rafmacalaba/datause-encoder-data huggingface dataset:
# **VII. References**

Alesina, Alberto, Silvia Ardagna, Giuseppe Nicoletti and Fabio Schiantarelli. 2005. “Regulation and Investment.” _Journal of the European Economic Association_ 3 (4): 791–825.

Antunes, Antonio and Tiago Cavalcanti. 2009. “Start-Up Costs, Limited Enforcement and the Hidden Economy.” Review of Economics and Statistics 91(1): 20-32.

Ackerlof, George A., (1970), "The Market for Lemons: Quality Uncertainty and the Market Mechanism." Quarterly Journal of Economics 84 (No. 3, August), pp. 488-500.

Barseghyan, Levon. 2008. “Entry Costs and Cross-Country Differences in Productivity and Output.” <u>Journal of Economic Growth, 13:145–167.</u> Besley, Timothy and Robin Burgess. 2002. “The Political Economy Of Government Responsiveness: Theory And Evidence From India.” _The Quarterly Journal of Economics_ 117(4): 1415-1451.

Dabla-Norris, Era, Mark Gradstein and Gabriela Inchauste. 2008. “What Causes Firms to Hide Output? The Determinants of Informality.” Journal of Development Economics 85 (1): 1–27.

Deininger, Klaus and Paul Mpuga. 2005. “Does Greater Accountability Improve the Quality of Public Service Delivery? – Evidence from Uganda.” World Development Volume 33, No. 1, pp. 171-101.

Djankov, Simeon, Rafael La Porta, Florencio López-de-Silanes and Andrei Shleifer. 2009. “Disclosure by Politicians.” NBER Working Paper 14703, _National Bureau of Economic Research_ , Cambridge, MA.

Fisman and Sarria-Allende. 2004. “Regulation of Entry and the Distortion of Industrial Organization.” NBER Working Paper 10929, National Bureau of Economic Research, Cambridge, MA.

Hirschman, Albert O. 1970. _Exit, Voice, and Loyalty: Responses to Decline in Firms, Organizations, and States._ Cambridge, MA: Harvard University Press.

Hirshleifer, Jack. 1980. “Privacy: Its Origin, Function and Future.” _Journal of Legal Studies_ 9 (4): 649–64.

Islam, Roumeen. 2006. “Does more Transparency go along with better Governance?” Economics and Politics, Volume 18, No. 2.

International Budget Partnership. http://www.internationalbudget.org/.

Kaufmann, Daniel, Kraay, Aart and Mastruzzi, Massimo. 2010. “The Worldwide Governance Indicators: Methodology and Analytical Issues.” World Bank Policy Research Working Paper No. 5430. Washington, DC: _World Bank_ .

29
equivalent). Sample selection is stratified by rural/urban location and by facility-type. The proportion of private/NGO facilities varies, from 1% of the sample in Mozambique to 42% of the sample in Kenya, and the proportion of rural facilities also varies, from 49% of the sample in Madagascar to 88% in Mozambique. All surveys are designed to be nationally representative, except for Nigeria, where data were collected in 12 of 36 states due to logistical constraints and Kenya, where data were representative at the county-level. Details of the sample are listed in **Table 1** .

3
development financing (Wolf, 2005). The composition of capital flows matters (Tong and Wei, 2011), and for a large open economy such as the Turkish, which presented an average current account deficit of 7.5 percent of GDP in 2010-2013, counting with long-term foreign investment matters. In addition, beyond the macroeconomic implications, firm-level evidence for some developing economies points to the existence of positive productivity spillovers over local suppliers (Javorcik, 2004; Kugler, 2006), as well as other positive pecuniary and knowledge-related externalities.

The purpose of this paper is twofold: first, assessing whether foreign direct investment in Turkey has contributed to growth by generating the micro-level externalities; second, if that is the case, empirically identifying significant driving factors for foreign direct investment, in order to help understand the reasons for Turkey ́s limited success in attracting inflows, and propose ways to increase the appeal to foreign investors.

The rest of the paper is structured as follows: section two presents firm-level evidence of the impact of FDI on productivity in Turkey; section three presents a cross country approach to the determinants of FDI attraction in Eastern Europe and Turkey; section four draws policy implications for Turkish policy makers; and section five concludes.

