rafmacalaba/datause-encoder-data huggingface dataset:
# **Conclusions**

Our aim in writing this paper was to document the Bank’s publication record and compare it to the records of other institutions, including the world’s top universities in economics. We quickly discovered that there is no institutional database that covers the full breadth of Bank publications; the Bank’s own Documents and Reports database fails almost completely to capture the largest and fastest growing component of the Bank’s publication portfolio—journal articles. These account for almost half of the 20,000 publications we unearthed by merging several databases. Since 1990, the Bank’s annual output of articles has grown while annual book and book chapter numbers have stayed fairly flat. Consciously or unconsciously the Bank has been diversifying its publications portfolio away from books.

The Bank now ranks second only to Harvard University in terms of the volume of journal articles it publishes each year in economics. Unsurprisingly, the Bank’s journal output—like that of other multilateral development agencies—is highly focused on development: the Bank produces two and a half times the output in the development field of the average university in our 14-university comparator group. Despite this focus, the median journal “quality” is only marginally lower among Bank-authored papers than among papers authored by faculty at the 14 universities. The Bank’s longish tail of “undistinguished work” (published in lower quality academic journals) is no longer than for most of the top universities.

Our interest in writing the paper was not just to examine the volume of publications, but also to assess their scholarly influence, again using data from other institutions to help set the Bank’s performance in context. Our use of Google Scholar citation data gives us a chance to probe broadly (getting citation data on books and working papers as well as journal articles) and deeply (picking up citations in new journals as well as established journals, journals from all disciplines, working papers, books, technical reports, dissertations, etc.). In terms of average citations, the Bank’s portfolio of journal articles ranks seventh behind Berkeley, Chicago, MIT, Harvard, Princeton and Stanford. This high average influence, coupled with its large size, gives the Bank’s portfolio a large aggregate influence: the Bank’s h-index of 192 (meaning it has published 192 articles each of which has been cited at least 192 times) is bettered only by Berkeley, Chicago, Harvard and Stanford amongst the 14 universities we consider. Among the Bank’s portfolio of journal articles, one finds some very highly cited papers: Bank staff authored 28
14. Henderson, V., 2002, “Urbanization in developing countries", World Bank Research Observer, 17, 1: 89-112.

15. Henderson, J. V., Z. Shalizi and A. J. Venables (2001), “Geography and development," Journal of Economic Geography 1, 81-105.

16. Hazell, P. and S. Haggblade, 1990, “Rural-Urban growth linkages in India," PRE Working paper#430. World Bank.

17. Hazell, P.,C. Ramasamy, and V. Rajagopalan, 1991, “An analysis of the indirect e ff ects of agricultural growth in the regional economy," Chapter 8 in P. Hazell and C. Ramasamy (eds) The Green Revolution Reconsidered: The Impact of High-yielding rice varieties in South India. Johns Hopkins Press, Baltimore.

18. Jacoby, H. G. 2000, “Access to Markets and the Bene fi ts of Rural Roads‘’, Economic Journal, Vol. 110, pp. 713-37.

19. Johnson, D.G, 2000, “Population, Food and Knowledge", American Economic Review, Vol. 90(1), p.1-14.

20. Lanjouw P. and Gershon Feder, 2001, “Rural Non-Farm Activities and Rural Development: From experience towards Strategy,” Rural Strategy Background Paper # 4, World Bank.

21. Lanjouw J. and P. Lanjouw, 2001, “Rural Non-farm Employment: Issues and Evidence from Developing Countries,” Agricultural Economics, vol. 26, no. 1, pp.1 —24.

22. Lewis, B. and E. Thorbecke, 1992, “District level economic linkages in Kenya: Evidence based on a small social accounting matrix," World Development, Vol. 20(6), pp.881-897.

23. Mellor, J, 1976, The New Economics of Growth: A Strategy for India and the Developing World, Cornell University Press, Ithaca.

24
product the price of which is currently the closest to the market price (milk for children), a 60 percent increase would be needed to match the market price—a significant price increase.

