rafmacalaba/datause-encoder-data huggingface dataset:
Considerable differences emerge across product types and exporter income levels. Some have argued that South-South trade was less disrupted during the crisis. While this appears to be the case if one examines aggregate trade patterns across all goods, there is a large decline in manufacturing trade with Sub-Saharan Africa. We indeed find a small positive quantity effect for total exports from low-income countries to Brazil and Indonesia, but there is an almost equally large negative net entry effect. Furthermore, the quantity effect for manufactures alone is negative. We also find evidence consistent with the view that credit constraints could account for the price increases which occurred in the manufacturing sector. For the United States, for example, price increases were most significant in sectors which are typically credit constrained.

In Section II, we describe the conceptual approach. Results are presented in Section III, and Section IV concludes. We focus on the decomposition of changes in trade into entry, exit, price and quantity changes. Our ability to separate price and quantity changes allows us to identify the upward trajectory of prices for manufacturing, which could be consistent with a role for credit constraints in explaining some—but obviously not the majority—of the collapse in world trade.

# **2. Conceptual Approach**

Our approach is inspired by earlier work analyzing the 1997 Asian crisis by Bernard, Jensen, Redding, and Schott (2009). However, we go one step further: we decompose the observed changes in trade at the intensive margin into price and quantity changes. For each importing country, we can tabulate the total number of products that entered or exited or continued in the market for imports from a given partner country. Most products fall into the ―continuing‖ category, for which we can assign further divisions. For each continuing product, 5
which exceeds $100m will leave the bank rather than the amount in excess of $100m. However, for identification of uninsured deposits for FDIC purposes, only the amount of each deposit exceeding $100m is technically uninsured.

- _(12) Other Sensitive Funds/Deposits_

Total of all funding sources that may exhibit unusual credit sensitivity that are not already defined.

- _(13) Treasury, Tax, and Loan_

The sum of the Treasury, tax, and loan balances.

- _(14) Fed Discount Window_

Total borrowings at the discount window.

# **Non-bank Assets (Parent)**

- _(15) Short-Term Internal Investments_

Foreign deposits placed (from parent's perspective). Other short-term liquid assets.

Note: Typically, these parent company assets are placed in affiliated bank liability accounts, such as foreign deposits taken and, therefore, are also reflected on the bank's asset side--presumably in money market assets. An understanding of how these funds flow from the parent to affiliate and back is critical in an analysis of the Funds Flow Analysis report to avoid double counting. It must be assumed that the bank's liabilities to the parent will have priority on the bank's liquid assets. Therefore, for analysis purposes, they must be subtracted from the money market asset number for an assessment of bank level liquidity.

- (16) _Short-Term_ _<u>External</u> Assets:_ Cash, foreign deposits placed, other short-term liquid assets.

Note: Ensure that these assets are not carried in the ‘consolidated’ Funds Flow Analysis MMA figure (4). They represent liquid assets the parent maintains outside of its own corporation and are available to the parent over the listed total MMA figure in the Funds Flow Analysis Report.

# **Non-Bank Liabilities (Parent)**

- _(17) Commercial Paper, etc._

Total commercial paper issued by the parent company or subsidiary, master notes, and any other short-term liability, including term debt or debt payments that are approaching maturity.

58
**A2 United States**

**Table A2: Regional disparity** trends **In the** United States

|**Year**|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw)|Relative<br>mean<br>Deviation<br>(_<br>RW)|Unweighte<br>gini index<br>_(GU)_|d Weighted<br>gini index<br>_(GW)_|Theil<br>index**(T)**|
|---|---|---|---|---|---|---|---|
|1990|2.871|0.229|0,159|0.132|0.118|0.049|0.013|
|1991|2.522|0.207|0.147|0.122|0.109|0.046|0.011|
|1992|2.281|0.189|0.139|0.114|0.102|0.044|0.010|
|1993|2.157|0.180|0.130|0.105|0.097|0.042|0.008|
|1994|1.966|0.167|0.122|0.096|0.092|0.039|0.007|
|1995|1.976|0.166|0.120|0.095|0.091|0.038|0.007|
|1996|1.943|0.163|0.121|0.096|0.090|0.039|0.007|
|1997|1.927|0.162|0.122|0.097|0.090|0.039|0.007|

