Neumayer (2003) also finds that the degree of good governance, past colonial ties, population, and per capita GDP had a positive effect on the size of foriegn aid. Kuziemko and Weker (2006) find that being a rotating member of the UN Security Council had a positive effect on aid transfers from the USA and the UN. These studies focus on the quantity or size of aid. A foreign donor may benefit directly from in-kind or tied aid because such transfers may promote trade between the donor and the recipient (Jepma, 1991).
As Kanbur (2006) noted, tied aid may also be used as a way of redistributing income within the donor country, given that such aid may be raised from general taxation within the donor country but must be spent on purchasing the output of a particular industry or the services of consulting firms in the donor country.
Schultz (1960) noted that farm surpluses in the US and Europe were used as tied foreign aid with the goal of protecting the income of farmers in\ these donor countries. Focusing on the size of foreign aid, Maizels and Nissanke (1984) undertook a study using data for eighty developing countries over the period 1969-70 and 1978- 80. It is found that the magnitude of bilateral donors' aid was consistent with a "donor interest" model, where aid was given for political, security, and trade interests, while the magnitude of multilateral donor aid was consistent with a "recipient need" model, where aid was given in response to shortfalls in the recipient country's resources. Burnside and Dollar (2000, p. 864) also found "no significant tendency for total aid or bilateral aid to favour good policy. On the other hand, aid that is managed multilaterally is allocated in favour of good policy."13 Alesina and Dollar (2000, p. 55), also find that for bilateral donors determinants for instance "voting patterns and colonial past in the United Nations clarify the distribution of aid more than the institution of politics and economic policy of recipients." Kanbur et al. (1999) strongly advocate for a common pool approach to foreign aid, where all bilateral donors coordinate their efforts by putting their aid in a common pool. Foreign aid is given to the leader of the recipient country for the provision of a public good which is assumed to fully depreciate in each period. The leader can embezzle all the aid or part of it. In particular we assume that all of untied aid can be embezzled but tied aid cannot be embezzled or resold in the market. It is in this sense that tied aid controls moral hazard behavior. This interpretation is consistent with how in-kind or restricted transfers are used to induce incentive-compatible outcomes as in, for example, Besley and Coate (1991), Blackorby and Donaldson (1988), and Nichols and Zeckhauser (1982).
Gahvari and de Mattos (2007) show that combining a conditional cash transfer with an in-kind transfer can reduce or eliminate the deadweight losses of in-kind transfers. Gahvari and de Mattos (2007), cash transfers are used in our model because of the possible deadweight loss of in-kind transfers and in-kind transfers are used to control moral hazard behaviour or relax self-selection constraints.
The focus on whether aid improves GDP growth can be traced back to the two-gap model (Chenery and Strout, 1966), which remains the most influential theoretical underpinning of the aid effectiveness literature. In this model, developing countries face constraints on savings and export earnings that hamper investment and economic growth. Aid flows are meant to fill the gap between investment needs and domestic savings. Even though this model had been the target of severe criticism almost since its inception, it had provided the underlying principles both for early aid policies (Easterly 1999) and for regression specifications of most empirical papers, which focused on the aid-growth and aid-savings relationships. Most early authors concluded that aid had no significant impact on growth and investment. It had shown that aid had increased fruitless public expenditure (Mosley and others, 1992) and to fail to promote investment. The latter point is confirmed by Boone (1996) and Reichel (1995) who find a negative relationship between savings and aid, and point to a substitution effect. This result is amended by Hadjimichael (1995), who notes that the relationship between aid and domestic savings is negative in most countries, but positive for good adjusters. The latter point is confirmed by Burnside and Dollar (2000) (henceforth BD), who showed that aid can be effective when policies are good. The BD paper had elicited abundant comment from researchers (to cite only a few, see Guillaumont and Chauvet (2001), Collier and Dehn (2001), but their results had been challenged as being "extremely data dependent" (Dalgaard and Hansen, 2001, Clemens, Radelet and Bhavnani, 2004). As argued by Svensson (2000) and Murshed and Sen (1995), a recipient government and a perfectly altruistic donor can had conflicting objectives, as the former represents a variety of stakeholders, including wealthy individuals who might influence the aid distribution. If foreign aid is misallocated and misused, then it cannot be expected to had a significant impact on growth. Third, as suggested by Boone (1996), aid effectiveness should not be measured by its impact on GDP growth. Aid could be increasing consumption rather than investment, which would explain the disappointing results of studies on growth, but still reduce poverty through either "higher consumption of the poor or greater