rafmacalaba/datause-encoder-data huggingface dataset:
_24_ _Estimates and Projections of Mortality by Cause_ Table A2. Countries or territories with reliable reports on causes of death, and year of data used, by region

|_Country or territory_|_Year_|_County or territory_|_Year_|
|---|---|---|---|
|_Industria market economies_<br>|<br>|_Industrial nonmarket economies_<br>||
|Australia<br>|1985<br>|Bulgaria|1985|
|Austria<br>|1985|Czechoslovakia|1985|
|Belgium<br>|1986|German Dem. Rep.|1985|
|Canada<br>|1985<br>|Hungary|1985|
|Denmark<br>|1985|Poland|1985|
|Finland<br>|1985|Romanla|1984|
|France<br>|1985|U.S.S.R.|1986|
|Germany, Federal Rep. of<br>|1985<br>|Yugoslavia|1985|
|Greece<br>|1985|||
|Iceland<br>|i985|_Latin America and the Caribbea_|_n_|
|Ireland<br>|1985|Argentina|1985|
|Italy<br>|1985<br>|Chile<br>|1985|
|Japan<br>|1985<br>|Costa Rica<br>|1985|
|Luxembourg<br>|1985<br>|Cuba|1985|
|Malta<br>|1985|Uruguay|1985|
|Netherlands<br>|1985|||
|New Zealand<br>|1985|_Middk East and North Africa_||
|Norway<br>|1985<br>|Israel|1985|
|Portugal<br>|1985|||
|Spain<br>|1985|_Asia_||
|Sweden<br>|1985|Hong Kong|1985|
|Switzerland<br>|1985<br>|Singapore|1985|
|United Kingdom<br>|1985<br>|||
|United States of America|1985|||

Note: Data for around 1970 for all these countries or territories except the German Democratic Republic, the U.S.S.R., and Israel were also used. The U.S.S.R. data, though considered reliable, could not provide estimates for many of the specific causes used here.

Life expectancy is assumed to rise over time following a logistic curve that has a minimum of 20 and a maximum of 75.8 for males or 82.5 for females. The slope of the curve is estmated using a regression equation that has on the right-hand side the slope over the previous quinquennium and the female secondary enrolment ratio (or the percent urban, in the few cases where this is not available). In comparisons among a variety of countries, the median slope in the past, applying the logistic curve, was -.035, and the quartiles were -.017 and -.053. To eliminate extreme trends-including a few increases in mortality-no country is allowed to have a slope falling outside these quartiles. The estimated slope, or the quartile value if appropriate, is assumed to apply for three quinquennia, after which all countries are assumed to revert to the median slope.

Infant mortality is assumed to fall over time also following a logistic curve, from a maximum of 200 per thousand to a minimum of 4 per thousand. The median slope across a variety of countries with data was .052, with the quartiles being .025 and .120. A slope is estimated for each country from its performance in the preceding quinquennium, and applied for three quinquennia, unless this slope falls outside the quartiles, in which case the appropriate quartile value is used.
are robust to mis-specifications, and Mazumder (2015) finds that the attenuation bias in the rank-rank slope estimate is significantly smaller compared to that in intergenerational elasticity (IGE). Nybom and Stuhler (2016) use rich data from Sweden to analyze life-cycle bias and attenuation bias in IGE and rank based measures. They find that the rank-based measures are more robust to both life-cycle bias and attenuation bias when compared to the estimates of IGE.

Although attenuation bias due to measurement error has been a central focus of the literature on intergenerational mobility, a second potentially important source of bias arising from coresidency restrictions common in household surveys has received much less attention. Since standard household surveys, especially in developing countries, do not collect information on nonresident children, the estimates of intergenerational persistence are likely to suffer from significant truncation bias. We are aware of only two papers that analyze the implications of coresidency restrictions in household surveys for estimating intergenerational persistence in economic status. In a recent paper, Emran, Greene and Shilpi (2018) show that intergenerational regression coefficient (IGRC), the most widely used measure of intergenerational mobility in development literature, suffers from strong downward bias due to truncation as a result of coresidency criteria used for household membership in a survey. IGRC is estimated as the slope of a level-level regression, usually for intergenerational schooling persistence. This also suggests that some of the IGE estimates for developed countries may be significantly downward biased even after correction of attenuation bias due to measurement error when the data do not include all the children because of nonresidency at the time of the survey.<sup>3</sup> Using the British Panel Household Survey, Francesconi and Nicoletti (2006) report 12% - 39% downward bias due to coresidency in short panels when estimating persistence in the Hope-Goldthorpe index of occupational prestige. To the best of our knowledge, there is no evidence in the literature on how the rank-based the economic literature by Dahl and Deleire (2008).

