rafmacalaba/datause-encoder-data huggingface dataset:
in real terms. Third, when defining “real terms”, we have focused on the price levels that matter most for measuring global poverty, namely those faced by the world’s poorest countries.

This paper provides a new set of global poverty estimates from 1990 to 2012, using the 2011 PPPs and an international poverty line that was revised in accordance with these three principles. The new line is still derived from the same fifteen national poverty lines that Ravallion et al. (2009) used to define the $1.25 poverty line in 2005 PPPs. The new value of the international poverty line in US dollars is $1.90, in 2011 PPPs.<sup>6</sup> The new estimates suggest that 897 million people, or 12.7% of the world’s population, live below the international poverty line in 2012. The level of poverty using the new line and PPPs is similar to estimates based on the 2005 PPPs and the $1.25 line. This is best seen comparing the estimates for 2011, a year for which we have numbers from both the old and the new methods: the $1.25 line (and 2005 PPPs) yielded a global poverty rate of 14.5%, compared to 14.1% under the new line and method. The regional distribution of poverty also remains largely unchanged, with Sub-Saharan Africa being home to both the highest incidence of poverty and to the largest absolute number of poor people. South Asia is the region with the second most poor, followed by East Asia and the Pacific. The achievement of the first Millennium Development Goal (MDG) of halving global poverty from 1990 to 2015 is maintained, and simple extrapolations confirm findings that the goal of ending extreme poverty by 2030 remains ambitious.

The estimates produced in this paper apply the same methods as Chen and Ravallion (2010) and are based on data from PovcalNet, the database maintained by the World Bank for monitoring global poverty in recent years.<sup>7</sup> The most recent version of PovcalNet, contains data from more than one thousand surveys covering 131 developing countries, and 21 high income countries. In addition to household surveys and PPP data, the poverty estimates rely on intertemporal and spatial price deflators and adjustments, which can have large effects on both the international poverty line and on estimates of poverty rates for individual countries. The paper

> 6 A second poverty line of $3.10 in 2011 PPPs has also been proposed as the comparable equivalent to the $2 dollar-a-day poverty line in 2005 PPPs, commonly used as a poverty line for middle-income countries. 7 PovcalNet is perhaps the most commonly used data tool for estimating global poverty counts. It is an online tool, maintained by the World Bank, which allows analysts to specify parameter values such as the global poverty line, and then estimate the number of poor people in the world based on their assumptions. For more details, see: http://iresearch.worldbank.org/PovcalNet/index.htm.

4
**Table S12: Effects of Parent Insurance for Eligible children with/out an Ineligible Sibling**

||Any Provider|Pharmacy|EMP|Public Facilities|Private Facilities|
|---|---|---|---|---|---|
|Panel A: Ever Visit|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|0.014|0.004|0.222***|0.067|0.006|
||(0.065)|(0.082)|(0.052)|(0.084)|(0.081)|
|Parent Enrolled*Ineligible Sibling|-0.009|0.075|0.023|-0.018|-0.066|
||(0.200)|(0.233)|(0.113)|(0.211)|(0.195)|
|Ineligible Sibling|-0.039|-0.059|0.004|-0.018|0.007|
||(0.038)|(0.046)|(0.019)|(0.042)|(0.039)|
|Observations|2172|2172|2172|2172|2172|
|R-squared|0.073|0.061|0.144|0.04|0.075|
|P-value of Enrolled + Enrolled*Ineligible|0.982|0.716|0.016|0.800|0.731|
|Panel B: Number of Visits|(1)|(2)|(3)|(4)|(5)|
|Parent Enrolled|1.905***|0.530*|0.633***|0.417|0.257|
||(0.686)|(0.315)|(0.172)|(0.306)|(0.311)|
|Parent Enrolled*Ineligible Sibling|-3.206*|-0.945|-0.349|-0.986|-0.768|
||(1.690)|(0.812)|(0.334)|(0.780)|(0.624)|
|Ineligible Sibling|0.445|0.114|0.042|0.199|0.098|
||(0.336)|(0.162)|(0.069)|(0.160)|(0.117)|
|Observations|2172|2172|2172|2172|2172|
|R-squared|0.067|0.05|0.1|0.027|0.037|
|P-value of Enrolled + Enrolled*Ineligible|0.402|0.583|0.324|0.430|0.332|

