rafmacalaba/datause-encoder-data huggingface dataset:
We construct a measure of household wealth by aggregating a number of household assets and dwelling characteristics using the first principal component. Similar wealth indices have been used extensively in the medical, demographic, nutritional, and economics literatures. The exact variables included in the wealth measures vary by country because of differences in the assets and dwelling characteristics that were collected in the surveys (see Appendix Table 1). As a robustness check, we test whether our results are sensitive to using consumption or education as an alternative measure of socioeconomic status, or to using only a common set of assets to construct the wealth index in all countries.

There are substantial differences across the countries we study in their level of development. Four of them, Chile, Colombia, Ecuador and Peru, are classified by the World Bank as uppermiddle-income countries, while Nicaragua is classified as a lower-middle-income country. Chile is the richest of the five countries, with GDP per capita in 2010 above US $ 15,000, and Nicaragua is the poorest, with GDP per capita below US $ 3,000. The other three countries, Colombia, Ecuador, and Peru, all have per capita GDP levels between US $ 8,000 and US $ 9,500. The average grades of completed schooling of adults in each country follows the same pattern as GDP per capita, with approximately four more grades of schooling in Chile than in Nicaragua. Like other countries in Latin America, the countries we analyze are highly unequal. The Gini coefficient of household per capita income ranges from 0.48 for Peru to 0.56 for Colombia. In comparison, the Gini coefficient for Sweden is 0.25, and that for the United States is 0.41. The average Gini for OECD countries (excluding the two Latin American countries, Chile and Mexico) is 0.31.

# **3. Results**

The aim of this paper is descriptive. In our main results we simply compare the TVIP scores for children in the top and bottom quartiles of the distribution of wealth. Because associations between TVIP scores and wealth could differ between urban and rural areas, we calculate separate wealth indices and conduct separate analyses for urban and rural areas.

Table 2 shows that differences in language development between richer and poorer children within countries are statistically significant and large. Differences across quartiles are biggest in urban Colombia (1.23 standard deviations) and rural Ecuador (1.21 standard deviations). Appendix Table 2 shows that, as expected, the differences between children in the richest and poorest deciles (as opposed to quartiles) are substantially larger—in both urban Colombia and rural Ecuador they are 1.64 standard deviations.

5
- Rutherford, Thomas F., Elisabet E. Rutstrom and David G. Tarr (2000) “A Free Trade Agreement Between the European Agreement and a Representative Arab Mediterranean Country: A Quantitative Assessment,” in Bernard Hoekman and Jamel Zarrouk (eds.), _Catching Up with the Competition: Trade Opportunities and Challenges for Arab Countries,_ Ann Arbor: University of Michigan Press.

- Rutherford, Thomas F., Elizabet E. Rutstrom and David G. Tarr (1997), "Morocco's Free Trade Agreement with the EU: A Quantitative Assessment," _Economic Modelling_ , Vol. 14, No. 9, 237-269, April.

- Rutherford, Thomas F. and David G. Tarr (2010), “Regional Impacts of Liberalization of Barriers against Foreign Direct Investment in Services,” the case of Russia’s accession to the WTO,” _Review of International Economics_ , Vol. 18(1), February, 30-46.

- Rutherford, Thomas F. and David G. Tarr (2008), “Poverty Effects of Russia’s WTO Accession: modeling ‘real households’ with endogenous productivity effects,” _Journal of International Economics_ , Vol 75 (1), pp 131-150.

- Rutherford, Thomas F. and David G. Tarr (2003), “Regional Trading Arrangements for Chile: Do the Results Differ with a Dynamic Model?” _Economie Intenationale_ , No. 94-95, 2003, 261-282. Available at: http://www.cairn.info/revue-economie-internationale-2003-2-page-261.html.

- Rutherford, Thomas F. and David G. Tarr (2002), “Trade Liberalization, product variety and growth in a small open economy: a quantitative assessment,” _Journal of International Economics_ , Vol. 56 (2), March, 247-272.

- Schiff, Maurice and L. Alan Winters (2003), _Regional Integration and Development_ , Oxford: Oxford University Press and the World Bank.

- Shepotylo, O. and V. Vakhitov (2015), "Impact of Services Liberalization on Productivity of Manufacturing Firms" _Economics of Transition_ , Vol. 23 (1), January, 1-44.

- Smith, Alasdair, and Venables, Anthony J. (1988), "Completing the Internal Market in the European Community: Some Industry Simulations," _European Economic Review_ , 32, 1501-1525.