provision of services to the poor." As noted by Meyer (1995), participation by NGOs in foreign aid had intensified significantly during the last two decades. Their number had grown exponentially; the size of some of them makes them significant players in social welfare and employment markets at the national level; the funding attracted had increased enormously; and their visibility to the general public had never been higher. NGOs are perceived as having two distinctive features that differentiate them from other donors. First, it advocates the most vulnerable populations and their motivation is widely perceived as mainly altruistic. Second, their actions at the grassroots stage are shown as conducted at private-sector stages of cost management and efficiency, while achieved development objectives and serve the needs of many people (Rose-Ackermann, 1996).
Kosack (2003) finds that aid can directly increase welfare but only in democracies. However, there is strong evidence that foreign aid had an indirect impact on poverty and well-being through its impact on pro-poor expenditures of recipient countries (Mosley and Hudson, 2001, Verschoor and Kalwilj, 2002, Gomanee and Morrissey, 2002, and Gomanee and others, 2003). Le and Winters (2001) provide an excellent conceptual framework in evaluating the impact of aid policies on poverty in Viet Nam. The general perception is that it had little impact on poverty reduction since successive governments directed little expenditure towards the social sectors. Moreover, a fiscal response model for Papua New Guinea indicates that foreign aid had led to small increases in investment expenditures but to minor reductions in health and education expenditures (Feeny and McGillivray, 2003).
The early literature initiated by McKinnon (1973) and Shaw (1973) suggests that financial liberalization encourages investment and therefore exerts a positive effect on long-term growth. Following the seminal empirical work of King and Levine (1993), the relationship between finance and growth had been a subject of considerable academic interest and intense policy debate (see, e.g., Bell and Rousseau, 2001; Rousseau and Wachtel, 2002; Beck and Levine, 2004; Levine, 2005). The bulk of cross-country evidence appears to suggest that financial development had a positive impact on economic growth (see Ang, 2008c, 2009a for a survey of literature), although case studies indicate that the direction of causality is less unambiguous (see, e.g., Ang and McKibbin, 2007). These two strands of literature, i.e., the aid-growth and finance-growth links, had recently been combined under an integrated framework by Nkusu and Sayek (2004). It is argued that financial development may exert an indirect positive effect on the aid-growth relationship through the conduct interest rate and exchange rate management, where the effectiveness of these policies depend on the absorptive capacity of the local financial markets. Significant inflows of foreign aid puts upward pressure on the real exchange rate that can be translated into higher prices. The presence of a broad and deep financial system provides the necessary instruments that could effectively sterilize these undesirable impacts. In other words, foreign aid functions effectively when aid flows are better managed in the context of deeper and more efficient financial systems. Therefore, it appears plausible that one of the underlying reasons that aid is less effective in spurring development than is expected may be due to the failure of financial systems in ensuring an efficient allocation of aid resources. However, in contrast to Nkusu and Sayek (2004), the focus of the present study is on financial liberalization rather than financial development. The presence of a more liberalized financial system also effectively reduces barriers and restrictions on interest rate and exchange rate controls, providing the monetary authorities with greater flexibility to conduct monetary and exchange rate management (Caporale and Williams, 2001; Kletzer and\ Kohli, 2001). Foreign aid helps to increase domestic savings or directly increase productivity of capital-promoting economic growth (Domar, 1946). Aid payments help to either close the gap between savings and investment, or the gap between export and import, which occur in developing countries because of limited resources. In addition to the savings and investment gap, Bacha (1990) asserts that developing countries' governments had weak revenue-raising capacities, causing a third _scale gap. Foreign aid may close this gap and thus stimulating investments and economic growth. During the ColdWar, western countries, as well as communist Warsaw Pact countries, tried to inuence the political elite in developing countries, using foreign aid payments to implement their respective ideologies [Alesina and Dollar (2000) and Wood (2005)]. These aid payments often ended in incumbents' pockets and were hardly able to promote economic growth [Alesina and Weder (2002)]. Nowadays, we can still observe such patterns by donor states. For example, Nigeria receives the larget amount of ODA in the world with 10.8 billion U.S. dollars (2007), and it is not farfetched to believe that the recent rise in oil prices and corresponding Nigerian oil deposits had played an important role in this context. As this brief discussion shows, aid might had a positive impact on economic development if donors and/or receivers are benevolent, but aid can also be an obstacle for growth if political or personal interests are involved.