**Table 10: Prices, Subsidies and Reform Scenarios**

||**Initial price**|**Subsidy**|**Final price**<br>**(Scenario1)**|**Final price**<br>**(Scenario 2)**|**Final price**<br>**(Scenario 2)/**<br>**Initial price**|
|---|---|---|---|---|---|
|Flour|0.090|0.940|0.372|1.030|11.4|
|Flour for bread|0.037|0.922|0.314|0.959|25.9|
|Semolina|0.080|0.831|0.329|0.911|11.4|
|Rice|0.140|1.419|0.566|1.559|11.1|
|Sugar|0.250|1.068|0.570|1.318|5.3|
|Tea|1.500|3.597|2.579|5.097|3.4|
|Macaroni|0.200|1.194|0.558|1.394|7.0|
|Vegetable oil|0.600|2.802|1.441|3.402|5.7|
|Tomato paste|0.600|1.541|1.062|2.141|3.6|
|Milk for children|7.500|4.750|8.925|12.250|1.6|
|Milk|0.975|1.647|1.469|2.622|2.7|

Source: Libyan Household Consumption Survey 2007/8; Libyan authorities; and Bank staff calculations.

These price increases would affect the poor in greater proportion than the rich. The total monetary impact of a complete removal of subsidies (scenario 2) on households would be equivalent in magnitude to the total estimated monetary value of subsidies received by households, namely 1.3 billion LYD (Table 11).<sup>14</sup> The total impact of a 30% reduction in subsidies (scenario 1) is estimated at 385 million LYD. The impact would be regressive in that poorer households would be affected more than richer households, as indicated by the greater loss in per-capita spending for lower quintiles (Table 12). This is to be expected since food subsidies were shown to benefit the poor in greater proportion. For example, with an elimination of subsidies, the first quintile (the poorest 20% of the population) would bear a cost of 348 million LYD. And at 18.9 percent, the decline in per-capita spending of the lowest quintile if food subsidies were eliminated is nearly 4 times that of the highest quintile (4.9 percent). This would be a disproportionate cost for poorer households.

**Table 11: Aggregate Monetary Impact of Subsidy Reform on Welfare (Million LYD)**

||**Q1**|**Q2**|**Q3**|**Q4**|**Q5**|**Total**<br>**scenario 2**|**Total**<br>**scenario 1**|
|---|---|---|---|---|---|---|---|
|Flour|-25.2|-21.1|-18.3|-15.1|-9.6|-89.3|-26.8|
|Flour-bread|-89.4|-82.7|-71.9|-59.8|-47.5|-351.2|-105.4|
|Semolina|-4.1|-3.5|-2.7|-1.9|-1.0|-13.2|-4.0|
|Rice|-42.3|-37.4|-33.1|-26.5|-18.1|-157.4|-47.2|

14 Note that these are upper bound estimates based on Laspeyers’ estimations.

14
|**Corporate Governance Benchmark**|**Evaluation of DBSA**|
|---|---|
|_Ownership structure_is disclosed and transparent,<br>including major share ownership (including legal<br>ownership of state shares) and voting rights. Any<br>special rights or agreements that may alter the<br>ownership or control structure (e.g., golden shares<br>or special veto powers granted the state), are<br>disclosed and transparent. Responsibility for<br>exercising the state’s ownership (i.e., the ownership<br>entity) are disclosed and transparent.|The ownership structure (100% government of<br>the Republic of South Africa), and the<br>responsibility of the Minister of Finance for<br>exercising the state’s ownerships rights, are fully<br>disclosed and transparent. There are no special<br>arrangements altering the ownership or control<br>structure.|
|The_state ownership policy_is disclosed, including<br>overall objectives of state ownership, the state’s<br>role in SFI corporate governance, and the manner in<br>which the ownership policy is implemented.|As noted, the state ownership policy for SOEs<br>generally is framed largely by the PFM Act, the<br>National Treasury regulations issued pursuant to<br>that Act, and the Protocol, as well as, for DBSA<br>specifically, the DBSA Act and the Article 17<br>regulations. The Minister of Finance in the<br>Annual Report provides his assessment of the<br>performance of DBSA and his expectations<br>regarding strategic direction and future<br>performance. As noted, the Minister also reports<br>to two Parliamentary committees in this regard.|
|_Board structure_is disclosed, which includes<br>bylaws, board size, board membership, board<br>member selection process, board member<br>qualifications, other directorships held by board<br>members, criteria for independence, any member’s<br>material interests in transactions or matters<br>affecting the SFI, as well as board committees’<br>terms of reference and committee membership.|Board and Board committee size, membership<br>and records of attendance, Board committee<br>responsibilities and processes, and basic<br>information about the academic qualifications and<br>current employment of Board members are<br>disclosed. Bylaws, other directorships held by<br>Board members, and member’s interests in<br>transactions or matters affecting the SFI are not<br>disclosed publicly. However, the Corporate<br>Secretary maintains a register of Board members’<br>declaration of interests which is updated annually<br>and at the beginning of every Board and Board<br>committee meeting.|
|The_number of board and committee meetings and_<br>_the attendance records_of individual members is<br>disclosed in the annual report.|Fully disclosed in the Annual Report.|
|_Senior management structure_is disclosed, which<br>includes responsibilities, reporting lines,<br>qualifications and experience.|Senior management is disclosed in the Annual<br>Report along with basic information regarding<br>their academic qualifications.|

