rafmacalaba/datause-encoder-data huggingface dataset:
|Figure 6<br>|Projected Population (billions) in Less Developed Countries,<br>1985.2025, Compared with U.N. and Previous World Bank Projections ......<br>|xdii|
|---|---|---|
|Figure 7<br>|Projected Population (billions) in More Developed Countries;<br>1985.2025, Compared with U.N. and Previous World Bank Projections ......<br>|<br>diii|
|Figure 8<br>|Percentage Distribution of World Population by Continent, 1985 ....<br>..<br> <br>|....<br>xiv<br>|
|Figure 9<br>|Percentage Distribution of World Population by Continent, 2000 ....<br>..<br>|.... xxiv<br>|
|Figure 10<br>|Percentage Distribution of World Population by Continent, 2025 ..<br>|xxiv<br>|
|Figure 11<br>|Population in Less Developed Countries by Age and Sex, 1985 .<br>..<br>|xxvii|
|Figure 12<br>|Population in Less Developed Countries by Age and Sex, 2025 ..<br>|xxvii|
|Figure 13<br>|Population in More Developed Countries by Age and Sex, 1985 ..<br>|xxvii<br>|
|Figure 14<br>Figure 15<br>|Population in More Developed Countries by Age and Sex, 2025 ..<br>Assumed Total Fertility Trend, Less Developed Countries, and U.N.<br>and Previous World Bank Trends ..<br>|xxvii<br>xxx|
|Figure 16<br>|Assumed Total Fertility Trend, More Developed Countries, and U.N.<br>and Previous World Bank Trends ..<br>|xxx|
|Figure 17<br>|Assumed Life Expectancy Trend, Less Developed Countries, and U.N.<br>and Previous World Bank Trends ...<br>|xx|
|Figure 18<br>|Assumed Life Expectancy Trend, More Developed Countries, and U.N.<br>and Previous World Bank Trends ..<br>|xxx|
|Figure 19|Percentage Contribution to World Population Growth from World Bank<br>Regions and Nonborrowers, 1985-2100 ..|xavii|
|Exhibit 1<br>|Countries and Economies Classified by Income Group (based on GNP<br>per capita in 1987 U.S. dollars) ..<br>|xcii|
|Exhibit 2|Countries and Economies Classified by World Bank Region and Country<br>Department..|xodv|
by the beneficiaries, th. If the eligible countries capture all of the benefits of this tariff reduction (i.e. 8, = 1), then the prices they receive on their exports should increase by the amount of the tariff decline. However, if export price increase is less than the tariff decline (ie. 8; < 1), then the importers are capturing a portion of the tariff rents created by the preferential market access. We should point out that the traditional tariff pass-through effect, given by a; in (1) and (2), can be found from our estimation. More specifically, given our definitions of the prices, we have a, = 1-—,. Suppose we were to estimate that 6, = 0.25. This implies that the pre-tariff price decreases by 0.25% (or the post-tariff price increases by 0.75%) if there is 1% tariff increase. Thus the tariff pass through rate is 75%. 4.1 Data & Methodology The United States International Trade Commission (USITC) collects and makes available very disaggregated and detailed customs data. The data include the customs value, unit prices, duties paid in a given 8-digit category from any country for any year between 1989 and 2002°. The USITC data is further classified according to whether the imports entered the US under a specific preference program (such as NAFTA, CBTPA, GSP or AGOA) or no program (meaning under MEN)’.

We use HS 8-digit level disaggregated data on customs value, quantity and duties collected from each country in our sample for the time period 1989-2002. The prices received by the exporters, denoted as Doar are unit prices, calculated as the ratio of customs value to number of units of category k in year t from country 7. The average US import price, p2OTM is the unit prices received by exporters from the rest of the world!”. Tariffs imposed on the exports of country i in category ‘Data were obtained from the USITC website at <u>http://www.dataweb.usitc.gov.</u> The customs value data exclude insurance and freight.

