rafmacalaba/datause-encoder-data huggingface dataset:
# Policy Research Working Paper Series

|Title|Author|Date|Contact<br>for paper|
|---|---|---|---|
|WPS1278 Regulation and Commitment in the<br>Development of Telecommunication<br>in Chile|Ahmed Galal<br>s|March 1994|B. Moore<br>38526|
|WPS1279 Optimal Hedging Strategy Revisited:<br>Acknowledging the Existence of<br>Nonstationary Economic TimeSeries|Ying Qian<br>Ronald Duncan|March 1994|S- Lipscomb<br>33718|
|WPS1 280 TheEconomic Impact of Export<br>Controls: An Application to Mongolian<br>Cashmere and Romanian Wood Prod|Wendy E.Takacs<br> <br>ucts|March 1994|M. Patefia<br>37947|
|WPS1281 Human and Physical Infrastructure:<br>Public Investment and Pricing Policie<br>in Developing Countries|Emmanuel Jimenez<br>s|April 1994|L. Longo<br>37786|
|WPS1282Copper and the Negative Price of<br>Storage|Donald Frederick Larson|April 1994|A. Kimrn<br>33715|
|WVPS1283 Interest Rates in Open Economies:<br>Real Interest Rate Parity, Exchange<br>Rates, and Country Risk in Industrial<br>and Developing Countries|Dipak Das Gupta<br>Bejoy Das Gupta|April 1994|B. iCim<br>82467|
|WPS1 284 The Soviet Economic Decline:<br>Historical and Republican Data|William Eastedy<br>Stanley Fischer|April 1994|R. Martin<br>31320|
|WPS1 285 Capital Fundamentalism. Economic<br>Development, and Economic Growth|Robert G. King<br>Ross Levine|April 1994|P. Sintim-Aboagye<br>38526|
|WPS1286 Economic Transformation and the<br>Fiscal Crisis: A Critical Look at the<br>Central European Experience of the 1|Luca Barbone<br>Domenico jr- Marchetti<br>990s|April 1994|S. Harbi<br>37143|
|WPS1 287 Unstable Inflation and Seignorage<br>Revenues in Latin America How Man<br>Times Can the Govemment Fool Peop|Jacques Morisset<br>y<br>le?|Apnl 1994|D. Jenkins<br>37890|
|WPS1288 The Public Finance of Infrastructure:<br>Issues and Options|Vinaya Swaroop|April 1994|C. Jones<br>37699|
|WPS1289 A Fiscal Needs Approach to Equali-<br>zation Transfers in a Decentralized Fe|AnwarShah<br>deration|Apnl 1994|C Jones<br>37754|
|WPS1290 Oil Price Instability, Hedging, and an<br>Oil Stabilization Fund: The Case of<br>Venezuela|Stijn Claessens<br>Panos Varangis|April 1994|D. Gustafson<br>33714|
|WPS1291 ASurvey of Viet Nam's Legal<br>Framework in Transition|Natalie G. Lichtenstein|April 1994|M. Rangarajan<br>81710|
any occupation.1<sup>8</sup> For the rest of the sample<sup>(9417observations),10percentareeitherchild</sup> labor (less than 14 years of<sup>age,9percent) ortoo old(morethan70yearsof age)andthusare</sup> dropped.1<sup>9</sup> But some of the parents of<sup>these individualsdid noteitherparticipateinthelabor</sup> force or report their labor<sup>forceparticipation, furtherreducing the sizeof thesample.20Moreover,</sup> a number of the PSUs showed<sup>no employment diversificationwhicharedroppedto avoid perfect</sup> fits in the regression analysis.<sup>Splittingthesamplebetweenmalesandfemales,weendupwith</sup> a final sample of 2090 observations<sup>(in152PSUs)fordaughtersand2948observations(in242</sup> PSUs) for sons.

