rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Exhibit 11 Exhibit 12<br>Technology Concentration Patenting Revealed Comparative<br>(Adjusted Herfindahl Index) Advantage (RCA)<br>0.14 2.5<br>US Source: USPTO and Hu Drugs and<br>(2006).<br>Medical<br>0.12 Japan<br>2.0 Computers &<br>Korea Communications<br>0.10 Taiwan Electrical &<br>Electronic<br>China 1.5<br>0.08<br>Singapore<br>1.0<br>0.06<br>0.04 0.5<br>0.02<br>0.0<br>0.00<br>Source: USPTO and Hu (2006).<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004<br>China Malaysia Korea SingaporeTaiwan, China Japan US<br><!-- End of picture text -->

# **_How Good is East Asian Patenting?_**

Although the volume of patenting in economies such as Korea and Taiwan (China) equals or exceeds that in most developed economies, is the same also true of the _quality_ of their innovations? The technological or economic value of patents can vary enormously. In fact the distribution of patent values is highly skewed. A survey of the realized economic value of patents in Germany and the US, for example, found that the top 10 percent of patents accounted for over 80 percent of economic value. (Scherer and Harhoff, 2000). Thus a simple count of patents may not provide an adequate summary of the quality of the underlying innovations.

A particularly useful feature of patents is that they contain citations to the previous patents and scientific literature which define the “prior art” to which the patent is making an original contribution. Trajtenberg et al (1997) suggest measuring the quality of patents with indexes of patent “generality” and “originality” based on patent citations. A patent is said to have greater generality and impact if it is cited more often by a wider range of technology classes. A patent is said to be more basic or original if it cites a wide range of patent technology classes.

US patents generally have higher generality and originality indexes across all technology fields compared to Japan and other East Asian economies.<sup>29</sup> Exhibits 13 and 14 show.that Japanese patents generally achieve quality ratings that are 80 to 90 percent or more of US quality ratings. Korea, in particular, is close to Japan in most technology areas, even matching or exceeding it in some. Taiwan (China) tends to achieve somewhat lower generality and originality scores, but still scores 70-80 percent of US levels.

> 29  Some East Asian economies have few if any patents in some technology fields, resulting in few citations with which to compute generality or originality indexes.  The discussion therefore focuses on Korea and Taiwan (China), economies with sufficient patenting activity for meaningful measurement.

26
9

## TABLE I

# Lending for MSWM Projects in AFRICA. FY74-88

|||LOAN<br>ANOUNT|TOTAL<br>PROJECT<br> <br>COST|<br>MSWM<br>COMPONENT<br> <br>COST|<br>MSWM AS<br>PERCENT<br>OF TOTAL<br>PROJECT||STATUS AS|
|---|---|---|---|---|---|---|---|
|COUNTRY|PROJECT TITLE||**(USS**MILL|ION)|<br>COST|<br>FY|<br> <br>OF 6/30/89|
|Benin|Cotonou Water/Sanitary<br>Eng Project|5.0|6.3|0.7|10 9%|81|COMPLETED|
|B. Faso|Urban Development Project|(8.2)*|(10.8)*|<br>NA|NA|78|COMPLETED|
|Burundi|Urban Development Project|15.0|16.7|0.4|2.2%|80|COMPLETED|
|Djibouti|Urban Development Project|5.0|15.2|1.0|6.8%|85|ONGOING|
|Gambia|Urban Management/Development<br>Project|<br>(11.5)*|(12.4)*|<br>NA|NA|84|ONGOING|
|Guinea|Conakry Urban Development Project|15.2|16.4|3.3|20.1%|84|<br>ONGOING|
|Ivory Coas|t Second Urban DeveLopment Project|(51.0)*|(104.0)*|<br>NA|NA|82|COMPLETED|
|Kenya|Site and Service Project|(16.0)*|(29.4)*|<br>NA|NA|75|COMPLETED|
|Lesotho|Urban DeveLopment Project|6.0|7.1|0.1|1.4%|80|COMPLETED|
|Liberia|Monrovia Urban Development Project|10.0|13.4|0.6|4.7%|82|COMPLETED|
|Madagascar|Antananarivo<br>Water and San Project|20.5|33.7|3.5|10.4%|80|COMPLETED|
|Mali|Mali Urban Developmert Project|12.0|15.3|1.4|9.2%|79|COMPLETED|
|Mauritius|Urb.n Rehab&Stormdrainage Project|15.0|24.5|0.7|2.7%|81|COMPLETED|
|Nigeria|Urban Development Project|17.8|36.6|0.03|0.1%|80|COMPLETED|
|Nigeria|Anambra Water/Sanitation<br>Report|67.0|120.0|5.4|4.5%|82|ONGGING|
|Nigeria|Lagos Swm and Stormdraii.age<br>Projett|72.0|164.3|111.8|68.0%|86|ONGOING|
|Nigeria|Infrastructure<br>Development Fund|69.5|96.8|3.8|3.9%|88|ONGOING|
|Uganda|Water Suppty & Sanitation Rehab|28.0|30.8|2.8|9.1%|85|ONGOING|
|Zambia|Lusaka Squatter Upgrade/Site<br>Service|<br>20.0<br>......|41.3<br>......|0.3<br>--<br>--|0.7%<br>......|75|COMPLETED|
||TOTAL FOR AFRICA REGION:<br>AVERAGE PER PROJECT:|378.0<br>~~<br>25.2|638.4<br>~~~~~~~<br>..<br>--<br>--<br>42.6|135.8<br>~..**.**.<br>..<br>...<br>.<br>9.1|.......<br>21.4%|||

