THRESHOLD AND INTERACTION EFFECTS IN THE TRADE-POVERTY RELATIONSHIP

Transcription

1 THRESHOLD AND INTERACTION EFFECTS IN THE TRADE-POVERTY RELATIONSHIP Vincent Leyaro School of Economics, Universy of Nottingham, UK March 2009 Abstract Although the levels of global poverty have generally declined over the past 30 years, still one sixth of the world s population lives in extreme poverty; most of whom are in Africa, where condions are growing inexorably worse. This happens at the time that most developing countries have increasingly integrated into the global trading system. There is a passionate debate on whether more openness to trade reduces or worsens poverty. The argument that has emerged recently is that, the relationship between trade and poverty is heterogeneous in nature, involving a number of thresholds. That is, the effects of trade on poverty are contingent on other prior factors (first nonlineary). But even this contingent relationship is non-linear in nature, as trade affects poverty differently given different levels (thresholds) of prior factors(second nonlineary). Making use of an up-to-date panel data set for developing countries and econometric techniques that address measurement errors in variables, omted variables, endogeney biases, and persistence in the series, this paper explores the empirical link between trade and poverty, by identifying and exploring two channels in the trade-poverty relationship as a first step. In the second step, we employ Hansen (2000) endogenous threshold regression technique that locates breaks and locations in the data to establish the thresholds in the trade-growth-inequaly-poverty nexus. What emerges, also found in other studies, is somewhat revealing. That is, the effects of trade on poverty in developing countries are condional on both growth rates and levels of inequaly, amongst other factors. The effects appear to be greater or less beyond certain levels of growth and inequaly respectively. Furthermore, Africa does not appear to be an exception to the general results for developing countries. Outline 1. Introduction 2. Effects of Growth and Inequaly on Trade-Poverty Nexus 3. Threshold-Interaction Effect Model 4. Data Descriptions 5. Results and Discussion 6. Conclusion Author Vincent Leyaro is a research student in the School of Economics. I am very grateful to my supervisors, Professor Oliver Morrissey and Dr Trudy Owens for guidance and comments. Contact lexvl@nottingham.ac.uk

2 1 1. Introduction Effects of trade liberalisation on the welfare of the poor in developing countries have recently become one of the biggest concerns for researchers and policy makers. Although the levels of global poverty 1, 2 have generally declined over the past 30 years, mainly due to a significant reduction of poverty in Asia (Sala-i-Martin, 2007; Nissanke et al. 2007), one sixth of the world s population live in extreme poverty - subsisting on less than a dollar a day; while half of the developing world s populations live on less than 2 dollars a day (Harrison et al. 2006). This happens at the time when most developing countries have increasingly integrated into the global trading system. Greenway et al. (2002) show that between 1980s and 2000s over 100 developing countries have undertaken trade liberalization. Given these patterns and trends, is easy to see why crics of globalisation blame most of the woes of globalisation on openness to trade, especially s effects on poverty and income distribution. In spe of s perceived potential for promoting growth and reducing poverty, the effects of trade liberalisation on poverty are ambiguous. There is evidence that openness to trade creates a conducive environment for economic growth that benefs the poor (Bhagwati, (2004), Goldberg et al., (2004, 2007), Sala-i-Martin (2007), Nissanke et al., (2007)). However, crics are sceptical of the basic argument that trade liberalization generally stimulates economic growth and reduces poverty. As a result, evidence regarding the poverty impact of trade liberalization remains contentious and tenuous. And the questions are: Does trade liberalisation reduces or exacerbates poverty? Are there differences between countries in the way openness affects the poor? Are the short-term effects different from the long-term effects? Some researchers argue that there are differences, as the relationship between trade and poverty involves a number of heterogeney. 1 During the last three decades the world has seen poverty rates fall by about two thirds. The share of the population of the developing countries living below a dollar per day declined from 40 per cent in 1980 to 21 per cent in 2001 (Sala-i-Martin, 2007). 2 Global individual inequaly, that is, the cross countries individual inequaly fell over the past 30 years, perhaps for the first time in history (Sala-i-Martin, 2006) However, a review done by Goldberg and Pavcnik (2007) on various measures of inequaly suggest that most of the developing countries experienced an increase in inequaly during the past two decades.

