rafmacalaba/datause-encoder-data huggingface dataset:
et al., 2004 and Bradford, 2003), and quantity-impact measures (using NTB data as in Leamer, 1990 and Harrigan, 1993 or as residuals of gravity-type equations as in Mayer and Zignago, 2003). However, to our knowledge, there exists no attempt to estimate those in a consistent way for a wide variety of countries at the tariff line level. Because trade policy is determined at the tariff line level (Men Shirts made of cotton) and not at the more aggregate industry level (Apparel), it is important to measure its restrictiveness at the most disaggregated level. Otherwise aggregation bias could lead to misleading conclusions.<sup>4</sup> We estimate AVEs of NTBs as follows. Using data on two broad types of NTBs —Core NTBs (price and quantity control measures, technical regulations, as well as monopolistic measures, such as single channel for imports) and agricultural domestic support— at the tariff line level for each country, we estimate their impact on imports following Leamer’s (1990) comparative advantage approach (see also Harrigan, 1993 and Tre fl er, 1993). The logic of this approach is to predict imports using factor endowments and observe its deviations in the presence of NTBs. This is done for each HS six-digit tariff line in which at least one country has some type of NTB (around 4800 tariff lines). The estimated impact of NTBs on imports varies by country (according to country speci fi c factor endowments). We then convert the quantity impact of NTBs on imports into a price equivalent(or AVE) by simply moving along the import demand curve using import demand elasticities estimated earlier.

The reminder of the paper is organized as follows. Section 2 describes the methodology used to estimate ad-valorem equivalents of NTBs, whereas section 3 presents the methodology used to estimate the trade restrictiveness indices. Section 4 describes the data, whereas section 5 describes the empirical results. Section 6 concludes.

> 4 For example, imagine that the Apparel industry is composed of two tariff lines: shirts made of cotton and shirts made of synthetic fabrics. Assume that country A imports an equal amount of both types of shirts. It has a tariff of 100 on cotton shirts, for which the import demand is perfectly inelastic and no tariffs on synthetic fabrics that had a fairly elastic import demand. Clearly, the tariff structure of country A does not impose any welfare cost. However if one were to aggregate cotton and synthetic fabrics into Apparel, one would fi nd that the (average) 50 percent tariff on Apparel has a welfare cost, given that import demand for Apparel is not perfectly inelastic.

4
Policy Research Working Paper 10754

# **Abstract**

Changes in the sentiment of migration-related news published in destination countries affect the timing of migrants’ journeys to these countries. Using geo-localized data on migrants in Libya and the complete record of news articles in their country of destination, this paper shows that a worsening news sentiment leads to migrants staying longer in Libya, slowing down their journeys to their final destinations. The paper validates these results by showing that the effect is concentrated in locations with internet connections.

The results indicate that changes in news sentiment have a significant impact only for some groups of migrants and under specific conditions, suggesting a limited effect on overall migrant movements. Finally, the paper provides suggestive evidence that a worsening news sentiment in the preferred destination induces substitution across destination countries, yet it does not make migrants return to their country of origin.

This paper is a product of the Office of the Chief Economist, Middle East and North Africa 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://www.worldbank.org/prwp. The authors may be contacted at michele.dimaio@uniroma1.it, nelmallakh@worldbank.org, and valerio.leonesciabolazza@uniroma1.it.

_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
A Conceptual Framework For Retirement Products: Risk Sharing Arrangements Between Providers And Retirees

- [45] **SOA** , 2001, 2001 Retirement Risk Survey: Key Findings. <u>http://www.soa.org/sections/retirement/framework.html.</u>

- [46] **Wadsworth, Michael, Alec Findlater and Tom Boardman** , 2001, Reinventing Annuities, The Staple inn Actuarial Society.

- [47] **Warshawsky, Mark J.** , 2001, Private Annuity Markets in the United States: 1919 – 1984, in Brown, Jeffrey R., Olivia S.  Mitchell, James M.  Poterba and Mark J. Warshawsky, eds.: _The Role of Annuity Markets in Financing Retirement_ (The MIT Press, Cambridge, MA.).  Also in _Journal of Risk and Insurance_ , 55(3): 518-528.

- [48] **Wilhelm, Mark O.** , 1996, Bequest Behavior and the Effect of Heirs' Earnings: Testing the Altruistic Model of Bequests. _American Economic Review_ 86(4): 87492.

