rafmacalaba/datause-encoder-data huggingface dataset:
|Construction|-3.1|2.4|1.9|-5.5|1.0|1.4|
|---|---|---|---|---|---|---|
|Wholesale & Retail trade|-5.3|10.8|10.3|-4.5|1.1|2.2|
|Transport, Storage|-8.9|4.9|4.1|-2.9|1.0|1.9|
|Financial and Insurance Activities|-2.2|5.5|7.2|-3.8|0.0|1.7|
|Real Estate Activities (OD)|3.8|3.6|3.7|3.7|1.0|2.0|
|Public Administration and Social Security|3.0|-0.5|1.8|-7.8|1.7|2.6|
|Other Private Services|5.0|5.1|4.8|5.0|2.2|2.5|
|Inflation [3]<sup>b</sup>|10.7|8.9|12.2|29.2|21.0|13.0|
|_Inflation [COICOP]_<sup>_b_</sup>|||||||
|Food and Non-Alcoholic Beverages|15.5|13.3|13.4|38.7|||
|Alcoholic Beverages and Tobacco|21.2|5.6|4.3|65.0|||
|Clothing and Footwear|9.7|10.1|10.4|18.7|||
|Housing and utilities|6.9|6.3|10.8|14.1|||
|Furnishing and HH Equipment<br>Maintenance|10.3|8.4|12.5|31.8|||
|Health|11.3|8.4|9.4|16.7|||
|Transport|11.1|1.6|23.9|50.5|||
|Communication|3.5|0.7|2.4|3.4|||
|Recreation and Culture|6.4|4.5|8.6|41.6|||
|Education|5.4|1.3|4.8|9.7|||
|Restaurants and Hotels|7.3|8.5|12.5|32.4|||
|Misc.|11.9|11.6|10.6|29.1|||
|Population (million) [4]<sup>a</sup>|227.2|231.4|235.8|240.5|245.2|249.9|

Source: a: Pakistan Bureau of Statistics (PBS) and World Bank (WB), b: Statistical Supplement Note: COICOP categories aggregate information from 283 expenditure items into 12 main categories Besides the large heterogeneity in the growth rates across the different sectors of the economy, there is additional variation in terms of the labor intensity of each of these sectors, which does not necessarily match the sector's contribution to the overall GDP. As Panel (a) in Figure 1 shows, while the agriculture sector represents about 23 percent of total GDP in FY19, it absorbs almost 32 percent of the labor force of the total labor force. This implies that expansion in the agriculture sector has the potential to positively impact the welfare of a larger number of households.<sup>8</sup> In addition to economic sectors presenting different levels of labor intensity, workers are not equally distributed across sectors. For instance, individuals working in agriculture are more likely to be at the bottom of the welfare distribution, further reinforcing that which sector expands or contracts produces different welfare implications; see Panel (b) in Figure 1.

# **Figure 1: Employment Distribution Across economic sectors**

**Panel (a) Labor shares and sectoral contributions to GDP.**

> 8 Subject to market structure and government redistribution of the benefits of growth in other economic sectors.

5
Ireland, on the other hand, similarly has several systemically large banks, but its debt-to-GDP ratio is relatively low at 0.27.

The huge size of many countries’ largest banks reflects that many banks’ liabilities have grown faster than GDP over the last two decades. This is evident in Figure 1. This figure displays the percentages of large banks that are defined as systemic by their liabilities-to-GDP ratios in excess of various benchmarks for each of the years since 1991. The percentages of systemically large banks reached a temporary peak in the 1996-1998 period. In the early years of the new millennium, the relative frequencies of systemically large banks declined, but these frequencies reached new highs in the 2006-2007 period, before a slight drop-off in 2008. The percentage of banks with a liabilities-to-GDP ratio exceeding 0.5, for instance, reached a high of 3.36 in 2006, and declined to 2.71 in 2008.

# **2.2. Bank market valuation, CDS spreads and other variables**

In the empirical work below, we relate two variables using market prices to the systemic bank size and national public finance variables. Using stock price data, we first construct bank’s market-to-book ratio as the market value of the bank’s common equity divided by the book value of common equity. The market value of a bank’s common equity is available from Datastream. The market-to-book ratio should reflect any costs or benefits of systemic bank size to bank shareholders. The market-to-book has a sample mean of 1.45 in the overall sample, as seen in Table 4.

