rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8: Manufacturing Imports of Industrial and Developing Countries with and without China from 1991/92 to 2001/02 and 2001/02 to 2007/08**

||Share of|Imports in D|omestic|Annual Imp|ort Growth|Ann|ual Import Gro|wthdue to||
|---|---|---|---|---|---|---|---|---|---|
||D|emands (%)||(%|)|1991/92|- 2001/02|2001/02|- 2007/08|
||||||||Market||Market|
|CountryGroup|1991/92|2001/02|2007/08|91/92 -<br>01/02|01/02 -<br>07/08|Demand<br>Change|Share<br>Change|Demand<br>Change|Share<br>Change|
|Industrial Countries (5)||||||||||
|From Developing without China|2.7|5.7|6.8|9.6|8.8|1.6|8.0|5.8|3.0|
|From China|0.6|2.4|5.1|17.0|20.0|1.6|15.4|5.8|14.2|
|Developing Countries (7)||||||||||
|From Developing without China|2.7|5.5|6.2|11.8|18.7|4.1|7.7|16.3|2.4|
|From China|0.3|1.8|4.9|23.8|36.9|4.1|19.7|16.3|20.5|
|All Sample Countries (12)||||||||||
|From Developing without China|2.7|5.7|6.7|9.8|10.4|1.9|7.9|7.5|2.9|
|From China|0.5|2.3|5.0|17.6|22.5|1.9|15.7|7.5|15.0|

Note: Data is based on two-year averages of 1991-92, 2001-02, and 2007-08. Sources: Based on UN COMTRADE Statistics (trade data) and UNIDO database (production data).

Table 8 shows the performance of developing countries with and without China for our sample countries. This table revises two important conclusions reached in this paper. First, most of the market share gains attained by the developing countries in the markets of industrial countries are driven by exports from China, which accounts for 72 percent of market share gains during the 2000s. In terms of annual market share gains, all developing countries excluding China increased their market shares in the markets of industrial countries only at 3 percent p.a. The ratio for China for the same period was 14 percent p.a.

Second, China also accounts for the bulk of the increases in South-South trade during the 2000s. China accounts for 82 percent of the market share gains of developing countries in the markets of our seven large developing countries. Without China, market share gains of all developing countries in our sample would be 2.4 percent p.a. versus 20.5 p.a. for China. This relationship holds true for all countries in our sample. Thus, these generalizations about SouthSouth trade need to be reanalyzed and China has to be treated separately.

20
# **1 Including direct air capture technologies**

In this Section, we include direct air capture technologies (DAC) in our modeling framework. We treat DAC like an additional economic sector with zero annual emissions (i.e., _a_ ̄ _DAC_ = 0) and some abatement potential, that we call _Z >_ 0 (in GtCO2 yr<sup>_−_1</sup> ). DAC is unique in that its emissions rate is not the same as its abatement potential, as was assumed in every other sector.

## **1.1 Augmented model framework**

A social planner tasked with decarbonization with DAC technologies at their disposal solves the following cost-minimization problem, Subject to : _ai,t_ +1 = _ai,t_ + ∆ _t_ ( _xi,t − δiai,t_ ) _,_ Extending this formulation to a model with uncertainty about the RCB is straightforward. We take the marginal abatement cost of DAC from International Energy Agency (2022) as $100–$300 tCO<sup>_−_</sup> 2<sup>1.</sup> In each simulation we show below, we assume a marginal abatement cost of $200 tCO<sup>_−_</sup> 2<sup>1.Anylower</sup> marginal cost we would judge to be unrealistically cheap, and any higher cost causes model behavior to be completely unchanged in all of our simulations. We translate this marginal abatement cost to a marginal investment cost using (2.12). We assume the capital depreciation rate of DAC is 5% yr<sup>_−_1</sup> and that, at maximum, the abatement potential from DAC technologies is 5 GtCO2 yr<sup>_−_1</sup> (Terlouw et al., 2021).

## **1.2 Results**

We find that the impact of including DAC in our model for abatement investment is consistent across temperature targets, see Figure 1. For both temperature targets we explored, we find that there is practically no change in the value of learning; in other words, the investment strategies with and without DAC are essentially identical. This owes to the carbon price being too low to induce the social planner to invest heavily in DAC; it is cheaper to simply abate emissions at their source, given the relatively cheap cost of abatement investment in other sectors compared to DAC.