- Tarr, David G. (2014), _Applied Trade Policy Modeling in 16 Countries: Insights and Impacts from World Bank CGE Based Projects_ , Singapore: World Scientific Publishers.

- Tarr, David G. (2013), “Putting Services and Foreign Direct Investment with Endogenous Productivity Effects in Computable General Equilibrium Models,”. in _Handbook of Computable General Equilibrium Modeling_ , Peter B. Dixon and Dale W. Jorgenson (eds.), Amsterdam: North Holland, Elsevier B.V., pp. 303–377.

- Viner, Jacob (1950), “The Economics of Customs Unions,” in _The Customs Union Issue_ , P. Oslington (ed.), Oxford: Oxford University Press, 41-81.

- Wonnacott, Paul and Ronald Wonnacott (1981), “Is Unilateral Tariff Reduction Preferable to a Customs Union? The Curious Case of the Missing Foreign Tariffs,” _American Economic Review_ , Vol. 71 (4), 704-714.

- World Bank (2016), “Potential Macroeconomic Implications of the Trans-Pacific Partnership,” _Global Economic Prospects_ , Chapter 4, 218-255.

46
it comes to the digitalization of the informal sector, mobile money is the predominant avenue (Islam and Jolevski, 2019). We see widespread adoption of mobile money among informal businesses as well. Between 40% and 50% of informal businesses in India, Mozambique, Zambia, and Zimbabwe, and over 80% in Somalia report using mobile money in their current operations. In contrast, only 7% of enterprises in Iraq and less than 3% of informal businesses in Lao PDR use mobile money, as the currently prevalent M Money offered by Lao Telecom was not operational at the time of the survey. Data on the use of mobile money by formal micro-enterprises is available only for India, Zambia, and Somalia.<sup>15</sup> Interestingly, micro-enterprises appear more likely than their informal counterparts to use mobile money in India and Zambia but less likely to do so in Somalia.<sup>16</sup>

# **Operations**

The ability of informal business owners to set their own hours and work in a largely unregulated setting has been provided as a potential reason for the sector's persistence in the developing world (Maloney, 2004; Loayza and Rigolini, 2011). To investigate these rationales, the Informal Sector Enterprise Surveys first ask whether the business was started by its current owner. Those that did are then asked a battery of questions on potential reasons for starting an informal business. <mark>The vast majority of informal businesses (over 90%) were started by their current owner, which, interestingly is slightly less than formally registered micro-enterprises (95%), which are likely more transferable across owners due to their legal status. Female informal business owners are more likely to have started the businesses: 94% compared to 88% for men. The shares of informal businesses started by their current owner vary somewhat across cities, ranging from 56.2% in Varanasi (India) to 99.1% in Pakse (Lao PDR).</mark> Table 4 presents descriptive statistics on the reasons for starting an informal business. While over 83% mention hours or location as a reason, the inability to find another source of income is also listed by 64% of owners. Importantly, <mark>over 89% of these owners listed the opportunity to develop a profitable business, with men and women equally likely to provide this reason. There is some cross-city variation, however: ranging from 70% in Nampula (Mozambique) to 100% in Sehore (India). For formal micro-enterprises, this variable was only collected in</mark>

> 15 Importantly, the question wording in the India survey was more general, referencing digital payments, rather than mobile money specifically.

> 16 This difference is not statistically significant in Zambia.

17
# **E.12 Placebo Test with Nearest Neighboring Out-of-sample Sections**

We calculate the distances between the centroid of a section in the sample and all out-of-sample sections. We then use the minimum distance to identify the nearest neighbor.

**Table E.12:** Placebo: Reported Cases using Nearest Neighboring Section

||Control Mean|Pooled|CM|NFA|Difference|N|
|---|---|---|---|---|---|---|
|Ebola Cases|||||||
|Total|0.222|0.057|0.043|0.069|-0.026|5,440|
||(0.957)|(0.063)|(0.076)|(0.072)|(0.078)||
|Confrmed|0.029<br>(0.371)|0.01<br>(0.023)|0.001<br>(0.03)|0.016<br>(0.027)|-0.015<br>(0.033)|5,440|
|Negative|0.169<br>(0.653)|0.042<br>(0.04)|0.033<br>(0.047)|0.049<br>(0.046)|-0.015<br>(0.047)|5,440|
|IHS(Ebola Cases)|||||||
|Total|0.153|0.041|0.033|0.048|-0.015|5,440|
||(0.419)|(0.031)|(0.036)|(0.035)|(0.036)||
|Confrmed|0.018|0.008|0.004|0.012|-0.009|5,440|
||(0.164)|(0.012)|(0.016)|(0.014)|(0.017)||
|Negative|0.125|0.032|0.024|0.038|-0.013|5,440|
||(0.37)|(0.025)|(0.029)|(0.028)|(0.028)||