Our second dependent variable is a bank’s CDS spread. We construct a bank’s yearly CDS spread as the average of daily CDS spreads, provided that there are at least 100 daily CDS spreads. We obtain CDS information from Markit. Typically, several CDS contracts are traded for a given major bank differing in the duration of the contract and in the definition of the deliverable bank liabilities in case of a specified credit event. Following Jorion and Zhang (2007), we consider 5-year CDS contracts as these contracts are the most liquid and constitute the majority of the entire CDS market. We further select on CDS contracts for senior unsecured 9
<!-- Start of picture text --> 40<br>g<br>oO<br>= 30<br>= 30 29<br>n oO2 27<br>ES 24<br>=2<br>22<br>3 5<br>es. 20 18<br>c=<br>OO<br>SE<br>as<br>2 10<br>Dd)<br>Cc<br>oO<br>on<br>[S)<br>0 Informal Formal<br>Base wave formality<br>1998-2006 [NNN 2006-2012 MEE 2012-2018<br><!-- End of picture text -->
- Family planning conscious efforts by inJividuals and couples to - regulate their fertility saves lives and improves the health of women. Indeed it was to save women's lives that Margaret Sanger founded the modern family planning movement. Yet sev'ent **y** ears later, 500,000 women die each year from pregnancy-related causes, 99 percent of them in developing countries (WHO, 1985). Many of those pregnancies are unwanted and could be prevented by family planning.

We now have far more effective family planning methods than were available to Margaret Sanger and solid c_idence that these methods are successfully employed by millions of couples in developing and developed countries alike (World Bank, 1984). But a majority of developing country couples do not use effective contraceptive methods and lack their health and other benefits. And while the essential facts remain the same, much more is now known about the impact of family planning on women's health, at different ages and stages in the family and reproductive life cycle, in different social-, economic and cultural circumstances, and in relation to organized efforts to provide family planning services on a broad scale.

We will try to summarize the main facts about the effect of family planrting on maternal health and to suggest how they can be translated into stronger policies and programs to save women's lives and improve their health. We will examine the following four areas:

- direct effects of family planning on maternal health through the prevention of health risks associated with pregnancy:

- a) preventing maternal deaths; and b) preventing maternal ill health.

- the health effects of fertility control - mainly the side effectF, complications and health benefits of various contraceptive methods;

- the effect of organized family planning programa on maternal health;

- the cost-effectiveness of family planning services in preventing maternal mortality and morbidity.

# We begin by saying what this paper w'll not cover.

We will not focus on the critical role of family planning in social and economic development. Rapid population growth inhibits overall development mainly by forcing governments and individuals to spend scarce resources on current consumption rather than investing them for future social and economic returns (World Bank, 1984). As development proceeds, fertility falls. When family planning services are made widely available and accessible, fertility falls more rapidly than would otherwise be the case, thereby slowing population growth. Slower population growth, in turn, makes possible better employment opportunities, education and health services. These opportunities and services, in turn, reduce fertility norms. Better
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 16 Table 6. The length of protected coast (in km) globally and by region over time (for selected years) under Strategies 1 to 4. 2010 is included to illustrate the large increase in protected length in Upper middle income countries in 2015. RCP 8.5 SLR and Risk Intolerant Protection are considered.

|**Year**|**2010**|**2015**|**2030**|**2050**|**2100**|
|---|---|---|---|---|---|
|**East Asia & Pacific**|31,300|31,400|32,100|37,000|49,100|
|**Europe & Central Asia**|4,600|4,600|4,600|4,600|5,000|
|**High Income**|104,100|104,100|104,100|104,500|108,600|
|**Latin America & Caribbean**|17,000|17,100|16,700|16,800|24,200|
|**Middle East & North Africa**|6,500|6,500|6,800|6,900|7,800|
|**South Asia**|3,500|3,500|3,900|4,800|6,700|
|**Sub-Saharan Africa**|4,100|4,100|4,100|4,200|6,400|
|**Total (global)**|171,100|171,200|172,300|178,800|207,800|

Table 7. The length of protected coast under CBA Protection, RCP8.5 and SSP2 scenarios.

|**Year**|**2010**|**Length of Protected Coast (2015 to**<br>**2100) (km) and percentage change**<br>**relative to 2010**|
|---|---|---|
|**East Asia & Pacific**|31,300|46,600 (+48.9%)|
|**Europe & Central Asia**|4,600|5,600 (+21.7%)|
|**High Income**|104,100|97,000 (-6.8%)|
|**Latin America & Caribbean**|17,000|16,900 (-0.6%)|
|**Middle East & North Africa**|6,500|8,100 (+24.6%)|
|**South Asia**|3,500|9,300 (+165.7%)|
|**Sub-Saharan Africa**|4,100|11,300 (+175.6%)|
|**Total (global)**|171,000|195,000 (+14.0%)|
Hallegatte, S. and P. Dumas (2008), “Can Natural Disasters Have Positive Consequences? Investigating the Role of Embodied Technical Change”, Ecological Economics 68, 777–786.

