rafmacalaba/datause-encoder-data huggingface dataset:
network of correspondents with country-specific expertise, but the final ratings are determined centrally by a very small number of people.

In the decentralized type, views are solicited from experts only for countries in which they have direct experience. Two examples are the UNECA’s Africa Governance Indicators (Economic Commission for Africa, 2005) and the World Governance Assessments (Hyden, Court and Mease, 2004). The Africa Governance Indicators (AGI), covering corruption and other governance issues, are based on surveys of elites in 28 countries, conducted in 2002-2003. The AGI “expert panels” varied in size from about 70 to 120 across countries. From 83 total questions in the survey, responses to 7 were used in constructing a “Corruption Control” index for each country.<sup>4</sup> World Governance Assessments were conducted in late 2000 and early 2001 in 22 developing countries from various regions, including Bulgaria, Kyrgyzstan and Russia from the ECA region. In each country, 35 “well-informed persons” were asked 30 questions, including 3 pertaining to corruption (in business licensing, in the judiciary, and favoritism in applying regulations). Data in 6 of the 22 countries were deemed to be of unacceptably low quality, so the publicly available data set covers only 16 countries.<sup>5</sup> At this time it is unclear whether or not the World Governance Assessments and the Africa Governance Indicators will fulfill their original intentions to expand their country coverage, and to track changes over time.

Managers of business firms may be viewed as merely a special category of “wellinformed persons.” The distinction nevertheless is important. Questions in the enterprise surveys place a greater emphasis on experience, and less on perceptions. Moreover,

> 4 See http://www.uneca.org/agr/. 5

> See http://www.odi.org.uk/WGA_Governance/Index.html.

9
|Outcome|Table|Population|Primary?|Rounds|Description|
|---|---|---|---|---|---|
|Reported Child<br>Difficulties|VI|Child|Yes|4|The standardized reported child difficulties and<br>reported pro-social behaviors are created using<br>the Strengths and Difficulties Questionnaire<br>(SDQ) and administered to the parent. Details<br>on construction of these variables can be found|
|Reported Pro-Social<br>Behaviors|VI|Child|Yes|4|at http://www.sdqinfo.com/c3.html.|

74
Nonrenewable resources, Income Inequality and per Capita GDP: An Empirical Analysis

# 6. REFERENCES

Alessandrini, Michele and Tullio Buccellato. 2009. “Natural Resources: A Blessing or a Curse? The Role of Inequality.” _Discussion Paper 98_ 2009(October).

Barbier, Edward B. 2014. “Account for Depreciation of Natural Capital.” _Nature_ 515:32– 33.

- Barbier, Edward B. 2015. “Wealth Inequality.” Pp. 1–30 in _Nature and Wealth_ , vol. 1, edited by Intergovernmental Panel on Climate Change. Cambridge: Palgrave Macmillan. Retrieved

(http://ebooks.cambridge.org/ref/id/CBO9781107415324A009).

- Barro, Robert J. and Jong Wha Lee. 2013. “A New Data Set of Educational Attainment in the World, 1950–2010.” _Journal of Development Economics_ 104:184–98. Retrieved (http://dx.doi.org/10.1016/j.jdeveco.2012.10.001).

- Bourguignon, Francois and Christian Morrisson. 1990. “Income Distribution, Development and Foreign Trade.” _European Economic Review_ 34(6):1113–32. Retrieved (http://linkinghub.elsevier.com/retrieve/pii/0014292190900716).

- Bunte, Jonas. 2011. “Why Does the Severity of the Dutch Disease Vary across Countries ?” Pp. 1–29 in _International Political Economy Society Conference Paper_ . Retrieved (https://ncgg.princeton.edu/IPES/2011/papers/S900_rm2.pdf).

- Cleveland, Cutler J. et al. 1996. “Natural Capital , Human-Made Capital , Economic Growth , and Sustainability.” 1–24.

Darvas, Zsolt. 2012. _Real Effective Exchange Rates for 178 Countries: A New Database_ .

- Dasgupta, Partha. 2010. “The Place of Nature in Economic Development.” Pp. 4977– 5046 in _Handbook of Development Economics_ , vol. 5, _Handbook of Development Economics_ . Elsevier. Retrieved March 23, 2016

(http://www.sciencedirect.com/science/article/pii/B9780444529442000124).

