rafmacalaba/datause-encoder-data huggingface dataset:
# **_2._** _The Ras Gas Project_

The Ras Gas project, while a typical example of its kind, represents a milestone in the annals of project financing because of its recourse to global bond markets.<sup>5</sup> Capital markets debt wvas instrumental in the successful design and financing of the project because it provided flexibility not otherwise available through the syndicated loan market. Its USD 1.2 billion bond offering completed December 1996 is the largest for any international project to date, the first for a LNG project, the first capital markets financing for a Qatari issuer, and the first for a Middle Eastern issuer with a maturity beyond 7 years. To put the Ras Gas financing into perspective, the total amount of project bonds issued in 1996 was USD 4.79b (with Ras Gas accounting for _25%_ of this amount) while total bank lending to projects amounted to USD 42.83b. By 1999, the proportion of project debt raised in bond markets had grown from 10.06% in 1996 to 21.62%.6 Ras Laffan Natural Liquified Gas Company Limited is a joint venture between the Qatar General Petroleum Corporation (66.5%) and Mobil Corporation of the US (26.5%), located in Qatar (Persian Gulf).<sup>7</sup> Ras Gas, a Qatari company, has the right to develop lOm tons of liquified natural gas (LNG) annually from Qatar's North Field, the world's largest unassociated natural gas field with about 380b cubic feet of confirmed recoverable reserves (about 9% of world gas reserves). To this end, Ras Gas has constructed a 5.2 MMTA (million metric tons per annum)

> 5 The following project description draws on its bond offering prospectus (Goldman Sachs, 1996), Standard and Poor's (1996a, 1999, 2000) and Randolph and Schrantz (1997). According to Greg Randolph, Goldman Sachs, Ras Gas, whose structure is much copied in the energy sector, exemplifies state-of-the-art project design and financing. **6** The use of public debt markets for project financing is a relatively recent phenomenon. By 1999, global project lending by banks had increased to USD 72.392b (USD 56.65b in 1998) while global project bond issuance rose from USD 9.979b in 1998 to USD 19.966b (Pepiatt and Rixon, 2000); for earlier data and an excellent overview of the project finance market, see Esty (2000). For a description of the syndicated loan market's role in more traditional project finance, see Esty and Megginson (2000).

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<!-- Start of picture text --> Welfare Gains Liberalization<br>60<br>50<br>Long Run Gain<br>Welfare Gain<br>40<br>30<br>20<br>10<br>0<br>−10<br>0 0.1 0.2 0.3 0.4 0.5 0.6<br>Distortion Slope<br>% welfare gain<br><!-- End of picture text --> Figure 3.1: Welfare Gains From Liberalizing Distortions

# _γ_ = 0.4, the gains reach 32%<sup>15</sup> .

The figure also reveals a significant divergence between the welfare gains with and without consideration of transition dynamics. The gray line, which reports the welfare gains from steady state to steady state improvements in consumption, shows that ignoring transition dynamics significantly underestimates the overall potential gains in welfare. Continuing with the examples of China and India, long run gains . are only half as large as the overall gains<sup>16</sup> The divergence between the two measures of welfare gain arises as a result of the temporary gain in consumption that the liberalization of distortions allows in the early years of the transition path. We show this in figure 3.2, which reproduces the time series of consumption along the transition path from liberalizations at the bottom, middle, and top of the range of distortions that we consider: _γ ∈{_ 0.1, 0.3, 0.6 _}_ .

15All elasticities are read off Table 4 in Hsieh and Klenow (2007) . Albeit at a weaker degree, there is also evidence of correlated distortions in the United States, which we use as the efficient benchmark in our calibration. Had we subtracted the estimated elasticity for the US from the estimates for China and India, the corresponding slopes of distortions would have been 0.3 and 0.2 respectively, leading to welfare gains of 17% and 10%/

> 16Given the abstraction from physical capital in our model, our finding that the full dynamics of _TFP_ matter for the quantification of the welfare gains is a verification of a more general theoretical result established in Basu et al. (2012), who show that, to a first order approximation and for general conditions about the production side of the economy, the present value of _TFP_ and the growth rate of the capital stock are sufficient for characterizing welfare in a country.