- [49] **Yaari, Menahem E.** , 1965, Uncertain Lifetime, Life Insurance, and the Theory of the Consumer. _The Review of Economic Studies_ , 32(2): 137-150.

- 34 -
reconcile the difference in results observed above (between cross section and panel) by decomposing growth “convergence” (or lack of) into 3 components: (i) a coefficient representing the “structural’ mobility, or change in inequality, (ii) the “pure” mobility effect (which tries to address the Galton fallacy) and the “re-ranking” effect. The authors of this study will explore this approach as a next step of the analysis with the aim to assess the relative important of “pure” mobility versus re-ranking and therefore the relative role of short term (re-ranking) versus medium to long term changes in the distribution.

# **VII. Conclusions**

Looking at cross sectional data for 2005 and 2008, the paper demonstrates that Egypt achieved impressive poverty reduction between 2005 and 2008, thanks to rapid economic growth. However, inequality also increased during this time, attenuating the impact of growth on poverty reduction. The shape of the growth incidence curve for overall Egypt suggests that the rich gained more than the poor, especially in rural areas. However, growth incidence curves ignore the fact that households can move to a different ladder in the distribution over time. With the panel data available for Egypt 2005-2008, it is actually possible to trace such movements between 2005 and 2008.

Longitudinal changes capturing actual movement of households suggest a very different pattern of mobility compared to cross-section growth incidence curves. Growth incidence curves constructed on panel data allow seeing how each person’s welfare evolved over time. From the panel perspective, growth in Egypt was pro-poor. The welfare of an average person who was poor in 2005 increased by almost 10 percent per year between 2005 and 2008; this was sufficient to move this household out of poverty. But growth also exposed some non-poor to negative dynamics, making them poor. Panel data also reveal that many middle-class households were exposed to significant risks. In the period 2005-2008, only 45 percent of the population in Egypt remained out of poverty and near-poverty. This means that 55 percent of Egyptians experienced poverty or near-poverty between 2005 and 2008, even though the poverty and near-poverty rates at a given point of time within his period fell from 46 percent to 36 percent.

This high mobility is a reflection of real economic phenomena and not a statistical artefact. The paper looks in detail at the statistical indices of mobility and mobility profiles, and finds that new HIECPS data 2005-2008 generate reasonably robust estimates of mobility. In the face of strong mobility upward and downward mobility even considerable increases of inequality observed over the period are minor factors of social welfare dynamics.

20
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62
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

||Unit|**Sudan**<br>Resource-rich<br>countries<br>**2009**<br>**Mid-2000s**|Low-income<br>countries<br>**Mid-2000s**|Middle-income<br>countries<br>**Mid-2000s**|
|---|---|---|---|---|
|Open defecation|% pop|43<br>28|38.3|11|
|||**2005**<br>**Mid-2000s**|**Mid-2000s**|**Mid-2000s**|
|Domestic water consumption|liter/capita/day|53<br>115|50.9|196|
|Revenue collection|% sales|64<br>60|94.1|99|
|Distribution losses|% production|40<br>40|34.8|29|
|Cost recovery|% total costs|62<br>67|89.5|86|
|Operating-cost recovery|% operating costs|86<br>94|125.2|121|
|Labor productivity|connections per employee|93<br>96|175.9|203|
|Total hidden costs|% of revenue|121<br>194||67|
|||**Scarce water**<br>**resources**<br>**S**|**udan**|**Other**<br>**developing**<br>**regions**|
|Average effective tariff|U.S. cents per m3|60|57|3–60|

_Source:_ Demographic and Health Survey (DHS) and AICD water and sanitation utilities database (www.infrastructureafrica.org/aicd/tools/data).Note: Access figures calculated by the AICD based on the 2000 and 2006 DHS figures published by the Joint Monitoring Program (WHO 2010). — = Not available.

Only 40 percent of urban dwellers rely on advanced water technologies for their water supply. Close to 40 percent of the population has access to utility water—mainly stand posts—in urban areas, and around 5 percent in rural areas. Almost 70 percent of rural Sudanese rely on wells and boreholes, 30 percent higher than in urban areas. It is very striking that there is little difference in the reliance on surface water—around 25 percent of both rural and urban Sudanese obtain their water from open sources.

