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

This paper is the result of a collaboration between the World Bank and UNICEF to estimate the global and regional magnitude of child poverty between 2013 and 2019 and specifically includes nowcasts of extreme child poverty for 2020 – 2022, to help monitor progress towards SDG 1.1.1.

The trend analysis shows that in the absence of COVID-19, we would have expected a reduction of 79.7 million children in extreme poverty between 2013 and 2022; but our estimates suggest a reduction of only 49.2 million children, due to pandemic disruptions. The extreme child poverty nowcasted estimate in 2022 (15.8 percent) is on par with the child poverty rate in 2019, indicating approximately three years of lost progress.

With a staggering 333.5 million children living in extreme poverty in 2022, addressing the structural root causes that affect children living in extreme poverty is imperative. A key step towards that is to monitor and understand which children and their families are being left behind, what are their characteristics and where do they live, and why existing policy interventions are not effective in curbing child poverty for countries to develop the appropriate policy portfolios to address and end extreme child poverty for good.

The analysis shows that these policy portfolios need to consider both the common and distinct vulnerabilities and deprivations of children living in fragile and conflict prone settings, children in large households, younger children, children in rural settings, children in households where the head has no or little education - as these are more likely than other children to be living in extreme poor households. In addition, the focus needs to be on the large concentration of children living in extreme poverty in Sub-Saharan Africa, where a staggering 40 percent of children live in extreme poverty. And while about 45 percent of extremely poor children live in low-income countries; half of all extremely poor children live in lower middle-Income countries. As a result, the design of effective policy portfolios needs to take into consideration too that context matters. For example, technical capacity, financial resources, political consensus, and perceptions, attitudes, and social norms might greatly differ across countries where child poverty reduction policies and programs are applied.

Ultimately, further research and analysis are warranted to understand the diverse policy portfolios countries need to adopt to address child poverty, including further analyzing the trends in child poverty reduction and the policies and programs that are making the biggest impact – and how these can be brought to scale to reach the SDGs of ending extreme child poverty.

9
<!-- Start of picture text --> Figure 3: Consumer Confidence Index<br> 65.00<br> 60.00<br> 55.00<br> 50.00<br> 45.00<br> 40.00<br> 35.00<br> 30.00<br> 25.00<br> 20.00<br>Jul-98 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Ene-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 Jul-13 Jan-14 Jul-14 Jan-15 Jul-15 Jan-16<br><!-- End of picture text --> Source: di Tella Institute’s “ICC”—Índice de Confianza del Consumidor. The index for the capital Buenos Aires was used, since it dates back to the late 1990s (for comparison sake). The last observation is April 2016.

Economic growth was on the order of 9 percent each year over 2003-2005. Inflation was brought down to the single digits in 2003 and 2004, but accelerated in 2005. Fiscal policy retained surpluses, and monetary policy was broadly consistent with inflation objectives. The exchange rate stabilized and stayed below 3 pesos per dollar, with sustained current account surpluses. It should be noted that favorable fiscal results were also dependent upon export and financial transaction taxes (not shared with the provinces) that together comprised about 1/5 of federal tax revenues.

One important policy development in 2004 was the passage of a Fiscal Responsibility Law. The law (25.917) established a ceiling on provincial debt service of 15 percent of current revenues (net of transfers to municipalities). If a province’s debt service were to pass the limit, the province was required to present plans of primary surpluses that would lower the debt burden to levels compatible with the debt service limit. Provinces that were not in compliance could no longer borrow, other than to roll over existing debt. It also established a federal fiscal council for coordinating fiscal policy across the levels of government, set standards for reporting fiscal accounts on an accrual basis, and called for the explicit accounting of tax expenditures and fiscal guarantees. Note that no quantitative targets or guidelines were established for the deficit or debt of the federal level of government.

In 2005, the government successfully launched a global debt exchange on $82 billion in claims. The offer implied a net present value haircut of about 75 percent. About 76 percent of 18
highest priority in Northeast Brazil. These findings indicate that policies targeting this group would contribute to a reduction of poverty. One such initiative could be increased access to childcare and other social services.