_Notes_ : Standard errors clustered on section. Treatment effects estimated using OLS including matching-triplet and week fixed effects. Significance: * p < 0.10, ** p < 0.05, and *** p < 0.01. The three major cities in our study districts (Bo Town, Kenema Town, and Makeni Town) are excluded as potential nearest neighbors.

A45
TABLE 8: SUMMARY OF IDENTIFIED E-PROCUREMENT IMPACTS ACROSS INDIA, INDONESIA, ANF BANGLADESH

|Outcome|Indicator|India|Indonesia|Bangladesh|
|---|---|---|---|---|
|Access|Number of bids submitted|||1.6-2.2 bids|
||Non-local winners|11%|23%|3.7-6.3%|
||Road Quality on Audit|9.7% higher quality|N/A|N/A|
||Winning Rebate|||7.4-8.0%|
|Economy|Time Overruns||||
||Cost Overruns||N/A||
||Lead Time||54.13 days|15.6-19.2 days|
||Submission Period|N/A|N/A|4.1-6.9 days|
|Efficiency|Decision Period|||2.0-2.2 days|
||Signing Period|N/A|N/A|4.1-6.9 days|

Source: Lewis-Faupel et al. for India and Indonesia and this study for Bangladesh 95. Based on the different estimation strategies, delivering a range of savings impacts, it is possible to broadly estimate the total public money saved by the e-procurement system (Pollitt et all, forthcoming). While our estimation does not encompass all expected monetizable impacts of e-procurement, it does provide a useful lower bound estimate of the total savings achieved. Using the total savings formula in section 4, the predominantly positive impact of e-procurement on access, economy and efficiency can be translated into savings estimates for the government and for bidding firms. We estimate that, between 2012 and 2016, the three government agencies saved USD 424 million to USD 471 million in purchase prices, and USD 35 million to USD 42 million in administrative costs. These figures dwarf the cost of setting up the e-procurement system, estimated at USD 70 million. Bidders’ transaction cost savings are also positive albeit rather low: USD 4 million to USD 7 million. If e-procurement were to cover 100 percent of public procurement spending, the GOB could save up to USD 1.76 billion per year, assuming that the same average savings due to increasing winning rebates would accrue to the whole budget. The full calculations can be found in Appendix 9.9. In addition to the cost savings of the GOB due to lower purchase prices, there would also be macroeconomic effects. These effects have been estimated by assuming that the government increases the provision of public services using the savings from the lower purchase prices. Including multiplier effects, e-procurement is estimated to increase Bangladesh’s GDP by between 0.48% and 0.54% in 2019 or, in absolute terms, between USD 0.94 million and USD 1.07 billion (in 2011 prices, between 36
Figure 1. Power generation capacity by fuel type Figure 2. Capacity and hourly peak generation <!-- Start of picture text --> 12000<br>12000<br>10000<br>10000<br>Import<br>8000 Hydro 8000<br>6000 Gas 6000<br>Fuel Oil<br>4000 4000<br>Diesel Derated Capacity (MW)<br>2000 2000 Peak Generation (MW)<br>Coal<br>0 0<br>2010 2015 2010 2011 2012 2013 2014 2015<br>MW MW<br><!-- End of picture text --> Source: BPDB (2016) Power policies now face a significant challenge in securing primary fuels for generation. The domestic gas sector has supplied all of the gas used by power plants from onshore gas fields. In the Gas Master Plan (Wood Mackenzie, 2006), combined production from these fields was projected to decline beginning in 2018 as new discoveries failed to meet growth in demand for gas and existing reserves were reaching production capacity. As a result, government policy now targets the import of coal, natural gas in the form of LNG, and power coming mainly from India. Bangladesh has no experience with importing coal or LNG as of 2017 and will have to develop the infrastructure needed to import these energy sources. Power imports from India began in 2013 with 500 MW capacity and has increased to 660 MW. Power import is poised to increase significantly over the coming years to potentially reach up to 3,000 MW in the medium term.