° Until 2000, when CBTPA was implemented, the main preference scheme under the CBI was duty-free treatment on the portion of the value-added created in the beneficiary country and the inputs (fabric and yarn) imported from the US. In other words, MFN tariff was paid only on the portion of the imputs imported from third countries. However, all shipments into the US in the same 8-digit category were compiled together and listed under the MFN category. The level of tariff collected in a given 8-digit category is, therefore, an average rate of the tariff paid on individual shipments. Since we do not have shipment level data, we rely on these averages. After 2000, we have separate data for exports entering under CBTPA (where the tariff is zero) and under the old scheme (listed as MFN). The results in Table 5 indicate that the coefficients of the Tariff Difference variable are not statistically different under the two regimes.

0We should note the unit prices are product-specific, such as dozens of shirts or pants. They are not measured by 10
# **4.2. Questionnaire design, expenditure data collection using SWIFT and survey implementation**

The questionnaire used in the survey was specifically developed for this study by the authors. It has four areas of focus: (i) socioeconomic characteristics, annual expenditure levels, and poverty; (ii) exposure to the 2015 floods; (iii) impacts of the 2015 floods; and (iv) behaviors associated with living in risk prone areas.

# _Annual expenditure levels and poverty_

Collecting expenditure data is costly and increases significantly the duration of interviews. Beyond budgetary and data processing issues, it also reduces the quality of the data by reducing the space available for the other questions in the survey and by increasing survey fatigue from respondents. To avoid these issues, the survey adopted the SWIFT approach to estimate household expenditures and poverty rates of sample areas from a few simple questions (Yoshida et al., 2015). More details on SWIFT are provided in Appendix 2.

Instead of collecting household consumption expenditure data directly, the SWIFT approach uses the expenditure data of a subsample of households which is representative of the GAMA region from the national household surveys (GLSS6, 2012/2013) to identify a set of around 20 simple questions that can best predict the expenditure level of any given household. The questions usually include demographics, education, and housing characteristics of households. Based on those, a statistical formula calibrated on the GAMA subsample of the national survey – here GLSS6 – provides an estimate of household expenditures. As the SWIFT methodology does not collect data on food consumption and other monetary variables, these expenditure estimates reflect the socioeconomic profile of a household in the medium term, and do not account for short-term variations in consumption patterns.

# _Exposure to the 2015 floods_

There is no unique definition of being “affected” by a flood. In the questionnaire, we define being affected as experiencing one of the following effects, which can be “localized” or “non-localized”:

- Localized impacts (due to impacts on the household’s dwelling):

- Asset losses;

- Damages to dwelling (such as having water in the house);

- Significant time (one day or more) spent cleaning the house, discharging water from house, or cleaning in and around the house due to flood water;

- Non-localized impacts (can be due to impacts elsewhere):

- Loss of water or electricity services;

- Loss of significant share of income, including one day of work missed or more (due to transportation, illness, work place damages, or forced to stay home with dependents, or other reason);

- Being fined or reprimanded due to late arrival at work;

- Experience illness or other health effects;

- Having children miss one day or more of schooling;

- Having to change food intake (either quantity or quality).

10
treatment effects are presented in Appendix C.

The main outcomes of interest in the randomized survey experiment relate to respondents’ support for subsidy reform. This is measured individually for each of the outcome questions and collectively by aggregating the results for the questions about unconditional and conditional support into two indexes (unconditional support index and conditional support index). Specifically, using an approach similar to that used in related randomized survey experiments by Alesina et al. (2018) and Karadja et al. (2017), indexes are created that are an unweighted average of the z-scores of the outcome variables, oriented so that a higher index means more support for reform. The answers to each outcome question and the unconditional and conditional indexes are presented in Tables 4, and 5.