_Notes:_ The sample is covered children aged 11 and under (N=2172). Siblings are ineligible if they are 12-15 at baseline. Above regressions are estimated coefficients from 2SLS-IV estimates where "Parent Enrolled" is instrumented with random assignment status Parent Enrolled*Ineligible Sibling is instrumented with random assignment status*Eligible Sibling. The dependent variable in Panel A is whether or not the child has visited various providers over the past year. The dependent variable in Panel B is the number of times the child has visited various providers over the past year. Regressions control for household size, household size squared, the inverse hyperbolic sine of parental income, parent's years of education, age of child, age of child squared, gender, whether the child was sick in the past year, the number of times sick, total number of health visits, and survey round and market fixed effects. Individuals without valid income data were imputed to be the median and regressions were run with a dummy variable indicating the missing value. Robust standard errors in parentheses, clustered at the family level. *** p<0.01, ** p<0.05, * p<0.1 _Source:_ Authors’ analysis based on data described in paper.

12
<!-- Start of picture text --> NGA Cross-Validation Model | GHS NLSS<br>3°<br>2<br>a3”<br>5<br>S<br>3=<br>z<br>5<br>°<br>10 12 14 16 18<br>;<br>——— NLSS_2018.19 ———— _mi_m=6<br>—— _mi_m=12 —— _mi_m=all<br><!-- End of picture text -->

<!-- Start of picture text --> NGA Cross-Validation Model | GHS NLSS<br>[e}27©<br>S<br>B<br>Ex<br>(6)5<br>z<br>50<br>3<br>x=fo}<br>o<br>10 12 14 16 18<br>x<br>—— NLSS_201819 ——— _mi_m=6<br>—— __mi_m=12 —— _mi_m=all<br><!-- End of picture text -->

<!-- Start of picture text --> NGA Cross-Validation Model | GHS NLSS<br>3 °<br>2<br>3Ex<br>8<br>3<br>3%<br>§<br>o<br>10 12 14 16 18<br>x<br>—__mim=t2 ——__mi_meall<br><!-- End of picture text -->

<!-- Start of picture text --> NGA Cross-Validation Model | GHS NLSS<br>2<br>2(e)a<br>Se<br>a<br>=<br>1S)Sx<br>2<br>5<br>Bey<br>=[<]<br>oO<br>10 12 14 16 18<br>x<br>—— NLSS_2018.19 ———— _mi_m=6<br>—— _mi_m=12 —— _mi_m=all<br><!-- End of picture text -->
|Industrial Enterprise RestructuringProject(IERP)|Marilyn Swann Manalo|
|---|---|
|Industrial Enterprise RestructuringProject(IERP)|Marilyn Swann Manalo|
|Industrial Enterprise RestructuringProject(IERP)|Marilyn Swann Manalo|
|Industrial Enterprise RestructuringProject(IERP)|Marilyn Swann Manalo|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Roads and Bridges Management and Maintenance Project|Dieter E. Schelling|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Education Sector Strategic Program(ESSP)|Xiaoyan Liang|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Health and Nutrition Project|Cornelis P. Kostermans|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|Urban Rehabilitation and Employment Generation Project|Bernard A. Becq|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
|FIRST ROADS AND COASTAL SHIPPING|Abdelmoula M. Ghzala|
Figure 3. Plurilateral Regional Trade Agreements of Eurasian Countries <!-- Start of picture text --> Estonia<br>Armenia Iceland, Liechtenstein,<br>Norway, Switzerland,<br>Russian Federation<br>Belarus<br>Ukraine<br>Kazakhstan EU<br>Kyrgyz Republic<br>Moldova Serbia<br>Tajikistan<br>Montenegro<br>Croatia<br>Uzbekistan<br>FYMR Albania Kosovo<br>Turkmenistan Georgia<br>Bosnia and Herzegovina<br>Azerbaijan<br>Pakistan EC-OCT<br>Afghanistan Turkey Iran<br>Romania<br><!-- End of picture text --> Source: Authors’ elaboration using data from the WTO and the Global Preferential Trade Agreements Library accessed through WITS (2012).