The NLSS 1995/96 also contains<sup>awide rangeof variableson householdstructure,education,</sup> income and asset ownership<sup>whichcan beusedas controls inthe regression.Theyincludehouse-</sup> hold size, composition of household<sup>(share of femaleadults,shareof children,share of youngand</sup> share of old in total household<sup>size)aswellasindividual'smaritalstatusand gender.The human</sup> capital of an individual is<sup>measuredbyher/hislevelofeducation andage.21The educationvari-</sup> able codes different levels of<sup>education(e.g.0if illiterate,1 if literatebutno schooling,andso</sup> on). We also define a set of dummies<sup>(15 to be exact)depictingthe ethnicityof the individual.Fi-</sup> nally, we use two alternative<sup>representationsof themostcriticalvariablein ouranalysis:parental</sup> occupation. First, we define a<sup>dummy indicatingif atleastone of theparentswasemployedin</sup> non-agriculture (ne). Second, to<sup>allowfordifferentialimpacts,wedisaggregateanddefinethree</sup> different dummy variables: (i)<sup>whetherboth parentsareemployedinnon-agriculture</sup><sup>_(nP),(ii)_if</sup> only father was employed in<sup>non-agriculture</sup><sup>_(nf)_and</sup><sup>_(iii)_ifonlymotherwasemployedinnon-</sup> agriculture (n<sup>m</sup> ). The summary<sup>statisticsof theexplanatoryvariables arepresentedin appendix</sup> Table A.1.

> 18A large fraction of those not reporting occupation are in fact child labor with age 14 years or less. 1<sup>9</sup> Note that the empirical results<sup>reportedinthefollowingsectionsremainunchangedeven if weuseanyother</sup> cut-off age (e.g. dropping those<sup>below20 yearsof ageandabove65yearsof ageandso on).</sup>

> 20For 8394 individuals left in the sample, both parents reported participation in the labor force in the case of 6874 individuals, and for rest of<sup>the observations, employmentstatus ofeitherfather(347observations)ormother</sup> (1173) are missing. Note that<sup>if weusedummiestocapturethemissingparentalinformation,the samplesizecan</sup> be increased but the qualitative<sup>resultsremainunaffected.</sup> 211n addition to human capital,<sup>agecaptures anycohorteffectalso.</sup>
15

**-.p:i** oi :. t age in 1lnding Th- ,'osest studies to the present one are !.Lo:. of :..indert and Morton t:X-l arnd Chowdrv (1988), where the authors :. o.. ed the relation het,teen **hi.** contractual interest rates and the default. histories of borrowers in the former episodes of lending (1820 through the 1930s). The important similarity to the present paper is that both of those studies focus to a period of boom in the financial markets. A boom that was separated from the previous widespread non-payment **crisis** by a period of stagnancy in lending. Their conclusions are that **defaulters were** not penalized, in fact it appeared that they paid less than **governments** with "unblemished records". Our finding is in contrast to **theirs despite** v.erv similar methodology and same data sources. The difference, **as** demonstrated in this paper, is a consequence of **how** one **defines**

"unblemished repayment record". in their investigations, countries in the unblemished record group contain not only governments with good repayment records, but also governments that had no record because they were not sovereign in the former episodes of lending.

Our result, that creditors paid attention to repayment record of borrowers, is consistent with that of Eichengreen and Portes (1989). The atlttors, again, as in this study, investigate a period of buoyancy (the 1920s) and suggest that in pricing of foreign bonds investors discriminated borrowers according to their past repayment record The remaining studies that investigated the impact of borrowing experience on later market access has a fundamental difference from the present one. Specificallv, thev _foc_ s to a period of stagnancy in private n.I-:. g. following a widc-;prca3d o is The overall conclusion of these sr :tes iS *hat badlv a ( .l;aved hoI- rr r5 W(ere not discriminated against; th.re ...s a rereral cut:-of' of > '.r. and the borrowers that behaved
measures of cough, other respiratory symptoms, and fever. Anthropometric measurements are made for children under 5. We focus on all DHS clusters in the oil producing Niger-Delta region and in CrossRiver State. Although Cross-River was delisted as an oil producing state in 2009, it is in close-proximity to current offshore oil and gas wells off the coast of Nigeria.

