rafmacalaba/datause-encoder-data huggingface dataset:
Figure 12: Malawi: Calorie composition (1990-2008) <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>maize wheat millet sorghum rice cassava s/potato i/potato<br><!-- End of picture text --> Source: Authors calculations based on FAOSTAT (for production data) and FAO conversion factors.

56. The value chain analysis covers five levels of maize production differentiated on the basis of farm management, crop varieties, and irrigation. Farm level profits are based on selling to a small trader at roadside location somewhere near the farm. Assembly level costs then include storage, fumigation, and transportation of the maize over an indicative distance of 130km into a mill location where the accumulated SV can be compared with import parity. For export parity, further transport costs to Zimbabwe were taken into account as described in the discussion of Malawi’s final trade competitiveness. Details of these value chain assumptions are set out in Table 9 below.

23
method is preferable to selecting an aggregate approach, as has been carried out in previous policy assessments (International Telecommunication Union, 2020). Nightlight luminosity is measured in ‘Digital Numbers’ (DN) and the mean luminosity is used to allocate high, medium, and low consumption tiers for broadband spending (>5 DN, <5 DN and <1 DN respectively). ARPU estimates for these consumption tiers are then adapted for Côte d’Ivoire, Mali, Senegal and Kenya (high: $8, medium: $6, low: $2), and for Tanzania and Uganda (high: $8, medium: $3, low: $2), using GSMA data as the median value (GSMA, 2020), and then low and high values selected based on consumer price research for different mobile services. Revenue is converted over the 10-year assessment period to the present value (PV) in the initial year using a discount rate of 5% to represent the time value of money due to inflation, with this value informed by IMF consumer price projections (International Monetary Fund, 2021).

# 3.3. High-resolution spatial estimation of least-cost networks

The network design module estimates the least-cost design to connect communities without cellular coverage. Firstly, a baseline is established for existing infrastructure using a range of data sources. Long distance fiber links are extracted from the African Terrestrial Fiber map (Network Startup Resource Center, 2020), and fiber Points of Presence (POPs) are estimated based on large settlements exceeding 20,000 inhabitants located within 5 km of a fiber edge. Secondly, existing sites in each region are estimated to obtain the total existing site density using either geolocated site data, or disaggregated estimates of tower counts by country, as detailed in Section S1 of the Supplementary Materials.

A standard way to dimension a cellular network is using three cells per site, thus producing geometric hexagons (Holma and Toskala, 2012). Of the total existing site density in each area, the hypothetical MNO modeled has a site density relative to its market share. So, an MNO with 30% market share, is treated as having a site density which is approximately 30% of the total site density. Sites are allocated 15
WPS3308

# **THE REGULATORY AND SUPERVISORY FRAMEWORK FOR FIXED INCOME MARKETS IN EUROPE**

## HUBERT GRIGNON DUMOULIN AND MOGENS KRUSE<sup>∗</sup>

## (The study describes the regulatory and supervisory framework as of July 2003.)

**Abstract:** This paper looks at the development and regulation of fixed income securities markets in Europe.  Fixed income securities markets in Europe have historically been characterized by a number of national markets that were interconnected via the foreign exchange markets.  They are presently undergoing major changes in size, infrastructure, and regulation.  The paper describes the current state of the on-going European regulatory and supervisory reform and the main drivers behind it.  The authors conclude that European fixed income securities market regulation and infrastructure are not (yet) homogeneous.  In some countries fixed income market regulation has been developed after intense political reflections on ways and means of promoting safe and efficient capital markets.  In other countries, fixed income market regulation is a product of learning-by-doing (e.g, ad hoc reflections based on negative market experiences, financial scandals, etc.).  To illustrate the heterogeneity in the European fixed income markets, the reports includes two examples:  France as an example of a country from the euro area, and Denmark as an example of a country outside the euro area.

## **JEL Classification Code:** G18, K22

## **Keywords:** Debt Markets, Fixed Income Markets, Regulation, Supervision

## World Bank Policy Research Working Paper 3308, May 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> ∗ - This study was prepared by Hubert Grignon Dumoulin (Hubert.grignon <u>dumoulin@cec.eu.int), Senior Officer at</u> the French Securities and Exchange Commission detached at the EU Commission, and Mogens Kruse (mkr@nationalbanken.dk), Legal Advisor at the Danish Central Bank.  The authors are grateful to Jeppe Furbo Ladekarl and his colleagues from the World Bank ́s Financial Sector Operations & Policy Department for their assistance and support in the research and preparation of this paper, to Ove Steen Jensen and Karsten Biltoft from Danmarks Nationalbank and to Patrice Aguesse from the French Securities and Exchange Commission for providing comments and suggestions.