Swaroopa et al. (2000) analyze the fungibility of aid in federal systems and that aid merely substitutes for spending that the government would had undertaken anyway. Svensson (1999) was the rest who considered the interaction of aid and policy variables, which had become a commonly-used concept to evaluate aid effectiveness, which we had adopted in our empirical analysis. Svensson found a weak significant negative impact of aid on growth, but a positive and significant effect in democracies. Islam (2005), focuses on political stability deafened by assassinations, revolutions, riots, and strikes, showing that aid promotes growth only in a politically-stable environment. Further, Economides et al. (2008) investigates the relationship between aid, growth, and rent-seeking activities. A significant positive effect of aid on growth, which is mitigated by an endogenous increase in rent-seeking activities, triggered by the very same rise in aid.
Azariadis and Stachurski (2005) had noted that generally poverty-trap models seem to be lacking testable quantitative implications, some studies had attempted to investigate their predictions in the context of aid flows.
Easterly (2006) claimed that the emphasized details are not regular with a low-income poverty corner due to inadequate aid, as development is lesser in aid-concentrated countries than in alike underdeveloped countries that obtain minute aid, while aid had increased in the end as a percent of revenue in Africa, but Africa's development rate had decreased eventually.
Kraay and Raddatz (2007) had recently tested whether the savings and increasing returns patterns predicted by poverty-trap models are supported by the data. It is shown that there is no supporting evidence either in the behavior of saving and per capita income, or technological non-convexities, in favor of a poverty trap. The authors also fail to find evidence for the existence of a high-growth which countries may be capable to reach with suitably huge aid inflows. In 1987 Paul Mosley suggested that while aid seems to be effective at the microeconomic level, any positive aggregate impact of aid is much harder to identify (Mosley, 1987). He labelled this the micro-macro paradox, and it challenged the conclusions of the seminal works by (Papanek, 1972, 1973). Now, after more than twenty years, Rajan and Subramanian (2008) (hereafter RS08) conclude "it is difficult to discern any systematic effect of aid on growth'. At the same time, microeconomic evaluations, including rigorous contributions to the programme evaluation literature by development economists, remain largely positive. In 1994, the Economist magazine concluded from the results of Boone (1994) that 'Aid [goes] Down the Rathole'. Djankov et al. (2008) argue that aid had analogous effects to a natural resource curse. Their core result is that foreign aid had a statistically significant negative effect on changes in political institutions (specifically democracy) and this effect is larger in magnitude than that caused by natural resource windfalls. Similarly, Rajan and Subramanian (2007) find that the rate of growth of value added by the manufacturing sector in developing countries had been undermined by a detrimental effect of aid inflows on governance quality. Using a more sophisticated theoretical approach, namely an augmented Solow-Swan growth model, Dalgaard and Erickson (2009) find that the predicted increase in the level of GDP per capita accruing from aid inflows to sub-Saharan Africa over the past 30 years is between 4 per cent and 7 per cent, depending on the assumed share of capital in value added. This converts to a considerably smaller elasticity of aid to growth than the value of 0.1 posited by RS08. Dalgaard and Erickson conclude that, viewed through the lens of a neoclassical growth model, the disappointing growth performance of Africa does not necessarily imply an aid effectiveness puzzle. Rather, expectations with respect to the potency of aid had been systematically too high. Ashraf et al. (2008) and Acemoglu and Johnson (2007) find that the initial economic impact of gains in life expectancy from disease eradication may be a reduction in per capita incomes