Three main types of experimental analysis are conducted. The first tests H2 by examining if providing any information (i.e., pooling all three treatments) about the negative aspects of energy subsidies shifts support for reform (across all countries). This is examined by conducting pooled (across treatments and countries) Ordinary Least Squares (OLS) regressions with country-fixed effects with a dummy variable that takes on the value of 1 if the respondent belongs to any treatment group and the value of 0 if the respondent belongs to the control group. This approach is in line with what was undertaken by Alesina et al. (2018, 2022). This analysis can be expressed formally as follows: where _i_ denotes individuals, _j_ denotes countries, and _β_ 1 captures the average difference between respondents in any treatment group ( _T_ ) and the control group about the outcome of interest ( _Y_ ). Further, _Xi_ is a vector of variables that controls for potential imbalances in background characteristics (age, sex, location, income, education level, perceived relative income, actual income, energy consumption, trust in government, belief in government capacity, knowledge of subsidies) between treatment and control groups, _θj_ captures country fixed effects, _εij_ is the model error term (clustered at the country level), and _β_ 0 is the 27
- 7- That he settled all obligations for general social and common needs, and

- 8- That he meets other requirements defined by the law or other regulations.

## **<u>Proposed text</u>** <u>:</u>

## **Article 8b**

Person who meets the following requirements will get permit for opening of a trade shop:

# **1- That he is citizen of Bosnia and Herzegovina,**

- 2- That he is capable to work,

- 3- That he meets health requirements if health is defined as a special requirement for retail trading of certain products in trade shops,

- 4- That he has business premises that meet general and special requirements,

- 5- That he does not work or already have a trade shop open,

- 6- That he was not prohibited to work in this business by a legally effective decision on introducing a protective measure,

- 7- That he settled all obligations for general social and common needs, and

- 8- That he meets other requirements defined by the law or other regulations.

## **<mark>FBiH AMENDMENT</mark>**

Based on chapter III. article 1 item d of FBiH Constitution (FBiH Official Gazette No.: 1/94, 13/97, 16/02, 22/02, 52/02) FBiH House of Representative at _________session on ______ 2003 passed the:

## **LAW**

## **ON AMENDMENTS TO THE LAW ON TRADE**

## **Article 1**

In the Law on trade (FBiH Official Gazette No. 2/95, 19/96) article 8b, paragraph 1, item 1 is to be amended and read as follows: ‘‘That he is citizen of Bosnia and Herzegovina ’’

## **Article 2**

This law becomes effective on the eighth (8) day from the day of its publishing in the FBiH Official Gazette.

No. : Chair Page 45 of 51
Finally multi-round surveys are more costly, more time-consuming, and more complex to organise given the need to link data from one round to the next.

Another alternative is to collect mortality data through systems for the continuous follow-up of a community. If all households are visited routinely on a regular basis, it is possible to record all deaths that occur in the population during a specified period. Changes in the denominator population can also be recorded as they occur. When a death occurs, the household can be visited within a planned interval of time, and the most appropriate respondent chosen to report on symptomatology. While this approach is attractive, it has several disadvantages as a method for the measurement of cause-specific mortality. Firstly, the intensive follow-up of the community may itself result in a modification of mortality patterns relative to the general population. If field-workers visit a household when an adult member is severely ill, there would be considerable pressure to assist with transport to a medical facility. Secoadly, as with multi-round surveys, continuous follow-up studies are generally restricted to the entire population of a limited geographical area, thus reducing their representativeness with regard to the general population. Thirdly, deaths among adults are rare events, and field-workers may tire of asking routine questions that almost always result in negative responses. Thus while it is important to improve the quality Qf data collected through continuous follow-up systems, these are not the method of choice for the provision of nationally representative data on cause-specific mortality.

# 4. COLLECTION, CLASSIFICATION AND PRESENTATION OF DATA CONCERNING CAUSE OF DEATH

# 4.1 Introduction

This section focuses on methods for the retrospective diagnosis of cause of death by questioning relatives about the symptoms associated with the death. Techniques used in previous studies of cause-specific mortality are reviewed with respect to the choice of interviewer, questionnaire design, and the diagnosis and classification of cause of death. This is followed by a discussion of the prospects of obtaining a diagnosis of some of the major causes of adult death on the basis of symptoms reported by relatives. The section ends with a discussion of alternative methods of presentation of cause-specific mortality data.

