rafmacalaba/datause-encoder-data huggingface dataset:
12 correlated shocks. In order to better understand how these propagation mechanisms affect the Ras Gas project and its bond yields we use the JP Morgan emerging market bond regional indices, i.e., Asia, Middle East, Europe and Latin America. We summarize the direct and indirect effects via Kepco credit spreads of the various variables on Ras Gas yield spreads and their first differences in the following table:

|Dependent Varia|ble|_Ras Gas Y_|_ield Spread_|_Changes in_|_RG Spread_|
|---|---|---|---|---|---|
|Effect||Direct|Indirect|Direct|Indirect|
|Variable|Description|Coefficient|Coefficient|Coefficient|Coefficient|
|RGS(- 1)|Lagged Ras Gas spread|persistent||||
|BRENT|(Log) oil price|indeterm.|+|indeterm.|+|
|BRENT<14|(Log) oil price below USD 14|insign.|+|indeterm.|+|
|BRENT: 14-23|(Log) oil price: USD 14 to 23|indeterm.|+|+|+|
|BRENT>23|(Log) oil price above USD 23|indeterm.|+|indeterm.|+|
|KORELES|Kepco yield spread|+||+||
|KORELES(-I)|Lagged Kepco spread|insign.|persistent||persistent|
|KEPCO|(Log) Kepco stock price|insign.|-|indeterm.||
|KRW|(Log) Korean Won FX rate|+|+|insign|+|
|KOSPI|(Log) Korea Stock Price Index|insign||insign||
|KR rating|Korean country rating index|+|+|insign.|+|
|ASIA|Emerging debt returns Asia|||||
|EUROPE|Emerging debt returns Europe|||||
|LAT|Emerging debt returns Latin Am.|||||
|MEA|Emerging debt returns Middle East|||||

Exhibit 4. Explanatory Variables

# **_S._** _Empirical Analysis of Interacting Risks: Breach-of-Contract Factors_

In the sequel, we investigate how risk factors are transmitted through the nexus of contracts represented by the take-off agreement and bond covenant to bondholders. Our analysis relies on daily data covering the period from January 1997 to March 2000 that is drawn from various sources, mainly Bloomberg, IDC and Baseline. Whenever we found missing observations we cross checked the time series with other news sources and filled in the missing data or, if this was not possible, deleted the observation. Depending on the included variables we have 725 to 750 observations before taking lags. Our dependent variable is the spread of the 17 year Ras Gas bond'<sup>3</sup> yield over the 10 year US Treasury yield. The explanatory variables are the above mentioned risk factors affecting the contractual relationships at the heart of the Ras Gas project.

> '3 The bond is highly liquid and widely traded contrary to the 10 year one and, therefore, constitutes a much better measure of investor and market assessment of the project's prospects.
# **Table A5: Panel B – Nested Logit Regressions, 2005**