> 3Their evidence also shows that truncation bias is much less (less than one-third) in another widely used measure called intergenerational correlation (IGC) which estimates Pearson correlation between economic status of parents and children.

2
Detailed analysis of these lagging regions indicates the following socio-economic characteristics (Massum 2008; Government of India 2008; World Bank 2005a; World Bank 2005b; World Bank 2005c; World Bank 2006; World Bank 2007; World Bank 2008a).

- These lagging land-locked/border countries/states/provinces/districts have an estimated 400 million people of which an estimated 200 million people are poor (reference year of 2005).  This is about 50 percent of South Asia’s estimated total number of poor for the year 2005.

- Much of the population is rural (90 percent) and most are engaged in lowproductivity agriculture.

- The human development indicators tend to be below the comparable national average and many indicators are lower than the average in South Asia.

- Infrastructure is on average poorer than rest of the respective countries and poorer than the average for South Asia

- The border regions on average tend to be more vulnerable to water shortages and flooding problems than other parts.

A review of history suggests that not all areas were lagging and poor for all the time. For example both Afghanistan and Nepal prospered in the 18<sup>th</sup> and 19<sup>th</sup> centuries on the basis of free trade and commerce with neighbors including Central Asia, Middle East, Indian sub-continent and China. Over the years, conflict and border restrictions removed this key source growth. A more dramatic example is that of North-East India (the so-called 7 sisters). The partition of Indian sub-continent into Pakistan and India brought havoc to the economies of these seven sisters, especially the booming state of Assam, by cutting off its sea-access and sharply raising the transport distance with the rest of India (see Box 1). The Kashmir valley was a prosperous and peaceful tourist resort until conflict between Pakistan and India took its toll. The Federally Administered Territories of Pakistan (FATA) and the NWFP were similarly prosperous and peaceful trading outposts until regional and global conflicts converted many parts of these border areas into conflict prone, security risk regions with low per capita incomes, high incidence of poverty and low human indicators.

Apart from being poor, the lagging regions also share a number of common vulnerabilities. First and foremost is the vulnerability to natural disasters. Figure 9 shows the impact of natural disasters in terms of the share of GDP lost. South Asia has lost a significant amount of its GDP because of natural disasters. This loss has been especially significant for Maldives, Bangladesh, Sri Lanka and Pakistan. The impact of natural disaster is particularly strong in South Asia because of its high population density. The losses are typically not insured in the financial market. It is the poor who are adversely affected by disasters.

16
Table 2. Main activity: share of women and men spending time across categories, and minutes in the last 24 hours

||Share spe<br>time<br>|nding any<br>(t>0)<br>|Minutes:|full sample|Minutes<br>spending|: only those<br>any time (t>0)|
|---|---|---|---|---|---|---|
||Women<br>(Y/N)|Men<br>Y/N)|Women|Men|Women|Men|
|Activity categories:|||||||
|(1) Unpaid work|0.91|0.48|209.7***|55.6***|231.2***|116.5***|
|(1a) Domestic (cook/clean)|0.86|0.34|117.9***|26.2***|136.5***|76.6***|
|(1b) Water and fuel coll.|0.15|0.11|8.9|7.8|59.4*|72.9*|
|(1c) Childcare|0.36|0.17|79.4***|20.9***|218.2***|121.0***|
|(2) Off-farm and/or agr. work<sup>(3)</sup>|0.76|0.89|275.3***|396.0***|363.6***|443.4***|
|(2a) Wage|0.22|0.37|106.2***|177.6***|473.4|476.6|
|(2b) NFE|0.22|0.22|93.6|89.0|426.9|410.6|
|(2c) Agriculture|0.36|0.47|70.4***|124.9***|194.2***|263.2***|
|(3) Leisure|0.96|0.97|151.2***|171.7***|156.8***|176.2***|
|(4) Traveling|0.57|0.67|41.6***|57.7***|73.1***|86.4***|
|(5) School|0.12|0.04|9.6**|5.1**|82.0|114.9|
|(6) Sleep|1.00|1.00|658.0|658.1|658.3|658.1|
|(7) Eating/drinking|1.00|1.00|75.1|75.7|75.9|76.3|
|(8) Other|0.10|0.10|19.4|19.9|196.2|205.7|
|Number of respondents|1,977|1,648|1,977|1,648|||