50
# **1 Introduction**

Calculating multidimensional measures of poverty has become commonplace in many developing countries, as a way to complement traditional monetary indicators. The breadth of multidimensional measures, compared to traditional approaches, is viewed as an advantage by researchers, enabling them to aggregate a large number of welfareassociated variables into a single measure. Multidimensional measures are also attractive to governments, as they are conducive to a more precise understanding of the determinants of individual welfare and quality of life, allowing the identification of those fields where public policy may have a larger impact.

Despite this popularity, multidimensional measures are not free from the criticism faced by other approaches to measuring poverty. As with any indicator that attempts to summarize a complex phenomenon into a single index, multidimensional poverty measures may be hard to interpret if unaccompanied by additional information. In this sense, rather than focusing solely on the measure that aggregates across dimensions, analyses of multidimensional poverty also tend to contain separate information about each dimension. This, however, increases the number of indicators to be tracked, and reduces the usefulness of the summary measures.

These difficulties become compounded when tracking the evolution of multidimensional measures across time. An exact identification of the contribution of each dimension to the evolution of multidimensional measures would require tracking the transition of individuals in and out of poverty, and the dimensions by individual. This exhaustive would defeat the of of analysis purpose aggregation poverty measures.

Decomposition approaches, whereby poverty measures are split up into the contribution of broadly defined determinants of interest, are a reasonable compromise between a comprehensive analysis and a completely aggregated one. As expressed in Ferreira and Lugo (2013), these approaches aim for a “middle ground”, which can be informative of multidimensional poverty as well as expeditious. While these approaches are deeply rooted in the traditional monetary poverty research, they have been less developed in the literature of multidimensional measurement. Apablaza and Yalonetzky (2013) propose approaches to decompose multidimensional poverty measures statically or dynamically. Yet, their focus is on two out of the three measures of multidimensional poverty: the average deprivation share among the poor 2
# **Tables**

## Table 1: Marriage penalties and premiums

|Dependent variable|||Working|||
|---|---|---|---|---|---|
|Sample|SAR|BGD|IND|MDV|NPL|
||(1)|(2)|(3)|(4)|(5)|
||_Panel A: _|_Women_||||
|Post-marriage _×_ No child sample|-0.117***|-0.0890***|-0.121***|-0.120**|-0.0229|
||(0.005)|(0.014)|(0.006)|(0.044)|(0.017)|
|Post-marriage _×_ Ignore child sample|-0.157***|-0.107***|-0.162***|-0.240***|-0.0755***|
||(0.004)|(0.013)|(0.006)|(0.041)|(0.015)|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|19987|3875|10734|1018|4350|
|R-squared|0.536|0.337|0.496|0.565|0.366|
||_Panel B_|_: Men_||||
|Post-marriage _×_ No child sample|0.128***|0.170***|0.130***|0.174***|0.132***|
||(0.015)|(0.037)|(0.019)|(0.041)|(0.028)|
|Post-marriage _×_ Ignore child sample|0.117***|0.166***|0.116***|0.108**|0.155***|
||(0.010)|(0.032)|(0.013)|(0.040)|(0.022)|
|Birth Cohort FE|Yes|Yes|Yes|Yes|Yes|
|Country _×_ Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|16328|2400|10036|721|3155|
|R-squared|0.611|0.307|0.631|0.698|0.499|

Note: Standard errors in parentheses clustered at the cohort level. All estimates weighted by cohort size. SAR - South Asia Region, BGD - Bangladesh, IND - India, MDV - Maldives, NPL - Nepal. No child sample refers matching postmarriage observations in the sample without children. Ignore child sample refers matching post-marriage observations in the full sample. Cohort FE refer to birth cohort. *** _p <_ 0 _._ 01, ** _p <_ 0 _._ 05, * _p <_ 0 _._ 1.