# **2. FDI and Growth in Turkey: Evidence from Firm Level Data**

In order to respond to the question on the impact of foreign direct investment on growth, we examine whether multinational firms increased the productivity of Turkish firms who received FDI between 2005 and 2012. We also analyze how this affects the productivity of domestic firms operating in the same or different sectors than the foreign affiliates of the multinationals.

## **2.1. Data and empirical approach**

Our data come from the ORBIS database (also known as AMADEUS for European countries) which is compiled by Bureau van Dijk Electronic Publishing (BvD). It covers 60 countries worldwide, including both developed and emerging countries. ORBIS has financial accounting information from detailed harmonized balance sheets and profit and loss accounts of all companies. In terms of coverage, the database is crucially different from the other data sets that are commonly used in the literature, such as Compustat (for the United States), Compustat Global, and Worldscope databases, in that 99 percent of the companies in ORBIS are private, whereas the data sets mentioned contain information mainly on large listed companies. A fundamental advantage of our data is the detailed 4
# Growth-Oriented Adjustment Programs: A Statistical Analysis

by Riccardo Faini (Johns Hopkins University) Jaime de Melo (The World Bank and CEPR) Abdel Senhadji-Semlali (University of Pennsylvania) and Julie Stanton (University of Maryland)

# Table of Contents

|1.|Introduction|1|
|---|---|---|
|2.|External Environment and Adjustment Lending|2|
|3.|An Implementable Model to Measure Effectiveness<br>of IMF-WB Programs|6|
||3.1<br>Alternative Approaches to Evaluating Adjustment<br>Programs<br>3.2<br>Implementation|6<br>8|
|4.|Statistical Results|10|
|5.|Sustainability<br>of Adjustment:<br>Output Loss Estimates<br>from Investment Cuts for a Group of Intensive<br>Adjustment Lending Recipients|13|
|6.|Conclusions|20|
|Foot|notes|23|
|Refe|rences|24|
|Appe|ndix|27|

* We thank Bela Balassa, Ajay Chhibber, Patrick ConrJay, Vittorio Corbo, Peter Montiel, John Nash, and Dani Rodrik for helpful comments. Helpful comments were also received from seminars at Berkeley, Urbino, and at a conference on Adjustment Lending held at the World Bank. This paper is part of RPO 675-32.
# **Annexes**

## **Table A. 1: Per Capita Household Income-Based Summary Statistics and Income Inequality Measures (in LVL)**

||**Natio**|**nal Level**|**Urb**|**an Area**|**Rura**|**l Area**|
|---|---|---|---|---|---|---|
|**Point Estimates**|**1997**|**2000**|**1997**|**2000**|**1997**|**2000**|
|Mean|59.55|75.33|61.49|82.35|54.59|58.11|
|Median|50.32|61.61|51.28|63.79|48.28|54.93|
|Coefficient of variation (CV)|953.48|2001.1|786.46|2193.97|1314.25|968.49|
|Kurtosis|560.49|1101.62|31.05|807.18|699.69|22.05|
|Skewness|14.66|27.53|3.94|24.2|20.85|1.62|
|Gini Coefficient|31.6|34.8|30.32|35.14|34.81|31.41|
|E(0)|16.81|20.38|15.40|21.56|15.95|13.67|
|E(1)|18.09|27.86|16.59|29.47|25.85|17.61|
|E(2)|31.62|78.20|21.62|86.43|69.33|21.91|