## <u>Notes:</u>

(1) Sample includes men and women self-reporting in the LSMS+ time diary module, aged 18 and older. All estimates weighted by household sampling weight.

(2) Significant differences in minutes spent across men and women (separately for full sample and among t>0) are indicated by asterisks; ***p<0.01 **p<0.05 *p<0.10 (3) As seen in Table 1, the time use module also included categories for (a) making goods (furniture, pottery, baskets, clothing), as well as (b) work for other household free of charge as an exchange laborer, but nearly no respondent reported time in these areas.

# 3.2 Labor

Adult individuals are economically active in Cambodia, even women despite having high unpaid work burdens. Table 3 reflects summary statistics as reported in the labor module across agricultural and offfarm activities. The labor module provides employment information as well as the hours worked in the last seven days. Seventy-seven percent of women and 88 percent of men reported being involved in any activity across agricultural and off-farm work, and about 21 percent of women and 27 percent of men reported being involved in more than one activity in the last 7 days (with greater shares in rural areas).<sup>20</sup> Roughly equal shares of men and women (about 45 percent) reported being involved in agriculture; about 16 percent of men and 18 percent of women reported themselves as owners/managers of NFEs; but there were wider gender gaps in wage employment (44 percent of men and 26 percent of women).

> 20 Among those in multiple areas of work, the most common combinations were respondents involved in either NFE or wage work along with agriculture (Hasanbasri et al., 2021b).

10
**_Closing the infrastructure gap._** An export push requires an infrastructure push in Africa, because many of the bottlenecks pertain to lack of infrastructure (figure 24 and 25). Sub-Saharan Africa lags at least 20 percentage points behind the average for International Development Association countries on almost all major infrastructure measures.<sup>36</sup> In addition, quality of service is low, supplies are unreliable, and disruptions are frequent and unpredictable (figure 26)—all pushing up production costs, a critical impediment for investors (table 15). There are also large inequities in access to household infrastructure services, with coverage rates in rural areas lagging those in urban areas. The region’s unmet infrastructure needs are estimated at $22 billion a year (5 percent of GDP), plus another $17 billion for operations and maintenance.

**<u>Table 15: Impact of unreliable infrastructure services on the productive sector</u>**

|Service problem|Sub-<br>Saharan<br>Africa|Developing<br>countries|
|---|---|---|
|_Electricity_|||
|Delay in obtaining electricity connection (days)|79.9|27.5|
|Electrical outages (days per year)|90.9|28.7|
|Value of lost output due to electrical outages (percent of turnover)|6.1|4.4|
|Firms maintaining own generation equipment (percent of total)|47.5|31.8|
|_Telecommunications_|||
|Delay in obtaining telephone line (days)|96.6|43.0|
|Telephone outages(daysperyear)|28.1|9.1|

_Note:_ Data for Sub-Saharan Africa are for 6 countries; data for developing countries are for 55 countries.

_Source:_ World Bank Investment Climate Assessments.

Recent progress is encouraging. Except roads, indicators of infrastructure access rose between the 1990s and 2000s (table 16). The Africa Partnership Forum reported steady improvements in effectively using existing infrastructure and in increasing public investments. Countries are also undertaking regulatory and policy reforms, especially for water, telecommunications, and transport (Africa Partnership Forum 2006b). Twenty of the largest African countries have or are formulating reform agendas for water and sanitation.

> 36 An important exception is the penetration of fixed-line and mobile telephones, where Sub-Saharan Africa leads low-income countries by as much as 13 percent. The largest gaps are for rural roads (29 percentage points) and electricity (21 percentage points).

