined markedly.

By the mid-2000s, however, the loss of textile and sugar preferences together with rising unit labour costs impacted severely on external oriented sectors and posed serious macroeconomic challenges for Mauritius. The unemployment rate rose while the debt and balance of payments position worsened. Thereafter, the succession of oil price shock, global financial crisis and the Euro sovereign debt crisis pushed domestic growth momentum below its potential level. Real GDP growth declined to an annual average of 4.2 per cent during 2006-2014. The contribution to growth of the manufacturing, tourism and construction sectors declined while the services sector, in particular, financial services, ICT, real estate and professional - supported the economy.

Government undertook a number of bold reforms, including opening the economy to foreign skills and investment and enhancing the business climate to boost growth and integrate the island into the global economy. As a result, Mauritius attracted significant FDI inflows in the tourism, financial services and real estate sectors.

Mozambique

Mozambique is a resource-rich country with a wide range of natural and mineral resources including natural gas, coal, titanium, gemstones, bauxite, etc. The country is also rich in terms of hydro, faunal and energy resources. Currently, the key drivers of growth are foreign direct investment (FDI), mostly oriented to the extractive sector, and increasing public investment in infrastructure. The fastest growing sectors are the extractive sector, driven by coal exports, and the financial sector fuelled by credit expansion and increased income, particularly on main urban areas but the main contributor to the growth in the recent years has been agriculture sector. Other dynamic sectors are services, transport and communication and construction, broadly related to government investment in infrastructure development and private investment.

The country’s macroeconomic and political stability along with a wide range of structural reforms conducive to significant improvement in business environment, have been crucial to stimulate growth and promote employment.

Namibia

Namibia’s economic activity is concentrated in primary and tertiary sectors and the economy is highly dependent on trade. The primary and tertiary sectors contributed an average 20.3 per cent and 55.4 per cent to GDP, respectively, between 2000 and 2013. Humavindu and Jesper (2013) found that mining and government services are the key sectors of the Namibian economy, while manufacturing and services sectors are important in terms of output, with the agricultural sector important in terms of employment and income. The extraction of minerals is particularly important among the primary activities, contributing on average 11.4 per cent to GDP between 2000 and 2013. Farming, fishing and tourism are also other significant sectors to the economy. The dependence on trade makes exports and the terms trade significant growth determinants in Namibia. Niishinda and Ogbokor (2013) found that a one per cent increase in export results in an additional 0.8 per cent increase in GDP. Furthermore, Kandenge (2010) found that one per cent deterioration in the terms of trade reduces GDP by 0.7%.

Seychelles

The tourism industry is the main pillar of the Seychelles economy, accounting for 25 per cent of total GDP. In addition to being the main source of foreign exchange earnings, the industry covers a wide range of economic activities such as employment, construction, banking and commerce. Earnings from tourism make up for 28.0 per cent of GDP, totalling approximately US$400 million a year. Over 200,000 tourists visit the country per year, with the majority of them originating from Europe. The majority of the revenue from fisheries (fishing and fish processing) sector comes from the export of tuna, the latter accounting for 22.0 per cent of GDP and more than 50.0 per cent of the country’s total exports. Aside from tuna, the industry also generates its revenue from manufacturing, construction, industrial fishing and licensing fees paid by foreigners fishing the Seychelles territorial waters.

The growing financial services sector is expected to eventually overtake the tourism industry as the main driver of the Seychelles economy. In addition to the Central Bank,

10 the financial system is made up of nine banks and two non-banking institutions. The banking system provides a wide range of financial services, such as current accounts, saving accounts, loans, foreign currency accounts as well as financial leasing.

South Africa

South Africa is an important hub in the global mining value chain, a regional assembly hub in the automotive value chain and a key player in regional finance and retail value chains. It should thus capitalise on these links as engines of growth at home. Both South Africa’s retail and its financial services industry are the most sophisticated on the continent and both have significant presence in the region. In the services, the finance and insurance, real estate and business subsectors take the lead, with finance and retail in particular tapping into the more dynamic growth of the wider Southern African region.

Growth has been quite volatile and strongly correlated to the global economic performance. Over the period 2004 to 2007 the economy recorded its fastest growth since the 1960s, averaging 5.4 per cent per annum. Globally, this period was marked by a strong bull-market and booming commodities markets. Domestically, household consumption expenditure and fixed investment activity elevated economic growth substantially, with the export sector also providing considerable impetus over the years 2005 to 2007. Final consumption expenditure by households was supported by the low interest rate environment, rising real disposable income, increased job creation and ease of access to credit.

Over the period 1994 to 2012 the largest contributions to overall economic growth emanated from household spending (with 74.8 per cent average annual contribution to overall economic growth, 29.3 per cent fixed investment, and government expenditure 18.2 per cent. Further contributions came from building and upgrades of new and existing stadiums for the 2010 FIFA World Cup, the Gautrain rapid rail link, investment in energy infrastructure, transport and logistics networks and fuel pipelines.

The tertiary sector has grown considerably over the years and accounted for about 70.0 per cent of gross domestic product in 2012, up from 60.0 per cent in 1994. Financial services sector, largely driven by a strong banking system, amounted to 22.0 per cent of GDP in 2012, up from 12.0 per cent in 1994. Key related services

11 sectors in the trade sector are retail, wholesale and motor trade, catering and communication. Manufacturing dominates the secondary sector in GDP and is the fourth largest sector in the economy.

Though relatively small, the construction sector played an important role in boosting economic activity during 2003 to 2007 as the demand for residential and non-residential construction escalated. The services sector has been the pillar of overall economic growth, increasing their share steadily over the past two decades. The financialisation of the economy, proliferation of business services and the telecommunications sectors have underpinned the robust output performance of the services sector, while retail and wholesale trade sectors further entrenched their strong status.

Swaziland

In the past two decades Swaziland has been stuck in a low-growth trap, with exogenous shocks reinforcing existing structural constraints to growth. The dawn of South Africa’s majority rule, which ended her political and economic isolation, eroded some of Swaziland’s advantages as a destination for investment. As foreign direct investment inflows declined, real GDP growth fell from an annual average of 7 per cent between 1980 and 1992 to less than 2.5 percent for the period 1993 to 2008. In 2011 the country suffered its worst fiscal crisis since independence due to reduced SACU revenue as a result of the slowdown of the South African economy. In a nutshell, between 2009 and 2011 the economy grew at an average of less than 1.3 per cent, whiles between 2012 and 2014 the economy grew by an average of 2.7 per cent.

Economic growth in Swaziland is largely driven by developments in the agriculture, forestry and manufacturing sectors, whose combined share averaged about 48 percent of GDP in the period 1990 to 2000. Worth noting is that since independence in 1968, agriculture and forestry remained the drivers of the economy contributing about 33 percent of total output, with manufacturing activity largely restricted to commercial agro- processing of sugar, wood pulp, citrus fruit, pineapples, cotton and meat. Since then, the economy has been diversified considerably and agriculture's share of total output, decreased gradually to about 9 per cent the early years of 2000. However, the agriculture and forestry sectors remain the backbone of the Swazi economy and major employers,

12 and the foundation of most manufacturing activity. The share of manufacturing rose from 22.3 percent in 1980 to 35.7 per cent before the 2008 global crisis. As a result of the limited size of the local market, the bulk of the output from the agriculture and manufacturing sector is exported, while a considerable share of inputs and final consumption goods are imported.

Tanzania

The economy of Tanzania is largely dependent on agriculture for employment with more than 70 per cent of the population who lives in rural areas depend much their livelihood on it. Until 1980s the public sector played a leading role in the economy. The economy was mainly driven by agriculture sector and state-owned manufacturing industries with an average economic growth rate of 2.2 per cent, while the rate of inflation was on the higher side of an average of 30.6 per cent.

Following the economic crisis of the 1980s economic reforms were introduced, where the economy started been transformed from a command to a market economy. The country moved from reliance on control mechanisms towards a predominantly market oriented environment. Economic performance responded favourably to reform efforts in 1986-1994, with the rate of economic growth averaging 3.8 per cent, up from the 2 per cent attained between1981-1985. Comprehensive package of policies were embarked on, which reduced the budget deficit and improved monetary control, substantially depreciated the overvalued exchange rate, liberalized the trade regime, removed most price controls, eased restrictions on the marketing of food crops, freed interest rates, and initiated a restructuring of the financial sector.

