rafmacalaba/datause-encoder-data huggingface dataset:
coming out of poverty. In short, the core results reported in the introduction are not highly sensitive to the omission of these admittedly important countries.

# **Table 9. Alternative Estimates of Global Self-Reported Food Insecurity Trends after Allowing for Omitted Countries (Millions of People)**

||**Estimated change in global food insecurity,**|
|---|---|
|**Estimation scenarios**|**2005–06 to 2007–08**|
|Raw results, 69 countries (excluding China),_covering 57%_<br>_of developing world population_|−132|
|As above, plus pessimistic assumptions for 16 omissions,||
|_covering 67% of developing_|−60|
|_world population_||
|As above, plus a 3-percentage-point reduction in China,<br>_covering 87% of world population_|−100|

_Source_ : Author’s calculations from GWP data (Gallup 2011), FAO Global Information and Early Warning System data (2010), and ILO food inflation data (2011). _Note_ : See text in this section for more details regarding the assumptions and data as well as table A3.

Another objection may be that the 2005–06 GWP results are less reliable than subsequent rounds because the first round of the GWP may be regarded as a trial run for Gallup. We have the option of using the second round of the GWP (2007) as a base year instead of the 2005–06 round, but the 2007 round contains fewer countries and does not include China. Nevertheless, the 2007 GWP round includes India and other large countries and therefore covers approximately 43 percent of the population in the developing world. A second potential problem with using the 2007 round as a base year is that maize and wheat prices were already increasing in 2007, so it is difficult to regard 2007 as a pure precrisis period. Thus, we might underestimate the food insecurity impacts of the crisis if the 2005–06 round is shown to be unreliable. However, we note that there is no analogous problem with the 2008 data. The vast majority of the GWP surveys in 30
# **Figure 5: Structure of Production (other than fossil fuels)**

<!-- Start of picture text --> Leontief σ = 0<br>Composite of Value-added (incl. Business Services) and Energy Composite of Other Goods and Services<br>Leontief<br>CES σ = 0.5 σ = 0<br>Coal ....<br>Other Other<br>Good and Service Good and Service<br>Value-added and Business Services Energy Composite<br>CES<br>σ = 1.25 Cobb-Douglas<br>σ = 1<br>Business Services<br>.... Value-added Electricity<br>Gas-Oil Composite<br>Cobb-Douglas<br>Cobb-Douglas<br>σ = 1 σ = 1<br>Business .... Business Sector Skilled CES<br>Service Service Specific Capital Unskilled Labor Labor σ = 3<br>Resources<br>GAS OIL (refined)<br><!-- End of picture text --> 79
<!-- Start of picture text --> tr ur TAYLOR ., AND LIVERPOOL MODELS N ON THE U.S. ECONOMY . - IN ...<br>U.S.A<br>09-<br>04 08 44<br>03<br>06-<br>0Cl<br>0 - 0 05 -T'<br>04<br>-0 I8<br>02-<br>-0 3<br>01-<br>-0 4<br>-nb~~~~~~~~~~~~~~~~~~~~~~0<br>.0 7~~~~~~~~~~~~~~~~~~~~~0<br>3986 1966 1986 1988 1989 1990 1985 1986<br>S-T I3etER6ST RATE<br><!-- End of picture text --> <!-- Start of picture text --> U.S.A<br>09-<br>08 44<br>06-<br>0 05 -T'<br>04<br>02-<br>01-<br>1985 1986 1987 98a 1989 199<br>CAP<br><!-- End of picture text --> U.S.A U.S.A <!-- Start of picture text --> 45.<br><!-- End of picture text --> <!-- Start of picture text -->
-22-<br>-2 4<br>-2  6-<br>-2 8<br>3-Sd<br>38J-<br>-44<br>-4  7<br>-<br>I  983  3f  1987  1988  3989  1990<br>COCHR~~~~~~~~~~~~~~~<br><!-- End of picture text -->

<!-- Start of picture text --> 3 5-<br>6 2-<br>OS<br>- 933! 1986 fol987 399 99 98 19 9<br><!-- End of picture text -->
OZIER - REPLICATION REDUX – p. 34 of 41 statistical work by Croke, et al. (2017), both illustrate how the medical approach to systematic review may hobble our understanding in matters of public health.

