Applied Econometrics and International Development Vol. 18-2 (2018)
POSITIVE TIME PREFERENCE AND ENVIRONMENTAL DEGRADATION: THE EFFECTS OF WORLD POPULATION GROWTH AND ECONOMIC ACTIVITY ON INTERGENERATIONAL EQUITY, 1970- 2015 Christopher E.S. WARBURTON* Abstract This paper investigates the effects of population growth and economic activity on intergenerational equity and environmental preservation. Time series data from 1970 to 2015 suggest that positive time preference, induced by poverty and virtually unrestrained economic activity, pose dynamic and systemic risks to environmental preservation more so than the population growth. With the aid of economic models and empirical evaluation, the paper concludes that the viability of environmental conservation will ultimately be dependent on levels of poverty, life expectancy, the availability of diverse sources of income, restrained commercial consumption of environmental resources, and the preference for long-term investment. Keywords: Cointegration, Conservation, Environmental Degradation, Foreign Direct Investment, Intertemporal Consumption, Overlapping Generations, Pollution, Population Growth, Poverty, Saving JEL Classification: O13, O14, O15, O44, Q53, Q54 1. Introduction This paper investigates the effects of population growth and economic activity on intergenerational equity and environmental preservation. Time series data from 1970 to 2015 suggest that positive time preference, induced by poverty and virtually unrestrained economic activity, pose dynamic and systemic risks to environmental preservation more so than the population growth. With the aid of economic models and empirical evaluation, the paper concludes that the viability of environmental conservation will ultimately be dependent on levels of poverty, life expectancy, the availability of diverse sources of income, restrained commercial consumption of environmental resources, and the preference for long-term investment. The equitable use of environmental resources is nothing new, but it started to gain noticeable and accelerated multilateral attention in the 1990s. Multilateral population policies (facilitated by United Nations Conference on the Environment and Development) and the signing of the Kyoto agreement to foster a global climate policy became evident indicators of the movement to preserve the environment. The judicious use of environmental resources has been controversial; partly because of divergent self-interests and less forceful multilateral policies. Over the years, and without adequate compensation, nations that were slow to grow or catch up with richer nations have espoused a sense of entitlement to extract resources just as their rich counterparts had done in bygone years. It was also fashionable to argue that financial wealth can compensate for environmental degradation through a substitution channel. The basic idea is that capital can be decomposed into interdependent and offsetting components, with one or more of the components offsetting the depreciation or ______* Christopher E.S. Warburton, Ph.D., Department of Political Science and Economics, East Stroudsburg University, PA, USA. Email: [email protected]
5 Applied Econometrics and International Development Vol. 18-2 (2018)
degradation of another to ensure the constancy of aggregate capital over time. Consider the composition of capital as defined by Equation 1: K= Kn + Kh + Km; (1) where K is for total capital stock, Kn is for natural capital stock, Kh is for human capital stock, and Km is for man-made capital stock. The natural capital stock includes renewable and non-renewable (critical) natural resources that are irreparable and susceptible to extinction. While some of these assets can be monetized, their true social value is usually indeterminate; a problem that is more pronounced when monetary and social values become impractical.1 Equation 1 provides a theory for a school of thought that supports weak sustainability. Implicitly, if K is not declining—as a result of offsetting changes in the assorted categories of capital stock—then there should be reason to accept some measure of sustainability. The idea that Km can replace or substitute Kn is problematic and not entirely convincing. A relatively appealing view has been to preserve Kn in pecuniary and or physical terms (strong sustainability). It is noteworthy that a subset of capital, which is essential for human existence (critical capital), cannot be substituted by other forms of capital. The bifurcated approaches pose interesting conceptual developments in the literature and the approaches have been summarized pretty well by Hanley et al. (2013). Two strands have been identified: (i) the outcome approach and (ii) the chance approach. In the outcome approach, there is a presumption that consumption is linear and insusceptible to diminishing returns. This may very well be the case when life expectancies are very short as those in some African countries (see Table 4). The chance (equity) approach requires contemporary generation to equitably save proportional capital stock for a subsequent generation. Essentially, saving must be somewhat equal to consumption. The issue of sustainable development has become a matter of population dynamics, positive time preference, saving or investment, and intergenerational equity or ethics, which is sometimes presumed to be subjective and complicated by preferences for appropriate discount rates. Obviously, the issues are not as simple as they might seem and the central variables are usually confounding and subject to careful analysis. Exogenous conditions or circumstances, policy contingencies, and data interpretation, tend to complicate the economic analyses. For example, population data must be studied carefully. The data may occasionally project growth without death rates and life expectancies. However, the data are almost always contingent on policy choices that may or may not be implemented. This paper is designed to investigate some of the central issues outlined above.
1 Human capital is also beset with its own form of degradation. Structural and long-term unemployment devalue the worth of human capital. In many societies, investment in human capital is rapidly diminishing, with no assurance of a reversal in the downward trend. This means that the retraining that is so essential to salvage structural unemployment is not readily available. The man-made category of capital includes all man-made facilitators of production; viz: machinery, roads, computers etc. Invariably, this form of capital is conspicuously associated with social policy in the form of capital consumption allowance; see Hanley et al (chapter 6) for further reading on the decompositions of capital and sustainable development.