# 4.2 Techniques used in previous studies.

Several studies in the past decade have attempted to establish causes of death through retrospective interviews of relatives of the deceased.

In Nigeria, Bradley and Gilles (1984) used enumerators with at least 7 years of schooling to interview a relative of the deceased within one month of the death. The presence or absence of each of a list of 25 symptoms was recoreded, but no additional questions were asked. Of 52 deaths of adults aged 15-44 years, 42 were recorded as due to ill-defined conditions, while in children ill-defined conditions 18
Table A.1 (continued): Baseline statistics and balance test

||||Tes|t of rando|mization ba|lance (N=|999)||
|---|---|---|---|---|---|---|---|---|
||Sample|Assigne|d therapy|Assign|ed cash|Assign|ed both|F-Test|
|Baseline covariate|Mean|Coef.|_p_ value|Coef.|_p_ value|Coef.|_p_ value|_p_ value|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Committed theft/robbery in past 2|0.53|0.05|0.51|0.01|0.64|-0.02|0.66|0.80|
|weeks|||||||||
|Number of nonviolent stealing|5.09|-0.36|0.58|-0.46|0.68|0.39|0.70|0.88|
|incidents|||||||||
|Number of felony stealing incidents|0.43|0.06|0.77|0.17|0.60|-0.17|0.67|0.86|
|Disputes and fghts in past 2 weeks|2.16|0.14|0.80|0.33|0.63|-0.68|0.25|0.68|
|(0-9)|||||||||
|Aggressive behaviors (mean of 19),|0.00|0.05|0.66|0.13|0.22|-0.23|0.09|0.23|
|z-score|||||||||
|Conscientiousness index (0-24)|15.36|-0.05|0.74|-0.22|0.32|-0.01|0.98|0.05|
|Neuroticism index (0-21)|12.09|-0.07|0.77|0.18|0.64|0.11|0.85|0.34|
|Grit index (0-21)|13.76|0.07|0.59|-0.08|0.83|0.00|0.99|0.24|
|Reward responsiveness index (0-24)|14.75|-0.15|0.48|-0.03|0.95|-0.25|0.70|0.95|
|Locus of control index (0-24)|14.45|-0.09|0.77|-0.43|0.15|0.45|0.29|0.00|
|Impulsiveness index (0-21)|9.39|0.39|0.38|0.18|0.66|-0.88|0.10|0.33|
|Self esteem index (0-24)|13.47|-0.08|0.78|-0.11|0.65|0.12|0.75|0.89|
|Patience in game play index (0-6)|4.11|0.08|0.48|-0.07|0.77|0.03|0.94|0.83|
|Time inconsistency in game play|3.27|-0.22|0.03|-0.05|0.62|0.13|0.34|0.01|
|index (0-6)|||||||||
|Risk aversion index (0–3)|1.56|-0.02|0.89|-0.05|0.56|0.09|0.43|0.61|
|Self-reported patience (mean of 7),|0.00|-0.08|0.62|-0.13|0.25|0.15|0.42|0.33|
|z-score|||||||||
|Declared Risk Appetite (mean of 6),|0.00|0.01|0.94|-0.02|0.88|-0.10|0.65|0.94|
|z-score|||||||||
|Cognitive ability (z-score)|0.00|0.15|0.05|0.14|0.20|-0.29|0.01|0.04|
|Executive function (z-score)|0.00|0.07|0.18|0.10|0.45|-0.25|0.06|0.16|
|R-Squared||0|.17|0.|11|0.|33||
|_p_ Value on F-statistics on all<br>covariates||0|.53|0.|66|0.|25||

_Notes:_ Column (1) reports the sample mean. A small number of missing values are imputed at the median. Columns (2)-(7) report the coefficients and _p_ values from ordinary least squares regressions of each baseline covariate on three indicators, one for assignment to each treatment arm, controlling for block fixed effects. Column (8) reports the p value from a joint test of statistical significance of all three treatment indicators.