3 2 Winters et al. (2000, 2004) and Nissanke et al. (2007) argue that trade affects poverty through two major channels, eher directly or indirectly. Indirectly, trade is expected to affect poverty contingent on other factors such as: growth; inequaly; inial levels of development; international capal movements, labour migration; technological progress; information; vulnerabily; instutions and the complementary policies. 3 Of these, the growth-inequaly-poverty link (which is sometimes assumed to be causal) is the most important channel. By promoting economic growth and influencing income distribution (lower inequaly), trade is considered as a powerful instrument for poverty reduction in developing countries. Although any trend in poverty and income inequaly observed so far cannot exclusively be attributed to more openness to trade whout rigorous analysis, there are concerns that openness to trade, as has proceeded to date, may have adverse effects on poverty and income distribution (Nissanke et al. 2007). The risks and costs associated wh increased openness can be serious for fragile developing economies and the world s poor. In addion, the linkages between trade and growth established in previous studies have been questioned. There is thus a general agreement on the association between trade and growth, but no consensus on a causal link between trade liberalisation and growth (e.g. Ackah and Morrissey, 2007; Rodriguez and Rodrik, 2001; and Harrison, 1999). 4 Whether the growth and income distribution effects of trade are strong enough such that poverty falls as a result of openness to trade remains unclear (Ravallion, 2005). Ltle empirical evidence is available today to address this question. Winters (2004), Ravallion (2004), Goldberg et al., (2004, 2007) and Harrison (2006) all acknowledge that they can only review the indirect evidence regarding the linkages between openness and poverty. 5 Of these, most of which are ced in Winters et al. (2004) and Ravallion (2004) have typically used computable general equilibrium (ex-ante) to disentangle the complex 3 Directly, trade is expected to affect poverty through three major channels: change in relative prices faced by households as consumers and producers; the market for labour (i.e. employment and wage adjustments) and role of taxation and government revenue. 4 However, Baldwin and Sbergami (2000) argue that the fragily of the relationship between openness and growth in the earlier lerature is generated by false imposion of a linear relationship. 5 In their comprehensive survey, Winters et al. (2004, p 73) wrote that there are no direct studies of the poverty effects of trade and trade liberalization. While Goldberg and Pavcnik (2004, 2007) in their review acknowledged that, while the lerature on trade and inequaly is voluminous, there is virtually no work to date on the relationship between trade liberalization and poverty.

4 3 linkages between trade reforms and poverty. 6 7 Although such research shed light to our understanding of the channels through which trade reforms could affect poverty, however, is important for us to be able to look at actual data, that is, ex post evidence of the impact of trade reforms on the poor. Even though, the conclusion that has emerged from the scant ex post studies, as surveyed by Winters (2000, 2004), Ravallion (2004) and Harrison (2006), found no evidence in the aggregate data that trade reforms is bad for poor, but such results are not robust. That has made some researchers, as in the case of trade-growth relationship, to argue that the fragily in the trade poverty results observed recently emanate from the false imposion of a linear relationship between the two by the empirical researcher. Arguing that, linear relationship has no support beyond the most basic theoretical models; non-lineary is the rule rather than the exception. That is, the relationship between trade and poverty is contingent in nature, involving a number of thresholds. First (nonlineary), the effects of trade on poverty is condional on other factors, chief amongst these are growth and inequaly. Second (non-lineary), even this contingent relationship is non-linear in nature, as trade affects poverty differently given different levels (thresholds) of growth and/or inequaly. Only a few studies are exceptions that have looked at trade-poverty this way so far, which includes Agenor (2004), who finds that there is a nonlinear relationship between measures of poverty and globalization. Agenor finds that at low levels, globalization appears to hurt the poor, but beyond a certain threshold, seems to reduce poverty. 8 More recently, Liang (2006), investigates the globalisation-poverty nexus in China, paying special attention to the nonlineary of impact of globalization on the poor; found strong evidence that, there exist a threshold in the relationship between globalisation and poverty: globalization is good for the poor only after the economy has reached a certain threshold level of globalisation. What all these recent developments seem to point out is that, possibly lack 6 The reasons given for the dearth of such empirical evidence include: lack of comparable and qualy data; use of aggregate data; measurement errors; and weak econometric techniques to address heterogeney and endogeney biases. 7 To circumvent these shortcomings, researchers like Bhagwati and Srinivasan (1999), Winters (2004) and Dollar et al. (2004) have called for the use of in-depth individual country case studies. Others like Edwards (1993) and Ravallion (2005), have argued to understand the precise channels through which trade affects growth and inequaly, and then poverty, requires us not only to look at the history but also at the microeconomics of trade, growth, inequaly and poverty. But Baldwin (2003) notes, both approaches are needed. Cross-country empirical studies are appealing as they allow authors to generalize beyond one specific case study and this can be useful in verifying how general are the links identified in case studies. 8 For earlier related studies, see Dollar and Kraay (2001, 2002).