Log Odds ratio between Owning with and without a Mortgage

|**Country**|**lage**|**female**|**hhsize**|**lnincome**|**hhnodep**|**hhdep**|**house**|**secedu**|**teredu**|**fulltime**|
|---|---|---|---|---|---|---|---|---|---|---|
|_New EU Countries_|||||||||||
|Czech Republic|-0.20 ***|0.01|-0.07 **|-0.07 ***|0.07 *|0.13 **|2.94 ***||1.07|0.10|
|Estonia|2.65 ***|0.02|0.15 *|-0.82 ***|0.86 **|-0.34|-0.52 ***||3.79|-0.46|
|Hungary|2.00 ***|-0.04|0.21 ***|-0.55 **|-0.32 ***|-0.83 ***|-0.17 *|-1.44 *|-4.96 ***|-0.34 *|
|Latvia|0.81 *|-0.29|0.26 **|-0.02|-0.42|-1.00 **|0.46|7.83 *|4.61|0.35|
|Lithuania|3.20 ***|-0.10|0.38 **|-0.92 ***|-0.48|-1.77 ***|0.17||5.58 **|-0.22|
|Poland|1.38 ***|0.25 **|0.21 ***|-0.26 **|0.31 *|-0.37 *|1.01 ***|3.12 **|-1.70|-0.59 **|
|Slovak Republic|0.12|-0.01|0.10 ***|-0.03|0.15 **|-0.04|2.04 ***|-1.17 ***|-1.40 ***|0.01|
|_EU-15 and Cyprus_|||||||||||
|Austria|0.42 ***|0.11|0.08 *|-0.09|-0.03|-0.74 ***|0.18 *||-2.92 **|-0.57 **|
|Belgium|3.19 ***|0.16|0.22 ***|-1.24 ***|0.35 **|-1.21 ***|0.17|-13.16 ***|-11.94 ***|-0.66 **|
|Cyprus|2.74 ***|0.11|0.19 ***|-0.85 ***|-0.23|-1.07 ***|0.06|-0.19|-2.74|-0.65|
|Finland|-3.29|0.21|-0.12|-1.32|0.02|0.39|-1.21 ***|-21.07 ***|-20.55 ***|-0.48|
|France|2.18 ***|0.05|0.02|-0.71 ***|0.04|-0.52 *|0.05|-9.34 ***|-15.14 ***|-0.66 ***|
|Greece|2.03 ***|0.08|-0.05|-0.73 ***|0.52 ***|-0.08|-0.11|-10.73 ***|-14.69 ***|-0.59 **|
|Ireland|0.78 **|-0.02|0.05|-0.04|-0.24|-0.93 **|0.40 ***|7.74 ***|3.96|-0.46|
|Italy|1.41 ***|0.11|0.14 ***|-0.33 ***|0.29 *|-1.06 ***|-0.04|1.60|-4.99 **|-0.76 ***|
|Luxembourg|2.57 ***|0.09|0.28 ***|-0.81 ***|0.04|-0.95 ***|0.53 ***|-0.15|-4.40 ***|-0.56 ***|
|Portugal|3.01 ***|0.06|0.01|-1.11 ***|0.42|-0.60|1.00 ***|-18.88 ***|-22.63 ***|-0.94 **|
|Spain|2.04 ***|-0.16|0.27 ***|-1.14 ***|0.68 ***|-0.53 **|2.68 ***||-11.79 ***|-0.16|
|Sweden|1.44 ***|0.10|0.27 ***|-0.60 ***|0.27|-1.06 ***|1.74 ***|3.34 *|-0.89|-0.98 ***|
|United Kingdom|2.04 ***|0.14 *|0.41 ***|-0.72 ***|0.14|-1.01 ***|0.27 ***|-3.08 ***|-4.10 ***|-0.76 ***|

Note: ***, **, * denote significance at the 99%, 95% and 90% levels respectively.

37
Table 11. Average share of respondents conducting distinct activities together within a day (pairwise distribution), men

