rafmacalaba/datause-encoder-data huggingface dataset:
incomplete electrification) means that some non-food goods found in urban areas will not be consumed in rural areas.

In addressing these concerns in applied poverty measurement (including the Bank’s PAs), the non-food component of the poverty line is typically set according to food demand behavior in each sub-group of the population for which a poverty line is to be determined. Different methods are found in practice, but they share the common feature that the non-food component of the poverty line is found by looking at the non-food spending of people in a neighborhood of the food poverty line, which is the cost for that sub-group of a reference food bundle (which may itself vary according to differences in relative prices or other factors). Thus spending on nonfood items such as clothing and housing is included consistently with the food poverty line. This typically entails a larger (sometimes appreciably larger) allowance for non-food spending in urban areas.

While this approach appears to be a reasonable and operational approach to the problem of setting a non-food poverty line, it may well introduce some degree of relativism into the poverty measures, depending on the properties of the food Engel curves, which may shift with factors that are not deemed relevant to absolute welfare comparisons. For example, better-off urban consumers may choose to cut their food spending to afford certain non-food goods that are not even available to rural consumers.

We use two poverty lines, $32.74 and $65.48 per person per month, both at 1993 PPP, interpreted as the “$1 a day” and “$2 a day” lines ($1.08 and $2.15 more precisely). The international rural line is converted to local currency by the Bank’s 1993 consumption PPP rate. We then use the ratio of the urban poverty line to the rural line from the PA (generally the one closest to 1993 if there is more than one) to obtain an urban poverty line for each country corresponding to its PPP-adjusted “$1 a day” rural line.<sup>13</sup>

> 13 For example, the $1.08 a day at 1993 Purchasing Power Parity represents 1.53 Yuan per person in China, which is equivalent to 2.42 Yuan at 1999 prices; this is the rural poverty line used for the 1999 household survey data for China.  The poverty lines used by the Bank’s Poverty Assessment for China imply an urban-rural differential of 1.37, so the urban poverty line for 1999 is 3.32 Yuan per person.

10
- Gains in statistical precision from aggregating sources of corruption data likely are far more modest than often claimed, because the assumption of independent error in measurement among data sources is violated.

- For various reasons, changes over time in corruption ratings should be interpreted with extreme caution.  For example, changes in perceptions indicators from one year to the next often are intended to correct ratings regarded in hindsight as incorrect.

- Enterprise surveys such as BEEPS measure only corrupt transactions between public officials and business firms, and in that sense provide a more limited picture than more broadly-defined corruption measures.  The advantage of firm surveys such as BEEPS is in providing narrow, specific indicators such as bribes paid in tax collection or in business licensing, and in providing objective measures on share of firm revenues or contract values paid as bribes to public officials.  The BEEPS also allows firm-level analyses, e.g. on which types of firms pay more in bribes.

- Changes over time in corruption levels as measured by firm surveys can produce valid inferences if the survey questions and sample design are identical in both periods, and if other factors are controlled for where necessary.  For example, perceptions that corruption is an obstacle to doing business are potentially affected by optimism, or by prevailing economic conditions.

- Data from BEEPS, as well as from the WEF executive opinion surveys, show improvement in the region in most but not all types of administrative corruption between 2002 and 2005, and little change in “state capture.”

- Most of the broad, perceptions indicators of corruption show somewhat greater improvement in ECA than in non-ECA countries on average between 2002 and 2005.

- Sources – including firm surveys - disagree markedly on which ECA countries have improved and which have not.  This apparent disagreement is in part attributable to the fact that changes in expert assessments often reflect corrections rather than a belief that corruption has actually improved or worsened.  In general, the rankings provided by different sources show convergence between 2002 and 2005, so in that sense are not really inconsistent.

- Detailed corruption questions in BEEPS and WEF can be used to shed light on what aspects of corruption are emphasized by the broad, perceptions-based indicators.  The latter appear to measure primarily administrative corruption, rather than “state capture,” and appear to measure corruption in public procurement particularly poorly.

- More research is needed to understand better the informational content and possible biases in existing corruption indicators.

4
With regard to the relationship between computer-related skills and earnings premiums, the results are more heterogeneous; there is as yet no consensus on whether this relationship is driven by an increase in computer-specific human capital or changes in the organization of the workplace.

# **III. Data**

This paper uses the STEP Skill Measurement surveys from eight low- and middle-income countries: Armenia, Bolivia, Colombia, Georgia, Ghana, Kenya, Ukraine, and Vietnam. It gathers information from urban adults between the ages of 15 and 64 and includes three unique modules that cover various measures cognitive and noncognitive skills.