6 Applied Econometrics and International Development Vol. 18-2 (2018)
In the next Section, I discuss some historical theories of population and sustainable development. In Section III, I discuss the issues of intertemporal consumption and positive time preference with the aid of Diamond’s overlapping-generations model and Romer’s exposition. The importance of saving (conservation) is highlighted in the Section as a prelude to the consumption and long-term investment problems, which are discussed in the subsequent Section. Econometric discussions of global output and long-term investment for sustainable development are provided in Section V. Concluding remarks and policy implications are provided at the end of the paper. 2. The population-development literature The enhancement of human welfare has been periodically punctuated by the issue of population growth and concerned theorists have provided several propositions to deal with population growth. Though the problem of population growth was well known in the eighteenth century, the Reverend Thomas Malthus became the first theorist to successfully provide a celebrated theory of population growth in his work work, Essay on population (1798). Evidently, Malthus was concerned about the geometric progression of population growth, relative to the arithmetic progression of food supply (output). He somberly predicted that without diseases, wars, and untimely death (positive checks), food supply will not keep pace with population growth that doubles every 25 years. A schematic representation of the Malthusian theory has been provided in Table 1, which shows how population growth was expected to progressively outpace food supply by large margins. Today, though the Malthusian projections have not materialized in the doleful form that was envisaged in the eighteenth century, the relationship between population growth and output continues to excite controversy over resource allocations across generations. Table 1: The Malthusian Theory of Doom Years 1 25 50 75 100 125 150 175 200 Population 1 2 4 8 16 32 64 128 256 Food Supply 1 2 3 4 5 6 7 8 9 1960 1985 2010 X X X X X X World Population 3.032 4.846 6.93 X X X X X X GDP per Capita 450.72 2618.28 9513.31 X X X X X X Source: Van den Berg, 159. World Population and GDP per capita (current US$) by author (datasource:worldbank.org). Malthus was convinced that population will double every twenty five years. Blaug (1997) questions the Malthusian data: “From dubious American data that did not distinguish between fecundity and immigration, he [Malthus] inferred that an unchecked population will double itself every 25 years, implying a growth rate of just under 3 per cent per annum (actually, rates of 5 percent per annum seem to be biologically possible)” (Blaug, 68). Yet, one can hardly make a convincing argument that Malthusian apprehension was misplaced. While the world population did not double between 1960 and 1985, for example, the population of the world grew at an average annual rate of about two percent between 1960 and 2015. National output per capita more than double between 1960 and 1985, and grew at an average annual rate of about 6 percent. Evidently, the population of the world has grown in absolute terms from 3.032
7 Applied Econometrics and International Development Vol. 18-2 (2018) billion in 1960 to 7.357 billion in 2015, and it took about 39 years for the global population to double in absolute terms (see Figure 1). This growth continues to cause concern about the inadequacy of nonrenewable resources for current and subsequent generations. Between 1960 and 1985, national output per capita more than double. Of course, Malthus could not have envisioned the technological changes and interventions that are retarding population growth and increasing output in the contemporary global economy. Malthus could hardly have conceptualized the paradox of waning population and increased output, or the comovement of high output and low life expectancy or ageing population. In the 1990’s, just about the time when the world doubled its population (in absolute terms) relative to the 1960’s, multilateral efforts were urgently pioneered to deal with population growth and sustainable development. For example, the link between sustainable development and population dynamics was recognized by the Rio Declaration (1992), pioneered by the United Nations Conference on the Environment and Development (UNCED) and promoted by a Programme of Action, which was put forward at the International Conference on Population and Development (ICPD) (held in Cairo in 1994). “Both political declarations highlighted the importance of promoting human wellbeing in harmony with nature, and to this end emphasized the need for a two-pronged approach, notably sustainable patterns of consumption and production – which is the hallmark of the green economy – and policies that address population dynamics” (UNFPA, 2012, p.1). Figure 1: World Population Growth (billion)
Source: Worldbank .org. Notes: Between 1960 and 2015 (55 years) the population of the world grew at an average annual rate of 2 percent. National income grew at a rate of approximately 6 percent for the same period. Slightly over a decade later, the least developed economies subsequently attracted attention for their expanding youth population. By 2011, it was estimated that about 60 percent of the population of the least developed economies was under the age of 25 and that by 2050, the population in this age group will expand by an additional 60 per cent (UNFPA, 2011, p.7). It was hoped that this cohort of the population will contribute to human capital and productivity. A United Nations (UN) report maintains that although population growth rates have slowed down, the world’s population is still growing by an additional 81 million people per year and that the global economy will need to support approximately 8.4 billion people by 2030 (UN.org, 2015,3). With the exception of Europe, where total population is projected to decrease by slightly less than 1 percent by 2030, it is
8 Applied Econometrics and International Development Vol. 18-2 (2018) estimated that all other regions will grow by at least 10 percent over the next 12 years. Additional sources of population data include: (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Report (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme. It is projected that Africa, the poorest and least developed continent, will account for more than 40 percent of the absolute increase in population so that, by 2030, the region will account for nearly one fifth of the world’s total population (UN.org, 2015, 3). But Africa has virtually invisible population encumbrances; two of which are stillbirths and very low life expectancy.2 A Lancet study reports that between 1990 and 2000, and 2000 and 2010, Sub-Saharan Africa accounted for about 40.5 percent of global stillbirth (Blencowe et al, p. e102); excluding Northern Africa. Further, the authors write: “Our estimates suggest that 2.6 million…babies were stillborn at 28 weeks or more in 2015. This represents a large burden for women, families, communities, and health- care providers. Progress in reducing stillbirth rates is slower than that required to meet targets set to end preventable stillbirths, and considerably slower than for maternal mortality reduction and for child mortality reduction, especially after the first month of life. Despite this large burden, stillbirths remain barely visible on the global policy agenda” (Blencowe et al, p. 105). The gloomy African picture is compounded by the abysmal rate of emerging life expectancies in the continent, which currently shows no sign of reversal. Some of the African countries have done comparatively well in the past (Guisan and Exposito, 2016, p.88), but nearly half of the countries in Africa have life expectancies that are below 53 years (see Table 2). More so, about 24 percent of the African countries have life expectancies that are less than 50 years. Table 2: Short Life Expectancy in Africa Country Life GDP Country Life GDP Expectancy (per capita) Expectancy (per capita) CAR 45 325.72 Cameroon 50 1,495.44 Lesotho 46 1,352.48 Mali 50 745.87 Sierra Leone 46 455.59 Equatorial Guinea 50 12,278.13 Zimbabwe 47 917.56 Somalia 50 NA Guinea-Bissau 47 582.37 Nigeria 50 2,455.92 Zambia 47 1,627.28 South Africa 51 7,488.99 Afghanistan 47 617.89 Malawi 52 481.45 Swaziland 47 3,906.26 Uganda 52 662.43 DRC 47 387.97 Guinea 52 735.72 Chad 49 859.65 Ivory coast 53 1,552.77 Mozambique 49 515.39 Niger 53 391.13 Burundi 49 218.28 Botswana 53 7,483.13 Angola 50 3,582.65 Source: list25.com (updated 19 May, 2016) and World Bank’s WDI (2017). Notes: The author has rounded the ages to reflect discrete age numbers. DRC is for Democratic Republic of Congo and CAR is
2 For international comparative analysis, the WHO defines stillbirth as a baby born with no signs of life at or after 28 weeks' gestation.
9 Applied Econometrics and International Development Vol. 18-2 (2018) for Central African Republic. Average life expectancy for the 25 countries is 49 years. GDP is reported as constant 2010US$. The Europeans have an unpleasant story of their own. While Africans are dying prematurely, Europeans are experiencing an ageing or fertility problem (see Table 4). The World Health Organization (WHO) reports that the world is aging rather rapidly and that nearly two billion people across the world can be expected to be over 60 years old by 2050 (WHO).3 It is unlikely that Africa will be a significant component of the ageing statistic, which is projected to be more than three times the 2000 statistic. It is estimated that 80% of older people will be living in low- and middle-income countries in 2050. “The pace of population ageing is much faster than in the past. All countries face major challenges to ensure that their health and social systems are ready to make the most of this demographic shift.” 4The natural rate of population growth is consistent with the projection of the WHO (see Table 3). Table 3: The Ageing Population of Europe Country Population over 65 Country Population over 65 (%) (%) Japan 23.6 Estonia 18.8 Italy 22.4 Lithuania 18.8 Greece 21.4 Spain 18.8 Germany 21.2 Austria 18.8 Portugal 20.8 Belgium 18.2 Finland 20.5 Netherlands 18.2 Bulgaria 20.0 Czech Republic 18.1 Sweden 19.9 Slovenia 18.0 Latvia 19.4 Switzerland 18.0 Malta 19.2 Hungary 17.8 France 19.1 United Kingdom 17.8 Denmark 19.0 Romania 17.3 Croatia 18.9 Source: www.worldatlas.com (updated April 25, 2017) The natural rate estimates the net birth rate, which is the difference between crude birth and crude death rates without the effects of migration. Of course, regional variations can be expected. If, on the aggregate, average death rates are exceeding birth rates (see Table 4), what is accounting for the environmental devastation that imperils the future of the world? One can reasonably redirect attention to poverty, unemployment, and the increasing positive time preference to ensure the survival of the indigent and unfortunate. An overlapping-generations model with intertemporal consumption and saving (conservation) is somewhat instructive and revealing. Table 4: The Declining Natural Rate of Population Year Change of Rate in 5 Year Rate years 1960-1965 0.069462 1990-1995 -0.15296 1965-1970 0.066563 1995-2000 -0.12827 1970-1975 -0.05115 2000-2005 -0.05366 1975-1980 -0.08679 2005-2010 -0.01565 1980-1985 0.001801 2010-2015 -0.03862 1985-1990 0.006515
