rafmacalaba/datause-encoder-data huggingface dataset:
Table 8. Simulated impact of PIT and GST reforms on poverty and inequality

|||Gross market<br>income plus<br>pension|Disposable<br>income|Consumable<br>income|Final<br>income|
|---|---|---|---|---|---|
|Poverty|2017 baseline|8.2|8.2|9.3||
|($3.20/day, %)|2021 simulation|7.2|7.2|8.7||
||2022 simulation|6.7|6.7|9.3||
|Gini index|2017 baseline|38.7|37.8|36.1|31.3|
||2021 simulation|38.7|37.9|36.3|31.4|
||2022 simulation|38.7|37.8|35.9|31.8|

Note: The results for 2017 present the baseline; the 2021 simulation incorporates the PIT reform and the 2022 simulation reflects both PIT and GST reforms. Source: World Bank staff calculations using 2017 BLSS.

Table 9. Concentration of old and new PIT schedule and sales tax/GST

|Old PIT<br>schedule|New PIT<br>schedule|Sales tax|GST|
|---|---|---|---|
|Poorest<br>0.0%|0.0%|2.5%|1.6%|
|2<br>0.0%|0.0%|4.3%|2.6%|
|3<br>0.3%|0.1%|4.4%|3.4%|
|4<br>0.5%|0.3%|5.2%|4.2%|
|5<br>1.6%|0.9%|8.0%|5.6%|
|6<br>2.9%|2.0%|7.9%|7.3%|
|7<br>5.0%|3.8%|9.9%|8.2%|
|8<br>9.6%|8.2%|11.7%|11.2%|
|9<br>18.7%|17.4%|16.2%|18.3%|
|Richest<br>61.3%|67.3%|29.9%|37.7%|

_Source_ : Staff calculations using BLSS 2017.

# 6. Conclusion

The analysis presented in this paper provides insights into the distributional impact of Bhutan’s tax-benefit system. To our knowledge, this is the first comprehensive analysis undertaken on this issue. The CEQ methodology is used to provide an assessment of the impact of tax-benefit policies as of 2017 as the baseline and then is supplemented with simulations to estimate the impact of two major fiscal reforms in Bhutan: the personal income tax reform in 2020 and the introduction of the GST in 2022.

The results in this paper show that the fiscal system in Bhutan plays a modest yet positive role in reducing inequality, while it slightly increases poverty. Taxes and social spending combined contribute to a decrease in inequality in Bhutan, while leading to a small increase in poverty as of 2017 (before the major fiscal reforms). The increase in poverty is mainly due to the burden of indirect taxes and social contributions that are not offset by other direct transfers. Disaggregating the results by fiscal intervention shows that households in the bottom 80 percent are net receivers of fiscal interventions, with fiscal benefits primarily occurring through education and health benefits which are both progressive. In fact, most households do 26
# **Cocaine Production and Trafficking: What Do We Know?**<sup>♣</sup>

Daniel Mejía<sup>♦</sup> and Carlos Esteban Posada<sup>•</sup>

## **INTRODUCTION**

The nature of illegal and black markets makes it very difficult to collect data such as quantities transacted, intermediate and final goods’ prices, and other relevant market characteristics like the quality of the product being dealt and the distribution of profits within the industry. Illegal drug markets are not the exception.<sup>1</sup> For instance, in measuring consumption “buyers cannot report a price in dollars per standardized unit, but only how much they spent on some quantity of white powder, the contents of which is unknown.” (Reuter and Greenfield, 2001, p. 169). Notwithstanding the difficulties of collecting accurate data, the market’s numbers on the size, quantities, prices, etc. always attract a good deal of

### **_Keywords_** : Cocaine; Narcotraffic; War on Drugs.

### **_JEL classification_** : K42; C81; H56.

♣ Chapter prepared for the _Project on the Development Impact of the Illegal Drug Trade_ , The World Bank. The authors wish to thank Norman Loayza, Phil Keefer and workshop participants at the World Bank for helpful comments and suggestions. Diana Jaramillo provided excellent research assistance. As usual, the views expressed in this article are solely those of the authors and do not compromise Banco de la República, Colombia or its Board of Governors.