In 1990’s, the reforms were intensified, resulting in major progress in macroeconomic stabilization and an acceleration of economic growth. Unification of the exchange rate and liberalizing imports, allowed the private sector to trade freely, fuelling an export boom that restored Tanzania’s foreign exchange reserves. The restructuring of the financial sector, including the licensing of foreign banks, provided financing for private investment. Public finances were subjected to stricter discipline. As a result, the economy recorded relatively good performance with inflation finally started coming down to a single digit. Economic growth increased substantially from 3.7 per cent in 1995 to 7.4 per cent in 2005, while inflation decelerated from 27.4 per cent to 4.4 per cent in the same period. The major drivers of the economy were agriculture, manufacturing, construction and trade activities.

The GDP estimates for 2005 onwards have been rebased to 2007 base year, with a view to encompass more contemporary structure of the economy evident in year 2007. The new series of real GDP estimates are expressed in 2007 prices compared to the 2001 prices in the previous series.

According to the rebased GDP numbers of 2007, real GDP grew by 7.3 per cent in 2013 compared to a growth of 5.1 per cent in the previous year of 2012. Activities which recorded higher growth rates than in the previous year include: electricity supply, construction, professional, scientific and technical activities.

Highest growth rate was recorded in construction (18.9 per cent) followed by information and communication (13.3 per cent) and electricity supply (13.0 per cent). High growth in construction was due to the increase of construction in infrastructure and buildings projects in the country while strong growth in communication and electricity supply activities was on account of increase in usage of mobile phone and electricity services respectively.

Major contributors to growth in 2013 were construction (22.6 per cent), agriculture, forestry and fishing (10.9 per cent), wholesale and retail trade (7.5 per cent), and information and communication (7.3 per cent). Construction activity expanded on account of increased construction and maintenance of infrastructure and buildings.

Over the last decade, the economy has shown signs of structural transformation with steadily increasing shares of total GDP from the services sector, which averaged to (55 per cent), manufacturing (10 per cent) and from construction (7 per cent). Communications is the fastest growing services sub-sector, averaging 17 per cent growth per annum since 2003. In contrast, the share of the agricultural sector in total GDP has fallen from around 23 per cent in 2004 to 12 per cent in 2013.

In the medium term, growth will also be supported by the ongoing investments in infrastructure and the recently discovered natural gas reserves. The investments towards harnessing gas potential is expected to lower cost of power generation and reduce demand for expensive heavy oil imports. The reduction in oil imports is expected to have significant impact on real GDP growth mainly through costs of production and balance of payments.

Zambia

Zambia’s growth in the recent past has largely been driven by growth in the telecom and transport, wholesale and retail, mining and agriculture sectors. In addition, the construction sector has also been a key driver through construction of housing units and shopping malls and construction of production facilities in the multi-facility economic zones in Lusaka and the Copperbelt provinces. This is in addition to private sector infrastructure development such as power plants (thermal and hydro power stations in the southern and north-western parts of Zambia, respectively). Further, public sector investment favourably impacted economic growth mainly in form of the on-going infrastructure development that includes roads, railway, airports, schools, energy and health facilities.

Zimbabwe

Zimbabwe’s annual economic growth rate averaged around 3% from 1961 to 2014, weighed down by the decline in economic activity from 1998 to 2008, with annual growth averaging - 7 per cent over the crisis period. Since 2009, the economy has been, however, rebounding, registering significant growths averaging 8 per cent per annum. An analysis of the source of growth in Zimbabwe, shows that growth from 1980 to 1998 was driven mainly by investment in human capital. The diversification of the economic base from agriculture to secondary and tertiary sectors has been an important source of total factor productivity in the economy. Trade liberalisation has also been an important source of growth by promoting the competitiveness of country’s domestic products. The increase in human capital and the corresponding rising skills levels fostered productivity in the labour intensive manufacturing sub-industries and agriculture sectors of the economy, thereby driving growth. Human capital grew on the back of increased Government spending on education and a favourable return to education.

The recent growth in the economy since the adoption of the multicurrency system in 2009, had been driven by capital accumulation driven by huge investment for infrastructure, expansion projects and new investments in mining. The discovery of diamonds, the expansion of PGMs and resuscitation of gold and nickel mines in the country have been a boon to the economy. Reflecting this, mining contribution to GDP increased from 3.8 per cent in 2007 to 11 per cent in 2012. It is against this background,

15 that the country’s growth dynamics has become highly sensitive to movements in the international mineral commodity prices. The investment in mining has been mainly from foreign direct investment. The rapid expansion in services such as tourism, financial and distribution have also been important sources of growth. Over 60.0 per cent of the country’s non-agriculture formal employment is in the services sector. The recent growth in the economy has been supported mainly by the highly capital intensive mineral sector. This means that the growth has been less on employment creation and therefore poverty reduction.

In the medium term, the drive for value addition and beneficiation, will allow growth to be driven mainly by human capital and productivity and tacitly impacting on poverty reduction. The country also intends to improve efficiency of production by recapitalising and retooling the manufacturing sector. Growth in this sector has over the years been affected by use of antiquated machinery and equipment. Moreover, addressing infrastructure deficit particularly in the energy sector will go a long way in fostering economic growth in the economy.

3. LITERATURE REVIEW

3.1 Theory of economic growth

The literature on economics is replete with studies on the sources of economic growth. Classical economists such as Adam Smith, David Ricardo and Thomas Malthus expended much effort to comprehend the concept of economic growth. According to Adam Smith3 countries become prosperous when they have good institutions that create favourable rules of the game – rules that encourage the creation of wealth. Since then, a vast variety of research has been carried out attempting to understand why some countries grow faster in the long run while others fail to. Neoclassical economists in the 1950s resuscitated research on economic growth. The models of Solow (1956) and Swan (1956) became pillars of the new growth theories. Application of these models culminated into a consensus on the understanding of drivers of growth by both classical and neoclassical economists. This understanding identifies technological progress as the sole lasting source of growth, given that the law of diminishing returns over time eliminates any growth that emanates from physical accumulation.

3 In his book entitled An Inquiry into the Nature and Causes of the Wealth of Nations – published in 1776

The modelling framework assumes that the production possibilities of an economy are described as:

(1)

Where and represent inputs, namely; capital, labour and technological progress. The production function has two key properties. First, it is able to exhibit constant returns to scale, in the sense that if labour and capital are increased by a factor of , output should be able to respond by the same .4 Second, the production function obeys the law of diminishing returns to capital. Furthermore, technical progress is exogenous and, capital and labour are substitutable. A common specification of the neoclassical production function is a Cobb-Douglas function defined as:

(2)

In this case, represents the proportion of national income for owners of capital, and is the fraction due to workers. In order to account for unemployment, equation 2 is defined in terms of output per capita. Of course output per capita and output per worker coincide if the population is equal to the labour force. If labour is separated into the total number of workers and skill quality , then output per worker can be obtained by dividing equation (2) by :

(3)

Where and denote output per worker and capital per worker, respectively. Equation (3) identifies sources of growth in output per worker. In other words, policymakers can increase output per worker by increasing all or one of the following: investment in physical capital, or the amount of skilled labour in the economy through education or technological progress. The dynamics of this framework have several implications. The first one arises due to diminishing returns to capital which means that the rate of economic growth due to capital accumulation will slowly approach zero. Even though the economy will be richer, it will have become stagnant. The second implication is due to differences in the capital stocks across countries. Hence, poorer countries with smaller stocks of capital will realise higher marginal products of capital than their richer counterparts. Diminishing returns would imply that a unit increase in the stock of capital,

4This assumption is anchored on the principle of replication.

