Debt, Corruption and Investment in East Africa: A Panel ARDL Analysis

We set out to investigate the relationship between public debt and private investment using a panel of four countries in East Africa for the period 1992-2015. The results from the Autoregressive Distributed Lag Models show that Public Debt (PD) crowds out both Private Domestic Investment (PDI) and Foreign Direct Investment (FDI) in the long run, although the magnitude of the impact is greater for the former category. We fail to find evidence of any short run significant relationship in either case. However, the importance of institutional quality in enhancing relationship in question is unquestionably confirmed in the data. The effect of PD on either PDI or FDI is observed to change when the corruption control improves. The immediate recommendation is the need to design fiscal policies to tame the growing debt that appears to discourage private investment in the region. A proper debt management system coupled with clear policies to improve the institutional quality would likely boost private investment in East Africa. The anti-corruption measures already in place should be enhanced to create a conducive investment climate for the private sector to thrive.


Introduction
The East African countries have ambitious plans to transform their economies to middle income levels in the next decade. In an ardent effort to achieve this target, member countries have for the last decade undertaken public investment in infrastructure as well as investor-friendly strategies including but not limited to investment incentives and institutional enhancements. Intuitively, the strategies aim at creating a private-sector-driven economy. Given the small tax base and inadequate revenue characteristic of developing countries and East Africa in particular, the main source of finances in the development and expansion of infrastructure is public debt secured both domestically and externally. While public investment may be complementary to private investment particularly where the former increases capital productivity of the private sector, increases demand for inputs, and improves aggregate demand and savings, the link between public debt and private investment is still an empirical question. In principle, however, since the supply of money is fixed, domestic borrowing by the government may be at the cost of private investment since it will be withdrawn from the productive uses. Limited credit availability offered at high interest rates crowds out the private sector just as increased domestic borrowing may also result into high interest rates that cause an increase in cost of production, making tradable goods expensive and noncompetitive in foreign markets. Regarding these theoretical arguments, the key question still attracting debate is the extent to which public debt would quantitatively impact private investment and via which channels this would be possible. The current paper seeks to appreciate these concerns with reference to East Africa.
Certainly the empirical arena is not in scarcity of studies that examine the issue in question. However, a detailed scrutiny of the available studies informs us of a divergence in the findings. For example, while the likes of inter alia [1] for Senegal, [2] for Bangladesh, and [3], report a crowding in effect of public debt on private investment, the crowding out finding is visible in other studies such as [4]- [9], among others. Yet different schools of thought agree on a non-linear relationship between the variables in question, pointing to a threshold effect. [10] and [11] inter alia, fall under the latter category. However, still among these, differences are not uncommon. For example, while the likes of [10] argue that debt affects private investment up to a threshold level and becomes positive beyond that threshold, the category of [11] argues that public debt first affects private investment positively until a certain threshold beyond which the effect turns out to be negative. Moreover it is not surprising to find studies that appear inconclusive regarding the relationship in question (e.g. [12]).
Perhaps the reasons for the mixture of evidence regarding the debt-investment nexus are not self-explanatory but could have basis in type of debt considered, type of investment examined, sampling, methodology and data used. It appears too that overall; the results depend on the country or region under analysis. We note that there is scanty literature on studies that capture the linkage in question for the east African countries. Yet the region is continuously engaged in achieving greater regional integration and the Eat African monetary union (EAMU) in particular which, via the EAMU Protocol inter alia, requires a ceiling on gross public debt of 50 percent of GDP in net present value terms as one of the four primary convergence criteria 1 According to [13], the share of government debt 1 The other three primary convergence criteria are: a ceiling on headline inflation of eight percent; reserve cover of 4.5 months of import; and, a ceiling on the overall deficit of three percent of GDP, including grants. All the four must be must be attained and maintained by each Partner State, for at least three years, before joining the Monetary Union. , all the East African countries had debt to GDP ratios below 50% of GDP with the exception of Kenya whose debt to GDP ratio was above the ceiling of 50% of GDP, debt sustainability is a key issue partly due to its direct or indirect effects on other key variables including but not limited to private investment.
With the aim of harnessing the private investment potential to promote economic growth and development in the region, the five partner states of the East African Community (EAC) agreed to cooperate in the areas of investment and industrial development [14] as outlined in the EAC Treaty (Chapter 12, Articles 79 and 80). The governments finance the activities of the EAC and each has a Ministry of EAC to coordinate and facilitate the activities of the common market. The cooperation that has been further enhanced by the coming into force of the EAC Protocol, seeks to rationalize investments with a view of achieving balanced and sustainable growth besides promoting the EAC as a single investment area. As evident, the Public debt in early 1990s was above 80 percent of GDP and be-   with Kenya being among the region's major beneficiaries [15]. FDI, is considered vital for economic development of capital scarce countries, as it provides not only financial assistance but also capital, technology, new jobs, management skill and expertise [9]. In Figure 3, FDI was less 1 percent of GDP in early 1992 in Uganda, Kenya, Tanzania and Rwanda and began to slightly increase. Uganda has had the highest percentage compared to the rest throughout the period under study. In 1998, FDI as a percentage of GDP stood at 3.1 in Uganda, followed  Years   0  20  40  60  80  100  120  140  160   1992  1995  1998  2001  2004  2007  2010  2013  1992  1995  1998  2001  2004  2007  2010  2013  1992  1995  1998  2001  2004  2007  2010  2013  1992  1995  1998  2001  2004  2007  2010   investment that compliments private investment, prudent and sustainable debt management is imperative because a continual rise of domestic debt causes interest rates to soar and crowd-out private investors and annual interest increments on external debt could exceed all other spending [13]. Moreover, debt overhang in the long run discourages private investment due to perceived higher tax burdens. Additionally, poorly done public investment, such as poor infrastructure, would increase the cost of doing business and discourage investors.
Therefore, there is need for efficiency in execution of public financed projects.
The financing of public sector investment, however, whether through taxes, issuance of debt instruments, or inflation, can reduce the resources available to the private sector and thus depress private investment activity. We focus our study on three specific objectives. First is to investigate the effect of public debt on the level of private domestic investment. Second, we examine the impact of Public Debt on Foreign Direct Investment. Lastly, we analyze the impact of institutions on Private Investment. The latter objective is grounded in the argument that the perception of the institutional quality may, in addition to, inter alia the fear of prudent debt management and sustainability, and, debt overhang, impact on private investment. Specifically, institutional quality might reduce economic uncertainties, determine the ease of establishment and doing business and ensure efficient utilization of resources to avoid unsustainable debt levels in the public sector. By implication, sound and efficient institutions are likely to enhance good governance which is a cornerstone to efficient resource allocation and utilization. On the other hand, bad institutional quality such as corruption would likely lead to inefficiency in government spending, poor tax administration, misallocation of resources and embezzlement. With weak institutions in place, public borrowing may continue to increase to fund the ever increasing public expenditure on ineffective administration. The consequence might be an increase in the production costs (for example, poorly done infrastructure), which discourages private investment. According to the World Economic Forum [16], weak institutions remain one of the major challenges affecting not only East Africa countries but also Sub-Saharan Africa as a whole. Yet, as the authors assert, the legal and institutional framework within which economic agents interact would determine competitiveness, and influence decisions whether to or not to invest as well as how benefits/costs associated with development strategies and policies are distributed in an economy. However, it is not illogical to argue that some countries experiencing bad institutional quality could as well attract foreign investment on basis of other reasons, say, the presence for scarce minerals.
In The rest of the paper is organized as follows. In the next section, a detailed analysis of both theoretical and empirical literature is presented. This is followed by Section 3 where the model and data are discussed. We then present and discuss our results Section 4, and conclude in Section 5.