- Fum, Ruikang Marcus and Roland Hodler. 2010. “Natural Resources and Income Inequality: The Role of Ethnic Divisions.” _Economics Letters_ 107(3):360–63. Retrieved (http://dx.doi.org/10.1016/j.econlet.2010.03.008).

- Gill, Indermit S., Ivailo Izvorski, Willem van Eeghen, and Donato De Rosa. 2014. _Diversified Development: Making the Most of Natural Resources in Eurasia_ . Washington, DC 20433, USA: The World Bank. Retrieved

- (http://elibrary.worldbank.org/doi/book/10.1596/978-1-4648-0119-8).

- Gylfason, Thorvaldur. 2001. “Nature, Power and Growth.” _Scottish Journal of Political Economy_ 48(5):558–88. Retrieved (http://doi.wiley.com/10.1111/14679485.00215).

- Gylfason, Thorvaldur and Gylfi Zoega. 2002. _Inequality and Economic Growth: Do Natural Resources Matter ?_

- Loayza, Norman, Alfredo Mier y Teran, and Jamele Rigolini. 2013. _Poverty, Inequality, and the Local Natural Resource Curse_ . The World Bank. Retrieved (http://elibrary.worldbank.org/doi/book/10.1596/1813-9450-6366).

18
partnership with multilateral organizations and by ensuring contractual flexibility to absorb unexpected commodity price shocks. Second, cross-country panel regression shows that less-democratic and resourcedependent (i.e., dependencies in natural resources and in aid) governments are not likely to honor foreign investment contracts in weakly institutionalized political environments, and they tend to breach contracts especially after large global shocks, notably natural disasters. Finally, this paper shows that BOC events tended to be clustered in a particular region (such as Latin America and the Caribbean (LAC), Europe and Central Asia (ECA), and South Asia) in the past, and it is likely to be repeated in countries that had prior contract breach experiences. It appears that contracts in the energy sector face relatively higher BOC risk in general, but the risk is particularly high when input fuel costs rise.

The paper is structured as follows. Section 2 introduces the data and Section 3 explains the context. Section 4 provides theoretical predictions. Sections 5 and 6 carry out empirical analyses on the triggers of breach of contract. Section 7 concludes.

# **2. Data**

This paper gathers contractual dispute information from several data sets to form a universe of dispute cases. Two main data sets are merged to establish my baseline sample.

The first main data set is taken from the World Bank’s _Private Participation in Infrastructure (PPI) Database_ ,<sup>4</sup> which has details on contractual arrangements used for each project. The data set covers 5,237 public-private infrastructure projects from 1984 to 2012, and includes information including investing country, sponsors’ originating country, project status (e.g., completed, under construction, cancelled, in distress), financial closure year (when contracts were agreed), contractual period, sectoral affiliation, procurement type, and supports of international financial institutions. If contracts are in distress or are cancelled due to some disputes, we defined them as “projects under contractual disputes”. There are projects which were cancelled due to the sponsor’s unilateral actions (e.g., change in business strategy), expiration of contracts, or war/civil conflict. These are excluded from dispute cases. Data on the exact timing of disputes were also collected from the detailed documentation available for each contract, which is used for the hazard analysis below.

As the PPI data only cover projects with significant private participation, they should be complemented with other data sets that cover general development projects without private participation. To this end, the PPI data are merged with the investment arbitration case data from _UNCTAD Database of Treaty-based Investor-_

> 4 The data set is publicly available at http://ppi.worldbank.org/explore/Report.aspx.

4
by 10 percent (World Bank, 1998a). This finding suggest that education returns have not been overstated in typical rate of return studies that ignore training and demand factors - the firm characteristics.<sup>7</sup>

|||||**Table**|**5:Schooli**|**ng**||||
|---|---|---|---|---|---|---|---|---|---|
|**Variable**||**1**|**993**||||**1**|**999**||
|**Edu**|**Wage**|P>Izl|**Produ**|**ctivity**|**P>Izl**|**Wage**|P>Izl<br>|**Productivity**|P>jzl|
|ooled|0.09|0.00||0.02|0.00|0.10|0.00|0.04|0.00|
|Micro|0.07|0.00||0.07|0.04|0.07|0.00|0.05|0.16|
|Small|0.10|0.00||0.01|0.37|0.09|0.00|<br>0.08|0.00|
|Medium|0.09|0.00||0.00|0.64|0.10|0.00|<br>0.00|0.81|
|Large|0.09|0.00||0.02|0.15|0.10|0.00|<br>0.05|0.01|

_Source-_ Author's calculations based on ENTRAM-ENESTYC An additional year of schooling yields a 10 percent increase in wages in small, medium and large firms, but only 7 percent in micro firms. However, additional schooling has a greater productivity enhancing effect for workers in micro and small firms. In micro firms, however, the effect of schooling on productivity decreased over time by 2 percentage points.

