rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1 Summary of Temperature Trend Studies in the Himalayas (NS= Number of stations; DT= Temperature data type (RG= reanalyzed grid; S=Station; M=monthly); SS= Statistical significance)**

|**Region**<br>|**NS DT**|**Trend(°C/Decade)**|**Analysis Period**|SS|**References**|**Remarks**|
|---|---|---|---|---|---|---|
|Western Indian Himalaya|RG(M)|Tmax: 0.09|1901-2003|NA|Dash et al.(2007)|Shimla (Himachal<br>Pradesh) and<br>Jammu & Kashmir<br>regions in India|
||10 S(M)|Tav, Northwest: 0.11 (A*), 0.14<br>(W**)|1876-2006|5%|Bhutiyani et al.(2010)||
||8 S(M)|Tav: 0.23-0.43 (W); Tmax: 0.06 to<br>0.17(W);Tmin: 0.336 to 0.833(W)|1975-2006|≤5%|Dimri and Dash(2011)||
|||||||Shimla (Himachal<br>Pradesh) and|
||4 S(M)|Tav,Northwest: 0.16(A),0.17(W)|1901-2000|5%|Bhutyani et al.(2007)|Jammu & Kashmir|
|||Tmax, Lower Indus: All seasons<br>and year (0.053 to 0.113; 0.139|20th century||||
||1 S(M)|Annual)|(90-100years)|5%|Singh et al.(2008)||
|Pakistan Himalaya(Upper Indus)|6 S(M)|Tmax: 0.27-0.55|1961-1999|5%|Fowler and Archer(2005)||
|||Tmax: Upper:0.45 (W);|||||
||20 S(M)|Middle:0.42(W);Lower:0.23(W)|1967-2005|1%|Khattak et al.(2011)||
|Eastern Indian Himalaya|RG(M)|Tmax: 0.1(A)|1901-2003|NA|Dash et al.(2007)||
|||Tav: -0.2 to 0.8; Tmax: 0.1 to 0.9;|||||
||8 S(M)|Tmin: -0.5 to 0.6|1960s/70s-2000|5%|Jhahaaria and Singh(2010)||
||RG(M)|Tav,Bramhaputra Basin: 0.06|1901-2002|NA|Immerzeel(2007)<br>Immerzeel(2008)||
|Tibet Autonomous Region|97 S(M)|Tav:0.16(A),0.32(W)|1955-1996|≤5%|Liu and Chen(2000)|Warmingafter 1950s|
|||||||Pronounced|
||90 S(M)|Tav: 0.36(A)|1961-2007|5%|Wanget al.(2008)|warmingafter 1998|
||1 S(M)|Tav: 0.86(W),0.62(A)|1959-2007|5%|Yanget al.(2011)||
||5 S(M)|Tav: 0.23 (A)- Mt. Qomolangma<br>region,0.3(A)-Tingri|1971-2004|1%|Yanget al.(2006)|YZR, Regionally<br>averaged trend|
||9* S(M)|Tav: 0.28(A), 0.37 (W), 0.35(F+),<br>0.24(Sp++),0.17(Su+++)|1961–2005|5%|You et al.(2007)|Regionallyaveraged|
|Nepal|RG(M)|Tmax:0.4 to 0.9 (A); NS<sup>#</sup>-1.2 (W)|1971-1994|≤10%|Shreshta et al. (1999)||
||RG(M)|Tamx:0.4-0.9(A);0.1-1.2(W)|1977-2000<br>|NA|Tsheringet al.(2010)<br>Tshe-ringet al.(2010)||