The STEP surveys provide a direct, objective assessment of _reading proficiency skills_ , which is scored on the same scale as the OECD’s PIAAC assessment. The assessment includes three parts that together provide a measure of reading proficiency among adults in each of the countries surveyed. The items used in STEP were developed based on the literacy frameworks developed for PIAAC, which defines literacy as: _“understanding, evaluating, using and engaging with written texts to participate in society, to achieve one’s goals, and to develop one’s knowledge and potential”_ (OECD, 2012). This definition provides a broad understanding of the processes and goals of literacy as measured in STEP. The main aspects of the construct—contexts for reading and underlying cognitive processes required to complete the presented tasks—have been taken into consideration when selecting the texts and developing items included in the STEP literacy assessment (Pierre et al., 2014; Educational Testing Service, 2014). The scores of the assessment range from 0 to 500 and represent six levels of proficiency.<sup>8</sup> In addition to reading proficiency, several items on the STEP survey capture data on _computer use at work_ , which we use as a second measure of cognitive skills for this study. This variable provides the frequency and complexity of computer use on the job and was selected to help determine the extent to which these skills are being rewarded in the labor market. Respondents report the frequency with which they use computers at work—never, a couple of times a week, or more than three times per week—followed by a description of the computerrelated tasks required in their job. Four levels of computer use are defined: level 1 includes browser-based tasks (such as use of email and the internet), level 2 includes basic Microsoft Office functions (such as word processing and graphics), level 3 includes basic programming (such as spreadsheets or databases), and level 4 includes advanced programming tasks (such

> 8 See Pierre et al. (2014), p. 83, for a detailed description of the assessment and levels of proficiency.

8
- <u>Mendelsohn, R., Nordhaus, W. and Shaw, D.: 1994. “The Impact of Global Warming on</u> Agriculture: A Ricardian Analysis.” _American Economic Review_ **84** : 753-771.

- <u>Mendelsohn, R, Dinar, A and Sanghi, A.: 2001. “The effect of development on the</u> climate sensitivity of agriculture”, _Environment and Development Economics_ , **6,** 85-101.

- <u>Mendelsohn, R., and Dinar, A.: 1999. “Climate Change, Agriculture, and Developing</u> Countries: Does Adaptation Matter?” _The World Bank Research Observer_ **14** : 277-93.

- <u>Mendelsohn, R. and Dinar, A.: 2003. “Climate, Water, and Agriculture.”</u> _Land Economics,_ **79,** 328-341.

- <u>Molua, E.: 2002. "Climate Variability, Vulnerability and Effectiveness of Farm-Level</u> Adaptation Options: the Challenges and Implications for Food Security in Southwestern Cameroon." _Environment and Development Economics_ **7,** 529–545.

- <u>Reilly, J.: 1995. "Climate Change and Global Agriculture: Recent Findings and Issues."</u> _American Journal of Agricultural Economics_ **77,** 727-733.

- <u>Rice Research and Development (RRDI): 2003. “Agroecological zones in Sri Lanka and</u> Rice Cultivation”, webpage (http://www.rice.ac.lk/research_disciplines2_map.html).

- <u>Rosenzweig, C., and Parry, M.: 1994. “Potential Impacts of Climate Change on World</u> Food Supply.” _Nature_ **367** : 133-138.

- <u>Washington, W., Weatherly, J. Meehl, G., Semtner, J., Bettge, T., Craig, A., Strand, W., Arblaster, J., Wayland, V., James, R., and Zhang, Y.: 2000. “Parallel Climate</u> Model (PCM): control and transient scenarios”. _Climate Dynamics_ , **16,** 755-774.

- <u>White, H.: 1980. “A Heteroscedasticity-Consistent Covariance Matrix Estimator and a</u> Direct Test for Heteroscedasticity.” _Econometrica._ **48,** 817-830.