3 World Health Organization, “Ageing and Health,” 5 February 2018; www.who.int 4 Op.cit.
10 Applied Econometrics and International Development Vol. 18-2 (2018)
Source: United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition. Notes: The natural rate quinquennially declined at an average rate of about 5 percent, with the previous five years as reference point. Rates are expressed per 1,000 population on an annual basis. 3. Diamond’s intertemporal consumption model The satisfaction that individuals get from consumption (utility) relative to their income and risk aversion is not an arcane concept. The intertemporal variety, otherwise known as an “overlapping-generations model,” is very instructive for explaining consumption between two time periods. In this paper, I recall the fundamental arguments of Diamond’s model (1965),5 which provides some very attractive properties for explaining the task at hand. The utility function has underlying and practical arguments that are relevant to the conservation puzzle. People tend to consume more as income increases at the outset (C1t). However, they must borrow from second period resources to satisfactorily survive when income is inadequate in the first period. As people get older, their appetite for consumption diminishes; meaning that if they have to live for two periods, they must also plan on smoothing consumption over their lifespan. Accordingly, there must be a parameter of risk that straddles the two periods, given the available technology (At) and the limited nonrenewable resources from which humans can generate income (Yt). The risk parameter can be characterized as “constant relative risk aversion,” because of its consistency across time periods regardless of the level of consumption. Notably, the model presupposes that people will live for two periods. However, in some areas of the world where the life expectancy is very short (40 to 50 years), the model is not necessarily relevant to the concept of saving or conserving for old age. For example, in parts of Africa where poverty is real and life expectancy is low, the concept of conservation is inapplicable. Since altruistic humans usually care about themselves and their children’s welfare, their appetite to consume across time periods is somewhat constrained by a bounded risk parameter denoted as theta (θ) in this case. The standardized version of the risk parameter cannot be greater than 1 or 100 percent. The fact that second period consumption (C2t+1) does not have the same weight as first period consumption— because humans have a positive time preference—suggests that second period consumption must be discounted by a factor of ρ (rho). From the foregoing discussions, the lifetime utility of rational humans with long life-expectancy can be specified as follows: 1 1 C1t1 C 2 t 1 0, 1 Ut * , . (2) 1 1 1 lnc if 1 The utility function (Equation 2) is equally appealing because it captures the diminishing marginal utility across time periods as people get older.6 Invariably, humans face an economizing problem. They have a finite amount of
5 See also Romer’s exposition of the model in Chapter 2 of Advanced Macroeconomics. Some minor contextual and notational modifications have been provided with detailed contextual explanations. 6 It must be noted that the first derivative is positive (Uc' 1t C 1 t > 0) while the second derivative is 1 negative (U’’C1t = C1t 0 ).
11 Applied Econometrics and International Development Vol. 18-2 (2018) financial resources or critical capital, but their wants are infinite (unlimited). Accordingly, the lifetime constraint must consider the economizing problem: 1 AYCttt1 C 2 t 1 ; (3) 1 rt1 where AtYt is the amount of income that can be generated for consumption in periods 1 and 2. The latter period coincides with the inception of a newer generation. When humans are willing and able, they prudently save or conserve economic resources during the first period. Saving or conservation can be transferred over to the second period for old age consumption and bequest. Since part of second period resources can be consumed in the first period when first period income is inadequate, the intensity with which economic resources will be depleted will depend on the economic circumstances and the disposition to sparingly consume nonrenewable economic resources. Therefore, second period consumption has to be discounted by the amount of borrowing or financial degradation to which second period consumption can be subjected (1/1+rt+1). In effect, it is precocious for the young to consider the extent to which first period consumption can be augmented by second period resources when there are second period obligations. However, positive time preference and the scope of financial devastation in the first period will unavoidably be influenced by the level of poverty and accessibility or exposure to economic resources. Equation 3 summarizes the arguments of the lifetime constraint, which must be evaluated against the objective of utility maximization in two periods (Equation 2). Given the fundamental arguments of Equation 3, the second period (future value) consumption can be derived as a residual, including the value of resources that will be transferred over from the first period (1+rt+1):
C2t 1(1 r t 1 ) * AYC ttt 1 . (4)
First period consumption (C1t) and early consumption of a portion of second period’s income (C1t*(1+rt+1)) reduce the amount of economic resources—including saving or conservation (AtYt*(1+rt+1))—that are available for second period consumption (C2t+1). The benefits and costs that are associated with intertemporal human decisions can be derived from a constrained optimization specification, which maximizes the lifetime utility function of humans (Equation 2) subject to their finite and limited economic resources (Equation 3). Equations 2 and 3 can be combined for optimization purposes to derive shadow prices: C 1 1 C 1 C L= 1t * 2t1 AY 2t1 C . (5) t t 1t 1 1 1 1 rt1 The combination of the two equations gives a “Lagrangian function” (Equation 5), which should also be considered to be a tribute to the Italian Mathematician, Joseph Louis Lagrange, who lived for parts of the eighteenth and nineteenth centuries and has been widely credited for producing multiple mathematical algorithms. The first order conditions for optimization and derivation of shadow prices are subsequently provided by taking the derivative of the Lagrangian with respect to consumption in two periods and the lifetime constraint:
12 Applied Econometrics and International Development Vol. 18-2 (2018)
L C1t 0 →→ C1t ; (6) C1t
L 1 1 1 1 C 2t1 0 →→ C 2t1 ; (7) C2t1 1 1 rt1 1 1 rt1
L C2t1 and AtYt C1t 0 . (8) 1 rt1 Shadow prices are social evaluation prices that are variously used to evaluate opportunity costs in the absence of precise market values.7 They provide a conceptual framework for cost-benefit analysis and marginal rates of substitution. Equations 6, 7, and 8, collectively known as “Euler equations,” can be utilized to derive critical values for social evaluation after utility has been maximized. By substituting the sensitivity parameter of the constraint (λ) or the optimal value of first period consumption into Equation 7 and rewriting the optimal consumption of period 2 in terms of period 1, Equation 7 can be rewritten as:
1 1 C 2t1 C1t . (9) 1 1 rt1 That is, 1 C2t1 1 rt1 . (10) C1t 1 Equation 10 presupposes that the propensity to consume more or less over time will depend on the preferred saving or conservation rate (rt+1), but also on whether that rate is greater or less than the discount rate (ρ) of future consumption. Theta is a sensitivity (elasticity) parameter that measures the strength of substitution that is associated with the relative price differentials of intertemporal consumption rather than income. Implicitly, theta is indicative of the amount by which intertemporal consumption will change as a result of interest rate adjustments between the two time periods. If humans accelerate the saving or conservation rate relative to the discount rate, they will be reducing their positive time preference. Alternatively, if the discount rate increases faster than the saving rate, the positive time preference will be accelerated. So, what are the guiding principles for optimal consumption in the first period? The optimal principle can be derived by multiplying both sides of Equation 10 by C1t (to eliminate second period consumption) and substituting the result into the constraint: 1 1 1 r 1 1 r C t1 C A Y C t1 C A Y , (11) 1t 1 r 1 1t t t 1t 1 1t t t t1 1 1
7 See Brent (2017) for detailed analyses of cost-benefit tradeoffs, including the anticipatory value of goods and services that are not yet available in the market place though consumers are willing to pay (WTP) for them.