***A=Annual; **W=Winter; +F = Fall; ++Sp=Spring;+++ S= Summer; # NS = Not significant)**

1
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Height-for-Age Z-Score|VI-<br>VIII|Child|Yes|4|The height-for-age (length-for-age) z-score is<br>calculated using the 2006 WHO child growth<br>standards. See the following for more details:<br>Leroy, Jef L (2011). zscore06: Stata command<br>for the calculation of anthropometric z-scores<br>using the 2006 WHO child growth standards.<br>http://www.ifpri.org/staffprofile/jef-leroy.|
|Neonatal Mortality|VI|Child|Yes|3,4|=1 if child died at or before 31 days.<br>=1 if child died between the ages of one month|
|Post-Neonatal Mortality|VI|Child|Yes|3,4|and one year. Defined for those that survived<br>the first month of life.|
|Parenting Practices<br>Percentage Score|VI|Child|Yes|4|Percentage score across a series of 16<br>questions on parenting practices related to<br>addressing behavior problems to interacting<br>with the child (the total number of questions<br>asked various by the age of the child). These<br>questions are only asked of living children.|
||||||=1 if mother exclusively breastfed for the first<br>|
|Exclusively Breastfed for<br>First Six Months|VI|Child|Yes|3,4|6 months of life, is still exclusively<br>breastfeeding for those under 6 months, or who<br>breastfed until death.|
|Standardized Malawi<br>Development Assessment<br>Tool|VI|Child|Yes|4|The standardized Malawi Developmental<br>Assessment Tool (MDAT) is a test of fine<br>motor skills, language, and hearing<br>administered directly to the child.|

73
**Figure 2. Bank Deposit Accounts per population against Financial Sector Depth and GDP per capita**

<!-- Start of picture text --> 4,000 4,000<br>3,000 3,000<br>2,000 2,000<br>UAE<br>Lebanon<br>1,0000 Yemen OmanAS lge yriariaTunisia E gyp tMoroccoJordan 1,0000 Y emen E gyp Syria Morocco t JordanTunisiaAlgeria Oman<br>0 50 100 150 0 5,000 10,000 15,000 20,000<br>Bank Deposits as % of GDP GDP per Capita<br>Deposits accounts per 1,000 Adults Deposits accounts per 1,000 Adults<br><!-- End of picture text --> Sources: CGAP and World Bank Financial Access 2010, International Financial Statistics, World Development Indicators. Iran data for 2008, all other data 2009.

# _Industry Structure_

# **Financial inclusion in the MENA region is characterized by NGO-dominated microcredit**

**sectors** , **postal networks and state banks.** A few countries have introduced legislation that allows for other legal forms of microfinance provider, such as finance companies or banks, but as yet there are few microfinance institutions that are legally allowed to offer (voluntary) deposit services in MENA.  Savings and loan cooperatives are not a major source of financial services in much of the MENA region.  Table A1.3 (Annex 1) sets out a typology of microfinance providers presented in table form. „Industry‟ is defined here as the set of providers of financial services to low income and microenterprise clients.

# _Banks and Financial Inclusion_

# **Direct commercial bank involvement in microfinance in the MENA region is very limited.**

Banks prefer to provide wholesale financing through specialized microcredit providers. Bankmicrocredit provider linkages build on the relative strengths of both types of entity – banks are able to raise funds from deposits and from the market and often have excess funds looking for returns, while MFIs are closer to clients, have tailored lending methodologies and delivery mechanisms, and lack funds. One of the big three Lebanese MFIs, Ameen, provides financial services in partnership with four Lebanese banks. Moroccan banks have set-up microcredit associations or foundations, rather than provide microloans directly. In Egypt two private sector banks offer microcredit in cooperation with microfinance service companies.

# **State banks are significant providers of financial services for low income people and**

**microenterprises in some MENA countries.** In Algeria, for example, the top 3 providers of loans to micro and small enterprises are public banks, with private banks and NGOs (associations) far behind, although the market share for private banks was growing (IFC survey, 2006).  While in Syria, half of MSEs interviewed for the IFC (2008) used banking services from

7
- A. Ferrer-i Carbonell. Income and wellbeing:an empirical analysis of the comparison income effect. _Journal of Public Economics_ , pages 997–1019, 2005. 9

- S. Frederick and G. Loewenstein. _Well-being: the foundations of hedonic psychology_ , chapter Hedonic adaptation. Russell Sage, 1999. 2

