rafmacalaba/datause-encoder-data huggingface dataset:
28

# **Appendix 1: Vulnerability calculations**

The results by country using the first set of data sources are shown in tables A1.1–A1.3. For these tables, import and export statistics from the IEA are always used if available. Tables A1.4 (1999–2008) and A1.8 (1999–2009) give the results of decomposition analysis by country. Tables A1.5–A1.7 (1999–2008) and tables A1.9–A1.11 (1999–2009) give the distribution of the oil share effect, energy intensity effect, and offsetting coefficients by income and region. Figure A1.1 reports the results in figure 8 for the second period. For decomposition analysis, where net oil imports calculated from the import and export statistics from the IEA differ markedly from those calculated taking the difference between production and consumption statistics from the IEA and EIA databases, the country is dropped. For the remaining countries for which both the IEA and EIA data are available, the data sets of production and consumption statistics that more closely approximate net imports derived from the IEA’s import and export statistics are used.

Table A1.1: Vulnerability and changes in vulnerability (percentage of GDP)

|**Country**|**V**<br>**1999**|**V**<br>**2008**|**V**<br>**2009**|∆**V**<br>**99–08**|∆**V**<br>**99–09**|**Data**<br>**source**|
|---|---|---|---|---|---|---|
|Albania|2.7|4.9|1.3|2.2|-1.4|IEA|
|Algeria|-17.5|-32.8|-23.1|-15.3|-5.6|IEA|
|Angola|-75.7|-76.7|-52.0|-1.0|23.7|IEA|
|Antigua and Barbuda|3.4|12.1|8.7|8.6|5.2|EIA|
|Argentina|-0.8|-1.5|-1.2|-0.7|-0.4|IEA|
|Armenia|1.4|3.0|2.1|1.6|0.7|IEA|
|Australia|0.4|1.3|0.9|0.9|0.6|IEA|
|Austria|0.7|2.1|1.4|1.4|0.7|IEA|
|Azerbaijan|-23.0|-57.8|-47.6|-34.8|-24.6|IEA|
|Bahamas, The|2.4|14.4|10.1|12.0|7.7|EIA|
|Bahrain|-17.1|-22.1|-12.5|-5.0|4.7|IEA|
|Bangladesh|1.0|4.1|2.3|3.1|1.3|IEA|
|Barbados|2.1|6.4|4.8|4.3|2.8|EIA|
|Belarus|6.1|8.0|6.5|1.9|0.4|IEA|
|Belgium|1.5|4.8|3.0|3.4|1.5|IEA|
|Belize|2.9|10.1|5.4|7.2|2.5|EIA|
|Benin|3.0|14.8|10.5|11.8|7.5|IEA|
|Bermuda|0.7|2.8|1.9|2.1|1.2|EIA|
|Bhutan|1.6|4.5|3.0|2.9|1.4|EIA|
|Bolivia|0.1|-0.4|0.5|-0.5|0.4|IEA|
|Bosnia and Herzegovina|2.1|5.2|3.7|3.1|1.6|IEA|
|Botswana|1.2|5.0|3.5|3.8|2.3|IEA|
|Brazil|0.8|0.3|0.0|-0.5|-0.8|IEA|
|Brunei Darussalam|-25.8|-44.3|-32.1|-18.5|-6.3|IEA|
|Bulgaria|4.1|6.8|4.3|2.6|0.1|IEA|
|Burkina Faso|1.6|3.9|2.4|2.3|0.9|EIA|
|Burundi|2.2|4.0|3.6|1.9|1.4|EIA|
|Cambodia|3.0|11.6|6.9|8.6|3.9|IEA|
|Cameroon|-6.0|-8.8|-4.4|-2.8|1.6|IEA|
|Canada|-0.8|-3.0|-2.3|-2.2|-1.6|IEA|
# **Table 3: Regression Results**