Theoretical Overview
Investment models usually distinguish two separate elements in the investment process: the determination of a desired capital stock and the specification of an adjustment process by which the gap between existing and desired capital stock is filled [17].
One such model that hold relevancy to our study is the accelerator theory that can be traced back to [18], where he explained that demand for capital depends on the acceleration of demand of a finished product. Since Clark focused on quantity as opposed to price, his model was regarded as being 'Keynesian' in spirit and has been referred to as the rigid or simple accelerator model of investment [19]. While the rigid accelerator model explains investment as a function of output growth only and assumes that the desired stock of capital is attained in each time period, its counterpart, the flexible accelerator model came later as a result of reformulating the rigid model to take into account the influence on in- On the other hand, Tobin's q-theory extends the neoclassical theory by incorporating adjustment costs to account for losses in output. In addition to postulating that investment depends upon the ratio of the market value of a firm's assets to their replacement cost, i.e. the q-ratio [22], the model uses the shadow price of capital services, referred to as the user cost of capital, to define the optimal level of capital stock, which implies a high degree of perfection in the capital markets. In essence, the theory contends that as firms maximize the present value of their profits, capital stock will adjust accordingly until no more profits can be made [23]. Consequently, the increase in capital is through investment, while a decrease is through depreciation.
The current study adopts the Accelerator Model which appears to have more relevance to the developing country setting where the underdeveloped equity and bond markets are part and partial of the economies. Other models seem to lack this attribute. For example, the Tobin Q theory of investment has been criticized for oversimplifying rational expectations and efficient markets, and the possibility of generating different investment behavior from the specification of the firm's alternative objective and production function [23]. Moreover, as argued by critics, a model attributed to Jorgensen equally suffers from several restrictive assumptions running from rational expectations, unitary elasticity of substitution between capital and labor, exogenously determined output prices, and constant cost of capital among others 2 .