- <u>World Bank: 2001. Sri Lanka Integrated Survey (SLIS) 1999-2000, Washington DC</u>

<u>World Bank: 2003. Global Economic Prospects, Washington, DC, USA.</u> 24
Figure <u>6.1: Headcount Poverty and Education Attainment in Haiti, 2001</u> <!-- Start of picture text --> 80<br>70 Metropolitan<br>60 Urban<br>50 Rural<br>40 Total Haiti<br>30<br>20<br>10<br>0<br>None Primary Secondary Tertiary<br>Source: Own calculations based on HLCS 2001.<br><!-- End of picture text --> Table <u>6.2: Highest Education Level Completed (percent), 2001</u>

||Q|uintile|||
|---|---|---|---|---|
|1(poorest)|2|3|4|5 (richest)|
|No education<br>53.2|50.7|46.1|38.9|21.1|
|Primary<br>33.4|32.0|32.3|34.1|29.3|
|<br>Secondary<br>13.3|17.1|21.1|26.1|44.6|
|<br>Tertiary<br>0.1|0.2|0.6|0.9|5.0|
|Note: Age 15 and above included. No. observ|ations 20,|074.|||
|_Source:_ Own calculatio|ns based|on HL|CS 200|1.|

Elder household heads are more likely to experience poverty than younger household heads. In Haiti, only 40 percent of households headed by a member younger than age 25 are below the indigent poverty line. This compares to 52 percent for households headed by a member older than 65 years of age. The latter group has the lowest average income of any age group, which may be explained in part by lack of old age pensions in Haiti. The P0 of the population groups aged 25 to 44 and 45 to 65 was 47 and 51 percent, respectively, in 2001. Thus the older the head of household, the more likely they are to be poor. This therefore does not reflect a life-cycle profile of poverty, but illustrates that many households are born poor (mainly due to inadequate assets) and assets are only sparsely accumulated, and when reaching old age most are depleted. As there are very few social protection programs available for the elderly, there currently exist few mechanisms that can take households headed by an older person out of poverty. Other countries in the region such as Brazil have changed this pattern by introducing old age pensions for all poor old age people. Finally, it is worth noting that for all age groups the likelihood of falling below the poverty line is more than double for urban and rural dwellers than it is for metropolitan dwellers.

Female-headed households are marginally more likely to be poor than maleheaded households. As a whole, 50 and 48 percent of female- and male-headed 33
The 1982 cohort is the first to incorporate the possibility of post-reform entry, as described in detail in Section 3 using the 1985 cohort as an example. As described with the 1985 example, there is a stock of students at each age that does not enter school when fees are in place, but would have entered if given the opportunity to enter for free ( _Sz,a − Sz,a,pre_ ). For the 1982 cohort, these students have the opportunity to enter at age 12; at this late age, there is a possibility that they may be tied to some other activity that constrains them from entering school. The further this earliest post-reform entry age is from the legal entry age of seven, the greater the decline in entry for would-be post-reform entrants<sup>1</sup> _/e_<sup>_a−_7</sup> : A.5
**Figure 1. Poverty Rates, 2008–15, Percent of Population**

**Figure 2. Poverty Rates by Location, Percent of Population**

<!-- Start of picture text --> 40<br>34.1 35.8 35.0<br>35 32.4 32.0<br>30.0 29.8<br>30 27.6<br>25<br>20<br>15<br>10<br>5<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>2008 2009 2010 2011 2012 2013 2014 2015<br>Yerevan Other Urban Rural<br><!-- End of picture text --> _Source_ : ILCS data. _Note_ : All calculations are based on the upper poverty line (2009 methodology).

_Source:_ ILCS data. _Note_ : All calculations are based on the upper poverty line (2009 methodology).

Between 2008 and 2009, poverty in Yerevan shot up from 20.1 to 26.7 percent of the population, but then eased slightly, to 25.0 percent in 2015. In 2015 living conditions in Armenia, notably access to economic opportunities, differ substantially by location, from 25.0 percent in Yerevan to 30.4 percent in rural areas and 34.4 percent in other urban areas. Regional disparities are linked to structural differences related to employment patterns and economic activity. Households in rural areas have benefited from increasing agricultural sales but except in Yerevan urban households continue to suffer from weak domestic labor markets and limited job creation.

Although the most recent decline in poverty allowed a significant number of households to escape economic poverty, patterns of economic mobility demonstrate that a large number of households are still vulnerable to economic shocks and at risk of falling back into poverty. In these circumstances, nonmonetary measures of poverty are useful for monitoring well-being. For example, the measure of multidimensional poverty looks into the endowment of households and describes their capacity to participate in society beyond simply generating income.

# 3. Multidimensional Poverty: A New Indicator

The NSSRA and the WB collaborated closely in formulating the new measure, consulting national line ministries, international organizations, and civil society to identify dimensions and indicators that describe functioning, well-being, and endowments.<sup>3</sup> These consultations allowed the team to customize the components of the measure of multidimensional poverty in order to raise its policy relevance as a robust indicator of well-being in Armenia.