**Figure A. 1: Per Capita Expenditure-based Lorenz Concentration Curves**

<!-- Start of picture text --> 1<br>Lorenz 1997<br>Lorenz 2000<br>0.8 45 degree line<br>0.6<br>0.4<br>0.2<br>0<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Ccumulative percent of population<br>ccumulative distribution<br><!-- End of picture text --> 36
|**Table 9. E**|**ffect of C**|**ONAFE o**|**n Failure and**|**Repetiti**|**on Rates,**|**1998-2001**|
|---|---|---|---|---|---|---|
||1998|1999|2000|2001|Ave|Trend|
|||**Less-d**|**isadvantaged**<br>**Failure**|**Group**|||
|Noncon|4.38|4.03|3.88|3.47|3.94|-0.29|
|Con|9.65|7.12|6.28|5.36|7.1|-1.37|
|Diff|5.27|3.09|2.4|1.9|3.16|-1.08|
||||**Repetition**||||
|Noncon|4.42|3.85|3.85|3.47|3.9|-0.29|
|Con|7.66|7.52|6.45|6.62|7.06|-0.42|
|Diff|3.25|3.66|2.59|3.16|3.16|-0.13|
|||**M**|**id-range Gro**<br>**Failure**|**up**|||
|Noncon|6.58|6.21|5.3|5.09|5.79|-0.54|
|Con|7.61|7.25|6.94|6.54|7.08|-0.35|
|Diff<br>|1.03|1.04|1.64<br>**Repetition**|1.45|1.29|0.19|
|Noncon|5.3|5.67|5.44|4.34|5.19|-0.31|
|Con|8.44|6.66|7.16|6.08|7.08|-0.66|
|Diff|3.14|0.98|1.72|1.74|1.89|-0.35|
|||**Disa**|**dvantaged G**<br>**Failure**|**roup**|||
|Noncon|6.66|4.55|5.37|5.02|5.4|-0.41|
|Con|7.72|8.25|7.26|6.66|7.47|-0.42|
|Diff|1.06|3.71|1.89<br>**Repetition**|1.65|2.08|0|
|Noncon|7.39|4.89|6.22|5.09|5.9|-0.56|
|Con|7.87|7.59|7.28|6.36|7.28|-0.48|
|Diff|0.48|2.7|1.06|1.27|1.38|0.08|

Source: _Estándares Nacionales_ 1998-2001. Data are percentages of all students in sample. “Failure” is the percent of students that have failed a grade. “Repetition” is the percent of students that have failed a subject or grade.

# **Conclusions**

This analysis used student test scores and school-level indicators to evaluate the effectiveness of CONAFE in improving the quality of primary and _telesecundaria_ education. We have used five approaches to measure CONAFE’s effects. First, we compared the disadvantage of all CONAFE and non-CONAFE schools to examine the effectiveness of CONAFE’s targeting 36
**5**

Datt (1996; 2002) and for China by Ravallion and Chen (2004). To accommodate such differences, we rewrite (1) as a weighted sum of the contributions to poverty reduction of both the agricultural and the non-agricultural sectors: with _a_ denoting agriculture, _n_ non-agriculture, and _q_ any constant (1< _q_ <0). A meaningful choice for _q_ is _q=(Yai/Yi)=sai_ the share of agriculture in total GDP in country _i._ It follows that ( _1-q_ ) equals ( _Yni/Yi)=sni_ , the share of non-agriculture in total GDP in country _i_ and (2) becomes: Using lower cases to represent rates of change for _Pi_ and _Yi_ gives: where _yki_ is the growth rate of per capita GDP in sector _k_ = _a_ , _n_ , ε _ki_ the elasticity of total poverty with respect to per capita GDP in sector _k_ , and _ski_ the share of sector _k_ in total GDP.

From equation (4) it is clear that the impact of a sector (e.g. agriculture) on poverty depends on how its pace of growth performs compared with the other sector (non-agriculture). In addition, a large literature exists, showing that accelerating agricultural growth will induce changes in other sectors, resulting in higher non-agriculture growth. While the reverse interaction might also hold, the literature suggests that these effects are smaller. The growth effect of a sector therefore will have two components: the _direct_ contribution (or the size of _ya_ ) and an _indirect_ effect, this being additional changes in poverty resulting from the induced change in the growth performance of the other sector (the effect of _ya_ on _yn_ ).

In addition, an acceleration in the pace of per capita agricultural growth ( _ya_ ) will have a more marked effect on poverty than an identical increase in the rate of non-agricultural growth ( _yn_ ) if ε _nsn_ < ε _asa._ . The participation effect therefore has two elements: an _elasticity_ component and a _share_ component. Even though agriculture is the largest sector in the economy of most developing
Grimm, M., A. Munyehirwe, J. Peters and M. Sievert (2017) A First Step Up the Energy Ladder? Low Cost Solar Kits and Household’s Welfare in Rural Rwanda. _World Bank Economic Review_ , 31(3), 631‐649.

Golumbeanu, R., and D. Barnes. (2013). “Connection Charges and Electricity Access in Sub‐Saharan Africa.” _World Bank Policy Research Working Paper_ No. 6511.

IEA, (2011). _World Energy Outlook_ . International Energy Agency, Paris.