There is significantly greater usage of improved sanitation technology types in urban areas (figure 14). Urban areas have twice the level of access to flush toilets or improved

**Figure 14. Limited access to advanced water technologies in urban Sudan**

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latrines compared to rural areas. In the case of flush toilets, the access rate in urban areas is 14 percent compared to 1 percent in rural areas. Sixty percent of the urban population relies on improved latrines compared to 32 percent of the population in rural areas (figure 15). Some 68 percent of the rural population defecate in the open compared to 12 percent of urban dwellers.

Deep boreholes and hand pumps are the predominant technology used for water supply in the poorer households, and water filters (improved sources of water) are the water supply modalities in wealthier households. Around 41 percent of the poorest quintile of households derive their water from wells and

> <sup>24</sup>
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is an event that lowers oil prices amid falling oil output and industrial production. A positive speculative demand shock (such as the residual in Figure 2.F) is identified as one that raises oil inventories, increases prices and oil production, and reduces industrial production.

**_Elasticity restrictions._** Restrictions are imposed on the short-run price elasticity of oil demand. The impact price elasticity of demand is assumed to be non-positive; the median draw in the range -0.2 to -0.1 is used, in line with estimates of the elasticity since the 1980s in Baumeister and Peersman (2013).

**_Data_** . The exercise set uses monthly data from January 1980 to April 2020. Global industrial production is the production-weighted average of industrial production in 31 advanced economies and 47 EMDEs, in an unbalanced sample depending on data availability. Data for industrial production in April is estimated as the level predicted by the global manufacturing purchasing managers’ index. Global oil production is from the International Energy Agency (IEA) from 19872020 and the U.S. Energy Information Administration (EIA) from 1980-86. Oil prices are the unweighted average of Brent, West Texas Intermediate, and Dubai crude oil prices from the World Bank’s Pink Sheet, measured in U.S. dollars. OECD inventories use IEA data from 1991-2020 and EIA data from 1987-1990. In April 2020 and prior to 1987, percent changes in U.S. inventories, which account for about one-third of OECD inventories,  are used to proxy for changes in OECD inventories.

# **2.3 Results**

The oil price plunge in 2020 has been mostly demand-driven, although supply factors have also played a role. The SVAR decomposition suggests that two-thirds of the price decline in the six months ending in April 2020 has been due to falling demand.<sup>4</sup> The demand and supply factors surrounding the recent plunge in oil prices are discussed below.

**_Demand decline resulting from lockdowns._** The single largest factor driving the collapse in oil prices has been the sharp reduction in oil demand arising from government restrictions to stem the

> 4 In contrast, other research finds that only around one-third of the fall in oil prices can be attributed to demand conditions, while supply factors explain most of the remainder of the fall (Groen and Nattinger 2020). Instead of industrial production as a proxy for oil demand, these other models use asset prices which have considerably more resilience than real activity indicators (in part reflecting monetary policy measures). If anything, other factors, in particular the widespread anticipation of a delay in OPEC+ negotiations, point to an even greater role of demand than estimated here.

6
# **References**

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<mark>Allison, Lee, Yu Liu, Samuele Murtinu, and Zuobao Wei (2023). “Gender and firm performance around the world: The roles of finance, technology and labor.”</mark> _<mark>Journal of Business Research</mark>_ <mark>, 154: 113322.</mark> Aristei, D. and M. Gallo (2016). “Does gender matter for firms’ access to credit? Evidence from international data.” _Finance Research Letters_ 18: 67–75.

<mark>Asiedu, Elizabeth, James A. Freeman, and Akwasi Nti-Addae (2012). "Access to credit by small businesses: How relevant are race, ethnicity, and gender?"</mark> _<mark>The American Economic Review</mark>_ <mark>102(3): 532-537</mark> _<mark>.</mark>_ <mark>Asiedu, Elizabeth, Kanyama, Isaac, Ndikumana, Leonce and Nti-Addae, Akwasi (2013). “Access to Credit by Firms in Sub-Saharan Africa: How Relevant Is Gender?”</mark> _<mark>The American Economic Review (Papers and Proceedings)</mark>_ <mark>103(3): 293-97.</mark> <mark>Bacchetta, P., K. Benhima, and C. Poilly (2019). “Corporate Cash and Employment”</mark> _<mark>American Economic Journal: Macroeconomics</mark>_ <mark>11(3): 30-66.</mark> <mark>Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine (2007). “Finance, inequality and the poor.”</mark> _<mark>Journal of economic growth</mark>_ <mark>12: 27-49.</mark> Becker, Gary S. (1957). **The Economics of Discrimination.** Chicago: University of Chicago Press.