Empirical Literature
It has been pointed out earlier that the field under analysis is not in scarcity of empirical references. Interestingly however, are the divergent findings on effects 2 A detailed analysis of the theories of investment can be found in [24]. To begin with, in demonstrating crowding out effect, using growth accounting [5], using a panel of 38 advanced and emerging economies between 1970 to 2007, show that the adverse effects on growth of initial debt largely slows down labor productivity growth as a result of reduced investment which reduces growth of capital per worker. A similar finding can be found in [25], who, in their study with focus on the North African countries, report a negative relationship between debt service and economic growth through its adverse effect on investment and export multiplier for all the countries they tested. Still in the same vein [12]), using OLS, found out that PD decreased PI in a study on Public Debt's impact on growth, investment and unemployment in Pakistan. A study on the same country Pakistani for the period 1981 to 2007 was carried out by [9].
The results out of the OLS estimate uncover a discouraging role of PD in FDI.
Support for the crowding-out effect can further be located in a study by [6], where he uses a novel data for local public debt issuance for China during the period 2006-2013. The results show that local public debt issuance crowded out investment by private manufacturing firms by tightening their funding constraints. An earlier study by [8] for Nigeria with focus on private sector investment yields similar outcomes. Relatedly [26], in their study on Nigeria, using Multiple Regressions, found an inverse relationship between external debt and investment volume during for the period spanning from 1980 to 2008. A crowding-in-effect is likewise found by [1] in the case of Senegal. Similar studies on individual East African countries have likewise provided support for the crowding-out role of public debt to investment. For example [27], in a study on Kenya, using the co-integration technique, found that debt service ratio negatively influenced private investment. The "debt overhang effect" is likewise found in [28] who uses a similar methodology but for the analysis of Kenyan data during the period 1967-2007. Still, with focus on Kenya during another period from 1980 to 2013 [29], using Granger causality, established not only a unidirectional causality from debt using Granger causality test, but also a negative debt-effect on Private Investment.
The other school of thought identifiable from the empirics includes protagonists for the crowding-in effect of debt on private investment. For example, [4] uses Unit Root test and co-integration in investigating the effect of public borrowing on Private Investment in Pakistan, between the fiscal years 1971/72 and 2005/06 to provide evidence of a crowd in effect. In his view, the result is attributable to sustainable debt levels, excess liquidity in Banking system, and, ex-penditure on goods with positive externalities. Similarly, [2] employs the co-integration and error correction model techniques to establish a crowd in effect of public debt on private investment in Bangladesh, for the period from 1976 to 2006. Elsewhere in [3], focus is shifted to South Africa and FDI during the period 1983 to 2013, Here, the authors, using Vector Error Correction Model, likewise find a positive relationship between PD and FDI in the Long Run.
Further support for the crowding-in effect can be found in a study on Sri Lanka by [30] where the Vector Error Correction Model (VECM) was estimated on data spanning from 1978 to 2015. [31] concurs with the latter author but only in the case of external debt. In their finding, external debt crowds in domestic investment in the long run.
The third strand of literature consists of categories of authors that provide inconclusive evidence on the relationship in question. For example, the second analysis of the aforementioned study by [6] indicates that local public debt issuance did not affect state-owned and foreign firms in China during the period 2006-2013. Similarly [32], did not find any evidence of debt overhang nor crowding-out effect in Bangladesh in the short run.
Beside the three strands presented above, we can also point out that other studies that argue for nonlinearity of the model that produces varying results depending on the threshold level of public debt. In this category, we identify among others, a study by [10]. We have earlier on argued that the role of institutions in the perceived link between public debt and private investment is theoretically plausible. The empirical arena however, is still characterized by scarcity in terms of related studies.
A few available scholarly works focus on the direct effect of individual as well as aggregate institutions on private investment. The results re however still mixed up. For example, while the likes of [33] and [34] find an adverse impact of corruption on FDI, others such as [35] report a detrimental effect.