> ♦ Corresponding author. Department of Economics, Universidad de los Andes. E-mail: <u>dmejia@uniandes.edu.co</u>

> • Research Unit, Banco de la República, Colombia. E-mail: <u>cposadpo@banrep.gov.co</u>

> 1 See Reuter (2001) and Thoumi (2005a).
Table D.2 (continued): Program impacts on self-control skills, 12–13 month survey only

||||IT|T regression|(N=943r|esponses fro|m 943 su|bjects)|
|---|---|---|---|---|---|---|---|---|
|||Control|Ther|apy only|Cas|h only||Both|
|Outcome||mean|ITT|Std. Err.|ITT|Std. Err.|ITT|Std. Err.|
|||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||Most of the time, I will do things for no|1.379|-0.138|[.059]**|-0.006|[.064]|-0.075|[.066]|
||other reason than that I will enjoy them.||||||||
||When I am doing well at something, I will<br>always like to be doing it.|2.059|0.061|[.053]|0.086|[.052]*|0.013|[.056]|
||When I get something I want, I can jump<br>with happiness and it gives me plenty<br>strength.|2.079|-0.027|[.056]|0.108|[.056]*|-0.096|[.060]|
||When I see a chance to get something I<br>really like, I can jump with happiness on<br>the spot.|1.966|-0.044|[.065]|0.013|[.065]|-0.129|[.064]**|
||When good things happen to me, it<br>afects me strongly.|1.611|-0.023|[.069]|-0.082|[.071]|-0.057|[.071]|
||I can jump with happiness when I win a<br>lucky ticket.|2.207|-0.053|[.057]|0.098|[.053]*|-0.070|[.059]|

_Notes:_ The table reports long-run (12–13 month) intent to treat estimates of self control outcomes. N=943 because 4 respondents did not answer all questions. We calculate the impact of each treatment arm in the short and long run, controlling for baseline covariates and block fixed effects. We focus on pre-defined composite measures, typically defined by survey module. The overall summary indexes are the standardized mean of its composite outcomes, standardized. Heterosketastic robust standard errors are reported in brackets. *** p<0.01, ** p<0.05, * p<0.1 xxv
# **1    Introduction**

Biofuels production and consumption has been rapidly growing in the last few years. Led by Brazil and the United States, global production of fuel ethanol more than doubled during the last four years, increasing from 31.3 billion liters in 2005 to over 72.8 billion liters (estimated) in 2009 (F.O. Licht 2009). Although being produced in smaller quantities than ethanol, the relative growth experience by biodiesel is even stronger, surpassing 12.8 billion liters in 2008 (estimated) up from 3.9 billion liters in 2005 (F.O. Licht 2008).

Currently, biofuels provide for over 1.5% (about 34 mtoe) of the energy used for transport (IEA 2008). The Energy Information Administration (EIA) of the United States Department of Energy projects the 2030 world energy consumption of liquid forms on 112.5 million barrel of oil equivalent per day (about 238 EJ/yr). Of this, 60% or 142.8 EJ/yr would be consumed by the transport sector. On the other hand, EIA (2009) projects that the total production of biofuels will be between 10.1 and 15.1 EJ per year by 2030 depending on the assumptions on oil prices.<sup>2</sup> Several reasons can be advanced as fueling growth in biofuels. Salient drivers are increased oil prices over the past decade, as well as oil price volatility. This has led to increased public support for renewable fuels (e.g., subsidies, mandated consumption, etc.) by many countries (REN21 2009). The oil crisis of the 1970s prompted some interest in biofuels. However, that impulse was short lived and faded in most countries with the subsequent decline in the price of petroleum. Brazil is an exception to this pattern, with ethanol production continuing to expand (with the help of blending mandates) throughout the 1980s.<sup>3</sup> Without blending and oxygenations mandates, and other forms of intervention, the market for biofuels would be limited when the price of crude oil is below US$60-70 per barrel (OECD-FAO 2009).