NOTE: * Loan and Project Cost quoted but not included in the totals due to non-availabitity of SWM data.
# **Table 3: Urban access, Cash crop potential and Non-farm Employment: Non-Linear Effect**

|Dependent variable: Type of<br>|Mar<br>|ginal effects<br>||
|---|---|---|---|
|Employment in Non-Farm|Multinomia|l Logit regres|sions|
|sector|(1)|(2)|(3)|
|**_Wage employment-Low Return_**||||
|Cash Crop Suitability Index|-0.032<br>||-0.030<br>|
||(1.34)||(1.03)|
|Cash Crop Suitability Squared|0.007<br>(0.94)|||
|Distance to Major cities (log)||0.011<br>(1.10)||
|Distance* C. Crop Suitability||-0.003|0.003|
|||(2.10)*|(0.67)|
|Test of joint significance (χ<sup>2</sup>)|3.75|4.43|3.41|
|P-value|0.15|0.11|0.18|
|**_Wage employment-High Return_**||||
|Cash Crop Suitability Index|0.020||0.129|
||(0.38)||(2.90)**|
|Cash Crop Suitability Squared|-0.022<br>(1.23)|||
|Distance to Major cities (log)||-0.040<br>(3.11)**||
|Distance* C. Crop Suitability||-0.006|-0.033|
|||(2.48)*|(3.95)**|
|Test of joint significance (χ<sup>2</sup>)|12.92|28.32|25.71|
|P-value|0.002|0|0|
|**_Self-Employment_**||||
|Cash Crop Suitability Index|0.057||0.049|
||(1.00)||(1.11)|
|Cash Crop Suitability Squared|-0.034<br>(1.82)|||
|Distance to Major cities (log)||-0.013<br>(1.04)||
|Distance* C. Crop Suitability||-0.007|-0.017|
|||(2.38)*|(2.16)*|
|Test of joint significance (χ<sup>2</sup>)|12.4|13.19|13.41|
|P-value|0.002|0.001|0.001|
|Loglikelihood<br>|-7228|-7207|-7218|
|Pseudo R<sup>2</sup>|0.09|0.1|0.1|

All regressions include individual characteristics (gender, education, age), household characteristics (size, land ownership, asset ownership, electricity connection), village characteristics (if village has electricity, NGO programs, credit programs, market), distance to town and an intercept term as regressors. Robust z statistics in parentheses Significant levels: * = 5%, ** = 1%
# **1 Methodology**

This analysis uses a fixed sample of FCS countries for the period of analysis, 2001-2013. While the FCS list can change from year to year, with some countries exiting the list and others re-entering the list, it is difficult to interpret trend changes and correlations between sets of variables, when the composition of a sample varies from year-to-year. Keeping a constant sample enables time series trends to be more meaningfully interpreted as developments for a specific set of countries, rather than possible composition affects, which could not be ruled out if the sample varied from year to year. Similarly, understanding the correlation between the likelihood a project will obtain a satisfactory outcome rating and whether the project is in an FCS country is difficult to interpret when the sample changes from year to year, but has a more straight-forward interpretation when a simple, time-constant categorization of countries is adopted.