17 in both a rich and poor country, will result in faster output growth in the poor country than in the rich one. It is easy to realise that the poor country will gradually catch up with its rich counterpart over time. This is termed the absolute convergence hypothesis. The third implication involves the differences in the savings rates. The absolute convergence hypothesis assumes identical savings rates between both the poor and rich country. However, if the savings rates differ between the two countries, then the poor country will only catch up with the rich one, if the savings rate in the poor country exceeds that of the rich country. This is referred to as the conditional convergence hypothesis. Sala-i- Martin (1995) distinguishes enduring and short-lived sources of growth. He concludes that countries can be able to achieve enduring growth rates over time if their main source of growth is “productivity improvements” manifested by increases in technological progress. He points out that this happens “because knowledge has no frontiers.” However, rapid growth dependent on high savings and investment rates which are attained through capital accumulation or increasing skill levels would decelerate and eventually cease.

The neo-classical models provided a basis for what is currently understood as endogenous growth which emerged from the pioneering work of Romer in 1986.Unlike the neo-classical models which assume diminishing returns to capital, the endogenous growth model assumes increasing returns to capital. Endogenous growth models identify the rate of accumulation of physical and human capital, and technological progress as determinants of long-run economic growth (Arrow, 1962). Investment in human capital (e.g. expenditures on education, training, and research and development) could have a positive impact on economic growth. This outcome is possible if high skills and training are accompanied by the process of innovation, which leads to a faster rate of technological progress. Hence, investment in education may not only make contribution to growth via improvements in the quality of the workforce, but also via innovation driven by research and development. On the empirical front, most studies use the share of investment to GDP as a proxy for physical capital and level of formal education (e.g. school enrolment ratio) as a proxy for human capital (Romer, 1986) and, technological progress is often represented by expenditures on research and development. Endogenous growth models identify good policies as key drivers of long-run economic growth. Unlike their neo-classical predecessors, endogenous growth theorists predict that convergence between poor and rich countries is not feasible due to the assumption of increasing returns to scale.

There are two other strands of the literature on the economics of growth, namely; the cumulative causation theory (Myrdal, 1957, and Kaldor, 1970) and the new economic geography theory (Krugman, 1991). Both theories attach a significant weight to initial conditions. For example, the cumulative causation theory argues that initial conditions affect economic growth over time, creating inequalities between countries. Such inequalities cannot disappear unless deliberate policy interventions are put in place. Petrakos and Arvanitidis (2008: 13) highlight that the new geography theory vividly associates economic growth with “compound effects of increasing returns to scale, imperfect competition and non-zero transportation costs.” Hence, the process of economic growth can be self-reinforcing due to spatial distribution of economic activities. There are cases where economic activities may cluster in certain locations where demand is high leading to both backward and forward linkages of firms and scale economies.

3.2 Empirical evidence on the sources of growth

There is vast empirical literature on sources of economic growth (see Temple, 1999; Ahn and Hemmings, 2000 - for surveys). Growth accounting studies provide contrasting results on the sources of economic growth. For example, Young (1994) found that in Singapore economic growth during the period 1960-1970 was a result of accumulation of physical and human capital rather than technological progress. On the contrary, Bosworth and Collins (1998) found that in China technological progress appeared to have accounted for more growth than accumulation of physical and human capital during the period 1960-1970. The stark contrast indicated the complexity of the process of economic growth. Consequently, according to Bloch and Tang (2004: 245) the neoclassical approach remained limited to provide “practical guidance for sustained economic development.” They argue that neo-classical growth accounting focuses on what they call “proximate determinants” of growth, that is, accumulation of capital and total factor productivity or technological progress without any reference to the sources of technological progress itself. Consequently, the literature distinguishes between proximate and deep determinants of economic growth.

The deep determinants of economic growth are institutions, openness to trade and geography. Easterly (2001) argued that institutional quality is crucial for economic growth such that poor institutions inhibit growth even if factors such as foreign aid, debt forgiveness, family planning, infrastructural development, education and foreign

19 investment are abundant. This finding built on North and Thomas (1973) who pointed out that lack of institutions such as protection of property rights impede investment in both forms of capital and impairs economic growth. There is also cross-country evidence of a positive association between property rights and economic growth (Hall and Jones, 1999; Rodrik, 1999). Furthermore, Knack (2002) indicates that well-defined property rights encourage technological progress and innovation and boost efficiency gains. There is evidence that socio-cultural institutions such as trust and ethnic diversity (Easterly and Levine, 1997), political institutions such as type of regime (democracy), stability of government, political violence and volatility (Bunetti, 1997) and macroeconomic policies (Barro and Sal-i-Martin, 1995) affect economic growth in one way or another. Moreover, a number of studies confirm that presence of a causal relationship between good institutions and per capita income with causality running from the former to the latter (Acemoglu et al., 2001, 2002, 2003). Tang et al. (2003) underscores that good quality institutions accelerate technical change and enhance long run economic growth rate.

Geography is another deep determinant of economic growth. Bloch and Tang (2004: 248) highlight that the impact on economic growth is felt in several ways such as “health, population growth, food productivity, resources endowment and mobility of factors of production.” For example, on the one hand, there is empirical evidence that latitude (Hall and Jones, 1999) and winter frost (Masters and McMillan, 2001) stimulate economic while tropical climate (Gallup et al., 1999) and adverse disease ecology (Acemoglu et al., 2001) inhibit economic growth, on the other hand. Areas where diseases such as malaria are rife may lose much of the labour force in death. In addition, Sachs and Warner (2001) observe that resource rich countries grew slower than their resource-poorer counterparts between 1900 because they are plagued by the Dutch disease and the high probability of civil conflicts. Moreover, proximity to coastal waters provides an inexpensive highway to global markets and boosts economic growth, while landlocked states lack such propinquity with deleterious effects on their economic growth. Frankel and Romer (1999) discover that trade is low in landlocked countries than in countries with access to coastal waters. This results shows that geography can also affect economic growth through trade openness.

The third deep determinant of economic growth is openness to trade. Bloch and Tang (2004) propound that trade enhances per capita income growth directly through comparative advantage. In an indirect manner, trade openness bolsters economic growth

20 by improving efficiency through technology transfer, economies of scale and international competitiveness. In addition, there are close links between capital flows such as FDI and trade openness. Lensink and Morrissey (2006) find that FDI contributes positively to economic growth. A number of studies explore the various factors that have a bearing on trade openness which may retard its impact on economic growth. For example, trade distortions tend to decelerate economic growth (Edwards, 1998), export orientation boosts economic growth (Balasubramanyam et al., 1996) and trade protectionist policies reduce and hurt labour and total factor productivity (Lee, 1996).

Figure 2: Structure of the Sources of Economic Growth

Economic Growth

Capital accumulation TFPG

Proximate determinants

Human capital Physical capital development development

Institutions Openness to trade Deep

determinants

Geography

Source: Authors

In the context of Africa, the following sets of factors have been identified in the literature to have impact on economic growth in Africa. The first set of factors is classified under macro-economic fundamentals. These include inflation (Grier and Tullock, 1989; Fischer, 1991; and most recently Seleteng et al, 2013), the degree of openness to international trade (Knight et al., 1993; Frankel and Romer, 1999), the extent of financial development (Mckinnon, 1973; King and Levine, 1992) and fiscal policies (Easterly and Rebelo, 1993). The second set of factors comprise countries’ institutional environment such as political stability, civil liberties and ethnic fractionalisation (Easterly and Levine, 1997; Kormendi and Meguire, 1985). The third set includes geographic factors such as access to the sea, tropical climate and natural resource abundance (Sachs and Warner, 1997).

4. EMPIRICAL ANALYSIS 4.1 Methodology

Since T > N, the empirical strategy used is based on panel time-series analysis. Panel time-series allows us to deal with important econometric issues such as heterogeneity, endogeneity bias and cross-sectional dependence. Panel time-series methodologies have several advantages. First, it allows us to specifically analyse the SADC case, amid all its idiosyncrasies and differences inherent within, without necessarily treating it as an outlier or as a dummy, and therefore enables us to get a clear picture of the region. Second, the issue of statistical endogeneity (unobserved individual effects which are nested in the error term might be correlated with the regressors), and heterogeneity of intercepts are dealt with by the two-way Fixed Effects (FE) with robust standard error estimator, which provides consistent estimates in dynamic models when T → ∞. Third, economic endogeneity (reverse causality) may be present, for instance, higher growth might generate lower inflation, or vice versa (Kocherlakota, 1996). We therefore use the Generalised Method of Moments (GMM) to deal with this problem.