> 2 The original units are in million barrels per day (4.8-7.2). For conversion, a barrel a day is about 50 tons of oil per year. A ton of oil contains 41.868 Gj.

> 3 It should be noticed that low crude oil prices in the 1990s proved challenging to the ethanol industry, which only resumed significant growth earlier this decade with the raise in crude oil prices and the advent of flex fuel vehicles (able to use ethanol and gasoline in any blend).

2
20 side (e.g., minimizing the risk of household over-indebtedness, through financial literacy programs and consumer protection frameworks).

# **4. Benchmarking – A macro-quantitative approach to identifying the financial**

# **possibility frontier**

In this section, we will discuss how the concept of a financial depth frontier can be partly operationalized using the benchmarking exercise developed by Beck et al. (2008) and De la Torre, Feyen and Ize (2013). Specifically, using a large cross-country panel, a timevariant benchmark for different financial sector indicators can be constructed by using the predicted value of regressions of financial sector indicators on an array of country characteristics proxying for the different frictions discussed above (e.g., income, size, population density, and demographic structure). As discussed by Barajas et al. (2013), this benchmark is not the equivalent to the frontier as it does not take into account long-term and deep-rooted institutional characteristics of countries. Including specific institutional measures raises (i) concerns of endogeneity of such measures to the specific financial system outcome variable and (ii) measurement concerns about the institutional indicators.<sup>12</sup> Rather, we can see this benchmark as representing a structural depth line, i.e. the level of financial development predicted by structural country characteristics that are not directly related to policies and/or the financial sector. The gap between the actual and predicted level of financial development can then be related to different policies.

The benchmarking exercise estimates the following regression

> 12 One can also interpret GDP per capita, one of the explanatory variables, as capturing institutional quality on a very general level, as it is empirically highly correlated with general institutional quality indicators, as those by Kaufman, Kraay and Mastruzzi (2011).
statistics, denoted as m. The estimated structural parameters are those that minimize the weighted average distance between the set of simulated statistics and the set of data statistics. Because the simulated statistics depend on the underlying structural parameters, minimizing this distance will provide consistent estimates of the structural parameters under certain conditions. The estimator of � where the subscript n refers to the number of households in the sample and s denotes the number of simulations. W<sup>c</sup> N is a positive definite matrix that converges in probability to a deterministic positive definite matrix W . I use the inverse of the covariance matrix of the data moments as the weighting matrix W<sup>c</sup> N . The covariance matrix is computed using a standard bootstrap method with 1000 bootstraps.

The statistics to be matched are listed in table 11. The first four moments correspond to the proportion of households that belonged to the formal sector in 2005, conditional on the education of their head and on their employment sector in 2004. Statistics from 5 to 14 are related to the labor income process from the formal and informal sectors<sup>17</sup> . Statistic 5 captures the persistence of labor income in the formal sector, by regressing the 2005 labor income of formal households on their 2004 labor income. The next moment captures the returns to education on the formal labor income by computing the mean difference between labor income of household heads with less and more than nine years of schooling that belonged to the formal sector in 2005. Moments 7 and 8 describe the returns to age in the formal sector, by comparing the 2005 labor income of household heads older and younger than 35 years and household heads older and younger than 50, conditional on being employed in the formal sector. Moment 9 captures the variance of the income shocks received by formal households in 2005. These shocks are the residuals from the OLS regression of formal labor income in 2005 on 2004 labor income, education, age and age squared for households observed in the formal sector at 2005. The same statistics are used to capture the income process of the informal sector (moments 10 to 14).