5 4 of robust relationship in the trade-poverty transmission might be due to false imposion of a linear relationship, instead of non-linear relationship that involve threshold levels. Which implies a more complex and heterogeneous relationship between trade and poverty across different countries? This paper is in this tradion, as seeks to determine whether there is a cross-sectional evidence of heterogeney in openness-poverty relationship related to growth and inequaly. Meaning testing two hypotheses: i) whether the heterogeney in trade-poverty transmission given growth and inequaly are best captured by linear interaction effects ii) or such transmission is of threshold type, that is, non-linear interaction effects. Making use of an up-to-date panel data set for developing countries that span over for 75 countries and econometric techniques that address measurement errors in variables, omted variables, endogeney biases, and persistence in the series, this paper explores the empirical link between trade and poverty condional on growth and inequaly as a first step. In the second step, we employ Hansen (2000) endogenous threshold regression technique that locates breaks and locations in the data to establish the thresholds in the trade-poverty relationship. After comparing the tradional approaches of imposion of linear interaction terms, quadratic and exogenous sample splting wh the formal endogenous threshold method, we argue in the favour of the latter method for capturing this heterogeney. Specifically, our aim is to add to the lerature by empirically exploring the contingent and threshold effects in the trade-poverty linkage. Liang (2006), to the best of our knowledge is the only paper to apply endogenous threshold regression techniques. What emerges, also found in other studies, is somewhat revealing. That is, the effects of trade on poverty in developing countries depend (or are condional) on both growth rates and levels of inequaly, amongst other factors. The effects appear to be greater or less beyond certain levels of growth and inequaly respectively. Furthermore, Africa does not appear to be an exception to the general results for developing countries. The plan of the paper is as follows. The next section reviews the theoretical and empirical lerature on trade, growth, inequaly and poverty, organised according to the channels we analyse. Section 3 justifies the threshold-interaction effects specification employed, outlines the endogenous threshold regression technique to be used. Section 4 describes the data. Section 5 presents and discusses the empirical results on the existence of

6 5 threshold and interaction effects in the trade-poverty relationship given growth and inequaly. Section 6 concludes. 2. Effects of Growth and Inequaly on Trade-Poverty Relationship The impact of trade on poverty is not clear cut, as different countries experiences different effects. Winters et al. (2000, 2004), Goldberg et al. (2004), Bhagwati, (2004), Nissanke et al. (2007) amongst others, have identified a number of potential channels through which trade is expected to affect poverty, eher directly or indirectly. Factors to consider include: growth; inequaly; change in relative prices faced by households as consumers and producers; the market for labour; changes in government revenue, taxation and spending; trade shocks, risks and vulnerabily; international capal movements and labour migration; technological progress, information and instutions. More recently is argued, even whin these transmission mechanisms there exist potential non-linearies and threshold effects; which results into a more complex and heterogeneous relationships. The first and most important of the mechanisms through which trade affects poverty is the growth-inequaly-poverty nexus. That is, trade affects growth and income distribution independently or trade and growth jointly affects income distribution, and then each of them on turn affect poverty. If the distribution of income does not change, growth implies that poverty rates will decline. Poverty can also decrease if inequaly declines even in the absence of growth. If trade and growth combined causes an increase in income disparies (inequaly), the benef to the poor is reduced and they may become worse off. The oppose also is true. This implies that to estimate the effects of trade openness on poverty we need to consider: s effects on growth; s effects on inequaly; the effects of growth on inequaly; and the combined effects of both trade and growth on inequaly and then on poverty (Winters et al. 2004; Bhagwati, 2004; Sala-i-Martin, 2007; Nissanke et al. 2007). If trade does not affect inequaly then s impact on poverty should come from s effects on growth. One of the effects therefore is from trade to growth, essentially export-led growth, and then from growth to poverty reduction. The channels through which trade affects growth are numerous, including: increased specialization; economies of scale (i.e. a greater exploation of increasing return); importing ideas, knowledge and benefing

7 6 from better inputs and technology capacies from abroad; increased competion; increased availabily of capal; technological progress and instutions, policies and polical process. Even though, theory does not predict a simple relationship between exposure to trade and growth. 9 This has made prominent skeptics of trade liberalization like Krugman (1994) Harrison and Hanson (1999) and Rodrik (2001) to argue that the effect of openness on growth is, at best, tenuous and at worst doubtful. Lee ( ), Harrison (1996), Baldwin (2003) and Yanikkaya (2003) point to the lack of robust theoretical foundations that clearly demonstrate and establish how trade is linked to economic growth. As a result, economists such as Harrison (1996, 2004), Berg and Krueger (2003) and Berggren and Jordahl (2004) argue that empirical work is needed to resolve the debate. 10 Most studies that have used eher trade shares (of GDP) or indices of trade policy/liberalisation find a posive association between trade and growth. The most heavily ced papers amongst these are Dollar (1992), Ben-David (1993, 1998), Sachs and Warner (1995), Edwards (1998), Frankel and Romer (1999). 11 More recent studies essentially replicate these seminal papers using different sample size, methodology, time period or addional measures of openness. 12 However, results are more mixed when trade policy measures are used. Lee (1993), Harrison (1996), Edwards (1998) and Irwin (2002) find a significant and negative relationship between tariff rates and growth. Vamvakidis (2002) finds a posive relationship between average tariffs and non tariffs barriers and economic growth after 1970 but not before. Clemens and Williamson (2001) find that before 1950 the association between average tariffs and growth was posive, but negative thereafter. DeJong and Ripoll (2004) find that the relationship between tariffs and growth depends on the level of development - negative among the richest countries 9 Grossman and Helpman (1990) model, for instance, does not provide a define answer as whether trade intervention will increase or decrease the long run growth rate. Rivera-Batiz and Romer (1991) points out that the impacts of trade restriction on growth are very complicated as; there some models in which trade restrictions can slow down the world wide rate of growth. there are others in which they can speed up world wide rate of growth (p.532). 10 Baldwin and Sbergami (2000) argue that the fragily of the relationship in the earlier lerature is generated by false imposion of a linear relationship between openness and growth. 11 Their findings are similar to earlier studies: Ian Ltle, Tibor Scovsky, and Maurice Scott (1970), Bela Balassa (1971), Ann Krueger (1978), Jagdish Bhagwati (1978), Demetris Papageorgiou, Michael Michaely, and Armeane Choksi (1991) 12 These include: Haan & Sturm (2000), Miller and Upadhyay (2000), Mathew J. Slaughter (2001), Alcala and Ciccone (2001), Dollar and Kraay (2003), Warner (2003), 2004), Wacziarg and Welch (2003), Alcala & Ciccone (2004), Bolaky & Freud (2004), Lee et al. (2004), Bergen and Jordahl (2005), to mention some, who found similar results.