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**Findings:** The proportion of scientific articles in the databases is the same as the proportion of documents published in peer-reviewed scientific journals (“AIDS”, “Sexually Transmitted Diseases” and “The Pan American Journal of Public Health” are examples of such publications).  Among the three Andean countries, Peru had the highest proportion of papers published in these journals, probably due to the fact that it has one of the longest records in HIV research in Latin America.  Peru’s academic sector has been actively involved in the process since the beginning of the epidemic.  Ecuador, on the other hand, is the country with the highest proportion of non-published papers or papers without publication status data (40%). In Bolivia, the proportion of non-published papers is 25%. Non-published papers have an impact on the use and quality of the information; nonpublished papers complicate access to information and the socialization of the findings. They generally make the papers produced weaker, because they have not undergone the detailed and enriching process of peer review.

# **Type of epidemiological design**

In **Peru** , 49 documents used various kinds of cross-sectional designs (86%). Among these, 28 documents used cross-sectional serological surveys supplemented by surveys with socio-demographic and behavior variables (49%); 18 used knowledge, attitudes and practice (KAP) surveys (32%); and three used STI bacteriological studies supplemented by socio-demographic surveys (5%). Of the remaining eight documents (14%), there were four cohort studies (7%), two case-control studies (3%), one case series study (2%), and one clinical trial (2%). In **Ecuador** , 36 documents used various kinds of cross-sectional designs (90%). Among these, 20 documents used KAP surveys (50%); 10 used serological surveys with or without supplementing KAP surveys (25%); and six included crosssectional survey studies about other issues (15%). Of the remaining four documents (10%), there was one experimental design study, one quality-control study for laboratories, one census, and one case series study. In **Bolivia** , 24 documents (96%) used cross-sectional design or survey design with different variations. Among these, 16 documents used KAP surveys (64%); seven used HIV serological surveys with or without supplementing KAP surveys (28%); and one was a syphilis serological survey (4%). The remaining document used a longitudinal design, made up by a cohort of people living with HIV (4%).

In Figure 8 below, cross-sectional studies or surveys and their variations appear in different shades of blue.

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Table 6. Parental Consanguinity and Likelihood of being Severely Stunted, Age 5-13

|||Ordinary Le|ast Squares||IV-LIML|Estimator|
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Parents are Related|0.026|||0.035|0.356*|0.401*|
||(0.046)|||(0.047)|(0.198)|(0.239)|
|F-coefficient of Inbreeding||0.109<br>|||||
|||(0.372)|||||
|Parents are 1st Cousins|||0.018<br>||||
||||(0.047)||||
|Parents are 2nd Cousins|||0.062||||
||||(0.070)||||
|Age|0.021***|0.022**|0.022***|0.020***|0.018***|0.017***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|
|Male|-0.014|-0.014|-0.014|-0.012|-0.017|-0.016|
||(0.026)|(0.026)|(0.026)|(0.025)|(0.030)|(0.029)|
|Wealth Index|-0.007|-0.008|-0.007|-0.019|0.019|-0.019|
||(0.022)|(0.023)|(0.023)|(0.020)|(0.029)|(0.020)|
|Dummy for Sindh province|0.084|0.080|0.083||0.144||
||(0.062)|(0.062)|(0.063)||(0.096)||
|Dummy for KPK province|0.230|0.229|0.237||0.300*||
||(0.174)|(0.175)|(0.175)||(0.157)||
|Father's Years of Education|0.002|0.002|-0.005|0.007|0.000|0.004|
||(0.005)|(0.005)|(0.009)|(0.005)|(0.005)|(0.005)|
|Father's Years of Education*Sindh|-0.003|-0.003|-0.005|-0.009|0.001|-0.002|
||(0.007)|(0.007)|(0.008)|(0.006)|(0.010)|(0.008)|
|Father's Years of Education*KPK|-0.014|-0.014|-0.015|-0.008|-0.010|-0.001|
||(0.017)|(0.018)|(0.018)|(0.011)|(0.014)|(0.011)|
|District Fixed Effects|No|No|No|Yes|No|Yes|
|**Test statistics**|||||||
|Hansen J statistic|||||2.40|2.67|
|p-value|||||0.121|0.102|
|F-statistic, Instruments|||||8.27|5.30|
|F-probability|||||0.001|0.007|
|CLR Test|||||4.07|6.53|
|p-value|||||0.056|0.016|
|95% Confidence Interval, Based on|||||[0.01,|[0.30,|
|CLR Test|||||0.91]|2.42]|
|Number of observations|1285|1285|1285|1285|1285|1285|

Notes: Village-level cluster robust standard errors are reported in parenthesis. Columns (4) and (6) control for factors related to village location with district fixed effects. The Hansen J statistic tests for over-identification. We report cluster-corrected F statistics to test for weak instruments and then implement the conditional likelihood ratio (CLR) test developed by Moreira (2003) to test for weak instrument bias. The CLR test tests the hypothesis that the instruments and the coefficient on the endogenous variable are jointly zero. We also show the 95% confidence interval suggested by the CLR test. The CLR test is generalized for clustered dependence in error terms using the minimum distance approach by Finlay and Magnusson (2009).