- Sebastian Galiani, Paul J. Gertler, and Raimundo Undurraga. The half-life of happiness: Hedonic adaptation in the subjective well-being of poor slum dwellers to the satisfaction of basic housing needs. _Journal of the European Economic Association_ , 16 (4):1189–1233, January 2018. 2, 3, 4, 6, 7, 8, 10, 11, 19, 29, 32

- J. F. Helliwell. How’s life? combining individual and national variables to explain subjective well-being. _Economic Modelling_ , pages 331–360, 2003. 9

- J. F. Helliwell and R. D. Putnam. _The Science of Well-Being_ , chapter The Social Context of Well-Being, pages 435–459. London: Oxford University Press, 2005. 9

- W. S. Jevons. _Essays on Economics_ . London: Macmillan, 1905. 2

- Daniel Kahneman and Angus Deaton. High income improves evaluation of life but not emotional well-being. _PNAS_ , 107(38):16489–16493, 2010. 6

- Miles Kimball, Ryan Nunny, and Dan Silverman. Accounting for adaptation in the economics of happiness. working paper, 2015. 2, 3, 4, 8, 10

- Botond Koszegi. Ego Utility, Overconfidence, and Task Choice. _Journal of the European Economic Association_ , 4(4):673–707, 2006. 4

- Botond Koszegi and Matthew Rabin. Reference-Dependent Consumption Plans. _American Economic Review_ , 99(3):909–936, 2009. 4

- George Loewenstein. Anticipation and the Valuation of Delayed Consumption. _The Economic Journal_ , 97(387):666–684, 09 1987. 3

- Graham Loomes and Robert Sugden. Disappointment and Dynamic Consistency in Choice under Uncertainty. _The Review of Economic Studies_ , 53(2):271–282, 04 1986. 2

- Shigehiro Oishi and Ed Diener. Can and should happiness be a policy goal? _Policy Insights from the Behavioral and Brain Sciences_ , 1(1):195–203, 2014. doi: 10.1177/ 2372732214548427. URL `https://doi.org/10.1177/2372732214548427` . 13

15
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# **Table 14. Benchmarking ICT indicators**

<!-- Start of picture text --> Unit Nonfragile, low- Burkina Faso Middle-income<br>income countries countries<br>2005 2005 2007–0) 2005<br>GSM coverage % population under signal 48 41 61 97<br>International bandwidth Mbps/capita 6 5.8 14.6 30<br>Landline Subscribers/100 people 1 0.7 0.9 9<br>Mobile phone Subscribers/100 people 15 5.3 16.8 87<br>Burkina Faso Countries without Countries with Other developing<br> US dollars 2007 submarine cable submarine cable regions<br>Price of monthly mobile basket 14.6 11.1 11.12 9.9<br>Price of monthly fixed-line basket 11.2 13.6 13.58 —<br>Price of 20-hour Internet package 90.5 68.0 47.00 11.0<br>Price of a call to the United States per minute 0.4 0.86 0.48 0.66<br>Price of an inter-Africa telephone calls per minute 1.6 0.7 0.57 n.a.<br>Source: Mali 2005 data together with benchmarks are taken from the AICD database. Mali 2007–08 data are compiled from a variety of World<br>Bank sources (including an information and communication technology for development database) to give a sense of the progress made during<br>the past four to five years; Ampah and others 2009; AICD national database (www.infrastructureafrica.org/aicd/tools/data).<br>Note: Numbers reported in the table as of 2007. GSM = global system for mobile communications; Mbps = megabits per second.<br>— = Not available.<br>n.a. = Not applicable.<br>Figure 21. Impressive expansion of the Internet market in Burkina Faso<br>a. Internet service trends b. Benchmarked against West African peers<br>0.8 16 9 160<br>0.7 14 8 140<br>7<br>0.6 12 120<br>6<br>0.5 10 100<br>5<br>0.4 8 80<br>4<br>0.3 6 3 60<br>0.2 4 2 40<br>0.1 2 1 20<br>0.0 0 0 0<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person) Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>Internet users Internet users<br>basket for internet<br>International internet bandwidth<br>International internet bandwidth / Price<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Cape Verde Senegal Nigeria Gambia, The Togo Ghana Cote d'Ivoire Guinea-Bissau Benin Mauritania Mali Guinea Burkina Faso Niger<br><!-- End of picture text --> _Source:_ World Bank, including AICD analysis.

