rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1-2. Adult mortality** in **Botswana by age and gender,**<sup>1991</sup><sup>**and**1997/98</sup>

<!-- Start of picture text -->
-<br>1.L4%<br>0.8  0.2%  - I  -<br>L.<br>0.0%  - '<br>15  20  25  30  35  40  45  50<br>Age  __<br>MALE  mortality 1991  census  - FEMALE  mortality  1991  census<br>- - Male mortality 1997/8  survey  - - - Female morality  1997/8  survey  !<br><!-- End of picture text -->

As indicated above, however, these raised mortality rates are substantially below the rates that had been projected for the year 1997/98 - for example,<sup>see</sup> Botswana MFDP (2000a). In addition, early estimates for the total head count from the Botswana population census in 2001 indicate a total of almost 1.7 million.<sup>This</sup> is close to projections obtained before HIV/AIDS was accounted for, and is at<sup>the</sup> very high end of AIDS scenario projections produced prior to the census (most<sup>of</sup> these were in the range 1.4-1.6 million). In the case of Botswana, it may be that the period from infection to death is higher than was previously expected, but this may not apply across all of the Southern African countries.

A recent study by the Medical Research Council (MRC) in South Africa (MRC 2000) documented the steady rise in adult mortality during the 1990's, with female mortality in the 25-29 age group increasing by a factor of 3.5 between<sup>1985</sup> and 2001. AIDS deaths were broadly compatible with a number of different projection models, in particular the ASSA600 model produced by the Actuarial Society of South Africa (ASSA). The report concluded: While there is inevitably some degree of uncertainty because of the assumptions<sup>underlyingboth</sup> the model and the interpretation of the empirical data, we estimate that about<sup>40percentof the</sup> adult deaths aged 15-49 that occurred in the year 2000 were due to HIV/AIDS<sup>andthat abuut20</sup> percent of all adult deaths in that year were due to AIDS. When this is combined<sup>withthe excess</sup> deaths in childhood, it is estimated that AIDS accounted for about<sup>25</sup> percent<sup>of all deaths in the</sup> year 2000 and has become the single biggest cause of death. The projections<sup>show that, without</sup> treatment to prevent AIDS, the number of AIDS deaths can be expected to grow.<sup>withinthe next</sup> 10 years, to more than double the number of deaths due to all other<sup>causes,resultingin5 to7</sup> million cumulative AIDS deaths in South Africa by 2010.

_3_
59. The estimate in this study should exceed the World Bark's Atlas estimate ($310) because it is derived by an exchange-rate-based conversion. WEFA's estimate ($305) seems unacceptable because it provides an esfimate lower than the exchange rate conversion, although it claims to follow the PPP methodology (Taylor, 1986). Gordon study ($1,485) applied the PPP concept in general but has weak statistical support and strong assumptions. So it is hard to compare his estimate with the results deuived in this study.

60. Taylor' **s** tudy presents an altemativ **e** stimate ($785) from the production-side. This estimate was used by IMF to give a revised weight for China in its _World Economic Oulook AF, 1993)._ Taylor's procedure is much different from the standard "industry of origin" approach. "Industry of **orgin",** which originated fiom Rostas (1948) and Paige and Bombach (1959), has been systematically used by the Internional Comparisons of Output and Productivity Project (ICOP) at Grongn University since 1983 (Maddison and van Ark, 1988). An obvious difference in Tayloe **s** tudy is that only China's gross values of output were used as weights to denve sectoral PPPs rather than the quantities of both the counties being compared as the "industry of origin" approach suggested. Considering the nature of PPPs over sectors or over the entire economy as an aggregated ratio of prices, one-way-weiting approach applied in the ag on seems inapproprate. Another possible source of eror is to geneate PPPs for missing sectors consing of a lot of non-tradables (e.g., services) by PPPs for maaring sectors which obviously inclde mostly trdables based on an input-output relationship (Taylor, 1992 pp.27).

