rafmacalaba/datause-encoder-data huggingface dataset:
# Triggers of Contract Breach:

Contract Design, Shocks, or Institutions?<sup>1</sup> Manabu Nose<sup>2</sup> Key Words: Contract design, Obsolescing bargain, Natural resource curse, Natural disaster JEL classifications: F21, H54, O10, O19 Sector Board: FPD, EPOL, SDN 1 This paper was prepared as a background paper for Chapter 2 of the 2013 World Investment and Political Risk report annually published by the World Bank (Multilateral Investment Guarantee Agency). I benefited from useful inputs from Nathan Jensen and Thomas Mahaffey, and interesting discussion with Conor Healy and Cherian Samuel. Additional data supports from the World Bank’s Infrastructure Policy Unit of Sustainable Development Network and Global Facility for Disaster Risk Reduction (GFDRR) are greatly acknowledged. Zuhaib Khero provides assistance with initial stage of contract-level data construction. The findings, interpretations and conclusions expressed in this paper are entirely those of the author, and do not necessarily represent the views of the World Bank or the IMF.

2 The International Monetary Fund, 700 19th Street, N.W., Washington, DC, 20431 USA; Email: MNose@imf.org; Tel: +1-202-623-4693; Fax: +1-202-589-4693.
**Figure 6: Goods export-employment elasticities in lower-income countries, by type of GVC specialization**

<!-- Start of picture text --> 0.15<br>0.10<br>0.05<br>0.00<br>-0.05<br>-0.10<br>Total goods Agriculture Mining Manufacturing Low-tech mfg<br>Commodity exporters Limited manufacturing<br><!-- End of picture text --> Source: Own computations. Data: ETD and Comtrade data, covering 37 countries (see Appendix 2) and 35 sectors for the period 1995-2018. Note: Export-employment elasticities at the country-sector level have been estimated using employment (in natural logs) as dependent variable and the instrumented trade variables (in levels and natural logs) as independent variable. For more information on the instruments, see Appendix 1. The panel data regressions control for country-sector, country-time, and sector-time fixed effects. Only results significant at the 10% level and above the critical value for the Kleibergen-Paap test statistic are reported. GVC taxonomy group by World Bank (2020).

# 3. Measuring jobs in exports: The role of supplying sectors

## 3.1 Do jobs in exports grow more strongly than other jobs?

Countries’ share of employment in exports in total employment varies considerably and is larger in small economies, but smaller in commodity-exporting countries. Analysis based on the OECD TiE database finds that smaller economies tend to show higher shares of employment in exports in their total employment, because their smaller market size dictates more openness to trade in the first place (Figure 7). The share of employment that is dependent on exports, including employment in supplying sectors, was 50 percent or higher in Ireland, Malta and Luxembourg in 2018, contrasting sharply with the United States where the share was less than 9 percent. However, commodity-exporting countries, even smaller ones, show a smaller dependence on employment in exports. Employment shares in exports range only between 10 and 15 percent in Saudi Arabia, Argentina, Colombia, Brazil and Australia. These low shares imply that exports of extractives in high- and middle-income countries are less labor-intensive, which is further aggravated by the “Dutch disease” reducing the competitiveness of non-commodity sectors or other constraints to diversifying the economy. The case of Argentina illustrates how closely the employment share supported by exports correlates with a country’s endowments of natural resources (Figure 8, left panel).

also highlights the role of rigid labor markets, low levels of human capital, underdeveloped capital markets, or an ineffective policy environment as possible factors (Kruse 2023).

11
stringent. Table 3 presents those five principal categories. In order to further illustrate what portfolio of policies might underpin those restrictiveness scores, we provide an example for each category taken from the ̳policy summary‘ section of our database (all examples refer to one country, Brazil, only to provide a consistent illustration).

