rafmacalaba/datause-encoder-data huggingface dataset:
than coca bush cultivation is explained by the fact that, since intense aerial spraying started in Colombia in 2001, there are an increasing proportion of coca fields that are newly planted, and these are known to be less productive than more mature fields. However, as we will see below, although it might be true that aerial spraying decreases the average age of coca fields, and this, in turn, decreases the yields, there is new evidence of an increase in yields per hectare coming from different strategies implemented by illegal groups engaged in cocaine production in response to the increase eradication measures taken by the Colombian and US governments.

Although UNODC’s and the State Department’s estimates of potential cocaine production show a similar proportional decrease between 2001 and 2003, the latter source shows a reversion of the decreasing trend for 2004 and 2005 due to higher yields per hectare, while the former shows an increase in 2005 that is only due to an increase in the fields surveyed, and, as a result, the number for 2005 is not really comparable to the numbers observed for previous years.

# iv. Average Purity

UNODC and ONDCP also gather statistics on the average purity of cocaine by using information from laboratories and cocaine shipment seizures in producer and transit countries respectively, as well as the information at the retail level from street seizures in consumer countries. On one hand, between 2002 and 2004 the average purity of cocaine in the producer countries ranged between 82% and 95% (UNODC, 2006). On the other, because purity levels in consumer countries are obtained from drug seizures, many times done at the retail level, the average purity of cocaine varies very widely, even for a given transaction size in a given 15
Milanovic (2016) and Chandy and Siedel (2017a, 2017b). We find that such adjustments have little effect on global poverty measures, but substantially revise upwards both global and national inequality, and significantly change our understanding of the distributional nature of growth and ‘shared prosperity’.<sup>3</sup> Notably, the relationship between observed levels of inequality and country income levels changes significantly, strengthening evidence of a Kuznets curve where inequality first rises and then falls with economic development (Kuznets, 1955).

The remainder of the paper proceeds as follows. Section 2 presents the data compiled for the analysis presented in this paper. Section 3 discusses reasons why gaps exist and presents our analysis of the differences between survey means and national accounts estimates (both in terms of levels and change over time) based on our newly compiled data. Section 4 considers two adjustments to survey data to reconcile these differences and describes the implications of each for global poverty and inequality measurement; the final section offers some concluding thoughts.

# **2. Data**

# **_National accounts_**

Our main comparison between survey and national accounts data focuses on comparing the survey household consumption aggregate with the component of national accounts that corresponds to household expenditure, known as household final consumption expenditure (HFCE) and established in the 1993 System of National Accounts.<sup>4</sup> While per capita HFCE is the variable that conceptually most closely corresponds to the measures from household surveys,<sup>5</sup> we also compare survey estimates to per capita gross domestic product (GDP) estimates, for two reasons. First, GDP is a more frequently cited indicator of economic development and may be measured with less noise than HFCE, which is sometimes measured as

> 3 Shared prosperity is an indicator of shared growth developed by the World Bank, measuring the income growth of the bottom 40 percent of the distribution in each country. It is often compared to the average growth of the country.

> 4 The definition of HFCE is broader than what is typically included in household surveys, encompassing items like spending of non-profit entities such as religious groups, NGOs and foundations. Previous literature (e.g. Ravallion, 2003, and Deaton, 2005) has referred to HFCE as private consumption expenditure (PCE), defined under earlier systems of national accounts.

> 5 Anand and Segal (2008), Ravallion (2003), Deaton (2005) use HFCE (or the older measure, PCE) to compare with consumption from surveys.

