rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 8:** Selected UMICs overperformers

<!-- Start of picture text --> a) China b) Malaysia<br>0.25 0.25<br>0.20 0.20<br>0.15 0.15<br>0.10 0.10<br>0.05 0.05<br>0.00 0.00<br>Other non-overperformers (i) LMICs Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs Other non-overperformers (ii) UMICs<br>Overperformer (CHN) Overperformer (MYS)<br>c) Kazakhstan<br>0.25<br>0.20<br>0.15<br>0.10<br>0.05<br>0.00<br>Other non-overperformers (i) LMICs<br>Other non-overperformers (ii) UMICs<br>Overperformer (KAZ)<br>Digital Infr. Digital Infr.<br>Digital Infr.<br>Human K. & Labor Mkt Human K. & Labor Mkt<br>Human K. & Labor Mkt<br>Reg. & Ethics Reg. & Ethics<br>Reg. & Ethics<br>Innov. & Integr. Innov. & Integr.<br>Innov. & Integr.<br><!-- End of picture text --> <u>Source: The AIPI score and pillars for 2023 are obtained from the IMF website [https://www.imf.org/external/datamapper/datasets/AIPI]. Notes: Population size for 2023, used for the weighted analysis, is sourced from the “wbopendata” module in Stata (Azevedo, 2020).</u> Weighted average AIPI scores per group in parenthesis. Dotted yellow shows the weighted average pillar scores for non-overperforming LMICs and dotted red shows them for non-overperforming UMICs. The “spider
Chart 13 Variance Contributions of the Global Factors (Percent of total variation) <!-- Start of picture text --> Policy rates<br>50<br>40<br>30<br>20<br>10<br>0<br>70-24 70-98 99-24 70-84 85-98 99-07 08-19 20-24<br>Inflation<br>50<br>40<br>30<br>20<br>10<br>0<br>70-24 70-98 99-24 70-84 85-98 99-07 08-19 20-24<br>Output growth<br>50<br>40<br>30<br>20<br>10<br>0<br>70-24 70-98 99-24 70-84 85-98 99-07 08-19 20-24<br><!-- End of picture text --> Sources: Authors’ calculations based on data from January 1970 through March 2024. Notes: The chart presents the average variance contribution of the global policy rate factor to the variations in country-specific policy rates, inflation, and output growth in eight advanced economies. See Section IV.2 for estimation details.

Second, the importance of the global interest rate factor has increased significantly since the end of the 1990s.<sup>44</sup> On average, this global factor explained about 40 percent of the interest rate

> 44 As a robustness check, we calculated the variance contribution of the global rate factor using other subsample periods (which are not based on the start of ECB) or excluding some large economies (such as the United States or the euro area). The key patterns reported above on how the importance of the global rate factor has evolved over time are unchanged.

39
1,003|2,840|320.3|
|Largest MPF|224,161|681,231|57,267.8|
|Average MPF|48,997|133,456|11,851.4|
|Market Total|881,944|2,402,210|213,324.9|
|Share of Largest Six MPFs|83%|87%|83%|
|Herfindahl Index|1,491|1,638|1,510|

Source: HFSA raw data.

11. The co-existence of large and small institutions is to a large extent due to the diversified nature MPF sponsors. Table 4 shows how the ten MPFs sponsored by financial institutions tend to be much larger. Average assets, contributions and members in this first group are much higher than for the other two groups.<sup>4</sup> The five MPFs sponsored by large employers have between 8,000 and 20,000 members, mostly employees of the sponsoring company or the group of companies in the sector (i.e., the electricity sector). The three MPFs in the third group have on average 60,000 members, although there is a large difference between the smallest and the largest MPF in this group.

12. The MPFs in the first group account for 87 percent of the market, however measured, reflecting the profit oriented nature of the sponsor and the more aggressive marketing tactics. The MPFs sponsored by employers or considered to be independent represent only around 5 and 8 percent of the market, respectively. These MPFs are open to members who are not employees of the sponsor(s), and most of them would actually welcome an increase in membership as a means to reduce average operating costs.