The marginal investments in DAC allows the social planner to invest slightly less resources in hardto-abate sectors, see Figure 2. We find that most sectors – waste, forestry, transport, energy, and 3
dependence of real GDP growth, for the full sample (left panel) and the advancedcountry sample (right panel). The CD test statistic is above 40 for both samples, overwhelmingly rejecting the null. Table 1 also reports the exponent of cross-sectional dependence _α_ along with 95% confidence bands, for both the advanced and full country samples. The estimated value of _α_ is 1 in the advanced-country sample (with a confidence region reaching well above 1) and .94 in the full sample. In both cases, the results point to the presence of strong common factors in the output growth data, consistent with the findings of, e.g., Kose, Otrok and Whiteman (2003).

Table 1: GDP growth: cross-sectional dependence

||All countries|Advanced countries|
|---|---|---|
|Pesaran CD statistic|42.343|41.661|
|Exponent of CSD|0.943|1.004|
||(0.906, 0.979)|(0.905, 1.102)|
|Number of countries|117|21|

Notes: GDP growth is the first difference of the log of real GDP. ’Exponent of CSD’ is the exponent of cross-sectional dependence of Bailey, Kapetanios and Pesaran (2015), and values in parenthesis are its 95% confidence bands. The sample period is 1970-2016.

# **4.1 Model estimation results**

In order to estimate the factor-augmented dynamic spatial model (1), we first need to determine the number of unobserved common factors. To do so, we compute the _IC_ , _BIC_ and _HQ_ information criteria proposed by Choi and Jeong (2018) setting the maximum number of factors to 5.<sup>11</sup> The results are shown in Table 2. The upper panel of the table reports results for the full sample, and the bottom panel reports results for the advanced-country sample. For the full sample, both the _IC_ and _HQ_ criteria suggest one factor while the _BIC_ criterion suggests zero factors. For the advanced country sample, the _BIC_ and _HQ_ suggest one factor while the _IC_ criterion suggests two factors (by a very narrow margin). We opt for employing one factor in all the estimations below.<sup>12</sup>

> 11Setting the maximum number of factors to 3 gives very similar results.

> 12Ductor and Leiva-Leon (2016) also employ a single factor to capture the common component of GDP growth across countries.

12
# **_Trade Facilitation_**

The results confirm the significant gains to be realized in complementing trade liberalization with trade facilitation improvements. The addition of trade facilitation improvements to trade liberalization within GAFTA increases overall welfare gains to the MENA region from $913 million to some $3 billion (some 0.1% increase to base GDP), some three times. Similarly, the welfare gains under trade liberalization within GAFTAEU increases from $1.8 billion to $7.2 billion (0.82% increase to base GDP), some four times with the inclusion of trade facilitation improvements.

<!-- Start of picture text --> Figure 8: Trade Facilitation improvements significantly increase the welfare gains from<br>liberalization.....<br>3.85<br>4.00<br>3.50 3.24<br>GAFTA GAFTA-EU<br>3.00<br>2.50<br>1.88<br>2.00 1.76<br>1.50<br>1.01<br>1.00 0.76<br>0.65<br>0.53<br>0.43<br>0.50<br>0.18 0.13 0.05 0.17 0.05 0.17 0.11<br>0.00<br>RONAF ROMIDE MOR TUN RONAF ROMIDE MOR TUN<br>without Trade Facilitation with Trade Facilitation<br><!-- End of picture text --> As regards the distribution of the gains, all the various MENA sub-regions share in the significant welfare gains. Welfare gains to Morocco, RONAF, and ROMIDE, regions which had small welfare gains under GAFTA, are shown to at least triple when GAFTA is complemented with trade facilitation; in Tunisia the welfare gains almost doubled (figure 8). Similar significant benefits to the MENA regions are also observed when GAFTAEU is complemented with trade facilitation improvements (figure 8). In both cases, the expansion in trade is proportional to the improvements in welfare, however, the increase in trade is not as significant as the welfare increases. For instance, in Morocco 16
# **Box 3: What could be driving the HH Concentration in Traffic of Extra-regional Routes?**

An initial assumption that is often stated is that outside airline traffic is mainly driven by tourism. One method for testing for this is by examining the relationship between hotel rooms and airline seats, which show a nearly linear relationship, and carry a correlation of 0.983 (Figure 39a).