Four panel data methodologies are used and then compared in the analysis. In particular, the Fixed Effects (FE) model specification acknowledges cross-section heterogeneity and assumes a different intercept for each country included in the sample. It can be argued that there is reverse causality or economic endogeneity, implying that higher growth actually generates higher inflation and not the inverse (Bittencourt, 2012). Therefore, Generalised Method of Moments (GMM) deals with the endogeneity problem in the dataset. Countries in the SADC region are striving towards common goals and therefore are likely to pursue similar macroeconomic policies, implying that there is between-country dependence. The Seemingly Unrelated Regressions Estimators (SURE) model deals with cross-country dependence. Before the regressions are run, unit root tests are performed in order to determine the order of integration of the variables.

The estimated heterogeneous dynamic equation is therefore as follows,

(4) whereby GR denotes growth rates of real GDP, INF are the inflation rates, INV shares of gross fixed capital formation to GDP, EDU is the public spending on education as share of GDP, GOV is the share of final government consumption expenditure to GDP, OPEN is a measure of

23 trade openness, M2 is the share of liquid liabilities to GDP and INST is the institutional variable which proxies level of democracy.

a) Fixed Effects Estimator

First, the Chow (1960) F-test was used to test for fixed effects. We tested the null of no individual effects ( ) against the alternative that individual effects are not all equal to zero. In this case, F=1.59 leading to a rejection of the null at all levels of significance. Therefore, the conclusion is that countries in the SADC region are not homogenous and hence these differences have to be controlled for. Second, to decide between using a fixed or random effects model, a Hausman (1978) test was used. The null hypothesis is that the preferred model is random effects versus the alternative of fixed effects. The null hypothesis was rejected at all levels of significant hence the preferred method is fixed effects in this case (see Table 1a in the Appendix).

Consider the following two-way error component regression model:

(5)

where = unobserved individual effect

unobserved time effect

stochastic disturbance term 1, 2, …, N 1, 2, …, T

If and are assumed to be fixed parameters to be estimated and then (5) represents a two-way fixed effects (FE) error component model. Note further that the are assumed independent of the stochastic disturbance term for all and . Since , FE is the appropriate estimator to use in this case. Furthermore, as already discussed, the FE estimator reduces statistical endogeneity and when , FE reduces the Nickell Bias5. The choice of a two-way fixed effects estimator is informed by the fact that countries are different and hence this caters for cross-sectional heterogeneity. In addition, there were periods of high inflation episodes observed in the SADC region during our sample period, hence the time-effects takes this into account through the use of time dummy variables.

5 When the bias tends to zero, i.e. Judson and Owen (1999) argue that when the FE estimator provides the best alternative in dynamic thin panels.

b) Difference and System GMM Estimators

Difference and system generalised method of moments (DIF-GMM and SYS-GMM) for dynamic panels have gained much popularity in recent years. This is due to the fact that these estimators are able to circumvent several modelling concerns such as endogeneity of regressors, which lead to problems of inconsistent and biased estimates. Research papers that propose the use of generalised method of moment estimators include Holtz-Eakin, Newey and Rosen (1988), Arellano and Bond (1991), Arellano and Bover (1995); and Blundell and Bond (1998).

A recurring debate in the literature is that, growth, inflation and investment are three endogenous variables (Temple, 2000). Therefore, to investigate this, the Hausman (1978) test for endogeneity is conducted and it confirms endogeneity in the model, as we reject the null hypothesis of exogeneity of the regressors with a Hausman test statistic of 18.57. Consequently, the use of DIF-GMM and SYS-GMM is necessary since these estimators are designed to deal with the endogeneity problem, and also to fit linear models with a dynamic dependent variable, additional control variables and fixed effects (Roodman, 2009). Other studies such as Cukierman et al. (1993) use several indicators as instruments, including central bank independence and turnover of central bank governors. However, due to data unavailability of such indicators in the SADC region, our DIF-GMM and SYS-GMM methods uses lagged values of GR, INF and INV as instruments. In particular, since growth, inflation and investment are assumed to be endogenous, they are instrumented with their first lags. Consider the following data generating process:

(6)

where

[ ] [ ] [ ]

Cross-sectional units are indexed by and time is indexed by . A vector of control variables is represented by and this may include lagged values for both dependent variable and controls.

The fixed effects and idiosyncratic shocks are represented by and , respectively. The panel has ( dimension and may be unbalanced. When is subtracted from both sides of equation (6), we get an equivalent equation of growth presented as:

(7)

In DIF-GMM, estimation occurs after the data is differenced once in order to eliminate the fixed effects, while the SYS-GMM augments the DIF-GMM by estimating both in differences and in levels (Roodman, 2009). Therefore, SYS-GMM augments the DIF-GMM by making an assumption that first differences of instrument variables are uncorrelated with FE and thus allows for the introduction of more instruments, thereby improving efficiency. Therefore, the extra moment conditions embedded within the SYS-GMM estimators render it to be a better estimator. When using these two estimators, caution needs to be exercised with respect to the number of instruments used. In particular, numerous instruments can overfit the endogenous variables and therefore the results will not be robust. This paper uses the Sargan (1958) test (an equivalent of Hansen (1982) test) to test for over-identification of restrictions.

c) Seeminlgy Unrelated Regression Estimators (SURE)

This estimator was proposed by Zellner (1962) and this allows for cross-sectional dependence and therefore captures efficiency due to the correlation of the disturbances across country- specific equations. As discussed earlier, countries in the SADC region are striving towards common goals and therefore are likely to pursue similar macroeconomic policies, implying that there might be cross-country dependence in the sample. The reason for the interdependence emanates from the fact that over the years countries experience increasing economic and financial integration, which implies strong interdependence among countries (Baltagi, 2008).

The presence of cross-sectional dependence implies that FE estimators are still consistent although inefficient; hence the standard errors are biased. Therefore, Seemingly Unrelated Regressions Estimator (SURE) deals with cross-country dependence. The SURE is based on large-sample properties of large T and small N datasets in which . Hoyos and Sarafidis (2009) point out that panel data sets usually exhibit cross-sectional dependence, which usually arise due to the presence of common shocks and unobserved components that become part of the error term.

Therefore, testing for cross-sectional dependence is important in estimating panel data models. For this paper, the sample is, T = 33 and N = 13 (T > N) and the approapriate test is the Breusch-Pagan (1980) Lagrange Multiplier (LM) test. In this case the null hypothesis of no cross- sectional dependence was rejected for the CMA, SACU and SADC regions, indicating that there is indeed cross-sectional dependence in these regions and this warrants the use of a SURE models (See Tables 7 and 8). As highlighted by Bittencourt (2012) the SURE estimates different

26 country time series, which are then weighted by the covariance matrix of disturbances. Therefore, this methodology disaggregates the analysis further, in order to allow for a more in- depth view of the effects of the several variables on growth in the region.

4.2 The Data

The dataset used is obtained from the World Bank Development Indicators (WDI), IMF International Financial Statistics (IFS) and Polity IV database, for the period 1980 to 2012. The growth variable used in the analysis is the growth rate of real GDP. The control variables are standard in the growth literature as discussed in Durlauf et al. (2005) and Levine and Renelt (1992) who used Leamer’s extreme bounds analysis to analyse growth accounting regressions. Levine and Renelt (1992) found that inflation, investment’s share of GDP, initial level of GDP, secondary-school enrolment rate, average annual rate of population growth and trade are robust in the growth regressions.

We follow their work and use a set of variables that control for factors associated with economic growth. These control variables are rather standard in growth literature and their description is presented in table 1.