Literature Gap
In sum, empirical literature provides mixed results and calls for further debate

Theoretical Framework and Model Specification
As presented earlier, the current study adopts the Flexible Accelerator Model with a few extensions because it takes into account uncertainty common to developing economies. According the model, Capital is adjusted towards its desired level hence firm net investment is proportional to change in desired capital.
Demand for capital increases when demand for output increases, hence the name accelerator model [36]. The model assumes that where: t Kp * stands for desired stock of capital by private sector in period t; is the expected level of output in period t.
Following [20], when the adjustment mechanism is introduced, then actual private capital adjusts to the difference between the desired private capital in period t and actual private capital in the previous period (t − 1): Simplifying Equation (2) where: where = t PDI is already defined while t PDI * represents the desired gross private domestic investment in the steady state. However, in the steady state, gross private domestic investment is given by: where, t PDI * means that the desired capital stock is related to the expected level of output such that .  (5) and (6) we get: Assuming that PD and other relevant variables denoted by vector X affect the speed of adjustment which determines the closure of the gap between the desired and actual gross Private Domestic Investment in each period. The coefficient β will hence vary with the factors that influence Private Domestic Investment and FDI. Hence if PD complements PDI, it speeds up the adjustment of the actual investment to the desired level of private investment and vice versa. Hence the speed of adjustment β is written in a linear form as: where 0 b is the intercept, b are the coefficients to be estimated, and t X is a vector of other relevant variables that may affect PDI. Substituting Equation (7) into Equation (8) and then re-arranging we obtain: However Equation (9) cannot be estimated because it contains some variables that are unobserved, e t Y , as well as depreciation rate, δ, whose data in SSA is lacking [20]. Therefore assuming that the depreciation rate is set equal to zero per cent, the model can be expressed in a general way as: In addition to PDI model specification, the study also estimated the impact of PD on FDI using the following equation: To evaluate the complementarily between PD and some policy variables in stimulating PDI and FDI, we interact PD with corruption index. By interacting PD with corruption index (PD × COR) the argument is that the impact of PD on PDI and FDI might be lower if there is good governance and hence low corruption levels.  (15) From Equation (15), if 8 0 α < , and the absolute value exceeds 2 0 α > it implies that a one percentage point increase in PD yields a negative impact on PDI as corruption decreases. Conversely, if 8 0 α > , PD increases PDI if corruption levels decrease with it. The interaction term between PD and corruption in Equations (13) and (14) test if the rise in PD is as a result of increased corruption levels in the countries under review and whether it lowers or increases PDI or FDI.
Additionally, according to the crowding out hypotheses, the accumulation of a large debt may stifle economic growth through lower investment. On this basis, we assume that public debt would be beneficial to investment up to a certain threshold. Once debt surpass this threshold, it will start to be a constraint to investment. Therefore in order to check for the Public Debt sustainability threshold or to determine the PD-GDP ratio turning point, we introduce a nonlinear relationship between public debt and domestic investment, as in [10] and [11] inter alia. Intuitively, nonlinearity implies that the debt effect on investment is conditioned by the level of public debt. Hence, the following model is run: PD is the Public Debt Squared. The inclusion of the squared variable affords us the opportunity to investigate the non-linearity effect of public debt on private investment, as well as analyzing the values of public debt thresholds. As in [37] and [38] we calculate the thresholds only when both coefficients of debt and debt squared are statistically significant. Taking the first order conditions, Equation (16) Equation (18) is the debt-GDP ratio turning point or the threshold level of debt. Here, 1 θ is the coefficient of the linear term and 2 θ is the coefficient of the quadratic term.