The results also reveal a positive and statistically significant effect of schooling on productivity in both 1993 and 1999 (an increase from 2 to 4 percent). The positive effect of schooling on productivity in large firms in 1999 is statistically significant compared to 1993.

However, the hypothesis which states that the effect of education on wages and productivity would be the same must be rejected since the difference was greater for wages, meaning that workers' benefits are higher than those of firms.<sup>8</sup> This means that workers have greater benefits than firms from schooling.

> 7A potential source of bias could arise **in** standard rate of retums to schooling studies because most of them do not control for the ability of the individual. Card (2001) uses several instruments to correct this problem. He shows that the coefficient of the schooling variable is even larger after controlling for the endogeneity of schooling. 8We performed hypothesis tests for the equality of the wage and productivity coefficients. Results are on Appendix C.
**<u>Table 6: Determinants of being rationed conditional on being sick</u>**

||(1)|**Adults**|(2)|(3)<br>**Chil**|(4)<br>**dren**|
|---|---|---|---|---|---|
|Year dummy, 2002 = 1|-0.079***||-0.035|-0.115***|-0.023|
||(2.82)||(1.13)|(4.34)|(0.78)|
|Total asset value (log) [α1]|<br>-0.016***||<br>-0.017***|<br>-0.018***|<br>-0.017***|
||(5.63)||(5.79)|(6.69)|(6.05)|
|Assets*year [β1]|0.007*||0.008*|0.019***|0.017***|
||(1.67)||(1.96)|(5.08)|(4.51)|
|Cement floor|-0.040***||-0.041***|-0.041***|-0.044***|
||(4.59)||(4.82)|(5.56)|(6.01)|
|Pit latrine|-0.025***<br>||-0.025***<br>|-0.033***<br>|-0.032***<br>|
||(3.65)||(3.67)|(5.67)|(5.57)|
|Distance to hospital|-0.000<br>||-0.000<br>|0.002***<br>|0.002***<br>|
||(1.34)||(0.97)|(6.71)|(7.01)|
|Age in years|<br>-0.003***||<br>-0.003***|<br>0.013***|<br>0.013***|
||(3.80)||(3.61)|(6.19)|(6.16)|
|Age squared|0.005***||0.005***|-0.062***|-0.062***|
||(6.25)||(6.07)|(3.97)|(3.99)|
|Female|-0.000||-0.000|-0.003|-0.004|
||(0.01)||(0.05)|(0.55)|(0.82)|
|Education|<br>-0.006***||<br>-0.006***|<br>-0.004***|<br>-0.004***|
||(7.77)||(7.74)|(5.50)|(5.49)|
|Orphan [α2]||||0.006|-0.005|
|||||(0.76)|(0.43)|
|Orphan*year [β2]|||||0.024|
||||||(1.39)|
|Other relative [α3]||||0.018**|0.032***|
|||||(2.46)|(3.37)|
|Other relative*year [β3]|||||-0.027**|
||||||(2.09)|
|Household size|-0.003***||-0.003**|0.001|<br>0.001|
||(2.68)||(2.55)|(1.17)|(1.50)|
|Malaria|<br>-0.047***||<br>-0.048***|<br>-0.041***|<br>-0.042***|
||(7.31)||(7.52)|(7.29)|(7.50)|
|Intensive|<br>-0.017||<br>-0.018|<br>-0.048***|<br>-0.048***|
||(131)||(139)|(541)|(542)|
|Rural area [α4]|.<br>0.056***||.<br>0.052***|.<br>0.060***|.<br>0.052***|
||(5.09)||(4.72)|(5.95)|(5.19)|
|Rural*year [β4]|-0.021||-0.019|-0.035***|-0.027**|
||(1.41)||(1.27)|(2.71)|(2.08)|
|East|0.042***||0.095***|0.054***|0.128***|
||(5.13)||(8.44)|(7.37)|(11.51)|
|North|-0043***||-0038***|-0010|0041***|
||.<br>(4.61)||.<br>(3.05)|.<br>(1.22)|.<br>(3.11)|
|West|<br>-0.062***||<br>-0.043***|<br>-0.024***|<br>0.026**|
||(7.16)||(3.72)|(3.10)|(2.19)|
|East*year|||<br>-0.094***||<br>-0.098***|
||||(7.27)||(9.40)|
|North*year|||<br>-0.007||<br>-0.068***|
||||(0.35)||(5.10)|
|West*year|||<br>-0.038**||<br>-0.073***|
||||(2.22)||(5.70)|
|Observations|13,966||13,966|15,430|15,430|
|Pd Rrd|010||010|008|008|
|seuo -squae<br>Loglikelihood ratio|.<br>-5,637||.<br>-5,603|.<br>-5,466|.<br>-5,419|
|χ<sup>2</sup>forα1+β1= 0|8.28||7.31|0.01|0.04|
|Prob value|0.004||0.007|0.754|0.842|
|<br>χ<sup>2</sup>forα2+β2= 0|||||<br>2.16|
|Prob value|||||0141|
|<br>χ<sup>2</sup>forα3+β3= 0|||||.<br>0.000|
|<br>Prob value|||||0.987|
|χ<sup>2</sup>forα4+β4= 0|12.64||11.50|9.77|10.10|
|<br>Prob value|0.000||0.001|0.002|0.002|