It is convenient to assign a value to each of these five categories of regimes on an openness scale from 0 to 100 with intervals of 25.<sup>14</sup> We call the resulting score a services trade restrictions index (STRI). As the example in Table 2 shows, most policy regimes have more than one provision in place per sub-sector and mode of supply, in which case the assigned score (shown in the right-most column) reflects the overall restrictiveness of all measures evaluated simultaneously.<sup>15</sup> Since the STRI focuses mainly on the set of measures that discriminate against foreign services and service providers, the greatest level of openness is associated with a value of zero. However, since the STRI does not adequately cover complementary areas of non-discriminatory prudential and pro-competitive regulation, and since it is likely that the results of liberalization depend on the existence of such complementary regulation, we cannot claim that a zero value of STRI is necessarily immediately desirable from a broader welfare or development perspective.

> 13  Further information about the OECD‘s work in this area, which focuses on Member economies, can be found at http://www.oecd.org/trade/stri and is described in OECD (2011).  The ability of their index to capture trade costs in services is explored in OECD (2009b).

> 14  At this level, basic STRI scores are no more than  ̳labels‘ attached to the five ordered categories of restrictiveness, but as soon as these scores are further processed, either by aggregation or by use in a quantitative model, the specific values assume a cardinal meaning that implies the five categories are  ̳equidistant‘ in terms of restrictiveness.  The alternative approach of ranking policy bundles aims to avoid exactly this assumption.

> 15  The Section 4 of the Database Guide (Borchert, Gootiiz and Mattoo, 2012) contains three examples—from Burundi, Thailand and India—that illustrate how a portfolio of several measures is being assigned to one of the five basic scores.  In principle, policy measures can be divided into two tiers.  The first tier measures include those that affect market entry decisions most significantly, such as a limit on foreign ownership and the number of licenses. The second tier measures are those that affect operations of service providers, such as restrictions on the repatriation of earnings.  The second tier measures do not contribute to overall restrictiveness when first tier measures are prohibitive.  In contrast, if the first tier measures are not prohibitive then second tier measures are also considered in determining the overall restrictiveness score.

13
|If there is a law,<br>does it prohibit<br>retaliation by the<br>employer for<br>bringing a com-<br>plaint or participat-<br>ing in any manner<br>in an employment<br>discrimination pro-<br>ceeding?<br>Yes|Protection Against Dis-<br>crimination Act (2004),<br>Arts. 5, additional pro-<br>visions, Sec. 1 (3)|No|No provisions could be<br>located<br>Yes|Code du Travail (2011),<br>Art. 36, Para. 4|Yes<br>Equal Treatment Act<br>(1994), Sec. 8(a)|No|No provisions could be<br>located|No<br>No provisions could be<br>located|
|---|---|---|---|---|---|---|---|---|
|In the private sec-<br>tor?<br>Yes|Protection Against Dis-<br>crimination Act (2004),<br>Art. 50 (administrative<br>protection)<br>Protection Against Dis-<br>crimination Act (2004),<br>Art. 71 (judicial protec-<br>tion)|Yes|Ley Federal del Trabajo<br>(1970), Arts. 2, 3, 133<br>Ley Federal para Preve-<br>nir la Discriminación<br>(2003), Art. 43<br>No|Code du Travail (2011),<br>Arts. 9, 41|Yes<br>Equal Treatment Act<br>(1994), Secs. 10, 12 (2)|Yes|Employment and Labour<br>Relations Act, (2004),<br>Arts. 7(4)(g), 37(3)|Yes<br>Decree No.<br>10/2012/QH13 Labor<br>Code (2012), Art. 3 Pa-<br>ra. 7, and 194 Para. 6|
|If there is a law,<br>does it prohibit<br>retaliation by the<br>employer for<br>bringing a com-<br>plaint or participat-<br>ing in any manner<br>in an employment<br>discrimination pro-<br>ceeding?<br>Yes|Protection Against Dis-<br>crimination Act (2004),<br>Arts. 5, additional pro-<br>visions, Sec. 1 (3)|No|No provisions could be<br>located<br>Yes|Code du Travail (2011),<br>Art. 36, Para. 4|Yes<br>Equal Treatment Act<br>(1994), Sec. 8(a)|No|No provisions could be<br>located|No<br>No provisions could be<br>located|
|Does the law:|||||||||
|Shift the burden of<br>proof for antidis-<br>crimination claims<br>from the employee<br>to the employer?<br>Yes|Protection Against Dis-<br>crimination Act (2004),<br>Art. 9|Yes|Ley Federal del Trabajo<br>(1970), Art. 784<br>No||Yes<br>Equal Treatment Act<br>(1994), Sec. 10 (1)|Yes|Employment and Labour<br>Relations Act (2004), Art.<br>7(8)(a)|<br>No<br>Decree No.<br>10/2012/QH13 Labor<br>Code (2012), Art. 192<br>Para. 2(a)<br>Civil Proceeding Code<br>(2012), Art. 79 Para. 1|
|Allow class action<br>lawsuits for em-<br>ployment discrimi-<br>nation on religious<br>grounds?<br>Yes|Protection Against Dis-<br>crimination Act (2004),<br>Art. 71 Paras. 2, 3<br>Civil Procedure Code<br>(2007), Art. 379 Para. 1|<br>Yes|Ley Federal del Trabajo<br>(1970), Art. 697<br>Yes|Code du Travail (2011),<br>Art. 549|Yes<br>Equal Treatment Act<br>(1994), Sec. 10 (2)|No||Yes<br>Civil Proceeding Code<br>(2012), Art. 31 Para. 2<br>(a)|
|EDUCATION|Bulgaria||Mexico|Morocco|Netherlands||Tanzania|Vietnam|
|Participation in Education|||||||||
|Does the lawprohibit discri|mination in school admissio|n on r|eligiousgrounds in:||||||

