rafmacalaba/datause-encoder-data huggingface dataset:
30 National Social Marketing Centre. 2010. ―PSI/Kenya insecticide-treated net social marketing programme‖. The National Social Marketing Centre. Available at http://www.nsmcentre.org.uk/sites/default/files/PSI%20ITN%20FULL%20case%20study _0.pdf. Accessed on 9<sup>th</sup> June 2011.

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- Noor, A. M., V.A.  Alegana, A.P. Patil, and R.W. Snow. 2010. ―Predicting the Unmet Need for Biologically Targeted Coverage of Insecticide-Treated Nets in Kenya.‖ Am. J. Trop. Med. Hyg., 83(4), 2010, pp. 854–860

- Noor, A. M., A. A Amin, W. S Akhwale, and R. W Snow. 2007. ―Increasing coverage and decreasing inequity in insecticide-treated bed net use among rural Kenyan children.‖ _PLoS medicine_ 4 (8): e255.

- Noor, A.M., P.W. Gething, V.A.  Alegana, A.P. Pati, S.I. Hay, E.Muchiri, E. Juma, and R.W. Snow. 2009. ―The risks of malaria infection in Kenya in 2009.‖ BMC Infectious Diseases 9:180.

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- Snow. 2007. ―The decline in paediatric malaria admissions on the coast of Kenya.‖ _Malar J_ 6 (1): 151.

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- Radelet, Steven. 2010. _Emerging Africa : how 17 countries are leading the way_ . Baltimore, Maryland: Center for Global Development.
# Appendix A: R-Coefficient Calculations

> R-coefficients were calculated for a set of World Bank and ADB projects by taking the following steps:

- classifying projects into types appropriate for estimating recurrent costs;

- identifying recurrent costs in each case;

- calculating recurrent costs as a ratio of capital costs for each type of project.

# World Bank Projects

Using the World Bank Data Bank, Project Appraisal Documents (PADs), were scanned to identify those that appeared to include enough information about recurrent costs to enable estimation of the r coefficient. Some 123 PADs were drawn upon.

> The classification of projects by sector generally followed that of the WB site. The level of detail and quality of data varies significantly across the PADs, despite the overall

> similarity of document and annex structure. Different sectors tend to have different

> analytical characteristics, which have implications for the ease of identification of incremental recurrent costs attributable to investments made under the projects.

The PADs normally identify 'incremental operating costs' during project implementation (as these are sometimes covered by a WB loan). The scale and type of these costs during the project implementation period are rarely similar to the incremental recurrent expenditure implications for governments thereafter.

> Sometimes relevant material about incremental cost issues is contained in the economic analysis even if it is not summarised in the financial analysis tables. Calculating r

> coefficients therefore typically involves the process of scanning all sections of the PAD.

9
economy. As in Loayza, Fajnzylber and Calderón (2002), these variables are measured as contemporaneous period averages.

Finally, the third model we consider includes two variables from the preceding specifications – female education and inflation – and adds infrastructure, whose empirical significance for growth has been recently stressed by Calderón and Servén (2004). In the empirical specification we use as infrastructure measure the number of main telephone lines per capita, expressed as the average over the preceding period.

# _III.3 Data_

Despite the huge progress made in recent years, poverty data are still very scarce, at least in relation to the size of the standard cross-country time-series growth dataset. In our case their scarcity becomes severely binding because estimation of (21) above requires a minimum of two poverty observations per country – and a minimum of at least three in order to allow generating instruments from the lagged values of the poverty measure.

To overcome this limitation, we adopt a different strategy: rather than using LSMSbased poverty data, we construct a set of poverty figures using a lognormal approximation.<sup>18</sup> We base this choice on recent work by Lopez and Servén (2005), who compare the quintile income shares generated by a lognormal distribution with their observed counterparts using data from approximately 800 household surveys. They find that the lognormal approximation fits the data extremely well, and are unable to reject the null hypothesis that per-capita income follows a lognormal distribution. In the case of per capita expenditure, the lognormal specification can be formally rejected, but it still provides an excellent empirical approximation to the data.