Estimation Technique
Since it is likely that investment in previous periods could affect current invest-  [45] inter alia, the estimates obtained from the ARDL method of co-integration analysis are unbiased and efficient, since they avoid the problems that may arise in the presence of serial correlation and endogeneity For these reasons, we opt for the ARDL approach for our study.
Expressing our model in an ARDL framework, we get:

Data
The study is based on the EAC involving Kenya, Uganda, Tanzania and Rwanda leaving out Burundi and Southern Sudan due to unavailability of data, for the period under study. To some extent thee EAC countries are homogenous in terms of general policies on investment for implementation in the common market. Panel secondary data used covers the period from 1992 to 2015. The period is selected based on the availability of data. Note also that during this period these countries appear to have developed strategies and policies to promote good governance as well as private and foreign investment. It is also observed that the corruption levels in the region were increasing during this period despite the anti-corruption measures in place. Moreover, during this period, the IMF and World Bank came up with concessional facilities for HIPCs to augment their development prospects. Table 1 displays the definition of variables used plus the source of data. Descriptive statistics and the pairwise correlation of the variables can be found in Table 2 and Table 3 respectively. The Private Domestic Investment ranges between 9.983 and 33.24, indicating that 5.098 of the values deviate from the mean. The mean of the Private Domestic Investment is closer to the maximum, hence negatively skewed. The PD as a percentage of GDP ranges between 19.19 with a deviation of 30.15 from the mean. FDI ranges between 0 and 6.48 with a deviation of 1.82. Similarly, the correlation matrix, in Table 2 shows that most of the variables have optimum linearity and hence the study is relevant. This is because all the values are less than 0.8 (threshold used to establish absence of collinearity problems). However, it is noted that Inflation (INF) and Real Interest Rate (RINT) are highly correlated but since the two variables were each used for two different models, they do not interfere with the results.

Impact of Public Debt on Private Domestic Investment
In Table 5    the null hypothesis implies that PMG is the preferred estimator, where the estimator restricts Long Run equilibrium between variables to be homogenous across countries or a subset of them. The Hausman test with an h-statistic of 2.34 and a p-value of 0.8860, which is greater than 5 percent level of significance implies that there is slope homogeneity and that PMG is the preferred estimation model. An important note here is that error correction term (ECT) coefficient of −0.5582 at one percent level of significance, is indicative of a disequilibrium in the previous period being corrected at a speed of 55 per cent to reach a steady state.
In the Short Run, PD is observed not significant whereas in the Long Run both variables attract an inversely relationship. Specifically, a unit increase in PD would decrease PDI by approximately 0.0687 units, which indicates that PD crowds out PDI. This confirms the priori expectation and is in line with the findings in [10] [25] and [29]. It can be argued that the long run effects of PD, of borrowing or adjust to productive use of borrowed funds so as to encourage private investment. We purport further that the observed impact of PD on PDI could also be driven by the level of corruption control. We come back to this issue later.
Besides the PD-PDI relationship, Table 5    FDIs. This is consistent with the findings in [49], where host-country institutions have little support for FDI. Table 7 addresses the third specific objective, i.e. investigating the influence of corruption control on the PD-PDI Nexus. It is clear from the results that once public debt is interacted with corruption-control in order to factor in institutional quality, the relevant interaction coefficient is positive (0.005) and greater than PD coefficient in the short run. By implication, a one unit increase in PD leads to an increase in PDI as institutional quality grows with it. Note that the marginal change in PDI as PD debt grows, taking into account corruption control, is given by 2   factoring in corruption (−0.01) is less than −0.0687 (impact of debt without factoring in institutions as in Table 5). Therefore, from the short run and Long

Assessing the Influence of Corruption Control on the PD-PDI Nexus
Run PMG results, the impact of institutions proxied by corruption control supports the hypothesis that good institutions matter for Private Domestic Investment. Corruption control would imply a reduction in use of public authority for private gains, low cost of investment and efficient mobilization and use of public resources that would lead to a reduction in debt levels. Moreover, when governance levels improve, the capture of the state by the elite decreases and small scale private domestic investors could also be awarded contracts based on merit and not by influencing public officers [20]. Furthermore, with good governance, public projects that compliment PDI will be executed efficiently.  value exceeds coefficient of PD (−0.1731), it implies that a one percent increase in PD yields an improvement on FDI as institutional quality grows with it.