Anti-Discrimination Law and Shared Prosperity 56
adverse conditions in Tanzania through trade. For each trading partner, we report the percentage of Tanzania dry years in which that country does not also experience a dry year. We report these percentages for each decade of the 21<sup>st</sup> century.

- We calculate how often each of Tanzania’s key (or potential) trading partners experiences a dry year in the same year that Tanzania does not experience a dry year. This metric indicates the likelihood that Tanzania will experience non-adverse climate conditions in the same year as her trading partners experience adverse conditions, therefore offering the potential for Tanzania to benefit through trade. For each trading partner, we report the percentage of dry years in that partner-country in which Tanzania does not also experience a dry year. We report these percentages for each decade of the 21<sup>st</sup> century.

We combine the 22 GCM realizations from the CMIP3 archive by first performing the above calculations for each GCM realization individually, and then calculating the mean across the ensemble of GCM realizations.

# **4.2.2 – SEVERE DRY EVENTS IN TANZANIA**

Table 7 shows the number of dry years in Tanzania in each decade of the 21<sup>st</sup> century, along with the percentage of Tanzania dry years in which each trading partner does not experience a dry year. The decadal occurrences are reported as the mean of the decadal occurrences of the individual climate model realizations. With this measure, a dry year occurrence of 0.5, as reported for the decade from 2000-2009, indicates that across all of the GCM realizations in that decade, the occurrence of dry years in Tanzania was half as common as in the historical period (which was one year on average per decade given the way the dry year has been defined). In addition, the number of years in which Tanzania experiences a dry year but another trading partner does not is reported as a percentage of the Tanzania dry years in that decade. For example, in the case of Canada, the value of 100% indicates that in all of the years in which Tanzania experienced a dry year, Canada did not simultaneously experience a dry year.