<!-- Start of picture text --> Figure A2: Reago.al disparIty trends d2he US<br>0.250<br>0.200<br>*: f<br>E<br>0.050 . - - -- - . - I- _ _ _ _ _ _ _ _ _ _<br>0.000<br>1990 1991 1992 1993 1994 1995 1996 1997<br>Year<br>Simple coefficient of varation (CVu) Weighted coefficient of variation (CVw) Relative mean Deviation (Rw)<br>Unweighted gini index (Gu) Weighted gini index (Gw) -Theil index (T)<br><!-- End of picture text --> 3 6
Our empirical strategy relies on the parallel trend assumption of the difference-indifference approach. It assumes that the difference in the outcome among exposed and control households would be similar had the attacks not occurred. Any difference between treatment and control is unlikely to have changed over 22 months –between Waves 1 and 2– since the identification strategy compares households within districts of Mogadishu and any other shock is likely to have affected in a similar way both exposed and control households. We also provide evidence to support the conditional independence assumption. Table 8 in the Appendix presents an OLS regression for the exposure of households to attacks in Mogadishu. The consumption level of households, their location and socio-economic characteristics are not associated with the propensity from being exposed to an incident. This suggests that terrorist incidents are likely to be exogenous, conditioned on location fixed effects and household characteristics.

In addition, we employ an instrumental variables (IV) identification strategy to further validate the results from the difference-in-difference approach. For this, we obtain a shift-share type of instrument based on Bartik (1991), exploiting the spatial variation of incidents against civilians and changes in the number of US air/drone attacks against Al-Shabaab in Somalia.<sup>21</sup> US air/drone attacks against Al-Shabaab were manually identified from ACLED data among all events recorded between Waves 1 and 2 of the SHFS. The US air/drone activity against Al-Shabaab is mainly concentrated on South West Somalia, with the number of attacks increasing from 4 in the first semester of 2015 to 21 in the last semester of 2017 (Figure 7 in the Appendix).

To support the validity of the instrument, we examine the location of both, US air/drone strikes against Al-Shabaab, and attacks from Al-Shabaab against civilians in Somalia, with an emphasis on Southern Somalia (Figure 8 in the Appendix). The locations of these two types of incidents are not spatially correlated. US air/drone attacks occurred in locations which are different to those of incidents against civilians. This could be because US air/drone strikes usually target high-profile members of Al-Shabaab and operation centers that are based in areas they already control, which is less often the case for the location of attacks against civilians.<sup>22</sup> US air/drone attacks are thus likely to increase the number of attacks from Al-Shabaab against civilians –as terrorist

> 21 Bartik (1991) combines national changes in employment with the composition of local industry to capture local demand shocks in the labor market.

> 22 The lack of spatial correlation between the location of US air/drone strikes against Al-Shabaab and attacks from the latter against civilians, as well as the different nature of these incidents suggest it is unlikely that causality runs in the opposite direction, such that US air/drone strikes target regions with high activity from Al-Shabaab against civilians.

12
**Figure 1: Global importance of Emerging Markets and EM7 A. EM share of Global Output**

<!-- Start of picture text --> Percent<br>80<br>60<br>40<br>20<br>0<br>1990s 2000-08 2010-15<br>China Brazil India Russia Mexico<br>Indonesia Turkey Other EM G7<br><!-- End of picture text -->

# **C. Global integration of EM and AM**

# **B. EM contribution to global growth**

<!-- Start of picture text --> Percentage point<br>3.5<br>2.5<br>1.5<br>0.5<br>-0.5 1990s 2000-08 2010-15<br>China Brazil India Russia Mexico<br>Indonesia Turkey Other EM G7 World<br><!-- End of picture text -->

# **D. Global integration of EM7 and G7**

<!-- Start of picture text --> Percent Percent<br>EM 2000 (2005) EM 2013 (2014) EM7 2000 (2005) EM7 2013 (2014)<br>100 70<br>AM 2000 (2005) AM 2013 (2014) G7 2000 (2005) G7 2013 (2014)<br>60<br>80<br>50<br>60<br>40<br>30<br>40<br>20<br>20<br>10<br>0 0<br>Trade Remittances FDI flows Trade Remittances FDI flows<br><!-- End of picture text --> _Note:_ EM stands for emerging markets; EM7 for the group of 7 countries: Brazil, China, India, Indonesia, Mexico, Russia, and Turkey; G7 stands for the group of 7 countries: Canada, France, Germany, Italy, Japan, United Kingdom, and United States; AM for advanced markets.