1
Policy Research Working Paper 9743

# **Abstract**

Developing countries suffer from rising urban pollution levels, with associated negative effects on health and worker productivity. This paper studies how managers in developing country cities cope with the polluted environment. High-resolution pollution measurements were collected in Ugandan cities and matched with a novel firm survey. The analysis finds that firms locate in close proximity to major polluted roads, which bundle a bad (exposure to pollution) with a good (market demand). Higher ability managers do not avoid polluted areas; instead, they adapt to the pollution by protecting their workers through the provision of equipment and flexibility in work schedule.

This paper is a product of the Urban, Disaster Risk Management, Resilience and Land 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 nlozano@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
Policy Research Working Paper 6349

# **Abstract**

This paper examines the extent to which policy restrictions on foreign direct investment in the Indonesian service sector affected the performance of manufacturers over the period 1997–2009. It uses firmlevel data on manufacturers’ total factor productivity and the OECD’s foreign direct investment Regulatory Restrictiveness Index, combined with data from Indonesia’s input-output tables regarding the intensity with which manufacturing sectors use services inputs. Controlling for firm-level fixed effects and other relevant policy indicators, it finds, first, that relaxing policies toward foreign direct investment in the service sector was associated with improvements in perceived performance of the service sector. Second, it finds that this relaxation in service sector foreign direct investment policies accounted for 8 percent of the observed increase in manufacturers’ total factor productivity over the period. The total factor productivity gains accrue disproportionately to those firms that are relatively more productive, and that gains are related to the relaxation of restrictions in both the transport and electricity, gas, and water sectors. Total factor productivity gains are associated, in particular, with the relaxation of foreign equity limits, screening, and prior approval requirements, but less so with discriminatory regulations that prevent multinationals from hiring key personnel abroad.

This paper is a product of the International Trade Department, Poverty Reduction and Economic Management Network; and Poverty Reduction & Economic Management Sector Department, East Asia and Pacific Region. 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://econ.worldbank.org. The author may be contacted at gvarela@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
reinforce the messages from the recent growth econometrics literature. For example, Francois and Reinert (1997) note that exports become increasingly producer (business) service intensive (in terms of combined direct and indirect linkages) with increasing industrialization, while Francois and Woerz (2008), mapping measures of Leontief-based direct and indirect service intensity to production and productivity indicators, find that service sector openness has boosted the competitiveness of more technology and skill intensive industry in the OECD, at the expense of less ICT and skill intensive sectors. Robinson et al. (2002) stress indirect effects from services sector trade liberalization on the efficiency and output of other sectors in the economy, working through inter-industry input-output relations induced by imports of high-quality services.<sup>2</sup> A basic message of this literature is that, at least for the OECD, the service sector is critical to the overall cost structure and recent performance of industrial economies.

In this paper, we work with a panel of global input-output data (a set of global social accounting matrices spanning intermittent years from 1992 to 2007) that covers not only key OECD economies, but also a range of developing countries as well. Our use of this 15 year panel of input output data sets what we do apart from the existing literature on services linkages, as we are able to trace more long-run patterns. Working with these data, we examine the structure of services embodied in trade on a value added basis. This includes not only the direct and indirect contribution of services to value added contained in a given country’s exports, but also the extent to which third-country value added in services, through intermediate linkages of imported goods and services, are also embodied in production and trade. This exercise serves to highlight the importance of non-tradables in trade, and how this has evolved since the early 1990s. It also reinforces the importance of nontraded, domestic activities for the competitiveness of goods on export markets. Not surprisingly, the ratio of value added to gross trade values has been falling for goods since 1992, consistent with the now common observation that production chains for goods have become more complex. However, it turns out that this same pattern is also seen for services. Yet there is a marked difference with respect to goods. In goods sectors, value added, even when we account for linkages and

> 2 Also see Kravis, Heston and Summers (1983), Inman (1985), Park and Chan (1989) and Uno (1989).