In terms of policy, one must weigh expected costs against expected benefits. If there is any uncertainty around our beliefs about a program’s effect, then _“the costs of proceeding when there is still substantial doubt as to the outcome needs to be weighed against the cost of missing an intervention that may be valuable,”_ as one anonymous referee of this manuscript has pointed out. In the case of deworming, one must further consider the likelihood that public (or NGO) financing of deworming medications influences uptake. Ahuja, et al. (2017) illustrate that even under pessimistic interpretations of the re‐analysis of Miguel and Kremer (2004), the incredibly low cost of deworming relative to other interventions still makes it highly cost‐effective in high‐worm‐prevalence settings.<sup>29</sup> Ahuja, et al. (2017) also point out that preventative health investments are precisely the ones that are least likely to be invested in by the poor; Kremer and Glennerster (2011) discuss how up‐front costs for health technologies—deworming medication, bed nets to protect against malaria, clean water, and so forth— sharply reduce take‐up, even when the cost itself is very low.

The “worm wars” teach us that scientific inquiry is difficult, and that those of us producing and communicating our findings do make mistakes along the way. Deworming remains lower‐cost than almost any other intervention, and our best estimates still suggest that it has lasting benefits. Because it requires up‐front investments and has substantial positive externalities, people are likely to underinvest in it as individuals. The World Health Organization and other coordinating entities can continue to play a role in ensuring deworming benefits do reach people, however. As for the replication effort? After all is said and done: most of the original study findings hold up, but the externality benefits probably do not reach as far as six kilometers. That just doesn’t make a very exciting headline.

29 Note that the full list of authors of this study is Ahuja, Baird, Hicks, Kremer, and Miguel.
**Table 5: Investment Climate Scores of 23 Cities in China**

ICA <u>City Score</u> Hangzhou A+ Guangzhou A+ Shanghai A+ Shenzhen A+ Chongqing A Changchun A Tianjin A- Dalian A- Beijing A- Zhengzhou A- Wuhan B+ Nanchang B+ Xi'an B+ Changsha B+ Chengdu B Guiyang B Kunming B Nanning B Harbin B- <u>Lanzhou B-</u> _Source_ : Dollar and others (2003) The efforts to reduce transaction costs are being buttressed by investment in infrastructure and housing. China’s municipalities are keenly aware that in the highly charged competitive environment, the quality of services provided by public utilities, urban transport, and the abundance of housing can tip the economic balance in the favor of one city over another. Hence, there is an ongoing infrastructure race as well. As we noted above, the leading cities are able to provide adequate level of infrastructure services for industry but in many instances, they still fall short of the standard achieved by the industrialized countries. The building of highly capital intensive physical infrastructure is hampered by the state of the capital markets and in particular by the thinness of structured bond markets that would permit cities to raise funds more 26
their “equilibrium” values in order to make relevant value judgments. On the connection between social security contributions and individual benefits, the literature has focused on pension reform and used as outcome indicators the extent to which pension regimes are based on individual capitalization systems. The highest score is given to pension regimes that feature a fully funded capitalization system with individual accounts managed by private companies; and the lowest score is given to pension regimes that are mostly based on a pay-as-you-go system (see Samwick 2000 for a cross-country application). Ideally, similar indicators could be constructed for other social security benefits (such as health insurance, family allowances, etc.) Regarding the mechanisms for negotiation and peaceful resolution of labor disputes, a key outcome indicator is the incidence of worker strikes. This can be measured, for instance, by the number of hours not worked due to strikes, per year and worker in the modern sector. The extent of unionization has also been used as an indicator of the lack of efficiency of manager-worker relations and even the degree of government involvement in the negotiation process (which would be the case if legislation favors the formation and membership of unions). However, without contextspecific analysis, it is difficult to ascertain whether unionization responds to excessive government regulations, ineffective labor-dispute courts, or the most prevalent forms of industrial organization in the country.

Finally, on the pervasiveness of public employment in the economy, common indicators are the ratio of public to total employees in the non-agricultural sector and the share of government expenditures devoted to the public sector payroll, among other measures. The first indicator denotes the labor resources in the economy occupied by the public sector, while the second measures public employment in relation to the size of government (a large share is likely to represent overstaffing in the public sector).

As noted before, the database compiled by Rama and Artecona (2000) contains useful cross-country information on a number of labor outcome indicators, such as unemployment rates, wages, conditions of work, trade unions and collective bargaining, and public employment.

24
2.2 Figure 1 shows world production of raw sugar (including the raw sugar equivalent of directly produced refined sugar), refined sugar and non-centrifugal sugar. Between 1964 and 1993 world production of centrifugal sugar grew at an annual rate of 0.97%. Production crossed 100 mln MT in 1982 and reached a peak in 1992 (117.7 mln MT).

