rafmacalaba/datause-encoder-data huggingface dataset:
<!-- Start of picture text --> Gra h 1.8: Tectonic Plates of the Americas<br>Plate Aactc No<br>Malor ecoi plates of -he r-Plate<br>were a e by 97A2tarctica P str en,3 wh w s aa<br><!-- End of picture text -->

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> The occurrence of natural hazards has increased in the last century and accelerated more recently. Throughout the twentieth century, the Latin America and Caribbean region was hit by 1,309 catastrophes (Graph 1.9). During the period 1970-99, Latin America and the Caribbean region were affected by 972 catastrophic events, 43 of which were classified as major disasters (Table 1.6).6 Graphs 1.9 and 1.10O clearly reflect an increasing trend in the frequency of disasters over the past three decades.

**Graph 1.9: Occurrence of Natural Disaster Events in Latin America** and the Caribbean by Decade (1900-99)

<!-- Start of picture text --> 450<br>400<br>350<br>300<br>250<br>200<br>150<br>100<br>50<br>1900 1910 1920 1930 1940 1950 1960 1970 1980 1990<br><!-- End of picture text --> Source: EM-DAT: The OFDA/CRED International Disaster Databasewww cred be/emdat -Universite Cathohque de Louvain - Brussels - Belgium

> **6** The impact of these events on macroeconomic variables is analyzed in the next section.

25
Using the prism of FDI as a case study, our basic message is like many other observers of the eighteen year old Chinese "economic miracle." While China has made remarkable strides in moving towards a market-oriented economy, greater attention is now required on the internal structure of the country's development and on the inequities that have come to exist as a result of the reform process.<sup>5</sup> _15_ An analysis of the prospects and problems of China's enterprise reform program is contained in Broadman (1995a).

18
Policy Research Working Paper 7987

# **Abstract**

This paper documents access to services and ownership of infrastructure-related durables in the water, energy, telecom, and transport areas, based on harmonized household survey data covering 1.6 million households in 14 Latin American countries during 1992 to 2012. The paper provides a systematic disaggregation of access and ownership rates at different levels of income and over time, and econometrically derives the country infrastructure premium, a measure of how much a household benefits from simply being located in a given country. The results show extensive inequality of access, within countries across the income distribution, but also across countries for households at similar levels of income. For water and electricity, for example, up to two-thirds of the variability in individual percentile access to infrastructure services and consumption of related assets can be explained by country residence only. In addition, few country fundamentals appear to be significant in explaining this variability, pointing to policy differences as an important determinant. The paper derives the income elasticity of infrastructure access for the full set of indicators, and uses these to estimate the time that would be needed to close the remaining gap for households at different levels of the income distribution under a “business as usual” hypothesis. Under that scenario, universal access appears to be decades away for many countries in the region. The last part discusses the policy challenges, arguing that in a context in which public budgets face strong constraints and significant increases in private investment are unlikely to be forthcoming, a large part of the solution lies in refocused investment strategies, better demand management, and improved public spending efficiency.

This paper is a product of the Sustainable Development Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mfay@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
# **1. Introduction**

Guinea has rich natural resource endowments, especially in mining, agriculture and hydropower. It has the world’s largest reserves of bauxite, the ore used to produce aluminum. Guinea’s bauxite reserves are estimated at more than 40 billion tons. Guinea also has one of the largest unexploited iron deposits known in the world, in the Simandou mountains, as well as large reserves of gold, diamonds, uranium, and some 20 other minerals. The country’s water potential is estimated at 27,000 m<sup>3</sup> per capita per year. Large rivers such as the Niger, Senegal and Gambia have their source in Guinea, making the country the “water tower” of West Africa. With such important endowment of water resources, Guinea can develop a hydroelectric potential estimated at more than 6,000 megawatts. Moreover, Guinea’s huge agricultural potential remains largely unexploited. Of 6.2 million hectares of arable land, only 25 percent is farmed and of 364,000 hectares for irrigation potential, less than 10 percent is developed.