Thus, in line with the recent Independent Evaluation Group’s (IEG) study on WBG assistance to FCS (World Bank 2013 [5]), a fixed sample of FCS countries is used. Adopting a similar approach to the IEG paper, the analysis focuses on IDA-only countries and considers Bank operations in 34 fragile and conflict-affected states against that of countries that were never classified as FCS as a benchmark for measuring portfolio trends and outcomes. The 34 FCS countries include 22 always on the FCS list and 12 on the list for part of the review period (Table 1). The categorization is derived from the FCS lists for FY06 to FY13.<sup>1</sup> Partial FCS countries are those classified as FCS in at least two years during this period. Myanmar was left off the list of always FCS as it received no IDA financing during FY01-12. South Sudan is not covered in the IEG report but is included in the analysis in the sample of always FCS.

The underlying data in all sections of this paper is built up from project by fiscal year observations rather than portfolio aggregates. This means that in section 2 where an overview of the FCS and IDA portfolio is provided, the data is compiled by summing across the total number of projects for which a record exists in each fiscal year. It is important to note that the data does not come from earmarked portfolio amounts to specific countries, regions, sectors or networks. Rather it focuses on lending operations based on information recorded in the World Bank’s lending operations database in each fiscal year.

In sections 2 and 3 all operations that are IDA financed are considered. This includes development policy operations. This enables a complete mapping of IDA resources to FCS and other IDA-only countries since FY01.

> 1The FCS lists can be found on the FCV Group website: http://go.worldbank.org/BNFOS8V3S0

5
<!-- Start of picture text --> i|<br>2.2<br>= 00<br>1.75<br>1.20<br>1.25<br>1.00<br>oO.74<br>O.>0<br>O24<br>lesa 1See@ IFS 1FS0 1992 1994 1996 1s 2000<br><!-- End of picture text -->
<!-- Start of picture text --> Electricity access proxy<br>30<br>25<br>.20<br>45 = Benchmark<br>=—<—— Counterfact<br>-10<br>05<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Sectoral GVA shares<br>75<br>70<br>65<br>60<br>55"50 — 5:enc fai farming<br>45 eee Benc industry<br>4035 ees BENC SErViCesS<br>30 ==----Coun farming<br>2520 =----Coun indusry<br>15 —----Coun services<br>.0510 uo<br>00<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Average no. of workers per establishment<br>35<br>-30 a 4sors ‘‘ 7L<br>yg i nd ———Benc farming<br>2015 ~VAPMs as wmwees BeneBence servicesindustry<br>10 === Counfaming<br>5 =—=—= Coun indusry<br>rr === Counserices<br>0<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted sectoral real GVA<br>12<br>10J<br>0.8 ——— Benc farming<br>0.6 ———=mn BencBene industrysevies<br>94 -----Coun farming<br>_ =----Coun indusry<br>| °? ~7==-Coun services<br>0.0 ——_— eer<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->

<!-- Start of picture text --> Normalized GDP per capita<br>4<br>ra ia<br>2 = Benchmark<br>= Counterfact<br>1<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text --> <!-- Start of picture text --> Predicted expenditure shares<br>0.9<br>.07 fc ef jee[beneBence conspriv inv<br>0.6 ees Benc publ inv<br>05 w= Benc fix cost<br>04 =< Councons<br>0.3 =< Counprivinv<br>aA iat ED OD 2 jam couneannyCounfix cost<br>0.0 SN ,<br>1970 1975 1980 1985 1990 1995 2000 2005 2010<br><!-- End of picture text -->
These findings suggest that conflict has important consequences for the structure of economic activity. The impact of a surge in conflict-related casualties is much stronger on formal and illicit activities than on overall economic activity. This is shown by Figure 3, which traces the evolution of aggregate, formal and illicit economic activity based on the estimated coefficients in our preferred specifications and the assumed 0.25 elasticity of aggregate economic activity to nighttime light intensity. Both panels in this figure are computed for an increase in conflict-related casualties by one standard deviation. In panel 3a the shock lasts for one quarter only, whereas in panel 3b it continues over time. This figure also shows the predicted impact on informal economic activity based on equation (6) and the estimated sectoral weights in total economic activity.