Two important items must be noted concerning hotels and air transport development:

- Stay-over tourism is the type of tourism that brings the largest benefits to the economy of the islands. It is fully contingent upon the availability of proper hotel infrastructure that takes some lead to time to develop

- The almost linear relationship between the hotel rooms and estimated seats also indicates that the best predictor of the air passenger demand is the installed and functional hotel capacity.

A scatter graph of hotel rooms and market concentration of air services in the hands of airlines (measured by the HH Index) yields a distinct hyperbolic curve suggesting that competition among airlines is related to the draw hotel rooms provide for tourism (Figure 29b). This relationship leads very quickly to a series of market distortions. The concentrated/captured market will quickly lead to bundled packages that might, as in the case of OECS, create pressure for subsidies.

This suggests that concentration in air transport with the outside world is directly related to the development and size of the overall stay-over tourism sector in the given island.

**Figure 29: The relationship between the hotel industry and air transport in the Caribbean**

<!-- Start of picture text --> a. Hotel Rooms and Estimated Airline Seats b. Hotel rooms and airline market concentration<br>12,000,000 4,000<br> 3,500<br>10,000,000<br> 3,000<br>8,000,000<br> 2,500<br>6,000,000 2,000<br> 1,500<br>4,000,000<br> 1,000<br>2,000,000<br> 500<br>0 -<br> - 20,000 40,000 60,000 80,000 - 20,000 40,000 60,000 80,000<br>Hotel Rooms Hotel Rooms<br>Source: Author’s calculations on data provided by Diio (2014).<br>HH Index<br>Estimated Scheduled Airline Seats<br><!-- End of picture text --> 60
26 Regrssion 12 Dependent variable: Correlation of Real Per Capita GDP Growth with OECD Average Real Per Capita GDP Growth, 1960-95 Number of observations: 155 Mean of dep. var. = .246648 Std. dev. of dep. var. = .267756 Sum of squared residuals = Variance of residuals = .049896 Std. error of regression = .223373 R-squared = .344712 Adjusted R-squared = .304039 LM het. test = .866619 [.352] Durbin-Watson = 1.95295 (<.711] = .769712 (.681] Ramsey's RESET2 = .034630 [.853] F (zero slopes) = 8.47520 (.000] Schwarz B.I.C. = -2.73913 Log likelihood = 17.5644

|Variable|Estimated<br>Coefficient|Standard<br>Error|t-statistic|P-value|
|---|---|---|---|---|
|SSA|-.717189|.199481|-3.59529|[.000]|
|ASIA|-.712958|.231572|-3.07877|(.002]|
|ECA|-.638812|.239380|-2.66861|(.0081|
|MENA|-.822765|.233648|-3.52138|(.001]|
|LAC|-.692913|.232984|-2.97408|[.003]|
|OECD|.082437|.068673|1.20043|[.232]|
|LOGQAV6095|.119230|.029067|4.10185|(.000]|
|OIL|-.077165|.065326|-1.18123|(.239]|
|COMMOD|.026310|.057571|.456996|[.648]|
|MICROSTATE|.013354|.053808|.248181|(.804]|
|Standard Er|rors are hete|roskedastic-c|onsistent (HCTY|PE=2).|
|Transmigration Project(04)|
|---|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|Nucleus Estates and Smallholders Project(07)- NES Project(07)|
|BAPINDO Project(05)|
|BAPINDO Project(05)|
|BAPINDO Project(05)|
|BAPINDO Project(05)|
|BAPINDO Project(05)|
|Public Works Manpower Development Project|
|Public Works Manpower Development Project|
|Public Works Manpower Development Project|
|Public Works Manpower Development Project|
|Public Works Manpower Development Project|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Polytechnics Project(02)|
|Pulpand Paper EngineeringProject|
|Pulpand Paper EngineeringProject|
|Pulpand Paper EngineeringProject|
|Provincial Health Project|
|Provincial Health Project|
|Provincial Health Project|
|Provincial Health Project|
|Provincial Health Project|
|Provincial Health Project|
|Power Project(12)|
|Power Project(12)|
|Power Project(12)|
|Power Project(12)|
|Power Project(12)|
|Power Project(12)|
|Power Project(12)|
|Power Project(13)|
|Power Project(13)|
|Power Project(13)|
|Power Project(13)|
|Power Project(13)|
orphanhood can be large, especially for orphans who have lost their mother. One hypothesis for the resilience in education and health outcomes in some situations on the one hand, and their decline in others, is the role of extended family networks. Traditional support systems to orphans may be undermined by the pressure of large increases in the number of orphans. If orphaned children are increasingly living in households that are less willing or able to invest in their human capital, then these shifts could have major implications for long-run poverty and human development in countries hard-hit by the HIV/AIDS pandemic.