24
# **Ethiopia’s Growth Acceleration and How to Sustain It - Insights from a Cross-Country Regression Model**

## **_Lars Christian Moller_**

_The World Bank Group_

## **_Konstantin M. Wacker_**

_The World Bank Group and University of Mainz_

## **<u>JEL Classifications</u>** <u>: O47, O55</u>

**<u>Keywords</u>** <u>: Economic growth, determinants of growth, economic growth policy</u>

## **<u>Acknowledgements</u>** :

This paper contributes to a forthcoming World Bank report on the same topic. The authors would like to thank Mesfin Bezawagaw, Clarie Hollweg, Ashagrie Moges, and Eyasu Tsehaye for helpful support and inputs. Useful comments on previous drafts were received from: Ed Buffie, Kevin Carey, Guang Chen, Pablo Fajnzylber, Maya Eden, Senidu Fanuel, Verena Fritz, Michael Geiger, Fiseha Haile, Ruth Hill, Jan Mikkelsen, Aart Kraay, Pedro Martins, Andrea Richter, Greg Toulmin, and Shahid Yusuf and the participants at the Ethiopia Country Economics Day held at the World Bank. Thanks also to Chris Papageorgiou for making the reform index data set of Prati _et al_ . (2013) available. The paper benefitted considerably from the World Bank project ‘Benchmarking the Determinants of Economic Growth in Latin America and the Caribbean’ (Araujo _et al._ , 2014), funded by the Latin America and Caribbean Office of the Chief Economist as well as the corresponding background work by Brueckner (2013).
Table 1: **Number of legal entities and employment size characteristics; 1989-1993 (end-period) as shares**

||--|No. of emp|loyees--|--||
|---|---|---|---|---|---|
|Year|**1-20**|<br>**21-50**|<br>**51-300**|**>300**|**Number**|
|1989|38|18|25|19|13568|
|1990|_59_|15|16|1O|27662|
|1991|72|12|11|5|50746|
|1992|78|10|9|3|67505|
|1993|82|9|7|2|83535|

Source: Statisztikai Havi Kozlemenyek Table 2: Employment equation Employment (end93/end92)

|dW93|-0.71|T stat<br>-2.3|
|---|---|---|
|dREVS93|0.77|12.0|
|Investment|8.37|6.6|
|Bankruptcy|-37.7|-12.8|
|Bad Debt|-6.9|-5.4|
|Constant|100.3|3.4|
|F<br>**1(2**|141.9<br>.23||

Sample: LMFS (without agriculture, budgetary sphere and fims being privatized (1#3235) dW93=Average wage end93/end92; dREVS93= Revenues 93.4/92.4; Investment New installed capacity; Bankruptcy= process under way June 93 -June 94; Bad Debt = Firm's bad debt Table 3: Characteristics of 'strategic finms', 1989-1993 (1993 constant prices: **HUF** bn)

|Year Net Sales<br>1989 578007|Exports<br>232273|Profit<br>before tax<br>41759|Assets<br>383275|Equity<br>239727|Employm.<br>103422|
|---|---|---|---|---|---|
|1993 230977|108037|(-8547)|256560|136012|58111|

Source: Ministry of Finance 3
**ANNEX B Literature review: Estimation of weight parameters for risk factors**

|**Author**<br>**Country/Variable**|**Methodology**|
|---|---|

**1. Linear Approximation: Forecast points as input in the construction of the forecast distributions from key risk factors**

|**factors**<br>Elekdag and<br>Kannan(2009)|World, IMF's Oct 2008 World<br>Economic Outlook (WEO)/<br>Forecast of global real GDP<br>growth|OLS estimation of the elasticity of global growth with respect to<br>each risk factor. Dependent variable: global growth; Independent<br>variables: lag of global growth and standardized risk factors|
|---|---|---|
|IMF, WEO Oct 2015|World /Forecast of global real<br>GDP growth|Same as in Elekdag and Kannan(2009)|
|Blix and Sellin<br>(1998)|Sweden/Inflation, GDP growth|Elasticity of inflation or the variable of interest with respect to<br>each risk factor. Does not suggest any estimation method|
|**2. VARs and DSGE M**|**ethods: Forecast distribution is i**|**nferred directly from the forecasting model**|
|Cogley et al (2005)|United Kingdom/Inflation|The weight parameters are obtained from BVAR with stochastic<br>volatility|
|Alvaro and<br>Maximiano (2003)|Euro Area/ Inflation, GDP growth|The weight parameters are estimated in VAR forecasting model|
|Smets and Wouters<br>(2004)|Euro Area/ Many macro variables|The weight parameters are obtained from the sticky prices DSGE<br>model using a Bayesian technique|
|Gürkaynak et al<br>(2013)|U.S./Inflation, real GDP growth,<br>Interest rate.|The weight parameters are obtained from an estimated DSGE<br>and a VAR and forecast performances of the two approaches<br>are compared|
|Wolters (2013)|U.S./Inflation, real GDP growth,<br>Interest rate.|The weight parameters are obtained from a DSGE model and<br>BVAR model. The forecast performance of the two approaches<br>are compared|