13 Applied Econometrics and International Development Vol. 18-2 (2018)
1 or 1 . (12) C1t 1 1 AtYt (1 ) (1 rt1 )
Equation 12 functionally helps to define the saving/conservation challenge: S(r) 1 C1t (AtYt ) : 1 (1 r) S(r) . (13) 1 1 1 (1 r) 1 Intertemporal saving, investment, or conservation will increase only if (1 r) is increasing. Implicitly, saving, investment, or conservation becomes a problem when θ is greater than 1, or when risk aversion dominates societal decisions to prevent saving or conservation from increasing in r.8 4. (a) The consumption issue So, what are the drivers of the contemporary consumption frenzy? In many ways, the foregoing discussions about consumption are very revealing. Poverty, a very serious problem, has been superficially integrated into discussions about environmental conservation. When poverty is prevalent in any locality and the desire for global corporate profits is unrelenting and poorly targeted, the welfare of subsequent generation will be severely discounted by undesirable time preferences and the concept of “sustainable development” can neither be feasibly implemented nor significantly achieved. Today, the global forests are virtually empty after long years of illegal logging and poaching. Since humans live on land, the resources on land are readily accessible for prompt exploitation with unintended consequences for the ecosystem. Scientists have discovered that humans can devastate a fauna by indirect or direct means. Indirect defaunation occurs when a fauna is destroyed through human activity that is not specifically aimed at animals; for example, habitat destruction in tropical forests. Defaunation is a process of deforestation, which the Food and Agriculture Organization defines as “the clearing of forests to use the land for other purposes, or to leave it as unused wasteland” (FAO, 2012, p. 9). By 2002, it was estimated that the cumulative loss of global forest land (over a period of 5 000 years) was about 1.8 billion hectares, an average net loss of 360 000 hectares per year (FAO, 2012, p.9 and Williams, 2002). Defaunation has been the result of population growth, demand for food consumption, and fuel. It gained heightened attention in the aftermath of deforestation that occurred in the temperate forests of Asia, Europe, and North America.9
1 12 8 1 Since the derivative of (1 r) is (1 r) , saving can only increase in r when θ < 1; see Romer’s discussion of household behavior in Chapter 2. 9 As environmental degradation became more apparent in the 1990s, strident efforts were made to save the forests of the world. The initial procedural and substantive issues were contentious, but an international
14 Applied Econometrics and International Development Vol. 18-2 (2018)
Forests have reliably provided income for rich and poor, and the forests have become indispensable sources of wealth for the poor and unfortunate. A meta-analysis of over 51 case studies from 71 countries indicates that forest incomes represent an average of about 22 percent of total income for poor populations (Cheng et al. p.2). The adversity of the poor has been exacerbated by structural changes to the global economy as well as market and policy failures. International trade restrictions in the form of tariff and non-tariff barriers have generated adverse social external effects for cash crop producers in tropical regions. The value of the traditional agrarian goods produced by the least developed countries (LDCs) has plummeted and there is no apparent superstructure for a transition from an agrarian economy to a service or industrial economy in some of the starving LDCs of Africa that have inadequate human capital. Not surprisingly, some have called for an African Marshall plan many years ago (see Guisan and Exposito). Poorer farmers are left with no alternative but to reevaluate their comparative advantages and shift from low value cash crops that are virtually untradable in global markets, to commercial scale high value illegal but tradable products. For example, logging was expedited in Guinea-Bissau, after the price of cashews, Guinea-Bissau’s main export, dropped unbearably. About 80 percent of the population is dependent on cashew production for financial stability. The price decrease caused a significant loss of income that had to be augmented by income from forest resources. A local can be paid between $2 and $6 to cut down a rosewood tree, as opposed to between 2¢ and 50¢ for a kilogram of cashews (Marino, 2014). In Guinea-Bissau, almost 50 percent of the population lives below the international poverty line of $1.25 per day and the life expectancy is 47 years. Logging rosewood trees has provided an inconvenient but attractive comparative advantage. Timber exports to China from the country jumped from 80 cubic meters in 2008 to more than 15,000 cubic meters in 2013. That is, the consumption of the poor will always be augmented by economic resources that should be saved or conserved except alternative sources of income are provided. “The local populations [of Guinea-Bissau] use wood from the forests as their primary source of energy. They also use the animals as a source of protein in their diets, but ‘at this pace, deforestation is going to destroy the animals’ natural habitats and cause their disappearance.’ This continued logging of the rosewood tree will lead to destabilization of the local habitat and essential aspects of the local population’s livelihood” (Marino, 2014). Unlike indirect defaunation, direct defaunation is the deliberate and systematic killing of animals for subsistence and international commerce. During the trying 1990s, Redford (1992) wrote: “Many large animals are already ecologically extinct in vast areas of neotropical forest where the vegetation still appears intact” (Redford, p.421).10 Of the Amazon, he observed: “During the height of the skin trade, many animals with valuable skins were killed.