# Method

We use a market potential approach to link gas flaring locations to the risk of exposure to gas flaring for each DHS cluster. Specifically, we calculate the inverse distance weighted average flare volume from each flare site for each DHS cluster: Where df−1 is the inverse of the distance8 between cluster c and flaring location f ; and FVf is the gas flare volume in flaring site f . This inverse-distance weighted flaring volume for each cluster (RFct) is our key variable of interest. There are some advantages to taking this market potential approach: first, this approach does not treat each flaring site as equal but takes into account the volume of gas flared in each location as well as the fact that flare sites closer to DHS clusters should have more impact on health outcomes than flare sites farther way. Secondly, this approach allows us to take into account the potential effect of offshore flaring, treating it in an equivalent way to onshore flaring. If instead, we used flaring averages for the local administrative areas where the DHS cluster was located, the offshore sites would be missed. About half of all of Nigeria’s flaring happen offshore and threequarters of these flaring locations are located within 60 km of areas where people live, which is well within the distance that the air pollution from flaring has been shown to travel.

We have three groups of outcome variables. First, the reported incidence of diseases in young children such as fever, cough, diarrhoea, and respiratory issues.<sup>9</sup> Second, anthropometric measurements yield z-scores for height-for-age (HAZ), weight-for-height (WHZ), and weight-for-age (WAZ). A child is considered stunted, wasted or underweight if HAZ, WHZ or WAZ for the child is less than two standard deviations below the median measurement for the reference group (WHO Multicentre Growth Reference Study Group, 2006). Third, measures of mortality at the cluster level: specifically, infant mortality (the proportion of children who died before they are 12months old), child mortality (the proportion of children who died when they were between 12 and 59 months old), and under-5 mortality (the proportion of children under 59months old who died).

> 8 We use the Stata routine _geonear_ to calculate the Euclidean distance between each DHS cluster and each flaring location.

> 9 This is defined as reporting one of the following symptoms: cough, blocked nose, short breath, and blocked chest.

7
# **_4.a A Case Study of Complaints about Alleged Dumping Practices_**

From 1995 to date (2004), the MEIC’s Investigating Authority has received six complaints about alleged dumping practices, which will be analyzed below.<sup>18</sup> Out of these, four concerned the manufacturing sector (Chapters 25 to 99 of the Central American Tariff System - SAC), while only two of the cases involved the agricultural and agroindustrial sectors (Chapters 01 to 24 of the SAC).<sup>19</sup> This distribution is consistent with Costa Rica’s import structure, which—as a consequence of the high tariff protection prevailing over the most important agricultural products for domestic consumption—is mostly concentrated on manufactured products.

**Table 4.1   Costa Rica: Dumping Cases**

|**Year**|**Product**|**Sector**|**Related Countries**|**Complainant or Applicant**|
|---|---|---|---|---|
|1995|Acrylic sheets|Manufactures|Mexico|Acrílicos de Centroamérica S.A.|
|1996|Refrigerators|Manufactures|Mexico|Atlas Industrial S.A.|
|1996|Fresh onions|Agriculture|USA, Nicaragua and Guatemala|National Association of Onion Producers|
|1996|Fiber cement sheets|Manufactures|Mexico|Ricalit S.A.|
|1997|Pasta|Agroindustry|Chile|Pastas Roma S.A.|
|1998|Sanitaryware|Manufactures|Venezuela|Incesa Standard S.A.|

Source: Prepared by the authors based on data drawn from the resolutions on the dumping cases dealt with by the MEIC.

It is also worth pointing out that all of the complaints relate to imports originating in countries of the Americas; Mexico stands out for having three complaints against it, while two concern South American countries, and one, the United States together with two Central American countries.

This structure is extremely interesting since, although Mexico is not a prominent trade partner for Costa Rica, it is clear that the trade liberalization stemming from the Free Trade Agreement signed by both nations in 1995 revitalized trade links to a sufficient extent for Costa Rica’s producers engaged in supplying the domestic market to pay much more attention to the prices offered by Mexican suppliers.