3
11 THE INTENSIVE MARGIN IN TRADE EKS find that average exports per firm are very similar across four origin countries (Brazil, Denmark, France and Uruguay) for which they have customs data. They regress average exports per firm on origin and destination fixed effects and find that the origin fixed effects differ little across their four origins. Running the same regression in our dataset (but pooling across years and including year fixed effects), we find that origin fixed effects do vary significantly across countries (the coefficient of variation in the estimated origin fixed effects ranges from 0.81 to 2.56, depending on the sample used) and are higher for countries with higher GDP per capita and higher total exports.<sup>17</sup> Moreover, origin-year and destination-year fixed effects are not enough to capture the variation in ln _xij_ : a regression of ln _xij_ on origin-year and destinationyear fixed effects yields an R-squared of 0.65 when only country pairs with _Nij >_ 100 are considered and only 0.37 when all country pairs are considered.

Using firm-level export data for the U.S., Bernard et al. (2009) present a similar decomposition to the one we present above for multi-product firms, except that they cannot allow for destination fixed effects because their data is for a single origin. They find that IME<sup>_p_</sup> is around 0.23, which is not far from our finding of around 0.29. On the other hand, contrary to our results, Bernard et al. (2007) find that average exports per product per firm increase with distance. We believe that the difference arises from the fact that, by having data for multiple origins, we are able to control for destination fixed effects. In fact, Table 6 shows that regressing ln _x_<sup>_p_</sup> _ij_<sup>on ln</sup><sup>_distij_</sup> with only origin and year fixed effects but without destination fixed effects yields a positive and significant coefficient as in Bernard et al. (2007), whereas the coefficient becomes negative and significant when destination fixed effects are added. The same happens when regressing ln _xij_ on ln _distij_ .

# **3. The Intensive Margin in the Melitz-Pareto Model**

In this section we ask how the Melitz model with Pareto distributed firm productivity stacks up relative to the findings of the previous section. We focus on the implications of this model for the intensive margin elasticity. We start with the simplest model, which entails a continuum of single-product firms with a Pareto distribution for productivity as in Chaney (2008) and Arkolakis et al. (2008). We derive a series of properties of this model, and then explore their robustness to allowing for destination-specific demand and fixed trade cost shocks at the firm level as in EKK, for multi-product firms, and for granularity.

> 17For this purpose, we run regressions of the estimated origin fixed effects on population, GDP, GDP per capita, and total exports, jointly and separately.
access in isolation. However, for localities connected to fast internet, individuals who are in locations in which a main road becomes one kilometer closer are 1.6 percentage points more likely to work in very highly skilled professions. It is very interesting to note that, while road and internet accessibility complement each other to further reduce the creation of unskilled jobs by about 2 percentage points, they further increase the creation of skilled jobs by an average of 2 percentage points. This makes it possible to conclude unequivocally that road and digital communication infrastructure investments act as complements to allow migration of employment from unskilled sectors of activity to those requiring a certain level of qualifications.

These results are consistent with those obtained when using the WBES data relating to the number of full-time employees with a high level of qualifications. Roads and the internet play complementary roles in increasing the number of qualified full-time workers in companies. Internet-connected firms that moved 10 kilometers closer to the nearest main road had 0.980 more high-skilled, full-time employees at the end of the last fiscal year compared to firms that had experienced an improvement in their accessibility to only one of the two types of infrastructure. The results also show that the combination of the two infrastructures additionally improves employment, even for the suppliers of low-skilled labor. Indeed, we show that firms in the second group have 0.447 more unskilled workers than those in the first group. The combination of these two results (means that the firms in the first group have a statistically higher demand for labor, both for high-skilled and low-skilled workers, compared to the second group of firms, which demands fewer workers of either skill level. Roads and the internet together enhance job creation; at the same time, the results do not contradict the result of labor migration from unskilled to higher-skilled employment. Unfortunately, because of the significant difference in the number of responses, it is difficult to determine whether, among the WBES responding firms, the joint effect of the presence of these two types of infrastructure is greater on skilled or unskilled employment.

28
results for BBC both for the full sample and the most recent periods. Tables 3-8 report the same results but only for the full sample, while the remaining results are relegated to Tables 4-6.