Absolute value of z-statistics in parentheses * significant at 10%; ** significant at 5%; *** significant at 1% 24
Policy Research Working Paper 9954

# **Abstract**

Using geo-coded, firm-level data on more than 51,000 establishments in 649 metropolitan areas in 98 developing economies, from the World Bank’s Enterprise Surveys and a new global database on city-level mobility and congestion, this paper estimates the “pure” firm productivity gains of urban density, net of negative externalities associated with limited mobility, crime, and pollution. The results suggest that the average size of agglomeration economies in the developing world is comparable to the one observed in advanced countries, but the magnitude of the benefits of density on firm productivity substantially varies across firms. Returns to urban density are higher for firms operating in the tradables sector, exporters, foreign-owned firms, larger firms, and more experienced firms. Agglomeration economies are lost through both limited uncongested mobility and congestion, but the latter has a stronger negative effect on agglomeration economies and reduces relatively more the agglomeration benefits of firms in the non-tradables sector than those producing tradables.

This paper is a product of the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/ prwp. The authors may be contacted at mburger@ese.eur.nl, eianchovichina@worldbank.org, or prottoy.akbar@aalto.fi.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
- Yao, S., 1999, Economic Growth, Income Inequality and Poverty in China under Economic Reforms, _Journal of Development Studies_ 35(6), 103-130.

- Zhao, Y., 1999, Leaving the Countryside: Rural-to-Urban Migration Decision in China, _The American Economic Review_ 89(2), 281-286.

Zhou, Q., 1994, _Rural reforms and Development in China_ (Hong Kong, Oxford University Press).

Zhu, L., 1991, _Rural Reform and Peasant Income in China_ (London, Macmillan).

- Zhu, L. & Z. Jiang, 1993, From brigade to village community: the land tenure system and rural development in China, _Cambridge Journal of Economics_ 17, 441-461.

- Zhu, N., 2002, _Analyse des migrations en Chine: mobilité spatiale et mobilité professionnelle_ , Thèse de doctorat, CERDI, Clermont-Ferrand, Novembre 2002.

24
INTRODUCIVIN **xix**

**_(based_** _on_ **_GNP per capita_** _in_ **_1990 U.S. dollarsi)_**

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# _Market-level data_

Maize price effects are estimated using price data from the Agricultural Market Information System (SIMA), which is run by the Ministry of Agriculture of Mozambique. The database tracks weekly retail prices for a range of core agricultural products (particularly maize, cassava, rice and beans) in 25 markets in cities and towns spread across all provinces in Mozambique.