due to the increase in population and dependency ratios. The simulation model employed by Ashraf et al. (2008) focuses on demographic impacts and resulting dependency ratios, capital/labour ratios and land/labour ratios. If, in addition to being a major cause of childhood death, diseases like malaria pose significant constraints to the growth of industries like tourism (by depressing demand) and food processing (by complicating recruitment of skilled labour to rural areas), the economic benefits of malaria eradication would be larger and would accrue more quickly. Furthermore, as Asharaf et al. point out, complementary policies such as population control and enhanced investment levels (potentially through foreign aid) could speed the realization of benefits from disease eradication and life expectancy improvement. Nevertheless, the point remains that for some important types of aid, the realization of growth benefits may require a full generation of elapsed time. One strand of recent literature emphasizes the need to 'open the black box' (Bourguignon and Sundberg 2007) incorporating political economy aspects. Others had pursued nongrowth (meso-level) aid outcomes, pointing to the multi-dimensional objectives of aid. Mishra and Newhouse (2007), for example, uncover a small but statistically significant effect of health aid on infant mortality.
Masud and Yontcheva (2005) also find that aid helps reduce infant mortality, but this effect is only significant for aid provided by nongovernmental organizations (NGOs) rather than bilateral aid. Easterly (2009) documents substantial improvements across a wide range of social indicators in sub-Saharan Africa since 1960, but he does not relate these improvements to aid. With few exceptions (e.g.,Sachs 2005, 2006), findings at the meso-level had not been deployed to argue for aggregate aid effectiveness. This is despite increasing evidence those outcomes at this level do had substantial macroeconomic effects (Cohen and Soto 2007).
Arndt et al. (2007) provide a comprehensive case study of Mozambique in which it is attempted to evaluate the effect of aid on different proximate drivers of growth. Starting with long-run growth accounting estimates, it is found that aid had played a critical role in rebuilding infrastructure and expanding access to health and education. While aid had supported rapid reconstruction and had crowded-in private investment, it also had generated important governance and economic management challenges; thus there is no guarantee that higher growth associated with aid is sustained over the long-term. This provides supporting evidence for the Collier and Hoeffler (2004) argument that aid can be particularly beneficial in post-conflict environments. Deaton (2009) had argued, few studies had dealt with the endogeneity of aid in a convincing manner.
Lancanster (1999) noted that though foreign aid had continued to play an important role in developing countries, especially sub-Sahara Africa, it is interesting to note that after half a century of channeling resources to the Third World, little development had taken place. In almost all of sub-Saharan Africa there is a high degree of indebtedness, high unemployment, absolute poverty and poor economic performance. The average per capita income in the region had fallen since 1970 despite the high aid flows. This scenario had prompted aid donor agencies and experts to revisit the earlier discussions on the effectiveness of foreign aid.
Chapter 3
Research Method
The following Section 3.1 describes data collection and section 3.2 describes the research method used in this study.
3.1 Data Collection
The data used for this study consists of one independent variable that is US Economic aid that affects the percentage change in real GDP growth. The data is acquired from Central Bank of Pakistan - Handbook of statistics 2005, Central Bank of Pakistan Annual Reports 2005 -2009. The sample consists of thirty years (1980 to 2009). The dependent variables used are the percentage change in real GDP; The Foreign Exchange Reserves; The Exchange Rate; The Debt on GDP and The Unemployment Rate. Independent variable is US Economic aid from the US in million dollars.
