rafmacalaba/datause-encoder-data huggingface dataset:
Brown C, Werick W, Leger W, Fay D. 2011. A Decision-Analytic Approach to Managing Climate Risks: Application to the Upper Great Lakes. _JAWRA Journal of the American Water Resources Association_ **47** (3): 524–534. DOI: 10.1111/j.17521688.2011.00552.x.

Bryant BP, Lempert RJ. 2010. Thinking inside the box: A participatory, computerassisted approach to scenario discovery. _Technological Forecasting and Social Change_ . Elsevier Inc. **77** (1): 34–49. DOI: 10.1016/j.techfore.2009.08.002.

Bureau of Reclamation. 2012. _Colorado River Basin Water Supply and Demand Study: Study Report_ . .

Cervigni R, Liden R, Neumann JE, Strzepek KM. 2015. _Enhancing the Climate Resilience of Africa’s Infrastructure: The Power and Water Sectors_ . World Bank: Washington, D.C. DOI: 10.1596/978-1-4648-0466-3.

Coastal Protection and Restoration Authority (CPRA). 2012. _Louisiana’s Comprehensive Master Plan for a Sustainable Coast_ . Coastal Protection and Restoration Authority of Louisiana: Baton Rouge, Louisiana.

Dessai S, Hulme M. 2007. Assessing the Robustness of Adaptation Decisions to Climate Change Uncertainties: A Case Study on Water Resources Management in the East of England. _Global Environmental Change_ **17** (1): 59–72.

Groves DG, Bloom E, Lempert RJ, Fischbach JR, Nevills J, Goshi B. 2014a. Developing Key Indicators for Adaptive Water Planning. _Journal of Water Resources Planning and Management_ . American Society of Civil Engineers (10.1061/(ASCE)WR.19435452.0000471).

Groves DG, Fischbach JR, Bloom E, Knopman D, Keefe R. 2013. _Adapting to a Changing Colorado River_ . RAND Corporation: Santa Monica, CA.

Groves DG, Fischbach JR, Kalra N, Molina-Perez E, Yates DN, Purkey DR, Fencl A, Mehta V, Wright B, Pyke G. 2014b. _Developing Robust Strategies for Climate Change and Other Risks: A Water Utility Framework_ . Denver, CO.

Groves DG, Fischbach JR, Knopman D, Johnson DR, Giglio K. 2014c. _Strengthening Coastal Planning: How Coastal Regions Could Benefit from Louisiana’s Planning and Analysis Framework_ . Santa Monica, CA.

Groves DG, Knopman D, Lempert RJ, Berry SH, Wainfan L. 2008. Presenting Uncertainty About Climate Change to Water-Resource Managers. RAND Corporation.

Groves DG, Lempert RJ. 2007. A new analytic method for finding policy-relevant scenarios. _Global Environmental Change_ **17** (1): 73–85. DOI: 10.1016/j.gloenvcha.2006.11.006.

Groves DG, Sharon C. 2013. Planning Tool to Support Planning the Future of Coastal Louisiana. _Journal of Coastal Research_ . The Coastal Education and Research Foundation **67** : 147–161. DOI: 10.2112/SI_67_10.

Haasnoot M, Kwakkel JH, Walker WE, ter Maat J. 2013. Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. _Global Environmental Change_ **23** (2): 485–498.

38
anocracy specifically influence a state’s risk of either civil war onset or political disorder. If spatially dependent relationships exist, neighboring regime characteristics should possess greater influence than states some distance away and equal to that of domestic considerations, as had been shown for other neighboring political attributes on civil war.