Hallegatte, S. and M. Ghil (2008), “Natural Disasters Impacting a Macroeconomic Model with Endogen‐ ous Dynamics”, Ecological Economics 68, 582–592.

Hallegatte S, Ranger N, Mestre O, Dumas P, Corfee‐Morlot J, Herweijer C, MuirWood R (2011), Assessing climate change impacts, sea level rise and storm surge risk in port cities: a case study on Copenhagen. Climatic Change, in press. doi: 10.1007/s10584-010-9978-3 432 Hallstrom, D.G., and V. K. Smith, 2005. Market responses to hurricanes, Journal of Environmental Eco‐ nomics and Management 50 (2005) 541–561 Henriet, F. and S. Hallegatte, 2008. Assessing the Consequences of Natural Disasters on Production Net‐ works: a Disaggregated Approach, FEEM Working Paper No. 100.2008 Hochrainer, S. (2009), Assessing the Macroeconomic Impacts of Natural Disasters – Are There Any?, World Bank Policy Research Working Paper 4968.

Holden, S., and Shiferaw, B. (2004). Land degradation, drought and food security in a less‐favoured area in the Ethiopian highlands: a bio‐economic model with market imperfections. Agricultural Economics, 30: 31‐49 Horridge, M., Madden, J., Wittwer, G., 2005. The impact of the 2002–2003 drought on Australia. J. Policy Modeling 27, 285–308.

Jaramillo, C. R. H. (2009), Do Natural Disasters Have Long‐Term Effects on Growth?, Universidad de los Andes, mimeo.

Jonkman S.N., Bočkarjova M., Kok M., Bernardini P., 2008. Integrated hydrodynamic and economic mod‐ elling of flood damage in the Netherlands, Ecological Economics Volume 66, Issue 1, 15 May 2008, Pages 77‐90 Kroll, C. A., J. D. Landis, Q. Shen and S. Stryker (1991), Economic Impacts of the Loma Prieta Earthquake:A Focus on Small Business, Studies on the Loma Prieta Earthquake, University of California, Transportation Center.

Lindell, M. and Prater, C. (2003) “Assessing Community Impacts of Natural Disasters” Natural Hazards Review 4(4) pp. 176‐185 Loayza, N., E. Olaberria, J. Rigolini and L. Christiansen (2009), Natural Disasters and Growth‐Going beyond the Averages, World Bank Policy Research Working Paper 4980.

McCarty,C and S. K. Smith (2005), Florida’s 2004 Hurricane Season: Local Effects, Florida Focus, University of Florida, http://www.bebr.ufl.edu/system/files/FloridaFocus1_3_2005_0.pdf.

24
Table 2: The size distribution of formal and informal plants in the Mexican census. 2018.

||**To**|**tal**|**Formal**|**plants**|**Informa**|**l plants**|
|---|---|---|---|---|---|---|
|Size|Share of<br>plants|Share of<br>workers|Share of<br>plants|Share of<br>workers|Share of<br>plants|Share of<br>workers|
|1-10|95.89%|43.70%|89.10%|26.00%|99.32%|89.22%|
|11-20|2.06%|6.22%|5.28%|7.57%|0.44%|2.98%|
|21-50|1.19%|7.84%|3.21%|9.99%|0.18%|2.65%|
|51-100|0.39%|5.81%|1.10%|7.75%|0.03%|1.09%|
|101+|0.46%|36.42%|1.31%|48.69%|0.03%|4.06%|
|**Total**|**100%**|**100%**|**100%**|**100%**|**100%**|**100%**|

**Notes:** Formal establishments are registered with SAT, IMSS, or both, as self-reported by the respondent. Establishments that are not formal are informal.

# **2.2 The Life Cycle of Formal and Informal Plants**

We estimate size at birth as the average size of formal and informal plants ages 0-4 in the 2018 cross-section: 6.1 workers among formal establishments and 1.9 among informal plants (2.9 for the total average). To estimate growth rates, we exploit the panel dimension of the data. INEGI has developed identifiers that track establishments across the 2008, 2013, and 2018 waves of the census. Busso et al. (2018) developed synthetic identifiers to follow establishments from 1998 through 2008, which we leverage in our analysis.<sup>15</sup> Busso et al. (2019) first exploited the synthetic identifiers to try to compute the life cycle of formal and informal plants in Mexico, but they focused on informality on the intensive margin while our definition looks at the extensive margin. Moreover, while they restrict their analysis only to the balanced panel (the panel of surviving plants between 1998 and 2018), our estimates of the average growth rates leverage micro data from entering and exiting establishments as well as in Eslava et al. (2022). Our analysis also corrects for differences across sectors.