|||||S|martphon|e sample|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||Agri-<br>Cultur<br>e|Nonfarm<br>employ-<br>ment|Obtaining<br>services/<br>resources<br>for HH|Cooking,<br>cleaning<br>in house|Care of<br>children,<br>elderly|Transport|School/<br>studying|Leisure|Comm.<br>Activitie<br>s|Personal<br>care|Eating/<br>drinking|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Agriculture|(1)|0.03|||||||||||
|Nonfarm employment|(2)|0.01|0.01||||||||||
|Obtaining services/<br>resources for HH|(3)|0.02|0.04|0.02|||||||||
|Cooking, cleaning in house|(4)|0.02|0.01|0.02|0.00||||||||
|Care of children, elderly|(5)|0.00|0.00|0.01|0.01|0.00|||||||
|Transport|(6)|0.06|0.10|0.13|0.04|0.02|0.18||||||
|School/studying|(7)|0.00|0.00|0.00|0.00|0.00|0.01|0.01|||||
|Leisure|(8)|0.10|0.12|0.11|0.10|0.04|0.41|0.03|0.50||||
|Community Activities|(9)|0.00|0.00|0.00|0.00|0.00|0.02|0.00|0.03|0.00|||
|Personal care|(10)|0.01|0.02|0.01|0.02|0.01|0.08|0.00|0.15|0.01|0.01||
|Eating/drinking|(11)|0.01|0.07|0.02|0.04|0.01|0.11|0.00|0.39|0.01|0.06|0.01|
||||||Recall s|ample|||||||
|||Agri-|Nonfarm|Obtaining|Cooking,|Care of|Transport|School/|Leisure|Comm.<br>Pe|rsonal care|Eating/|
|||culture|employ-|services/|cleaning|children,||Studying||Activities||drinking|
||||ment|resources<br>for HH|in house|elderly|||||||
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(11)|
|Agriculture|(1)|0.00|||||||||||
|Nonfarm employment|(2)|0.00|0.00||||||||||
|Obtaining services/<br>resources for HH|(3)|0.00|0.01|<br>0.00|||||||||
|Cooking, cleaning in house|(4)|0.00|0.00|<br>0.00|0.00||||||||
|Care of children, elderly|(5)|0.00|0.00|<br>0.00|0.00|0.00|||||||
|Transport|(6)|0.01|0.03|<br>0.02|0.00|0.01|0.00||||||
|School/studying|(7)|0.00|0.00|<br>0.00|0.00|0.00|0.00|0.00|||||
|Leisure|(8)|0.11|0.22|<br>0.10|0.11|0.04|0.43|0.01|0.42||||
|Community Activities|(9)|0.00|0.00|<br>0.00|0.00|0.00|0.00|0.00|0.01|0.00|||
|Personal care|(10)|0.00|0.01|<br>0.00|0.00|0.00|0.01|0.00|0.03|0.00|0.00||
|Eating/drinking|(11)|0.00|0.03|<br>0.00|0.00|0.01|0.01|0.00|0.72|0.00|0.01|0.00|

Notes: (1) Pooled sample of individuals across days 5, 8 and 10.

27
**Table 5. Labor Restructuring Measures around the World**

(percent)

||_Latin_<br>_America_|_Asia_|_Africaand_<br>_MiddleEast_|_Developed_<br>_Countries_|_Transition_<br>_Economies_|_All_|
|---|---|---|---|---|---|---|
|Downsizing|82.2|58.3|79.7|79.2|76.2|78.2|
|Voluntarydownsizing|32.5|12.5|45.3|28.6|14.3|32.5|
|Age-biased downsizing|57.4|29.2|54.7|54.5|26.6|49.7|
|Skill-biaseddownsizing|12.5|13.9|9.4|15.6|11.9|13.0|
|Female-biaseddownsizing|5.0|8.3|14.1|0.0|4.8|5.8|
|Employmentguarantee|8.4|20.1|51.6|13.0|52.4|28.2|
|Paycut|8.9|0.0|1.6|13.0|7.1|7.5|
|Sample|32.8|7.8|20.8|25.0|13.6|100.0|

_Source:_ Data collected by authors.

**Table 6. Simple Correlation of Labor Downsizing Measures**

<!-- Start of picture text --> ..-z Volunamy AWaid ill-biased Fm1e-biased rrploypmit<br>downsang domn7g doiWmng domsizing gatee<br>Dmwsizng<br>VoluitarydofiziZng 0.36568 1<br>A4biaged dwnsizitg 0.5239a 0.4483e 1<br>Skill-biaseddoWMiZiDg 0.20373 0.1447 0.1184 1<br>Fenmle-biadowning 0.0643 0.0342 -0.0261 0.1508 1<br>Enployrnt garantee 0.1036 0.027 0.0113 0.0365 0.2126e 1<br>PNav c -0.0456 -00099 -0.009 -0.0584 -0.0803 -0.0788 1<br><!-- End of picture text -->