The next four moments relate to households’ consumption behavior. These moments correspond to the 5th percentile of the distribution of households’ percapita expenditure in 2005; the proportion of households that owned radio, TV sets, VCRs or computers in 2005; the 2005 ratio of percapita

> 17The households’ data (MxFLS) includes retrospective information for the years 2004 and 2001 regarding labor decisions, which is used to compute these moments.

24
Policy Research Working Paper 8787

# **Abstract**

This paper assesses the international comovement of gross capital flows in a setting simultaneously encompassing aggregate inflows and outflows. It uses as empirical framework a multilevel latent factor model, implemented on flow data for a large sample of countries over more than three decades. On average, common shocks account for over 40 percent of the variance of both inflows and outflows, although with major differences between advanced countries and the rest. Among the former, global and group shocks dominate capital flows, and the same shocks drive gross inflows and outflows. Among the latter countries, idiosyncratic shocks tend to play the leading role, and gross inflows exhibit less commonality with outflows. The latent factors configure an international financial cycle that closely tracks the trends in a handful of global “push” variables. Recursive estimation of the factor model reveals a rising trend in the exposure of countries’ flows to the international cycle—especially for advanced economies—up to the global financial crisis. Exposure to the cycle is robustly related to countries’ external financial openness and the (lack of) flexibility of their exchange rate regime.

This paper is a product of the Development Research Group, Development Economics. 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/research. The authors may be contacted at lserven@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
Figure 16: Sweden has a larger share of microenterprises among its manufacturing firms than almost any other OECD country <!-- Start of picture text --> 100<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>enterprises in manufacturing, 2013<br>Microenterprises as percentage of total<br>Slovak Republic Japan Ireland Switzerland United States Germany Luxembourg Estonia New Zealand Denmark Israel Austria United Kingdom Netherlands Norway Iceland EU27 average Spain Portugal Italy Finland Belgium France Slovenia Sweden Poland Czech Republic Greece<br><!-- End of picture text --> Source: OECD 2014, Enterprises by business size (indicator) Note: Microenterprises are defined as those with fewer than 10 employees.

Rigid labor market regulations are often also analyzed in terms of their possible effect on selfemployment. The evidence in this regard is mixed. Some studies highlight a positive relationship between the strictness of employment protection legislation and the incidence of self-employment (OECD, 1999). Others do not find a robust relationship and rely on a combination of factors to explain the large disparities observed in self-employment rates across OECD countries (Figure 17). These factors include taxation and tax evasion opportunities, the share of workers in the public sector, product market regulations and unemployment benefits (Torrini, 2005; Robson, 2003).

In examining the role of employment protection legislation, two possible and opposite transmission channels are identified. First, where labor markets are rigid, workers are more likely to move toward self-employment than to start and expand a business (Jütting et al., 2008). Second, where there is limited flexibility in the labor market, self-employment may actually mask dependent employment relationships. As mentioned in the discussion of temporary contracts, employers may use selfemployment as a means of undermining the intended effects of employment protection legislation and “employ” workers on a self-employment basis to economize on nonwage labor costs or to circumvent the effects of regulations on their ability to hire and fire employees.

31
_Table 1. Breakdown of Sample by School 7ype, Classes and Pupils_ (percent)

|||||||_Final Sa_|_mple_|||
|---|---|---|---|---|---|---|---|---|---|
||_Origin_|_al Schoo_|_l Sample_|_Scho_|_ol_|_Cla_|_rs_|_Pup_|_ils_|
|_Schoo! Type_|A|**_%b_**|_Response_<br>_Rate(%b)_|X|V|_N_|V|_N_|_%'_|
|Former A|14|12|64|9|**1OC**|<br>19|**IOC**|<br>642|5'|
|Former B|**19**|16|53|10|12|44|23|1840|27|
|High Fee Paying|9|8|78|7|8|14|7|408|6|
|Low Fee Paying|15|13|73|11|13|34|18|1380|20|
|**District Council**|61|52|80|49|57|81|42|2657|38|
|Total|118|100|73|86|100|,92|100|6927|100|

_Notes:_ a A = Former Government Group A; B = Former Govemment Croup B; HFP = High Fee-Paying; LFP = Low Fee-Paying; DC = District Council _b_ Percentage of original sample c Percentage of final sample

# _Variables_

13. The variables on which information was collected were at thrae levels, describing differences between students in their backgrounds, between classes, and between schools. Three questionnaires were used to collect this information, addressed to the students, their Grade 7 teachers, and the heads ef their schools. In addition, other information was collected from the Ministry of Education's statistical returns, the ED46 (Part II). Data for the study were collected between July and December 1990.