We first sort establishments in the 2018 cross-section into formal and informal using the self-reported registration with SAT and IMSS as described above. We then treat establishments that were informal (formal) in 2018 as informal (formal) every period back through 1998. That is, we do not estimate separate profiles for plants that transition in and out of informality during their life cycle and for plants that never changed their

> 15Busso et al. (2018) use the location, the name, and the 6-digit sector to find establishments in two consecutive waves of the census.

7
# **Figure 1: Participation rates in MGNREGS are only weakly correlated with the incidence**

<!-- Start of picture text --> of poverty across the states of India<br><!-- End of picture text --> <!-- Start of picture text --> .7<br>r=0.13<br>.6<br>Rajasthan<br>.5<br>Chhattisgarh<br>.4<br>.3<br>.2<br>.1<br>Bihar<br>.0<br>10 15 20 25 30 35 40 45 50 55 60<br>Headcount index of rural poverty 2009/10 (% below poverty line)<br>Participation rate in MGNREGS (share of rural households)<br><!-- End of picture text --> _Source_ : Dutta et al. (2012) 19
the source of a tiny fraction of all jobs found by workers: 9 percent of jobs in Albania, 5 to 6 percent of jobs in Jordan and Morocco, and down to less than 1 percent of jobs obtained in Mexico.

**Table 1: Main methods used by employed workers to find jobs**

||||Percent wh|o used meth|od to find job|**Sierra**||
|---|---|---|---|---|---|---|---|
||**Albania**|**Jordan**|**Mexico**|**Morocco**|**Romania**|**Leone**|**Turkey**|
|Friends and Relatives|28.8|41.0|53.0|70.9|37.2|46.0|59.7|
|Direct Application to Employers|52.7|37.1|32.0|40.4|36.4|41.1|22.9|
|Newspaper ads|0.5|8.5|8.9|0.1|15.7|5.0|4.5|
|Internet|0.3|1.0|1.6|1.5||0.0|5.0|
|Public or Private Employment Agency|9.1|6.2|0.6|5.5|2.5|2.2|2.7|
|Other|8.5|6.2|3.9|12.5|8.2|5.8|5.2|
|SampleSize|14653|904|117924|712|36542|504|3302|

_Source:_ Albanian data from Quarterly Labor Force Survey 2019; Jordan is from New Work Opportunities for Women Pilot Impact Evaluation 2010–2013; Mexico is from Trimester 1, 2014 National Survey of Occupation and Employment (ENOE); Morocco is from Household and Youth Survey 2009– 2010; Romania is from Household Labor Force Survey 2021; Sierra Leone is from 2014 Labor Force Survey; and Turkey is from Vocational Training for the Unemployed Impact Evaluation 2010–2012.

_Note:_ Morocco survey allowed multiple methods to be used, so responses add up to more than 100 percent. Romania survey combines newspaper advertisements and internet advertisements into one response category. Carranza and McKenzie (2023) provide full details.

Job matches between workers and firms can be highly location-, sector-, time-, and firmspecific, so that it seems unlikely in most cases that a central planner or government will do better than the private market in filling most job openings. In line with Table 1, throughout history and the growth of most countries, large increases in labor supply have been absorbed by the market without the need for the government to help millions of people find jobs. So why should the government get involved in providing job training and search assistance rather than leaving it to the private market?

Perhaps the most common reason that governments provide these services is based on political pressure for a government to show it is doing something about employment—and providing job training or search assistance is often politically easier than addressing other barriers to creating jobs. In political terms, the success of these programs can be based on visible inputs— like the number of people who finished a training program, or the number of towns with jobassistance centers—rather than the more difficult-to-observe effects like the number of workers 5
- 47 -

Net foreign reserves have grown rapidly in recent years in Turkey, and Table 23 below shows that the ratio of broad money to foreign reserves have declined. Though the ratio does not look particularly high as compared to a number of other large capital importer countries, whether the reserves are all freely usable is an issue of concern in the case of Turkey. While the central bank has fairly conservative foreign exchange management policies, and it does not engage in foreign exchange guarantees, forward contract or swap positions, a large part of the net international foreign reserves represent foreign currency deposits with the central bank by foreign residents of Turkish origin through the Dresdner Scheme'<sup>9</sup> or similar arrangements. Although the balance of such deposits does not exhibit high volatility, its stability cannot be taken for granted.