Lee, K., E. Miguel, and C. Wolfram. (2016). “Appliance Ownership and Aspirations among Electric Grid and Home Solar Households in Rural Kenya.” _American Economic Review: Papers and Proceedings_ , 106(5), 89‐94.

Lee, K., E. Miguel, and C. Wolfram (2018). “Experimental Evidence on the Demand for and Costs of Rural Electrification”, NBER Working Paper 22292.

Lenz, L., A. Munyehirwe, J. Peters and M. Sievert (2017). “Does Large Scale Infrastructure Investment Alleviate Poverty? Impacts of Rwanda’s Electricity Access Roll‐Out Program.” _World Development_ , 89, 88‐110.

Perez‐Sebastian, F. and J. Steinbuks (2017). ʺPublic Infrastructure and Structural Transformation.ʺ _World Bank Policy Research Working Paper_ No. 8285.

Peters, J., M. Sievert, A. Munyehirwe, and L. Lenz. (2014) “The provision of grid electricity to households through the Electricity Access Roll‐out Programme”. Final Report on behalf of the Policy and Operations Evaluation Department (IOB) of the Netherlands Ministry of Foreign Affairs. Available online at http://www.iob‐ evaluatie.nl/EARP.

Peters, J. and M. Sievert (2016). “Impacts of Rural Electrification Revisited – The African Context”. _Journal of Development Effectiveness_ , 8(3), 327‐345.

Rud, J. P. (2012). “Electricity Provision and Industrial Development: Evidence from India”. _Journal of Development Economics_ , 97(2), 352‐367.

29
**Table A.8.  Unrestricted irrigation: median** **_Ascaris_ infection risks for children under 15 from the consumption of raw wastewater-irrigated carrots estimated by 10,000 Karavarsamis−Hamilton Monte Carlo simulations**<sup>_a_</sup>

|**Number of**|**Median**||
|---|---|---|
|**_Ascaris_ eggs**|**_Ascaris_**|**Notes**|
|**per litre of**|**infection**||
|**wastewater**|**risk pppy**||
|100–1000|0.86|Raw wastewaters in hyperendemic areas.|
|10–100|0.24|Raw wastewaters in endemic areas.|
|1–10|2.9 × 10<sup>–2</sup>|Treated wastewaters.|
|1|5.5 × 10<sup>–3</sup>|Wastewater quality required to comply with the 1989 and<br>2006 WHO Guidelines.|
|0.1–1|3.0 × 10<sup>–3</sup>|Highly treated wastewaters.|
|0.1|5.5 × 10<sup>–4</sup>|Wastewater quality recommended by Blumenthal_et al_.<br>(2000) to protect children under 15.|
|0.01–0.1|3.0 × 10<sup>–4</sup>|Treated wastewaters in non-endemic areas.|

> _a_ Assumptions: 30–50 g raw carrots consumed per child per week; 3–5 mL wastewater remaining on 100 g carrots after irrigation; _N_ 50 = 859 ± 25% and α = 0.104 ± 25%; no _Ascaris_ die-off .

_Source:_ Mara and Sleigh, 2010b.

# **A.5. Restricted Irrigation: Norovirus Infection Risks**

In the 2006 Guidelines health risks for restricted irrigation were estimated by assuming the fieldworkers involuntarily ingested wastewater-contaminated soil. An ingestion of 10−100 mg of soil per day for 300 days per year was used to represent labor-intensive agriculture in developing countries. With hygiene education it is not infeasible to postulate that the quantity of soil ingested can be substantially reduced, perhaps to 1−10 mg per day. Table A.9, which gives norovirus infection risks for this quantity of soil ingestion for 300 days per year, shows that a norovirus reduction of 1 log unit results in a norovirus infection risk of 0·32 pppy (i.e., one episode of norovirus diarrhoea every three years), which is higher than the tolerable norovirus infection risk of 0·13 pppy determined in section A.3 (one episode of norovirus diarrhoea every seven years), but acceptable if combined with sustained hygiene education and the provision of (a) hand-washing facilities on or adjacent to the site being irrigated, and (b) oral rehydration salts/solutions whenever required.