||Model|1|Mod|el 2|
|---|---|---|---|---|
||Coef.|Std. Err.|Coef.|Std. Err.|
|Constant|-10.641***|1.558|-10.771***|1.549|
|Tariff Rates|-1.155***|0.318|-1.163***|0.318|
|Port Efficiencyof Importer|0.307*|0.163|0.338*|0.160|
|Port Efficiencyof Exporter|0.924***|0.148|0.938***|0.146|
|Customs Environment of Importer|0.472**|0.199|0.486*|0.199|
|RegulatoryEnvornment of Importer|0.281*|0.144|0.264|0.144|
|RegulatoryEnvornment of Exporter|0.620***|0.132|0.580***|0.131|
|Service sector infrastructure of Importer|0.729***|0.224|0.657**|0.224|
|Service sector infrastructure of Exporter|1.943***|0.216|1.943***|0.217|
|GNP of Importer|0.915***|0.014|0.915***|0.014|
|Per capita GNP of Importer<br>|-0.182***|0.037|-0.210***|0.037|
|GNP of Exporter|1.246***|0.014|1.241***|0.014|
|Per capita GNP of Exporter|-0.226***|0.029|-0.251***|0.029|
|Geographical Distance|-1.258***|0.025|-1.225***|0.025|
|Adjacencydummy|0.336***|0.114|0.426***|0.108|
|MembershipDummyfor anyFTA|||-0.021|0.078|
|ASEAN MembershipDummy|0.509***|0.190|||
|NAFTA MembershipDummy|-0.645|0.501|||
|LAIA MembershipDummy|0.593***|0.154|||
|AUNZ MembershipDummy|1.118|0.858|||
|MERCOSUR MembershipDummy|0.229|0.302|||
|EU MembershipDummy|-0.515***|0.106|||
|Dummyfor anyCommon Language|||0.823***|0.061|
|English Language Dummy|0.808***|0.089|||
|French Language Dummy|-1.413***|0.500|||
|Spanish Language Dummy|0.598***|0.098|||
|Arabic Language Dummy|-1.223|0.992|||
|Chinese Language Dummy|1.747***|0.406|||
|German Language Dummy|-0.826|0.505|||
|Portuguese Language Dummy|0.569|0.986|||
|Russian Language Dummy|2.026***|0.362|||
|Year 2000 dummy|-0.031|0.039|-0.038|0.039|
|Adjusted R-squared|0.758||0.755||
|Number of the observations|7,904||7,904||

Note: The significance levels at 10%, 5% and 1% are denoted by “*”, “**”, and “***”, recpectively. Source: Authors’ calculation.

32
especially in sectors that are typically dependent on external finance and have few collateralizable assets. We find that both higher SFD and DFD, directly and indirectly, increase the relative volume of bilateral FDI in financially vulnerable sectors. Directly higher financial development improves access to external finance for those firms which require outside capital to expand in foreign markets and produce there at their first-best levels. This is the main positive effect of financial development on FDI. Indirectly, greater financial development also promotes overall economic activity, notably in financially vulnerable sectors. This raises the volume of producers in source countries and generates agglomeration benefits in destination countries. Any negative FDI-specific disintegration effect or general competition effect that financial development may induce on the destination side tends to be dwarfed by these positive effects of financial development. The overall effects of higher SFD and DFD on the relative volume of bilateral greenfield FDI in financially vulnerable sectors are economically substantial and complementary. They are larger at the intensive margin (average size of bilateral greenfield FDI projects) of FDI than at the extensive margin (number of bilateral greenfield FDI projects) of FDI. Our key findings are robust to the use of data on the number of bilateral M&A transactions.

Our results suggest that countries wishing to facilitate the internationalization of their firms and to attract foreign multinational enterprises should implement measures to improve access to external finance. Higher financial development may, in that way, increase international transfers of technology and promote long-run economic growth. Our findings also complement the literature that highlights the opportunistic use of internal capital markets by multinational enterprises. For many of these firms, it is likely that the depth of their internal markets cannot be dissociated from the depth and stability of their home country’s financial system and that host countries’ financial development still influences their location choices. Finally, the high sensitivity of FDI to external finance availability that we have systematically found suggests that tight credit conditions have played a role in the drastic decline of global FDI flows during the recent global financial crisis.

40
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As Särndal and Lundström (2015) argued, the calibration approach can easily leverage the wealth of information available on non-response; if the auxiliary variables are explicative of the non-response, the calibration estimator reduces the no-response bias. In this way, the estimates jointly smooth the non-response bias and calibrate the known population totals. This approach has three advantages for the method often adopted for producing survey estimates, which is based on performing preliminary adjustments for non-response based on no-response propensity scores and which produces weights defined at the individual level, implying that people in the same household may have different sampling weights.