_2.5 Does the Risk of Conflict Recurrence vary across Regime Neighborhoods?_ A risk profile of civil war prone states based on internal and neighboring characteristics cannot ignore the considerable influence of the conflict trap. A state runs a 44% risk of returning to civil war up to five years after the cessation of the previous conflict (Collier et al., 2004: 83). The pathdependent effects of conflict, and the dynamics that evolved during a war, may lead to differential rates of recurrence across risk-prone states (Doyle and Sambanis, 2000). The risk of recurrence has been shown to be strongly related to political and economic advancement (Walter, 2004). It stands to reason that the political institutions both within and across states are of particular importance in creating a stable post-war environment, or alternatively, creating an environment of hostile and unstable relations which may exacerbate the already feeble governing institutions in a state.

However, it is also true that factors affecting the risk of recurrence are different from those across all civil wars. The airing of grievances and the cessation of conflict creates new opportunities for reinstated or new governments to redress the causes and consequences of the previous conflict. A failure to do so is presumed to lead to quick recurrence. Post-conflict third-party intervention is often couched in terms of improvements in political participation, as this factor particularly is important to post-conflict societies (Doyle and Sambanis, 2000). The lack of democratic institutions, which is generally insignificant in many onset studies, is shown by Walter to powerfully predict recurrence (2004: 384). Walter also finds the risk of recurrence is due to growth experienced in the interim between the cessation of conflict and the onset of another; should people believe their lot is not improving, rebel recruitment prospects improve (2004:372, Elbadawi, 2001, Collier and Hoeffler, 2001; Fearon and Laitin, 2003; Gates, 2002).

If internal political factors essentially make or break the conflict trap, the political environment in which a state is operating should have a considerable impact on its ability to hinder renewed conflict activity, for two separate reasons: 1) surrounding regime characteristics create a context in which neighboring intervention is harmful to continuing peace or where states are unable to withstand the negative implications of ‘bad neighborhoods’ due to a weakened postwar state; 7
<mark>Firms that are innovators (50 percent) are more evenly distributed across sectors, firm size, and region. The decision to define innovators more inclusively – by their introduction of new products and services – instead of by whether they conducted research and development was intentional. It is important to keep in mind that the many small adaptations and improvements in products and services that firms make, while minor relative to current practice, are cumulatively responsible for the bulk of productivity gains over time (Kim, 1999). It is therefore significant that most high-skill Tanzanian firms are innovators rather than exporters.</mark>

|**Table 1. Distribution of**|**High-Skill Firms**<br>**Region**<br>|**by Sector, Fir**<br>|**m Size, and**<br>|
|---|---|---|---|
||High-Skill<br>Firms|Exporter<br>Firms|Innovator<br>Firms|
|**_Sector_**||||
|Food|0.50|0.22|0.32|
|Textile & Garments|0.76|0.13|0.72|
|Fabricated Metals|0.60|0.09|0.53|
|Furniture|0.64|0.10|0.60|
|IT|0.60|0.12|0.55|
|Hotels & Restaurants|0.49|0.03|0.46|
|Construction|0.60|0.20|0.43|
|Transportation|0.60|0.26|0.42|
|**_Firm Size_**||||
|Small(5 - 19)|0.54|0.07|0.49|
|Medium(20 - 99)|0.68|0.23|0.53|
|Large(100+)|0.70|0.24|0.52|
|**_Region_**||||
|Arusha|0.73|0.04|0.71|
|Dar es Salaam|0.61|0.18|0.49|
|Mbeya|0.47|0.04|0.43|
|Mwanza|0.36|0.16|0.24|
|Zanzibar|0.73|0.08|0.69|
|**_Total_**|0.59|0.13|0.50|
|Source: 2015 Tanzania Enter<br>Note: The column “High-Sk<br>Exporters, asInnovators oras|prise Skills Survey<br>ill firms” includes fir<br> bothconcurrently.|ms that are de|fined either as|

Table 2 presents unconditional pair-wise correlations (and their statistical significance) of selected key variables that will be the focus of the analyses. The correlations relate exporter and innovator status (a proxy for skills demand), education and skills composition, in-service training, and summary measures of skill problems and job vacancies. These unconditional correlations reveal important relationships between different variables, as described below. These will be further 19
# **ANNEX.  Comparison of LIDCs and LICs**

This paper discusses LICs following the World Bank Group definition of countries with per capita gross national income below $995 per year. This group includes 33 countries. It differs from other reports (such as IMF 2018a and World Bank and IMF 2018c), which include additional middleincome countries following the IMF definition of low-income developing countries (LIDCs). The term “LIDC” refers to countries with low per capita gross national income and comparatively weak socioeconomic indicators.