> 4 However, the smallest MPF belongs to this group.
WPS3889

# Comparative Advantage, Demand for External Finance, * and Financial Development

Quy-Toan Do Andrei A. Levchenko The World Bank International Monetary Fund

### **Abstract**

The differences in the levels of financial development between advanced and developing countries are large and persistent. Theoretical and empirical literature has argued that these differences are the source of comparative advantage and could therefore shape trade patterns. This paper points out the reverse link: financial development is influenced by comparative advantage. We illustrate this idea using a model in which a country’s financial development is an equilibrium outcome of the economy’s productive structure: financial systems are more developed in countries with large financially intensive sectors. After trade opening demand for external finance, and therefore financial development, are higher in a country that specializes in financially intensive goods. By contrast, financial development is lower in countries that primarily export goods which don’t rely on external finance. We demonstrate this effect empirically using data on financial development and export patterns in a panel of 96 countries over the period 1970-99. Using trade data, we construct a summary measure of a country’s external finance need of exports, and relate it to the level of financial development. In order to overcome the simultaneity problem, we adopt a strategy in the spirit of Frankel and Romer (1999). We exploit sector-level bilateral trade data to construct, for each country and time period, a predicted value of external finance need of exports based on the estimated effect of geography variables on trade volumes across sectors. Our results indicate that financial development is an equilibrium outcome that depends strongly on a country’s trade pattern.

JEL Classification Codes: F02, F14, O16, O19.

Keywords: trade patterns, demand for external finance, financial development, gravity model.

## World Bank Policy Research Working Paper 3889, April 2006

_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
# **Monitoring, research, and evaluation**

National data on breastfeeding and complementary feeding are needed to help governments assess progress in improving feeding practices. All South Asian countries except Maldives have recent data on feeding practices and should continue to collect data about every three to five years.

Only three countries (Afghanistan, Bangladesh, and Pakistan) have health systems that routinely monitor the number of caregivers who receive information and counseling on breastfeeding and complementary feeding. The data gap in other countries makes it challenging for administrators to hold themselves and others to account for delivering these services and ensuring that children in greatest need are reached. Data on the quality of services are also lacking. All countries in the region should examine how to integrate indicators on the coverage of counseling services into health information systems, and ensure that the quality of services is monitored as part of supervision.

Evidence is lacking on the impact of large-scale programs to improve complementary feeding in South Asia (Bhutta et al. 2013; Aguayo 2017), particularly programs employing a mix of sectors, strategies, and interventions to address both demand- and supply-side barriers. One exception is the Alive & Thrive project in Bangladesh, which had the explicit aim of learning how to strengthen delivery systems to programmatically achieve behavior change at scale (Piwoz et al. 2013).

Well-designed operational research is needed to understand the program-impact pathways to success–or failure–in improving feeding practices (Frongillo 2017). Evidence is also needed on how to effectively engage the private sector in improving complementary feeding (van Liere et al. 2017).

Notable evidence gaps exist on what works, and through what pathways, to improve breastfeeding practices for certain countries (Afghanistan, Nepal, and Pakistan), on continued breastfeeding, and on policies and workplace interventions for working mothers (Benedict et al. 2018b). Addressing these gaps will provide additional evidence on how to protect, promote, and support recommended breastfeeding practices in South Asia.

# **Conclusion**

The poor quality of
# **SERVICE EXPORT SOPHISTICATION AND EUROPE’S NEW GROWTH MODEL**

SUSANNA LUNDSTROM GABLE AND SAURABH MISHRA *

**OFFICE OF THE CHIEF ECONOMIST EUROPE AND CENTRAL ASIA THE WORLD BANK**

**Keywords:** services, trade, structural transformation, productivity, economic growth, sophistication, economic integration, globalization, Europe.

**JEL Classification:** F01, O4, O52

* _Gable_ : The World Bank (e-mail: <u>slundstrom@worldbank.org) and</u> _Mishra_ : The World Bank (e-mail: <u>saurabhmishra@thepulselab.org). This research paper was prepared as a background paper for the 2011 Europe</u> and Central Asia Flagship Report ―Restoring the Luster of the European Growth Model‖. We are grateful for valuable comments from Indermit Gill and Yue Li. We are also grateful to Ivan Kandilov, Thomas Grennes for sharing their data on direction of trade. The views expressed in this paper are those of the authors, and do not necessarily represent those of the World Bank group or its executive directors. Any remaining errors are solely ours.
# Annex 2:  Assigning Questions to Governance Dimensions

This annex provides summarize how the individual indicators from the WGI data sources are assigned to the six governance dimensions corresponding to the six aggregate WGI indicators in the most recent year in which they appear in the WGI dataset. For most data sources, this is 2023 (the most recent year available in the 2024 WGI update). However, for a few discontinued data sources, the most recent year the source is used is earlier than 2023 (see Table 1). Note that many of the WGI data sources have changed over time, so the description provided here may not apply to all previous years. For a complete description of the assignment of individual indicators to aggregate clusters over time, please visit <u>www.govindicators.org, and consult the source data Excel files available on the documentation page of</u> the WGI website here.
Figure 2. Guerrilla-Controlled Territories and Administrative Boundaries <!-- Start of picture text --> (a) Departmental Boundaries<br>Under Guerrilla Control<br>(b) Municipal Boundaries<br>Under Guerrilla Control<br><!-- End of picture text --> _Notes:_ The figure presents the areas under guerrilla control in red and shows that these areas do not coincide with the administrative departments and municipal boundaries of El Salvador today.