C.D. Due to data constraints, global trade (exports plus imports) from 2000 and 2013; remittances (inflows plus outflows) data from 2000 and 2013; foreign direct investment (FDI) flows (inflows plus outflows) from 2000 and 2014; and international investment position (IIP, including direct investment, portfolio investment, financial derivatives, and other investment assets and liabilities) from 2005 and 2013.

15
Section 4 discusses the evolution of leverage of the sector since the 1997 Asia crisis. Section 5 summarizes the main results and raises the policy issues that seem to emerge from the simple analysis of the data presented in the paper.

# **2. Data and methodology**

The paper focuses on stock market listed companies operating in developing countries.<sup>4</sup> Distinction is made between utilities and transport companies. This is due to historical differences in the types of regulation and levels of competition between the two sectors, which have also influenced the financing structures in these companies. The utilities sample is further broken down into electricity, gas and water sectors, and the transport sample into infrastructure and services providers. However, the scope for detailed monitoring of the evolution of the financing structure for any company in developing countries is limited, since few regulators in these countries tend to collect this data systematically. Also, in many countries an increasing number of small local companies are taking over the public management and provision of services in sectors such as water and sanitation and transport services. Unless these smaller companies are listed, they will not be covered here. There is, however, a large enough set of companies that report their financial structure in their home countries to ensure the statistical representativeness of the sample collected here.

The sample data were collected from the companies listed in the Thompson Financial Datastream database. Datastream holds accounting and financial market data for publicly traded companies in a large number of countries, collected from companies’ group financial accounts and regional data providers. The data were collected for a period of 12 years from 1991 to 2002. The total number of observations in each year is presented in Table 1. A more detailed data description is provided in the Appendix.

For utilities, the data are for 121 companies from 16 countries. These include 90 electricity companies<sup>5</sup> , 23 gas distribution companies and 8 water distribution companies. For transport, the data limitations were more constraining and required a more complex screening and aggregation process. Only companies that are at least partly subject to government regulation are considered. Attention is restricted to the following transport sub-sectors: rail and road passenger transportation companies, airlines and airports, and shipping ports. In order to have representative sample sizes, companies had to be aggregated into two categories, depending on whether they were transport service or transport infrastructure providers. The final dataset comprises 23 transport infrastructure and 23 transport service companies in 15 developing countries.

> 4 This is clearly a biased sample but it is the only one for which this kind of data is available.

> 5 For the purposes of this study, both electricity transmission and distribution companies are included under the electricity sector.

4
**Figure 5: Estimated change in incomes due to past climate change versus initial income level, municipal level in Chile**

<!-- Start of picture text --> 10<br>8<br>6<br>4<br>2<br>0<br>0 500 1000 1500 2000<br>-2<br>-4<br>-6<br>-8<br>-10<br>Change in income due to<br>past climate change (% change)<br><!-- End of picture text -->

**Income per capita without climate change (USD/month)**

There is a slight, statistically insignificant, negative relationship, indicating that recent climate change has not had any significant effect on the income distribution, but that it has contributed slightly towards poverty reduction in all but the most northern municipalities.

# **6. Expected future climate change in Chile**

Having quantified the impacts of climate change during the last 50 years, we now turn to an assessment of the likely impacts of possible climate change during the next 50 years. For that purpose we will use the regional climate projections made by Working Group 1 for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, which provides a comprehensive analysis based on a coordinated set of 21 Atmosphere-Ocean General Circulation Models (Christensen et al 2007). The use of several different models allows an assessment of the level of confidence with which predictions can be made.

According to the model simulations reported in Christensen et al (2007), temperatures are going to increase faster in the northern part of Chile than in the southern part (see Figure 6). This thus corresponds well to the pattern observed in the past.