8 7 and posive but not significant among the poorest countries. This is supported by Ackah and Morrissey (2007), who found that for low income countries tariffs appear to be associated wh higher growth, whereas only for middle income and rich countries is there a negative impact of tariffs on growth. Yanikkaya (2003) finds that trade barriers are posively and in most cases significantly associated wh growth, especially in developing countries. A number of empirical studies, as pointed above, have suggested that there exist thresholds in the impacts of openness on growth, through which openness may affect the poor. For example, Edwards (2001) investigates the effects of capal market openness on economic growth, and his empirical results suggest the existence of a threshold in development levels, i.e., an open capal account can posively affect growth only after a country has achieved a certain degree of economic development. In a more recent study, wh the help of a dataset covering 83 countries over the period , Girma et al. (2003) empirically explore the heterogeney in the openness-productivy growth relationship, and find evidence that there exist thresholds in the effects of openness on growth that depend on the level of natural barriers. If trade affects inequaly, the second channel is from trade to changes in inequaly, essentially the effect of trade on distribution of income (or, in some cases consumption expendure) among the poor, and the effect of this on poverty. 13 One of the few uncontroversial predictions of trade theory is that a change in a country s exposure to international trade affects the distribution of resources whin that country. The question then is what are the causal links through which trade impacts on inequaly? Goldberg and Pavcnick (2004, 2007), Sala-i-Martin (2007), Ravallion (2004) and Milanovic (2002) review both theoretical linkages as well as empirical evidence on what has been the impact of trade on various measures of inequaly. According to Goldberg and Pavcnick (2004, 2007), trade openness is expected to affect individuals through three main channels: changes in their labour income; changes in relative prices and hence consumption; and changes in household production decisions. Much of the focus 13 The Gini index used in this paper is from World Bank s staff estimates based on primary household survey data obtained from government statistical agencies and World Bank country departments. It measures the extent to which the distribution of income (or, in some cases, consumption expendure) among individuals or households whin an economy deviates from a perfectly equal distribution. This is unlike measures that focues on relative incomes of skilled and unskilled workers.

9 8 however, both in theory and empirics, has been on the first channel, 14 that is, changes in labour income, and particularly changes in the skill premium. Openness to trade is expected also to affect labour income through transional unemployment; industry wage; uncertainty and labour market standards. Increase in the skill premium is the main contributing factor to raising labour income (wage) inequaly. In their simplest form, the Heckscher Ohlin (HO) and Stolper- Samuelson (SS) theorems say that countries export goods intensive in their abundant factor. Thus, Krueger (1983) and Bhagwati et al. (2002) argue that, since developing countries are likely to have comparative advantage in goods made wh unskilled labour, trade reforms should be pro-poor, raising the wages of the unskilled in unskilled-labour abundant countries. Hence, expanding trade opportunies should cut poverty and reduce inequaly whin poor countries. However, evidence suggests that the poor in developing countries are generally not better off following more than two decades of trade liberalization, and that the distributional change went in oppose direction from the one suggested (see Sala-i-Martin, 2007; Goldberg et al. 2007). Goldberg et al. (2004, 2007), have suggested reasons for this, including: price data which are incomplete and often endogenously determined (not always determined by trade); unskilled labour intensive sectors were highly protected prior the tariff reduction during trade reforms; evidence shows no labour reallocation; and the shift in comparative advantage towards China. Because of the lack of direct evidence on HO-SS effects, researchers have considered various extensions of the original model to explain the wage inequaly. The widening wage gap between skilled and unskilled labour has mainly been due to an increase in the demand for skilled and well educated workers. According to the recent work by Feenstra and Hanson (1996, 1997, 1999, and 2003) intermediate goods and outsourcing is one channel that can explain part of the observed increase in demand for skilled workers in both developed and developing countries, and hence increase in the skill premium globally. Another channel is the increase in capal flows and complementary of capal wh skilled labour. This is a case where trade liberalization has been associated wh massive increase in international capal flow to developing 14 This is because the main focus of trade openness-inequaly research has been to the middle-income developing countries as data i.e. formal labour markets surveys are readily available.