61. Penn World Table's results ($3,248 in Mark 4 and $2,368 in Mark 5) were extrapolated firm Kravis' estfimate ($1,930) which followed the ICP methodology. So only Krais' study and the present 26
**Table 1. Poverty (at $2 per day) and Related Indicators by Country Groupings**

||Poverty<br>Incidence<br>.(percent)|Poverty<br>Gap Ratio|Female<br>Illiteracy<br>(percent)|Life<br>Expectancy<br>(years)|Percent<br>Population<br>Covered|
|---|---|---|---|---|---|
|_By Region_||||||
|Europe and Central Asia|20|6|5|69|96|
|Latin America and the Caribbean|29|16|13|70|90|
|Other|66|29|39|66|93|
|_By Per-Capita Income_||||||
|(over $2996)|22|13|11|70|87|
|($1,446-$2,995)|24|9|12|69|86|
|($755-$1,445)|50|19|25|70|92|
|Blend|83|40|52|62|99|
|**All Middle-income Countries**|**57**|**25**|**32**|**67**|**93**|
|Low-Income Countries|||46|57|100|
|High-Income Countries|0|0|..|78|100|

Note: ".." denotes data not available. Source: _World Development Indicators 2000._ earlier decades-in Brazil, for example, the net primary enrollment rate was 80 percent in 1980 as compared with 97 percent currently-and disadvantaged educational access of females. But it also reflects the low quality of primary schooling provided in many countries and smaller secondary enrollments. Life expectancy at birth in the MICs rose in most cases over the past decades to an average of 67 years and compares favorably with that of low-income countries (57 years), but still lies substantially below that of high-income countries (78 years). While some health indicators such as child immunization approach high-income country levels in most MICs, others are widely dispersed. Access to sanitation in countries such as Bulgaria, Korea, Romania and Poland lies in the 95-100 percent range, but reaches lows in the 10-20 percent range in Egypt, India and Uzbekistan. Access to drinking water is also substantially dispersed. The incidence of communicable diseases such as tuberculosis and the prevalence of HIV/AIDS are at levels somewhat closer to those of low- than high-income countries.

# **Volatility in MICs Increases Poverty**

20. Economic growth in MICs has in general been more volatile than in high-income countries, though less volatile than in low-income countries (Figure 7) Moreover, growth volatility increased somewhat in the MICs in the 1990s compared to earlier decades.

<!-- Start of picture text --> Figure 7. Growth Volatility<br>EMIcs OHighimome *LICs 7<br>8<br>6 6 9 6 5<br>6- 58<br>29<br>21 24<br>2<br>0<br>1970s 1980s 1990s<br>Source World Bank staff estimates<br><!-- End of picture text --> 7
# Model 3: The Impact of Decentralization when Resource and Preference Costs are Simultaneously Determined

Table 7 summarizes the estimation results for the model specification which assumes that resource and preference costs are jointly determined. We use this model specification to better capture the impact of autonomy of decentralized units on efficiency of road service provision. We see that the model specification does not fit the data as well as the specification on independent determination of resource and preference costs. This may be that the countries in the data base fall mostly in the category where these two costs are indeed independently determined. The effect of decentralized maintenance with this specification is the same as that for model 1, where resource costs initially increase, then decrease as the level of decentralization increases (second order effect less significant). In the case of decentralized administration costs, the effect is also the same as that in model I in terms of the shape; resource costs decreasing non-linearly (second order effect) and then increasing at slower rate (third order effect).

Table 7: Simultaneous Determination of Resource **and Preference Costs**

|Variable|Const|ruction|Main|tenance|Admin|istration|
|---|---|---|---|---|---|---|
||Resource|Preference|Resource|Preference|Resource|Preference|
|fiscal|1.71|0.25|... :00-2 Iti|-0.59|0.37|0.66|
|decentralization'|(0.17)|(0.12)|(-.79)|(-0.52)|(0.94)|(0.60)|
|fiscal|0.03|-0.01|-0.06|0.02|-00|0.002|
|decentralization<sup>2</sup>|(0.14)|(-0.31)|(-1.28)*|(0.74)|(--.O6)|(0.95)|
|fiscal|-0.001|0.0001|0.0004|-0.0001|.|-0.0001|
|decentralization<sup>3</sup>|(-0.50)|(0.46)|(1.06)|(-0.79)|(2_83)t|<br>(-0.58)|
|R-squared|0.70|0.38|0.76|0.51|0.77|0.54|
|Observations|261|261|261|261|261|261|

Fiscal decentralization measured separately as the percentage local government contribution to construction, maintenance, administration, and road provision in general in the first, second and third order respectively.

Shaded portions significant at 90% level of confidence.

* significant at 85% level of confidence.

'em significant at the 80% level of confidence.

Construction costs are neutral with respect to decentralization, as in the case of model 1, and preference costs, unlike the case in model 1, are not affected by any type of decentralization. The results with the respect to the impact of decentralization on resource costs are very robust, whereas those with respect to preference costs are sensitive to model specification.