The presence of old-aged people (above 65 years of age) in the household is an important factor contributing to poverty reduction in the Northeast and elsewhere in Brazil. The poverty rate for households with children under the age of six is four-and-a-half times the poverty rate of households with members aged 66 or older. Furthermore, we find that old-age poverty experienced a fall since the middle of the 1990s. Moreover, we find that there is no regional differences in the probability of falling below the poverty line for the elderly in Brazil.

The poverty profiles indicate that blacks and _mulattos_ are significantly poorer than their white counterparts. This finding is confirmed by the probit regressions for the 1980s and the beginning of the 1990s, even when controlling for other household head characteristics.

Informal sector workers are much more likely to experience poverty than formal sector worker. Current labor market programs such as unemployment insurance, training, and severance payments have little poverty impact as they are highly directed towards formal-sector employees. The informal-sector workers are therefore left out. The importance of extending the social safety nets to the informal-sector may create a positive welfare impact. One route to increase well-being of informal-sector workers is to supply programs that increase skill levels and hence productivity.

Providing better access (full property rights) to cheaper capital to informal producers could also help the progression of poverty, as productivity in the informal sector is often limited by credit constraints.

Finally, though poverty reduction over the last decades appeared to be mainly driven by federal policies, it appears that some states were more successful in reducing poverty than others. In particular, the relatively good performance of Maranhão, Piauí, Ceará, Paraíba, and Rio Grande do Norte that reduced the headcount poverty ratio by 9 or more percentage points in the 1981-99 period indicates scope for regional coordinated poverty reduction strategies.

40
regime. This is exactly the approach that was followed in the preceding section for international trade.

Following the example of trade, for instance, one method to evaluate the redistribution impact of labor migration would be to introduce barriers to migration in a model of the world economy and to see how the international allocation of production and consumption would be affected if those barriers were modified. This is a difficult exercise, which would require data on stocks of migrants in host countries by country of origin, the wage differential between countries, the cost of migration, and also some rather strong assumptions on migration behavior, and parameters that may determine the volume of immigration in host countries. In the absence of such a model both for labor and capital flows, and in view of the extremely imprecise data on both migration stocks and remittances, we have not pursued that direction here, leaving the issue of international redistribution through those two types of flows open to future research.<sup>21</sup>

# **VI.  Conclusions**

Although this paper is about international inequality, we know that the actual level of global inequality of income is extremely high – with a Gini coefficient between 0.64 (Milanovic 2005) and 0.66 (Bourguignon and Morrisson 2002). If this level of inequality were to exist within a single country, that country would probably experience substantial social strife. That this does not happen in the world simply means that, as of today, there is

> 21 A recent simulation conducted for the Global Economic Prospects 2006 of the World Bank (World Bank, 2005c) uses a global Computable General Equilibrium model to estimate the impact on global income of an increase in migration equivalent to 3 percent of high-income countries’ labor force.  The simulations reveal an increase of 0.9 percent in global real income, adjusting for purchasing power parity, given the change in location of migrants’ own consumption.  In the simulation, most of the gains accrue to the new migrants themselves, while the only declines in real income are experienced by “old” migrants, i.e. the existing migrants prior to the increase in migration. We could not use these results in the framework developed in this paper because the assumption of identical income within all countries prevented to distinguish between nationals, old  and new migrants.

22
lowest prices and ambitious coverage targets. Economic and financial models are important decision making tools that policymakers or regulators should use for the reconciliation of all these objectives

|Table 1: Examples of selection c<br>**Infrastructure transaction**|riteria<br>**Structure of financial proposal**|
|---|---|
|**Peru : Lima electricity distribution**<br>**privatization**|**Highest dollar value offered for**<br>**assets**|
|**Argentina: Buenos Aires water**<br>**concession**|**Maximum discount to existing tariffs**|
|**Philippines: Power generation BOTs**|**Lowest price (cents per kilowatt-**<br>**hour) of power to be supplied**|
|**Chile : south access to Conception**<br>**toll road**|**Minimum toll and minimum one-**<br>**time subsidy**|
|**Turkey: electricity distribution**<br>**concession for Istanbul**|**Minimum margin on distribution**<br>**required by the operator**|
|**Venezuela: Mobile telephony**<br>**concession**<br>Source: Guasch (2004).|**Highest concession fee paid to**<br>**government**|

# **III. The procurement of PPI projects in Korea**

In the first section we reviewed the principles and the main issues affecting the design and implementation of the procurement of PPI projects. This section reviews the experience of Korea in the development of PPI projects, and discusses more specifically how these projects are procured. South Korea is an attractive country for private investment in infrastructure sectors. The country credit worthiness<sup>14</sup> , the availability of local currency lending and the established track-record in managing infrastructure projects are its main assets.