10 9 countries, but such capal inflow requires a higher share of skilled labour. And since the foregoing theories are often used in terms of endogenous technological change, skillbiased technological change is another explanation for increased demand for skilled labour and hence increases in skill premium. Other channels are composional changes whin industries and changing returns to skill-intensive occupations. For most of these channels, Goldberg et al. (2004, 2007) found empirical evidence that suggest that they are affected by trade openness and they increase the skill premium. Two other channels through which trade may affect inequaly, in addion to labour markets effects, are household production and consumption. This is about the link between world prices and trade (policy), and the prices of the goods that poor households consume and produce. As the bulk of the world s poor (up to 80% in most countries) are self employed, in eher low-level agriculture or in the informal sector of the economy, their consumption and sources of incomes are directly affected by prices changes induced by international trade. These channels are very relevant in poorer developing countries, but there is limed empirical evidence due to data constraints; many surveys focus only on the formal labour market and/or do not contain information on household expendure or consumption. 15 Topalova (2004a) which have used household production in the relationship between trade reforms and inequaly and Porto (2006) and Appleton et al. (2007) who consider how trade policy affects welfare through consumption, are among very few studies. More work needs to be done to establish whether the findings of these studies are generalizable, which is the focus of this paper. The third channel is from growth to changes in inequaly and effects on poverty. The analysis of the link between growth and inequaly has a long tradion in economics, which mainly concerned wh a potential trade-off between reducing inequaly and improving growth, or whether there exists a virtuous circle in which growth leads to lower inequaly, wh lower inequaly in turn leading to faster growth. Thus, the relationship may go from growth to inequaly and from inequaly to growth. 15 Excluding self-employed, even if these are included, measure of net earnings or profs are missing, even if are available they tend to be noisy.

11 10 Kuznets (1955) was the first person to articulate the mechanism by which growth affects income inequaly; where inequaly tends to raise on the early stages of economic development and then fall on the later stages, hence the inverted-u hypothesis. Though the earlier cross-section studies gave a strong support to this hypothesis, recent studies have called these findings into question. Since then, recent studies went in two directions, to develop theoretical models that generate inverted-u hypothesis as well as empirical ones to test (Ravallion et al, 1994). Today, a number of economic models show that technological progress is the main determinant of growth and a cause of higher inequaly. On the effects of inequaly on growth, both the theoretical and empirical lerature is divided. Some studies conclude that inequaly leads to faster growth 16, whilst others suggest that inequaly is likely to lower growth (Banerjee and Duflo, 2003). 17 On the empirical front, the evidence suggests that the overall impact of eher growth on inequaly or inequaly on growth cannot be set a priori (Forbes 2000; Banerjee and Duflo, 2003). 18 While there is some consensus (at least in the cross country empirical lerature) on the lack of causaly from growth to income distribution; however, on the potential causaly from inequaly to growth, views are much more divided, wh some studies concluding that inequaly leads to faster growth, and others suggesting that inequaly are likely to lower growth. The three channels established so far are not independent, and one has to address the relationships between them. In so doing, a fourth channel is then in order, that is, the combined effects of both trade and growth on inequaly and from inequaly to poverty. Consequently, a diverse strand of empirical evidence exists to date on the relationship between trade and poverty. Not surprisingly therefore an extraordinarily wide range of approaches have been devised to analyse this linkage. Most studies that link poverty, inequaly and trade in developing countries may be categorized under four headings: 16 These include: Kaldor's hypothesis of marginal propensy to save; investment indivisibilies; the trade offs between efficiency and equaly; human capal accumulation due to home environment externaly; and technical change that enhance mobily and concentration of high abily workers which will generate growth (see Mirrlees (1971), Galor and Tsiddon (1997a, 1997b), Galor and Moav, (2002)). 17 Four theories can be put forward here: the polical economy argument; socio-polical instabily approach; the presence of cred constraints and x-efficiency (see Alesina and Perotti (1996), Banhabib and Tabellin (1996), Fishman and Simhon (2002), De La Croix and Doepke, (2004)) 18 One can find several explanations for these apparent contradictions of results. For example, Forbes (2000) explores the role played by five different factors: (i) use of different variables; (ii) different samples; (iii) data qualy issues; (iv) time span; and (v) omted variable bias in the papers using a cross section. She concludes that that the most likely reasons for the discrepancy are country specific, time-invariant, omted variable bias and the length of the period under consideration.