Moreover, of the six applications filed with the Investigating Authority, no dumping injury was found in two cases; two cases were dropped due to failure to submit the required information; in one case no measure was applied because it was not confirmed that the injury was directly caused by the imports, and only in one case it was decided to apply a tariff measure, which ended up being 0% since the imports virtually disappeared.

> 18 No complaints about alleged dumping practices or applications for countervailing measures were filed in Costa Rica prior to 1995.

> 19 The Central American Tariff System is derived from the Harmonized System and, therefore, it coincides with the latter as regards the first six digits of the headings or less.

30
have to wait until the year 1996 before we can observe whether an establishment first appeared in the data 11 or more years earlier.<sup>12</sup> (Table 3 here) Table 3 also shows many of the results one would expect. The accession percentage, separation percentage, job-creation percentage, and the job-destruction percentage all decrease with establishment age. Excess worker flows show this same general trend with the exception of the youngest age category. Table 3, however, does yield two results that may be surprising. First, net employment growth for firms 1-11 years old is negative. The overall positive growth (4.3%) comes from births (firms 0 years old).<sup>13</sup> We also observe that net-growth percentages are lowest for the youngest establishments, which contrasts with the results in Davis and Haltiwanger (1992). This result may point to the importance of credit-market failures that may impede younger firms from expanding.

Table 3 also shows the average of worker- and job-flows statistics for the period 19862001 separated by size category of the establishment. We define five categories based on the average of current- and previous-year employment of the establishment. The categories are: less than 50 employees, at least 50 employees but less than 100, at least 100 employees but less than 250, at least 250 employees but less than 1000, and at least 1000 employees. We see here that the accession percentage, separation percentage, job-creation percentage, and job-destruction percentage all decline with establishment size. That is, small establishments exhibit higher rates of both job and worker flows. This is consistent with the stylized facts of the literature.

We now turn to some of the more novel results of our study. Perhaps the most important economic event in Mexico over the period studied was the economic crisis of 1995. Indeed, the net-growth percentage of –4.6% in 1995 is the lowest net-growth percentage we observe. Earlier 15
**Table 8: Determinants of city height**

|**Dependent variable:**<br>**Average height**|**All with city income**<br>**[preferred specification]**|**All with NTLs**<br>**per capita**|**+ region**<br>**dummies**|**All with**<br>**country**<br>**income**|**+ region**<br>**dummies**|
|---|---|---|---|---|---|
|Ln Population 2000|0.251***|0.262***|0.266***|0.267***|0.269***|
||(0.0170)|(0.0155)|(0.0173)|(0.0171)|(0.0166)|
|Ln City Income 2000|0.185***<br>(0.0219)||0.0557<br>(0.0322)|||
|Ln NTLs per capita||0.209***<br>(0.0344)||||
|Ln Country Income 2000||||0.222***|0.102**|
|||||(0.0251)|(0.0352)|
|East Asia & Pacific|||0||0|
||||(.)||(.)|
|Europe & Central Asia|||-0.130<br>(0.0780)||-0.221*<br>(0.0874)|
|Latin America & Caribbean|||-0.139*||-0.195**|
||||(0.0693)||(0.0706)|
|Middle East & North Africa|||-0.197||-0.248*|
||||(0.104)||(0.111)|
|North America|||-0.198*||-0.315**|
||||(0.0876)||(0.0958)|
|South Asia|||-0.978***||-0.973***|
||||(0.111)||(0.110)|
|Sub-Saharan Africa|||-0.470***||-0.438***|
||||(0.0751)||(0.0776)|
|Constant|-4.047***|-1.650***|-2.880***|-4.683***|-3.328***|
||(0.286)|(0.254)|(0.289)|(0.352)|(0.362)|
|FE|No|No|No|No|No|
|Controls|No|No|Yes|No|Yes|
|R-squared|0.392|0.440|0.561|0.403|0.564|
|N. of observations|396|396|396|383|383|
|Standard errors in parentheses|* p < 0.05, ** p < 0.01, *** p|< 0.001||||