# **_A. PPI project development in Korea_**

Since the early 1960s, Korea has demonstrated an impressive record of economic performance. Much of its success stems from a commitment to infrastructure

> 14 Standard and Poors sovereign rating for Korea:  A+ (local) ;  A- (foreign)

18
**Table 6: Effects of local rainfall variability and policy action variables on consumption welfare**

|Sample:|(1)<br>Non-flood pro|(2)<br>ne Upazilas|(3)|
|---|---|---|---|
|Policy interactions variables|Credit|Safety-Net|Market|
|CV Rain CRU (β4)|**-1.794*****|**-1.638*****|-1.242|
||(-3.775)|(-3.799)|(-1.247)|
|Policy Action X CV Rain CRU (β6)|0.449|-0.0903|-1.306|
||(0.894)|(-0.214)|(-0.433)|
|Observations|3886|3886|3886|
|Test β4+ β6= 0|**-1.345*****|**-1.729*****|-2.548|
|F Statistic|8.066|12.66|1.407|
|F > Prob|0.00498|0.000467|0.237|

Robust t-statistics in parentheses based on PSU level clusters. *** p<0.01, ** p<0.05, * p<0.1. Full estimation results are in annex Table A9 We find that the net effect of rainfall variability and access to credit or safety-net on consumption is negative and significant (columns 4 and 5). That is, access to credit or safety-net does not completely mitigate the negative effects of rainfall variability on consumption welfare. On the other hand, we cannot reject the null that local rainfall variability has no effect on consumption in households with access to market.

# **6. Conclusions**

When households’ choices of diversity in economic activities are driven by push factors households attain income stability by sacrificing higher returns. We find that rural households in Bangladesh are pushed to diversify between sectors and employment types to cope with historical flood and local rainfall variability risks. However, our findings suggest households chose sectoral diversity to mitigate historical flood risks. That is, members of the same households choose occupations in different sectors to mitigate flood risks.

As expected, in the historically flood prone Upazilas local rainfall variability plays a less significant role in households’ occupational choice decisions. On the other hand in non-flood prone Upazilas local rainfall variability plays a significant role in household occupational and employment diversity choices. In particular, households living in Upazilas with high rainfall variability are more likely to be diversified between sectors, such as agriculture versus non-agriculture, as well as between self versus wage employment choices.

We find that, in non-flood prone Upazilas households with head and non-head members self employed in agriculture enjoy higher consumption welfare as compared with the households who choose to diversify. However, households faced with higher rainfall variability are less likely to have both members self employed in agriculture. Higher rainfall variability also accounts for significantly lower consumption welfare in non-flood prone Upazilas.

19
Kouame, C. Y., B.B. Kalimili and F. Pirlea, “Many African Economies are Larger than Previously Estimated,” World Development Indicators, 2019. <u>http://datatopics.worldbank.org/world-development-indicators/stories/many-economies-inssa-larger-than-previously-thought.html</u>

- Kuznets, S., “Economic Growth and Income Inequality,” _American Economic Review,_ 45, 1–28, 1955.

Lakner, C. and B. Milanovic, “Global Income Distribution: From the Fall of the Berlin Wall to the Great Recession,” _The World Bank Economic Review,_ 30, 203–32, 2016. doi:10.1093/wber/lhv039 Lakner, C., D.G. Mahler, M. Negre, and E.B. Prydz, “How Much Does Reducing Inequality Matter for Global Poverty?. _Policy Research Working Paper_ No. 8869, World Bank, Washington, DC, 2019. https://openknowledge.worldbank.org/handle/10986/31796

- Lind, J.T. and H. Mehlum, “With or Without U? The Appropriate Test for a U‐shaped Relationship,” _Oxford Bulletin of Economics and Statistics_ , 72, 109–118, 2010. doi: <u>10.1111/j.1468-0084.2009.00569.x</u>

- Milanovic, B., _Worlds Apart: Measuring International and Global Inequality_ , Princeton University Press, Princeton, NJ, 2005.