3.2 Research Method
To evaluate the relationship of variables the explanatory variables were regressed using Regression method called Quadratic Regression Model (Curve Estimation). In measuring impact of foreign aid on economy of Pakistan we had taken percentage change in real GDP, Change in Foreign Exchange Reserves, Exchange rate, Debt on GDP and Unemployment rate in percentage as dependent variable and independent variables is foreign aid in amount in million dollars.
3.2 Empirical Model/ Statistical Tool
The Quadratic Regression (Curve Estimation) function is;
Å·=b0 + b1x + b2x²
Where;
b0 is the constant term
b1 is the linear term
b2 is the quadratic term
Chapter 4
Result and Findings
4.1 Result
Many studies were carried out to analyze the impact of the foreign aid on the Economy as discussed in the literature review of section of this study. In order to analyze the impact of Foreign Aid on percentage change in Real GDP, Foreign Exchange Reserves, Exchange Rate, Debt on GDP and Unemployment Rate; Quadratic Regression (Curve Estimation) Function is used:
4.2 Findings
4.2.1 Percentage Change in Real GDP
This model summary table shows the strength of the relationship between the model and the dependent variable. R, the multiple correlation coefficients, is the linear correlation among the model-predicted and observed values of the dependent variable. Its large value indicates a strong relationship. The coefficient of determination, R Square, is the squared value of the multiple correlation coefficients. It shows that 75% of the variation in time is explained by the model. Adjusted R Square is a "corrected" R Square statistic that penalizes models with large numbers of parameters.
The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression sum of squares is greater than the residual sum of squares, which indicates that most of the variation in the proportion of Change in Real GDP is explained by the model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance.
The Quadratic model states that the expected proportion of Change in Real GDP is equal to 4.687 + 0.004*US Economic Aid - 1.5E-006*US Economic Aid
4.2.2 Foreign Exchange Reserves
The model summary table shows the power of the relationship among the model and dependent variable. R, the multiple correlation coefficients, is the linear correlation amid model-predicted and the observed values of the dependent variable. Its large value indicates a strong relationship. R Square, the squared value of the multiple correlation coefficients. It shows that 95.6% of the variation in time is explained by the model. Adjusted R Square is a "corrected" R Square statistic that penalizes models with large numbers of parameters.
The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression sum of squares is smaller than the residual sum of squares, which indicates that most of the variation in the proportion of Foreign Exchange Reserves is not explained by the model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance.
The Quadratic model states that the expected proportion of Change In Foreign Reserves is equal to 26762.744 - 21.929*US Economic Aid + 0.005*US Economic Aid
4.2.3 Exchange Rate
The model given shown above summarized the force of the association between the model and the variable which is dependent. R, the multiple correlation coefficients, is the linear correlation and Its large value indicates a strong relationship. R Square, is the squared value of the multiple correlation coefficients. It shows that 21.2% of the variation in time is explained by the model. Adjusted R Square is a "corrected" R Square statistic that penalizes models with large numbers of parameters.
The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression sum of squares is smaller than the residual sum of squares, which indicates that most of the variation in the proportion of Exchange Rate is not explained by the model. The significance value of the F statistic is more than 0.05, which means that the variation explained by the model is due to chance.
The Quadratic model states that the expected proportion of Exchange Rate is equal to 62.722 - 0.003*US Economic Aid + 2.16E-007*US Economic Aid
4.2.4 Debt on GDP
Model summary table had shown the amount of relation among the dependent variable and the model. R, the multiple correlation coefficient's large value indicates a strong relationship. the coefficient of determination, R square, is the squared value of the multiple correlation coefficients. It shows that 9.4% of the variation in time is explained by the model. Adjusted R Square is a "corrected" R Square statistic that penalizes models with large numbers of parameters.