These results point to a very important issue: when road provision activities are decentralized and the decentralized units have authority for both spending and quality standards they spend more on maintenance, have more efficient administration than 23
# **3. Roads and Poverty in Cameroon**

## **_Overview_**

Due a great diversity of climates, terrain and vegetation, Cameroon has several natural advantages that could help to sustain its development. Moreover the 402 km of coast boarding the Guinea Gulf ease trade for Cameroon as well as its closer neighbors. In fact Cameroon is among the ten richest countries in Sub-Saharan Africa. Cameroon’s wealth comes from oil and wood resources as well as a diversified agricultural production both in terms of foodproducing (maize, cassava, plantain banana, macabo, rice, millet, sorghum, groundnuts, etc...) and cash-crop (cocoa, coffee, cotton, rubber, banana, pineapple, etc...) that makes its agriculture the most prosperous in Central Africa.

The primary sector (agriculture) represented 22% of GDP in 2001; the industry sector was 33% and services 45% of GDP. However employment does not exhibit a similar pattern. In fact in 2001, 60.6% of total employment was in agriculture, 9.1% in industry and 23.1% in services (World Bank, 2009.3).

Figure 1 below presents the evolution of the poverty rate in Cameroon between 1996 and 2007. While poverty at the national level significantly declined between 1996 and 2007 (from 53.3% to 39.9%), this phenomenon encompasses heterogenous situations. Decrease of poverty rate mainly came from the reduction that occurred in urban areas where the proportion of households below the poverty line was 41.4% in 1996, 17.9% in 2001 and 12.2% in 2007. On the contrary, poverty rate in rural areas only lost 4 percentage points over the period.

<!-- Start of picture text --> 40,2<br><!-- End of picture text --> <!-- Start of picture text --> 1996<br>2001<br>2007<br><!-- End of picture text --> <!-- Start of picture text --> 52,1<br><!-- End of picture text --> <!-- Start of picture text --> 17,9<br><!-- End of picture text --> <!-- Start of picture text --> 10,9<br><!-- End of picture text --> <!-- Start of picture text --> 13,3<br>5,9<br><!-- End of picture text --> 8
WPS4228

# Poverty, Social Divisions, and Conflict in Nepal*

Quy-Toan Do† Lakshmi Iyer ‡

## **Abstract**

We conduct an econometric analysis of the economic and social factors that contributed to the spread of violent conflict in Nepal. We find that conflict intensity is significantly higher in places with greater poverty and lower levels of economic development. Violence is higher in locations that favor insurgents, such as mountains and forests. We find weaker evidence that caste divisions or linguistic diversity in society are correlated with the intensity of civil conflict.

Keywords: violent conflict, poverty, social polarization World Bank Policy Research Working Paper 4228, May 2007 _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

*We thank the Informal Sector Service Centre (INSEC, Nepal) for data on conflictrelated deaths. We thank Shawn Cole, Elena Glinskaya, Simon Johnson, Miguel Messmacher, Martin Ravallion, Debraj Ray, Eric Werker, Hassan Zaman and seminar participants at the World Bank, Harvard Business School, the Workshop on Endogenous Institutions and Political Conflict (UC Berkeley) and NEUDC 2006 for comments and suggestions. Tripti Thapa provided outstanding research assistance.

†Research Department, World Bank. qdo@worldbank.org ‡Corresponding author. Harvard Business School. liyer@hbs.edu
# **1 Introduction**

According to the WTO, the global value of cross-border services exports in 2007 was $3 trillion, or some 20 percent of world trade in goods and services. However, the share of services rises to almost 50 percent if transactions are measured in terms of direct and indirect value added content - that is, if trade is measured in terms of the value that is added by processing of imported components into final products for export as opposed to measuring trade flows on the basis of the gross value of goods crossing the border (Escaith, 2008). If we add in the sales of services by foreign affiliates of multinational firms, then the value of trade in services rises further. Data for 15 OECD countries puts the value of such sales at some $1.5 trillion in 2007 (WTO, 2009).