# **A.6 Restricted Irrigation:** **_Ascaris_ Infection Risks**

Table A.10 gives the equivalent calculations for _Ascaris_ as given in Table A.9 for norovirus. It shows that, in areas where ascariasis is hyperendemic, a 1-log unit reduction results in an ascariasis risk of 1·5 × 10<sup>−2</sup> pppy, which is close to the tolerable ascariasis risk of 1·2 × 10<sup>−2</sup> pppy determined in section 3.4.

87
_Figure 5: Pollution and City Competitiveness in terms of per capita GDP Growth_ PM 2.5 Concentration and City Growth 2006−2015 <!-- Start of picture text --> WHO Guideline ● ● ●<br>●<br>● ● ● ●<br>● ● ● ●<br>40 ●●●●●● ● ●●●● ●● ●●●● ● ●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●● ●● ● ● ● ●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●● ● ●●●●●●●● ● ●●●●● ● ●●● ● ●●●●●●●● ● ●●●●● ● ●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●● ● ●●●●●●●●● ●● ●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●● ● ● ● ●●●●●● ●● ●●●●●●●●●●●●●●●● ●● ●●●●●●● ● ●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●● ● ● ● ●●●●●●●●●●●●● ●● ●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●● ● ●●●●●● ● ●●●● ● ●●●● ● ●●●●●●●●● ● ●●●●●●●● ● ●●●●●●●●●●●●● ● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●● ● ●●●● ● ●●●●●●●●●●●●● ● ●●●● ● ●●● ● ●●●●●●●●●●● ●●●●● ●●●●●● ● ●●●●●●●●●● ●●●●●●●●●●● ●●●●●●●●●●●●●● ● ●● ●●●●● ● ● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●● ● ●●●●●●●●●●●●●● ●●● ● Regions ●●●●●● EAPECALACMENAOECDSAR<br>● ● ● ● ● SSA<br>−4 ● ● ●<br>●<br>●<br>0 25 50 75 100<br>PM 2.5 in 2015<br> Percent, 2006−2015<br>City GDP Growth per capita compared to National Growth<br><!-- End of picture text --> _Figure 6: Pollution and City Competitiveness in terms of Employment Growth_ PM 2.5 Concentration and City Employment Growth 2006−2015 <!-- Start of picture text --> WHO Guideline<br>Regions<br>10<br>● ● ● EAP<br>● ●<br>● ● ● ● ● ● ● ● ● ECA<br>0 ●●●●● ● ●●●●●●●●●●●●●●● ● ●●●● ● ●●● ● ●●●●● ● ●●● ● ●●●●●●●●●●● ● ●●●●●●●●● ● ●●●● ● ●●●●● ● ●●●●●●●●●●●●●●●● ● ●●●●● ● ●●●●●●● ● ●● ● ● ● ●●●●●●●●●●● ● ●●●●●● ● ●●●●●●●●●●●●●●●●●● ● ●●●●● ● ●●●●●● ● ●● ● ●●●●●●●●●●●●●●●●●● ● ● ● ● ● ●●●●●●●●●●●● ● ●●●●●●● ●● ● ● ● ● ●●● ● ●●●●●● ●● ●●●●● ● ●●●●●● ● ●●●● ● ● ● ●●●●●●● ● ●●●●●● ● ●●●●● ● ●● ● ● ● ●●●● ● ● ● ● ● ●●●●●●●● ● ●●● ● ●● ● ●●● ● ●●● ● ● ● ●●●●● ● ●●●● ● ●● ●● ● ●●● ● ●●●●●●●● ●● ●●● ● ●●●●●●● ●● ●● ●● ● ● ● ●● ● ● ●● ● ● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●● ● ●●● ● ●●●●●●● ● ●●●●●●●●●●●●●● ● ●● ● ●●●●●●●●●●●● ● ●●●●●●●●●●● ●●●●●●●● ●●●●●●●● ● ●●●●●●●●●●●●● ● ●●●●●●●●●●●●● ●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●● ● ●● ● ● ●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●● ● ●●●● ● ●●●●● LACMENAOECDSARSSA<br>●<br>●<br>0 25 50 75 100<br>PM 2.5 in 2015<br> Percent, 2006−2015<br>City Employment Growth compared to National Growth<br><!-- End of picture text --> 22
# _Indicators_

Table 4 shows the MDG indicators and their definitions used in the present study. Our list includes five health status indicators (stunting, underweight, infant mortality, under-five mortality, and HIV prevalence); the rest are intervention indicators. Our aim was to include as many of the official indicators as possible for each MDG.<sup>3</sup> We have included in our analysis five additional indicators that are almost always used in MDG monitoring exercises, drawn from the additional core intermediate indicators listed in Table 1. Specifically, we include the prevalence of stunted children (MDG1), the percentage of children aged one who have been fully immunized (MDG4)<sup>4</sup> , the percentage of children in malaria-endemic countries sleep under an insecticide-treated bednet (MDG4 and MDG6), antenatal visit coverage (MDG5), and whether or not a condom was used during sexual intercourse with a high-risk partner (MDG6).