The first advantage is that the sampling variance estimation is better founded and more transparent. Calibration estimators behave asymptotically, as they converge in probability to the regression estimator. They produce robust inferences with sound statistical properties for both the sampling design and the statistical model.

The s econd benefit is that fewer adjustment steps are needed, making the process flow for the estimation more straightforward. Särndal and Lundström (2005, pg. 88) note that if the information on non-response is available only at the sample level, the calibration approach can be carried out in two steps, where the first step calibrates on the known totals at the selected sample level, which includes both respondents and non-respondents. The two-step approach improves the accuracy of the estimates when the reasons for attrition are different from the reasons for sampling under-coverage. Thus, two calibrations are implemented: the first calibration adjusts the base weights for panel attrition and the second calibration uses the adjusted weights to produce the final weights to address the under-coverage.

Finally, the third advantage is that the calibration method produces estimates that are defined at the household level. That means all individuals in the same household have the same final weight, which significantly facilitates the consistency of estimates with variables defined at the individual level and those constructed with variables at the household level. ′ = �X , ... , X , ... , X � from administrative data, the latest census, or demographic tt tt,1 tt,pp′ tt,PP To introduce this estimator, let XX be a column vector of PP auxiliary = �X , ... , Xtt , ... , X � household k such that known totals for the population UU tt,kk tt,1,kk tt,pp,kk tt,PP,kk statistics. Let XX be a vector of auxiliary PP variables of the . �MMtt tt,kk tt Let X kk=1XX = XX are two possible situations regarding the relationship between the auxiliary variables defined tt,pp,kk,ii at the household level, X be the auxiliary variable defined for the ii-th individual of the kk-th household. There , and those defined at the individual level, X . Suppose the variable X can only be defined at the household level (e.g., the household's state of poverty). tt,pp,kk tt,pp,kk,ii In that case, all individuals in the household will have the same value of the household variable, tt,pp,kk 14
# **IV. AN ASIAN SKILLS INDEX (ASI)**

Constructing a composite index involves several choices in terms of methodology. We describe below some of the major methodological choices in constructing the ASI.<sup>12</sup> _a)_ **_SELECTING COUNTRIES USING k-MEANS CLUSTERING_** _:_ The constituent elements in any index clearly affect the behavior of the index. If an index is to serve its role as a tool for comparative measurement, the constituents in the index must be reasonably comparable. There are two important reasons for this comparability requirement. When there are missing values in the data set, replacing missing observations with mean values requires a similar grouping of countries. _“Cluster analysis serves as a method for selecting groups of countries for the imputation of missing data with a view to decreasing the variance of the imputed values._ ( _Handbook on Constructing Composite Indicators_ , p. 26). A second reason for comparability has to do empirical estimation. In the macroeconomics literature, empirical estimates of the role of human capital in real output range from nearly all to potentially none. Part of the reason for this is inclusion of economies at completely disparate levels of economic development.<sup>13</sup> We began with a list of 30 Asian countries<sup>14</sup> and then used GDP per capita to cluster the countries using “k-means”. k-means is an unsupervised machine learning algorithm for partitioning datasets into a set of k clusters such that objects within the same cluster are as

> 12 See _“Handbook on Constructing Composite Indicators – Methodology and User Guide”_ by the Joint Research Centre (JRC) of the OECD. <u>https://www.oecd.org/sdd/42495745.pdf</u> See also Greco et al (2019) for a comprehensive review of the methodological choices in composite indicators construction.

> 13 The Dow Jones Industrial Average (DJIA) is, by far, the best-known index of U.S. stock market performance. However, the DJIA has an inherent methodological flaw. The smallest stock in the index is Walgreens Boots Alliance (WBA) with a market capitalization value of $46.2 billion. The largest is Apple (AAPL) with $2,100 billion or $2.1 trillion - 45 times larger than WBA (values as of June 2021). However, a $1 movement in the stock price of both companies is treated exactly alike in the DJIA even though AAPL has a market capitalization that is 45 times larger than WBA. For comparative performance, conflating large and small companies leads to methodological problems. The Standard & Poor’s (S&P) 500 Index addresses this issue by weighting stock prices by market capitalization.