5
|Number of prime adults (15-60yo) in HH|-0.023|0.085**|0.084**|-0.019|0.085**|0.041|
|---|---|---|---|---|---|---|
||[0.605]|[0.021]|[0.019]|[0.671]|[0.021]|[0.239]|
|Number of elderly (60yo+) in HH|-0.079|-0.023|-0.004|-0.082|-0.019|-0.027|
||[0.612]|[0.855]|[0.969]|[0.600]|[0.880]|[0.804]|
|Household access to loan (0/1)|-0.030|0.346***|0.016|-0.025|0.346***|0.076|
||[0.769]|[0.000]|[0.852]|[0.812]|[0.000]|[0.369]|
|Normalized TLU index|-2.637+|1.632|2.046|-2.536+|1.661|1.912|
||[0.130]|[0.252]|[0.201]|[0.149]|[0.241]|[0.354]|
|Normalized agricultural asset index + Normalized land|||0.846***|||0.614***|
|<br>holdings|||[0.000]|||[0.003]|
|Household was affected negatively by some income|0.158|-0.052|-0.036|0.163|-0.052|-0.027|
|Shock During the last 12 months (0/1)|||||||
||[0.191]|[0.594]|[0.683]|[0.178]|[0.596]|[0.760]|
|Household has a migrant network (0/1)|0.066|-0.017|0.083|0.076|-0.016|-0.103|
||[0.573]|[0.855]|[0.358]|[0.517]|[0.868]|[0.230]|
|Average 12-month total rainfall (mm)|-0.002|0.001|-0.002|-0.001|0.001|-0.002|
||[0.416]|[0.444]|[0.199]|[0.469]|[0.426]|[0.207]|
|Annual Mean Temperature (∞C * 10)|0.007|-0.002|-0.022***|0.006|-0.002|-0.017**|
||[0.478]|[0.843]|[0.007]|[0.498]|[0.815]|[0.036]|
|HH Distance in (KMs) to Nearest Road|-0.014|-0.006|-0.003|-0.015|-0.006|-0.006|
||[0.334]|[0.643]|[0.831]|[0.303]|[0.635]|[0.626]|
|HH Distance in (KMs) to Nearest Population Center|0.004|-0.000|-0.011*|0.004|-0.000|-0.008|
|with +20,000|||||||
||[0.561]|[0.965]|[0.070]|[0.547]|[0.957]|[0.208]|
|Price of fertilizer in the EA|-0.001*|-0.000|-0.001|-0.001*|-0.000|-0.000|
||[0.085]|[0.473]|[0.251]|[0.084]|[0.460]|[0.513]|
|Price of maize grains in the EA|-0.002|0.002|0.000|-0.002|0.002|0.001|
||[0.229]|[0.317]|[0.893]|[0.246]|[0.290]|[0.614]|
|Total land area owned by HH in Acres|-0.000|0.000||-0.000|0.000||
||[0.746]|[0.755]||[0.733]|[0.804]||
|value of ganyu salary in the area|0.000|0.000||0.000|0.000||
||[0.538]|[0.373]||[0.501]|[0.370]||
|EA neighbor’s wage employment participation|0.010***<br>[0.000]|0.005***<br>[0.005]||0.010***<br>[0.000]|0.005***<br>[0.005]||
|EA neighbor’s self-employment participation|0.006***|0.013***||0.006***|0.013***||
||[0.004]|[0.000]||[0.006]|[0.000]||
|Share of total land cultivated in crops|||||||

62
# **<mark>Table 13: Asset Ownership at the Individual Level</mark>**

<mark>Coefficients, standard errors in parentheses</mark>

||Pooled|Mean|Impac|t by Roun|d (OLS)|Pooled|Impac|t by Roun|d (SAR)|
|---|---|---|---|---|---|---|---|---|---|
||N<br>(1)|R3<br>(2)|R1<br>(3)|R2<br>(4)|R3<br>(5)|Impact<br>(6)|R1<br>(7)|R2<br>(8)|R3<br>(9)|
|**_Panel A: Reported by Women_**||||||||||
|Value of livestock, conditional on screening|2,300|130|-20.0|-21.7|-39.3*|-27.2**|-18.1|-18.1|-36.9*|
|question [in Cedis]|||(21.1)|(20.9)|(20.4)|(12.2)|(21.0)|(20.9)|(20.4)|
|Value of tools, conditional on screening<br>question [in Cedis]|2,256|17.0|-1.21<br>(1.82)|-1.64<br>(1.75)|2.60<br>(1.71)|-.012<br>(1.03)|-.500<br>(1.8)|-1.00<br>(1.7)|3.20*<br>(1.7)|
|Value of durable goods, conditional on<br>screening question [in Cedis]|2,300|495|.755<br>(45.3)|5.56<br>(44.9)|219***<br>(44.0)|77.6***<br>(26.3)|1.70<br>(45.1)|6.00<br> <br>(44.7)|226***<br> <br>(44.1)|
|**_Panel B: Reported by Men_**||||||||||
|Value of livestock, conditional on screening<br>question [in Cedis]|1,780|556|-56.5<br>(63.2)|3.21<br>(62.6)|220***<br>(63.1)|55.7<br>(37.0)|-49.2<br>(63.0)|4.5<br>(62.3)|214***<br>(62.8)|
|Value of tools, conditional on screening|1,752|65.5|-4.88|-6.24|11.1*|.066|-4.40|-5.60|11.5*|
|question [in Cedis]|||(6.19)|(5.98)|(6.03)|(3.56)|(6.20)|(6.00)|(6.00)|
|<br>Value of durable goods, conditional on<br>screening question [in Cedis]|1,778|1,609|66.8<br>(192)|-146<br>(190)|765***<br>(191)|227**<br>(112)|68.8<br>(192)|<br>-138.1<br> <br>(190)|<br>798***<br> <br>(192)|