# List of countries in LIDCs and LICs

|**L**|**ow-Income Developing**<br>**Countries (LIDCs)**|**Low-Income Countries**<br>**(LICs)**|**Low-Income Developing**<br>**Countries (LIDCs)**|**Low-Income Countries**<br>**(LICs)**|
|---|---|---|---|---|
|1|Afghanistan|1<br>Afghanistan|31<br>Malawi|17<br>Malawi|
|2|Bangladesh||32<br>Mali|18<br>Mali|
|3|Benin|2<br>Benin|33<br>Mauritania||
|4|Bhutan||34<br>Moldova||
|5|Burkina Faso|3<br>Burkina Faso|35<br>Mozambique|19<br>Mozambique|
|6|Burundi|4<br>Burundi|36<br>Myanmar||
|7|Cambodia||37<br>Nepal|20<br>Nepal|
|8|Cameroon||38<br>Nicaragua||
|9|Central African Republic|5<br>Central African Republic|39<br>Niger|21<br>Niger|
|10|Chad|6<br>Chad|40<br>Nigeria||
|11|Comoros|7<br>Comoros|41<br>Papua New Guinea||
|12|Congo, Dem. Rep. of|8<br>Congo, Dem. Rep. of|42<br>Rwanda|22<br>Rwanda|
|13|Congo, Republic of||43<br>São Tomé and Príncipe||
|14|Côte d'Ivoire||44<br>Senegal|23<br>Senegal|
|15|Djibouti||45<br>Sierra Leone|24<br>Sierra Leone|
|16|Eritrea|9<br>Eritrea|46<br>Solomon Islands||
|17|Ethiopia|10<br>Ethiopia|47<br>Somalia|25<br>Somalia|
|18|Gambia, The|11<br>Gambia, The|48<br>South Sudan|26<br>South Sudan|
|19|Ghana|||27<br>Syrian Arab Republic|
|20|Guinea|12<br>Guinea|49<br>Sudan||
|21|Guinea-Bissau|13<br>Guinea-Bissau|50<br>Tajikistan|28<br>Tajikistan|
|22|Haiti|14<br>Haiti|51<br>Tanzania, United Rep.|29<br>Tanzania, United Rep.|
|23|Honduras||52<br>Timor-Leste||
|24|Kenya||53<br>Togo|30<br>Togo|
|25|Kiribati||54<br>Uganda|31<br>Uganda|
|26|Kyrgyz Republic||55<br>Uzbekistan||
|27|Lao P.D.R.||56<br>Vietnam||
|28|Lesotho||57<br>Yemen, Rep.|32<br>Yemen, Rep.|
|29|Liberia|15<br>Liberia|58<br>Zambia||
|30|Madagascar|16<br>Madagascar|59<br>Zimbabwe|33<br>Zimbabwe|

_(continues in the next column)_ Sources: IMF and World Bank.

16
# **Panel B: Two Stage Regressions (OLS in the First Stage, Ordered Logit in the Second Stage)**