21
**The Mechanics of Progress in Education:**

# **Evidence from Cross-Country Data**

# **1. Introduction**

In all countries education plays a key role in social and economic life, so it is not surprising that most government **s** eek, at least in rhetoric, to build education systems that offer the best possible services to as wide a segment of the population as possible. Despite the common goal, however, countries achieve very different results. On almost any measure of schooling outcome-whether related to coverage or student learning-low-income countries typically lag behind high-income countries. What are the sources of rich countries' advantage in education? What is the role of such factors as demographi **c** onditions, government fiscal capacity, prices, and policy choice? Does the impact of these factors change as countries grow rich, and if so, how? What do the patterns reveal about the nature of policy choice in education?

Answers to these questions are explored in this paper. In the process we hope to discover patterns in the relation between country wealth and various aspects of educational development, and to clarify the mechanics of progress in education as a country grows rich.' At the same time, we also expect to improve our understanding of policy options to improve education. Given the breadth of these issues, our study represents a partial treatment, both because it focuses on system-wide features rather than on pedagogical processes at the classroom level, and because it relies solely on quantitative data.<sup>2</sup> These limitations notwithstanding the findings reveal several key insights. The first is that systematic links exist between country wealth and most indicators of educational

> 'For a discussion of similar questions from the perspective of a production-demand framework based on data from 1960 to 1980 see Schultz, T.P. 1988 "Expansion of public school expenditures and enrollments: inter-country evidence on the effects of income, prices, and population growth," Economics of Education Review 7(2): 167-83.

> 2 The bulk of the data pertains to 125 countries around 1993, the latest year for which data on the most of the indicators used in our study are currently available or can be constructed, supplemented by data for 1975 for a smaller set of indicators and countries. These data come from a larger time-series database prepared by the Institute de Recherche sur l'Economie de l'Education (IREDU), Universite de Bourgogne, Dijon, France. The raw data come from various sources, including international organizations (e.g. UNESCO, the World Bank, ILO, and OECD), published and unpublished country and comparative studies, and doctoral dissertations. Data for all the indicators have been checked for consistency in their relation to each other, as well as across time. Where published data contain obvious inconsistencies they are replaced by data for a nearby year, or by simulations based on related data. Since the database was created, new data have become available, but rather than attempt to incorporate them we have decided simply to use what is already available in it.
23 unrealistic assumptions. It is not possible<sup>to expound all the</sup> models here. A fundamental problem is that each model<sup>seems to be</sup> a special case leading to particular results<sup>depending on the</sup> size of various parameters and so on. There has<sup>hardly been a</sup> search for general principles, and certainly no new<sup>paradigm has</sup> emerged so far. There is a new and highly sophisticated

- literature here, and also a new idea namely<sup>the role of trade</sup> policy in "profit shifting" as between oligopolistic<sup>firms-</sup> but since there is not a new paradigm there is certainly<sup>no "new</sup> international economics".