Notes: Standard errors are reported in parenthesis and are clustered at the individual level. *** denotes significance at 1%, ** at 5% and * at 10%. Standard errors are clustered by respondent and are shown in brackets. All outcome variables in Panel A (B) are measured conditional on a female (male) respondent interviewed. Control variables include household structure, household size, number of females in the household, number of household members under five years of age, dummy variable for household head being male, age of household head, dummy for whether household head has ever been to school, three dummies for the predominant chieftaincy institution in the community, number of sampled households situated around respondents’ house within a 200m radius (household density), distance between the house of the respondent and the main road demarcation.

35
<!-- Start of picture text --> +<br>°<br>3<br>:FE<br>§<br>.<br>o<br>s<br>2000 2005 2010 2015<br>Year<br>——— Rajasthan ————Other States (Avg)<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>75%<br>= 300+<br>50% m 150-300<br>100-150<br>650-100<br>25% w<50<br>0%<br>Output Employment Contract Employment<br><!-- End of picture text --> <!-- Start of picture text --> :<br>8<br>28<br>5<br>ss<br>:<br>r=]<br>:<br>ow 2000 2005 2010 2015 2020<br>Year<br>——— Rajasthan ————Other States (Avg)<br><!-- End of picture text --> <!-- Start of picture text --> 100%<br>75%<br>300+<br>50% w 150-300<br>w 100-150<br>50-100<br>25% <0<br>0%<br>Output Employment Contract Employment<br><!-- End of picture text -->
# **TABLE A4:  Bertelsmann Transformation Index (BTI)**

## **A4: Bertelsmann Tranformation Index (BTI)**

http://www.bertelsmann-transformation-index.de/atlas.0.html?&L=1 Founded by Reinhard Mohn in 1977 and headquartered in Berlin, the **Bertelsmann Foundation** is a non-profit organization dedicated to identifying social problems and challenges early on in order to develop and implement model solutions.

Starting in 2004, the Foundation began publishing the **Bertelsmann Transformation Index** (BTI), a global ranking that analyzes and evaluates development and transformation processes in 116 countries. The Bertelsmann Transformation Index provides the international public and political actors with a comprehensive view of the status of democracy and a market economy as well as the quality of political management in each of these countries.

The goal of a consolidated market-based democracy constitutes the BTI’s normative framework. The BTI analyzes the status of both democratization and market liberalization as it evaluates actors’ performance in managing these changes. The quantitative data collected for the Bertelsmann Transformation Index 2003 is outlined in two parallel indices: the **Status Index** and the **Management Index.** The Status Index (SI) shows the development achieved by 116 states on their way toward democracy and a market economy. States with functioning democratic and market-based structures receive the highest scores. The Management Index (MI) reveals the extent to which governments and political actors have been consistent and determined in their pursuit of a market-based democracy. Those states showing progress in the last five years and in which transformation has resulted from astute management receive the highest scores.

In the table below we list the variables included in each of the governance indicators. We use data from 2004, drawing 5 variables from the Management Index and 6 variables from the Status Index.

|**Table A4: Bertelsman Transform**|2005<br>**ation Index(119 develo**|2004<br>**ping c**|2003<br>**ountr**|2002<br>**ies)**|2000|1998|1996|
|---|---|---|---|---|---|---|---|
|**Voice and Accountability**||||||||
|Political Participation (SI)|X|X|..|..|..|..|..|
|Institutional Stability (SI)|X|X|..|..|..|..|..|
|Political and Social Integration (SI)|X|X|..|..|..|..|..|
|**Political Stability**||||||||
|NA|..|..|..|..|..|..|..|
|**Government Effectiveness**||||||||
|Consensus Building (MI)|X|X|..|..|..|..|..|
|Governance Capability (MI)|X|X|..|..|..|..|..|
|Effective Use of Resources (MI)|X|X|..|..|..|..|..|
|**Regulatory Quality**||||||||
|Price Stability (SI)|X|X|..|..|..|..|..|
|Competition (SI)|X|X|..|..|..|..|..|
|**Rule of Law**||||||||
|Rule of Law (SI)|X|X|..|..|..|..|..|
|Private Property (SI)|X|X|..|..|..|..|..|
|**Control of Corruption**||||||||
|NA|..|..|..|..|..|..|..|