23
7

# Research on Single-Sex Secondary Schooling

Non-American Western countries. The majority of research comparing single-sex and coeducational secondary schooling has been conducted outside the United States in developed Western countries.]A/ Those studies generally have focussed more on students' atti-udes about the social and psychological environments of their schools thaki **uui** academic attitudes and behaviors, and most of the studies have not measured achievement differences between the two school types. They typically involve small non-random samples of schools, and the data analyses are primarily descriptive. The research generally does not include adjustment for differences in the background characteristics of students who attend coeducational and single-sex schools, and often doesn't consider whether the effects might differ for boys and girls. Many of the studies are dated, probably not reflective of attitude changes about sex roles within the last decade.

Whereas single-sex schools, especially those for girls, were considered to emphasize control and discipline to a greater extent, coeducational schools were found to have a more relaxed and friendlier atmosphere.!-5/ There is, however, some disagreement about actual academic emphasis between the two school types, with some researchers finding girls' schools to evidence a more academic orientation, while others find no positive relationship between gender grouping and academic orientation.1 A cross-national study used 1970 IEA data measuring attitudes and achievement in science and reading among 14-year-olds in both single-sex and coeducational schools in the United States and England. While no differences were found between coeducational and a small sample of single-sex schools in ti.e Uniited
explain these differences by either introducing a stochastic productivity trend (Aguiar and Gopinath, 2007, 2008) or foreign interest rate shocks along with financial frictions (Neumeyer and Perri, 2005; Uribe and Yue, 2006). However, the theoretical literature still needs to understand: (a) the forces behind the differences in the TFP of emerging market and OECD economies. Are these differences mainly the reflection of policy reversals or frictions? (b) The mechanisms through which shocks to fundamentals may induce fluctuations in country risk spreads.<sup>12</sup> This section will focus on a more limited issue. We will try to shed light on the factors that determine the depth of recessions (or peak‐to‐trough phases of the cycle). Our strategy is two‐fold: first, we estimate a cross‐section regression where each observation represents an episode of contraction (as defined in section 2). The dependent variable is the average output losses, which is a measure of the cost of recessions. Based on the literature previously discussed we include as determinants of the cost of recession proxies for external shocks (foreign interest rate), macroeconomic instability (inflation, flexibility of exchange rate regimes), and other structural characteristics (trade openness, domestic financial development, quality of institutions), among others. Second, we explore the association between the average cost of recessions over the entire period of analysis and measures of the quality of labor and business regulations over a cross section of countries. The proxies for regulations are not used in the regression analysis since they are available only in the 1990s and do not show enough variability over time.

# **4.1 Determinants of the size of the recessions**

After implementing the Harding and Pagan methodology to identify peaks and troughs in quarterly data for real GDP in **35** economies over the period **1980‐2006** , we are able to detect 126 episodes of recession (see Section 2). For each episode we calculated the cumulative change in output (from peak‐to‐trough) and the duration in quarters, and we

> 12 In other words, it could be a supply shock that deteriorate the economic situation of the country, raising risk premium.

22
|Subsidized inputs*community has irrigation scheme (1/0)|0.690|(0.299)|
|---|---|---|
|Panel C|||
|Received subsidized farm inputs (1/0)|0.808|(0.321)|
|Received food/cash transfer (1/0)|2.072<sup>***</sup>|(0.528)|
|Subsidized inputs*food/cash transfer (1/0)|0.660|(0.237)|
|Panel D|||
|Community has irrigation scheme (1/0)|0.706|(0.375)|
|Received food/cash transfer (1/0)|1.738<sup>***</sup>|(0.287)|
|Irrigation scheme*food/cash transfer (1/0)|0.858|(0.447)|
|Panel E|||
|Received food/cash transfer (1/0)|1.810<sup>***</sup>|(0.300)|
|HH uses an irrigation scheme (1/0)|1.401|(0.773)|
|HH use an Irrigation scheme*food/cash transfer (1/0)|0.443|(0.263)|

Notes: All models include control variables such as household characteristics and location fixed effects. HH=household. Exponentiated coefficients. Standard errors in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01