# _Weather data_

Determining historical weather distributions as well as the timing, intensity and spatial location of anomalies requires long time series and spatially disaggregated data on weather observations. We first listed all weather-related natural disasters that took place in Mozambique within the time frame determined by the dates of the household surveys (IOFs) using the disaster profile information publicly available in the International Disaster Database (EM-DAT 2018). In addition to providing information on the timing, geographical coverage, and human impact of the disasters, the EM-DAT database provides estimates of their economic damages.<sup>2</sup> In a second stage, we initially cross the long list of floods with daily precipitation data recorded by the Tropical Rainfall Measurement Mission (TRMM) in the Multi-Satellite Precipitation Analysis (TMPA) for the period 2000 to 2012. These data have a resolution of 0.25 by 0.25 degrees, which in Mozambique corresponds to grids of approximately 25 by 25 kilometers (Huffman et al. 2007). In contrast to other high-resolution precipitation data sets such as CHIRPS and CRUTS, which combine remote sensing data with readings from weather, TMPA contains rainfall data based only from satellite records, ensuring consistency in the measurement methodology.

To obtain quantitative measures on the intensity and location of cyclones, we use daily wind speed data from the U.S. National Oceanic and Atmospheric Administration (NOAA) and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). This database stores wind speed records for the period 1950-2017 for grids with resolution of 0.25 by 0.25 degrees The GFS is a global weather forecast model that contains data on dozens of

> 2 For a disaster to be entered into this database, at least one of the following criteria must be fulfilled: 1) 10 or more people reported being killed, 2) 100 or more people being affected directly by the event, 3) declaration of a state of emergency by the corresponding authorities and 4) a formal request for international assistance.

8
|T|ABLE1. Workforce<br>1<br>Rural|byEmployment<br>993<br>Urban|Sector(%)<br>1<br>Rural|998<br>Urban|
|---|---|---|---|---|
|Public employees|3.12|20.01|5.90|21.51|
|Private employees|7.60|19.58|18.03|26.82|
|Self-employed|89.26|60.37|76.07|51.67|

_Source:_ Author’s calculations based on VLSS 1993 and VHLSS 2006 data.

Public sector and private sector workers are engaged in very different economic activities. A growing majority of public employees work in government, education, and health services (51 percent in 1993, 62 percent in 2006) whereas only 8 percent of private employees participated in these sectors in 1993. The remainder of public employees work in public firms, which operate in many industries: electricity and water production, mining, food and beverages, textiles, and other fields. Even when they operate in the same industry, however, public and private firms have very different characteristics. In 1998, the average public worker in the paper, metal, and plastics industries had 200 coworkers whereas the average private sector worker in this industry had only 35 coworkers. According to the 2006 Enterprises Survey, public firms are more capitalistic and generate more profits compared with private domestic firms (Vietnam SocioEconomic Development 2008). Within the private sector, foreign-owned enterprises are an exception, with a much larger size and more capital compared with domestic firms, but they represent only a small minority of the employed workers in the period that we consider (up to 5 percent in 2006).

# _Earnings in the public and private sectors_

On average, public employees earn more compared with private sector workers. In nominal terms, public employees earned 1460 dongs per hour in 1993, and private sector workers earned 1320 dongs. In 2006, public employees earned 9320 dongs per hour, and 8
Policy Research Working Paper 5182

# **Abstract**

Governments can significantly reduce earthquake mortality by implementing and enforcing quake-proof construction regulation. The authors examine why many governments do not. Contrary to intuition, controlling for the strength and location of actual earthquakes, mortality is lower in countries with higher earthquake propensity, where the payoffs to mortality prevention are higher. Importantly, however, the government response to earthquake propensity depends on country income and the political incentives of governments to provide public goods to citizens. The opportunity costs of earthquake mortality prevention are higher in poorer countries; rich countries invest more in mortality prevention than poor countries in response to a higher earthquake propensity. Similarly, governments that have fewer incentives to provide public goods, such as younger democracies, autocracies with less institutionalized ruling parties and countries with corrupt regimes, respond less to an elevated quake propensity. They therefore have higher mortality at any level of quake propensity compared to older democracies, autocracies with highly institutionalized parties and non-corrupt regimes, respectively. The authors find robust evidence for these predictions in our analysis of earthquake mortality over the period 1960 to 2005.

This paper—a product of the Macroeconomics and Growth Team, Development Research Group and the Global Facility for Disaster Reduction and Recovery Department—is part of a larger effort in the departments to study the main sources of vulnerability and to disseminate the emerging findings of the forthcoming joint World Bank-UN Assessment of the Economics of Disaster Risk Reduction.. Policy Research Working Papers are also posted on the Web at http://econ. worldbank.org. The author may be contacted at Pkeefer@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team