Table 1: Variables Description Variable Acronym Description Source Expected Sign Real GDP growth GR Real GDP growth (annual %) WDI n/a Inflation INFL Consumer price inflation (annual WDI +/- % changes in CPI) Investment GFCF Gross fixed capital formation (% WDI + of GDP) Openness OPEN (Imports + Exports) of goods WDI + and services (% of GDP) Government GOV General government final WDI +/- consumption expenditure (% of GDP) Money Supply M2 Money and quasi money( M2) [as IFS + % of GDP] Credit PSC Private sector credit extension (as IFS + % of GDP) Democracy INST Institutional variable [-10,+10] with a +10 a full consolidated demogracy Human capital EI Secondary school enrolment (% WDI + of corresponding population age group) Public spending on PSE Public spending on education, WDI + education total (% of GDP)

Population growth POPGR Population growth (annual %) WDI +/- Urbanisation UPOPSHR Urban population (% of total) WDI +/-

The coefficients of these variables are expected to exhibit expected signs consistent with literature based on a priori expectations. Inflation (INFL) – a negative relationship is expected between inflation and growth (Fischer, 1993); the ratio of gross fixed capital formation to GDP (INV)- a Solow determinant and it is expected that investment positively affects growth ( Bond et al, 2010); a measure of openness to trade - ratio of imports and exports to GDP (OPEN)– it is expected that more open economies in terms of trade display faster growth rates, mainly because higher exports imply an increased inflow of foreign exchange into the country and also imports of intermediate materials may be growth enhancing (Wacziarg and Welch, 2008). Moreover, a measure of conditional convergence, namely, lagged real GDP (Y1) is included as part of the explanatory variables; a measure of financial development, namely, the ratio of private sector credit extension to GDP (PSCE) or ratio of liquid liabilities to GDP (M2)– it is expected that wider access to finance increases economic activity (Levine et. al, 2000).

We account for institutions by using an institutional variable representing a measure of the level of democracy (INST) – and it is expected that more democratic countries tend to grow faster (Papaioannou and Siourounis, 2008). Moreover, Durlauf et. al, (2005) in their chapter in the Handbook of Economic Growth list different group of variables that have already been regressed against growth and these include, among others, a measure of the size of the government, measured as final government consumption expenditure as a share of GDP (GOV); public spending on education (PSE); school enrolment ratios - for both primary and secondary school enrolments (EL); urbanisation - share of urban population to total population (UPOPSHR); and population growth (POPGR). These variables were considered as part of the explanatory variable set. The number of countries included in the sample amounts to fifteen (T = 33 and N = 15) therefore NT = 495.

Table 2: The Correlation Matrix, SADC, 1980 – 2012 GR INFL GFCF GOV PSE INST M2 OPEN POPGR GR 1 INFL -0.13*** 1 GFCF 0.20*** -0.11*** 1 GOV 0.03 -0.09* 0.37*** 1 PSE 0.002 -0.04 0.14*** 0.19*** 1 INST 0.15*** -0.05 0.22*** 0.12*** 0.58*** 1 M2 -0.05 0.001 0.24*** 0.14*** 0.65*** 0.40*** 1 OPEN 0.21*** -0.04 0.45*** 0.40*** 0.17*** 0.08 0.21*** 1 POPGR 0.08* 0.001 -0.17*** -0.07 -0.58*** -0.36*** -0.55*** -0.27*** 1 */**/*** denotes significance at 1%,5% and 10%, respectively.

Table 2 presents the correlation matrix of the variables used, and inflation and growth depict a negative and statistically significant correlation to each other, Fischer (1993). Other control variables (with an exception of M2) have the expected signs. Investment is positively and significantly correlated to economic growth (Bond et.al, 2010), as well as openness to trade (Papaioannou and Siourounis, 2008). Moreover, population growth is also positively and significantly correlated to economic growth.

Government consumption and secondary school enrolment are positively correlated to economic growth, however, not statistically significant. A measure of financial development is negatively correlated with growth, however, not statistically significant either. In a nutshell, this initial inspection of data, with all its known caveats, confirms the a priori expectations with an exception of a measure of financial development.

4.3 Unit Root Testing

Consider the following data generating process:

(8)

We use the Im, Pesaran and Shin (2003) (IPS) unit root test as well as the Levin, Lin and Chu (2002) (LLS) specification to test for the presence of a unit root in the panel. The Levin, Lin and Chu (2002) (LLC) specification assumes a common unit root process, i.e. common for all cross-sections (assumes parameter homogeneity) as opposed to the IPS test, which assumes individual unit root processes, i.e., individual ’s for every cross-section (allows for heterogenous parameters). Since LLC does not consider a possible heterogeneity bias present in

the data, IPS generally would be the preferred test. However, LLC unit root test results confirm IPS test results, i.e. all variables are stationary, with the exception of M2 and INST, which are stationary in first differences. Therefore, the first differences of M2 and INST variable are used in the model, whereas the rest of the variables are used in levels. Results for unit root tests are reported in Table 3.

Table 3: Panel Unit Root Tests

GR INFL GOV OPEN GFCF M2 INST IPS W-stat Levels -4.67*** -3.64*** -1.97*** -1.57* -1.85*** 0.18 0.38 [P-value] [0.00] [0.00] [0.02] [0.06] [0.03] [0.57] [0.35] Differences -12.71*** -14.05*** -11.82*** -13.31*** -11.96*** -10.24*** -5.86*** [P-value] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]

LLC t*-stat Levels -3.19*** -3.44** -1.34* -1.76** -1.39* -1.56* -1.43* [P-value] [0.00] [0.02] [0.09] [0.04] [0.08] [0.06] [0.08] Differences -6.80*** -10.82*** -10.09*** -11.21*** -9.42*** -9.27*** -4.77*** [P-value] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00] [0.00]

***/**/* denotes significance at 1%, 5% and 10%, respectively. [P-values] are in square brackets. All the variables are expressed in logarithmic form except for the inst which ranges from -10 to +10.Due to space, unit root results on other variables such as PVT, PSE, EL, POPGR and UPOPSHR are not reported here. All these are I(1) with an exception of POPGR.

5. EMPIRICAL RESULTS

a) Fixed Effects

First, all the coefficients of the initial level of income are negative and statistically significant indicating that there is conditional convergence in the SADC region. Moreover, INFL estimates are negative and statistically significant against GROWTH, which suggests that inflation is detrimental to economic growth and inflation generally distorted the pace of overall economic activity in the SADC region during the period under review. For instance, dynamic inflation estimate in Model A indicates that an increase by 1 percentage point in the inflation rate leasds to a decline by about 0.2 percentage points in the annual economic growth rate.

Second, GOV variable (proxy for the size of the government) depicts negative, but statistically insignificant effect on growth. This shows that government spending may be detrimental to economic growth if such spending is channelled towards unproductive sectors (Barro, 1998). The variable proxying trade openness (OPEN) is positive and stastically significant in all the

30 models indicating that more open economies can indeed grow faster via increased flows of goods, capital, people and ideas (Wacziarg and Welch, 2008). This result regarding openness is important amid the objective of SADC to achieve regional integration, or trade openness, combined with economic growth and development.

Table 4: FE Models (using M2 as a proxy for financial development) Dependent Variable: GROWTH Model A Model B Model C Model D Constant 3.03 2.79 3.05 2.99 (1.15) (1.02) (1.16) (1.14) Y1 -0.20* -0.19* -0.19* -0.20* (-1.77) (-1.63) (-1.74) (-1.76) INFL -0.18*** -0.19*** -0.18*** -0.18*** (-3.22) (-3.44) (-3.15) (-3.24) GOV -0.11 -0.13 -0.12 -0.11 (-0.57) (-0.63) (-0.61) (-0.56) OPEN 0.74*** 0.79*** 0.72*** 0.74*** (4.55) (4.74) (4.34) (4.55) INV 0.11 0.10 0.11 0.11 (0.71) (0.62) (0.71) (0.67) M2 0.12 0.12 0.10 0.08 (0.41) (0.39) (0.36) (0.29) INST -0.01 -0.01 -0.01 -0.01 (-0.45) (-0.41) (-0.42) (-0.33) PSE 0.12 0.12 (0.37) (0.37) POPGR -0.02 0.64 -0.04 (-0.13) (1.07) (-0.28) EL 0.92* (1.67) F*test 6.30 6.83 6.10 7.24 [P-value] [0.00] [0.00] [0.00] [0.00] # of Obs 393 390 393 390 */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, PVT, LEI, and PSE are I(1) hence they are used in first differences across the models.T-ratios are in italics and in parenthesis.

Third, a proxy for physicall capital, INV presents a positive but statistically insignificant effects on GROWTH (Bond et al, 2010). In addition, the ratio of liquid liabilities, M2, amid information asymmetries and lack of experience by smaller entrepreneurs in terms of how to make better use of finance, in general present negative and not statistically significant estimates on growth. Regressions were also estimated using PVTas a proxy for financial development and the results are essentially similar (See Table 4(a) in the Appendix).