While the expanding economic importance of services has not gone unnoticed, traded services have not figured prominently in the economic growth and development literature, and have been downplayed in the input- output and CGE literature. Traditional international economics textbooks tend to assume (assert) that services are largely non-tradable. This started to change with the emergence of services on the international policy agenda in the 1980s, in part as a consequence of U.S. proposals to negotiate multilateral rules on policies affecting trade and investment in services.<sup>1</sup> Productivity trends in services have been a basic determinant of recent growth in the OECD. Working with the EU-KLEMS database, Inklaar, Timmer, and van Ark (2007) find rising investment in ICT (information and communications technology), as well as growth in human capital contributed, were both important drivers of labor productivity in commercial services in Europe, though there are marked differences across countries and sectors. How well this translated into overall productivity growth helps explain overall differences in performance across Europe. Similarly, Triplett and Bosworth (2004) find that in the United States productivity growth in services has been critical to overall US growth since the early 1990s. The literature using input-output analysis to study services in individual countries

> 1See Francois and Hoekman (2010) for an overview. This is a basic point to be taken from the theoretical literature on trade in services, including Markusen (1989), Francois (1988, 1990), van Marrewijk et al (1997), and Markusen Rutherford and Tarr (2005).

2
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37
Policy Research Working Paper 10336 Combatting Forest Fires in the Drylands of Sub-Saharan Africa Quasi-Experimental Evidence from Burkina Faso _Tung Nguyen Huy Guigonan Serge Adjognon Daan van Soest_ Development Economics Development Impact Evaluation Group March 2023 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
|0<br>20<br>40<br>60<br>80<br>100<br>evenue, excl. grants ( % GDP)||0<br>20<br>40<br>60<br>80<br>100<br>evenue, excl. grants ( % GDP)||
|---|---|---|---|
|0<br>100<br>200<br>300<br>R|400|0<br>R|20<br>40<br>60<br>80<br>100|
|_Notes:_The dotted line represents the result of a b<br>_Source:_Authors’ calculation based on the variable<br>**Table A2.**Bivariate Linear R<br> <br>Trade Openness|ivariate lin<br>s and data<br>egression|ear regression of reven<br>sources described in S<br>s of Revenues on E<br>|ue on the indicator shown on the x-axis.<br>ection III.<br>conomic Fundamentals<br> <br>Age Dependency Ratio|
|**2008-2011**<br>Mean<br>Slope in<br>Biv. Reg.|Sign.|**2012-2015**<br>Mean<br>Slope in<br>Biv. Reg.|**2016-2019**<br>Sign.<br>Mean<br>Slope in<br>Biv. Reg.<br>Sign.|
|Revenue (% GDPG)<br>27.7||28.8|27.8|
|Log GDP per capita<br>9.2<br>6.69|***|9.3<br>6.42|***<br>9.4<br>5.93<br>***|
|Share of Agriculture<br>in GDP<br>11.5<br>-0.67|***|10.9<br>-0.65|***<br>10.8<br>-0.58<br>***|
|Trade Openness<br>93.1<br>0.05|***|97.2<br>0.05|***<br>95.1<br>0.05<br>***|
|Age Dependency<br>Ratio<br>60.6<br>-0.34|***|59.4<br>-0.35|***<br>59.3<br>-0.30<br>***|

_Notes:_ The dotted line represents the result of a bivariate linear regression of revenue on the indicator shown on the x-axis. _Source:_ Authors’ calculation based on the variables and data sources described in Section III.

_Notes:_ Significance levels (Sign.) correspond to p-values in bivariate regression of revenue on respective indicator: _* p<0.10, ** p<0.05, *** p<0.01. S_ amples consist of a maximum of 182 countries (including high-income countries). _Source:_ Authors’ calculation based on the variables and data sources described in Section III.

**Table A3** . Data on World Bank Group Engagement in DRM

|Country|Income<br>|Region|WB|G Engagement|<sup>1</sup>|Level of WBG<br>|
|---|---|---|---|---|---|---|
||Group||2009-2011|2012-2015|2016-2019|Engagement<sup>2</sup>|
|Afghanistan|LIC|SA|0.159|0.170|0.077|Low|
|Albania|UMIC|ECA|0.066|2.553||High|
|Algeria|LMIC|MENA||||None|
|Angola|LMIC|SSA||0.756||Medium|
|Armenia|UMIC|ECA|0.930|1.323||High|
|Azerbaijan|UMIC|ECA||||None|
|Bangladesh|LMIC|SA|0.060|0.028||Low|
|Belarus|UMIC|ECA|0.312|||Low|
|Belize|UMIC|LAC||||None|
|Benin|LMIC|SSA|0.527||0.389|Medium|
|Bhutan|LMIC|SA||0.738|0.121|Medium|
|Bolivia|LMIC|LAC||||None|
|Bosnia and Herzegovina|UMIC|ECA||||None|
|Botswana|UMIC|SSA||||None|

28