**ANNEX C Literature review: Measurement of dispersion and skewness**

|**Author**|**Country/Variable**|**Methodology**|
|---|---|---|
|Mixon (2011)|U.S./S&P500|(25delta Put - 25delta Call)/50 delta|
|Bates (2001)|U.S./S&P500|Out of The Money (OTM) Call/OTM Put -1|
|Baksi et al (2003)|U.S./S&P100|The slope from regression log of Implied Volatility (IV) on log<br>of moneyness|
|Carr and Wu (2007)|Currencies: JPY/USD, GBP/USD,<br>GBP/JPY|Risk Reversal (RR[25])=25delta put-25delta call|
|Chicago Board of<br>Option Exchange<br>(CBO) (2010)|U.S./SPX 500|Skewness = 100-10*(Price of Skewness )|
|Elekdag and Kannan<br>(2009)|U.S./SPX 500|Skewness of the risk neutral density|

17
-8 -

Third, price distortions caused by government control of private school tuition, have limited the expansion of private education, prevented the appropriate use of the private sector and reduced<sup>competition and</sup> overall efficiency. Fourth, poor expenditure policy Ud to mistargeting<sup>within and across sectors.</sup> Relatively high public subsidies are provided for the types of programs that are not<sup>likely to reach the poor</sup> and the vulnerable. For example, 78 percent of the health spending is spent<sup>on curative hospital care in</sup> 1986, only 22 percent for primary care. In education, an estimated 23 percent<sup>of total government</sup> education spending (federal, state, and minicipio) goes to universities,<sup>and only 9 percent to secondary</sup> education, compared with 11 percent and 37 percent respectively, in<sup>Korea.</sup>

# ILI.B Project case-studies

This sub-section reviews several project cases based on the Bank's Project Performance<sup>Audit</sup> Reports (PPARs) and Project Completion Reports (PCRs). These reports do not<sup>systematically address</sup> overall econemy-wide policies or pricing policies for the sector.<sup>Often the macro and sectoral policy</sup> information is not included. Therefore, the linkage between policies and performance<sup>is not always clear</sup> from this documentation.

# A. Zaire: Education I. 1971-81

The project was designed to help finance construction, equipment<sup>and rehabilitation of</sup> existing/new facilities for the training of secondary teachers and teacher<sup>trainers. The IDA loan of US$6.5</sup> million was to cover about 55 percent of the cost. However, the project<sup>did not receive local currency</sup> funds on a regular basis which caused delays. Serious inflation, and a rapid devaluation<sup>of the Zaire</sup> resulted in high cost overruns and slowed down ir. v, ,mentation. By 1982, only<sup>a fraction of the physical</sup> components were completed and the rest of the loan was canceled. Civil wars also adversely affected the implementation, especially in the Shaba region. Other factors include,<sup>lack of support from Department of</sup> National Education, incomplete architectural plans aggregated by poor management<sup>and supervision. In</sup>
Table C.2 – _Continued from previous page_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|Access to electricity|0.477***|0.481***|0.428***|0.433***|0.424***|0.446***|0.369***|0.374***|
||(0.071)|(0.071)|(0.075)|(0.076)|(0.076)|(0.078)|(0.065)|(0.066)|
|**Dwelling tenure agreement**|||||||||
|No Agreement|||||||ref.|ref.|
|Written Agreement|||||||0.142|0.116|
||||||||(0.091)|(0.087)|
|Non-written Agreement|||||||0.021|0.010|
||||||||(0.068)|(0.067)|
|Not Applicable|||||||0.000|0.000|
||||||||(.)|(.)|
|**Dwelling tenure status**|||||||||
|Owner|||||||ref.|ref.|
|Rented|||||||-1.074***|-1.061***|
||||||||(0.124)|(0.124)|
|Free|||||||0.000|0.000|
||||||||(.)|(.)|
|Other|||||||0.685***|0.727***|
||||||||(0.110)|(0.112)|
|Urban district|0.234***|0.228***|0.120|0.115|0.115|0.122|0.085|0.081|
||(0.084)|(0.080)|(0.090)|(0.086)|(0.090)|(0.086)|(0.090)|(0.091)|
|Affected by food|||||0.513||||
||||||(0.423)||||
|Affected by food_×_Job density|||||-0.304||||
||||||(0.209)||||
|Flood event is likely||||||-0.316|||
|||||||(0.193)|||
||||||||_Continued o_|_n next page_|
Table 21. Indonesian Exports, ICT use, and Log of Skills Measures. Female workers. 2011-2015