|_Predicted Values in_<br>_Second Stage _|_H_|_istorical Variabl_|_es_|_Averag_|_e External Finan_<br>_Countries_|_cing Across_||_Historical Variabl_|_es_|
|---|---|---|---|---|---|---|---|---|---|
||1|2|3|4|5|6|7|8|9|
|Dependent Variable in|||||||CORE<br>(Averaged<br>across|INDEX8<br>(Averaged<br>across|INDEX2<br>(Averaged<br>across|
|Second Stage|CORE|INDEX8|INDEX2|CORE|INDEX8|INDEX2|countries)|countries)|countries)|
|External Financing|0.014<sup>a</sup>|0.041<sup>a</sup>|-0.008|0.014<sup>a</sup>|0.041<sup>a</sup>|-0.007||||
||[0.004]|[0.004]|[0.007]|[0.004]|[0.004]|[0.007]||||
|External Financing<br>(Averaged across|||||||0.118<sup>a</sup>|0.134<sup>a</sup>|0.003|
|countries)|||||||[0.029]|[0.033]|[0.002]|
|First Stage Adjusted R2|0.2093|0.2088|0.2088|0.209|0.2086|0.2086|0.4676|0.4004|0.4004|
|N|13688|13343|13343|13886|13541|13541|44|41|41|

a b c , , and represent significance at 1%, 5% and 10% respectively 47
recourse if the funds are stolen (Gross, Hogarth and Schmeiser, 2012). Moreover, an individual’s decision to remain unbanked can have long-lasting effects, since having a bank account can facilitate asset building and wealth creation that may allow for consumption smoothing at retirement or when faced with shocks (Rhine et al., 2006).

Despite evidence on the importance of financial inclusion in general, and the use of bank accounts in particular, little is known about the reach of the financial sector across countries and the policies that foster inclusion (see Demirguc-Kunt, et al., 2008).<sup>3</sup> Existing studies rely on country-level proxies (such as the number of bank accounts per capita), drawing on data collected from bank regulators and supervisors (e.g., Beck, Demirguc-Kunt, and Martinez Peria, 2007; Honohan, 2008; Kendall, Mylenko, and Ponce, 2010). Not only are these studies problematic because the proxies used have significant limitations (for example, the number of accounts per capita might overestimate the percentage of the population with an account because some people have more than one account or accounts may be owned by foreigners), but more importantly, the fact that the data used are aggregated at the country level makes it impossible to assess how the impact of policies varies across individual characteristics, such as income.

This paper studies the underpinnings of financial inclusion using a new global individuallevel database (for a detailed description of the data see Demirguc-Kunt and Klapper, 2012). The unique individual-level nature of the data—from the perspective of the users of financial services—allows us to disaggregate financial inclusion by key respondent characteristics, such as gender, age, education, employment status, and income, and to investigate how the factors and policies associated with greater inclusion vary according to individual-level characteristics. In particular, we are interested in analyzing whether policies to promote inclusion are especially

> 3 The exception is the US, where a sizeable literature studies the unbanked and their behavior See, for example, Barr (2004), Hogarth et al. (2004,2005),  Rhine and Greene (2006), Rhine et al. (2006) and Gross et al. (2012),

3
discourage investment in production capacity, which eventually causes prices to rise as existing oil fields that can be tapped at relatively low marginal cost are depleted.

**Figure 3.2 Evolution of Research and Development Expenditure in Select Integrated Oil and Service Companies**

<!-- Start of picture text --> (Billions of US Dollars; unless otherwise noted)<br>600 120<br>Research and Development Expenditure<br>500 100<br>Crude oil price, average (U.S. dollars a barrel, right scale)<br>400 80<br>300 60<br>200 40<br>100 20<br>0 0<br>1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> Source: World Bank; Bloomberg LP.; and WB staff calculation.

But the adage gained its currency before the advent of shale oil production, which can be turned on and off much faster than conventional production. The new shale oil production––made possible by new technologies such as hydraulic fracturing (“fracking”) and horizontal drilling—will lead to shorter and more limited oil-price cycles. In the narrow view, however, technological innovation--both among producers and users, are results from exogenous (i.e. independent and external) forces. The data seem to suggest otherwise—that technological change is, in economic parlance, endogenous to market developments. As shown in Figures 3.2 and 3.3, periods of high oil prices tend to stimulate expenditure in research and development and subsequently innovation.

