rafmacalaba/datause-encoder-data huggingface dataset:
percent; High Income Countries 79 percent; Sub-Saharan African 74 percent; and Middle East and North Africa 70 percent (Ackah, et al, 2008).

# **Definition of Scenarios**

We developed three scenarios and analyzed over the period 2009-2020.The first scenario is the Business as Usual ( _BaU_ ) scenario. This is the baseline scenario which incorporates a number of assumptions. The first set of assumptions is on the world prices of three sources of energy (coal, oil, and natural gas) which are exogenous variables in the model, whose values were derived from the projections calculated outside the model. Table 1 shows the price indexes of these three sources of energy used under the _BaU_ . The price of coal has the highest increase in 2004-20 (161 percent), followed by the price of natural gas (85 percent), and by the price of oil (113.7). There was an oil price spike in 2008 and 2009, but it stabilizes thereafter.

**Table 1:** **<u>World price index of energy</u>**

|Year|Worldprice of coal|Worldprice of oil|Worldprice of naturalgas|
|---|---|---|---|
|2004|100.0|100.0|100.0|
|2005|101.0|104.9|105.4|
|2006|102.1|110.0|111.1|
|2007|103.1|115.4|117.0|
|2008|104.2|121.0|123.3|
|2009|105.2|126.9|130.0|
|2010|106.3|133.1|137.0|
|2011|107.4|139.6|144.4|
|2012|108.5|146.4|152.1|
|2013|109.6|153.6|160.3|
|2014|110.8|161.1|168.9|
|2015|111.9|168.9|178.0|
|2020|114.6|168.9|178.4|

In 2004-2015 world prices of these commodities are exogenous, but after 2015 they are endogenous The second set of assumptions under the BaU scenario is the growth rates of gross domestic product (GDP) of the different countries and regions in the model. The GDP growth rates were based on the growth projections made by the World Bank. In Table 2 one can observe that China has the highest annual growth in real GDP followed by India. Developing countries have relatively higher real GDP growth than developed countries.

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savings. While Morocco and Portugal undertook a strong fiscal adjustment, Yugoslavia did not make a fiscal adjustment. This difference in response and the magnitude of the external transfer explains the difference in the effect on the private sector. Morocco and Portugal cushioned the effect on the private sector by reducing their fiscal deficits. But in spite of this fiscal adjustment the private sector had to transfer resources to the public sector. As explained before, in these two countries the public sector responded to the external shock by increasing its domestic PS debt. In Morocco the private sector transfer increased from 4.1 percent of GDP in 1982-84 to 6.0 in 1985-89; and in Por:;ugal it increased from -0.9 percent to 2.9 percent. Yugoslavia exemplifies a dramatic case. The drast:c fall in real net external savings had to be completely financed by an increase in private sector real net savings. This savings increased from 3.4 percent of GDP in 1982-84 to 17.5 percent in 1985-89.

Did the increasing reliance on private sector savings affect private sector activity and economic growth? The answer to this question is not straightforward. First, economic theory is ambiguous. On the one hand, there is the Keynesian Theory that states that the greater the public expenditures, the greater the rate of growth of GDP and the greater the private investment; and on the other, there is the Neoclassical Theory that emphasizes the crowding-out effect and predicts that the larger the fiscal deficits, the higher the cost of capital and thus the lower private investment. Second, available evidence is ambiguous concerning the predominant effect. Country studies indicate that:
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These engineering estimates can be different between the following two scenarios: (i) only road transport is assumed, and (ii) both rail and road can be used. The second scenario is referred to as the modal choice of rail transportation. Technically, even if rail is selected, road transportation is also generally needed, for instance, to bring products to a close rail station. _TC_ and _HR_ include all rail and road segments along the optimal route. Not surprisingly, as shown at the bottom of **Table 6** , rail transport tends to have lower transport costs and greater travel times than road transportation.

The results turned out to be broadly unchanged. Transport costs have a negative effect on shippers’ mode choice: The coefficient of _TC_ is negative and statistically significant ( **Table 10** ). Traditional firms are more likely to use rail services, holding everything else constant. Firms aimed at local markets do not use rail transportation, because their transport demand may be focused on short haul shipping. Rather, rail transport is more often used for international trading purposes. Both _EXPT_ and _IMPT_ have significantly positive coefficients.