**Figure 3. Shapley Decomposition of GDP per capita growth**

<!-- Start of picture text --> Benin: 2006-2015 Benin: 2010-2015<br>Employment rate Employment rate<br>Demographic effect Demographic effect<br>Within-sector Within-sector<br>Structural change- Structural change-<br>Dynamic Dynamic<br>Structural change- Structural change-<br>Static Static<br>-1.5% -0.5% 0.5% 1.5% 2.5% -1.5% -0.5% 0.5% 1.5% 2.5%<br>Burkina Faso: 1998-2014 Burkina Faso: 2005-2014<br>Employment rate Employment rate<br>Demographic effect Demographic effect<br>Within-sector Within-sector<br>Structural change- Structural change-<br>Dynamic Dynamic<br>Structural change-<br>Structural change-Static<br>Static<br>-1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% -1.0% 0.0% 1.0% 2.0%<br>Cote d'Ivoire: 2008-2014<br>Cote d'Ivoire: 2002-2014<br>Employment rate<br>Employment rate<br>Demographic effect<br>Demographic effect<br>Within-sector Within-sector<br>Structural change- Structural change-<br>Dynamic Dynamic<br>Structural change-<br>Structural change-Static Static<br>-1.5% -1.0% -0.5% 0.0% 0.5% 1.0% 1.5% 2.0% -2.0% -1.0% 0.0% 1.0% 2.0% 3.0%<br><!-- End of picture text --> Source: Author’s estimation based on data described in Section 2.

In Benin, the empirical results suggest that the modest output per capita growth during 2006-2015 has been mostly attributed to _static_ structural change. In contrast, within-sector productivity and dynamic structural change acted in the opposite direction. Specifically, annualizing growth rates, structural change and demographic changes accounted for 1.7 ppts and 0.7 ppts of the 1.1 percent per capita growth. However, these were partly neutralized by a decrease in productivity within sectors (-0.8 ppts) and lower employment rate (-0.6 ppts). Interesting insights emerge from further breaking down structural change. Over 130 percent of the structural change-induced growth can be attributed to static 17
some cases the analysis is presented for the year 2002 because data are available for most countries and because there have not been significant changes in the trend across this four-year period.

|**Name**|**Acronym**<sup>**12**</sup>|**Countries Included**|
|---|---|---|
|Central and Eastern Europe|CEE (4)|Czech Republic, Estonia, Latvia, Poland|
|South East Europe|SEE (2)|Bulgaria, Romania|
|Former Soviet Union<br>Low Income Countries|FSU-LI (6)|Armenia, Azerbaijan, Georgia, Moldova, Kyrgyz Republic,<br>Tajikistan|
|Former Soviet Union –<br>Middle Income Countries|FSU-MI (4)|Belarus, Kazakhstan, Russia, Ukraine|

_Description of Variables_

|**Indicator **|**Unit**|**Definition **|
|---|---|---|
|Average Cost<br>Recovery Price<br>(ACRP)|US$/m<sup>3</sup>|The cost of 24 hours of water supply, where costs include operation,<br>maintenance and necessary investments (equivalent to 4.0 percent of the<br>value of utilityassetsperyear)<sup>13</sup>.|
|Collection Rate|rate (as a<br>proportion of<br>billed<br>consumption)|This is the proportion of the billed charges to consumers of the respective<br>energy that is actually collected in some form by the utility/service provider.|
|Continuity of<br>Service|hr/day|Average hours of water service per day.|
|Cost of Assets|US$/capita<br>|Total annual net fixed assetsper(water)capita served|
|End User<br>Consumption|m<sup>3</sup>|This measure is the actual final consumption that is marketed by the utilities.|
|Investment|US$|Total annual investmentsper(water).|
|LaborCosts|US$|Total annual utilityreported labor costs(includingbenefits).|
|Net Fixed Assets|US$|Total annual net fixed assetsper(water).|
|Pipe Breaks|breaks/km/yr|Total number of pipe breaks per year expressed per km of the water<br>distribution network.|
|Population<br>Served|-|Population within the administrative area of the water utility and connected<br>to the services.|
|Total Revenues|%|Total annual operating revenues per population served expressed as a<br>percentage of theGDPper capita.|
|Unaccounted-<br>for-Water|m3/km/d<br>|Difference between water supplied and water sold as volume of water “lost”<br>per km ofwater distribution networkper day.|
|WAET|US$/m<sup>3</sup>|Weighted Average End-user Tariff, calculated by dividing the total annual<br>operatingrevenues bythe annualvolume ofwater sold.|
|Water Coverage|%|Percentage of the population with easy access to water services (either with<br>direct service connection or within 200m of a stand-post) divided by the<br>totalpopulation under the utility’s nominal responsibility.|
|Working Ratio|-|Total annual operational costs divided by the total annual operating<br>revenues.|

Source: World Bank, Benchmarking Water & Sanitation Utilities: A Start-Up Kit, 1999: <u>http://www.worldbank.org/html/fpd/water/topics/bench/bench_network_indicatordef.html</u> Average Cost Recovery Price – internal definition.

> 12 Number of counties shown in parentheses. 13 Adjusted to constant 2001 US dollars

24