# **III.B Geospatial variables**

We use geospatial data to test the validity of the local continuity assumption around the boundaries of guerrilla-controlled areas. Elevation was obtained from NASA’s Shuttle Radar Topography Mission (SRTM). Information on surface water bodies comes from the MERIT Hydro dataset. Agro-climatic yield rasters with a spatial resolution of five arc minutes (nine km) come from the Global Agro-Ecological Zones (GAEZ) project. For all yields, we are using the 30-year average beginning in 1961.

Figure C.1 maps guerrilla-controlled territories, altitude, and main rivers in El Salvador. It il16
trends in UK inequality over 1975-2000 using household-specific CPIs, but find that the results are highly sensitive to the choice of base year for the indices.

It is worth noting that these issues are closely related to, but conceptually different from, the question of whether inflation is more harmful for the poor than for the rich. This has been explored by a fairly sizable literature (see e.g., Easterly and Fischer 2001 for discussion and further references), focused on the workings of mechanisms such as indexation of prices, wages or financial assets and liabilities. Our analysis is not concerned with these issues, but with the measurement of real income and inequality under household heterogeneity.

This paper uses household survey data to explore the empirical significance of inflation inequality in nine episodes corresponding to four Latin American countries: Brazil (1988-1996), Colombia (1997-2003); Mexico (1984-1989; 1989-1994; 1994-1996; 1996-2002), and Peru (1995-1999; 1999-2001; 2001-2003). These countries seem well suited for this kind of analysis, given that some of them (e.g., Brazil, Colombia) are among the most unequal in the developing world, while others (e.g., Peru) not only suffer high inequality, but have also shown a deteriorating trend in recent years (see DeFerranti _et al_ 2004 for details).

> 1This issue has gone beyond scholarly debate, and a proposal currently with the House of Representatives (H.R.2035, 2001) would require the Bureau of Labor Statistics to build a special consumer price index for the elderly.

> 2Regarding the rural/urban inflation differential, Hobijn and Lagakos (2003) conclude that its fluctuations are mainly driven by the relative price of gas, due to the fact that rural households devote a much larger expenditure share to gas than do urban households. 3 Specifically, CPI-A is based on the expenditure patterns of the bottom 50 percent of the population, CPI-B refers to the next 30 percent, and CPI-C is built for the next
lack survey data in the early years.

The regional figures presented in this note are the unweighted averages for the same 15 Latin American countries throughout. We focus on unweighted averages because they capture more accurately the experience of the average country in the region and avoid letting the largest countries 4 Argentina, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Honduras, Mexico, Panama, Paraguay, Peru, Uruguay, and República Bolivariana de Venezuela. Because of data availability, this report focuses primarily on Latin America and covers only one country in the Caribbean, the Dominican Republic.

5 The SEDLAC database was developed in a collaboration between the Centro de Estudios Distributivos, Laborales y Sociales, Universidad Nacional de La Plata and the World Bank. In our analysis we use an augmented SEDLAC database; of the 238 surveys used in our analysis, 24 are original country household surveys for which we manually harmonized variables of interest to make them consistent with SEDLAC data definitions. Please refer to highlighted cells in the table in Annex 1 for the specific country-years that rely on these additional surveys.

4
# **I. Introduction**

In this paper, the impact of exposure to unimproved sanitation in Bangladesh on early childhood primary school enrollment is analyzed, using a pseudo-panel data for children ages 6 to 9 for the years 2007, 2011, and 2014. There is growing evidence that ill-health and poor nutrition in the early years of childhood have a lasting impact on later in life outcomes. This is especially true for the first 1,000 days of life. Failure to maximize a child’s development during this phase could have negative impact later in life in areas such as education, earnings, as well as mental health (Walker et al., 2007). Evidence indicates that poor health conditions early in life are associated with higher mortality (Bengtsson & Lindstörm, 2000) and poor adult cognitive development (Rong et al., 2017; Vogl, 2014). Children who are healthy early in life have better educational outcomes (Case & Paxson, 2010) and subsequently better economic outcomes (Lawson & Spears, 2016; Vogl, 2014; Cutler et al., 2010).

Unsanitary conditions during childhood are likely to result in poor health and nutrition both in the short and long-run. Exposure to fecal matter and bacteria because of unsafe disposal of feces, poor sanitation, and subsequently contaminated water and soil cause environmental enteropathy (George et al., 2018; Kosek et al., 2013), stunting (George et al., 2015; Spears et al., 2013), as well as diarrhea (Cronin et al., 2016), and a host of other water-borne diseases among young children (Mertens et al., 1990; Daniels et al., 1990). Although Bangladesh has substantially reduced its open defecation rates, its continued lack of improved sanitation facilities<sup>3</sup> (World Bank, 2018) could adversely affect children’s health, both early on and throughout their lives.