The national measure indicates whether people face deprivations related to education, health, labor, housing, and basic needs (details below). It also counts the typical number of household deprivations and examines overlaps of deprivations. The multidimensional poverty index (MPI) then aggregates this information into a single measure that combines deprivation incidence and intensity. _Incidence_ shows the

> 3 A summary of the national consultations is available upon request.

5
# **Governance in Health Care Delivery: Raising Performance**

Maureen Lewis (World Bank) and

## Gunilla Pettersson

(University of Sussex and World Bank)

**Acknowledgements:** We are grateful to Rebecca Kinsey who compiled much of the original data, especially the perceptions-based corruption data, and to Gerard La Forgia, Philip Keefer, Alexander Preker, George Schieber and Juan Pablo Uribe for providing invaluable insights and useful comments.

**Keywords:** Governance, corruption, health, health care systems, health care performance, indicators, human resources for health, health expenditures

**JEL Classifications:** I1, I18, H4, H110
TABLE 5 NON-OFFICIAL EXTERNAL FINANCING BEFORE AND DURING ADJUSTMENT NET FLOW AS % OF GDP

||1975-80|1981-82|1983-84|1985-88|
|---|---|---|---|---|
|EIA|1.8|3.3|0.4|0.0|
|OAL|3.0|1.1|0.6|0.5|
|NAL|1.1|3.1|2.2|-0.2|

Source: World Bank data.

1 ot The .t cup **ta #..** o,^ **Ce'O"^^** d to tr **net** 19*d long **4'0t.ta-** C t. **#o. obtan;.** **_fro_** t. balance of **Dey.*nt.. *-.. nat** **_4_** O.* **_t4_ iiriaff.'t.** ^ c_.^*^tS dol **_a._** * **Psaia 5_ iq1** tat en.' nt e'0".a 44 **Qe** by tn. **_Oe_ data** . to (b b .o- **Ct.i** tc **dtlot** ed **... t. o'*** **_ataril)_** t?it tre **_c'ad-to,a_** no., *A All **ce.tii** .'eSbla sb *1 **5'** **_ieS_** _Eanea.at. *^_

**( to** "; do. **_(C_** 453.D C ( 1,3;)Cj
Bartolini, L. and Cottarelli, Carlo, _Designing Effective Auctions for Treasury Securities,_ Current Issues in Economics and Finance 3/9, FRBNY, <u>www.ny.frb.org, 1997</u> Beller, Alan, _Remarks at Bond Market Association Annual Meeting,_ New York, New York, April 2003 BIS Study Group on Fixed Income Markets, _The Changing Shape of Fixed Income Markets,_ BIS Papers No. 5, October 2001 Bond Markets Association, _Bond Markets 2000: A Conceptual Framework for Efficient Regulation of the Fixed Income Markets,_ BMA Member Exposure Draft, October 1998 Bond Markets Association, _Uniform Practices for the Clearance and Settlement of Mortgage-Backed Securities and Other Related Securities,_ 1990 Burnham, James B., _The Government Securities Act of 1986: A Case Study of the Demand for Regulation,_ Regulation: The CATO Review of Business & Government, www.cato.org, Washington DC, Summer 1990 Carey, Mark and Prowse, Stephen, _The Economics of the Private Placement Market,_ Board of Governors of the Federal Reserve, Staff Studies 166, December 1993 Chaplinsky, Susan and Ramchand, Latha, _The Impact of SEC Rule 144A on Corporate Debt Issuance by Foreign Firms,_ <u>www.faculty.darden.virginia.edu, October 2000</u> Committee on Payment and Settlement Systems _, Payment and Settlement Systems in Selected Countries, Part 15, Payment Systems in the United_ States, BIS, May, 2003 Dewar, Helen and Berry, John M _., White House Urges Senate to Raise Debt Ceiling Soon,_ Washingtonpost.com, May 2003 Dupont, Dominique and Sack, Brian, _The Treasury Securities Market: Overview and Recent Developments,_ Federal Reserve Bulletin, December 1999 El Daher, Samir, _Credit Ratings: An Introduction (and the Case of Sub-Sovereign Ratings),_ Infrastructure Notes, Urban No. FM-8c, The World Bank, July 1999 Fabozzi, Frank J _., Handbook of Fixed Income Securities,_ 6<sup>th</sup> Edition, McGraw Hill, 2001 Federal Reserve Board of New York, _Fedpoint 2: Primary Dealers,_ <u>www.ny.frb.org</u> Fleming, Michael J. and Garbade, Kenneth D., _When the Back Office Moved to the Front Burner: Settlement Fails in the Treasury Market after 9/11,_ FRBNY Policy Review, November 2002 50
# <u>Table 4. Linear regression model estimates (with country fixed effects)</u>