43
<mark>Coimbra C., and P. Esteves. 2004. “O</mark> il Price Assumptions in Macroeconomic Forecasts: Should we follow Futures Market Expectations?” _OPEC Energy Review_ <mark>28 (2): 87-106.</mark> Coppola, A. 2008. “Forecasting Oil Price Movements: Exploiting the Information in the Futures Market.” _Journal of Futures Markets_ 28 (1): 34–56.

Cortazar, G., H. Ortega, and C. Valencia. 2021. “How Good are Analyst Forecasts of Oil Prices?” _Energy Economics_ 102 (C): 1-11.

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Degiannakis, S., and G. Filis. 2017. “Forecasting Oil Price Realized Volatility Using Information Channels from Other Asset Classes.” _Journal of International Money and Finance_ , 76 (2017): 2849.

Dehghani, H. 2018. “Forecasting Copper Price Using Gene Expression Programming.” _Journal of Mining and Environment_ 9 (2): 349–360.

Diebold, F.X. 2015. “Comparing predictive accuracy, twenty years later: A personal perspective on the use and abuse of Diebold–Mariano tests.” _Journal of Business and Economic Statistics_ , 33(1): 1-1.

Diebold, F.X., and R.S. Mariano. 1995. “Comparing Predictive Accuracy.” _Journal of Business and Economic Statistics_ 13: 253–263.

Dooley, G., and H. Lenihan. 2005. “An Assessment of Time Series Methods in Metal Price Forecasting.” _Resources Policy_ 30 (2005): 208–217.

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63
23 entrepreneurs in protected sectors to alter their labor-leisure choices and work less. Although in most models trade distortions retard economic growth, Grossman and Helpman (1989) point out circumstances under which certain trade distortions accelerate growth.'" Thus, there is an important empirical question regarding the relationship between various trade policies and economic growth.

# 2. EmRirical Studies

In a thorough review of the trade-growth literature, Sebastian Edwards (1989) divides the empirical growth literature on trade policy into three categories:1" papers that attempt to link measures of either export growth or the role of exports in the economy to growth; investigations that use theoretical models to compute continuous, objective measures of trade distortions and then link these measures of trade intervention with growth; and large multicountry case-studies that attempt to link indicators of trade orientation with growth.

In synthesis volumes of multicountry-case studies, Krueger (1978), Bhagwati (1978), Balassa (1982), and The World Bank (1987) carefully document the experiences of many countries that have undergone trade li.beralization efforts. Although a wealth of information is contained in these studies, Edwards (1989) argues that methodological and statistical problems dampen the '"For example, in a world where technology is produced and where technology has external effects, a country with a comparative advantage in technology production can increase world growth by protecting the technology sector.

la Edwards (1989) also considers studies that try to uncover the determinants of exports.
# V. APPROPRIATE POLICY RESPONSES l/

The review of country experiences above gives considerable insight into the types of policy responses that stabilized rather than destabilized commodity exporting LDCs facing terms of trade shocks. Nevertheless, it may be helpful to consolidate the findings in the form of more general lessons or principles involved in boom management. That is the purpose of this Section. When discussing primary export booms, it is helpful to consider the appropriate response for the economy as a whole, before highlighting the possible roles of the private and public sectors. At this level of aggregation, macroeconomic policies should be designed to achieve three broad objegtiDvel:

- (i) limiting increases in consumption expenditures,

- (ii) allocating the resulting increase in domestic saving efficiently among alternative uses including: increases in official reserves and other foreign assets, reductions in foreign liabilities, increases in domestic capital investment, and supporting programs in trade, financial, and exchange rate liberalization,

- (iii) coping with the monetary, inflationary, and exchange rate aspects of the booms.

At the more disaggregated level, three additional sectoral obiectives should be addressed:

- (iv) improving the sectoral allocation and timing of domestic investment,

- (v) maintaining a reasonable "balance" between the public and private sectors of the economy,

- (vi) setting producer prices (relative to world prices) to achieve an efficient production response, on one hand, and appropriate levels of saving by commodity

/ This section is adapted from Cuddington (1988).