32
**Table 4.** Sensitivity of Optimal Motor Fuel Taxes to the values of several parameters

|Response (in cents|) of opti|mal mo|tor fuel|tax (∆|_tF_) to in|crease of:|
|---|---|---|---|---|---|---|
||||0.1 in|||/c10 in<br>|
||_εLL_|_ε_<sup>_c_</sup><br>_LL_|_ηMI_|_ηF F_|_ηMF_|_E_<sup>_PF_</sup>|
|Australia|-0.6|4.8|-7.3|-12.6|7.2|10.9|
|Austria|-1.4|21.0|-31.4|-28.0|6.7|10.8|
|Belgium|-2.2|27.3|-41.0|-38.2|10.3|11.0|
|Brazil|-0.9|9.0|-13.6|-19.2|10.0|11.2|
|Canada|-0.6|6.9|-10.4|-13.6|6.5|10.8|
|China|-0.3|1.7|-2.5|-14.8|16.0|12.2|
|Czech Republic|-1.1|15.9|-23.8|-25.7|9.1|11.0|
|Denmark|-1.5|30.8|-46.2|-37.1|6.0|10.7|
|Finland|-0.8|17.7|-26.5|-27.9|9.1|10.7|
|France|-1.8|34.4|-51.6|-50.1|14.1|11.2|
|Germany|-1.2|21.5|-32.2|-40.7|17.2|11.0|
|Greece|-1.0|14.1|-21.2|-27.0|12.0|10.9|
|Hungary|-1.5|20.0|-30.0|-29.8|9.2|10.9|
|Iceland|-0.6|8.8|-13.3|-18.3|8.6|10.6|
|India|-0.4|3.1|-4.6|-13.9|9.8|11.3|
|Ireland|-0.7|9.8|-14.7|-16.1|5.8|10.5|
|Italy|-1.5|26.3|-39.4|-41.9|14.1|11.0|
|Japan|-0.5|4.8|-7.3|-14.2|8.2|11.0|
|Korea|-0.3|2.4|-3.6|-18.3|13.7|11.4|
|Luxembourg|-1.4|13.9|-20.9|-30.5|15.7|11.1|
|Mexico|-0.2|2.3|-3.4|-10.8|8.7|11.0|
|Netherlands|-1.2|23.3|-35.0|-33.3|9.0|10.7|
|New Zealand|-0.4|3.1|-4.7|-10.6|6.0|10.7|
|Norway|-0.7|15.0|-22.5|-21.8|6.1|10.6|
|Poland|-1.0|15.1|-22.6|-24.7|8.8|10.7|
|Portugal|-1.1|22.2|-33.3|-30.9|7.9|10.9|
|Russia|-0.7|3.1|-4.6|-16.3|14.9|12.6|
|Slovak Republic|-0.8|11.8|-17.8|-21.4|8.7|10.7|
|South Africa|-0.3|1.5|-2.2|-10.2|7.0|11.4|
|Spain|-0.7|11.1|-16.7|-19.2|7.5|10.7|
|Sweden|-1.0|23.4|-35.0|-27.7|4.0|10.7|
|Switzerland|-0.7|8.9|-13.3|-15.4|6.0|10.7|
|Turkey|-0.5|4.0|-6.0|-14.4|8.2|11.0|
|U.K.|-1.0|5.2|-7.7|-24.6|16.5|12.3|
|U.S.|-0.6|2.8|-4.1|-10.3|8.4|11.8|
|Mean|-0.9|12.8|-19.1|-23.1|9.6|11.0|
|Median|-0.8|11.5|-17.2|-21.6|8.8|10.9|
|Standard Deviation|0.5|9.3|14.0|10.2|3.5|0.50|

13
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forming a mashup index. These gains often stem from the inadequacies of prevailing composite indices of the first type as characterizations of important development goals—combined with the desire for a single (scalar) index. As data sources become more open and technology develops, creative new mashups can be expected. It is a good time then to take stock of the concerns with existing indices, in the hope of doing better in the future.