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and another noting that now “I try to talk [with] customers in a nice and professional way. I try to make delicious meals and make my place of work look attractive to them...I make sure [my cooking] it's up to the customers’ standard, not watery food” (ICRW, 2015). In qualitative interviews with those who had gone through mentoring, one participant said “I used to not care much about my business, I could open late, and sometimes the shop would remain closed if I don’t feel like opening. Now I know that it is important to put effort into my business.” We incorporated this potential channel into the three-year follow-up survey, asking whether the business always opened at a set time each day. Column 5 shows only 34 percent of the control group do this, and training increases this by a significant 6 percentage points. Mentoring appears to have a greater impact than training alone on having regular opening hours. Appendix 11 shows that the route to more customers does not appear to be through price competition, with no significant impact of treatment on the prices charged (or input costs of) the most commonly sold goods.

A second reason firms were able to attract more customers was by diversifying the range of products they sold. Banerjee and Duflo (2008) note the preponderance of small businesses in developing countries all selling similar things to one another, with no reason for customers to seek them out in terms of either product line or shopping environment. Column 6 shows that firms assigned to training were 9 to 11 percentage points more likely to have introduced a new product to their business, significant at the 1 percent level. There was tremendous variety in the new products introduced, with almost every business that said it had introduced a new product giving a different answer for what product it had introduced. Examples included avocados, oranges, carrots, garlic, fertilizer, body oil, cowpeas, belts, hair oil, tea, tobacco and Weetabix. Since most firms were selling only a handful of products, this diversification could have attracted customers that would not have otherwise purchased from them.

# **6.3 Impact on Financial Control, Access to Finance, and Capital Levels**

We next examine the extent to which training enables firm owners to produce more or earn higher profits by managing resources more effectively, and overcoming financial constraints. Several studies have emphasized the possibility that business training may have its strongest impact on sales during a bad month by helping participants identify strategies to reduce downward fluctuations in sales through diversifying the products they offer, as well as being more proactive about alternative activities during slow months. McKenzie and Woodruff (2014) note however 19
Jack, W. & T. Suri. 2016. “The Long Run Poverty and Gender Impacts of Mobile Money.” _Journal of Development Economics_ 354: 1288-1292.

Ljungqvist, L. 2002. “How Do Layoff Costs Affect Employment?” _Economic Journal_ 112: 829853.

Levine, R., N. Loayza, & T. Beck, 2000. "Financial Intermediation and Growth: Causality and Causes." _Journal of Monetary Economics_ 46: 31-77.

Sargan, J.D. 1958. “The Estimation of Economic Relationship using Instrumental Variables.” _Econometrica_ 26: 393-415.

Shea, J. S. 1997. “Instrument Relevance in Multivariate Linear Models: A Simple Measure.” _Review of Economics and Statistics_ 79: 348–352.

World Bank. 2020. _Doing Business 2020: Comparing Business Regulation in 190 Economies._ World Bank Publications, The World Bank Group: Washington, DC.

World Bank. 2020. “World Development Indicators Database.” _The World Bank:_ < http://data.worldbank.org/data-catalog/world-development-indicators> 26
# **3.3. Sampling frame**

In the HIES 2016/17, the primary sampling units (PSUs) are the Census Enumeration Areas (EAs) used for Bangladesh’s 2011 Census of Population and Housing. EAs are small geographic areas consisting of an average of 110 households. The sampling frame for the selection of PSUs consists in the list of all EAs covering people residing in non-institutional dwelling units (98.5 percent of the population in Bangladesh).<sup>8</sup> This is an important departure from the HIES 2010, where the sampling frame for the selection of PSUs consisted in the list of all EAs in the Bangladesh IMPS that excluded some geographic areas, such as urban slums.<sup>9</sup> For the HIES 2016/17, the IMPS could not be used because the most recent version of IMPS based on the 2011 Census of Population and Housing included only 2,012 EAs, an insufficient number to serve as a sampling frame for this survey. Table 1 summarizes the main characteristics of the sampling frame for the HIES 2016/17:

**Table 1 Sampling frame for the HIES 2016/17**

|**Area**|**Number of**<br>**households**|**Number of**<br>**EAs**|**Mean number**<br>**of households**<br>**in EA**|
|---|---|---|---|
|Rural|24,588,843|228,202|108|
|Urban|5,020,815|43,872|114|
|City Corporation|2,253,737|21,266|106|
|**Total**|**31,863,395**|**293,340**|**109**|

Source: Bangladesh Bureau of Statistics, 2011 Census of Population and Housing

# **3.4. Stratification, sub-stratification, and domains of study**

In Bangladesh, the eight divisions are the first-level administrative geographical partitions of the country. As of 2016, the country had eight divisions: Barisal, Chittagong, Dhaka, Khulna,

> 8 The list of all EAs in the 2011 Census of Population and Housing includes 296,718 clusters, of which 293,340 EAs cover non-institutional dwelling units.

> 9 The IMPS is a master sample design that is updated after each Census of Population and Housing, and which has been used in Bangladesh as the sampling frame for the selection of PSUs in the HIES 2000, 2005, and 2010. Using a master sample frame for multiple surveys makes regular survey data collection more efficient. For details on how the Bangladesh IMPS Design was produced, see Maligalig and Martinez (2013).

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# **Table 8. Tax Incidence**

||**P**<br>Hou<br>in<br>Ml|**anel 1**<br>sehold tax<br>cidence<br>Fl||**Pa**<br>Imputed i<br>inci<br>Ml|**nel 2**<br>ndividual tax<br>dence<br>Fl||
|---|---|---|---|---|---|---|
|Exp. Quartile|ae-<br>headed|emae-<br>headed|Exp. Quartile|ae<br>holders|emae<br>holders||
|Poorest|0.71|0.71|Poorest|1.96|2.03||
|2|**0.37**|**0.74**|2|**1.01**|**1.16**||
|3|**0.31**|**0.52**|3|0.72|0.81||
|Richest<br>|**0.20**|**0.36**|Richest<br>|0.38|0.46||
|Total tax<br>incidence|**0.46**|**0.62**|Total tax<br>incidence|**1.07**|**1.19**||
|_Number of_<br>_households_|_1,755_|_553_|_Number of_<br>_respondents_|_1,726_|_1,731_||
||**P**|**anel 3**<sup>**a**</sup>|||**Panel 4**<sup>**b**</sup>||
||Hou<br>in|sehold tax<br>cidence||Ho<br>|usehold tax inci<br>|dence<br>|
|Exp. Quartile|Dual<br>headed|Female-only|Exp. Quartile|Married<br>male-<br>headed|Married<br>female-<br>headed|Single<br>female-<br>headed|
|Poorest|0.71|0.76<sup>d</sup>|Poorest|0.71|na<sup>e</sup>|0.73|
|2|**0.40**|**0.83**<sup>**d**</sup>|2|**0.36**|na<sup>e</sup>|**0.79**|
|3|0.33|0.43<sup>d</sup>|3|0.30|na<sup>e</sup>|0.48<sup>d</sup>|
|Richest|**0.21**|**0.42**<sup>**d**</sup>|Richest|**0.21**|na<sup>e</sup>|**0.45**<sup>**d**</sup>|
|Total tax<br>incidence|**0.47**|**0.65**|Total tax<br>incidence|**0.45**|0.56|**0.65**|
|_Number of_<br>_households_|_1,935_|_303_|_Number of_<br>_households_|_1,969_|_260_|_518_|

_Notes_ : The sample is restricted to landholding households and respondents. For in panels 1, 3, and 4, household tax incidence is calculated by dividing self-reported tax payments by household expenditure. In panel 2, individual tax incidence is calculated by dividing imputed tax payments (apportioned according to individual landholdings) by per capita expenditure. The imputation of individual incidence requires the land area, but because there are households for which that is missing, the sample size of male respondents is less than that of male-headed households.

Households and individuals are ranked by adult equivalence expenditure scales into quartiles. Tests of equality of means were conducted between (1) male- and female-headed households, (2) male and female landholders, (3) dual adult and female-only households, and (4) single female-headed and married male-headed households. Significant differences at p<0.05 are in bold. a Male-adult-only households are excluded in this panel because of small sample size. b Single male-headed households are excluded because of small sample size.<sup>d</sup> The sample size is less than 100 observations.<sup>e</sup> The sample size is less than 50 observations. Household sampling weights are used.