# **Trade Policy Barriers in Services**

We start the analysis of trade policy barriers in services with the examination of trade barriers in the Eurasian region, as domestic policy constraints may dampen competition and create anti-export bias. To conduct the analysis, we employ a new dataset constructed by Borchert et al. (2012a and 2012b), which provides trade restrictiveness indexes and detailed information on policy restrictions in the services arena. Restrictions to services trade considered in the dataset include: i) cross-border supply of services (mode 1), ii) supply of services through commercial presence (mode 3), and iii) temporary presence of natural persons (mode 4). The perspective is one of a foreign service provider that wishes to provide services to consumers in a foreign country, and therefore it considers policy measures that affect both domestic and foreign service provides as well as measures that discriminate against foreign service providers.

The database covers 103 countries across all regions and income groups of the world. For each country, five major service sectors are covered that encompass a total of 19 subsectors: i) financial: retail banking (lending and deposit acceptance) and insurance (automobile, life, and reinsurance); ii) telecommunications: fixed-line and mobile; iii) retail distribution; iv) transportation: air passenger (international and domestic), maritime shipping, maritime auxiliary, road trucking, and railway freight; and v) professional: accounting, auditing, and legal services (advice on foreign/international law, advice on domestic law, and court representation). Each subsector in turn covers the relevant modes of supplying the respective services, yielding overall 34 subsector-mode combinations: mode 1: financial, transportation, and professional; mode 3: all subsectors; and mode 4: professional.

20
2009 study. In these results we found that the short-term and long-term effects were similar to this study (e.g., increases in per capita expenditure, a shift towards nonfarm wage employment away from agricultural wage work, improvements in child schooling enrollment, and a reduction in transport costs).

# **8. Discussion and conclusion**

Few studies have examined how rural road impacts evolve over time. Roads are inherently long-term investments, and multiple factors may affect returns to roads, including initial area characteristics, road quality, and changes in complementary infrastructure (such as electricity access, for example). Lower transport costs and market development, for example, are two major changes expected from rural road improvements. However, these outcomes may improve at different rates over time and may interact with each other differently over both the short and long run. For example, transport costs might fall much faster initially before new markets spring up but still lead to quicker growth in markets with greater mobility of labor and goods. Conversely, growth in markets may lead to a further decline in prices and other costs due to competition. However, with better quality products and modes of transport, the decline in prices and costs may not be sustained in the long run.

Because of these issues, it is essential that data have enough variation in pre-program conditions and policies across targeted and non-targeted observations—covering a long enough period of time with sufficiently disaggregated information on sources of income generation—to be able to link the long-term effects of rural road development to household outcomes and welfare of the poor. A confounding factor is that unobserved characteristics affecting road development and outcomes are more likely to vary over a longer period of time.

21
# **<u>REFERENCES</u>**

Agell, J., H. Ohlsson, and Skogmann Thoursie P., 2006, “Growth Effects of Government Expenditure and Taxation in Rich Countries: A comment,” _European Economic Review,_ 50, 211-18.

Agénor, Pierre-Richard, _The Economics of Adjustment and Growth_ , Harvard University Press (Cambridge, Mass.: 2004).

------, "Fiscal Policy and Endogenous Growth with Public Infrastructure," _Oxford Economic Papers_ , 60 (January 2008), 57-88.

Agénor, Pierre-Richard, and Blanca Moreno-Dodson, "Public Infrastructure and Growth: New Channels and Policy Implications," in Public Expenditure, ed. By Maura Francese, Daniele Franco, and Raffaela Giordano, Banca d'Italia (Rome: 2007); and WB Working Paper.

Agénor, Pierre-Richard, and Kyriakos Neanidis, "The Allocation of Public Expenditure and Economic Growth," Working Paper No. 69, Centre for Growth and Business Cycle Research, University of Manchester (March 2006).

Ang, James B., 2009, “Do public investment and FDI crowd in or crowd out private domestic investment in Malaysia?” _Applied Economics_ , 41, 913–919.

Arellano, M. and S. Bond, 1991, “Some Tests of Specification for Panel Data: Montecarlo Evidence and Application to Employment Equations,” _Review of Economic Studies_ 58(2), 277-297.