Figure 1: **World production of sugar** <!-- Start of picture text -->
- CENTRIFUGAL<br>+NON  CENTRIFUGAL<br>Mt  21T  REFINED (BEET & CANE)<br>; __XS  YMCAL5NDRThAR<br><!-- End of picture text -->

Source: See Data S4 for details.

World production of refined sugar production has followed a similar pattern to the production of raw sugar, reflecting an average extraction rate of refined sugar from raw sugar of about 92.3 percent. By comparison world production of non-centrifugal sugar is much smaller, only about 12 mln MT in the early 1990s. As noted previously, a significant but unknown proportion of this is Indian khandsari.

In terms of raw sugar equivalents. India was the world's largest sugar producer during 1988-94, accounting for over 10.3 percent of the total production (see Data S4 for details). Its share of world sugar production is about half its share of world sugar cane production, because about 40 percent of its cane is used for khandsari and gur, and because of beet sugar production in other countries. If a quarter of the cane not used to produce mill sugar were used for khandsari production, the Indian share in world sugar production would be about 12 or 13 percent. India was followed by Brazil<sup>4</sup> (6.9 %), Cuba (6.7%) and China<sup>5</sup> (5.9%). Other large producers in the early 1990s were USA, Mexico, France, Germany and Thailand. India replaced Brazil as the world's largest producer of mill sugar in the late 1980s, but its total production could well have been ahead of Brazil's before this if khandsari were included.

> **5** Per capita consumption of China is very low. China is thus a large potential market for sugar.

6
**Annex 4: Number of Countries that Receive FDI, by Sector and Motivation**

|**2-digit ISIC **|**Natural-resources**|**Market**|**Efficiency**|
|---|---|---|---|
|15|11|17|15|
|16|6|3|3|
|17|7|24|6|
|18|12|19|13|
|19|7|17|9|
|20|10|21|15|
|21|2|29|2|
|22|3|32|4|
|23|2|15|1|
|24|1|34|1|
|25|5|33|4|
|26|6|29|9|
|27|11|24|9|
|28|3|37|1|
|29|1|30|3|
|30|0|5|3|
|31|2|27|6|
|32|0|13|2|
|33|1|18|7|
|34|2|19|3|
|35|1|13|5|
|36|10|19|13|
|45|0|109|0|
|50|0|109|0|
|51|0|109|0|
|52|0|110|0|
|55|0|110|0|
|60|0|101|0|
|61|0|36|0|
|62|0|31|0|
|63|0|97|0|
|64|0|66|0|
|72|0|0|94|
|Rest|2|31|0|

Source: Perea et al. (mimeo).

30
# **5. Policy volatility and long-term growth**

As noted in the introduction, the literature argues that policy continuity ought to be an important driver of economic growth in the long-run. In this section, we provide suggestive evidence that this is indeed the case.<sup>4</sup> In Figure 8 we show that policy volatility is negatively correlated with long-term growth during the 1940-2010 period. Long-term growth is measured by the percentage point change of the GDP per capita of each country (relative to the GDP per capita of the US). Positive values indicate countries that were catching-up with the US, while economies with negative values diverged from the US. Policy volatility is measured as the percentage point change between 1940 and 2010 of the share of the top three topics in the presidential speech of the initial year. Intuitively, this measure captures to what extent the top priorities circa 1940 were still relevant seven decades later. As mentioned previously, we pool the speeches from all countries and perform LDA in the collection of speeches to “discover” the topics in each speech. We then use a 10% minimum threshold to discard topics that appear infrequently in each country.

Figure 8. Policy volatility and convergence with the US GDP per capita, 1940-2010 (using topic modeling on pooled data of speeches from all countries) <!-- Start of picture text --> 0.3<br>Spain<br>0.2<br>0.1 Costa<br>Rica<br>0<br>Chile<br>Ecuador Mexico<br>-0.1<br>Peru<br>-0.2 Paraguay<br>Argentina<br>R2 = 0.40 Venezuela<br>-0.3<br>0.4 0.5 0.6 0.7 0.8 0.9 1.0<br>Low High<br>Policy volatility<br>(change in the share of the top three topics in speeches, 1940-2010)<br>Catching-up<br>Falling behimd<br><!-- End of picture text --> Note: This Figure shows the cross-country correlation between long-term growth and policy volatility. Longterm growth is measured as the percentage point change of the GDP per capita of each country (relative to the GDP per capita of the US). Policy volatility is measured as the percentage point change of the share of the top three topics in the presidential speech of the initial year. The topics were selected by the LDA using pooled data of all the countries, and using a 10% minimum threshold to discard topics with low frequencies.