# **Conclusion**

Most available estimates of the impact of conflict on economic activity focus on indicators giving a disproportionate weight to the formal sector. And this is despite informal and illicit activities being common, if not predominant, in conflict settings. Aggregate indicators such as GDP tend to underestimate informal sector activities, and to exclude illicit activities. Wages matter only for relatively better-off segments of the population, as the majority of those at work tend to be farmers or self-employed. Disaggregated indicators from non-traditional sources, from housing prices to construction permits to mobile phone traffic from registered firms, also refer to formal markets and organizations.

While some studies have analyzed the effects of conflict on informal and illicit activities, this paper is the first one to consider impacts across formal, informal and illicit activities simultaneously. The proposed methodology is firmly anchored in the literature, although it pays special attention to the dynamic structure of conflict impacts, and it controls for other variables that have been shown to affect economic activity in conflict settings. The main innovation of the paper comes from the reliance on non-traditional sources of data with a high degree of spatial granularity. Thus, nighttime light intensity is used to measure economic activity at the aggregate level, mobile phone traffic from registered firms for the formal sector, and the land surface devoted to poppy cultivation for illicit activities.

The results suggest that the main impact of conflict is not as much on aggregate economic activity as it is on its composition. Conflict escalation is associated with a major drop in formal sector activity, an increase in its informal sector counterpart, and a surge in illicit activities. Therefore, assessments that focus on indicators giving a disproportionate weight to the formal sector tend to overestimate the impact of conflict on economic activity.

19
# **Table A.10. The district fixed-effect RDD-DD regressions of households’ income and poverty using the sample without the** **<u>program 135 communes</u>**

|||||Dependen<br>|t variables<br>||||
|---|---|---|---|---|---|---|---|---|
|Explanatory variables|Log of per<br>capita income|Log of per<br>capita income<br>from wages|Log of per<br>capita income<br>from nonfarm<br>production|Log of per<br>capita income<br>from farm<br>production|Log of per<br>capita<br>remittances|Log of per<br>capita public<br>cash transfers|Log of per<br>capita income<br>from other<br>sources|Income poor<br>households|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Program *Post-program|-0.0517|0.4750|0.9983**|-0.4430*|0.5304|0.9442***|1.1550**|-0.0503|
|period|(0.0919)|(0.5635)|(0.4380)|(0.2309)|(0.5760)|(0.2807)|(0.4489)|(0.0598)|
|(Poverty rate – 50) *Post-|0.0044|-0.0997*|-0.1156**|0.0247|-0.0059|-0.0651**|0.0079|-0.0022|
|program period|(0.0111)|(0.0520)|(0.0560)|(0.0305)|(0.0663)|(0.0277)|(0.0531)|(0.0058)|
|Program * (Poverty rate – 50) *|-0.0085|0.0527|0.1009*|-0.0203|-0.0306|0.0529|-0.0019|0.0053|
|Post-program period|(0.0115)|(0.0570)|(0.0582)|(0.0311)|(0.0709)|(0.0342)|(0.0569)|(0.0063)|
|Age of household heads|0.0268***|0.0528***|0.0657***|0.1398***|-0.0501***|-0.1080***|-0.0588***|-0.0142***|
||(0.0026)|(0.0155)|(0.0099)|(0.0150)|(0.0099)|(0.0115)|(0.0126)|(0.0015)|
|Age squared of household|-0.0003***|-0.0008***|-0.0008***|-0.0014***|0.0007***|0.0014***|0.0007***|0.0001***|
|heads|(0.0000)|(0.0002)|(0.0001)|(0.0002)|(0.0001)|(0.0001)|(0.0001)|(0.0000)|
|Gender of household head|0.0166|0.0184|0.2169***|0.7117***|-0.3702***|-0.0586|-0.3139***|-0.0139|
|(male=1, female=0)|(0.0203)|(0.0978)|(0.0711)|(0.0886)|(0.0617)|(0.0631)|(0.0792)|(0.0100)|
|Ethnic minorities (yes=1,|-0.5658***|-0.3437*|-1.7880***|0.9697***|-0.4657***|0.4397***|-0.5626***|0.2113***|
|Kinh=0)|(0.0573)|(0.2002)|(0.1854)|(0.1433)|(0.1536)|(0.1129)|(0.1200)|(0.0242)|
|District fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|8.5712***|2.9181***|1.3450***|3.5091***|4.9270***|2.1595***|2.9019***|0.8883***|
||(0.0961)|(0.4433)|(0.2898)|(0.3731)|(0.3201)|(0.3182)|(0.3225)|(0.0496)|
|Observations|28,920|28,920|28,920|28,920|28,920|28,920|28,920|28,920|
|R-squared|0.389|0.180|0.117|0.160|0.241|0.164|0.101|0.275|

Note: This table reports district fixed-effect RDD-DD regressions of income and poverty of households using household-level observations. Robust standard errors in parentheses. Standard errors are clustered by district and village-year levels. *** p<0.01, ** p<0.05, * p<0.1.