Our study thus extends the range of indicators studied compared to previous studies. Unlike Moser et al. (2005) who focused on MDG outcomes (actually just one outcome: under-five mortality) and Victora et al. (2012) who focused on MDG interventions, but like Suzuki et al. (2012) we analyze data on both MDG outcomes and MDG interventions. While Suzuki et al. (2012) analyze more indicators than we do, we analyze more MDG indicators.<sup>5</sup>

> 3 Some official indicators – such as the number of tuberculosis (TB) cases detected and treated under DOTS – are not covered by the DHS or MICS, or in any other global household survey exercise. Deaths from malaria and TB are also not captured by regular household surveys like the DHS and MICs, partly because of sample size issues, but also because of the difficulty of ascertaining after the fact the cause of death. Maternal mortality is often computed using variables from the DHS, but maternal deaths are so rare that quintile-level analysis would be challenging.

> 4 The official indicator captures only measles immunization.

> 5 We include two additional MDG6 indicators – condom use and HIV prevalence.

5
would be important to assess the long-term impact on debt sustainability. Growth estimates are not dependent on debt burden indicators and therefore they may be considered optimistic, especially for those countries with already high debt ratios. This metric can be further expanded, through the consideration of indicators measuring the quality of the governance and strengths of fiscal institutions and the assessment of the domestic revenue generation capacity, which would further strengthen the discussion of fiscal space and indicate further policy options.

Figure 15. Projected Status of BRI Recipient Countries in 2023 (In percent of GDP) <!-- Start of picture text --> LIDCs EMs<br>45%<br>Tajikistan 18%<br>40%<br>16% Armenia<br>35%<br>14%<br>30%<br>12%<br>25% 10% Albania<br>20%<br>Cambodia 8%<br>15% Mongolia<br>Djibouti Timor- 6% Malaysia<br>10%<br>Leste 4%<br>5% Kenya Maldives 2%<br>Afghanista Pakistan<br>0% n 0%<br>0% 10% 20% 30% 40% 50% 0% 2% 4% 6% 8% 10%<br>Fiscal gap Fiscal gap<br>Low Moderate High Low<br>Sources: WEO, WIND Database and LIC DSF DSAs. Sources: WEO, WIND Database and MAC DSAs.<br>Growth gap<br>Growth gap<br><!-- End of picture text --> Long-Term Projections of debt-to-GDP Ratios To assess the impact of BRI financing and investment on growth and indebtedness in the long term, this section uses the results of SGE estimates from de Soyres et al. (2019). To compare the evolution of public debt-to-GDP ratio from 2016 to 2030 with and without BRI infrastructure investment, a number of assumptions, consistent with the previous sections, are made. We construct two scenarios.

- Under the _Baseline_ scenario, no BRI investment and BRI-related borrowing take place. Under the

- baseline, the primary-balance-to-GDP ratio and the real growth rate for all countries will remain constant until 2030 and it will be equal to their average values of the period 2007-2016.

- Under the _BRI scenario_ , annual growth would reflect the effect of trade-related infrastructure

- investment and the reduction of trade costs and policy reforms as in de Soyres et al. (2019).<sup>43</sup> Furthermore, the paper assumes that all the countries will have fully converged to their new equilibrium, post-BRI infrastructure, by 2030.

> 43 To obtain the projected average annual real growth rate for the baseline, we estimate the average real growth over the period 2000-2016 and we project the real GDP until 2030. Then we estimate the total growth between 2030 and 2016 and divide it equally among those years. For the BRI growth, we increase the projected baseline GDP of 2030, based on the estimates of de Soyres et al. (2019). Then as in the baseline, we estimate total growth between 2030 and 2016 and divide it equally among those years. The growth estimates are reported in annex XII.

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