The Government of Bangladesh (GoB) envisages a dramatic increase in coal‐fired generation to meet demand for base‐load power. The latest projections by the Power System Master Plan (JICA, 2017) forecast coal generation capacity to reach 20 GW by 2041. A number of coal‐fired projects are currently in different stages of planning. The Government agreed with India on a joint venture to build a 1,320 MW coal plant along with a coal import facility near Rampal, in the South‐West of Bangladesh (Sharda and Buckley, 2016). Construction of the facility began in 2016 with concessional finance from the Export Import Bank of India. Further coal project developments are underway for the South‐East and South‐West regions with plans for three more coal plants each with capacity in excess of 1,200 MW. To address demand growth in the immediate future, the Government tendered 700 MW of fuel oil generation capacity to meet near‐term demand growth for power, with plans to add a total of 1,000 MW of fuel oil generation capacity.<sup>4</sup> The limited supply of gas forced the power sector to invest in oil‐fired generation capacity and increase the imports of fuel oil. Fuel oil generation capacity grew from 300 MW in 2010 to 2,100 MW in 2015 (BPDB, 2016). The government regulates oil prices in Bangladesh, including the price of fuel oil, which largely reflected international prices as the country imports all of its oil from abroad. Between 2014 and

> 4 The Financial Express. “BPDB selects sponsors for seven more IPPs”, 2016. Source: <u>http://www.thefinancialexpress‐bd.com/2016/12/01/54877/BPDB‐selects‐sponsors‐for‐seven‐more‐IPPs</u>

4
# **V.3 Effects on input demands**

Next, we explore whether IDP inflows had any impact on the firm’s demand for inputs including labor and capital. The results of these exercise are useful to study the possibility that IDP may be increasing labor supply and as such may be modifying firms’ optimal combination between labor and capital.

Our estimates of the impacts of IDP in total employment are presented in Table IV. Additionally, we also estimate the effects of IDP inflows on employment and wages by type (blue- and white-collar) in Tables V and VI, albeit only between 2000 and 2010 due to data availability.

We find no consistent evidence of a significant effect of higher IDP inflows on formal employment or wages of any kind. Although some of the results are statistically significant for employment effects, these effects are not consistent and disappear once a different set of controls are included in the estimates. Our results are in line with the argument suggesting that IDP in Colombia have low levels of education and little experience on manufacturing jobs (see Ib ́a ̃nez and Moya, 2006). In fact, as forced migrants in Colombia mostly moved from rural to urban areas, they previously worked on agricultural activities which have low demand on urban regions. Displaced individuals, hence, may be searching for low quality jobs as they lack experience, which ultimately may suggest that they will end up joining the informal sector.

We also explore the effects of IDP inflows on firms’ capital demand measured as the logarithm of gross and net investment in Table VII. Similar to the estimates for employment, we are not able to distinguish a consistent statistically significant effect of IDP inflows in capital demand.<sup>19</sup> In sum, our results so far suggest that the effects of IDP on manufacturing formal firms are limited to their negative effects on the intensive and extensive margin of production with no other observed effects on the prices or input demands in the formal sector.

> 19As is common in most firm surveys a handful of firms report capital and investment figures, this explains the drop in the number of observations on Table VII. To address the concern that this sample was not selected we imputed capital figures to all the firms available in Table I by doing a two-step exercise: i) we estimate a regression of firm investment on production, intermediate consumption, and sector for the firms that have information available on all variables, ii) we use the estimated coefficients to predict investment figures for the firms that had missing information. We then re-estimate Table VII for all the firms included in Table I. We still find no effects of IDP migration on net investment.

19
# Table 10: Regression results from baseline specification using US BEA input‐output table coefficients

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|(12)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐ TFP Ack|erberg_et al_.‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐TFPQ Hsie|h and Kleno|w‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐0.097||||||‐4.065***||||||
||(0.117)||||||(0.000)||||||
|Entryt‐2||0.051|||0.068|0.069||‐1.539***|||‐1.422***|‐1.404***|
|||(0.260)|||(0.146)|(0.145)||(0.001)|||(0.005)|(0.005)|
|Conductt‐2|||‐0.129***||‐0.136***|‐0.135***|||‐2.554***||‐2.413***|‐2.331***|
||||(0.001)||(0.000)|(0.000)|||(0.000)||(0.000)|(0.000)|
|FDI Overallt‐2||||0.036||0.008||||0.829***||0.657**|
|||||(0.326)||(0.810)||||(0.003)||(0.018)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐YES‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|5598750|6046439|5679283|6486660|5598750|4983292|5598825|6046514|5679358|6486735|5598825|5598825|
|R2A|0.982|0.982|0.982|0.985|0.982|0.874|0.825|0.820|0.826|0.831|0.825|0.825|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.129|0.133|0.128|0.134|0.129|0.426|0.686|0.701|0.684|0.711|0.686|0.686|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