> 14 By Asian countries we mean all countries classified as being in either South Asia, East Asia, or Central Asia.

16
SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE underpricing to 57 percent (table 15). In terms of utility size, the SSEC encounters one of the highest hidden costs in Africa (figure 18). If SSEC is to expand access, redressing these hidden costs must be its first urgent priority.

**Table 15. Operational indicators for the SSEC**

|||System|Implicit collection|Average total|Average|Total hidden|Total hidden|
|---|---|---|---|---|---|---|---|
||Net generation|losses|ratio|cost|effective tariff|costs|costs|
||(GWh/year)|(%)|(%)|($/kWh)|($/kWh)|($ million/year)|(% revenues)|
|2006|39|50|40|0.37|0.23|8.7|187|

_Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Southern Sudan statistical handbooks 2006–08; World Bank (2011).

**Figure 18. Benchmarking the SSEC’s high hidden costs due to inefficiencies**

<!-- Start of picture text --> 300<br>250<br>200<br>150<br>100<br>50<br>0<br>Nigeria Sudan Liberia Niger Mali Sudan Ghana Burkina Cote Benin Cape Senegal South<br>(South) (North) Faso d'Ivoire Verde Africa<br>Collection Inefficiencies Underpricing Losses<br>Percentage of revenues<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009); Southern Sudan statistical handbooks 2006–08; World Bank (2011).

Improving South Sudan’s electricity situation requires a phased approach, starting with supporting and expanding off-grid electrification schemes. Programs that boost off-grid electrification through commercial means can be a viable option. These options not only enhance access to modern lighting in the absence of grid electrification but also boost private sector development. One such option is through the program Lightning Africa. Given the absence of any grid network in South Sudan, the most efficient and rapid way of providing lighting services to the people (particularly in rural areas) would be through the private sector by promoting the low-cost modern lighting offered by photovoltaic (PV) modules. The incorporation of South Sudan into th _e Lighting Africa program_ deserves consideration as a practical short-term solution to the country’s energy challenges.

Lighting Africa is a joint International Finance Corporation (IFC) and World Bank program that has to accelerated the development of commercial off-grid lighting markets in Sub-Saharan Africa. The program aims to mobilize the private sector to build sustainable markets to provide 2.5 million people with safe, affordable, and modern off-grid lighting by 2012 through a variety of tailor-made products, many of them in the form of PV solar lanterns. The longer-term goal is to eliminate market barriers for the private sector to bring these innovative products to 250 million Africans by 2030, who currently lack electricity that rely on costly and low-quality candles and kerosene lamps. Improved lighting provides 32
## Table 5

# Decomposition of Change Over Tine **in the Fraction** of Wage Workers Among Male Employed Workers, Within and **Between** Sectors, by Region

|Class of<br>Sectors<br>Countries<br>Analyzed<br>|Average<br>Fraction of<br>Wage Earners<br>(in percent)<br>|Total<br>Change<br>Per<br>Year|Sample<br>Size<br>(#of<br>Countries|Percent of<br>Intrasector<br>Change<br>)|Chanae due t<br>Intersector<br>Change|o:'<br>Residual<br>Interaction|
|---|---|---|---|---|---|---|
|Agricultur<br>Nonagriclu|e<br>lturea||||||
|World (Entire Sample)|53.9|.69|61|64|35|2|
|High Income<br>|79.1<br>|.54|22|40|61|-1|
|Latin America<br>|55.9<br>|.41|17|32|75|-7|
|Africa<br>|55.1<br>|.68|6|53|36|11|
|East Asia<br>|34.4<br>|.88|6|56|40|3|
|South & West Asia|35.0|.85|10|84|13|3|
|All 8 Sec|torsb||||||
|World (Entire Sample)|53.9-|.69|61|65|33|2<br>o|
|High Income<br>|79.1|.54|22|42|56|1|
|Latin America<br>|55.9|.41|17|36|71|-7|
|Africa<br>|55.1<br>|.68|6|59|35|6|
|East Asia<br>|34.4<br>|.88<br>|6<br>|49<br>|43|8|
|South & West Asia|35.0|.85|10|84|14|2|

aAgriculture includes forest.i irdy fishing.

bNonagriculture divided into the following ISIC categories: Mining; manufacturing; electricity, gas, and utilities; construction; wholesale and retail trade; transportation; business and personal services.