> 4 GDP growth data come from The Maddison-Project, http://www.ggdc.net/maddison/maddisonproject/home.htm, 2013 version.

19
<!-- Start of picture text --> cy Conflicts number (2011 to 2023)<br>C.Font aLGM [1 863 - 1010<br>[1010 - 1157<br>nee [J 1157 - 1304<br>2 River 1304 - 1451<br>Cee, (i 1451 - 1598<br>Ve [i 1598 - 1744<br>(1744 - 1801<br>° A Hl 1891 - 2038<br>ae I 2038 - 2185<br>HB 2185 - 2332<br><!-- End of picture text -->
PAYING THE PRICE FOR UNRELIABLE POWER SUPPLIES: GENERATION OF ELECTRICITY BY PRIVATE FIRMS IN AFRICA

# **Figure 9 Own-generated electricity as a share of installed generating capacity in Africa, 2005**

<!-- Start of picture text --> 70.00%<br>60.00%<br>50.00%<br>40.00%<br>30.00%<br>20.00%<br>10.00%<br>0.00%<br>Mauritania Equatorial Guinea Congo, Dem. Rep. Swaziland Mauritius Nigeria Gabon Guinea Niger Togo Tanzania Ghana Mali Uganda Congo, Rep. of Sudan Guinea-Bissau Malawi Angola Kenya Cote d'Ivoire Rwanda Zimbabwe Botswana Burundi Zambia South Africa Gambia, The Benin Cameroon Senegal Mozambique Ethiopia<br><!-- End of picture text --> Source: UDI World Electric Power Plants Data Base (WEPP).Country data on annual electricity imports came from IEA’s International Energy Annual 2004. Flow data were converted to generating capacity assuming continuous, uninterrupted annual consumption of imported electricity at an average peak load factor of 0.65.

The estimated startup value of in-house generating capacity, expressed as a percentage of the GNI and gross fixed capital formation (GFCF) in Sub-Saharan African countries is shown in figure 10. For most countries, the value of own generation relative to the size of their economies is not large. However, for five countries (Mauritius, Congo Republic, Mauritania, Swaziland, and Zimbabwe) it is greater than 4 percent of GNI, and for another five countries (Equatorial Guinea, Nigeria, Ghana, Malawi, and Togo) it ranges between 2–3 percent of GNI. Figure 12 also shows that for three countries (Mauritius, Congo Republic, and Swaziland), the value of own generation accounts for more than 20 percent of GFCF, and for five countries (Mauritania, Zimbabwe, Nigeria, Malawi, and Togo) about 10 percent of GFCF.

8
# **2.2 Structure and Cost of Public Pillars**

There are large differences in the structure of the zero pillars among these five countries (Table 3). Australia and Denmark offer universal pensions financed from general tax revenues. These are subject to clawback provisions. Denmark also pays a supplement to low income pensioners. The level of the universal benefit is 25 percent of the average wage in Australia for single pensioners and nearly 42 percent for couples. In Denmark the combined universal pension and supplement amount to 35 percent of average earnings.<sup>3</sup>

**<u>Table 3: Structure of Zero Pillars</u>**

|Country|Type|Replacement Rate<br>(Percent ofaverage wage)|Clawback or Top Up Provisions|
|---|---|---|---|
|Australia|Universal|Single rare: 25%<br>Couples: 41.7%|Income Test<br>40% clawback<br>above low income threshold<br>Asset Test<br>1.5 per mil clawback<br>abovehighasset threshold|
|Chile|Universal pension to<br>impoverished old people|Basic pension (PBS) 17%<br>Maximum pension with<br>solidarity (PMAS) 57%|Top-Up to Pillar 2 pension<br>Universal pension is means<br>tested|
|Denmark|Universal pension<br>Supplement to low-income<br>pensioners|Combined 35%|Universal pension:<br>30% clawback above high<br>income threshold<br>Supplement:<br>30% clawback above low<br>income threshold|
|Sweden|Minimum pension guarantee for<br>combined NDC and FDC benefits|About 30%|Top up to NDC and FDC<br>benefits|
|Switzerland|Minimum pension from pillar 1<br>Supplement for impoverished old<br>pensioners|Combined 24%|Supplement is means tested|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

Chile closed down its old social security system to new workers when it implemented its pension reform in 1981. However, a new basic solidarity pension (PBS) was introduced in 2008. This is offered to pensioners who do not have adequate balances to purchase a life annuity above the PBS level. The PBS currently amounts to approximately 17 percent of the average wage. Low-balance workers are compelled to use phased withdrawals and the government pays the PBS after their balances have been exhausted. In order to minimize the probability of triggering the PBS, the 2008 amendments to the pension law

> 3 The clawback provisions are discussed in section 2.4 below.