Despite its enormous potential, Guinea remains one of the poorest countries in the world. Guinea’s GNI per capita was estimated at US$830 in 2018 (current US$, Atlas method). With a Human Development Index of 0.459, Guinea was ranked 175<sup>th</sup> of 189 countries in 2018 (UNDP, 2018). The country’s Human Capital Index (HCI) is estimated at 0.37, which means that a child born in Guinea today will grow up to be, at best, 37 percent as productive as she could be if she enjoyed complete education and full health. Based on the HCI, Guinea was ranked 141 of 157 countries in 2017.<sup>1</sup> Poverty has increased from 49 percent (national poverty line) in 2002 to 55 percent in 2012, according to the most recent available national poverty survey. After the Ebola crisis, the poverty rate has probably further increased to about 60 percent in Guinea (World Bank, 2016).

Lack of economic growth largely explains the paradox between rich natural resources endowments and low level of human development outcomes and high level of poverty in Guinea. Indeed, since its independence in 1958, Guinea has experienced slow and volatile economic growth rates.<sup>2</sup>

> 1 The Human Capital Index has been recently developed by the World Bank. It quantifies the contribution of health and education to the productivity of the next generation of workers. For further information on the methodology, see Kraay (2018).

> 2 For further discussion on growth binding constraints in Guinea, see Mijiyawa (2017a).

2
Table 9: OLS and IV Estimates of Father’s Labor-Supply Models in Albania (2002 - 2012)

|Method||OLS|IV|IV|
|---|---|---|---|---|
|Instrument for Fertility:<br>|||||
|_(Number of children ever born)_<br>||—|_Same-sex_|_Two girls_|
|Survey Fixed-Efects<br>||Yes|Yes|Yes|
|District Fixed-Efects||Yes|Yes|Yes|
|Controls||Yes|Yes|Yes|
|Dependent variable<br>Mean|Obs.|(1)|(2)|(3)|
|_Panel A: All data_|_bases (LS_|_MS and DHS)_<br>|||
|_Worked in last_ 7 days<br>0.790|5460|0.0124*<br>(0.0067)|0.0581<br>(0.0445)|0.0257<br>(0.0314)|
|_Worked of-farm_<br>0.394|5460|-0.0234***<br>(0.0080)|0.0365<br>(0.0537)|0.0671*<br>(0.0401)|
|_Worked on-farm_<br>0.218|5460|0.0347***<br>(0.0066)|0.0125<br>(0.0396)|-0.0019<br>(0.0280)|
|_Self-employed_<br>0.198<br>_Panel B: Onl_|5460<br>_y LSMS _|-0.0137**<br>(0.0063)<br>_databases_|0.0215<br>(0.0437)|-0.0072<br>(0.0319)|
|_Hours per week (total)_<br>34.39|4530|0.1056|-0.2895|-1.1751|
|||(0.4501)|(2.8443)|(2.0713)|
|_Hours per week (of-farm)_<br>18.60|4530|-1.609***<br>(0.426)|-4.3926<br>(2.8683)|-0.8306<br>(2.1766)|
|_Hours per week (on-farm)_<br>9.15|4530|2.744***|2.3598|0.5399|
|||(0.390)|(2.2198)|(1.6274)|
|_Hours per week (self)_<br>7.65|4530|-0.741**|2.7154|-0.0305|
|||(0.331)|(2.3503)|(1.7875)|
|_Second occupation_<br>0.058|4530|0.008*<br>(0.004)|0.0321<br>(0.0289)|0.0406*<br>(0.0230)|
|_Log(Monthly Labor Income)_<br>193092.2|4530|-0.3816***<br>(0.1144)|-0.6700<br>(0.7176)|-0.0883<br>(0.5305)|

_Notes:_ Standard errors clustered at the PSU level are in parentheses. All the models include the following covariates: _Age, Age Squared, Age at First birth_ and _Years of Education_ . IV Models in columns (2) and (3) also include indicators for _Boy 1st_ and _Boy 2nd_ . The sample of fathers includes the husbands of the married women aged 20 to 49 years old with 2 or more children younger than 18 years old) Each model is estimated with survey and district fixed effects. * Indicates statistical significance at 10%. ** Indicates statistical significance at 5%. *** Indicates statistical significance at 1%.