The variables for level of democracy (INST), population growth (POPGR), both proxies for human capital -private spending on education (PSE) and secondary school enrolment ratio (EL) have the expected signs but statistically insignificant. Finally, the F* tests indicate that there is evidence of country fixed effects.

b) Generalised Methods of Moments (GMM)

The resuls from the difference and System GMM models, which takes care of endogeneity among the variables, exactly mimics the results of the FE models. Coefficients for initial level of income, inflation, government expenditures and openness to international trade, all depict the correct signs and are statistically significant at all levels. The results depict that openness to trade has a significant impact on economic growth across the SADC region. The significance in the openness indicator suggests that a 1.0 per cent increase in average growth rate of the trade sector raises real GDP growth by about 0.6 per cent to 0.7 per cent across all the four GMM models estimated. The coefficient of financial development variable as proxied by M2 was found to be statistically insignificant. The estimations were also carried out using private sector credit extension as a proxy for financial development and the results are similar (See Table 4(b) in the Appendix).

There is evidence that a faster growing government sector (proxied by government expenditures to GDP ratio) is associated with slower economic growth. A 1.0 per cent increase in the annual growth rate of the government expenditures to GDP ration depressing annual economic growth rate by about 0.46 per cent to 0.56 per cent. Higher inflation is also found to affect economic growth detrimentally, with a 1.0 per cent increase in inflation rate retarding economic growth rate by about 0.19 per cent to 0.21 per cent.There is also evidence of conditional convergence in the region, meaning that poorer countries in the SADC region are likely to grow faster and catch up with richer countries in the region.

Table 5: Dynamic Difference GMM Models (using M2-proxy for financial development) Dependent Variable: GROWTH Model A Model B Model C Model D GROWTH (-1) 0.01 0.02 0.01 0.03 (0.11) (0.20) (0.09) (0.20) Y1 -0.54*** -0.53*** -0.54*** -0.53*** (-2.96) (-2.99) (-2.80) (-2.88) INFL -0.19*** -0.21*** -0.20*** -0.21*** (-2.66) (-2.96) (-2.63) (-2.91) GOV -0.55*** -0.46*** -0.56*** -0.47 (-3.24) (-2.50) (-3.39) (-2.66) OPEN 0.72*** 0.61*** 0.71*** 0.60** (2.21) (2.02) (2.00) (1.83) INV 0.22 0.24 0.22 0.25 (1.12) (1.36) (1.05) (1.27) M2 0.09 0.08 0.10 0.08 (0.24) (0.21) (0.25) (0.22) INST 0.001 -0.003 -0.001 -0.005 (0.04) (-0.09) (-0.02) (-0.15) PSE 0.05 0.09 (0.11) (0.21) POPGR -0.10 -0.10 (-0.67) (-0.65) EL 0.08 0.18 (0.08) (0.20) 85.50 68.40 87.74 74.09 [P-value] [0.00] [0.00] [0.00] [0.00] # of Obs 303 303 303 303 Note: See note on Table 4.

Contrary to the DIF-GMM results, the SYS-GMM models depict that coefficient for GOV still has the correct sign although not statistically significant. In addition, POPGR and EL are now statistically significant and have the expected signs, meaning that investing in human capital has positive implications on economic growth in the SADC region, as suggested by economic theory. In a similar fashion, PVT was used and the results are more or less the same.

Table 6: Dynamic SYSTEM-GMM Models (using M2 - proxy for financial development) Dependent Variable: GROWTH Model A Model B Model C Model D Y1 -0.07*** -0.05*** -0.07*** -0.05*** (-4.68) (-3.20) (-4.51) (-3.20) INFL -0.06* -0.11*** -0.07** -0.11*** (-1.73) (-2.81) (-1.90) (-2.80) GOV -0.09 -0.11 -0.09 -0.10 (-0.93) (-1.12) (-0.87) (-1.05) OPEN 0.06 0.14* 0.07 0.15* (0.68) (1.65) (0.83) (1.66) INV 0.007 0.02 -0.02 -0.0002 (0.08) (0.22) (-0.15) (-0.00) M2 0.20 0.16 0.15 0.12 (0.24) (0.67) (0.61) (0.48) INST 0.01 0.01 0.01 0.01 (0.44) (0.51) (0.48) (0.52) PSE 0.18 0.10 (0.59) (0.33) POPGR 0.20*** 0.18*** (2.74) (2.41) EL 1.39*** 1.21*** (2.72) (2.33) AR (1) -6.58 -6.82 -6.07 -6.25 [P-value] [0.00] [0.00] [0.00] [0.00] Sargan Test 556.36 435.00 427.91 416.98 [P-value] [0.00] [0.00] [0.00] [0.00]

# of Obs 346 346 346 345 Note: See note on Table 4.

The second-order serial correlation test developed by Arellano and Bond (1991) depicts that there is no second-order serial correlation present, both in DIF-GMM and SYS-GMM models. In addition, the Sargan (1958) test for over-identification of restrictions was used and the results indicate that the restrictions are not over-indentified and therefore the results are robust and not weakened by many instruments.

c) Seemingly Unrelated Regression Estimators (SURE)

When we disaggregate the analysis further and make use of the SUR estimator that takes into account any between-country dependence present in the data, the findings are mixed. Table 6 shows that there is no β-convergence for most of the countries in the CMA region, except Namibia. This implies that countries in the region, except Namibia, are not growing faster enough to catch up with the bigger country, namely, South Africa. Moreover, the salient feature of the results is that spending on physical capital, the high extent of financial development, and faster population growth rate have the expected signs, for most countries in the CMA region.

Investment has a positive impact on economic growth for Lesotho, South Africa and Swaziland, meaning that these countries’ economic growth depends also to higher spending on physical capital. In Namibia, we observe a positive significant association between inflation and growth. This positive significant association can potentially be interpreted that despite increases in inflation, Namibia still managed to register positive growth rates, although these growth rates may still be below its potential growth rates. Public spending on education and the government spending seems to have depicted expected positive impact on economic growth only in Namibia.

Table 7: SURE results for CMA Dependent variable: GROWTH Lesotho Namibia South Africa Swaziland Y1 0.54 0.34*** 1.63 -0.12 INFL 0.72 1.12*** -1.04** 0.25 GOV 0.74 4.09*** -6.96** -1.01 OPEN -3.77*** -3.12*** -5.18 0.16 GFCF 1.53*** -1.87*** 3.86*** 1.79** M2 3.68*** 1.67 8.52** -1.14 INST 0.26*** - 1.04 - PSE -2.48*** 10.21*** -11.89*** 0.27 POPGR -2.39*** -2.49*** -1.59 0.65* Breusch-Pagan test of independence: , P-value = 0.0642 Note: See note on Table 4.

In a nutshell, the results in the SADC region are mixed. There seems to be β-convergence in Angola, Mozambique, Seychelles and Zimbabwe. Inflation is detrimental to economic growth in countries such as Angola, Seychelles, South Africa and Swaziland. In the rest of the other countries, inflation has a positive impact on economic growth. Government spending can both affect economic growth positively or negatively depending on whether such spending is channeled towards productive sectors or not. In a country such as South Africa, government spending has a positive effect on growth, whereas in a country such as Mauritius, the impact is negative. The rest of the findings are self-explanatory. SURE results for SACU and other SADC countries are available in the Appendix 8(a) and 8(b), and as expected, the findings are mixed.