12 11 theoretical or conceptual studies; cross-country regressions; micro econometric studies and general/partial equilibrium studies. Most studies, especially country studies, focus on Asia, 19 wh a few on Latin America and very few on Africa (Port et al on Zambia, and Appleton et al on Ghana, are partial exceptions). Our focus is on cross-country studies where only few studies exist. The most relevant empirical studies include Deininger and Squire (1996), Ravallion and Chen (1997), Lunderberg and Squire (2003), Dollar and Kraay (2002, 2004), Milanovic and Squire (2004), Ravallion (2001, 2004), Easterly (2004) and Aisbett et al. (2005). All of these studies indirectly examine the aggregate relationship between different poverty measures and trade openness. Some of these look on evidence only between growth and reduction of poverty. Others reviewed evidence, first on the linkages between trade and growth and secondly on the relationship between growth and poverty. Generally, the findings that growth tends to reduce poverty apply both when the source of growth is trade as well as other factors. 20 Thus, growth associated wh trade openness is as pro-poor as growth in general and since whin country inequaly does not systematically increase wh trade, we can say that openness also reduces poverty Berg and Krueger (2003 p37). However, the general conclusion is that there is no evidence on the aggregate data that trade reforms are uniformly eher good or bad for the poor (Ravallion 2004). Among the reasons attributed for this are: poor qualy and use of aggregate data; measurement errors; and weak econometric techniques to address heterogeney and endogeney biases. However, though trade affects poverty through growth and inequaly, is highly probable that this relationship is non-linear in many aspects and involves several thresholds. Recently, the possibilies of nonlineary and thresholds effects have been increasingly recognized as crical issues in the trade-poverty relationship. This suggests a false imposion of a linear relationship in the trade and poverty relationship, instead of a 19 The argument being that: this is the part of the world where there is a majory of poor people (in large developing countries like China and India), which has seen rapid trade growth associated wh increased openness, high economic growth rates and a rapid decline in poverty over the past years. 20 Such as fiscal, monetary or exchange rates, foreign aid, social factors, to mention a few.

13 12 non-linear one. Even though, very few studies exist that have tested for the non-lineary and presence of threshold effects in the trade-poverty relationship. The few exceptions are Agenor 2004; Sindzingre 2005; Liang 2006; and Nissanke et al Agenor 2004, for example, found that there is non linear relationship between measures of poverty and globalisation. He analysed two types of globalization effect on poverty. One is through output effect, where his results suggest the existence of an inverted U-shape relationship between globalisation and poverty. At low level, globalisation appears to hurt the poor; but beyond a certain threshold, seems to reduce poverty. This is from the fact that, at the inial stage, globalisation wh greater trade liberalization may lead to decline in the output of import-competing sectors lead to decline in both aggregate output and per capa income, hence worsen the welfare of the poor; while at following stage, wh the expansion of exportable sector, aggregate output will gradually increase resulting in poverty reduction. The second effect is through the relative wage effects via the impact of globalisation on skilled-unskilled wage differentials. Inially, greater openness may increase the wage differential between skilled and unskilled labour hence worsen the living condion of the later and increase poverty. This inial widening in wage gap may lead to an increase in investment in human capal and hence an increase in supply of skill labour over time, which then tend to narrow the wage differential across skill categories and reduce poverty in later stage. Again, this indicates an inverted U-shape relationship between globalisation and poverty. However, Agenor s work is limed in a number of ways, so in particular the way in which captures the nonlineary in the relationship. He includes a square term of globalisation index in his regression, s disadvantage being that the nonlineary in the trade-poverty is constrained to be of a particular format. In addion, the inclusion of square term implies that the number of thresholds is arbrarily chosen as being one, completely ignoring the possibily of multiple thresholds. More recently, Liang 2006, by paying particular attention to the nonlineary of the impact of globalization on the poor, he employed panel data covering twenty-five Chinese provinces over the period of and applied the endogenous threshold regression techniques to empirically investigate the globalization-poverty nexus in China. Unlike Agenor s work, this technique is able to dictate multiple thresholds unfettered. Estimation results provide strong evidence to suggest that there exists a threshold in the relationship between

14 13 globalization and poverty: globalization is good for the poor only after the economy has reached a certain threshold level of globalization. Sindzingre 2005 focuses on the non-lineary of the transmission of the impact of globalisation on poverty and the existence of the threshold effects by considering instutions as crical factors in creating these threshold effects. Instutions: their credibily and abily to be transformed by globalisation, according to him determine the whether the benefs of globalisation are harnessed and spread to the poor, whether these benefs are locked in by a particular groups wh the poor being excluded from them, or the negative shocks associated wh globalisation are transmted to the poor in unconstrained manner. This is due to the fact that instutions create discontinuies and multiple equilibria upon the impact of globalisation on the poor. Therefore the impact of globalisation on poverty in any given setting is posive or negative depending on the characteristics of certain instutions, such as: their historical depth; credibily; the ways they organize and support particular market structure. 3. Threshold-Interaction Effects Model 3.1 Modelling Threshold and Interaction Effects Existence of non-lineary and threshold effects in economic relationships has been widely studied in empirical works for sometimes now. As shown by Winters et al. (2000, 2004) and Nissanke et al. (2007), trade affect growth and/or inequaly (independently or jointly) and this mediates s effects on poverty. This means that the effects of trade on poverty are contingent on prior factors, implying a non-linear relationship. But this is only one type of non-lineary in trade-poverty relationship. To capture such relationship, one possibily is to allow for interactions effects, that is, effect of growth on poverty, effect of trade on poverty and an interaction term for both (which is the effect of growth given trade or of trade given growth). 21 Growth and inequaly therefore are mediating variables which influence the effects of trade on poverty. This is what called linear interaction method, which we did in our earlier work, where we include interaction terms between 21 Alternatively, one can model the channels through which trade affects poverty using a simultaneous equation system, such as two equations (e.g. one for trade on growth, and another for growth on poverty) estimated together.