42
concerns, so we may well have to reconcile ourselves to the fact that it will not always be possible to reach any unqualified conclusions about health-related inequalities and public expenditure incidence.

That said, the results call for more clarity and care in the analysis of health-related inequalities. In cases where both asset and consumption data are available, analysts are in a position to qualify any analysis of these issues by reference to parallel analysis based on alternative measures. However, few analysts are afforded such a luxury. In cases where only one SES measure is available, the potential sensitivity of the findings should be recognized. Analysts should be explicit about how socioeconomic status is measured, and about the limitations of conclusions and policy implications. Particular caution is required in contexts where there is considerable spatial variation in access to health services or in health-related attitudes and preferences, in particular if there is also reason to suspect that consumption and the asset index differ systematically across geographical areas.

Finally, the results also call for more careful research on how different dimensions of SES are related, and on the pathways by which different dimensions impact on healthrelated variables. Moreover, it should be noted that the measurement of socioeconomic inequalities in health-related variables provides only a descriptive perspective. These measures are of considerable interest in their own right, but should also be considered the starting point for further analysis aimed at understanding the determinants of health service utilization and at disentangling the sources of observed inequality.

15
- How does what goes on in other markets affect what goes on in the labor market? What role for infrastructure? For physical capital? For financial capital and credit? What determines factor pricing on these other markets?

- Which trade policies (e.g., open vs. closed economy) and commercial policies (e.g., exchange rate policy) are associated with the best labor market outcomes for the poor?

# 3. Supply of labor

- Why do people work informally?

- What are the educational and skill characteristics of the labor force and how have they changed over time?

- To what extent do the educational and skills supplied by workers conform to the education and skills demanded by employers?

- Is there evidence of structural unemployment and underemployment?

- **What are the effects of various policy interventions on those treated and on those not treated?**

- When educational expansion has taken place, how has employment of the educated responded?

# 4. Wage-setting mechanisms and institutions

- Are labor markets segmented?

- To what extent are wages and conditions of employment determined by supply and demand in various key sectors of the economy and to what extent by minimum wages, labor unions, and other institutional forces?

- **Using panel data, including new panels for additional countries: What are the patterns of earnings changes and their determinants? Why does the qualitative picture coming out of panel data analysis look so different from that found in analysis of comparable cross sections?**

- What are the determinants of self-employment earnings a) in agriculture, b) in non-farm rural employment, and c) in urban self-employment?

- Which policies have made a difference in each of these three areas, enabling workers to earn their way out of poverty?

# 5. Other institutions impinging upon labor market functioning

61
**Plann ng, and Research F-Policy,**

# **and Human Resources**

Reducing poverty is a major objective of economic policies in both developed and developing countries. It is important that limited govemment resources be channeled to the poor, but it is not always easy to identify the poor directly.

Which households should be given transfers (such as money, food stamps, vouchers, and rations) when reliable information on incomes is difficult to obtain? How much money (stamps, vouchers, rations) should be given? The answers to these two questions depend on the information available.

charactcristics and distribute transfers on the basis of those predictions. It appears that significant reductions in poverty can be achieved using this method.

Some of the variables that most reliably predicted income level in CoMe d'lvoire were: per capita floor area; whether the household was headed by a member of the Voltaic ethnic group (which is one of the poorest groups); the level of educational attainment of the head of household; whether the household owned a car, a bike, or a refrigerator.

Glewwe and Kanaan present a simple method for targeting when income is not observable but other characteristics that are correlated with income can be observed. Using simple regression techniques on comprehensive household survey data taken from Cote d'lvoire, they predict incomes based on observable household Several problems with this approach are discussed. For example, the cost of gathering information may at times outweigh the benefits. Also, basing transfers on a policy that favors one group over another might lead to public opposition.