Bangladesh has made significant progress towards addressing the problem of
<br>40<br>30<br>20<br>10<br>0<br><!-- End of picture text --> Source: Doing Business database, 100 represents the score of the leading countries.

Figure 4 shows that the emerging markets of the G20 lag substantially behind the global leaders. In relative terms within the group, Argentina lags about 15 percent behind leader Mexico.

> 51 Also see World Bank (2005) for a discussion of the investment climate more generally.

23
use at work across ISCO 2-digit occupations, workers and country-level characteristics based on PIAAC data and by subsequently imputing them into individual observations in country-level surveys included in the SEDLAC database. We then use this measure to create two categories among workers who are expected to benefit from GenAI use because of the nature of their occupations: those who have access to digital technologies, and those who do not. The size of the latter is an indicator of the number of workers who will not be able to enjoy the productivity benefits of GenAI even though their jobs could theoretically benefit from the transformation. We also discuss the detailed demographics of the groups that are most likely to be negatively affected by these infrastructure limitations.

Our findings indicate that between 30 and 40 percent of employment in the LAC is exposed in some way to GenAI. This exposure is linked with the economic status of countries, suggesting that income levels are a strong correlate of GenAI’s impact on labor markets. This total level of exposure includes three categories: exposed to automation, augmentation, and “the big unknown”. The latter includes occupations, which – depending on the progress of technology and the use of adjacent technological applications, such as LLM-based agents – could fall closer to automation or augmentation.

Certain characteristics consistently correlate with higher overall GenAI exposure. Specifically, urban-based jobs that require higher education, are situated in the formal sector, and are held by individuals with higher relative incomes are more likely to come into interaction with this technology. The share of jobs exposed to automation is relatively small but nontrivial at about 2 to 5 percent of total employment. Younger and female workers tend to face greater automation exposure, particularly in the finance, insurance, and public administration sectors. At the same time, the shares of jobs that could benefit from a productive transformation with GenAI are consistently higher than those with automation risks across all LAC countries, ranging between 8 and 12 percent of employment across countries. This is particularly the case for the jobs in education, health and
lower|30 or lower|

# IV. Main findings from the simulation: Stock and flow of primary teachers in EAP MICs in 2030

## A. Projected changes in teacher position needs vary widely across the region

Significant heterogeneity exists across the 22 EAP countries with respect to primary education teacher position needs by 2030. Only six countries – mainly in East Asia – are projected to need fewer teachers overall in the next 10 years, while most countries will need to expand their teacher workforce (especially

> 7 For UIS data, teacher attrition rate is calculated as “the number of leavers is estimated by subtracting the number of teachers in year t from those in year t-1 and adding the number of new entrants to the teaching workforce in year t. The attrition rate is the number of leavers expressed as a percentage of the total number of teachers in year t-1.” UIS website

> (http://uis.unesco.org/en/glossary-term/teacher-attrition-rate-education-level).

5
-30-

One of the major definitional problems arises from the differences in level of detail at which a "product" is specified. On the concept of the "product", one can usefully cite the 1977 Census of Manufactures as follows: "A "product" as used in the Census of Manufactures is the finest level of detail for which output information was requested. It is not necessarily synonymous with the term "product" as used in the marketing sense. In some cases it may be more detailed and in other cases, it may be more aggregative. For example, there is a long list of pharmaceutical preparations but a single item for all canned meats." (1977 Census of Manufactures, p. XXII, 1981a).

This creates the problem that in one country several heterogeneous products (in the marketing sense) may be regarded as one "product" in the census, while in the other country these products are specified separately. This problem is dealt with extensively in chapter VI.

Another important definitional problem concerns the different industrial classifications used in the countries involved. The United Nations Yearbook of Industrial Statistics classifies commodities according to ISIC. Unfortunately, none of the countries included in this comparison applies the ISIC classification. A strict adherence to ISIC by all countries would indeed simplify the process of international comparison, but the national statistical offices usually claim that their country's output structure is unique and specific enough to warrant a separate national classification system.

Data for the United States were taken from the 1977 Census of Manufactures (1981a and 1981b). In this census, information is classified according to the American Standard Industrial Classification (SIC), which assigns some 13,000 product items to approximately 1,500 product groups according to a 7 digit classification. Not all information is published by the census. Data are withheld for national security reasons for certain products. When the number of establishments reporting is limited, information is withheld so as not to violate the privacy of individual firms by providing classified