forest policy dialogue was launched in 1995 out of a compromise. The United Nations Forum on Forests eventually became an outgrowth of the formative efforts (FAO, 2012, p. ix and FAO, 2012, pp. 9-10). 10 Ecological extinction is defined as "the reduction of a species to such low abundance that although it is still present in the community it no longer interacts significantly with other species” (Redford, p. 420)
15 Applied Econometrics and International Development Vol. 18-2 (2018)
Since the collapse of the skin trade, tropical forest peoples have continued to hunt many of these animals because their meat is appreciated. The result of this widescale human activity has been the reduction or extinction of local game populations in virtually all areas of Amazonia” (Redford, p.420). Similar patterns of defaunation are also evident in vast areas of Africa where poverty levels are high and life expectancies are very low (see Table 3). Coincidentally, “most of the world’s forests are located in some of the poorest areas on the earth. Of the 1.3 billion people worldwide who live in extreme poverty on less than $1.25 a day, forests directly contribute to 90 percent of their livelihoods” (www.earthday.org). As forests are depleted, poor people go in search of more forests in order to subsist. “Even when the poor recognize that their practices are destructive, faced with feeding their families today or protecting forest land for future generations leaves them no choice. At the same time, expanding cropland and agricultural systems, building roads and harvesting wood for lumber leads to further deforestation. When logging companies look for new timber, poor communities who depend on the forests are again the losers” (www.earthday.org). Direct defaunation constitutes a consumption crisis in Africa and the rest of the world. Markets exist when there is interaction of buyers and sellers. While Africa is home to the world’s most iconic wildlife, its wildlife is hunted to extinction. For example, relative to its population in the 1960, the population of the Black Rhino has fallen by 97.6 percent. Lions are extinct in seven African countries and it is estimated that about 2,000 Grevy’s Zebra remain on the continent. Fewer than 1,000 Mountain Gorilla remains, and 35,000 African Elephants can be killed in one year (www.awf.org). These consumption preferences or perverse incentives have systemic consequences. Essentially, they involve the interdependent ecosystem, which inures on the quality of land, water, and air. The interdependence is further complicated by occasional and virtual geographic delineations that are amorphous and imprecise. While land drives a significant portion of the global economy, it also services the ecosystem by providing carbon storage and managing the natural flow of water (Cheng et al.1). Human activity (consumption) affects air quality and marine life. The National Oceanic and Atmospheric Administration (NOAA) finds that the majority of pollutants going into the ocean come from activities on land. “For more than 200 years, or since the industrial revolution began, the concentration of carbon dioxide (CO2) in the atmosphere has increased due to the burning of fossil fuels and land use change (e.g. increased car emissions and deforestation). During this time, the pH of the surface ocean waters has fallen by 0.1 pH units” (NOAA, 2011).11
To the extent that the ocean absorbs about 30 percent of CO2 emissions from land, there is a positive correlation between increases in CO2 emission from land and oceanic absorption of land emissions. CO2 emissions increase hydrogen ions and reduce carbonate ions that are essential for calcifying organisms such as oysters, sea urchins, corals, and calcareous plankton (NOAA, 2011).
11 The change in the pH scale, which is logarithmic, corresponds to 30 percent increase in oceanic acidity.
16 Applied Econometrics and International Development Vol. 18-2 (2018)
Global carbon emissions from fossil fuels have significantly increased since 1900. The Environmental Protection Agency (EPA) finds that between 1970 and 2011 or thereabout, CO2 emissions increased by about 90 percent; with emissions from fossil fuel combustion and industrial processes contributing about 78 percent of the total greenhouse gas emissions. Agriculture, deforestation, and other land-use changes have been the second-largest contributors to emissions (EPA, Global Greenhouse Gas Emissions Data April 13, 2017, www.epa.gov). Though poverty and unemployment have not significantly contributed to the retardation of environmental degradation, the global response to greenhouse gas (GHG) emissions has been much more deliberate, thoughtful, and relatively well coordinated since Kyoto. The market-based policy of capping and trading emissions has somewhat controlled the release of GHG emissions and trade regimes have put a cap on GHG emissions that permit polluters to trade emission allowances. Beyond the declarations of endangerment, enforcement provisions to save the global forests have been less stringent. Somehow, as opposed to specific technological and performance criteria, the emission trade allows the emission market to estimate the economically efficient or tolerable amounts for GHG emission, and the market mechanism has been generally cost effective (Ramseur p.2).12 With the ageing population and increased consumption, can the paucity of saving and overconsumption be salvaged by long-term investment? 4.(b) The issue of consumption and long-term investment Apparently, per capita income growth is volatile, but it is far in excess of the relatively stable and accommodative measure of population growth (see Figure 2 in the Annex). The natural rate of global population growth is less alarming. Ironically, long-term investment or foreign direct investment (FDI) has not necessarily salvaged consumption propensities. Rather, FDI has facilitated more of extractive (consumption) and service-oriented investments to maximize shareholders’ wealth. The belated marginal attempts to invest in green technology are encouraging, but they are not directly sensitive to the reduction of poverty in troubled areas. A foreign direct investment (FDI) is a long-term investment in the form of a controlling ownership of a business in one country by an entity that resides in a foreign country. The controlling ownership or management interest is usually about 10 percent, based on OECD and International Monetary Fund (IMF) guidelines. There are organic and inorganic forms of FDI. Technically, FDI can be characterized in terms of equity-, long-, and short-term capital (United Nations Conference on Trade and Development, UNCTAD, 2010, p.xiv). When businesses expand existing operations, they are engaged in organic extensions or investments. Alternatively, they promote inorganic extensions or investments when they purchase or merge with businesses in a target country. Apart from management interests, a broader definition of FDI encompasses mergers and acquisitions, intra-company loans, construction of new facilities, and reinvestment of profits for subsequent growth. Additionally, FDI usually
12 In the US, between 1990 and 2015, national concentrations of air pollutants improved 85 percent for lead, 84 percent for carbon monoxide, 67 percent for sulfur dioxide (1-hour), 60 percent for nitrogen dioxide (annual), and 3 percent for ozone (www.epa.gov). These gains were largely the result of the Clean Air Act, Clean Air Act 1990, which forced factories and businesses to install control technology to reduce toxic emissions by 90 percent between 1990 and 2000 (www.epa.gov). 17 Applied Econometrics and International Development Vol. 18-2 (2018) involves participation in management, joint-venture, transfer of technology and expertise. Stock of FDI is usually measured as the net (i.e., outward FDI minus inward FDI) cumulative FDI for any given period (see Figure 3), with direct investment excluding investment through purchase of shares (UNCTAD, 2010, p. xiv).13 Figure 3: Global Net FDI 1970-2016
Source: World Bank.org. Additional sources include: International Monetary Fund, international Financial Statistics and Balance of Payments databases, World Bank, International Debt Statistics, and World Bank and OECD GDP estimates. Notes: The long-term net investment of the world grew at an annual average rate of 0.07 percent, population, without death rates, grew at an annual average rate of about 2 percent, and national income per capita grew at an average annual rate of about 6 percent. FDI stock has increased insubstantially even before the pronounced volatility of FDI stock in the twenty-first century (See Figure 3). Assuming that saving approximates investment (S≈I), and that what is not invested or saved is consumed, [(Y-I=C), 6 - 0.07=5.93 percent], global output and investment patterns from 1970 to 2015 reveal that virtually everything that was produced was consumed. That is, given the output growth and low level of average long-term investment, global consumption virtually grew at the rate of global per capita output between 1970 and 2015. Of course, this rough estimation can be imprecise when some amount of spending, “consumption,” or absorption is also considered to be investment. It is equally noteworthy that this rough estimate does not say about income distribution. Absent any consensus on discount rates and cryptic indicators of intergenerational equity, the lopsided and disproportionate disparities in consumption and investment levels are ocularly disturbing, given the degradation of environmental resources and the expediting-role of long-term investment. The consumption bias of long-term investment is evidently corroborated by the focus of long-term investment spending (see Table 5). For example, in 2015, the greatest amount of spending occurred in manufacturing finance, and business activities. Sovereign wealth funds (SWFs), funds set up by or on behalf of sovereign states that can be used for foreign direct investment, have emerged as active sources of FDI in recent years. Between January to May of 2010, motor vehicles and other transport equipment attracted 36 percent (a lion’s share) of long-term spending, followed by mining, quarrying and petroleum (26 percent). Trade and business activities attracted
13 The United States remained by far the largest source of FDI worldwide, followed by Japan, China, the United Kingdom, Germany and Canada. While China was a net outward direct investor for the first time in 2016, it was a net inward investor in 2017 (Ibid).