**a** Significant at 1 percent, **b** Significant at _5_ percent., **C** Significant at 10 percent.

29
|**Y**|Institution of a regular<br>and ongoing<br>community monitoring<br>plan|Minutes from village<br>and staff meetings<br>Ratings on community<br>scorecards|Systematic user<br>engagement in the<br>development and<br>implementation of the<br>plan (demand-driven) and<br>incentives for providers to<br>respond to feedback from<br>monitoring|Bjorkman and<br>Svensson (2009)|
|---|---|---|---|---|
|**B I L I T**|# of “under-spends” by<br>government during<br>defined time period|Independent budget<br>analysis and review of<br>errata statements|Direct communication<br>with agency and<br>population not receiving<br>allocatedfunding|Malajovich and<br>Robinson (2006)|
|**O U N T A**|Probability of a public<br>sector entity receiving<br>a government audit|Computed based on<br>the frequency and<br>targeting of auditing<br>by the relevant<br>authority|Presence of a supply-side<br>accountability mechanism<br>supporting government<br>auditing function|Olken (2007)|
|**A C C**|Supreme Audit<br>Institution Strength<br>Index|Rankings by non-<br>government country<br>experts on a subset of<br>budget transparency<br>questions in the Open<br>Budget Index Survey<br>related to the presence<br>and functioning of the<br>Supreme Audit<br>Institution|Index shows relationship<br>with development<br>outcomes even when<br>controlling for other<br>factors.|Fukuda-Parr et al.<br>(2011)|
||Trust in participatory<br>budgeting and<br>perceived benefits|Survey ratings|Tax receipts or other<br>evidence available to<br>confirm revenue and<br>compliance related to<br>taxes|Cabannes (2004)<br>Schneider and<br>Baquero (2006)|
|**A T I O N**|Perceptions that<br>political institutions<br>represent citizens’<br>interests|Quality of Governance<br>Index (see Kaufman,<br>Kraay, and Mastruzzi<br>2003)|Existence of tax morale<br>reflecting sense of<br>individual’s obligation to<br>pay taxes as a<br>contribution to society|Schneider and<br>Baquero (2006)|
|**A R T I C I P**|Efficiency of<br>participatory budgeting<br>process|Independent review to<br>assess whether PB<br>process allocated<br>sufficient funds across<br>organizations to<br>implement planned<br>projects|Presence of participatory<br>budget—as reflected by<br>percentage of electorate<br>that participated in budget<br>process to set priorities|Schneider and<br>Goldfrank (2002)|
|**P**|Effectiveness of<br>participatory budgeting|Proxies based on<br>financial indicators of<br>local public<br>administration such as<br>level of own revenue<br>collection and|Need for improved local<br>government financial<br>management and<br>increased capital spending|Zamboni (2007)|

24
The paper proceeds as follows: In Section 1 we examine the evidence on aid allocations. Using data for a large cross-section of developing countries, we show that in the 1980s the amount of aid a country received was not correlated with institutional quality, as measured by the Freedom House democracy index or the ICRG rule of law index, once population and income level are taken into account. On the other hand, our data set suggests that in the 1990s the picture changed: countries with better institutions received significantly more aid. One standard deviation higher on the indices of rule of law and of democracy corresponded to 28% more overall aid and 50% more finance from the World Bank IDA facility (which is part of foreign aid). Our paper is not concerned with why this apparent shift in donor behavior has come about. It may be a spillover benefit from the end of the Cold War; it may reflect deliberate decisions by donors to stop funding corrupt governments; and/or it may be a coincidence. Our concern, rather, is whether or not this shift – whatever its source – led to an allocation of foreign aid that has more impact on growth and poverty reduction in the developing world, than aid has had in the past.

In Section 2 we turn to our policy question. Using a new cross-sectional data-set for the 1990s, and a measure of overall institutional quality that is currently popular in empirical growth studies, we find our original result to be quite robust if the focus is on choosing between hypotheses #1 and #2. There is simply no cross-country evidence that aid promotes growth in every environment. On the other hand, we find considerable support for hypothesis #2 in this new data-set, particularly when we estimate our model using instrumental variables techniques. In fact, we find the institutions-aid interaction to be more robust than institutions by themselves.

Given that the number of countries in the world is finite, we suspect that one can always make the coefficient of interest lose significance. We find specifications, for example, in which there is no significant link between institutions and growth. So, in choosing between hypotheses #2 and #3, we doubt that cross-country empirical work alone can settle the debate. Inevitably, there will be specifications in which the aidinstitutions interaction is not significant.