37
# TABLE OF CONTENTS

Page

## EXECUTIVE SUMMARY

|I.<br>INTR|ODUCTION: PURPOSE AND SCOPE OF REVIEW ..........................|1|
|---|---|---|
|II.<br>OVE|RVIEW<br>.............................|3|
|A.|Lending ................................................|3|
||1.<br>Trends in the Lending Portfolio ........<br>........................|3|
||2.<br>Regional Distribution and IBRD/IDA Commitments ....................|5|
||3.<br>Cofinancing ............................................|6|
||4.<br>Project Funds by Expenditure Category ...........................|6|
|B.|Sector Work .<br>..............................................|7|
|C.|Supervision and Project Evaluation: Lessons Learned ..............|9|
|D.|Policy, Research and Dissemination<br>.....................|9|
|E.|Population, Health and Nutrition Content of Projects ............<br>.|11|
||1.<br>Population ............................................|11|
||2.<br>Health ..............................................|12|
||3.<br>Nutrition .............................................|13|
|III.<br>SPEC|IAL TOPIC: PHN'S CONTRIBUTION TO THE BANK'S POVERTY||
|ALL|EVIATION OBJECTIVE ..|15|
|A.|Background ..........|15|
|B.|Context ..........|15|
||1.<br>What ..........|15|
||2.<br>Who ..........|16|
||3.<br>How..........|16|
|C.|The Bank's Role ...............................................|17|
|D.|Evaluation of FY92 Portfolio of PHN Projects: An Overview ...................|17|
||Category 1:<br>Multisectoral Interventions for Poverty Alleviation||
||(Poverty Projects) .17||
||Category 2:<br>Provision of Basic Social Services to the Poor||
||as Primary Objective .18||
||Category 3:<br>PHN Interventions Whose Benefits to Poor||
||Are Important But Not Primary Objective .18||
|E.|Special Features of Poverty Interventions in the FY92||
||Portfolio .20||
||1.<br>Poverty Information and Monitoring ..............................|20|
||<br>2.<br>Understanding and Responding to the Needs and Demands||
||of the Poor ..........<br>........................|20|
||3.<br>Targeting ..................................|22|
||4.<br>Reforms in Policy, Financing and Organization .......<br>.............|..<br>23|
||4.a<br>Institutional Reforms .................................|24|
||4.b<br>Financial Reforms ...........|25|
# **2   Background and Description of the Program**

In response to the preventable threats posed by poor sanitation and hygiene, the Water and Sanitation Program (WSP) launched two large-scale projects in December 2006—the Global Scaling Up Handwashing Project and the Global Scaling Up Rural Sanitation Project—to improve child health and welfare outcomes of rural households around the world. These projects were implemented by local, regional, and national governments, with technical support from WSP.

The Global Scaling Up Handwashing Project implemented in Peru, Vietnam, Tanzania, and Senegal, borrowed from commercial and social marketing to promote better hygiene. Communication campaigns and messages developed for this project were designed and strategically delivered across multiple integrated channels and in various settings in order to “surround” target audiences with handwashing promotion. Formative research conducted during 2007 with mothers and caregivers revealed that soap was not available for handwashing in most households in Peru, that there was a common belief that washing hands with water was sufficient, and that people did not know the critical times to wash hands with soap. In fact, the results of the structured observations in the baseline showed that soap was used in only 16 percent of the events in which it would have been necessary, that 20 percent of people were observed to wash their hands after fecal contact,<sup>18</sup> and that 25 percent of people washed their hands before eating.<sup>19</sup> Thus, the programs studied had sufficient scope to improve the hygiene habits of the treated households. These rates of soap use are lower than the observed rates in more developed peri-urban areas (47 percent after cleaning up a child in Northern England<sup>20</sup> ) and higher than the rates observed in poorer countries (10 percent after cleaning up a child in Nigeria, 3 percent after defecation in Ghana, or 1 percent in after toilet use in urban Burkina Faso<sup>21</sup> ).

> 18Fecal contact includes defecation, toileting of any kind, and cleaning a child who has defecated.

> 19At the baseline, structured observations of handwashing behavior were conducted in Peru as part of this study, in a subsample of 159 households (see Galiani and Orsola-Vidal 2010). 20 Curtis et al. 2003.

> 21 World Bank 2005.

7