60
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17
BENIN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# References and bibliography

This country report draws upon a wide range of papers, databases, models, and maps that were created as part of the Africa Infrastructure Country Diagnostic. All of these can be downloaded from the project website: www.infrastructureafrica.org. For papers go to the document page (http://www.infrastructureafrica.org/aicd/documents), for databases to the data page (http://www.infrastructureafrica.org/aicd/tools/data), for models go to the models page (http://www.infrastructureafrica.org/aicd/tools/models) and for maps to the map page (http://www.infrastructureafrica.org/aicd/tools/maps ). The references for the papers that were used to compile this country report are provided in the table below.

## General

AICD (Africa Infrastructure Country Diagnostic). 2010. ―ECOWAS’s Infrastructure: A Regional Perspective.‖ Africa Region, World Bank, Washington, DC.

- Banerjee, Sudeshna, Quentin Wodon, Amadou Diallo, Taras Pushak, Helal Uddin, Clarence Tsimpo, and Vivien Foster. 2009. ―Access, Affordability, and Alternatives: Modern Infrastructure Services in Africa.‖ AICD Background Paper 2, Africa Region, World Bank, Washington, DC.

- Foster, Vivien, and Cecilia Briceño-Garmendia, eds. 2009. _Africa’s Infrastructure: A Time for Transformation._ Paris and Washington, DC: Agence Française de Développement and World Bank. http://www.infrastructureafrica.org.

IMF (International Monetary Fund). 2003. Poverty Reduction Strategy Paper.

- ———. 2008. Poverty Reduction Strategy Paper. IMF Country Report 08/125. April 2008

- World Bank. 2005. Benin Investment Climate Assessment. Africa Region, Private Sector Unit. November.

- ———. 2011. ―Draft Program Document for a Proposed Grant to the Republic of Benin for a Seventh Poverty Reduction Support Grant (PRSG-7).‖ Africa Region, World Bank, Washington, DC. June.

- World Bank and IFC (International Finance Corporation). 2009. Enterprise Survey. Benin Country Profile. Washington, DC.

## Financing

Briceño-Garmendia, Cecilia, Karlis Smits, and Vivien Foster. 2009. ―Financing Public Infrastructure in

- Sub-Saharan Africa: Patterns and Emerging Issues.‖ AICD Background Paper 15, Africa Region, World Bank, Washington, DC.

44
**_-7_** -

P AKISTAN: The Pink funded an ERP study by the Pakistan Institute of Development Studies whose tariff recommendations were not accepted by the government largely because, in their view, it paid inadequate attention to the revenue and employment effects of the proposals. Subsequent analysis by the Bank considered the revenue and employment implications. It also recommended development of a broad-based sales tax to release the revenue constraint and enable further tariff reforms to proceed.

THflILAND: Revenue decline caused a reversal of previous tariff reductions in Thailand. A Bank review of SAL lending concluded that the Bank recommendations for revenue enhancement had focussed on one-time increases rather than elasticity-increasing tax reforms. It also suggested that short term revenue effects of tariff reduction may have been underestimated and contributed to policy reversal. An IMF study recommended tariff reductions to a level where they served a purely protective function. The revenue effects of these measures were expected to be compensated by reforms in domestic indirect taxation and the long term development of a consumption-type VAT.

-TURKEY: In Turkey, the prutection-reducing effect of tariff reforms over the period 1980-85 were partially offset by the introduction of a dollar denominated levy in 1985. While the Bank was separately recommending domestic tax reforms such Es the introduction of the VAT, these were slow in being implemented. It is clear that the tariff reforms did not ease the fiscal pressure and may have contributed to the growth of surtaxes on trade earmarked to Extra Budgetary Funds. A subsequent study took more explicit note of the need to coordinate tariff reductions and tax reforms. The report observed that

"trade taxes are an inefficient revenue collection device because they introduce production distortions in addition to consumer price distortion. They are however