**<u>Table 10. Alternative specific conditional logit model estimation</u>**

||<br>Coef.|<br>Std.Err.||<br>Coef.|<br>Std.Err.||Coef.|Std.Err.||
|---|---|---|---|---|---|---|---|---|---|
|Alternative-specific|var.|||||||||
|TC|-0.0601|(0.0202)|***||||-0.0585|(0.0193)|***|
|HR||||0.1615|(0.0999)||0.1284|(0.0925)||
|Case-specific var.||||||||||
|VALU|0.0013|(0.0017)||0.0010|(0.0018)||0.0013|(0.0017)||
|AGE|0.0301|(0.0145)|**|0.0250|(0.0131)|*|0.0285|(0.0137)|**|
|EDU|-0.2397|(0.1815)||-0.2404|(0.1784)||-0.2412|(0.1814)||
|FORN|-0.0060|(0.0068)||-0.0051|(0.0071)||-0.0052|(0.0069)||
|LOCL|-1.5714|(0.4389)|***|-1.6600|(0.4283)|***|-1.5595|(0.4408)|***|
|NATN|2.7379|(1.0919)|**|2.6759|(1.0684)|**|2.7597|(1.1077)|**|
|EXPT|1.2485|(0.6285)|**|1.0546|(0.6503)|*|1.0467|(0.6310)|*|
|IMPT|1.1022|(0.5021)|**|0.9479|(0.4779)|**|0.9898|(0.4736)|**|
|Constant|-5.0917|(1.1647)|***|-4.7861|(1.1476)|***|-5.2182|(1.2133)|***|
|Obs.|1822|||1822|||1822|||
|No. of cases|911|||911|||911|||
|Wald chi|79.62|||65.46|||88.08|||

The dependent variable is one if rail is selected. Robust standard errors are shown in parentheses. *, ** and *** indicate the statistical significance at 10, 5 and 1 percent, respectively.
accumulation. Monticone (2010) uses data from Italy to show that wealth has a small but positive effect on financial literacy. Behrman et al. (2010) use an instrumental variables regression analysis to show that financial literacy also has a causal effect on wealth accumulation in the U.S. Jappelli and Padula (2011) estimate an intertemporal model of investment in financial literacy, which it is shown that financial literacy and wealth are jointly determined and correlated over the life cycle. Meanwhile, Hastings and Mitchell (2011) provide experimental evidence from Chile to show that financial literacy is correlated with wealth, but that measures of impatience might be a more important determinant. Behrman et al. (2010) focuses on the relationship between financial literacy, education, and household wealth accumulation in Chile. Using a nationally representative data set, they find that financial literacy has even stronger effect than educational attainment on wealth.

One cross-country study of note which addresses the effects of both education and income on financial literacy is Jappelli (2010), which analyzes data from a survey of 55 countries. The study shows that economic and financial literacy (as perceived by each country’s business leaders) is positively associated with human capital indicators (math and science test scores and college attendance), but negatively associated with living in a country with a generous social security system. The study hypothesizes that this is because the relative lack of opportunity for private capital accumulation decreases incentives to acquire financial literacy.

# **_Geographic and racial/ethnic disparities in financial literacy are common_**

Most country surveys indicate strong regional disparities in financial literacy, as measured by awareness of financial terms and institutions, particularly between urban and rural areas. This likely mirrors the differences in access to finance, and is especially prominent in developing countries. For instance, in Ghana, 52 percent of urban adults have commercial bank accounts, versus just 21 percent of rural adults. While rural adults are more likely to use informal financial products, this usage only partially close the access gap. Given that the level of urbanization is still quite low in many African countries—in Malawi, for instance, only 16 percent of those surveyed lived in urban areas—this disparity could potentially be more important than those between gender and age groups. This may partially reflect differences 13
# **Table 4: Welfare Effects of MFN and Bilateral Trade Facilitation India and Pakistan**

(Equivalent variation in US$ million at 2007 prices)

|**Country**|**MFN**|**MFN+BTF**|
|---|---|---|
|Bangladesh|21.08|32.25|
|**India**|**160.71**|**1406.67 **|
|Nepal|18.01|32.22|
|**Pakistan **|**99.21**|**1229.08**|
|Sri Lanka|34.92|63.32|
|Rest of South Asia|15.72|21.38|
|China|-10.52|-102.62|
|USA|-18.39|-122.33|
|EU 25|-29.55|-200.94|
|Rest of the World|-66.71|-585.41|

Note: MFN = Most Favored Nation; BTF = Bilateral Trade Facilitation between India and Pakistan Source: GTAP simulation

**A number of sectors in India will benefit in terms of rises in exports to Pakistan due to the MFN status and improvement in bilateral trade facilitation** (Figure 7) **.** Such rises in exports from India would happen due to India’s unit cost advantage compared to Pakistan’s other trading partners.<sup>17</sup> Under the mere MFN scenario, the change in exports from India to Pakistan would vary from meat (348 percent) to vegetables, fruits and nuts (0.2 percent). Besides, India’s exports to Pakistan would rise for chemical, rubber and plastic, food processing, mineral fuels (petroleum, coal products), metals, machinery and equipment, textiles, leather products, dairy products, fishing, etc. However, under the MFN plus bilateral trade facilitation scenario there would be much larger rises in exports from India from these sectors.