In view of these results, we construct our poverty figures on the basis of the observed per capita income levels and Gini coefficients. In our regressions, we use three alternative poverty measures – the headcount, the poverty gap and the squared poverty gap – constructed in this manner, and in each case we experiment with three alternative poverty 18 The use of the lognormal approximation to the distribution of income dates back to Gibrat (1931). Under lognormality, given the Gini coefficient (g) it is possible to compute the standard deviation ( σ ) of the log of income as 1 + _<u>g</u>_ σ _=_ √ _2_ Φ<sup>_-1_</sup> ( ) 2 where Φ (.) is the standard normal cumulative distribution function. Using this expression and the log of per capita income ( _y_ ), we can compute the FGT family of poverty measures for a given poverty line _z_ as: 12
SADC’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE As of the baseline year, 2006, almost all of the power demand was being met. But this situation deteriorated considerably in the years that followed due to South Africa’s power supply crisis. The baseline total net demand for power in the SAPP was 259 terawatt-hours (TWh) in 2005, making it the largest power market in Sub-Saharan Africa. In the island states (all members of the SADC), about 95 percent of demand for power was being met.

**Table 3.2 Demand and suppressed demand in the Southern African Power Pool**

||Total net demand<br>in 2005|% suppressed demand<br>as a share of net<br>demand(2005)|Market demand<br>2015|Social demand with<br>national targets<br>2015|Total net demand<br>2015|
|---|---|---|---|---|---|
|Angola|2.1|21|6|1.9|7.9|
|Botswana|2.4|0|4|0.2|4.2|
|Congo, Dem. Rep. of|4.7|7|7.4|6.2|13.6|
|Lesotho|0.4|2|0.8|0.1|0.9|
|Malawi|1.3|4|1.9|0.4|2.3|
|Mozambique|11.2|4|15.7|0.7|16.4|
|Namibia|2.6|1|4.2|0.1|4.3|
|South Africa|215|0|316|3.2|319.2|
|Zambia|6.3|2|9|0.4|9.3|
|Zimbabwe|12.8|4|18|0.8|18.7|
|**SAPP**|**258.8**|**1**|**383**|**14**|**396.9**|
|WAPP|31.3|42|69.6|24.8|94.3|
|EAPP|100.6|1|144.8|24.2|169|
|CAPP|10.7|9|17.1|3.1|20.2|
|Island states|1.1|5|1.6|1.5|3|

_Source:_ Rosnes and Vennemo 2009. _Note:_ WAPP = West African Power Pool; EAPP-NB = East African Power Pool–Nile Basin (EAPP is expanded to include key Nile Basin trading partners Egypt, Ethiopia, and Sudan); CAPP = Central Africa Power Pool; SAPP = Southern African Power Pool; TWh = terawatt-hour. All figures are in TWh unless noted otherwise.

Power demand in the SAPP area is expected to increase by 40 percent over the next decade, a pace that is lower than power pools starting from a lower base. Taking into account the need to satisfy existing demand for power—plus the anticipated expansion in market demands driven by economic growth in commerce and industry, plus the need to provide additional power to support the planned expansion in electrification from 26 percent to 51 percent of households across the region—it is estimated that power demand as of 2015 could reach 397 TWh. This requires the development of 31,300 MW of new generation capacity (table 3.2). (These projections are based on economic growth forecasts prior to the onset of the global financial crisis of 2008. On the assumption that the economic crisis could halve anticipated economic growth rates over this region, the estimate of demand for 2015 would fall to 397 TWh.) Future power demand can either be met through expanding national production or expanding crossborder power trade within the SAPP. Two alternative scenarios will be considered in this report. The _trade stagnation_ scenario assumes that no additional cross-border interconnectors will be built, so that trade is constrained at the levels observed today, and countries are thus obliged to meet incremental 40
# Figure 4. Long-term growth prospects of EMDEs

# A. Long-term consensus forecasts: output growth

<!-- Start of picture text --> Percent Percent<br>4 2000 2007 2019 8<br>3 6<br>2 4<br>1 2<br>0 0<br>World Advanced EMDEs (RHS)<br>Economies<br>C. Potential output growth<br>Percent<br>7 Potential growth<br>6<br>5 1998-2018 potential growth<br>4<br>3<br>2<br>1<br>0<br>World AEs EMDEs<br>2003-07 2013-18 2019-27 2003-07 2013-18 2019-27 2003-07 2013-18 2019-27<br><!-- End of picture text -->