15 14 measures of openness and growth and/or inequaly in the reduced form specification regression (Leyaro, 2007). But the simple interaction effect model test only for a bilinear interaction effect, where the slope between poverty and trade changes as a linear function of growth and/or inequaly. Failure to obtain a statistically significant interaction effect using tradional product terms may reflect the presence of an alternative functional form rather than the absence of an interaction effect. It might be that changes in the relationship between trade and poverty are relatively large as one moves from low (high) growth rates (inequaly levels) to medium and then to high (low) growth rates (inequaly levels). Suggesting that, there exist potential threshold effects in trade-poverty transmission, given growth and/or inequaly. One strategy is to model the simple product terms in a polynomial regression by calculating the square of the mediating variables, i.e. growth and inequaly. This is the quadratic interaction effect model. Agenor (2004) is one exception work that sought to test the nonlineary in globalisation and poverty relationship, using a squared term of globalisation index in his regression model. Besides quadratic interaction effect model, another strategy is to exogenously spl the sample into pieces. For instance using different levels of growth and inequaly (i.e. above and below mean value) to identify nonlineary and threshold effects in the trade-poverty relationship. 22 But that is done in ad hoc and arbrarily way. In our case for example we exogenously imposed the threshold values by splting the sample data into two, above and below the 50 th percentile 23, to test for the presence of threshold effects in the tradepoverty linkage given growth and inequaly. This approach of splting one s sample into classes (groups) based on the arbrarily choice of threshold value in threshold tradional approach lerature is called exogenous sample splting. But both quadratic interaction effect and exogenous sample splting approach have obvious weakness, since they both assume that the non-lineary and so the threshold values in the trade-poverty transmission, given growth and/or inequaly are of particular form. The inclusion of square terms or ad hoc splting of the sample into groups implies that the threshold values 22 Most studies interact inial income (as a measure of development or instutions) wh measures of trade to test the hypothesis that trade affects different countries differently, i.e. less beneficial for inially poor countries than is for more advanced ones. 23 Which is the same as using the mean value

16 15 are chosen arbrarily as being one, completely ignoring the possibily of multiple break points. Hence, results basing on such ad hoc, arbrarily and exogenously splting of sample are very sensive to the choice of the threshold values. Such disadvantages that are inherent in quadratic models and exogenous sample splting approach in dealing wh non-lineary and threshold effects are also shared by many other economists. For instance Moschos (1998) employed a swching technique, which is completely different from tradional approaches, that is, the quadratic and exogenous sample splting. To determine the existence of threshold effects between export and growth given level of development, the swching point (threshold level) is arrived at data self rather than being determined exogenously. His results, which contradict the previously held view on the effects of export on growth, led him to cricise the earlier studies that the crical levels of development (threshold levels) were chosen arbrarily. 24 The view which is echoed by Gomanee, Girma and Morrissey (2003) who argued that under tradional approach both the number of regimes and location of sample spls are arbrarily chosen and not based on prior economic guidance. Since the threshold is not determined whin the model, is not possible to derive confidence intervals for the threshold. In addion to that, the robustness of the results based on such approach is also questionable, as they are likely more to be sensive to the choice of the threshold value. In a similar vein, Hansen, in series of papers on the subject of threshold regression analysis cricises the use of ad hoc sample splting in many areas of economic inquiry (see Hansen, 996, 1999, 2000). He noted that econometric estimator generated on the basis of exogenous sample splting may pose serious inference problems. In our case for instance, sample splting will imply that countries in the same group have the same growth rate and income distributions, a thing which is implausible given heterogeneous nature of countries in the sample for both growth and inequaly variables. Thus, for these reasons, arbrarily and exogenously splting of the sample in empirical estimation is highly disputed. Something which makes us to rethink the tradional approaches; and 24 This approach is close to another approach used in threshold analysis that is, the regression tree methodology, wh which the number and location of thresholds are endogenously determined through the method of data sorting. It weakness is that, this approach suffers seriously from the limation that fails to provide any distribution theory to test the statistical significance of thresholds.