Baldacci, Emanuele, Benedict Clements, Sanjeev Gupta, and Quing Cui, 2008, “Social Spending, Human Capital, and Growth in Developing Countries,” _World Development_ , Vol. 36, No. 8, 1317–1341.

Barro, Robert J., "Government Spending in a Simple Model of Endogenous Growth," _Journal of Political Economy_ , 98 (October 1990), s103-s25.

Barro, Robert J., "Economic Growth in a Cross Section of Countries,” _Quarterly Journal of Economics_ , vol. 106 (May 1991), 407-43.

Barro, Robert J., and Xavier Sala-i-Martin, _Economic Growth_ , 2nd. ed., McGraw-Hill (New York: 2003).

Benoit, E., 1973, _Defense and Economic Growth in Developing Countries_ . Lexington: Lexington Books.

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# **Figure 8 Banking Sector**

**Credit to GDP**

<!-- Start of picture text --> 200%<br>160%<br>120%<br>80%<br>40%<br>0%<br>China India East Asia Eastern Latin America G7<br>Europe<br>Deposits to GDP<br>160%<br>120%<br>80%<br>40%<br>0%<br>China India East Asia Eastern Latin America G7<br>Europe<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br>1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004 1991 1994 1999 2004<br><!-- End of picture text --> East Asia is the average of Indonesia, Korea, Malaysia, and Thailand. G7 is the average of Canada, France, Germany, Italy, Japan, United Kingdom, and United States. Latin America is the average of Argentina, Brazil, Chile, and Mexico. Eastern Europe is the average of Czech Republic, Hungary, and Poland. Source: World Bank World Development Indicators. The data source for Chinese deposits is Beck, Demirgüç-Kunt, and Levine (2006) for the years 1991, 1994, and 1999, and International Financial Statistics for 2004.
# **3 Characteristics of Electricity Sectors in Sub-Saharan Africa**

This section presents aggregate data on the characteristics of electricity sectors in the 48 countries of SSA unless otherwise noted. Detailed country-level data on the characteristics of electricity sectors in the countries studied in this paper can be found in annexes 2-7. Additional electricity sector characteristics related to operational efficiencies are discussed in section 0. Apart from the access data based on the Global Tracking Framework of the Sustainable Energy for All initiative (SE4ALL) and installed capacity data, all other data are for the reference year for the 39 countries covered in this study and for the most recent year for which data are available for the countries excluded from the study. As such, regional averages presented in this section do not represent averages of data in the same year but the data are from different reference years (except for installed capacity for which data was available for a common reference year).

## 3.1 Access

The regional access rate of 35 percent is less than half percent of the global average levels of access of 85 percent (SE4ALL 2015a). Mauritius and the Seychelles have 100 percent access to electricity, followed by Gabon (89 percent) and South Africa (85 percent). A further nine countries have access rates above 50 percent. Rural areas remain the most underserved in the world. In some countries, less than 5 percent of rural population has access to electricity.

## 3.2 Installed capacity and power mix

Installed capacity in Figure 2 consists of all domestic grid-connected capacity, including IPPs, gridconnected power supply for exports and industry, and emergency power. It excludes enclave-generation such as mines. To avoid double counting, country-specific estimates exclude capacity from imports.

**Figure 2: Power mix on installed capacity, 2014, percent of total**

<!-- Start of picture text --> 1% 0% 4% 4% 0%<br>6%<br>4% 0%<br>Hydropower<br>Gas<br>21%<br>47GW 47% Diesel and HFO 47GW<br>(excluding South (South Africa<br>Africa) Coal only)<br>Other renewables<br>26% 82%<br>Nuclear<br><!-- End of picture text --> _Source_ : World Bank staff calculations taking data from utility annual reports and other documents.

With a population of almost one billion people, installed capacity in all 48 countries in SSA in 2014 was 94 gigawatts (GW), up from 68 GW in 2005 (Eberhard et al 2011). The total regional capacity is less than that in Spain, whereas Spain has a total population of 50 million, against 962 million in SSA. South Africa with 47 GW accounted for half the capacity in SSA. Fourteen countries had power systems larger than 1 23
# _Comparison and Correlations between the Indexes on Ports_

The three indicators on ports used above should reflect overlapping information. Table 5 shows the correlations between the three indicators and the port efficiency measures by Blonigen and Wilson (2008)<sup>24</sup> from 2001 to 2006.