# C. Potential output growth

# B. Long-term consensus forecasts: Investment growth

<!-- Start of picture text --> Percent<br>EMDEs EMDEs excl. China World<br>12<br>10<br>8<br>6<br>4<br>2<br>D. Potential per capita output growth<br>Percent<br>6<br>Potential growth<br>1998-2018 potential growth<br>4<br>2<br>0<br>World AEs EMDEs<br>2014<br>2019-27 2013-18<br>2010 2011 2012 2013 2015 2016 2017 2018 2019<br>2003-07 2013-18 2003-07 2013-18 2019-27 2003-07 2019-27<br><!-- End of picture text -->

# D. Potential per capita output growth

Source: Consensus Economics, Haver Analytics, Penn World Tables, UN Population Prospects, World Bank. A. Bars show long-term (10 years ahead) average annual growth forecasts surveyed in respective years. Sample comprises 38 countries—20 advanced economies and 18 EMDEs—for which consensus forecasts are consistently available during 1998-2019. Aggregate growth rates calculated using constant 2010 U.S. dollar GDP weights. B. 10-year-ahead forecasts surveyed in indicated year. Aggregate growth rates are calculated using constant 2010 U.S. dollar investment weights. Sample comprises 23 advanced economies and 20 EMDEs. C.D. Period average of annual GDP-weighted averages. Estimates based on production function approach. World sample comprises 50 EMDEs and 30 advanced economies.
growth. The latter may occur when the marginal productivity of additional workers in expanding sectors is below that of existing activities in those sectors.<sup>8</sup> China’s development has been similar to that of other Asian countries—benefiting both from rapid within-sector productivity growth and significant structural change (Timmer et al., 2015). Over 1979-2018, productivity growth within sectors contributed 5.7 percentage points to aggregate labor productivity growth of 7.3 percent per year. The movement of workers between sectors generated the rest (Figure 2). The cross-sector term is positive, suggesting that labor generally moved to sectors with positive productivity growth, but its effect is very small.

**Figure 2 Within-sector productivity growth versus movements of labor**

(annual average contributions to growth in labor productivity, percentage points)

# **Figure 3 Labor moving out of agriculture**

(log ratio of sectoral to aggregate labor productivity in 1978, y-axis; change in employment share 1978– 2010, x-axis) <!-- Start of picture text --> Within-sector Between-sector Cross-sector 2.5<br>10 Utilities<br>2.0<br>8 Fin., real estate, business services<br>1.5<br>Distribution Trade, rest., hotels<br>6 1.0<br>Construction<br>Mining<br>0.5 Manufacturing<br>4<br>Government services<br>0.0<br>2 -0.4 -0.3 -0.2 -0.1 0.0 0.1 0.2<br>Agriculture -0.5 Other non-market<br>services<br>0<br>1979-2018 1979-1988 1989-1998 1999-2008 2009-2018 -1.0<br><!-- End of picture text --> Source: NBS; authors’ calculations.

Source: GGDC 10-Sector Database, Timmer et al. (2015); authors’ calculations.

Tracking the standard path of industrialization, labor moved from agriculture to industry. In 1978, 75 percent of the labor force was in agriculture. That share declined to 27 percent in 2018, as the tight controls on rural-urban migration were gradually relaxed and workers relocated to sectors with higher productivity growth (Figure 3). Between 1978 and 2010, the employment shares of manufacturing and construction increased by 6 and 7 percentage points, respectively.

Labor also moved to services, mainly trade, restaurant and hotel and non-market (community, social, and personal) services. By 2010, labor productivity in non-market services was less than a fifth of the economy-wide level, while the employment share had risen by 12 percentage points to 14 percent of the labor force (Figures 4 and 5). In contrast, the employment share of financial, real estate, and business services, where productivity growth is very high, has remained around 1 percent over four decades.

> 8 As in in McMillan and Rodrik (2011) and Timmer et al. (2015), this decomposition uses average rather than marginal labor productivity to compare productivity gaps across sectors. However, the reallocation of labor between two sectors affects output per worker in both sectors, likely reducing average productivity in the new sector. Hence, the gains from reallocation in the above analysis may be overestimated.

5
_Consolidated Banking Statistics_ published by the Bank for International Settlements, include both local claims extended by foreign bank branches and subsidiaries in the host country as well as cross-border claims extended from outside the host country.