4
2

**Figure 1. The main components of government employment**

<!-- Start of picture text --> / Adm inistration/<br>% liiXlXilll ,/ _~A d mnin istration<br>Health atio;<br><!-- End of picture text --> Even when comparisons are limited to monetary compensation, there are serious problems. If wage rates are derived from independent surveys or from official information on pay scales, it is impossible to treat non-wage allowances and other monetary benefits uniformly -because certain sources will include them in the wage package while others show only the basic salary. However, budgetary figures on the overall wage bill paid for through the central budget are generally reliable, because monetary allowances are usually captured in the central government budget, and properly classified under the wages and salaries item of the budget. For this reason, measuring the relative 'weight' of the government wage bill, by defining it as a fraction of GDP or of total expenditure, is both reasonable and reliable.

Compensation rates are a different matter: dividing the central government wage bill by the number of employees listed as working for the central government **will** always tend to inflate average compensation, because significant (and variable) numbers of employees are paid out of the central budget but work for, and are shown as part of, local government entities. Consequently, it is necessary, country by country and by recourse to different sources of information, to adjust the figures on personnel paid from the central government-in order to obtain a good approximation of average monetary compensation.

Assessing wage "adequacy", or even only ranking countries by relative levels of government wages, presents additional difficulties. Exchange rate problems make reliable conversion into a single numeraire difficult. Relating public wages in local currency to other meaningful variables also in local currency obviates the exchange rate problem, but raises the obvious issue of the appropriate denominator. The only reliable measure of the adequacy of government wages is through a statistically-representative - survey of public and private salaries for comparable skills, in a given country and at a given time. Because such surveys are not common (only a few among the World Bank's private sector assessment studies contain such information), this is an impossible standard when large-scale international comparisons are at issue. Accordingly, we have used in this study three measures of relative government wages: average central government wages relative to wages in manufacturing (the only statistic available, from
<!-- Start of picture text --> Inventory of Public Lands - Ahmedabad 2013<br>Land Use Classification Within the Registered Public Lands<br>Cantonment fr 7.06%<br>Airport I s.sex<br>Commerce J] 0.60%<br>Other, Multi-Purpose i 1.12%<br>Institution [ff 1.04%<br>Service | 0.25%<br>Utility [2.31%<br>Cemetery | 0.29%<br>Wholesale market 0.01%<br>Park, Open Space+Recreational Li 7 9.48%<br>NA- No Defined Use Lo 7 7.29%<br>0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%<br>Source: GIS database “FP_Boundary"<br><!-- End of picture text -->
# Figure 16: SAR: Investment needs

# A. Quality of infrastructure

# B. Infrastructure investment needs

<!-- Start of picture text --> Index, 1-7, 7 = best Percent of GDP<br>7 8<br>Advanced economies EMDEs SAR<br>6 7<br>6<br>5<br>5<br>4<br>4<br>3<br>3<br>2<br>2<br>1 1<br>0 0<br>Transport Electricity Water and Flood Irrigation<br>sanitation protection<br>C. Public health expenditure D. Human capital indicators<br>Percent of GDP Index, 0-1, 1 = best Number of years<br>3 EMDEs SAR Human Capital Index<br>0.8 Learning-adjusted years of school 12<br>(RHS)<br>2<br>0.7 9<br>0.6 6<br>1<br>0.5 3<br>0.4 0<br>0 SAR EMDEs Advanced<br>economies<br>E. Agriculture output F. Agriculture research spending<br>Percent of total output Percent of agriculture GDP<br>30 0.7 Range Median<br>0.6<br>25<br>0.5<br>20<br>0.4<br>15 0.3<br>10 0.2<br>0.1<br>5<br>0.0<br>0<br>1990 2021<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Sources: Agricultural Science and Technology Indicators; Haver Analytics; Rozenberg and Fay (2019); World Bank; World Health Organization.

Note: EMDEs = emerging market and developing economies; SAR = South Asia. B. Based on the preferred scenario in Rozenberg and Fay (2019).

C. Sample includes 152 EMDEs and 8 from SAR.

- D. Sample includes 138 EMDEs, 7 from SAR, and 35 advanced economies

F. Based on data for Bangladesh, India, Nepal, Pakistan, and Sri Lanka. “Range” reflect minimum and maximum values.