**Table 5: Correlations between the Indexes on Ports**

||_Port Congestion _|_Port Infrastructure _|_Sea Transportation _|_Port Efficiency_|
|---|---|---|---|---|
|_Port Congestion_|1.00|--|--|--|
|_Port Infrastructure (GCR)_|-0.16|1.00|--|--|
|_Sea Transportation (WCY)_|0.02|0.92|1.00|--|
|_Port Efficiency (BW)_|0.29|-0.63|-0.47|1.00|

(Source) Port Congestion: Authors’ calculation based on _Containerization Yearbooks_ . Port Infrastructure: _Global Competitiveness Report_ . Sea Transportation: _World Competitiveness Yearbook_ . Port Efficiency: Blonigen and Wilson (2008).

Our port congestion index partially correlates to the port efficiency measurement by Blonigen and Wilson. No significant correlation, however, is found with the indexes from _GCR_ and _WCY_ . Our port congestion index represents narrowly-defined physical congestion / utilization of ports and possibly some rents from the higher demands and technical efficiency. The other two indexes reflect survey opinions that reflect much a much wider scope and perceptions. Our index does correlate to the port efficiency index by Blonigen and Wilson which is supposed to cover all port-related costs incurred by transporters, because it is the value of the port-specific fixed effects. The indexes from _GCR_ and _WCY_ also correlate to the port efficiency index, showing that both of the indexes also contain information on the costs on ports.

If the port efficiency measurement of Blonigen and Wilson is regressed on our port congestion index, time dummies and constant, the estimated coefficient of our index is 0.049, significant at the 1 percent level. The regression can explain around 15 percent of the total sum of the squares. For the same example above, the expansion of port capacity by 18 percent for China in 2006 will brings about the fall in the port efficiency measurement by 1.3 percent. Because the port

> 24 The journal article only puts a table showing a measurement averaged throughout the years from 1991 to 2003 on each foreign port. We take simple averages of ports in a country to obtain the index of the country, and assume the port efficiency measurements do not change over time from 2001 to 2006 to calculate the correlations in Table 5.

24
Why should we care about a relationship between aggregate economic outputs and growth? Climate change is expected to have profound effects on the hydrological cycle, leading to more variable rainfall patterns (Menon et al., 2013; Donat et al., 2016). So far, the climate-econometric literature has highlighted the cost of rising temperature on GDP growth (Hsiang and Kopp 2018). Accounting for changes in rainfall is likely to be equally critical when estimating the economic costs of climate change. However, climate models diverge significantly on the effect of climate change on precipitation across regions. This has two implications for predictions on future economic growth. First, it implies that the joint temperature and rainfall impacts may be significantly different from the predictions that rely only upon temperature. Second, these findings add further uncertainty to the future cost of climate change.

# **5- References**

Auffhammer, M., Ramanathan, V., & Vincent, J. R. (2006). Integrated model shows that atmospheric brown clouds and greenhouse gases have reduced rice harvests in India. _Proceedings of the National Academy of Sciences_ , _103_ (52), 19668-19672.

Barrios, S., Bertinelli, L., & Strobl, E. (2010). Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy. _The Review of Economics and Statistics_ , _92_ (2), 350-366.

Briant, A., Combes, P. P., & Lafourcade, M. (2010). Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations? _Journal of Urban Economics_ , _67_ (3), 287-302.

Brown, Casey, et al. "Is water security necessary? An empirical analysis of the effects of climate hazards on national-level economic growth." _Phil. Trans. R. Soc. A_ 371.2002 (2013): 20120416.

Burke, Marshall, Solomon M. Hsiang, and Edward Miguel. "Global non-linear effect of temperature on economic production." _Nature_ 527.7577 (2015): 235.

Burke, Marshall and Tanutama, Vincent (2019) "Climatic constraints on aggregate economic output.", NBER Working paper 25779 Dell, Melissa, Benjamin F. Jones, and Benjamin A. Olken. "Temperature shocks and economic growth: Evidence from the last half century." _American Economic Journal: Macroeconomics_ 4.3 (2012): 66-95.

Deryugina, Tatyana, and Solomon Hsiang. _The marginal product of climate_ . No. w24072. National Bureau of Economic Research, 2017.

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