Recent developments and many models are surveyed by<sup>Dixit</sup> (1987a). The work is ongoing. As Dixit says, "It<sup>is premature to</sup> draw confident or firm policy implications from<sup>such an</sup> unfinished and ongoing body of research." He also<sup>guesses that</sup> "research on static models of oligopolistic trade<sup>and trade</sup> policy has more or less run its course. But dynamics<sup>remains a</sup> rich area for research." (p 354). A beginning has<sup>been made with</sup> - empirical work some of which is surveyed in Helpman and Krugman (1989, Chapter 8), which also discusses the problems<sup>of</sup> quantification. But Dixit (1987a, p 359) concludes<sup>that</sup> "empirical work has lagged behind, and it needs<sup>to be improved</sup> greatly in both quality and quantity if we are<sup>to have a better</sup> idea of the scope and significance of these theoretical advances." Actually, he has reported an interesting<sup>piece of</sup> empirical work of his own in Dixit (1987b), where<sup>he applies the</sup> theory to the rivalry between US and Japanese firms<sup>in the</sup>
|Table 2: Training categories and trai|Technical<br>nee charac|teristics in the m|anufacturing|sector||
|---|---|---|---|---|---|
||<br>upgrading|Apprenticeship|Habilitation|Initiation|Qualification|
|Average trainee age|31.972|18.257|24.551|26.26|28.449|
||(10.520)|(2.430)|(7.570)|(10.360)|(10.120)|
|Average course duration (in hours)|40.413|809.58|1109.348|164.707|174.566|
||(44.970)|(501.700)|(508.450)|(372.330)|(201.130)|
|Share of trainees working|0.689|0.271|0.550|0.464|0.491|
||(0.460)|(0.440)|(0.500)|(0.500)|(0.500)|
|Average tenure of trainees (in months)|54.872|11.335|37.4|33.01|54.637|
||(70.560)|(14.880)|(46.290)|(46.630)|(54.630)|
|Log hourly wage|2.545|1.683|2.341|2.119|2.238|
||(0.693)|(0.449)|(0.553)|(0.567)|(0.606)|
|Share with schooling less than high school|0.237|0.442|0.149|0.344|0.259|
||(0.425)|(0.497)|(0.356)|(0.475)|(0.438)|
|Share with schooling equal to high school|0.635|0.516|0.767|0.568|0.678|
||(0.481)|(0.500)|(0.423)|(0.495)|(0.467)|
|Share with schooling more than high school|0.128|0.042|0.085|0.088|0.063|
||(0.334)|(0.201)|(0.279)|(0.284)|(0.243)|
|Share employed in year before training|0.775|0.35|0.305|0.233|0.443|
||(0.417)|(0.477)|(0.455)|(0.482)|(0.479)|
|Share employed in same firm in year before training|0.605|0.229|0.211|0.157|0.201|
||(0.488)|(0.420)|(0.499)|(0.497)|(0.456)|
|Share employed in same occupation in year before training|0.513|0.133|0.125|0.074|0.157|
||(0.499)|(0.340)|(0.494)|(0.482)|(0.431)|
|Share of workers in skill level 4|0.027|0.006|0.014|0.027|0.019|
||(0.162)|(0.077)|(0.116)|(0.124)|(0.112)|
|Share of workers in skill level 3|0.118|0.062|0.192|0.086|0.086|
||(0.323)|(0.241)|(0.394)|(0.274)|(0.271)|
|Share of workers in skill level 2|0.729|0.786|0.699|0.746|0.735|
||(0.444)|(0.410)|(0.458)|(0.437)|(0.441)|
|Share of workers in skill level 1|0.125|0.145|0.094|0.169|0.178|
||(0.330)|(0.352)|(0.292)|(0.366)|(0.377)|
|Number of trainees in manufacturing firms||||||
|2009|75,099|5,331|6,840|33,334|132,530|
|2010|166,694|7,660|9,358|49,237|46,670|
|2011|179,555|8,079|16,738|60,902|52,958|
|2012|164,784|7,550|11,568|50,279|61,214|
|Total|586,132|28,620|44,504|193,752|293,372|
|Age restriction|No|Yes|Yes|No|No|
|Education restriction<br>Note: Table reports means and standard devitations of attrib<br>levels 1 to 4 are based on the 1988 International Standard<br>description).|No<br>utes of SEN<br> Classificati|Yes<br>AI trainees in ma<br>on of Occupation|Yes<br>nufacturing firm<br>s (see Appen|No<br>s by training<br>dix Table A1|Yes<br> category. Skill<br> for a detailed|
Table 11 Teacher earnings differential and student performance

||Harmonize<br>|d test score<br>|Learnin<br>|g poverty<br>|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Monthly earnings differential|70.73**|72.08**|-0.15|-0.06|
||(28.00)|(31.86)|(0.13)|(0.17)|
|GDP per capita (in thousand)||-0.48<br>(4.53)||0.05<br>(0.05)|
|Countries|15|15|6|6|
|Adj. R2|0.33|0.33|0.24|0.42|
||Harmonize|d test score|Learnin|g poverty|
||(1)|(2)|(3)|(4)|
|Hourly earnings differential|69.67*|67.50*|-0.12|0.002|
||(32.55)|(35.66)|(0.11)|(0.17)|
|GDP per capita (in thousand)||0.95<br>(4.77)||-0.06<br>(0.07)|
|Countries|14|14|6|6|
|Adj. R2|0.28|0.28|0.23|0.40|

Notes: Each column represents a regression of test scores (harmonized test scores or learning poverty) on earnings differentials. Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Source: Harmonized test scores from Human Capital Index (2020). Learning poverty data from the World Bank (2019a). Earnings differentials from survey data listed in Table 1.

44