49
<!-- Start of picture text --> 3.5% —Household income<br>3.0%<br>25% Household consumptio<br>. expenditures<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> —agriculture —construction<br>008 —pvt. tradable —pvt. non-tradabl<br>0.06 me : consumptionP os _<br>0.04<br>;<br>0.02<br>0.00 Ce ee ele ee<br>-0.02 =<br>-0.04 <====2=-<br>-0.06<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text -->
1.5%<br>—Labor wage<br>1.0%<br>0.5% —Capital wage rates<br>0.0%<br>0.5%<br>-1.0%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->

<!-- Start of picture text --> —total ex. mining = —agriculture<br>—construction —pvti. tradable<br>2.0% pvt. non-tradable —govt. consumption<br>———<br>1.5% =ES<br>1.0%<br>0.5%<br>ee<br>0.5%<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> 50 —agriculture<br>construction ’<br>“0 —pvt.non-tradablepvt. tradable =”a<br>”<br>30 --govt. consumption ,--*<br>--total a<br>20 a<br>40 a<br>: res<br>0 es7 a 7<br>-10<br>2012 2015 2018 2021 2024<br><!-- End of picture text --> <!-- Start of picture text --> agriculture —construction<br>—pvt. tradable —pvt. non-tradable<br>0.6% )--govt. consumption<br>0.4%<br>0.2% mane<br>f ee<br>0.0% | : 1 1 re<br>-0.2% ~__—— —<br>0.4%<br>2012 2015 2018 2021 2024<br><!-- End of picture text -->
more sustainable in the long run. Public accountability is a guaranty of transparency and of better availability of information, which also help governments to build credibility. Public accountability provides access to policymakers and can hold them responsible for failures in implementing policies. In particular, freedom of press, free political parties and open elections contribute to government’s legitimacy and give voice to citizens in the decision-making process. On the empirical side, the literature on democratic participation has focused on the effects of transparency and accountability on growth, using data on civil liberties, political rights, and freedom of press from various sources. The empirical validation has, however, produced mitigated success<sup>18</sup> . The work of Pastor and Sung (1995) is one of the few to have been able to show a positive effect of various indicators of democratic institutions on private investment in the developing world.

# **2.3. Political Stability (** **_PS_ )**

The last set of variables is intended to measure the “Political Stability”. Political instability increases the uncertainty in the economy and deters the risk-averse entrepreneurs to take action for profitable investment opportunities. The political stability index includes the following variables from ICRG: “Government Stability”, “Internal Conflict”, “External Conflict”, and “Ethnic Tensions”. Various authors, using different indicators of political uncertainties, have brought empirical evidence that institutions associated with political instability hamper aggregate investment<sup>19</sup> .

All the political and governance indicators have been aggregated by using principal component analysis (PCA) to account for the multi-collinearity issue in using these potentially correlated variables in the same regression equation. In addition, we have generated a global indicator of governance ( _GOV_ ) which summarizes the 12
variables, β and θ are vectors of coefficients, ε and v are the disturbance terms of the earnings and selection equations, and σεvi is the covariance of the two error terms. Female labor force participation in Venezuela has been steadily increasing during the 1990s, from about 50 percent in 1992 to 63.2 percent in 2002. The pattern of employment of women, with respect of sector of employment is similar to that of men.

Table 1: Returns to Schooling and Average Years of Schooling by Sex: 1975-2002

||Rate of Ret<br>|urn (%)<br>|Average Years of Schooling<br>|
|---|---|---|---|
||All Men<br> <br>|Women<br>( corrected for<br> selectivity)|All Men Women|
|1975*|13.7 -|-|4.6 - -|
|1984*|11.2 -|-|6.6 - -|
|1987*|10.7 10.0|13.1|6.9 - -|
|1989*|9.6 9.4|11.3|7.7 - -|
|1992|8.8 8.4|11.9|8.0 7.6 8.7|
|1995|8.0 8.0|9.6|8.2 7.7 9.1|
|1996|7.6 7.4|10.1|8.4 7.9 9.4|
|1997|9.2 9.1|11.7|8.3 7.8 9.3|
|1998|9.0 9.2|10.5|8.7 8.0 9.9|
|1999|9.2 9.1|11.9|8.7 8.0 9.9|
|2000|8.0 7.9|9.4|8.7 8.0 9.9|
|2001|9.4 9.1|11.3|8.8 8.1 10.0|
|2002|10.49.9|12.7|8.9 8.310.1|
|Sources:|Psacharopoulos and Al<br>and Steier 1988;author|am 1991; Fiszbein a<br>s’ estimates for 199|nd Psacharopoulos 1993; Psacharopoulos<br>2-2002|