Table 8: SURE results for entire SADC countries Dependent Variable: GROWTH Y1 INFL GOV OPEN GFCF M2 INST PSE POPGR AGO -2.50*** -1.74*** -1.45 15.64*** 5.39 -10.76*** 7.32*** -39.08*** -12.11*** BWA 0.02 -0.06 -2.68 -1.11 4.17 -0.34 - -5.26 2.22 DRC -0.31 0.06 0.62** 1.17*** 0.72*** 0.44*** 0.73*** -0.55 -0.71 LSO 1.31*** 0.78*** -0.79 -4.43*** -0.68 3.39*** - -3.91*** 0.80 MDG -0.44 -0.44 1.01 6.88*** -2.77*** 0.19 - -0.55 -9.70 MWI 1.57*** 3.21*** 2.29*** -15.45*** 6.13*** 5.12*** 1.48*** 7.69*** 0.48*** MAU 0.18 -0.15 -8.48*** 2.96*** 1.87 -9.42*** - 3.07*** 0.57*** MOZ -0.16** -0.16 -0.43 2.13*** -0.46*** -1.44*** - 1.54*** -0.92*** NAM -0.06 1.49*** 7.51*** -4.94*** 0.47 0.03 - 6.95*** -5.41*** SYC -0.61*** -0.42*** 3.02*** -1.78** 4.94*** -8.95*** - 5.64*** -1.24*** SA -3.25 -4.62** 20.65** -10.05** 18.53*** 40.81*** 2.05*** -31.36** 2.04 SWA 0.88*** -12.16*** -15.88*** 0.55 14.21*** -18.92*** 3.75*** 26.11*** 14.21*** TZN 0.01 1.31*** 5.72*** 3.61*** -0.28 -0.50 - 12.44*** -29.68*** ZMB 1.03*** 2.66*** -6.95*** -13.74*** 9.46*** -3.59*** -0.49*** -10.92*** 21.81*** ZWE -1.08*** 2.27*** 2.09*** 6.02*** -5.50*** 1.46 0.72*** -3.25*** 6.15*** Breusch-Pagan test of independence: , P-value = 0.0042 Note: AGO-Angola, BWA-Botswana, DRC-Democratic Republic of Congo, LSO-Lesotho, MDG-Madagascar, MAU-Mauritius, MOZ-Mozambique, NAM-Namibia, SYC-Seychelles, SA-South Africa, SWA-Swaziland, TZN- Tanzania, ZMB-Zambia, and ZWE-Zimbabwe. */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, and PSE are I(1) hence they are used in first differences.

6. INFERENCE ON EMPLOYMENT AND POVERTY

Poverty has now become a subject of global interest. In fact, it is Goal 1 of the Millennium Development Goals (MDGs) to halve poverty by 2015 (Fosu, 2010). There is general agreement that aggregate income growth is necessary to reduce poverty (World Bank, 1990). In particular, macroeconomic variables such as inflation, unemployment, government expenditure and economic growth, have been proven to have an impact on poverty (Kashi and Shahiki Tash, 2014). In order to reduce poverty, economic growth has to be reflected in higher family incomes generated through productive jobs at adequate wages. Employment is the main source of household income; therefore, job creation and improved labour productivity are the fundamental mechanisms whereby economic growth can be reflected in poverty reduction, and growth can then be translated into better incomes (Islam, 2004; Osmani, 2002).

There are several studies that emphasize the importance of inequality in determining the responsiveness of poverty to growth. For instance, Easterly (2000) evaluated the impact of the Bretton Woods Institutions’ programmes by specifying growth interactivity with the level of inequality in the poverty-growth equation and found that the effect of the programmes was

36 enhanced by lower inequality. Adams (2004) provided elasiticy estimates showing that the growth elasticity of poverty is larger for the group with the smaller Gini coefficient.

Due to lack of comparable data over time and across the SADC region, this section focuses on making simple inferences on how growth, unemployment and poverty reduction are interlinked in the SADC region. Figure 3 depicts that most of the countries in the SADC region that experienced higher average economic growth rates during a ten-year period, also had lower unemployment rate levels. This implies that there is a negative relationship between economic growth and unemployment, and ultimately lower unemployment levels are conducive for poverty reduction. The same pattern can be observed when a ten-year period ranging from 1991 – 2011 is taken into consideration as depicted in the appendix.

Figure 3: Real Economic Growth Rate versus Unemployment Rate (2002 – 2012)

12.00 AGO 10.00

8.00 MOZ TZN 6.00 DRCMWI ZMB NAM 4.00 MAU BWA LSO (%) RSA MDG 2.00 SWA 0.00 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

-2.00 Real Economic Growth Rate Growth Economic Real ZWE -4.00 Unemployment Rate (%)

Source: World Bank Development Indicators

Ali and Thorbecke (2000) used cross-country African data and found that poverty is more sensitive to income inequality than it is to income Therefore, faster economic growth tends to lead to lower inequality through employment creation (inclusive growth). Employment lies at the heart of the concept of inclusive growth. Ianchovichina and Lundstrom (2009) justified that employment is a means through which all excluded individuals are able to achieve sustainably higher income levels. Hence, it can be deduced that higher economic growth leads to lower income inequality between the rich and the poor, and therefore leads to poverty reduction. Figure 4, depicts that the income inequality between the rich and the poor has been wide for most of the SADC countries. The inequality of income has been high in countries such as Namibia, Seychelles and South Africa, and low in countries such as Tanzania.

Figure 4: Real GDP growth versus Gini Coefficient (2002 – 2012) 12.00 AGO 10.00 8.00 TZN MOZ 6.00 DRC ZMB MWI NAM 4.00 LSO RSA SYC 2.00 MDG SWA 0.00

Real GDP GDP growth(%) Real -2.000.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 -4.00 Gini Coefficient

Source: World Bank Development Indicators

Figure 5 depicts that most countries in SADC have been registering high poverty headcount ratios as percentage of total population. When looking at the 10-year averages from 2002-2012, only Botswana, Namibia, Mauritius, Seychelles and South Africa registered poverty headcount ratios of below 50 per cent of their respective total populations. The figure further shows that countries that registered higher economic growth rates, such as Angola, Mozambique and Tanzania, also depicted higher poverty headcount ratios. The may be attributable to distributional effects across the countries. For instance, some countries may have grown faster due to sectoral performance in mining sector, but it may take time for the wealth derived from mining to be distributed into the households. Furthermore, the profits from the mining sector may be repatriated out of the country and therefore, the community in the mining area may not reap the benefits. Another example may be in the case of agricultural sector. Distributional effects from such a sector may be rapid and implying that households may rapidly enjoy the benefits accruing from growth in such a sector. The picture is more or less the same when the 10-year averages for 2001-2001 are considered as depicted in the appendix.

Figure 5: Poverty Headcount Ratio; US$2.00/day (PPP) versus Real Economic Growth (2001-2011)

12.00 AGO

10.00

8.00 MOZ TZA 6.00 DRC NAM ZMB MWI 4.00 MUS BWA LSO SYC RSA MDG Real GDP Growth GDP Real 2.00 SWA 0.00 0 20 40 60 80 100 Poverty Headcount Ratio at US$2.00 a day (PPP) (% of population)

Note: The countries in astericks are those with poverty headcount ratio of below 50 per cent. Source: World Bank Development Indicators

7. CONCLUSION AND POLICY RECOMMENDATIONS

Several conclusions can be drawn from the empirical results. On the positive side, there is evidence of conditional convergence among the SADC countries, meaning that poorer countries tend to be growing faster and catching up with richer countries in the region. Furthermore, there is evidence that countries that are more open to international trade are likely to experience higher economic growth. The development of the financial sector also significantly promotes countries’ growth rates. Human capital is also found to significantly influence economic growth in the region. On the negative side, factors such as inflation, government spending towards unproductive sectors, and political instability are found to retard economic growth in the region. However, caution has to be taken when interpreting these results because the macroeconomic policies and levels of economic development in respective countries are different.

The policymakers need to keep in mind that low inflation in the SADC region is a precondition for economic activity, and also that high inflation affects mostly the welfare of the poor. Therefore, low inflation is not just a necessary condition for economic activity, but also a sufficient condition for macro-economic stability. On the international trade front, policymakers in the SADC region should focus their efforts to ease restrictions on international trade, design strategic trade policies, and intensify efforts in trade negotiations to enable better access for exports. On the human capital front, policymakers in the region should continue to promote education policies (such as Free Primary and Secondary Education) and enhance the quality of the existing human capital stock.

The quality of the evidence presented is, to a certain extent, robust because we avoid using averages and take advantage of panel time-series analysis, which deals with important empirical issues, such as heterogeneity bias in dynamic panels and endogeneity in relatively thin panels. Essentially, this analysis is important because it allows us to specifically study the SADC region, instead of treating the region either as a dummy or as an outlier to be removed from the sample. Therefore, the analysis conducted here represents a step forward in terms of achieving insightful estimates, and in improving our knowledge on the subject in sub-Saharan Africa.