40
# **Acknowledgments**

This paper draws upon a wide range of contributions from sector specialists from the Africa Infrastructure Country Diagnostic Team; notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Rupa Ranganathan on power, Carolina Dominguez on water and sanitation, Michael Minges and Rebecca Meyer on information and communication technologies, Alberto Nogales on roads, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in the relevant World Bank country teams; notably Ousmane Diagana (Country Manager), Moctar Thiam (Sector Leader), Christian Vang Eghof (Country Officer), Matar Fall and Zie Coulibaly (water and sanitation), Fabio Galli, Papa Mamdou Fall, Pierre Pozzo di Borgo and Lucien Andre Aegerter (transport), Koffi Ekouevi and Phillippe Durand (power), Boutheina Guernazi (ICT), and Ibrahim Mamane and Afua Sarkodie (local consultants).
13

|**Classification**|**Oil trade status**|**No. of countries**|**Median V**|**Median**∆**V**|
|---|---|---|---|---|
|Developingcountries only|||||
|East Asia and Pacific|Importers|14|5.9|4.1|
||Exporters|3|-3.0|0.9|
|Europe and Central Asia|Importers|17|5.0|1.8|
||Exporters|5|-15|-2.7|
|Latin America and the Caribbean|Importers|23|8.0|5.9|
||Exporters|7|-2.7|-3.4|
|Middle East and North Africa|Importers|9|5.4|6.4|
||Exporters|3|-31|-1.2|
|South Asia|Importers|8|5.4|3.0|
||Exporters|0|n.a.|n.a.|
|Sub-Saharan Africa|Importers|34|5.4|4.0|
||Exporters|11|-19|-3.8|

_Sources_ : Author’s calculations based on data from EIA (2013a) and WDI. _Notes_ : Results by region exclude high-income countries. V = vulnerability; ∆ V = change in vulnerability; n.a. = not applicable.

Among net importers, high-income countries had the lowest median vulnerability and the lowest median increase in vulnerability. Upper-middle-income countries had the highest median vulnerability and increase in vulnerability. By region, Latin America and the Caribbean had the highest vulnerability and second highest increase in vulnerability. Among exporters, as expected, the Middle East and North Africa, with some of the major oil exporters, had the largest median vulnerability in magnitude. These countries also dominated the vulnerability index for highincome net oil exporters. Figure 3 illustrates the distribution of vulnerability in 2011 by oil trade status, and Figure 4 provides the distribution of the change in vulnerability between 2009 and 2011.

Figure 3: Countries’ Vulnerability by Oil Trade Status in 2011 <!-- Start of picture text --> 40 40 38 38<br>35 Net importers Net exporters<br>35 32 35<br>30 30<br>25 25<br>20 17 20 18<br>15<br>15 15<br>10 10 8<br>5 2 5<br>0 0<br>0 to 2.5 2.5 to 5 5 to 10 10 to 20 Above 20 Below -30 -30 to -15 -15 to -5 -5 to 0<br>Vulnerability as % of GDP Vulnerability as % of GDP<br>% of countries<br><!-- End of picture text --> _Source_ : Author’s calculations based on data from EIA 2013b.
# TABLE 3

FLOW OF NEW COMMITMENTS OF OFFICIALLY SUPPORTED MEDIUM- AND LONG-TERM EXPORT CREDITSa TO SUB-SAHARAN AFRICA, 1987-1989

|Year|SDRs. million|IUSS.million)|Share in lower<br>in_ome developing<br>countries.b Percent|Share in<br>all developing<br>countriesb. percent|Share in<br>all recipientsb,<br>percent|
|---|---|---|---|---|---|
|1987|2,584|(3,341)|35.3|11.0|7.1|
|1988|2,652|(3,564)|28.0|10.4|8.0|
|1989|3,274|(4,196)|31.4|10.5|7.9<br>**U**|

Source: OECD, Secretariat of the Export Credit Group.

Notes: a See notes to Table 2.

b The denominators are from, respectively, columns III, IV and V of Table 2, Part A.