14. The _outcome variables_ used were Grade 7 Examination scores in English and mathematics, obtained from the Examinations Branch of the Ministry of Education. These exams are scored on a stanine scale, from 9 (low) to 1 (high). In order for the results of the regressions to be more easily interpretable, these scores v.ere transformed (new score= 10-old score) so that 9, instead of 1, is the highest score. The _student variables_ include gender, cge, years spent in pre-school, years taken to complete primary education, days the pupil was absent from school, time devoted to English and mathematics homework, home language, number of children in the family and parents' educatiun levels.

_15. Class level variables_ report teachers' information (gender, age, qualification, experience), teacher's use of time for academic activities and games and sports, class size, class textbook availability in English and mathematics, and teaching load.

16. _School level variables_ include organizational information (streaming of pupils, number of sessions, teacher stability, distribution of time to academic and sports activities, school size, time devoted to school-based inservice activities), material and non-material inputs (textbook availability, library books, teacher experience, percentage trained teachers, professional support to teachers through sup rvision by the head teacher), social composition (ethnic and gender composition of the school, boarding status), and 4
# **V. Successful vs. unsuccessful self-employed**

In the last section we presented evidence that being an employer is one way to characterize the successful self-employed. By this definition, on average 7% of the self-employed (or 2.7% of all workers) in developing countries are successful; 10% of non-agricultural self-employed and 5% of agricultural self-employed (table 6). The regions with the highest percent of employers are the Middle East and North Africa (9.8% of all workers; 4.0% in agriculture and 5.8% in agriculture) and Latin America and the Caribbean (5.0% of all workers; 3.8% in non-agriculture and 1.2% in agriculture).

It is reasonable to assume that some self-employed have no desire to become employers. That is, some self-employed may be happy working for and by themselves, and consider themselves successful if they earn enough to provide for themselves and their family. To capture this possibility, we also consider as successful those self-employed who live in a household with a per capita consumption above the $2/day poverty line.<sup>5</sup> The proportion of workers who are successful and unsuccessful by this definition is presented in table 7. By this definition, on average 34% of self-employed (or 12% of all workers) in developing countries are successful (46% of non-agricultural and 23% of agricultural self-employed).

By both definitions of success, as per capita GNI increases, there is a net decline in unsuccessful self-employed and a net increase in successful non-agricultural self-employed.

The successful self-employed are slightly older, much more educated, more likely to work in retail and services, and much less likely to work in agriculture, compared to the unsuccessful self-employed (table 8). Men and women who are self-employed are equally likely to be successful, while self-employed who identify themselves as head of household are less likely to be successful than are spouses and other family members (table 9).

What happens to the unsuccessful self-employed as countries develop? As the per capita GNI of a country increases, the proportion of unsuccessful self-employed in both agriculture and non-agriculture falls, as the unsuccessful self-employed are absorbed into non-agricultural wage and salary employment and, to a lesser extent, as successful non-agricultural selfemployed (figure 10).

> 5 Households were identified as falling below the $2/day poverty line if the position in the distribution of per capita household consumption was less than the $2/day poverty rate reported by the POVCAL network of the World Bank. Where possible, we matched the reported poverty rate to the reported year of the survey.  Where this was not possible, we used the poverty rate calculated for the year before or year after.  Where there was a poverty rate reported in the POVCAL data for both the year after and the year before the reported year of the survey, we used the mean.

11