The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression sum of squares is smaller than the residual sum of squares, which indicates that most of the variation in the proportion of Debt on GDP is not explained by the model. The significance value of the F statistic is more than 0.05, which means that the variation explained by the model is due to chance.
The Quadratic model states that the expected proportion of Debt on GDP is equal to 171183.5 - 354.953*US Economic Aid + 0.177*US Economic Aid
4.2.5 Unemployment Rate
The model shown above reports the power of the relationship among the model and variable which is dependent. R is the multiple correlation coefficients and its small value indicates a weak relationship. The coefficient of determination, R^2 is the squared value of the multiple correlation coefficients. It shows that 68.7% of the variation in time is explained by the model. Adjusted R Square is a "corrected" R Square statistic that penalizes models with large numbers of parameters.
The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression sum of squares is greater than the residual sum of squares, which indicates that most of the variation in the proportion of Unemployment Rate is explained by the model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance.
The Quadratic model states that the expected proportion of Unemployment Rate is equal to 8.872 - 0.003*US Economic Aid + 1.06E-007*US Economic Aid
4.3 Summary Assessment of Research Hypothesis
Linear
Quadratic
Linear
Quadratic
Linear
Quadratic
Empirical Conclusion
H1
Foreign Aid explains Change in Real GDP
50.50%
66.70%
9.163
9.002
0.019
0.016
Accepted
H2
Foreign Aid explains Change in Foreign Exchange Reserves
61%
94.10%
13.49
64.48
0.008
0
Accepted
H3
Foreign Aid explains Change in Exchange Rate
9.90%
-5%
1.879
0.808
0.213
0.489
Rejected
H4
Foreign Aid explains Change in Debt on GDP
-5.10%
-20.80%
0.613
0.311
0.459
0.744
Rejected
H5
Foreign Aid explains Change in Unemployment Rate
64.10%
58%
15.294
6.571
0.006
0.031
Accepted
Chapter 5
Conclusion, Discussion and Implications
5.1 Discussion and Conclusion
The current study shows both encouraging with the pessimistic effect of foreign aid on the Pakistan's economy. On optimistic face, foreign aid had assist in enhancing the GDP Growth by means of structural revolution of the economy, laid basics of the industrial and agricultural sectors, provided technical assistance, strategy advice and up to date technology, helped in overcoming the budget deficits and the Balance of Payments deficits and had also invested in the ventures for the community development projects. Seeing the regression analysis of the GDP and the Foreign Economic Aid confirms the positive affect of Foreign Economic Aid on the GDP. GDP increases at the decreasing rate, as the flow of foreign capital increases. Thus, the overall impact of the Foreign Economic Aid on the economic of Pakistan is positive. Similarly, Foreign aid also helped in increasing the foreign exchange reserves of Pakistan and these foreign exchange reserves provide space for the government to maneuver exchange rates - typically to soothe the foreign exchange rates to offer a further positive economic situation. In addition, the higher the foreign reserves of a country, the superior position it is in to protect itself from speculative effects on the home currency.
However on the pessimistic side, Foreign aid moreover amplified Unemployment rate in Pakistan which had taken place of domestic savings, raise debt load. Like a variety of debt indicators shows that Pakistan's debt load amplified as the time passed and the country possibly will trapped in rigorous debt servicing crisis if the macroeconomic administration, household saving and foreign trade policies are not planned and implemented properly.
The policies are also vital in the efficacy of foreign aid, like the aid had a additional affirmative impact on growth accompanying good monetary, trade and fiscal policies .In the company of poor policies, conversely, aid had no constructive impact on growth. For that reason, there is a requirement of not just good policies but furthermore the execution of these policies with the appropriate check and balance of the aid-utilizing projects is essential to stay away from the misutilization and the misconduct of the foreign capital resources. Therefore we can state that the Foreign Economic Aid may possibly be supportive in boosting economic development of Pakistan merely in the existence of proper fiscal, trade and the monetary policies.