Corruption, Public Debt and FDI
Therefore, the impact of institutions proxied by corruption-control supports the hypothesis that good institutions matter for Foreign Direct Investment. Intuitively, corruption reduces Foreign Direct Investment, a finding consistent with [33] and [34]. The result is augmented in the short run PMG model where the interaction term is positive (0.0010), implying that corruption-control could improve FDI. Table 9 confirms our earlier finding of the adverse effect of public debt on private investment. However, this relationship is shown to be non-linear, as evident first order optimal outcome of public debt. The change of sign from a significantly negative to positive on the associated coefficients of the unsquared and squared debt variables respectively affords us the conclusion of a U-shaped curve. The finding is consistent with earlier submission in [11]. Additionally, there is threshold level identified here. Specifically, the debt-effect is expectedly negative until a threshold of about 94.93% of GDP when thereafter it starts to be beneficial to private investment in the East African countries under analysis.

Non-Linearity of Public Debt on Private Investment
M. B. Aswata et al. Implicitly, for public debt to have a positive impact on private investment, borrowed amounts must be invested productively and in a manner that is private-sector-enhancing or else the impact of debt on private investment may always be negative.
However, given the popular argument shared by [50] and [51] inter alia, that thresholds are sensitive to the time dimension, the variables used, the set of countries under consideration together with their economic characteristics, the threshold level reported here ought to be interpreted with caution. On the other hand, we fail to find evidence that the relationship between public debt and FDI is non-linear. The associated signs on the relevant coefficients potentially confirm a linear type of relationship.

Robustness Checks
In order to check the validity of our study, we conduct several robustness checks. First, we carried out a country-by-country analysis allowed by the ARDL technique. Since the results do not substantially alter the original findings, and due to space limitations, we do not present them here but they are available on request. Second, we adopted a common practice in several empirical works of using three-year averages of all the variables to eliminate short run fluctuations.
Here we notice a few ignorable changes that do not seriously alter interpretation of the original findings. Similarly, one would perhaps argue that the findings could be driven by the existence of more or less developed economies in East Africa in relation to their counterparts. We therefore dropped Kenya from the sample. In turn, we also dropped Rwanda to remain with the original EAC countries. Later we also dropped the most corrupt country just as we in turn dropped the country with the largest public debt on average during the study period. Unsurprisingly, the results were never substantially affected in each of the "droppings", although some control variables turned out more significant. Given the high similarity rate with the original findings, still we have spared space and not presented the robustness results here but they are available on request.

Concluding Remarks
The study analyzed the relationship between Public Debt and Private Domestic Investment and also investigated the impact of PD on FDI in the EAC countries.
The findings indicate a crowding out effect of Public Debt. It is further noted that the magnitude of the Public Debt's impact is greater for PDI as compared to FDI. Additionally, it appears that interaction of public debt and corruption control improves Private Investment in the Long run. This has the implication that enhancing of institutional quality is vital in the promotion and development of Private Investment. Our findings also point to a nonlinear relationship between public debt and domestic investment. The results for a panel of the 4 East African countries over the period 1992-2015, indicate that public debt lower than 94.93 percent of GDP is positively associated with private domestic investment.
Otherwise, once the debt exceeds this threshold, the relationship between public debt and investment becomes negative.
The results from the study have policy implications. The immediate recommendation is the need to design fiscal policies to tame the growing debt that discourages private investment. In addition to the urgent necessity to reduce reliance on non-concessional borrowing in refinancing the debt and lowering fiscal vulnerabilities, there is need for a proper debt management system to lower fiscal vulnerabilities, coupled with clear policies to improve the institutional quality in order to boost private investment in East Africa. Any existing fiscal adjustment efforts that are focused on both expenditures and revenues together with complimentary monetary policies deserve commendation. Also, the anti-corruption measures already in place and those in the pipeline should be strongly supported to create a conducive investment climate for the private sector to thrive.
Besides the policy implications, the study appears to have insinuated further debate in related areas. For example, the finding of an insignificant relationship between FDI and PDI may not be taken on face value. Perhaps, it would be interesting to find out the empirical rationale behind such an outcome in at a more detailed level regarding FDI spillover effects. A related area of interest but which was outside the scope of our study is decomposing private investment by category and taking them as separate dependent variables. Perhaps such an analysis would provide a more detailed picture of the debt-investment link. However, such a kind of analysis would be limited by data availability. A similar limitation is likely a hindrance to repeating the analysis by disaggregating public debt in order to determine which category of debt impacts greatly on investment. Otherwise once data gets available in future, such would be an interesting area to better understand the debt-investment nexus.

Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.