- Newhouse, D.L. and P. Vyas, “Estimating Poverty in India without Expenditure Data: A Surveyto-Survey Imputation Approach,” _Policy Research Working Paper_ No. 8878, World Bank, Washington, DC, 2019. <u>https://openknowledge.worldbank.org/handle/10986/31868</u>

Nolan, B., M. Roser and S. Thewissen, “GDP Per Capita Versus Median Household Income: What Gives Rise to the Divergence Over Time and How Does This Vary Across OECD Countries?” _Review of Income and Wealth_ , 65, 465–494, 2019. doi: 10.1111/roiw.12362

- OECD, _Measuring the Non-Observed Economy: A Handbook_ , OECD Publishing, Paris, France, 2002.

Palma, J.G., “Homogeneous Middles vs. Heterogeneous Tails, and the End of the ‘Inverted-U’: the Share of the Rich is What it’s All About,” _Cambridge Working Papers in Economics_ , no. 1111, Faculty of Economics, University of Cambridge, 2011.

- Pinkovskiy, M. and X. Sala-i-Martin, “Africa Is On Time,” _Journal of Economic Growth_ , 19, 311–338, 2014. doi: 10.1007/s10887-014-9103-y

Pinkovskiy, M. and X. Sala-i-Martin, “Lights, Camera... Income! Illuminating the National Accounts-Household Surveys Debate,” _The Quarterly Journal of Economics_ , 131, 579–631, 2016. doi: <u>10.1093/qje/qjw003</u> 33
# **Trade Facilitation and Country Size**

Mohammad Amin* and Jamal Ibrahim Haidar** Keywords: Country size, Trade facilitation, Openness JEL: F10, F13, F14, F15, F50, F55, H10, K00, L5, O20, 024 _______________________________

*Enterprise Analysis Unit, World Bank, Washington DC, 20433. Email: mamin@worldbank.org. **Paris School of Economics: Jamal.Haidar@ParisSchoolofEconomics.Eu
Policy Research Working Paper 10144

# **Abstract**

Can an implementation-driven analysis of project success be used as a more granular instrument for assessing the effectiveness of World Bank project? By focusing on how projects perform, this paper attempts to capture variation hitherto unexplored in the aid effectiveness literature. This offers greater precision for diagnosing implementation challenges throughout the project cycle, producing a cross-cutting instrument that reaches across country-, time-, and sector- based approaches. Using data from the Global Delivery Initiative’s “Delivery Challenges in Operations for Development Effectiveness” database and indicators from more than 5,000 lending projects (1995 –2015), the paper examines project performance and the achievement of development objectives across 42 specific delivery challenges. Bayesian model averaging is used for a holistic assessment of the relative impacts of each challenge alongside a battery of structural and contextual covariates. The findings show that issues of project design, ineffective monitoring, and weak organizational capacity have systematically hindered the World Bank’s performance and achievement of the indicators. Conversely, while financial instability and weaknesses in stakeholder engagement can hinder success, their identification and treatment ultimately improves project performance.

This paper is a product of the Governance Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ahagh@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
**Table 3. Share of Laborers (Age 16-50) Migrating for Work Outside the Home County (1995-2002)**

||**All**|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|---|
|All|0.158|0.088|0.098|0.110|0.144|0.155|0.182|0.219|0.265|
|Men|0.204|0.121|0.134|0.146|0.188|0.202|0.232|0.28|0.332|
|Women|0.110|0.052|0.06|0.072|0.097|0.106|0.132|0.159|0.197|
|Age 16-29|0.203|0.114|0.126|0.142|0.179|0.200|0.237|0.292|0.353|
|Age 30-50|0.107|0.058|0.065|0.070|0.100|0.103|0.121|0.146|0.180|
|Less than 8 years of schooling|0.100|0.054|0.06|0.068|0.094|0.100|0.116|0.144|0.180|
|Eight or more years of schooling|0.200|0.118|0.129|0.142|0.181|0.193|0.229|0.268|0.319|
|A member of the largest patrilineal clan in village|0.159|0.085|0.105|0.116|0.140|0.173|0.172|0.208|0.263|
|Not a member of the largest patrilineal clan in village|0.157|0.089|0.096|0.108|0.145|0.148|0.186|0.223|0.266|
|With young women (age 19-24) or men (age 21-26) in household|0.163|0.105|0.108|0.115|0.155|0.16|0.184|0.214|0.261|
|Without young women or men in household|0.153|0.075|0.091|0.105|0.134|0.15|0.181|0.223|0.268|
|Obs.|56342|6705|7138|7395|6650|7444|7072|6843|7095|

Source: 2004 Rural Household Social Network, Labor Allocation and Land Use Survey, RCRE, Ministry of Agriculture.