# **4.2.2 Effects of interventions on food consumption**

Table 7 shows the effects of different interventions on food consumption scores generated from IV Poisson regression. We find positive joint effects on FCS are lacking. In other words, there are no significant combined effects on food consumption scores from investing in irrigation and receiving input subsidies. Additionally, the findings demonstrate that households are significantly disadvantaged in FCS when they combine participation in input subsidies with food/cash transfers (Panel C). Households receiving input subsidies as well as food/cash transfers experience reduced FCS by a margin of 55 points (p<0.10). However, receiving food/cash transfers alone has significant favorable benefits on FCS (Panel C, D & F), implying that household receiving government food/cash transfers experience greater balance in food consumption. This finding is also obtained when we use quantity of subsidized fertilizers (Table A5).

<u>Table 7: Effects of interventions on food consumption score</u>

|Dependent var: FCS|Model 1||
|---|---|---|
||Coef.|SE|
|Panel A|||
|Received subsidized farm inputs (1/0)|29.146|(20.216)|
|HH uses an irrigation scheme (1/0)|72.026|(67.444)|
|Subsidized inputs*use of irrigation scheme (1/0)|-90.862|(77.749)|
|Panel B|||
|Received subsidized farm inputs (1/0)|31.290|(20.468)|
|Community has irrigation scheme (1/0)|47.295|(36.394)|
|Subsidizedinputs*communityhasirrigationscheme (1/0)|-66.622|(44.156)|

16
**Figure 23. Long-run prospects look promising for Uganda**

<!-- Start of picture text --> 30<br>25<br>20<br>15<br>10<br>5<br>0<br>Total costs, Revenue Effective tariff LRMC trade LRMC trade<br>historical expansion stagnation<br>US cents per kWh<br><!-- End of picture text --> _Source:_ Rosnes and Vennamo 2009; World Bank 2008 and 2010. _Note:_ LRMC = long-run margin costs.

# Information and communication technologies

## **Achievements**

Uganda has made notable progress in telecommunications, particularly through mobile networks, outperforming LICs in this area (table 13). Mobile penetration has risen from less than one percent in 2000 to 28 percent by 2009, consistent with what is observed in low-income African countries. This level of access is higher than the penetration rate due to shared usage in households and public mobile phone facilities. Furthermore, there are no infrastructural barriers to mobile access, with the Uganda Communications Commission (UCC) reporting that mobile cellular signals covered 100 percent of the population by December 2008b (UCC 2009). This is a major achievement compared to the LIC benchmark of only 56 percent. Meanwhile, in 2006 (the latest year available), the Uganda Bureau of Statistics (UBOS) reported that almost half of inhabitants already had access to telephone facilities (UBOS 2006), and the number of public pay telephones has increased more than 30 times since 2000 to almost 100,000 by 2009.

38
||more confirming lines to new Kenyan banks).|
|---|---|
|**Sierra Leone**|The IFC has provided US$1 million of LC confirmation lines to two largest local<br>banks and these lines are fully utilized; the banks are requesting the IFC to increase<br>this to US$3 million.|
|**Turkey**|The World Bank is implementing a US$300 million loan with Turkish Exim bank and<br>another US$300 million loan with the local development bank (TSKB) to finance<br>working capital and investment for exporters. The Bank also implemented a US$200<br>million SME credit line, including for exporters’ working capital or trade finance, and<br>an additional SME credit line of US$500 million is confirmed for FY10.|
|**Tunisia**|The World Bank is supporting a US$40 million Export Development Project that<br>includes: (1) the Export Market Access Fund, a matching grant program that provides<br>co-financing to firms, and (2) a Pre-shipment Export Finance Guarantees Facility.|
|**Ukraine**|The World Bank is implementing a US$150 million export development project with<br>Ukraine Eximbank (Ukrexim) and discussion are underway with Ukrexim bank for a<br>follow-up Export Development project later in 2009. Additionally, a US$250 million<br>Export Development 3 or EDP2 AF to augment financing support for exporters has<br>also been confirmed for FY10 (starting in July 2009).<br>IFC’s Global Trade Finance Program (GTFP) was launched in Ukraine in October<br>2005, and increased its program by US$500 million to US$1.5 billion in October 2008<br>and to US$3 billion in December 2008.|

_Source: World Bank and consultants’ survey reports_ 40