This paper offers a critical assessment of the strengths and weaknesses of existing mashup indices of development. One theme of the paper is the importance of assessing the (rarely explicit) tradeoffs embodied in these indices—for those tradeoffs have great bearing on both their internal validity and their policy relevance. Another theme is the importance of transparency about the robustness of country rankings. Clearer warnings are needed for users, and technology needs to be better exploited to provide those warnings. As it is, prevailing industry standards in designing and documenting mashup indices leave too many things opaque to users, creating hidden costs and downside risks, including the diversion of data and measurement efforts, and risks of distorting development policy making.

After describing some examples, the paper discusses the generic questions raised by mashup indices.

# **Examples of mashup indices of development**

A prominent set of examples of mashup indices is found in past efforts to combine multiple social indicators. An early contribution was the _Physical Quality of Life Index_ (PQLI) (Morris, 1979), which is a weighted average of literacy, infant mortality and life expectancy. Along similar lines, a now famous example is the _<u>Human Development Index</u>_ (HDI) that is published each year in the UNDP‘s _Human Development Report_ (HDR), which started in 1990. The HDI adds up attainments in three dimensions—life expectancy, schooling (literacy and enrollment rates) and log GDP per capita at purchasing power parity—after re-scaling each of them.<sup>3</sup> There have been a number of spinoffs from the HDI, including the ― _<u>Gender</u>_ _<u>Empowerment Measure</u>_ <u>,</u> ‖ which is a composite of various measures of gender inequalities in

> 3   See Anand and Sen (2000) for a useful overview of the construction of the HDI and how this has changed over time. The 2010 HDR introduced some further changes to the variables and aggregation function. I will comment on these changes later.

4
22

# **Scenario 4. Increasing retirement age (men to 65, women to 60) with increasing replacement ratio**

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.11|9.54|9.77|10.05|10.54|10.85|11.36|11.48|
|Total expenditure|8.49|9.63|10.77|11.57|12.65|14.18|14.76|16.20|16.92|
|Fund balance|-0.06|-0.52|-1.24|-1.80|-2.60|-3.64|-3.91|-4.84|-5.44|
|Average replacement ratio|29%|29%|31%|34%|37%|45%|46%|45%|48%|
|Dependency ratio|111%|119%|116%|113%|108%|96%|95%|101%|100%|
|Share of formal sectoremployment|59%|59%|59%|60%|60%|61%|61%|61%|61%|

Note: Retirement age increases by one year each year from 1997 to 2001 so that men aged 59 and women aged 54 in 1996 will retire in 2002.

# **Scenario S. Increasing** retirement **ages to 65 with** increasing replacement ratio

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.22|9.62|9.84|10.12|10.68|11.01|11.51|11.61|
|Total expenditure|8.49|9.64|10.72|11.47|12.42|13.13|12.90|13.88|14.42|
|Fund balance|-0.06|-0.43|-1.10|-1.63|-2.30|-2.45|-1.90|-2.37|-2.81|
|Average replacement ratio|29%|29%|31%|34%|38%|47%|49%|48%|50%|
|Dependency ratio|111%|117%|114%|110%|104%|84%|78%|82%|81%|
|Share of formal sector employment|59%|59%|59%|60%|60%|61%|62%|61%|62%|

Note: Retirement age increases by one year each year from 1997 to 2001 for men, to 2006 for women.