_Land Tenure Insecurity and Migration, Page 37_
|(1)<br>(2)<br>(3)<br>(4)<br>LS - BE<br>LS - BE<br>LS<br>LS<br>Avg. tari¤ rate (%)<br>FDI in‡ows (log)<br>Avg. tari¤ rate (%)<br>FDI in‡ows (log)<br>-37.038***<br>0.243<br>-38.720***<br>0.782<br>(10.604)<br>(1.864)<br>(10.259)<br>(1.880)<br>2.307***<br>-0.053<br>2.351***<br>-0.068<br>(0.606)<br>(0.107)<br>(0.594)<br>(0.111)<br>-1.154<br>1.174***<br>-0.991<br>1.113***<br>(1.387)<br>(0.244)<br>(1.508)<br>(0.332)<br>-0.075<br>-0.025**<br>-0.022<br>-0.041***<br>(0.071)<br>(0.013)<br>(0.111)<br>(0.013)<br>-4.318***<br>0.343<br>-4.641***<br>0.408*<br>(1.500)<br>(0.264)<br>(1.391)<br>(0.239)<br>-0.826<br>0.385<br>-1.733<br>0.641*<br>(2.144)<br>(0.377)<br>(1.925)<br>(0.336)<br>-0.890<br>0.439***<br>-0.854<br>0.421***<br>(0.818)<br>(0.144)<br>(0.785)<br>(0.157)<br>-2.221**<br>-0.127<br>-2.364*<br>-0.072<br>(0.937)<br>(0.165)<br>(1.289)<br>(0.217)<br>-0.016<br>-0.000<br>-0.042<br>0.007<br>(0.053)<br>(0.009)<br>(0.041)<br>(0.011)<br>0.997<br>0.091<br>0.784<br>0.170<br>(1.169)<br>(0.205)<br>(1.256)<br>(0.200)<br>-0.029<br>-0.050<br>-0.003<br>-0.055<br>(0.298)<br>(0.052)<br>(0.355)<br>(0.074)<br>-0.778<br>-1.093***<br>-1.233<br>-0.932***<br>(1.154)<br>(0.203)<br>(1.436)<br>(0.257)<br>0.112<br>-0.019<br>0.227<br>-0.065<br>(0.718)<br>(0.126)<br>(0.558)<br>(0.105)<br>0.001<br>-0.001<br>0.001<br>-0.000<br>(0.004)<br>(0.001)<br>(0.002)<br>(0.000)<br>290<br>290<br>121<br>121<br>0.480<br>0.863<br>0.465<br>0.851<br>121<br>121<br>121<br>121<br>NA<br>NA<br>12.69***<br>7.02***<br>NA<br>10.39**<br>YES<br>YES<br>NO<br>NO<br> for Columns (1) and (2); Robust standard errors in parentheses for Columns (3) and (4);<br> of 1%, 5%, and 10%, respectively.|
|---|
|Regressions<br>VARIABLES<br>Import elasticity<br>Import elasticity*Population (log)<br>Population (log)<br>Statutory Corporate Tax Rate<br>Transitional Economy Status<br>Distance to GVC hubs (log)<br>Political stability index<br>Domestic industrial capacity<br>(log)<br>Rents from resources / GDP<br>(%)<br>Capital / GDP (log)<br>Land / GDP (log)<br>Med/High-skilled labor / GDP (log)<br>Low-skilled labor / GDP (log)<br>Exch. rate appreciation<br>Observations<br>R-squared<br>Number of countries<br>Sanderson-Windmeijer F test<br>Kleibergen-Paap Wald test for Weak IV<br>Decade ...xed e¤ects<br>Notes: LS standard errors in parentheses <br>***, **, and * indicate signi...cance levels|

35