The overall return to one additional years of schooling steadily declined in the 1970s, 1980s and through the mid-1990s (Table 1). The benefits decline from 13.7 percent in 1975 to 8 percent in 1995, and 7.6 percent in 1996. Since then, returns have exhibited an increasing trend, reaching 9.4 percent in 2001 and 10.4 percent in 2002. Returns for men are consistently lower than returns for women and the difference in returns has been between 1.3 (in 1998) and 3.0 (in 1992) percentage points.

5
# **A.3 Filling Missing Data**

We proceed in two steps to fill the gaps in the BOP data. The first step is an internal fill. When the BOP data reports the total for a flow type and reports 3 out of the 4 sectors, we fill the fourth sector by subtracting the three reported sectors from the total, the residual being allocated to the missing sector. In the case of capital outflows (asset flows), we combine general government and central bank into a single public sector. So, when one or both of general government or central bank are missing data, we fill the public sector with the residual of the total minus banks and corporate sectors. After performing our internal filling exercise, we use external data to fill the remaining gaps.

We draw on 3 separate sources for data to construct measures of capital inflows that can be used when the BOP data is missing. The first is banking and bond data from the BIS, which is described in detail in Appendix B. We also draw on the International Investment Position (IIP) data that accompanies the BOP data, and the Quarterly External Debt Statistics (QEDS) data which is produced jointly by the World Bank and IMF. Both of these are stock measures, and have the same sector and capital flow type classifications as the BOP data. The QEDS data is quarterly and is compiled from a combination of data reported to the IMF via their Special Data Dissemination Standard (SDDS) and their General Data Dissemination System (GDDS), thus sometimes giving it better coverage than the reported IIP stock data. The IIP data comes either quarterly or annually.

The dataset with the broadest coverage by sector and capital flow type, and thus fills the most observations, is derived from the BIS data. The BIS produces a database on international bond issuances and databases on international banking flows (e.g. loans), which are 71
# II. Data and Methodology

The Brazilian Youth Well-Being Index uses as its starting point the index created by Duke University, departing from it based on data availability and appropriateness of indicators for the Brazil case.

# _Data_

Thirty-six indicators are used to construct the indices, which can be grouped into five categories (columns 1 and 2 of Table 1).<sup>8</sup> <u>Health</u> includes indicators on infant mortality and on AIDS. Behavior variables are those that measure the outcomes of risktaking behaviors, such as adolescent pregnancy (a result of risky sex), homicide/suicide, and substance use. AIDS is not included here recognizing that, in some instance, it may not be a result of risk-taking behaviors. School performance includes graduation rates, literacy, average education, and performance on standardized tests. Institutional <u>connectedness</u> variables include young people’s interaction with public institutions, including school, the labor market, and voting. Finally, the socioeconomic conditions include indicators related to poverty and household structure. The indicators in each category are presented in Table 1.

The indicators were selected based on two criteria. First, they must be indicators that are commonly used to track the well-being of youth and/or children. This includes indicators that are regularly used in various sectors to assess the situation of youth, indicators of childhood investment that have been shown to have significant impacts on the youth period, and general factors exogenous to the young person but important for his/her development (Bronfenbrenner, 1979, Kohler, 2004).<sup>9</sup> Second, they must be easily available to allow for future researchers to replicate this exercise. Since the indices are calculated at the state level, the set of potential

> 8 While the US CWI uses seven categories  (or “domains,” as used in Duke University, 2003), we collapse some of the categories from the US CWI, based on data availability and the youth development models that form the basis of the choice of variables for the Brazilian YWI.

> 9 A commonly used model in the public health field to understand youth development is the ecological risk framework.  The basic premise is that youth are a product of their own “hardwiring” and of their environment, which includes spheres of family, community, institutions, and the macro-environment).  The correlation between a negative environment and negative youth behaviors has been found to be very strong in the United States (Blum, 2002), the Caribbean (World Bank, 2003), and Brazil (Kohler, et. al. 2005), and new evidence in the United States suggests causal relationships (Roche, Ahmed, and Blum).

5