18 Applied Econometrics and International Development Vol. 18-2 (2018)
11 and 9 percent respectively (UNCTAD, 2010, p.15). Table 5: Estimated Global Inward FDI Stock by Major Industry, 2015 (Billions of dollars) Industry Monetary Value ($b) Extractive 1.4 Telecommunications 1.8 Others 2.3 Trade 2.6 Business activities 4.7 Finance 5.6 Manufacturing 6.5* Source: UNCTAD, FDI/MNE database (www.unctad.org/fdistatistics) and UNCTAD (2017, 21). Note: * Petroleum products, 0.4, Motor vehicles,0.5; Electronics, 0.6; Food and beverages, 0.8; other manufacturing, 1.2; Chemical products,1.3; and Unspecified,1,6. However, the long-term investment preferences are gradually being modified. The United Nations Conference on Trade and Development (UNCTAD) estimates that in 2009 low-carbon FDI flows into three key low-carbon business areas (renewables, recycling, and low-carbon technology manufacturing) accounted for $90 billion of long-term investment. “In its totality such investment is much larger, taking into account embedded low-carbon investments in other industries and [transnational company] TNC participation through non-equity forms. Already large, the potential for cross-border low-carbon investment is enormous as the world transitions to a low- carbon economy” (UNCTAD, 2010, p. xiv). Long-term investment is unpredictable, and over the years the fortunes of businesses have waxed and waned with changing political and economic circumstances. The viability of long-term investments could be linked to economic performance, the regulatory environment, and political risks. Between 1897 and 2007, six merger waves were detected; all of which were driven by different political and economic circumstances.14 The mergers of the 1980s were spurred by the desire to exploit natural resources of oil and gas while taking advantage of deregulation in the banking sector. During the fifth merger wave (1994-2001), long-term US investment focused on banking, finance, and communications. The investments accounted for 26.5 percent of all US deals between 1993 and 2004 (Gaughan, p.69). It might just be propitious to have a merger wave that is conducive to green technology and poverty reduction. Regrettably, while some of the poorer nations have been impoverished by market and policy failures from within and without their national boundaries, the political and/or infrastructural deficiencies in the poorer countries have not invited robust long- term investments. Over the years, political risk and inadequate human capital have created a push factor that redirects long-term investment to alternative targets. Asia is becoming the most attractive region for FDI (UNCTAD, 2010, p.19). In 2017 global FDI fell by 16 percent from the previous year. While developing Asia and Latin America and the Caribbean gained 2 and 3 percent respectively, Africa, developed economies and transition economies lost 1, 27, and 17 percent of FDI respectively (UNCTAD, 2018, p.4). The structural changes in the global economy are demanding investments in the service and technology areas that require human capital, reliable
14 The periodization of waves can be found in the work of Gaughan (2015), pp.41-74; viz: 1897-1904, 1916-1929, 1965-1969, 1981-1989, 1994-2001, and 2004-2007.
19 Applied Econometrics and International Development Vol. 18-2 (2018) infrastructure, and political stability to preserve the natural sphere (Figure 4a in the Annex). Can long-term investments increase consumption and conservation? The perverse tradeoff between consumption and conservation can be modified if saving and consumption can be proportionally increased over time. Implicitly, income must be increased without diminutive effects on saving and long-term consumption. Skillful investment can lead to increased income and less consumption of critical environmental resources when the economic and natural spheres are considered to be interdependent. Consider Figures 4(a) and (b), depicting income per capita (y, f(k)), conservation costs C(s), the services that nature can offer (s), the production function in the natural sphere (F(S)), available capital per worker (k), depreciation of capital (δk) and investment (δf(k)), based on the Solow model. At lower levels of production, the use of nature’s services is less intense and the marginal conservation cost is zero. Hence, the conservation cost curve is flat (see 0-s1- s2 in Figure 4(a)). At higher levels of output and intense use of nature’s resources (s), conservation cost increases with physical capital per worker (k) and the conservation cost begins to rise. Therefore, increases in output beyond k*2 will require an increase in conservation cost. The positive correlation between output and consumption of nature’s services can be modified by targeted investment in green technology and the reduction of poverty. The growth and saving-augmenting effects of investment are depicted in Figure 4(b). Progress in the economic sphere, which increases capital per worker, can inure on the production function of the natural sphere to such an extent that long-term investment and technology in the natural sphere generates efficiencies in the natural sphere. Instead of increasing output and the cost of conservation in tandem from B to D (see the Natural Sphere of Figure 4(b)), output can be increased from B to C while stabilizing the use of nature’s resources. Has global long-term investment shared any relationship with global income between 1970 and 2015? The next section investigates this relationship. 5. Econometric Discussions The essence of this section is to estimate the relationship between the change in world GDP per capita (WGDP), a proxy of global income or output, and change in global net foreign direct investment as a percentage of global GDP per capita (FDI/GDP). While data for WGDP are available from 1960 to 2015, collection of data for FDI/GDP started in 1970. Consequently, availability of long-term investment data has significantly influenced the empirical timeframe of this paper. Notwithstanding the data limitation, 45 years of data points are reasonably adequate for an analysis of output and long-term investment that is based on parsimonious specification; the observations are about 5.5 times the parameters to be estimated. Eviews 10, a product of HIS market, has been utilized for all empirical estimation in this paper. It is noteworthy that the scope this study and the quality of the data considerably reduce the significance of standard errors. The unit of analysis (the world) virtually exhausts all available information. Except for slight variations in the quality of the data, the population is adequately accounted for. Data for the income and investment analysis have been provided by the World Bank and a consortium of reputable global sources, including UNCTAD, the OECD, and IMF.
20 Applied Econometrics and International Development Vol. 18-2 (2018)
Pursuant to the earlier discussions in the paper, the relationship between global income and global long-term investment warrants an econometric investigation. The fundamental idea is to test the strength of the relationship between output and long- term investment; since output and long-term investment are probably the most obvious way of evaluating conservation for the welfare of subsequent generation. Implicit in this analysis, is the intensity of contemporary consumption or positive time preference—the preference to consume considerably more today rather than tomorrow. Cointegration and Vector Error Correction (VEC) models provide a useful paradigm for intertemporal analyses. They econometrically appraise short and long- term decisions as well as the relationship between short- and long-term variables. Unlike the generic or generalized form of the VECM specification, this paper models the VECM (Equation 14) without summation operators to reflect the single lag criterion proposed by the diagnostic tests in Table 6A in the Annex:
Ft0 1 F t 1 1 GZ ttt 1 1 ; (14) where F is for FDI/GDP (the target variable), G is for WGDP, Z is for the residuals 15 from the long-run cointegrating regression, α0 is a constant, μ is a perturbation expression, and α1, δ1, and ϕ are impact multipliers.