This paper is a product of the Welfare and Human Resources Division, Population and Human Resources Department. Copies are available free from the World Bank, 1818 H Street NW, Washington DC 20433. Please contact Brenda Rosa, room S9-137, extension 33751 (48 pages with tables).

The PPR Working Paper Scrics disseminates the findings of work under way in the Bank's Policy, Planning, and Research Complex. An objective of the series is to get these findings out quickly, even if presentations are less thar fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official policy of the Bank.

Produced at the PPR Dissemination Center
Table 4: Tests of the Calvo-Mendoza Hypothesis,1987-1995

|**Sample:**|**All**|Europe|Latin<br>**I**America|Asia+|
|---|---|---|---|---|
|||||_|
||M1||||
|**Log(R/Ml)**E-03|-2.03|-0.439|-17.0|-2.04|
||0,004|0.039|0.070|0.478|
|VJLog(RIMI)] E-02|4.77|11.2|-3.76|-10.8|
||0.003|0.000|0.001|0.646|
|Constant E-03|-0.258|_-1.05_|-6.41|-0.649|
||0.001|0.142|0.106|0.446|
|**R2**|0.018|0.049|0.080|0.000|
|Observations|1 577|960|232|289|
|Overall Significance|3.39|49.06|2.67|0.69|
||0.000|0 000|0.002|0.763|
||M2||||
|Log(RlM2) E-03|-2.47|-0.764|-3.16|0.044|
||0.0)01|0.037|0.150|0.894|
|V[Log(R/MZ)J E-02|7,43|15.4|13.7|-1.82|
||0.000|0.000|0.000|0.591|
|Constant E-03|-5.99|-2.19|-12.1|4.29|
||0.004|0.177|0.220|0.530|
|Ri|0.029|0.061|0.111|0.000|
|Observations|1769|1152|232|289|
|Overall Significance|5.38|74.34|3.41|0.55|
|<br>___________________________|0.000|0.000|0.000|0.879|

Notes: We report the summation of the fust six **I of** each variable and below it the P-Value ofthe test **under** the **null that** the coefEicients **an** jointly equal to zero. **All** models are estimated as pooled mgressions except the European ones where Hausman and Breusch-Pagan **tests** dictated a random effet model. F-tests for the pooled and Chi squared for the random **effects** regiessions were used to **evaluate** the **overall** significance of the specification.
**Table 2.4 Number of Days Ozone and PM,0 Levels Satisfied Air Quality Standards**

||_1990_|_1991_|_1992_|_1993_|_1994_|_1995_|_1996_|_1997_|_1998_|_1999_|
|---|---|---|---|---|---|---|---|---|---|---|
|Ozone|37|12|34|41|21|41|39|43|45|65|
|PM10|**--**|**=**|**--**|||273|186|212|176|345|
|_Source_|_:_SMA(199|9).|||||||||

For the period 1995-99, the highest value for ozone-0.349 ppm-was measured at the Pedegral station, which is located in the Southwest zone. Ozone air pollution at this station exceeded the standard for 276 days. The least polluted station was Chapingo in the Northeast, with a daily 1-hour maximum concentration of 0.210 ppm .

Air quality standards for PM1 O include a daily average (150 ,ug/m<sup>3</sup> ) and an annual average (50 ,ug/m<sup>3</sup> ). All stations violated both standards apart from the annual average standard at the Pedregal and Coacalco stations (formerly Villa de las Flores). The highest concentration-a daily maximum of 335 11g/m<sup>3</sup> (190 IMECA)-was observed in the eastern zone at the Nezahualcoyotl station. The maximum annual average of 94 tg/m<sup>3</sup> was observed at Xalostoc station. This station has traditionally recorded the highest particulate matter concentrations in the metropolitan area, exceeding particulate standards 58 days per year (16 percent of the year). As a result, in 1995 about 1.2 million people were exposed at least once a year to PM1 O concentrations above 300 Ag/m<sup>3</sup> , the trigger for a Phase I contingency (see table 2.6).