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# **3 Data**

Our unit of observation is an export spell - continuous exporting of product _k_ from country _c_ to the world market. The term “time” refers in this paper to the year when an export spell started unless stated otherwise. The final sample contains data on 1,299 products exported from 81 countries and focuses on export spells that started between 1980 and 1997. Many financially closed countries opened up their domestic stock markets for the first time during this period, with different countries allowing foreign investors to acquire equity stakes in domestic firms in different years. This wave of staggered financial liberalization happened before the more controversial explosion of cross-border financial flows in the 2000s that preceded the global financial crisis.<sup>9</sup>

## **3.1 Financial variables**

A stock market liberalization event occurs when the government of a country opens up its domestic stock market to the presence of foreign investors, allowing them to acquire equity stakes in domestic companies. Bekaert, Harvey, and Lundblad (2005) report the official years of these liberalization events, covering the period of extensive financial liberalization in many countries between 1980 and 1997.<sup>10</sup> Based on these years, we construct a liberalization dummy that is equal to one if a given country allows in a given year foreign investors to acquire shares in the domestic firms and zero otherwise. The liberalization dummy thus always equals one for the countries that implemented stock market liberalization prior to 1980. By the same token, the dummy always equals zero for the countries that did not liberalize their stock market until 1997. For countries that opened up their stock market to foreign investors during the period 1980-1997, the liberalization dummy takes value one if a given export spell is exposed to the liberalization event, i.e.

> 9 In most specifications, we follow the export spells until 2006, the year before the global financial crisis began. In one of the robustness tests reported in Online Appendix F3, we stop following the export spells already in the year 1997 rather than 2006.

> 10 Online Appendix A1 provides a list of the countries. The original database of Bekaert, Harvey, and Lundblad (2005) covers 91 countries, including 41 countries that liberalized their stock market at some point during the 1980-1997 period, 16 countries that liberalized their stock market prior to 1980, and 34 countries that kept their stock market closed to foreign investors during the whole period under consideration. In our final sample, the number of all countries declines from 91 to 81 and the number of countries that liberalized their stock market during the 1980-1997 period declines from 41 to 36 due to the lack of data for some of the control variables.

7
|ADMINISTRATIVE<br>CORRUPTION|Public<br>Official<br>Survey|Corruption index representing the widespread of bribes to obtain a<br>public services within own agency.|
|---|---|---|
|CORRUPTION IN<br>LAWS AND<br>REGULATIONS|Public<br>Official<br>Survey|Corruption index representing the widespread of bribes to bias a<br>regulatory/legal decision within own agency|

# **PANEL D: GOVERNANCE INDICES**

|**Variable Name**|**Source**|**Definition**|
|---|---|---|
|QUALITY OF<br>RULES|Public<br>Official<br>Survey|Percent of cases where rules/guidelines/regulations in the personnel<br>and budget management are simple, clear, easy to understand and do<br>not add too many administrative steps. Subdivided in the empirical<br>analysis into quality of the rules in personnel and in budget<br>management.|
|EFFECTIVENESS<br>OF RULES|Public<br>Official<br>Survey|Percent of cases where policy/guidelines/regulations in the personnel<br>and budget management are well supervised and strictly<br>enforced.Subdivided in the empirical analysis into effectiveness in<br>personnel management and effectiveness in budget management|
|AUDIT<br>MECHANISMS|Public<br>Official<br>Survey|Percent of cases where the decisions on personnel, budget and service<br>management are subject to external and/or internal audits. Subdivided<br>in the empirical analysis into audit in personnel management, audit in<br>budget management and audit in public contracts.|
|MERITOCRACY|Public<br>Official<br>Survey|Percent of cases the decisions on personnel management issues are<br>based on professional experience/merit/performance or education<br>levels.|
|WAGE<br>SATISFACTION|Public<br>Official<br>Survey|Percent of employees very satisfied or somewhat satisfied with their<br>wages and benefits.|
|VOICE|Public<br>Official<br>Survey|An index representing the existence of consumer feedback and<br>complaint mechanisms.|
|MISSION|Public<br>Official<br>Survey|Index determining the degree of understanding of agency's objective<br>and own tasks and responsibilities by public officials within the<br>institution.|
|RESOURCES ARE<br>ADEQUATE|Public<br>Official<br>Survey|Percent of cases where physical, financial and human capital resources<br>of the agency are adequate.|
|**PANEL E: REGION**|**AL VARIA**|**BLES**|
|**Variable Name**|**Source**|**Definition**|
|Language Stratification|LSMS<br>survey|Languague stratification of the region|
|Regional average<br>income|LSMS<br>survey|Average household Income of the region|
|Regional Dummies|Public<br>Official<br>Survey|Dummies for Lima, Selva, Resto de Costa and Sierra (reference region<br>is Sierra)|

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