4.3 The combined impacts of built-up growth and vertical layering on floor area In the previous section, we introduced the concept of Floor Area Adjusted Densities (FAPD), which can help differentiate between crowded and livable cities. Floor area is critical for cities to accommodate families and firms. It can be added through horizontal spread, vertical layering, or infill development. Here, we combine the results from the previous estimates of horizontal and vertical growth to estimate income and population elasticities for the total floor area. Income and population elasticities are inferred using 22
# **References:**

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Aizer, A. 2010. “The Gender Wage Gap and Domestic Violence.” _American Economic Review_ 100 (4): 1847-59.

Anderson, S. 2000. _The Economics of Dowry Payments in Pakistan._ Center for Economic Research, No. 2000-82 Angelucci, M. 2008. Love on the Rocks: Domestic Violence and Alcohol Abuse in Rural Mexico. The B.E. Journal of Economic Analysis & Policy, 8(1) (Contributions), Article 43 Arnold, F., S. Kishor, Roy, R.K. 2002. “Sex Selective Abortions in India.” _Population and Development Review_ 28 (4): 739-785.

Badawi, J. 1995. _Gender Equity in Islam_ . World Assembly of Muslim Youth Studies on Islam.

Bae, Y., S. Choy, C. Geddes, J. Sable and T. Synder. 2000. Trends in educational equity of girls and women. Washington, DC, National Center for Education Statistics, Department of Education Bankole A. 1995. “Desired Fertility and Fertility Behavior among the Yoruba of Nigeria: A Study of Couples’ Preferences and Subsequent Fertility. _Population Studies_ 49: 317-328.

Bardhan, P. 1974. “On Life and Death Issues.” _Economic and Political Weekly_ 9: 1293304.

Becker, S. 1996. “ _Couples and Reproductive Health: A Review of Couple Studies._ ” _Studies in Family Planning_ 27 (6): 291-306.

Berk, S.F. 1985. _The Gender Factory: The Apportionment of Work in American Households._ New York: Plenum.

Bhalerao V.R., M.M. Galwankar, S.S. Kowli, R.R. Kumar, and R.M. Chaturvedi. 1984. “Contribution of the Education of the Prospective Fathers to the Success of Maternal Health Care Programme.” _Journal of Postgraduate Medicine_ 30: 10-12.

Bianchi, S, M. Milkie, L. Sayer, and J. Robinson. 2000. “Is Anyone Doing the Housework? Trends in the Gender Division of Household Labor.” _Social Forces_ 79: 191-228.

Bhaskar, Venkataraman, and Gupta Bishnupriya. 2007. “India’s Development in the Era of `-` Growth.” _Oxford Review of Economic Policy_ 23 (2): 135 142.