**Figure 3.3 Clean vs Dirty Patents**

<!-- Start of picture text --> 20000 2.5<br>18000 Dirty Patents Clean Patents Ratio: Clean vs Dirty<br>16000 2<br>14000<br>12000 1.5<br>10000<br>8000 1<br>6000<br>4000 0.5<br>2000<br>0 0<br>1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 2011<br><!-- End of picture text --> Source: Aghion, Dechezlepretre, Hemous, Martin and Van Reenen (2012), calculations based on the PATSTAT database.

4
VAT revenues vary in importance between countries; the lowest ratios are found in the oil exporting countries. For these countries, oil revenues are the single most important source of fiscal revenue but these revenues do not accrue to the government through the VAT. Table 3 below shows VAT revenues as a share of GDP for the Balkan countries, the CIS and South Caucasus, the EU11, the Oil Exporters and the ECA average for 2007, 2009 and 2011.<sup>18</sup> They are the most important, as a share of GDP, in the Balkans. Figure 4 shows that the importance of VAT revenues varies with per capita income in ECA countries. VAT revenues as a share of overall revenues, as depicted along the Y axis, tend to decline with increases in per capita income.

**Table 3:  VAT Revenue as Share of GDP in 2007, 2009 & 2011**

|**Country Group**||**VAT Revenu**<br>|**e**<br>|
|---|---|---|---|
||**2007**|**2009**|**2011**|
|Balkans Average|11.1|10.5|10.7|
|CIS & South Caucasus Average|9.7|9.3|9.5|
|EU11 Average|8.4|7.8|8.1|
|Oil Exporters Average|5.3|4.7|4.4|
|ECA Average|8.7|8.2|8.4|
|_Source:_World Bank staff calculation|s based ongov|ernment acco|unts|

**Figure 4:  VAT revenues are a less important share of taxes in higher income countries**

<!-- Start of picture text --> 0 10000 20000 30000<br>GDP Per Capita (Constant 2000 USD)<br>Source: World Bank staff estimates based on government accounts.<br>Finally, Figure 5 below shows a scatterplot of the VAT revenue to GDP ratio graphed against the<br>net export to GDP ratio. The graph indicates a strongly negative relationship. Figures 4 and 5 together<br>imply that similar changes in net exports would have larger effects on countries with lower GDP.<br>.6<br>.5<br>.4<br>.3<br>.2<br>The Ratio of VAT Revenues To Total Tax Revenues .1<br><!-- End of picture text -->

> 18 These numbers are based on data received during 2012.

14
stock data rather than on a structural form-reduced form dichotomy. The corresponding estimable equation from this specification is (where the stock level is engogenous). Equation (44) forms the basis for equations estimated in that paper.

49. Although the estimable equations (42) and (44) are derived within an RE commodity market model and hence are compatible with the REH, it is nevertheless the case that they fail to give a satisfactory representation of the implications of the REH forJ commodity market behavior. First, it is not possible to use these equations to incorporate any advance information either on movements of the exogenous variables (e.g. policy announcements) or on the equation residuals (so called 'conjunctural analysis - see Keating, 1985). Second, the specifications are vulnerable to the Lucas (1976) critique since a change in the processes governing the exogenous variables x1 or x2 would alter the _0_ coefricients in equation (42) or the s coefficients in equation (44).

50. These observations prompt a search for a procedure intermediate between inference of the price adjustment equation from the structural equations and unrestricted estimation of an equation the specification of which is suggested by the rigorously derived model. Direct substitution of the processes for x1 and x2 into the price adjustment equation (28) gives If the parameters 1, (32 and -Y2 are imposed from the production and consumption 31
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 6. Shares of different factors involved in the time and cost of importing through gateways in West Africa**

<!-- Start of picture text --> a. Cost b. Time<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>Border Administrative Transport Port Border Administrative Transport Port<br>Share (%) Share (%)<br><!-- End of picture text --> _Source_ : Ocean Shipping Consultants 2008; AICD ports database; Teravaninthorn and Raballand 2009. Ports data are based on indicators from 2006/07.