Figure 3 shows that consistent with the other measures of foreign bank importance, data on foreign bank claims reveal a fairly steady increase in the role of foreign banks across developing countries.<sup>2</sup> As with other measures, foreign bank claims are largest and have witnessed the most sizeable increase in Eastern Europe and Central Asia and in Latin America. In Eastern Europe and Central Asia, the share of foreign claims to GDP rose from 8 percent in 1996 to 45 percent in 2008 and in Latin America this ratio increased from 19 to 44 percent over the same period. On the other hand, while Sub-Saharan Africa ranked among the regions with the highest level of foreign bank presence measured as a fraction of total bank assets, this region is at the bottom of the list when the importance of foreign banks is measured by the share of foreign claims to GDP. This might be due to the fact that relative to other regions, Sub-Saharan Africa receives fewer cross-border loans.<sup>3</sup> Nonetheless as with the other two regions, foreign claims to GDP for Sub-Saharan Africa rose from 7 to 15 percent. In the case of East and South Asia, foreign claims to GDP rose from 13 and 5 percent, respectively, to 22 and 18 percent, respectively. Finally, in the case of the Middle East and North Africa region, foreign claims to GDP rose from 9 to 15 percent between 1996 and 2008.

# **3. The Drivers of Foreign Bank Entry**

What is behind the rise in foreign bank participation in developing countries? The literature on the drivers of foreign bank participation has focused on four main sets of factors, namely: the desire of banks to follow their home customers abroad, the attractiveness of local profit opportunities in the host countries, the absence or elimination of barriers to foreign bank entry, and the presence of mechanisms to mitigate information costs of doing business in foreign markets. Below, we review and discuss the evidence on each of these factors.

> 2 The recent crisis appears to have primarily impacted the ratio of foreign claims to GDP for Eastern Europe and Central Asia, which fell from 54 to 44 percent of GDP between 2007 and 2008.

> 3 Unfortunately, the BIS does not report separate statistics for local claims and cross-border claims so we cannot know this for sure.

6
# **Figure 8: Normalized citation curves for World Bank and various universities**

- (a) Full curve

<!-- Start of picture text --> 0 .2 .4 .6 .8<br>Harvard<br>Oxford<br>Princeton<br>World Bank<br>Yale<br>4000<br>3000<br>2000<br>citations<br>1000<br>0<br><!-- End of picture text --> 37
# ANNEX 1 –Firms and Banks Sample Overview

This report reflects the results obtained from a survey of 425 firms in 14 developing countries of different income level across six regions (Figure 14). The same questionnaire developed by the World Bank was used by local consultants in these countries. It covers firms’ trade developments and prospects, their method and source of payment for preexport, import and export finance. The survey was conducted in March/April 2009 and covered the period from September to December 2008/first quarter of 2009 versus comparable periods in 2007. The objective was to highlight changes since the onset of the global financial crisis in the 4<sup>th</sup> Quarter of 2008. In all these countries, the surveyors targeted a large number of exporters and **Figure 14: Country Breakdown of Sample** importers from different industries and of **Country Breakdown of Sample** different size. Questionnaires were sent **Phillipines BRAZIL Chile 6% 3% 4% Egypt** by email and made available online. **4%** However, given time constraints and the **Ghana4%** low level of spontaneous response, most **India** questionnaires were administered **Indonesia 6% Kenya** directly or via phone interview by the **25% Peru 2% 3%** surveyors. As a result, the sample of **Sierra Leone** firms surveyed in each country is not **12%** homogeneous across countries, and **UKRAINE Turkey 6% 10% Tunisia** interpretation and comparison of results **9% South Africa** across countries need to be conducted **6%** Total number of firms: 402 carefully.

The Regions that are most represented in the sample are East Asia (32 percent) and SubSaharan Africa (24 percent). Almost two-thirds of firms are based in Low Middle Income countries (Figure 15). Overall, 75 percent of the firms in the sample declared that they were not part of a global supply chain. However the proportion in each country varies (Figure 16). More firms are part of a supply chain in Tunisia, Chile and Peru. On the contrary, very few firms surveyed in Ghana, Kenya and Sierra Leone are part of such a network. Similarly, 77 percent of the firms in the sample declared they are not partially or fully owned by multinational firms, except for two outliers, Chile and Peru (and to some extent India) where most firms surveyed is partially or fully owned by a multinational firm. Finally, most firms are SMEs except in Brazil, Turkey, India and Chile where the sample is dominated by large firms (Figures 17 and 18).

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