|Dependent variable:|Log CO2 e<br>industry rea|missions per<br>l value-added|millions of<br>(2012 euro)|Average an<br>emissions pe<br>(20|nual growth r<br>r industry rea<br>10-2019), % p|atio in CO2<br>l value-added<br>oint|
|---|---|---|---|---|---|---|
|Skill measure domain|Literacy|Numeracy|Problem<br>solving|Literacy|Numeracy|Problem<br>solving|
|Skill measure (standardized)|-0.17***<br>(0.01)|-0.16***<br>(0.01)|-0.17***<br>(0.01)|-0.17***<br>(0.02)|-0.17***<br>(0.02)|-0.19***<br>(0.02)|
|Female|-0.65***<br>(0.01)|-0.68***<br>(0.01)|-0.61***<br>(0.01)|-0.24***<br>(0.03)|-0.27***<br>(0.03)|-0.4***<br>(0.04)|
|Other controls: age, age square, country fixed effects|yes|yes|yes|yes|yes|yes|
|Constant|4.94|4.93|3.94|4.71|4.69|-7.93|
||(6.86)|(6.84)|(6.65)|(92.42)|(92.38)|(86.38)|
|Observations|73621|73621|48673|73686|73686|48735|
|R2|0.26|0.26|0.28|0.21|0.21|0.23|
|countries|21|21|17|21|21|17|

Estimated using ten plausible values for each skill measure following OECD (2013); jackknife standard errors reported in parentheses. Statistical significance at the 1, 5 and 10 percent levels denoted by ***, **, and * respectively.

17
<!-- Start of picture text --> i<e}<br>e<br>e<br>e<br>+ e<br>o e<br>iS} e<br>”n<br>oa<br>oO e<br>Nee<br>%e<br>e e@<br>°<br>ee<br>e<br>e<br>e<br>fo)<br>50 55 60 65 70 75<br>Small Government (% of GDP)<br>——— Fitted Values © High Income OECD Democracy<br><!-- End of picture text -->
**_Core Indicators for Determinants and Performance of Electricity Sector Reform in Developing Countries_**

||**II. Key Reform Steps**|||
|---|---|---|---|
|**Focus area**|**Indicator**|**Data Source**<br>**(e.g. UK)**|**Source/reference**<br>**(example)**|
|_? Electricity law or act_|**? Date of effectiveness (year)**|Regulator’s web-site|Bacon (1999)|
|_? Corporatisation_|**? Are state-owned enterprises corporatised?**<br>**(no/yes-fully, partially)**|Interviews|Bacon (1999)|
|_? Privatisation_|**? Start date (year)**<br>**? Privatisation proceeds from G, T, D**<br>**(national currency and US$ per kW, MWh)**<sup>*****</sup><br>**? Privatised electricity assets – G (MW), T**<br>**(MW), D (MWh) as % of total**||Zhang et al. (2002)<br>Siniscalco, et al.<br>(2001)<br>Bacon (1999)|
|_? Unbundling_|**? Vertical separation of G from T/D**<br>**(no/yes – legal, accounting, ownership)**||Bacon (1999)<br>Steiner (2001)|
|_? Private sector_<br>_participation (new entry)_|**? Are new private concessions and**<br>**greenfield investments allowed? (no/yes)**||Bacon (1999)|
|_? Retail competition_|**? Is retail competition allowed? (no/yes -**<br>**Down to what level?**||Bacon (1999)|
|_? Wholesale electricity_<br>_market_|**? Is a wholesale market established? (no/yes**<br>**– Pool, single-buyer, cost-based/price-based,**<br>**voluntary / compulsory participation,**<br>**demand participation, contract market**<br>**bilateral, forward), balancing, IPPs)**||Zhang et al. (2002)<br>Steiner (2001)|
|_? Regulator_|**? Is a regulator established? (no/yes –**<br>**independent, ministerial, other)**||Zhang et al. (2002)<br>Bacon (1999)|
|_? Network regulatory_<br>_reform_|**? Is there incentive regulation for T**<br>**networks? (no/yes – price -cap, partial**<br>**incentive schemes, other)**<br>**? Is there incentive regulation for D**<br>**networks? (no/yes – price -cap, partial**<br>**incentive schemes, other)**|||
|_? Composite reform_<br>_index_<br>* G: Generation,T: Tra|? Number of key reform steps taken<br>(alternatively weighted)<br>nsmission,D: Distribution||Bacon and Besant-<br>Jones (2002),<br>Drillisch et al.<br>(1998)|

20