VPercent of changes due to the three components may not precisely sum to one because of rounding. The change in workers in wage employments is summed across countries before total changes in the share of wage earners are calculated and decomposed for the sample and for the regional aggregations of countries.

See Appendix Table A-1 for composition of sample.
24 decision to export. The latter, if true, would actually suggest that trade patterns are not determined by product comparative advantage but rather by the economies of scale in export infrastructure linkagesl Moreover, the primary source for increasing returns to scale would seem to lie in the nature of the technology of production, like in the high capital costs and R&D expenditures of high technology industries, rather than in export infrastructure linkages.

# 4. Gains from Trade LiberaUzation

While the case for an activist trade policy in developing countries inspired by the new trade theory argu,nents is seriously eroded by the above arguments, the conventional wisdom of adopting a free trade policy is simultaneously reinforced by an imperfectly competitive world. Calculations of the gains from trade - the conventional "triangles" - based on the competitive neoclassical model provided numbers in the order of 0.5 to 2 percent of GNP. But Harris (1984) has estimated the static long-run gains to trade to Canada in an imperfectly competitive model to be a much higher 8-12 percent. Richardson's (1989) survey of empirical work concludes that, in imperfectly competitive markets, trade liberalization, rather than policy intervention to promote domestic producers, leads to gains from trade two to three times larger than those under perfect competition.20 These results strengthen the case for trade liberalization and may be the only item of relevance for developing countries. This is particularly important since the new trade theory was alleged to undermine the neoclassical prescription for trade liberalization which many developing countries had begun to follow since the eighties after weltering for decades iniprotectionist policies.

20/ According to Trefler (1993), thes **e** stimates are biased downwarda **s** dhey consider trade libenlization as an exogeou variable when. in fact, it is endogenous. By treating the level of protection endogenously, he has shown tat its retrictive impact on U.S. imports in 1983 was 10 dtmes the estimate derived from treadng the level of protection exogenously. This, therefore, opens up the possibility of even larger gains from **trade** liberalization **than** those estimated.
analysis of movements in and out of poverty. Table 1 is the transition matrix showing these movements.<sup>8</sup> On the main diagonal of Table 1 are those whose status did not change over the period considered: those who remained always poor also called chronic poor and those who never experienced poverty. On the other diagonal lay those who changed status from non-poor to poor or poor to non-poor. These are defined as out of poverty and entering poverty respectively.The latter group, considering the short time span, is rather numerous: if faced with shocks, about 13 percent of those non poor can easily fall below the poverty line. Although this is more than compensated for by those coming out of poverty (32%) which confirms the high vulnerability of Nigerian households and suggests a rather high exposure to shocks.

Different specifications for the probability of a household becoming poor in the second period (Equation 1) and for household’s expenditures in the first period (Equation 2) are tested. Overall results are convergent; for the sake of brevity only two specifications are presented:

1. Equation 1 expressed as a logit, equation 2 using OLS (Table 4)

2. Equation 1 expressed as a GME equivalent to logit, and equation 2 using a linear GME specification including an asset index<sup>9</sup> (Table 5) Table 2 shows that people living in urban areas are significantly less likely to be classified as poor during the second period than those in rural areas ceteris paribus. Compared to households in the North Central, only those in the South West are significantly doing better while as expected those in North West are more likely to be classified as poor. Also, household head’s education is related to being poor. However this is only significant after completing primary education. Looking at the OLS results, households where the head has completed primary education or above, have significantly higher consumption than those where the head has no education.

The gender of the household’s head is not significantly related to poverty, but for consumption per capita it is positive and significant at the 10 percent level. The head’s gender,

> 8The transition matrix is constructed using the household’s reported expenditure for the corresponding year, and uses population weights in order to make estimates nationally representative. The table is representative for those individuals present in Wave 1 of the GHS-Panel.

> 9The asset index, suggested by Filmer and Pritchett (2001) is constructed using principal-components analysis. The index takes into consideration whether or not the household owns a specific household asset. Additionally, it considers whether it owns its dwelling, the number of rooms in the dwelling, the type of building materials, the water source, and the toilet facilities of the dwelling.

15