**MFN status to India would have negligible impact on Pakistan’s sectoral exports to India** (Figure 8) **.** There would however be some rise in the exports of plant based fibers, animal products and metals from Pakistan to India. However, MFN status together with improvement in bilateral trade facilitation would result in some sectors in Pakistan to benefit in terms of increased exports to India. Such sectors include wool, wheat, textile, wearing apparels, leather products, metals, machineries, chemicals, etc.

**Mere MFN status to India would raise Pakistan’s import from India by 32 percent, whereas MFN plus enhancement of bilateral trade facilitation would lead to rise in such import by 202 percent** (Table 5). Under the MFN plus bilateral trade facilitation scenario, Pakistan’s total import would rise by 2.7 percent, which is 2.43 percentage points higher than that under the mere MFN scenario. On the other hand, **Pakistan’s exports to India would rise by a staggering 202 percent under the MFN plus bilateral trade facilitation scenario against only 0.4 percent under the MFN scenario** (Table 5) **.** Similarly, Pakistan’s total exports

> 17 From the WITS database 561 products at the 6 digit HS code are identified (which are outside of Pakistan’s positive list) where the unit costs of imports if they are sourced from India would be lower than the unit costs of imports if they are sourced from other countries. This suggests that there would be a possibility of change in import source in favor of India for these product categories when Pakistan extends MFN status to India. A discussion on these products is reported in Annex 3.

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between dependence on human capital and the availability of a skilled workforce.<sup>55</sup> From a broader perspective, financial development may also proxy for other country characteristics which play an important role for bilateral FDI in ED sectors. We control for the potential reliance of ED sectors on human capital by interacting sectorspecific human capital intensity with the average years of schooling in population over the age of 25 in 2000. To isolate the effects of financial development from overall economic development, we also include interactions between the ED variable and the first principal component (a “development index”) of three highly correlated variables: log of income per capita, average years of schooling and a measure of institutional quality.<sup>56</sup> Column (3) shows that inclusion of these additional terms has virtually no impact on the coefficient on the SFD-ED interaction term but slightly decreases the size of the coefficient on the DFD-ED interaction variable. This suggests that the latter previously picked up some effects of overall economic development which positively influences inward FDI in more ED sectors. Overall, the coefficients on the two interaction terms remain large, positive and statistically significant at the 1% level.

# _Reverse causality_

Our results may suffer from simultaneity bias. For example, among other factors, the entry of foreign banks can be explained by them following their domestic clients abroad.<sup>57</sup> If their entry fosters DFD by increasing the efficiency of the domestic banking system, reverse causality between FDI and financial development can occur. Alternatively, as suggested by Manova (2013), higher relative foreign demand for goods manufactured by sectors intensive in external finance can induce more FDI in those sectors, more borrowing in source and destination countries, and a positive relationship between the relative volume of bilateral FDI in financially vulnerable sectors and financial development despite the absence of financial constraints. In columns (4)-(6), we address the possibility of reverse causality between FDI and financial development in several ways.<sup>58</sup>

> 55The coefficient of correlation between the average years of schooling in population over the age of 25 and the log of the private credit to GDP ratio is equal to 0.50, statistically significant at the 1% level.

> 56We follow Manova (2013) by using the sector-specific measure of human capital intensity developed by Braun (2003). It corresponds to the 1986-1995 median of the industry’s mean wage over that of the whole manufacturing sector in the U.S. Data on income per capita come from PWT 7.0 (Heston et al., 2011). Data on human capital stock come from Barro and Lee (2010). The measure of institutional quality is the average size of three components of the Heritage Foundation’s Index of Economic Freedom: Investment Freedom, Property Rights, Freedom from Corruption (http://www.heritage.org/index/explore).

> 57See Clarke et al. (2003) for a survey of the literature.

> 58See also section 5.1.2 when we discuss firms’ ability to access external finance.