30
Table 4: All/Awareness/Management - Gap between self-reported and behavioral measures

|||All|||Awareness|||Management||
|---|---|---|---|---|---|---|---|---|---|
|||SR-Behaviora|l||SR-Behaviora|l||SR-Behaviora|l|
|||gap|||gap|||gap||
|Women<br>Behavioral measure<br>Years of education|(1)<br>-0.19***<br>(0.03)<br>-0.75***<br>(0.02)|(2)<br>-0.16***<br>(0.03)<br>-0.80***<br>(0.02)<br>0.03***|(3)<br>0.06<br>(0.23)<br>-0.80***<br>(0.02)<br>0.02***|(4)<br>-0.16***<br>(0.03)<br>-0.88***<br>(0.02)|(5)<br>-0.14***<br>(0.03)<br>-0.90***<br>(0.02)<br>0.03***|(6)<br>-0.04<br>(0.24)<br>-0.90***<br>(0.02)<br>0.03***|(7)<br>-0.19***<br>(0.03)<br>-0.79***<br>(0.02)|(8)<br>-0.16***<br>(0.03)<br>-0.84***<br>(0.02)<br>0.03***|(9)<br>0.09<br>(0.24)<br>-0.84***<br>(0.02)<br>0.02***|
|||(0.00)|(0.01)||(0.00)|(0.01)||(0.00)|(0.01)|
|Years of education X Women|||0.01|||0.01|||0.01|
||||(0.01)|||(0.01)|||(0.01)|
|Cognitive Ability||0.32***|0.22**||0.32***|0.22***||0.33***|0.23***|
|||(0.07)|(0.09)||(0.07)|(0.09)||(0.07)|(0.09)|
|Cognitive Ability X Women|||0.20*|||0.19*|||0.20*|
||||(0.10)|||(0.11)|||(0.11)|
|Social desirability index||0.45***|0.53***||0.45***|0.52***||0.43***|0.51***|
|||(0.04)|(0.05)||(0.04)|(0.05)||(0.04)|(0.05)|
|Social desirability X Women|||-0.16**|||-0.14**|||-0.16**|
||||(0.06)|||(0.07)|||(0.07)|
|Equitable beliefs regarding||-0.03**|-0.06***||-0.02|-0.05**||-0.04**|-0.05**|
|PSDM abilities||(0.01)|(0.02)||(0.01)|(0.02)||(0.01)|(0.02)|
|Equitable beliefs X Women|||0.05*|||0.07**|||0.04|
||||(0.03)|||(0.03)|||(0.03)|
|p(Edu. + Edu. X Women = 0)|||0.00|||0.00|||0.00|
|p(CA + CA X Women = 0)|||0.00|||0.00|||0.00|
|p(SD + SD X Women = 0)|||0.00|||0.00|||0.00|
|p(Equit. + Equit. X Women = 0)|||0.64|||0.42|||0.37|
|p(Women + Edu. X Women = 0)|||0.65|||0.97|||0.56|
|p(Women + CA X Women = 0)|||0.37|||0.67|||0.32|
|p(Women + SD X Women = 0)|||0.00|||0.00|||0.00|
|p(Women + Equit. X Women = 0)|||0.40|||0.53|||0.43|
|Observations|4459|4459|4459|4459|4459|4459|4459|4459|4459|
|R-squared|0.43|0.47|0.47|0.46|0.50|0.50|0.47|0.51|0.51|

_Notes:_ Results presented are OLS estimates that include controls for enumerator, age, father’s education, mother’s education and city. Outcome measures are standardized naive scores. Robust standard errors in parentheses. SR = Self-reported. PSDM = Problem-solving and decision-making. Edu. = Years of education. CA = Cognitive Ability. SD = Social Desirability. Equit. = Equitable beliefs. *** p _≤_ 0.01, ** p _≤_ 0.05, * p _≤_ 0.1
_Trend #2. The number of poor people in the world has declined by 375 million, the first such decline in history._ The most important point that I want to get across in this section is that poverty reduction in low-income countries is very closely related to the GDP growth rate in these countries. Hence, the accelerated growth of low-income countries has led to unprecedented poverty reduction. By poverty, we mean subsisting below some absolute threshold. Most poverty analysis is carried out with countries’ own poverty lines, which are set in country context and naturally differ. In the 1990s we have more and more countries with reasonably good household surveys and their own poverty analysis. Figure 8 shows five poor countries that have benefited from faster growth, and in each case significant poverty reduction has gone hand-inhand with faster growth. Poverty reduction here is the rate of decline of the poverty rate, based on the country’s own poverty line and analysis.

Figure 8. Poverty reduction in Bangladesh, India, Uganda, Vietnam, and China closely related to growth <!-- Start of picture text --> Percent per annum, 1992-98<br>10 GDP per capita growth rate 9.2<br>Poverty reduction 8.4<br>8 7.5<br>6.4<br>5.9<br>6 5.4**<br>4.6<br>4<br>3.1* 3.4<br>2.1*<br>2<br>0<br>Bangladesh India Uganda Vietnam China<br><!-- End of picture text -->

* Bangladesh figures are for 1992-2000   ** India figures are for 1993-99

China, for example, uses a poverty line defined in constant Chinese _yuan._ The poverty line is deemed the minimum amount necessary to subsist. In practice, estimates of the number of 16