Similarly, lengthy travel times in the Cotonou–Niamey corridor are mainly associated with inefficient operations in the Port of Cotonou, which is responsible for more than 70 percent of the time required to import to Niamey, similar to the time consumed at the Port of Abidjan within the context of the Abidjan– Ouagadougou corridor. Comparatively, the time spent at the Port of Lomé accounts for 53 percent of the total time involved in transporting goods using the Lomé–Niamey corridor. It is estimated that the delays in the Port of Cotonou impose a cost of around $180 per container. Time-consuming regulatory processes related to customs clearance and technical controls further augment the total time (figure 6b), accounting for about 23 percent of the total time. The average time to clear imports through customs in Benin is more than twice the time required in Sub-Saharan Africa (World Bank and IFC 2009).

# Roads

## **Achievements**

Benin’s has a well-developed road network with relatively high levels of traffic. The density of the country’s classified road network (75 km/1,000 km2) is comparable with that of the average low-income country (88 km/1,000 km2), but only one-third that of the average for the middle-income countries of Sub-Saharan Africa (278 km/1,000 km2). Traffic of more than 2,000 vehicles per day, concentrated in the North–South corridors, is much higher than the average of 1,100 vehicles in nonfragile low-income countries and close to the levels of traffic registered in middle-income countries (table 3).

10
||Ex-Factory<br>Mining<br>Products|**_HowFanHu Ouaw hmaaty_**<br>**_1r_**<br>Prices<br>Purchase Prices<br>Raw<br>All Raw Materials<br>Materials|
|---|---|---|
|1985|108.8|110.9<br>118.0|
|1986|100.6|107.5<br>109.5|
|1987|114.1|106.9<br>111.0|
|Aug.1988|108.8|117.8<br>123.9|

Source: China Price 2 (1989): 59-60.

Profitable intermediation, whether by state agencies or by legitimate or illegal private enterprise (sometimes involving persons with official connections) is an important componen of the "official speculation' (guandao) widely reported in the Chinese press. Beijing's large Yanshan Petrochemical Company reportedly "sold almost all their products to the State at official low prices" only to discover that 'most of their products' users paid much higher market prices" for the same materials [China Daily 6-3-1989, p. 1].

Although inflation of industrial prices seems to have acclerated in recent years, substantial price increases certainly occurred prior to 1984/85. Data in Table 6 show a rising trend for coal prices paid by electric power plants beginning as early as 1978/79. Data for the construction industry show a consistent pattern of rising building costs from the stat of annual time seIes data in 1978 [TJNJ 1988, p. 590]. Since these figures exclude land costs, it would appear that rising costs of construction mateials, which prompted complaints in the Chinese press during the late 1970s, also date back to this period. Interview data collected by foreign reserchers give a strong impression of rising machinery prices during the late 1970s and early 1980s [personal communication].

Information on prices paid by power plants for coal illustrates the possible inconsistency between information about inflation and the price changes implied by industrial output statistics. The electric power industry is dominated by large, stateowned enterprises. It seems reasonable to assume that thermal power plants obtain the bulk of their coal requirements through planned allocations at low official prices, and that they enjoy considerable protection from the "official profiteers' attacked in the Chinese press. This would imply that, relative to otber consumers of coal, power plants are somewhat insulatid from inflationary pressures, and that trends in their coal costs should _unmad_ the average rise in coal prices for the entire economy.

Data reproduced in Table 6 show that averagc coal costs in China's power industry havi risen steadily since 1978. The index of coal costs, which should represent an underestimate o the aveage rise in coal prices, shows an increase of 88.9 peracnt between 1978 and 1987. The implicit price indicator calculated from gross output at cunrent and constant prices for the coal industry, however, shows much smaller increases of 66.7 percent for the state sector and 48.9 percent for the (much smauller) collective mining sector. These data lead to the conclusion that the annual price changes implicit in statistics of gross output value at current (CVIO) and