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<!-- Start of picture text --> Age-groups: 45-54 years, 55-65, 65-74, 75-84,<br>All-cause deaths (expected): 2020 Excess deaths: 2020 Excess deaths: 2021<br>fie<br>=<br>a<br>©<br>x<br>o<br>no<br>=<br>FS64<br>=<br>1 2 3 1 2 3 1 2 3<br>GNI per capita tercile<br>Notes: Results are based on the country-specific Poisson model described in the text applied to the WHO Global<br>Excess dataset. It shows the median and interquartile ranges (uncertainty bars) of predicted mortality sex ratio<br>for each age group by country income (in three groups) as defined in Table 1.<br><!-- End of picture text -->
less than 40% in Qinghai (Tibet may be viewed as an exceptional case.) Figure 3: Sub-National Fiscal Revenue and Expenditure <!-- Start of picture text --> 18000<br>16000<br>14000<br>12000<br>10000<br>r evenue<br>8000 expendi t ure<br>6000<br>4000<br>2000<br>0<br>1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> Source : Authors calculations Figure.4: The sub-national governments’ self-support rate (%) <!-- Start of picture text --> 100. 0<br>90. 0 88. 6 89. 0<br>80. 0 80. 6 83. 0 79. 1 78. 4 81. 1 83. 7<br>70. 0 71. 8 64. 6 72. 0 67. 2 70. 4 64. 8 68. 1 72. 1 62. 4 63. 9 73. 5 70. 5 65. 1 61. 5<br>60. 0 58. 3<br>50. 0 53. 3 53. 4 53. 7 50. 8 49. 0 53. 0<br>40. 0 39. 3<br>30. 0<br>20. 0<br>10. 0 12. 9<br>0. 0<br>Bei j i ngTi anj i n Hei bei ShanxiI nner mongul i aLi aoni ng Ji l i nHei l ongj i angShai hai Ji angsuZhej i ang Anhui Fuj i anJi angxiShandong Henan Hubei HunanGuangdongGuangxi Hai nanChongqi ngSi chuanGui zhou Yunnan Ti bet Shaanxi GansuQi nghai Ni ngxi aXi nj i ang<br><!-- End of picture text --> Source : Authors calculations Note: The fiscal self-support rate is the share of own-source revenue in total revenue.

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**Table 4 Government Size and External Risk by Income Groups:**<sup>**OLS and Instrumental Variables Regressions'**</sup> (Dependent Variable: Government Consumption/GDP in 1985 International<sup>Prices)</sup>

|**Panel A**<br>**Independent Variables**|**High Income**<br>**Countries**<br>**OLS**|**High Income**<br>**Countries**<br>**IVb**|**Middle Income**<br>**Countries**<br>**OLS**|**Middle Income**<br>**Countries**<br>**IV**|**Low Income**<br>**Countries**<br>**OLS**|**Low Income**<br>**Countries**<br>**IV**|
|---|---|---|---|---|---|---|
|Openness|0.130*<br>(0.067)|0.051<br>(0.067)|0.204**<br>(0.080)|0.237***<br>(0.080)|0.147***<br>(0.054)|0.106<br>(0.070)|
|Terms of Trade Changes (TOT)|1.650|1.087|-1.965*|-2.364|-1.294|-1.201|
||(2.009)|(2.804)|(1.106)|(1.444)|(0.881)|(1.020)|
|Openness*TOT|1.445|1.723|-1.006|-1.307|-1.992**|-2.112**|
||(1.728)|(2.834)|(1.248)|(1.532)|(0.937)|(0.933)|
|Number of Observations|74|73|154|145|129|127|
|R-squared'|0.6844|0.5763|0.558|0.5548|0.5146|0.4317|
|**Panel**B<br>**Independent Variables**|High**Income**<br>**Countries**<br>**OLS**|**High**Income<br>**Countries**<br>**IV**|**Middle Income**<br>**Countries**<br>**OLS**|**Middle Income**<br>**Countries**<br>**IV**|**Low Income**<br>**Countries**<br>**OLS**|**Low Income**<br>**Countries**<br>IV|
|Openness|0.121<br>(0.086)|0.066<br>(0.087)|0.140<br>(0.093)|0.214**<br>(0.093)|-0.037<br>(0.081)|-0.093<br>(0.094)|
|Volatility in Terms of Trade (STOT)|-3.478***|-4.035***|0.434|-0.89|1.497***|1.751***|
||(1.165)|(1.026)|(0.482)|(0.544)|(0.563)|(0.630)|
|Openness*STOT|-0.031|-0.234|0.424|-0.36|1.596***|2.001 * * *|
||(1.134)|(1.167)|(0.652)|(0.742)|(0.453)|(0.584)|
|Number of Observations|65|64|154|145|129|127|
|R-squared|0.7789|0.6911|0.5452|0.5339|0.5225|0.4444|

significant at 1% level significant at 5% level * significant at 10% level ' Standard errors, corrected for heteroscedasticity, are in parentheses. Coefficients on the other<sup>regressors are not shown. Other regressors are the same as those listed in Table2.</sup> bFor all IV regressions in Table 4, the instruments for the following variables are the previous five year<sup>lag of itself and regional and decade dummy variables: per capita GDP;</sup> relative price of government consumption; openness; and political freedom.<sup>All other variables were treated as exogenous.</sup>

**£** The R-squared is not an appropriate measure of goodness of fit with instrumental variable regressions.
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