The change in the target variable, FYt – FYt-1, (ΔF) is dependent on the past value of long-term investment, past values of output and lagged ordinary least squares (OLS) residuals (Equation 15):
Ztt1 F 1 0 1 G t 1 ECT t 1. (15)
In effect, Zt-1 represents lagged residuals from the long-run cointegrating regression and phi (ϕ) is an error correcting estimator of the speed at which adjustment should take place when there is disequilibrium in the model. Phi is usually expected to be negative, less than 1, and significant. However, a relatively newer category of literature16 makes a case for abnormal coefficients that are greater than 1. In this paper, the coefficient can be rationalized as the speed at which long-term investment returns to its stable or expected (average) value after a period of deviation. The cointegrating parameter turns out to be weakly significant (0.05< ϕ <0.10). The tolerance for error is increased by 2 percent above the conventional level (see Table 6D in the Annex). This result can be expected because of the very weak relationship between output and long-term investment. The VECM has a very structured procedure and the results and diagnostics of the methodology have been reported in Table 6 in the Annex: viz: (i) a lag-length test (6A), (ii) a stationarity test (6B), (iii) a cointegration test (6C), (iv) the VECM results and coefficient test(s) (6D), and (v) a serial correlation test (6E). A dynamic stability test is discussed in the Appendix. The VECM shows that only 8 percent of the deviation of long-term investment from the expected value was corrected (Table 6D in the Annex). Analogously, fifty percent of the deviation of long-term investment from a stable value was corrected at a rate of ln2/γ; where γ is the estimated adjustment
15 More precisely, Zt can be specified as: ZFtt0 1 G tt . 16 See Narayan, and Smyth, p. 339.
21 Applied Econometrics and International Development Vol. 18-2 (2018)
0.69 17 parameter, 8.6 . This finding is consistent with discussions in Section 0.08 IV. Recall from the previous section that between 1970 and 2015, consumption growth was almost equal to per capita output growth, [(Y-I=C), 6 - 0.07=5.93]. Accordingly, there is no convincing evidence that GDP per capita Granger causes long-term investment (see the Wald test at the bottom of Table 6D). Of course, the Granger test is a correlative rather than causative metric. More pointedly, and for the time period under review, I fail to reject the null hypothesis that global output has no explanatory power over long-term investment. The Eviews 10 protocol requires the VECM system of equations to be estimated in order to unmask standard errors and p- values. In the primitive estimates, constants are included in the long-run relationships, which generate a marginal exaggeration of the number of constants to be estimated. For example, the primitive results report 3 constants for 2 equations. Are the series cointegrated? While global GDP is stationary, long-term investment is not (see Table 6B in the Annex). The dichotomy sets the stage for a cointegration test in order to evaluate the long-term relationship between the two variables. The rank and maximum eigenvalue tests suggest that there is one cointegrating equation18 (see Table 6C in the Annex). The VECM is then estimated after the diagnosis of the cointegration test (Table 6D). Since changes in long-term investments and output are not spontaneous but gradual, they may be sensitive to interrelated changes as time progresses. Therefore, they may not be independent of each other as a result of some time-sensitive feedback effects. In effect, econometric series can become autocorrelated (correlated with their own past values) or serially correlated (correlated across different series). Equation 14 suggests that long-term investment may be correlated with its past values and/or those of global output.19 Errors can no longer be presumed to be normally distributed when series are autocorrelated or serially correlated and coefficients can be wrongly estimated with exaggerated standard errors. The model exhibits no serial correlation (see Table 6E in the Annex) and it is dynamically stable (see the CUSUM recursive test in the Appendix). By and large, the econometric findings adequately support the theory of inadequate long-term investment and overconsumption as the population of the world ages.
17 log(1 ) Elbadawi (1997) proposes that the long-run (T) may be estimated asT ; where τ log(1 v ) is for the hypothesized dissipation rate, and γ is for the speed of adjustment. 18 The rank is the maximum number of independent vectors within the system of equations (matrix). Essentially, it cannot exceed the number of endogenous variables in the system. If the coefficient of the error correction term is zero, then there is no cointegrating relationship. To obtain a full rank, the number of independent vectors must coincide with the number of endogenous variables to be estimated. Full rank suggests that cointegration does not exist , because all the variables will be stationary I(0). Ideally, less than full rank is the preferred econometric standard (0 < r < n). The null for the maximum eigenvalue statistic, a less useful test, is Ho= r, with an alternative of Ha = r+1. 19 See Hill et al (pp.347-70) for further reading on serial correlation.
22 Applied Econometrics and International Development Vol. 18-2 (2018)
6. Conclusion This paper finds that environmental conservation is threatened by positive time preference and overconsumption than population growth. While much has been done to minimize air pollution, the protection of forest resources has been suboptimal and relatively inconsequential. The decimation of forest resources is largely due to poverty, commercial harvests (consumption), and the paucity of environmentally-friendly long- term investments. As the population of the world ages between 1970 and 2015, a larger share of global output has been disproportionately consumed. There has also been no compelling urgency to conserve environmental resources in areas of the world where human capital is unequivocally deficient and life expectancy is alarmingly brief. Clearly, without income from forests, the poor can derive income from alternative sources such as agro-based industries for some nontradable agricultural products, the construction of roads, bridges, schools, hospitals, and dams (infrastructure), and investments that are oriented toward conservation and the global service industry. The basis for intertemporal conservation becomes tenuous when life is agonizingly short and miserable. The empirical evidence suggests that successful conservation of environmental resources will ultimately be influenced by the reduction of poverty, the extension of life expectancy for saving or conservation, and restrained commercial consumption of environmental resources. Invariably, the preservation of environmental resources is inextricably linked to long-term investments that can appropriately counterbalance social responsibility and the maximization of shareholder’s wealth. References Blaug, M. (1997). Economic Theory in Retrospect. New York, NY: Cambridge. Blencowe,H., Cousens,S., Jassir,F.B., Say, L., Chou, D., Mathers, C., Hogan, D., Shiekh, S., Qureshi,Z.U., You, D., and Lawn, J.E. (2016, February). “National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis.” The Lancet Stillbirth Epidemiology Investigator Group, 4, e98-e108. Retrieved from http://www.lancet.com Brent, R.J. (2017). Advanced Introduction to Cost-Benefit Analysis. Cheltenham, UK: Edward Elgar. Cheng, S.H., Ahlroth, S., Onder,S., Shyamsundar,P., Garside,R., Kristjanson.P., McKinnon, M.C., and Miller D.C. (2017). “What is the evidence for the contribution of forests to poverty alleviation? A systematic map protocol.” BioMed Central, 6(10), 2-11. Diamond, P. (1965). "National debt in a neoclassical growth model". American Economic Review. 