Since we doubt that cross-country regressions will settle the policy debate, in the third, and concluding section we briefly survey other information that is relevant. The 7
Figure 1: Minimum Wages in Korea <!-- Start of picture text --> Nominal Min. Wage Inflation Real Min. Wage<br>(a) Minimum Wages and Inflation Trend<br>Korea USA Japan Korea USA Japan<br>UK Czech Rep. Hungary UK Czech Rep. Hungary<br>(b) Hourly Real Minimum Wages (c) Minimum-to-Median Wage Ratio<br>1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br>2013 2014 2015 2016 2017 2018 2019 2020 2013 2014 2015 2016 2017 2018 2019 2020<br>16<br>14<br>12<br>10<br>8<br>6<br>Growth Rate (%) 4<br>2<br>0<br>-2<br>12 .7<br>10<br>.6<br>8<br>.5<br>6<br>.4<br>4<br>Minimum-to-Median Wage Ratio<br>Hourly Wage (2020 constant US$)<br>2 .3<br><!-- End of picture text --> _Notes:_ Panel (a) shows the annual growth rates of nominal minimum wages (blue) and consumer price index (CPI, red), respectively, of which the difference is real minimum wages (orange bar). Panel (b) presents hourly real minimum wages for selective countries, while panel (c) compares their minimum to median wage ratios. All values in panels (b) and (c) are in constant prices at 2020 USD exchange rates. The data come from OECD Statistics Database.

# **2.3 Korean MNCs’ Foreign Affiliates**

Here we describe trends in Korean outward foreign direct investment (FDI) and employment by foreign affiliates. Korea first began collaborating with foreign affiliates in the mid-1990s; this activity increased significantly in the 2000s, as shown in panel (a) in Fig7
this operation. In practice, many women opted for prepaid ATM cards, which have low transaction costs but limited validity (typically one year). Most participants were able to open an account on time, but others did not. Just after the training sessions, local coordinators and supervisors continued the work on guiding project beneficiaries to open accounts and collect missing information. At the end of this process, the details of 939 bank or postal accounts were collected, which represents 99% of the women trained (and 94% of the target population). Ten participants could not open a bank account (typically because they did not have an ID card). For these ten women, it was agreed with the bank team to proceed with the payment in cash (the payment was video recorded).

**Training** Before receiving the cash grants, women had to attend a one-day training session. CAWTAR and the World Bank co-developed the trainer’s guide and the training material. The training covered three modules: i) Financial Planning and Budgeting Module, ii) Savings Module, iii) Debt Management Module. Each module included a series of definition, videos, exercises, and debates. Links to videos are provided in Figure A.2.

The first module of the training was devoted to financial planning and budgeting (2 hours). The trainers drew the attention of the participants to the difference between the different financial objectives (investment in an income generating activity, investment in human capital, consumption, coping with unforeseen situations). The participants were invited to choose a financial objective and prepare a plan and a budget for its achievement. A practical case was also presented.

The second module was focusing on savings (1.5 hours). The trainers presented the different savings services available locally, their characteristics, and modes of use. Scenarios were presented to the participants, who had to choose the savings products most suited to their needs.

The last module was devoted to the debt management (1.5 hours). The trainers presented the different sources of funding available locally to meet the needs of participants’ businesses. They also provided information about how to get a loan and how to calculate the cost of credit.

33
<!-- Start of picture text --> Panel A: Total formal borrowing Panel B: Total informal borrowing<br>Winsorized at 99th percentile Winsorized at 95th percentile<br>3i; ||1||| 3o||| ||I<br>||||<br>1| |<br>se}3Ss 1 | |<br>1|3|<br>|| |<br>| | |<br>2as | oO2 | |<br>a58 | ae38 |<br>| |<br>)||I<br>)| | I<br>=|.||<br>5| 3 | |<br>\ | | |<br>I\\ | | I<br>|||<br>|| |<br>oo<br>010000 20000 30000 0 1000 2000 3000 4000<br>Loan amount (100 ETB) Loan amount (100 ETB)<br>——— Control —————_ Treatment ——— Control —————_ Treatment<br>Observations = 83 Observations = 31<br>Amounts are conditional on formal borrowing Amounts are conditional on informal borrowing<br><!-- End of picture text -->
panel data to show that growth in per capita incomes is significantly and negatively associated with civil conflict.