**Table 23**

|||Turkey: System|Uquidity||
|---|---|---|---|---|
|Ratio<br>to|of Short-term Debt<br>Foreign Reserves|<br>Short-term Debt<br>as Flrcentage of Total Debt|Ratio of Broad Money<br>to Foreign Reserves|Ratio of Broad Money+ Repos<br>to Foreign Reserves|
|1993|2.4|28%|4.3|**4.3**|
|1994|1.3|17%|4.0|4.1|
|1995|1.1|21%|3.4|3.4|
|1996|1.2|26%|3.0|3.1|
|1997|1.2|27%|3.0|3.2|

Source: Own calculation based on Central Bank data.

**Table 24**

||**System Uquidity in**<br>|**Seleted Countries as of June 1**<br>|**997**<br>|
|---|---|---|---|
||Ratio of Short-terrm Debt<br>to Foreign Reserves|Short-term Debt<br>as Percentage of Total Debt|Ratio of Broad Money<br>to Foreign Reserves|
|Korea|3.0|67%|6.2|
|Indonesia|1 .6|24%|6.2|
|Thailand|1.1|46%|4.9|
|Philippines|0.7|19%|4.9|
|Malaysia<br>|0.6<br>|39%|4.0|
|Source: Goldstein (|1998), p<sup>11</sup>.|||

19 The Dresdner Scheme is a special arrangement between Dresdner Bank in Germany and the Central Bank of Turkey, to attract deposits of Turkish workers residing in Germany. The Central Bank of Turkey sets the interest rates offered under the scheme, with the Dresdner Bank acting as an agent to facilitate remittances back to Turkey.
blend mandate). On the other hand, as expected, higher mandates, tax exemptions for hydrous ethanol and gasoline prices in Brazil means higher ethanol and sugar prices.

Eliminating Brazilian ethanol tax exemptions and mandates reduces ethanol prices by 21 percent in 2010-11, very similar to the estimated effects of U.S. ethanol policies by Drabik (2011) for the same time period. But the marginal changes in Brazilian policies on ethanol prices between 2010-11 and 2011-12 are shown in this paper to be relatively small both individually and collectively. We find that observed market changes can only be explained by outward shifts in fuel transportation and sugar export demand curves, and reduced sugarcane supply due to bad weather.

Although the hydrous ethanol tax exemption always increases both anhydrous and hydrous ethanol prices (with or without the mandate), in principle it is very possible that the mandate in Brazil can also act as upper bound on ethanol consumed, (rather than a lower bound as is always the case in the United States), depending on the year and market circumstances. This outcome is more likely, _inter alia_ , when sugar demand is low and sugarcane supplies plentiful. In that case, the removal of the mandate will increase the ethanol price. The number of years in which this has happened in the past (if ever) and the extent to which ethanol prices would have risen with the elimination of the mandate await further research.

# **References**

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Crago, C., M. Khanna, M., J. Barton. E.Guiliani and W.Amaral. (2010). “Competitiveness of Brazilian Sugarcane Ethanol Compared to US Corn Ethanol,” _Energy Policy_ , 38(11), 7404-7415, November.

Lasco, C and M. Khanna, “US-Brazil Trade in Biofuels: Determinants, Constraints and Implications for Trade Policy” in Handbook of Bioenergy Economics and Policy, ed. by M. Khanna, J. Scheffran and D. Zilberman, Springer Publishers, 2009.

de Freitas, L. Ch., and Kaneko, S.,. (2011). “Ethanol demand in Brazil: Regional approach” _Energy Policy_ Volume 39, Issue 5, May 2011, Pages 2289–2298.

de Gorter, H., Drabik, D., and Erika M. Kliauga (2012): “Why Ethanol Prices Are So High in Brazil” _Biofuels_ Vol. 3, No. 4, p. 371-373 34
**<mark>The Impact of Infrastructure on Development Outcomes:</mark>**

**<mark>A Qualitative Review of Four Decades of Literature</mark>**

<mark>Vivien Foster, Nisan Gorgulu, Stéphane Straub, Maria Vagliasindi</mark><sup>*</sup>

**JEL:** O18, O47, Q40, R11, R12, R40

**Keywords:** infrastructure, development impact, internet, mobile telephony, energy, electrification, reliability of supply, transport, rural road, highway, railroad, port

> * Foster, Gorgulu, Vagliasindi: The World Bank, Straub: T <mark>oulouse School of Economics. We thank Pedro Bom and David Tuesta for sharing their data, and Estefania Vergara Cobos and Mumba Ngulube for their contributions.</mark>
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Dammert, Ana. 2007. “Child Labor and Schooling Response to Changes in Cocoa Production in Rural Peru.” IZA Discussion Paper 2869. Bonn: IZA.

27