# **Scenario 6. Gradual increases of retirement ages (see note below) with increasing replacement ratio**

||1995|1996|1997|1998|2000|2005|2010|2020|2030|
|---|---|---|---|---|---|---|---|---|---|
|PAYG Pension Fund (% of formal GDP)||||||||||
|Payroll contributions|8.43|9.24|9.65|9.89|10.18|10.74|11.10|11.61|11.68|
|Total expenditure|8.49|9.64|10.72|11.64|12.77|13.37|12.92|13.99|14.70|
|Fund balance|-0.06|-0.40|-1.07|-1.76|-2.60|-2.62|-1.83|-2.38|-3.01|
|Average replacement ratio|29%|29%|31%|34%|38%|47%|48%|48%|50%|
|Dependency ratio|111%|117%|114%|111%|106%|86%|77%|81%|80%|
|Share of formal sector employment|59%|59%|59%|60%|60%|61%|62%|61%|62%|

Note: Retirement age increases gradually (by two years each year) to 65 for men and 60 for women at the end of 1996, and then then retirement age for women increases gradually to 65. So that men aged 59 in 1996 will retire at the age of 61 in 1998, and those 58 in 1996 will retire at the age of 62 in 2000, and so on.
more commonly used and focuses on household spending at present. For our purposes, we will use the latter interpretation of the marginal effects.

# **4.2. Distance to family planning center as instrument**

We then estimate equations (1) and (2) jointly in an IV model (with 2SLS or IV-Tobit accordingly), where family size is instrumented by the distance to the nearest family planning center. We believe this instrument is appropriate in the context of Vietnam because it meets the exogeneity, relevance, and exclusion restriction conditions. In this section, we consider these three criteria in turn.

A major exogeneity-related concern with using public programs, including placement of family planning centers, as instruments is that these programs may have been established in response to local demand (Rosenzweig and Wolpin, 1986). The evidence suggests, however, that such demand response is not an issue in Vietnam, where family planning services were already offered at the commune level and reached virtually the whole population by the late 1980s (Goodkind, 1995; GDPFP, 2011). While little data exist on the local conditions when family planning centers were set up, it is generally the case with most policy implementation in Vietnam that the central government sets the national policies but it is the local governments that ultimately decide exactly how these policies will be implemented.<sup>29</sup> Thus the establishment of family planning centers was most likely decided by local governments. In theory, this might cause us to suspect that it was primarily local demand that drove the placement of the centers. But in an analysis of a survey of local governments' family planning efforts in 15 provinces across Vietnam, San et al. (1999) find that effort strength is mostly driven by the quality of local governments’ leadership and implementation

> 29 Scornet (2001) observes that local governments’ strong autonomy in implementing family planning policies takes its root in the traditional decentralization of monarchical governments in the past.

17
# **<mark>Foreword</mark>**

The work presented in this document grew out of the experiences of the AIDS Strategy and Action Plan program (ASAP) in Latin America. ASAP’s mission is to support countries in creating effective strategies in response to the HIV epidemic. It is now recognized that the most effective way to develop high-impact HIV policies and programs is through results-based management, a decision-making process that focuses on the use of evidence to make informed decisions. Therefore, an important aspect of strategic planning based on evidence is to provide such evidence—data that highlights the evolution and trends of the epidemic and its determinants—to decision-makers. The World Bank has worked in collaboration with the Ministries of Health from several countries to compile 15 years of locally-produced, primary-source data. The data for each country has been integrated into a document called “ _Database and References for an Evidence-Based Strategy”._ When using information from multiple and diverse sources, it is important: (i) to be able to understand the scope and limitations of the reported findings; (ii) to assess whether the studies can be accurately compared; and (iii) to take an in-depth look at the methodology used. Such an analysis can reveal, for example, the true representativeness of a sample, or the ability to generalize results to the study’s priority population. To this end, ASAP proposes an analysis tool in a questionnaire or verification list format to assess the methodological quality of data as reported,. The ASAP tool offers a practical approach that is especially useful for the kind of studies that are frequently used within the HIV field. The assessment tool can help to identify biases or methodological problems that may have emerged in design or implementation, providing a better understanding of the quality of information that the studies contain. Such a critical analysis will also help to identify the difficulties and constraints faced by researchers when working in the HIV field—especially when dealing with most-at-risk and hard to reach populations.

The utility of this methodology is illustrated by the rich experience acquired in Latin America by the ASAP team managed by the World Bank over the last two years.

5