This paper is closest to the literature that relies on data from value surveys to explore the link between attitudes towards revolution and the role of income, economic growth, and a range of other correlates such as religion and freedom (MacCullogh, 2004 and MacCullogh and Pezzini, 2010). In these studies, people’s tastes for revolution are revealed by their answers to the question whether they would like the organizational structure of society to be overturned by revolutionary actions. They identify people as having a preference for revolution if they answer “yes” to this question. Using probit regressions MacCullogh and Pezzini (2010) find that a number of individual characteristics seem to be robustly correlated with a taste for revolution. Men, the young, unmarried and individuals with political opinions to the left of the political spectrum are more likely to have a taste for revolution, while economic growth, being Christian, and being relatively affluent reduces the likelihood of having a preference for revolution. Average per capita income in a country and individual school attainment are not significantly associated with the likelihood of having a taste for revolution.

The literature on the political economy of terrorism consists mainly of case studies. A review by De Mesquita (2008) points to a lack of a thorough understanding of the explanatory factors of terrorism and highlights endogeneity issues that make it difficult to investigate the determinants of extremism. In a study of the main factors that led to sectarian radicalism in the Punjab,<sup>5</sup> Zaman (1998) shows that sectarian conflict has mainly been associated with the emergence of sectarian organizations and the proliferation of madrasas, but finds that economic and social volatility,

> 5 Punjab is Pakistan’s most populous province.

5
# **Incentives and Teacher Effort: Further Evidence from a Developing Country**

Hai-Anh H. Dang and Elizabeth M. King<sup>*</sup>

## JEL: C3, I2, O1

Keywords: teacher incentives, teacher effort, private tutoring, joint probit-OLS, random effects, Lao PDR Sector Board: EDU, POV

* Dang (hdang@worldbank.org) and King (eking@worldbank.org) are economists respectively at the Poverty and Inequality Unit, Development Research Group and Education Sector, Human Development Network at the World Bank, Washington, DC. We thank our colleagues Keiko Miwa, Dominique van de Walle and Jeffrey Waite for their contributions to the design and application of the school survey in Lao PDR and for helping us interpret some of our results; and Harold Alderman, Eric Gronqvist, Yang Huang, Dean Jolliffe, Phil Keefer, Xubei Luo, Salvador Navarro, Ha Nguyen, Daniel O’Brien, Forhad Shilpi, Trang Tran, Futoshi Yamauchi, seminar participants at the North American Econometric Society Winter Meeting (San Francisco), and International Food Policy Research Institute for useful comments on earlier drafts. We also thank the National Statistical Center, Lao PDR for permission to use the data. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author and should not be attributed to the World Bank, its Executive Directors or the countries they represent.
# _DATA APPENDIX_

# _I. THE FIRM DATA._

All firms in the manufacturing sector that follow the French accounting system known as the _plan comptable_ are required to file their balance sheets with the Banque des Donnees Financieres (BDF). The BDF has collected records from more than 900 industrial companies between 1974 and 1987. These records include more than 500 variables on each firm for each year. This data includes such information as:

- Chiffre d'affaires (Gross Sales)

- Exports

- Variation de Stocks (Product Inventory Variation)

- Achats (Purchases)

- Imports

- Effectif Total (Total Number of Employees)

- Masse Salariale Totale (Total Wages)

- Investissement Net Cumule (Cumulated Net Investment)

Other variables include short-term and long-term debts, Gross Fixed Capital Formation, Subsidies, Taxes, and Gross Values Added. For an extensive discussion of this data see Ministere de l'Industrie (1988).

Coverage of the manufacturing sector is extensive, but not complete. Small or medium size firms apparently can elect to pay their income tax "by default" and have the government estimate their profits in lieu of reported profits. This diminishes their incentive to file reports. Moreover, the sanctions for not reporting do not appear to be extremely strong and some modern firms apparently chose not to report all their information to the BDF. It is much more likely that selective information will be missing than that the firms do not report at all.

In a study based on the same data, Lorch (1989) concludes that "It seems fair to say that the BDF covers the sector of large manufacturing enterprises well, with few enterprises missing; among medium-size manufacturers the majority is covered by the BDF; and of small yet 'modern' enterprises the BDF contains only a sample which might not be representative".

One of the problems in working with the data was that zeros and missing values

- 31 -