55 (5): 1126–1150. Elbadawi, I.A. (1997). “Real Exchange Rate Policy and Export Performance in Three Arab Countries.” Paper presented at the ERF Fourth Annual Conference on Regional Trade, Finance, and Labor Markets in Transition. Beirut, 7-9 September. Field, B, and Field, M. (2016). Environmental Economics. New York, NY: McGraw Hill Food and Agriculture Organization. (2012). State of the World’s Forests. Rome, Italy: FAO. Food and Agriculture Organization. (2016). State of the World’s Forests. Rome, Italy: FAO Gaughan, P.A. (2015). Mergers, Acquisitions, and Corporate Restructurings. Hoboken, NJ: John Wiley & Sons Guisan, M.C. and Exposito, P.(2016). “Life Expectancy, Education, and Development in African Countries 1980-2014: Improvements and International Comparisons.”Applied
23 Applied Econometrics and International Development Vol. 18-2 (2018)
Econometrics and International Development, 16(2), 87-98. Hanley, N., Shogren, J.F., and White, B. (2013). Introduction to Environmental Economics 2nd ed. Oxford University Press. Hill, R.C., Griffiths, W.E., and Lim, G.C. (2011). Principles of Econometrics (4th ed.). Hoboken, NJ: John Wiley & Sons IHS Markit. (2017). Eviews 10. Long Beach, California: IHS Markit. Marino, E. (2014). “ Illegal-Logging in Guinea-Bissau.” Retrieved from http://www.borgenproject.org McKinnon,M.C., and Miller, D.C. (2017). “What is the evidence for the contribution of forests to poverty alleviation? A systematic map protocol.” BioMed Central, 6(10), 1-11. Narayan, P.K. and Smyth, R. (2006). “What Determines Migration Flows From Low-Income to High-Income Countries? An Empirical Investigation of Fiji-US Migration 1972-2001.” Contemporary Economic Policy, 24(2), 332-342. National Oceanic and Atmospheric Administration. (2011, August). “Ocean Pollution.” Retrieved from http://www.noaa.gov/resource-collections/ocean-pollution Ramseur, J.L. (2017, May). The Regional Greenhouse Gas Initiative: Lessons Learned and Issues for Congress. Congressional Research Service Report R41836. Redford, K.H. (1992). “The Empty Forest.” BioScience, 42(6), 412-422. Romer, D. (2018). Advanced Macroeconomics (5th ed.). New York, NY: McGraw Hill. United Nations. (2015). “Integrating population issues into sustainable development, including the post-2015 development agenda.” Report ST/ESA/SER.A/364, Population Division of the Department of Economic and Social Affairs United Nations Conference on Trade and Development. (2018, January). “Global FDI Flows Slipped Further in 2017.” Investment Trends Monitor, 28, 1-8. United Nations Conference on Trade and Development. (2017). World Investment Report 2017: Investment and the Digital Economy. Geneva, Switzerland: United Nations United Nations Conference on Trade and Development. (2010). World Investment Report 2010: Investing in a Low-Carbon Economy. Geneva, Switzerland: United Nations United Nations Population Fund. (2012). Population Matters for Sustainable Development. Retrieved from http://www.UNFPA.org. United Nations Population Fund. (2011). “Population Dynamics in the Least Developed Countries: Challenges and Opportunities for Development and Poverty Reduction,” Report of the United Nations Population Fund for the Fourth Conference on the Least Developed Countries (Istanbul, 9-13 May 2011), New York, NY United States Environmental Protection Agency. (2017, April). “Global Greenhouse Gas Emissions.” Retrieved from http:// www.epa.gov Van den Berg, H. (2017). Economic Growth and Development (3rd ed.). Hackensack, NJ: World Scientific. Williams, M. (2002). Deforesting the earth: from prehistory to global crisis. Chicago, USA, University of Chicago Press. ----(n.d.) “Africa is home to the world’s most iconic wildlife. But illegal poaching might destroy it forever.” Retrieved from http://www.awf.org -----(2018). “Deforestation and Poverty.” Retrieved from http://www.earthday.org
Annex on line at the journal Website: http://www.usc.es/economet/eaat.htm
24 Applied Econometrics and International Development Vol. 18-2 (2018)
Appendix
Figure 2: World Population Growth and Per Capita Income Growth
Data Source: World Bank’s WDI
Source: Van den Berg, Hendrik, Economic Growth and Development (2017), pp 280 and 283.
25 Applied Econometrics and International Development Vol. 18-2 (2018)
Dynamic Stability (CUSUM Recursive Test)*
The CUSUM test is a test of structural stability, using the chronological sequence of the available data. Inference is based on a sequenced and iterative sum of squares of residuals estimated from nested subsamples of the data that are increasingly larger. The test plots the sum of recursive residuals that are expected to stay within a critical boundary for structural stability under the null that the coefficients are constant. A sum that is outside of the critical boundary is indicative of a structural break at the point at which the sum began its movement toward the critical limit.
20
15
10
5
0
-5
-10
-15
-20 1980 1985 1990 1995 2000 2005 2010 2015
CUSUM 5% Significance
* CUSUM test with 5 percent error margins
The time-sensitive standard recursive residual is denoted as:
et wt ; 2 1 (1 xXtt '(1 ' X t 1 ) x t ) where Xt-1 is a matrix of regressors from period 1 to period t-1, xt is the row vector of observations on the regressors in period t, and et is yt-xt-1’ b; b is a vector of coefficients to be estimated. The CUSM test statistic is:
t w r ; wt r k 1 s where k is for the coefficients to be estimated, s is for the standard deviation, and wr is for the weighted residuals. The residuals are computed for t=k+1.
26 Applied Econometrics and International Development Vol. 18-2 (2018)
Table 6: Econometric Estimates and Diagnosis (Global Net FDI/GDP and Global GDP, per capita growth, 1970-2015) A. Lag-Length Criteria lag logL LR FPE AIC SC HQ 0 -0.85 NA 0.06 0.52 0.6 0.55 1 29.03 70.34* 0.001* -1.1* -1.85* -1.01* * Suggested lag length; LR= sequential modified likelihood ratio test; FPE= final prediction error; AIC= Akaike information criterion; SC= Schwarz information criterion; and HQ=Hannan-Quinn information criterion. B. Unit Root Tests Null hypothesis: Variables are not stationary Variable Augmented Dickey-Fuller test statistic (p-values in parenthesis) Global Net -1.52 (0.52) ** significance at the 95 percent FDI/GDP level Global GDP -3.35 (0.018)**
C. Cointegration Test (Trace and Maximum Eigenvalue)ψ Eigenvalue Trace Stat. Critical Prob Value (0.05) None 0.28 16.26 15.49 0.038** At Most 1 0.05 2.09 3.84 0.15 Eigenvalue Max-Eigen. Stat. Critical Prob Value (0.05) None 0.28 14.17 14.26 0.05** At Most 1 0.05 2.01 3.84 0.15 ψ Trace and Maximum Eigenvalue tests indicate 1 cointegrating equation
D. VECM Result and Wald Test Coefficient Std. Error t-Stat Prob Cointegrating Parameter -0.08 0.04 -1.81 0.07* Wald Coefficient Diagnosis Null: No causality runs from past global income to long-term investment Value df Probability t-statistic 0.67 40 0.51 F-statistic 0.45 (1,40) 0.51 Chi-square 0.45 1 0.50
E. Breusch-Godfrey Serial Correlation Testϕ Null Hypothesis: No serial Correlation F-statistic 0.63 Prob. (1,39) 0.43 Obs*R-squared 0.70 Prob. Chi Square (1) 0.40 ϕ Lagged residual dependent variable
Journal published by the EAAEDS: http://www.usc.es/economet/eaat.htm
27