Bellucci, Andrea, Alexander Borisov, and Alberto Zazzaro (2010). “Does gender matter in bank– firm relationships? Evidence from small business lending.” _Journal of Banking & Finance_ , 34(12): 2968–2984.

Berger, A. N. and G. F. Udell (1998). “The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle.” _Journal of Banking and Finance_ 22(6–8): 613–673.

Bertocchi, G., and M. Bozzano (2020). “Gender gaps in education”, in Klaus F Zimmermann (ed.), **Handbook of Labor, Human Resources and Population Economics** , 1-31. Available at <u>https://link.springer.com/content/pdf/10.1007/978-3-319-57365-6_26-1?pdf=chapter%20toc</u> <mark>Bertrand, Marianne (2018). “Coase Lecture – The Glass Ceiling”</mark> _<mark>Economica</mark>_ <mark>, 85(338): 205-231.</mark> 28
# 28

data suggest. The percentage increases in employment within the chemical and mechanical industries, for instance, are much smaller than those found in the official industry statistics presented in Table 4.3. In recent (1982-1985) years, the chemical industry in fact employed fewer permanent workers, while employment in the mechanical industry rose slightly during this period. Our data also indicate that, contrary to the official data in Table 4.3, employment of permanent workers in the textie, clothing and leather industry actually increased during the 19801985 period. Finally, some of the discrepancies between the output and employment trends can be attributed to the differential use of temporary workers by industries over time. For instance, as Table 4.6 demonstratc.. the food industry has significantly increased its reliance on temporary workers in the 1980s.

# _4.3.2 Employment of Temporary Workers_

Because the population and hence the labor force have been growing rapidly, the overall stagnation of permanent industrial employment is clearly a cause for concern. One possible explanation' of the phenomenon is that entrepreneurs have increasingly relied on temporary workers as a means of maintaining labor-input flexibility in the presence of rigid government enforcementof permanent employmeint. Tables 4.5 and 4.6 present tata from the sampled firms on the number of temporary workers in various industries and their wage bills relative to the wage bills of permanent workers.

As Tables 4.4 and _4.5_ show, the number of temporary workers employed in the industrial sector is approximately 40 percent of the number of permanent workers and seems to have remained fairly constant over the first half of the 1980s.2 There is, however, some fluctuation, which supports the managers' statements that they are using temporary workers as a way to adjust their labor cost to cyclical market conditions.

The number of temporary workers is, of course, a good measure of how many individuals have at one time or another 2. The figures for the food industry exclude one flour mill, which appears to have reported unreasonable data (when compared zo the other flour mills and other firms in the sample). Specifically, the numbers given were 13,200 temporary workers in 1980, 20 in 198Z 547 in 1983, 13,824 in 1984 and 700 in 1985.
**Figure 4: Biofuel production cost ($/Gj) from various feedstocks**

<!-- 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>$/Gj<br>Diesel<br>Gasoline Sugarcane (Brazil) Maize (US) Sugarbeet (EU) Wheat (EU) Cellulosic ethanol Jatropha Bd Microalgae Bd*<br><!-- End of picture text --> Source: IEA (2008). For gasoline and diesel prices, the range is given by wholesale prices (excluding taxes) in years 2007 and 2008 in the U.S.. The assumed energy content are as follows: ethanol 21.1 mj/lt; gasoline 32 mj/lt; biodiesel 33.3 mj/lt; and diesel 36.4 mj/lt. Capital and operating costs for processing algae oil into biodiesel were assumed equal to those of converting other vegetable oils into biodiesel and set to $0.122 per liter (Paulson and Ginder 2007). First-generation ethanol costs are from IEA (2008). * The upper values for feedstock costs would imply costs of production per Gj in the thousands for algae biodiesel.

# **4    Environmental Impacts of Second-Generation Biofuels**

In this section we discuss issues related to energy balance, GHG emissions, water use, and impact on biodiversity of second-generation biofuels. The interested reader is referred to some detailed and recent reviews published by OECD (2008), IEA (2008), and WWI (2007).

## **4.1    Fossil energy displaced by biofuels**

Many life cycle analyses (LCA) of different biofuels, produced following different technologies and in distinct regions have been conducted in recent years. A consensus seems to have been reached in that when considering only the LCA, biofuels can contribute to displace fossil energy and reduce GHG emissions in that process. The (fossil) energy balance of a sample of possible biofuel pathways is presented in Table 33