iii
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out that earlier research, notably Prusa (1992), had aggregated withdrawn and settled cases. Taylor uses data only for antidumping cases filed from 1990 through 1997 that ended in a withdrawn petition _without a suspension agreement or a voluntary restraint agreement_ . From an examination of monthly import data, Taylor finds no evidence of collusion in these cases. By design, Taylor’s analysis omits cases settled through collusion-friendly measures such as price floors or quantitative restrictions (such as voluntary export agreements).<sup>17</sup> Barfield’s (2003) review of the effects of antidumping actions in several U.S. high-technology industries is relevant here because these industries are precisely the kind where the necessary conditions for predatory or strategic dumping are most likely to be found. Yet Barfield concludes that antidumping, “while problematic in any sector, is particularly troublesome in high-technology sectors.” The cases Barfield reviews are consistent with Messerlin’s (1990) characterization of antidumping as a policy more likely to impede than to protect competition in the market of the importing country. As such, antidumping during this period was likely to benefit producers (in some cases including foreign producers) at the expense of consumers, the same result as with other types of protection. Barfield argues that antidumping is futile as a means to save uncompetitive companies and sectors, while it is damaging to market competition and impedes innovation—the lifeblood of high-technology industries.

Like Finger (1993, chapter 4), Barfield favors complete elimination of antidumping, with substitution of safeguard actions where domestic firms need more time to adjust to changing market conditions, while leaving evaluation of anticompetitive effects from possible predation to the antitrust authorities. Of course, both authors acknowledge that political opposition makes this “best” course of action unlikely. But Messerlin (1994) points to a variety of pitfalls from simply replacing antidumping by

> 17 WTO rules restrict the use of voluntary export restraints, which were common prior to the Uruguay Round. However, the WTO still permits use of price undertakings (price floors) to settle antidumping cases.  As Moore (2005) shows, the two policies have identical effects under perfect competition, but the results differ when firms have market power.

18
follow Lemke and Reed (2001) and Marinescu (2007) in jointly estimating (1) and (2) by maximum likelihood.<sup>22</sup> Similar to what we described for y2ij, using an account frequently (that is, three or more times a month), which we label y3ij, can only be observed as long as the individual owns an account. Hence, in order to analyze the determinants of using an account frequently, we run a sample selection model similar to that presented in equations (1) and (2), replacing y2ij with y3ij as the dependent variable and including x3i and z3ij as the vectors of country- and individuallevel variables, respectively. As above, since in principle we have to assume that the variables that affect the likelihood of having an account (x1i and z1ij) are the same as those that affect the frequency of using an account (x3i and z3ij), we estimate the selection and frequency equations a la Heckman (but with two dichotomous dependent variables) using maximum likelihood.

In addition to analyzing how different individual and country characteristics relate to greater financial inclusion, we also examine reported subjective barriers to financial inclusion. We identify these barriers based on the respondents’ answers to the following question: “ _Please tell me whether each of the following is a reason why you, personally, do not have an account at a bank, credit union, or other financial institution_ .” The reasons we analyze are: “ _(a) They are too expensive_ ”; “ _(b) You don’t have the necessary documentation (ID, wage slip)”_ ; “ _(c) They are too far away_ ”; “ _(d) You don’t trust them_ ”; and “ _(e) You don’t have enough money to use them.”_

> 22 Sartori (2003) points out that the Heckman-type estimator may perform poorly when the same variables are included in both the selection (likelihood of having an account) and the outcome (likelihood of using the account to save) equations.  This is especially problematic when estimations are conducted using small samples. Sartori (2003) proposes an alternative estimator that assumes that errors are near identical across equations to obtain more precise estimates. In unreported regressions, we applied the Sartori method. We found that the results based on the Sartori estimator are almost identical in size and in significance to those we obtain when using the probit Heckman-type model (without weights).  Our challenge is that the Sartori estimator does not allow for the use of weights which is very important when using individual-level survey data. Hence, given the similarity of the results and considering that we have a large sample, we prefer to estimate the Heckman-type model with two binary dependent variables using maximum likelihood.

16