Data from these 21 Sub-Saharan African countries show that orphanhood is common in many countries, although not all countries are experiencing rapid increases in rates of orphanhood. In many of the countries studied the orphan rate has remained stable—and even declined in some countries. This set of countries includes a group of countries where orphan rates have remained stable at a relatively low rate (this group consists primarily of the West African counties in the sample). It also includes a group of countries where the orphan rate is relatively high, but stable (Ethiopia, Madagascar, Mozambique and Uganda). In the sample, there is an orphanhood surge in 5 countries: Kenya, Malawi, Namibia, Zambia and Zimbabwe. These are all countries with a high prevalence of HIV. While there is a strong indication that HIV prevalence maps to orphanhood trends, this pattern is not always observed. For example, despite its high HIV rate, Mozambique has not experienced this surge in orphanhood.

The trends in living arrangements are less clear cut than suggested by the common hypothesis that increasing orphan rates are corroding the ability of families to care for orphans, at least in terms of co-residence. The main finding that emerges from the analysis is that, in many countries, there has been a shift towards grandparents taking on increased childcare responsibility. This suggests that care by surviving parents, other relatives, and non-relatives has been substituted with care by grandparents. While the trend is apparent among all orphan types, it is larger for single and double orphans. This trend is also especially evident in countries where the orphan rate has been increasing rapidly. Still, some large changes in living arrangements even in countries with low orphanhood rates are found, such as the substantial decline in nonorphans living with both parents in Cameroon, Niger, and Senegal.

The average age of caregivers among orphans is higher than among non-orphans. Grandparents who are caregivers are on average in their mid-60s, which raises concerns about the ability of this group to physically and financially care for young fostered children. Orphans 20
**_52_**

Table **13:** Distribution of Deviations of MWXR from IFS RF

|Deviation**(%)**|**1980**|**1981**|**1982**|**1983**|1984|**1985**|**1986**|**1987**|**1988**|
|---|---|---|---|---|---|---|---|---|---|
|**0**|**_15_**|14|14|12|**9**|**8**|**7**|**10**|**7**|
|**0.1-1.0**|**62**|_54_|**52**|**53**|49|52|**59**|**_50_**|40|
|1.1-2.0|2|**7**|**7**|**6**|12|**9**|**_5_**|**3**|**7**|
|**2.1-3.0**|2|2|2|2|**3**|4|4|4|**0**|
|3.14.0|**1**|2|2|**1**|**0**|**0**|**0**|**3**|**6**|
|4.1-5.0|**0**|**1**|2|**0**|**1**|2|**0**|**0**|**1**|
|**_5.1-6.0_**|**1**|**U**|**0**|**1**|2|**0**|**0**|2|**0**|
|**6.1-7.0**|**0**|**0**|**0**|**1**|2|**1**|**1**|**0**|**1**|
|**7.1-8.0**|**0**|**0**|2|**1**|**0**|**1**|**0**|**1**|**0**|
|**8.1-9.0**|**0**|**1**|**0**|**0**|**1**|**0**|**0**|**0**|**0**|
|**9.1-10.0**|**0**|**0**|**0**|**0**|**0**|**0**|**1**|2|**0**|
|**_10.1-15.0_**|**0**|2|**0**|**1**|**1**|2|**0**|2|**0**|
|**15.1-20.0**|**0**|**0**|**1**|2|**0**|**1**|2|**0**|2|
|**20.1-25.0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|**0**|
|**25.1-30.0**|**0**|**0**|**0**|**0**|**1**|**0**|**0**|**0**|**1**|
|**>30.0**|**0**|**0**|**0**|2|**1**|**0**|**1**|**0**|2|
|Total|**83**|**83**|**82**|**82**|**82**|**80**|**80**|**77**|**67**|

Source: **IFS**

# **Countries for Review and Adjustment for Volatility**

> **73.** Nevertheless, any large deviations should be taken as signals that the MWXR could be the preferred choice among the two rates. The results do suggest, however, that for three countries in particular the use of MWXR, instead of the official exchange rate, merits

> consideration. The three countries showing strong deviations are Argentina, Brazil, and Yugoslavia. Any decision on the preference order between MWXR and official rate would

> have to take into account relevant country-specific knowledge, not just the magnitudes of the deviations between the two rates.
Second, important differences exist between investor types. For all investor classes – apart from on-grid – policy and regulatory risk is the most important risk category. For ongrid investors, in turn, power sector context risk is the most important risk category, potentially because the future expansion of the power sector determines to a large extent the investment opportunities that arise and retroactive changes to expansion plans may pose a substantial risk. International investors assign more weight to country-context risk than domestic investors, potentially due to better local connections.