If air pollution goes above certain levels in one of the five zones a PCA is invoked (a contingency program). Table 2.5 describes the three levels of action: Precontingency, Phase I contingency, and Phase II contingency.

**Table 2.5 The Environmental Contingencies Program**

|_Target_|_Precontingency_|_PhaseI contingency_|_Phasellcontingency_|
|---|---|---|---|
|Publichealth|Suspend outdoor<br>sportactivitiesin<br>schoolsand<br>parks.|Epidemiologicalsurveillance<br>andcommunicationofrec-<br>ommendationstoaddress<br>health risks.|Suspend activitiesin<br>publicoffices,recrea-<br>tionalactivitiesand<br>publicservices.|
|Transport sector||Restricttraffic(nocirculation<br>ofhologram**11**vehicles<br>everyotherday).<br>Suspenduseofpublicly<br>ownedvehiclesby50%.<br>Improvetrafficcirculation.|Suspenduseofall<br>hologram**11**vehicles<br>and80%ofpublicser-<br>vice vehicles.|
|Industryandservices||Reducecertain industrialac-<br>tivities30-40%.Suspend<br>fuel distribution activities,<br>redbrickfabrication,andthe<br>thermoelectric powerplant<br>JorgeLuque.|Reduce industrialactiv-<br>ityby50%.|
|Publicservices**/**in-<br>frastructuremainte-<br>nance|Sus|pendmaintenanceofurbaninfr|astructure.|
|Additionalactions|Surveillanceand|controloffiresinnaturalandagr|iculturalareas_I_landfills.|

_Source:_ SMA (http://sma.df.gob.mx).

6
Bankscope reports data on board composition, as shown in Table A1 in the appendix. We will address this sampling issue below, but for the meantime it is important to notice that this incomplete coverage will result in underestimating the number of bankers-politicians.

The process of finding coincidences between politicians and bankers’ names involves four steps. First, we standardize the strings containing the names by converting them to lowercase only, and removing punctuations and titles (Sir, PhD, etc.). In the second step, we remove duplicate entries by determining in each of the datasets those observations that correspond to different spellings of the same name (for instance, with and without the middle initial). In a third stage, we pool the different datasets containing politician names and again determine those observations across the datasets that correspond to the same individual. Once the names have been cleaned in this way, we compare the names included in the politicians and bankers datasets to obtain the matching observations.

To determine whether there was a matching name in each step we use a _record-linkage algorithm_ that forms all the possible pairs of names within each country, and then ranks the pairs based on three standard measures of string similarity used in the record linkage literature: Bigram, Levenshtein, and Longest Common Subsequence.<sup>6</sup> When comparing two strings containing names, each of these criteria results in a value between 0 and 1 that measures the likeliness of the two names. We kept all pairs with a minimum value of 0.8 in at least one of the three criteria and then visually checked all pairs to determine whether they corresponded to an actual match.

Following the process above, after the second step we end up with 10,829 different politicians and 62,981 different bankers in 146 countries. The third step produces a total of 211 matching names across these two lists of people in this set of countries (see columns (3) to (5) in Table 1). Based on the raw numbers documented above, one might conclude that the mechanism cannot be of first order importance simply because it is highly infrequent. After all, having found only 211 politicians that are also bankers out of more than 70,000 names, and finding none in roughly half the countries considered looks unimpressive. We argue this not so simply because of the sheer size of the pool where both politicians and bankers are extracted.

The expected number of matches one would get by randomly choosing people for the politician and banker posts is much lower in many countries than the actual number of matches we found. We derive the probability of obtaining a given number of coincidences under the

> 6 The record linkage software we use is _Merge Toolbox (MTB),_ a Java based tool created by the members of the _Safelink_ project (see Schnell et al (2004)).

5