17 16 forcing us to make use of advanced regression technique, if we are to draw more convincing and robust conclusion. To address this caveats Hansen (1996, 1999 and 2000) develops new econometric techniques for threshold regression analysis, i.e., the endogenous threshold regression techniques; which has crical advantages over the tradional approaches. One, does not require any specified functional form of non-lineary, and the number and location of thresholds are completely endogenously determined by the data. Two, provides an asymptotic distribution theory to construct confidence intervals for the parameters. Henceforth, to test for non-lineary and existence of threshold effects and avoid the potential biases in the trade-poverty relationship: this paper employs endogenous threshold regression technique as proposed by Hansen (2000). 3.2 Formal Threshold Regression Technique The aim of this study is to explore the evidence of heterogeney in the trade-poverty transmission given growth and/or inequaly. We adopt the conventional specification used in growth cross-country regressions, following Barro (1991), Barro and Sala-i- Martin (1992), and Mankiw, Romer and Weil (1992), replacing growth wh poverty as the dependent variable (and adapting the set of explanatory variables). In line wh what we reviewed in the lerature, see Winters et al. (2000, 2004), Agenor (2004), Sindzingre (2005), Liang (2006) and Nissanke et al (2007); we are testing two hypotheses: i) the null hypotheses of no contingent relationship in trade-poverty transmission given growth and inequaly, best captured by linear interaction effects, against the alternative one of no condion relationship and ii) the null hypotheses of no threshold effects, that is, heterogeney in trade-poverty nexus given growth and inequaly, against the alternative one of presence of threshold effects in such transmission, best captured by non-linear interaction effects. As a result, we need baseline framework whin which to explore this relationship, to start wh. First we start by testing the direct hypotheses: the poverty reducing effect of growth and poverty worsening effect of inequaly. Thus the base model is: POV = α + ln GDPO + GINI + GROWTH + TRADE + μ (3.1)

18 17 POV is headcount poverty measured as % of population living in households wh consumption or income per person below the poverty line (as per World Bank, 1993). lngdpo, GINI andgrowth are the explanatory variables for the baseline specification. GROWTH. lngdpo is inial log of real income per capa which often is used to capture condional convergence. But can also be used to capture country s specific effects. The expected sign is negative implying that as countries grow, the poor or developing countries will catch up wh rich or developed countries. GINI is a measure of inequaly between 0 (everyone has the same income) and 100 (richest person has all the income). The sign is expected to be posive for the direct effect. GROWTH is per capa growth rate of a country by the average over the period. The sign is expected to be negative. TRADE, the measure of openness, is measured in three ways: exports over GDP ( XGDP ), imports over GDP ( MGDP ) and trade over GDP (i.e. export + imports/gdp). The subscript i indexes the individual country; and the subscript t indexes time while ε is an error term. But as discussed above, trade is unlikely to affect poverty directly in these reduced form aggregate data specification, rather condion on prior factors. Hence, we extend the baseline specification and allow for linear interaction effects to capture that s, such that the augmented model becomes: POV = α + X β 1 + S β 2 + X S β 3 + μ (3.2) x is a vector of explanatory variables as defined before. S is the vector of our measures of trade openness as define before. inequaly and growth - GINI GROWTH X S is the interaction terms between: ; inequaly and trade - GINI TRADE ; and growth and trade. Specification 3.2 implies that the effects of trade on poverty are contingent on prior factors, that is, growth and/or inequaly. Though linear interaction effects captures heterogeney in trade poverty relationship, as specified by 3.2, however, the results obtained from such specifications may be biased in

19 18 a number of ways. This is due limations inherent in such linear interaction effects approach, as restricts externalies generated by eher growth or inequaly on trade to be monotonically increasing (decreasing) wh growth and inequaly, and so is the oppose. Thus, much as trade affects poverty through growth and/or inequaly, might do so in non-linear form involving a number of thresholds. Two approaches that have been used to allow for this non-linear interaction effects are: quadratic interaction effect and exogenous sample splting approaches. But, as shown above, such approaches suffer a number of caveats. For comparison purposes, we employ these approaches too. But in addion to that, and unlike previous studies, we adopt a more formal approach to the modelling of heterogeney in the trade-poverty relationship. Given our prior belief that the effects of trade openness on poverty differ across countries based on the countries level of growth and inequaly, we are arguing that the relationship is discrete in nature (not continuous condioning influences).that is, the tradepoverty relationship is characterised by heterogeney condional on growth and inequaly. We do not know, however, how the coefficients on the openness variables vary wh growth and inequaly. In light of this, we formally apply the endogenous threshold regression technique as developed by Hansen (2000) to estimate the unknown thresholds or cut-off values. Once all the heterogeneous relationship are identified as involving thresholds, then what level of confidence can we attach to the posion of the threshold. That is, how confident can we be that any particular country lies above or below the thresholds? This final point is important in testing procedure since the standard econometric theory of estimation and inferences are rendered invalid. Though the standard econometric theory of estimation and inference is not valid, Hansen (2000) has provided an asymptotic distribution theory that enables one to make valid statistical inferences on the basis of threshold models. From 3.2, to start wh, the threshold regression model can be described as captured by eher of the single threshold variables, where in equation (3.3a) growth is the threshold variable and in equation (3.3b) inequaly is the threshold variable: POV POV = γ X + β ) + TRADE I ( GRWTH α ) + β TRADE I ( GRWTH > α + η i + λt ε (3.3a) 1 2 = γ X + β ) + + TRADE I ( GINI α ) + β TRADE I ( GINI > α + η i λ t ε (3.3b) 1 2