Third, there are striking differences between segments of the power sector. Investors perceive three segments as ‘ready’ for private solutions in Sub-Saharan Africa: power generation, off-grid electrification, and mini-grids. And when asked about the most attractive markets for investments over a three-year time horizon, respondents ranked nine countries as ‘positive’ on average: Côte d’Ivoire, Kenya, Mozambique, Nigeria, Rwanda, Senegal, South Africa, Uganda, and Zambia.

Fourth, besides understanding investors’ and financiers’ risk perception, a central motivation for the survey was to ‘reality-check’ existing, data-based assessments of countries’ readiness for investment. We find that survey respondents’ perception of countries’ readiness correlates very well with RISE 2017 scores (R2 of 0.47 for a sample of 35 countries). A similar picture emerges for the Power Sector Reform Index (R2 of 0.37 for a sample of 35 countries). Further disaggregating the RISE scores into its grid and off-grid components shows that they also correlate well with the assessment of the respective investor classes. Particularly the following components of the index show a high correlation with the subjective experience of off-grid investors: existence of an electrification plan, consumer affordability of electricity, and the existence of a framework for stand-alone systems. For grid-connected renewable energy particularly clear network connection procedures, carbon pricing and regulatory support for renewable energy are well corelated with the perception of investors.

In sum, despite many challenges, the survey underscores that past experience of power sector investments in Sub-Saharan Africa has been more positive than negative, and many of the surveyed equity investors and financiers plan to invest in the region in the next three years. Addressing the key concerns of investors – such as the certainty of cash flow, ease of entry and exit of the market, and clear investment priorities – can substantially increase the readiness of countries. Investors particularly assess positively those countries that have seen substantial power sector reform efforts, often over a decade or more, to provide adequate policy and regulatory frameworks for investment.

35
declined from 82 to 34 percentage points of GDP between 2000 and 2006, with the bulk of the drop between 2004 and 2006. Since 2006, however, the debt-to-GDP ratio begun to increase, reaching slightly over 50 percentage points of GDP in 2010.<sup>11</sup> Panel B of Figure 5 displays the evolution of the six determinants of foreign debt, as identied by equation (2.1). The average growth rate of real GDP between 2001 and 2010 was 4 percentage points, although with signicant volatility. The GDP deator in U.S. dollars (that is, the reciprocal of the real exchange rate) was highly volatile, and the country had a persistent current account decit net of interest payments. In addition, the large negative values of the debt shock during 2005 and 2006 (-9 and -26 percent respectively) reect the realization of the debt relief agreed under the HIPC initiative. FDI ows remained roughly constant, at around 1.5 percentage points of GDP, and the implicit interest rate was virtually constant at less than one percentage point.

The top panel of Figure 6 displays the evolution of foreign debt / GDP and the probabilities that debt will cross the proposed thresholds during 2011-2020. Here, the country-specic intercept is set at its estimated value based on historical data. The median foreign debt trajectory decreases from 52 to just over 5 percentage points of GDP by 2020. Likewise, the upper quartile of foreign debt decreases to 27 percentage points of GDP over the same horizon. These trajectories imply that the probability of foreign debt increasing to 60 percentage points of GDP during the next ten years is always smaller than 8 percentage points. Similarly, the probabilities that debt will increase to more than 80 or 100 percent of GDP never exceed 4 and 2 percentage points respectively. These predictions for foreign debt are, to a large extent, driven by the proposed intercept. Evaluating equation (3.3) at the estimated intercept implies very optimistic long run values of the endogenous variables: real GDP growth is 4 percent, the growth rate of the GDP deator in U.S. dollars is 5 percent, the ow of FDI is 3 percentage points of GDP, and the debt shock is -4.5 percentage points of

> 11Throughout this section, the data for the debt-to-GDP ratio and the endogenous variables are taken from the IMFWorld Bank May 2011 DSA (IDA and IMF, 2011).

21