||||||N|on-routine<br>|skill measure<br>|s|||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Dependent variable:||Ana|lytical|||Interp|ersonal|||Ma|nual||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|
|IDN exports per worker|-0.000378||-0.000392|-0.000535|-0.000417||-0.000427|-0.000536|0.000431||0.000436|0.000525|
||(0.000489)||(0.000487)|(0.000486)|(0.000417)||(0.000416)|(0.000414)|(0.000384)||(0.000383)|(0.000382)|
|Share of hhlds with computer||-0.185|-0.183|-0.2||-0.131|-0.129|-0.142||0.0858|0.0837|0.0943|
|||(0.0969)*|(0.0973)*|(0.0991)**||(0.0830)|(0.0830)|(0.0842)*||(0.0818)|(0.0816)|(0.0826)|
|IDN exports per worker *||||0.00342||||0.00261||||-0.0021|
|Share of hhlds with computer||||(0.00149)**||||(0.00118)**||||(0.00109)*|
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|
|R-squared|0.648|0.648|0.648|0.649|0.431|0.431|0.432|0.433|0.738|0.738|0.738|0.738|
|||||Routine ski|ll measures||||||||
|||Ma|nual|||Cog|nitive||||||
||(1)|(2)|(3)|(4)|(1)|(2)|(3)|(4)|||||
|IDN exports per worker|0.000064||0.0000744|0.000253|0.000317||0.000322|0.000402|||||
||(0.000917)||(0.000914)|(0.000928)|(0.000312)||(0.000311)|(0.000310)|||||
|Share of hhlds with computer||0.139|0.138|0.16||0.0706|0.0691|0.0788|||||
|||(0.127)|(0.128)|(0.131)||(0.0801)|(0.0802)|(0.0809)|||||
|IDN exports per worker *||||-0.00426||||-0.00191|||||
|Share of hhlds with computer||||(0.00252)*||||(0.000792)**|||||
|Observations|2,734|2,734|2,734|2,734|2,734|2,734|2,734|2,734|||||
|R-squared|0.915|0.915|0.915|0.915|0.759|0.759|0.759|0.759|||||

Source: Authors’ calculations based on Sakernas 2005-2010 and 2011-2015, Susenas 2005-2010 and 2011-2015, 2015-2016 Philippines STEP survey, and UN Comtrade database 2005-2010 and 2011-2015.

Notes: IV regressions. IV: IV: Global demand for foreign products excluding Indonesian products. Indonesian exports per worker in thousands of USD at 2010 prices and global demand for foreign products in millions of USD at 2010 prices. ICT variable at the province level is the share of households with computer. Province level controls include the age structure of the population, the share of urban population, real GDP per capita, and the minimum wage level. Industry level controls include the gender and educational composition of employment. All models include province, industry, year fixed effects, and province linear time trends. Robust standard errors between parentheses clustered by province-industry. ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.

40