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Appendix Appendix 1: GDP per capita growth

10.00 8.00 6.00 4.00 2.00 0.00 -2.00 -4.00 -6.00 -8.00 -10.00 1981-90 1991-00 2001-09

Source: IMF 2014

Table 1a: Hausman fixed versus random Coefficients (b) (B) (b-B) Sqrt(diag(V_b-V_b)) fixed random Difference S.E. linfl -0.1645377 -0.1506016 -0139361 - lgov -0.0246287 -0.1190631 0.0944344 0.0528856 lopen 0.509149 0.5485584 -0.0394095 0.0528856 lgfcf 0.1278662 0.1079211 0.019945 - lpopgr 0.0849156 0.115459 -.0305434 0.0677153 b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg

Test: Ho: difference in coefficients not systematic chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 39.07 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite)

Table 4(a): FE Models (using PVT as a proxy for financial development) Dependent Variable: GROWTH Model A Model B Model C Model D Constant 2.99 2.72 3.01 2.89 (1.14) (1.00) (1.14) (1.05) Y1 -0.20* -0.19* -0.19* -0.19* (-1.76) (-1.62) (-1.73) (-1.62) INFL -0.18*** -0.19*** -0.18*** -0.20*** (-3.24) (-3.50) (-3.21) (-3.49) GOV -0.11 -0.13 -0.12 -0.14 (-0.56) (-0.62) (-0.61) (-0.69) OPEN 0.74*** 0.79*** 0.72*** 0.76*** (4.55) (4.76) (4.36) (4.51) INV 0.11 0.10 0.11 0.11 (0.67) (0.62) (0.71) (0.68) PVT 0.12 0.01 -0.01 -0.03 (0.12) (0.05) (-0.05) (-0.19) INST -0.01 -0.01 -0.01 -0.01 (-0.50) (-0.40) (-0.43) (-0.34) PSE 0.10 0.10 (0.31) (0.32) POPGR -0.01 -0.03 (-0.11) (-0.26) EL 0.66 0.94* (1.08) (1.70) F*test 6.34 6.81 6.11 7.18 [P-value] [0.00] [0.00] [0.00] [0.00] # of Obs 393 390 393 390 */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, PVT, LEI, and PSE are I(1) hence they are used in first differences across the models.T-ratios are in italics and in parenthesis.

Table 4(b): Dynamic Difference GMM Models (using PVT as a proxy for financial development) Dependent Variable: GROWTH Model A Model B Model C Model D GROWTH (-1) 0.02 0.02 0.02 0.02 (0.16) (0.24) (0.18) (0.25) Y1 -0.53*** -0.53*** -0.53*** -0.53*** (-2.82) (-2.92) (-2.66) (-2.78) INFL -0.21*** -0.21*** -0.21*** -0.21*** (-3.03) (-3.30) (-3.01) (-3.03) GOV -0.59*** -0.49*** -0.59*** -0.51 (-3.25) (-2.51) (-3.34) (-2.63) OPEN 0.73*** 0.63*** 0.73*** 0.63** (2.29) (2.13) (2.03) (1.91) INV 0.22 0.25 0.22 0.24 (1.13) (1.34) (1.06) (1.26) PVT -0.07 -0.05 -0.06 -0.06 (-0.56) (-0.48) (-0.49) (0.45) INST 0.006 0.001 0.0004 -0.003 (0.17) (0.03) (0.01) (-0.09) PSE -0.002 0.06 (0.00) (0.12) POPGR -0.09 -0.09 (-0.62) (-0.59) EL 0.10 0.19 (0.11) (0.22) 64.85 64.57 51.37 42.70 [P-value] [0.00] [0.00] [0.00] [0.00] # of Obs 303 303 303 303 */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, PVT, LEI, and PSE are I(1) hence they are used in first differences across the models.T-ratios are in italics and in parenthesis.

Table 8(a): SURE results for SACU Dependent variable: GROWTH Botswana Lesotho Namibia South Africa Swaziland Y1 -0.33 0.62** 0.33*** 1.75** -0.12 INFL -1.15*** 0.60 1.07*** -1.15*** 0.22 GOV 0.93 0.57 4.07*** -7.41** -0.91 OPEN 0.24 3.98*** -3.104*** -5.78** 0.13 GFCF 2.31*** 1.54*** -1.87*** 4.47 *** 1.77** M2 -0.24 3.55*** 1.55 8.34*** -1.16 INST -0.31 -0.25*** - 1.27 - PSE -5.68 -2.59*** 10.07*** 12.71*** 0.42 POPGR 0.21 -2.34*** -2.47*** -1.65 0.66** Breusch-Pagan test of independence: , P-value = 0.0491 */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, and PSE are I(1) hence they are used in first differences.

Table 8(b): SURE results for other SADC countries (Excluding SACU) Dependent Variable: GROWTH Y1 INFL GOV OPEN GFCF M2 INST PSE POPGR AGO -0.99*** -1.26*** -0.33 6.72*** 0.13 -2.89*** -3.31 -1.48 -1.14 DRC 0.51** 0.59*** -0.67 0.96** 0.87 -0.07 1.19*** -0.31 -16.82*** MDG -0.47* 1.06** 1.88 4.21*** -1.19** 3.46*** - 0.29 -8.59 MWI -0.19 -0.22 1.93** -0.02 0.89 -0.28 0.16 -2.41 -1.28*** MAU 0.37 -0.05 -5.78 1.24 0.67 5.03 - 1.53 -0.26 MOZ 0.03 -0.26** -1.96*** 2.17*** -0.60 -6.53*** -1.24 0.25 -0.25 SYC -0.48** -0.44*** 2.71*** -2.11* 4.69*** -8.81*** - 4.14** -1.26** TZN 0.24*** 0.71*** -3.32*** -1.53** 5.52*** 7.70*** 0.22 2.08 -7.99*** ZMB -0.02 0.95** -3.55*** -3.32*** 3.53*** 0.47 -0.18*** -0.12 12.91*** ZWE -0.77*** -2.03*** 1.39 4.96*** 5.18*** -0.35 0.56*** 2.58*** 4.96*** Breusch-Pagan test of independence: , P-value = 0.0566 Note: AGO-Angola, DRC-Democratic Republic of Congo, MDG-Madagascar, MAU-Mauritius, MOZ- Mozambique, SYC-Seychelles,TZN-Tanzania, ZMB-Zambia, and ZWE-Zimbabwe. */**/*** denotes significance at 10,5 and 1 per cent levels, respectively. All variables are in logarithms with an exception of INST which ranges from -10 to +10. Variables: INST, M2, and PSE are I(1) hence they are used in first differences.

Figure 6: Real Economic Growth Rate versus Unemployment Rate (1991 – 2001) 8.00

6.00 MOZ MAU BWA 4.00 NAM LSO SWA TZN MWI

MDG 2.00 ZWE RSA AGO ZMB 0.00(%) 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 -2.00 Unemployment Rate (%)

-4.00 Real Economic Growth Rate Growth Economic Real DRC -6.00

Source: World Bank Development Indicators

Figure 7: Real GDP growth versus Gini Coefficient (1991-2001)

8.00

6.00 MOZ BWA 4.00 SYC LSO NAM SWA TZN MWI 2.00 MDG ZWE RSA AGO ZMB 0.00 0.00 10.00 20.00 30.00 40.00 50.00 60.00 70.00 80.00 -2.00

-4.00 Real Economic Growth (%) Growth Economic Real Gini Coefficient -6.00

Source: World Bank Development Indicators

Figure 8: Poverty Headcount Ratio; US$2.00/day (PPP) versus Real Economic Growth (1991-2001) 8.00

6.00 MOZ

BWA 4.00 NAM LSO SWA TZAMWI 2.00 RSA MDG AGO ZMB 0.00 0 10 20 30 40 50 60 70 80 90 100 -2.00

Real GDP Growth GDP Real Poverty Headcount Ratio at US$2.00 a day (PPP) (% of population) -4.00

-6.00

Note: The countries in astericks are those with poverty headcount ratio of below 50 per cent. Source: World Bank Development Indicators