rafmacalaba/datause-encoder-data huggingface dataset:
This paper consists of 5 sections. The next section describes the data and methodology used for the cluster analysis. Section 3 sets the stage by describing Argentine youth in general, while Section 4 applies the cluster methodology, discusses the clusters, and examines linkages between risk and protective factors and behavioral outcomes. Finally, Section 5 concludes the paper.

# **2. Data and Methodology**

This section describes the data used for the analysis below. Furthermore, the clustering procedure is described and variables defined. Findings from the cluster analysis are discussed in Section 4.

## **2.1 Data**

The data used are from a household survey conducted between November 26 and December 18, 2005 containing 2,009 individuals. Of these individuals, 1,275 were between the ages 15 and 24 and thereby classified as youths for this purpose. The questionnaire was done by FLACSO (Facultad Latinoamericana de Ciencias Sociales) in corporation with and financed by the World Bank, and used a methodology similar to that of EPH (Encuesta Permanente de Hogares) by INDEC (Instituto Nacional de Estadística y Censos). The interviews covered four cities in Argentina; Grand Buenos Aires, Neuquén, Posadas, and Salta, and data are representative for urban households with one or more youth in these areas. The sample of youth is not nationally representative, but the sample of youth will henceforth be referred to as Argentine youth for simplicity. Thus, it is important to stress that the term “Argentine youth” is too generalizing, and that conclusions only are based on youth in the four urban areas included in the sample. Still, findings provide valuable insights to these urban areas, and help specify what to look out for when trying to identify youth at risk in other areas. The data are named ESCVJA (Estudio Sobre Condiciones de Vida de Jóvenes y Adultos).

## **2.2 Methodology**

A cluster analysis is intended largely for generating rather than testing hypotheses. The cluster methodology is applied to organize youth into groups whose members are similar in some way (see, e.g., Berkin 2002, Fasulo 1999). Based on these clusters and their observed characteristics and behaviors, hypotheses of causality between observed factors and outcomes can be established.

The approach to clustering applied here is hierarchical, i.e., youth are divided into hierarchically related sets of clusters. Agglomerative hierarchical clustering is used, which means the process begins with each observation being considered as a separate group. The closest groups are combined, and this process continues until all observations belong to the same group. This process creates a hierarchy of clusters. Ward’s linkage method is used for choosing what should be compared between groups that contain more than one observation. This method minimizes the sum of within group variance of all 4
**These results reveal** that **the** interactions **of** agricultural and **macroeconomic policy are** important. A careful analysis of the likely impacts **of alternative** U.S. policy options from the standpoint of trading partners requires joint consideration of both agricultural and macroeconomic policy alternatives.

# Summary

This study reports the estimated effects of macroeconomic policy on U.S agriculture using a model of U.S. corn, sorghum, and soybeans (USAGM.K1S) that includes the role of U.S. agricultural policies and related livestock markets. The macroeconomic effects of monetary and fiscal policy are estimated using the FAIRMODEL of the macroeconomy. The results show that the effects of U.S. macroeconomic policies on prices and exports can be substantial. Price effects of recent and pending macroeconomic policy adjustments on the order of 15 percent or more are not unreasonable. Furthermore, the extent of response depends heavily on current economic circumstances. Thus, a policy response capability for countries that trade with the U.S. requires an ability to sort out the effect of current economic circumstances on U.S. policy effects. This study has attempted to develop a model that can help to serve these purposes.

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21 level, "...extensive water subsidies have led to inefficient use of land and water resources as well as capital, labor and materials." (Wahl 1989: 45). The costs of many subsidy-supported projects sites exceeded benefits. "Consequently, dams have been placed where rivers, in the absence of the irrigation subsidy, would have been left in their natural state. Furthermore, low-cost water has provided little incentive for careful use of the resource. This means that water has been diverted to uses other than those that would produce the greatest economic benefits and has, for the most part, continued to be used for the original purposes." (Wahl 1989).

## **_The PASTEN Mechanism in Indonesia_**

The PASTEN system, which is administered by the Provincial Water Resources Srvices with guidance from the Directorate General of Water Resources Development (DgWRD) of the Ministry of Public Works in Indonesia, is a process of determining fixed proportions of water to allocate it across tertiary-level irrigation units (Howe 1990).

The PASTEN allocation mechanism, described in Box 2, takes into account different onfarm canal conveyance and operational losses, it takes into account the different cropping patterns in a simple way that can be adjusted as crop stages change, and it provides a set of computable guidelines for the water management team who must allocate the water by controlling flow rates at various gates. No consideration is given, however, to economic values of different crops, differing productivity of individual tertiary units, and to opportunity costs of water not used in other projects on the river basin. The PASTEN mechanism is incompatible with the efficiency criteria because it sets proportions of water to be applied to each crop based upon the maximum physical yield possible.

# **Box 2: Principles of the PASTEN Allocation Mechanism.**

The basic design consists of biweekly calculations of the "full water requirement" of each tertiary unit given the crops and their growth rates in each unit. To this amount is added the amount of water that can be expected to be lost before application (terrain, soil type, canal loss). The sum is an estimated "full diversion requirement" (DR) for each unit. The full diversion requirement is compared to the known total water available in the system to estimate a "PASTEN Index," or K value, which is actually a ratio of available water to DR. When available water resources at least meet DR, the K value is equal to I or more. A water shortage means that K is less than 1. The allocation of water then is done in fixed proportions across all irrigation units. Proportions allocated are based on the ideal water applications given no scarcity, and the delivery and application losses of each unit.

Source: Howe (1990).
Source: Own estimates based on Turkey 2016 HBS. Note: changes calculated from Market income plus pensions to Disposable income.

Panel (a) of Figure 8 shows the Kakwani coefficient and the marginal contribution to inequality of all direct taxes in Turkey in 2016. In general, direct taxes are progressive; the Kakwani coefficient for most of the direct taxes is positive. They are also broadly inequalityreducing as shown by a positive marginal contribution to the Gini coefficient. However, there is substantial heterogeneity across tax categories. For instance, payroll income tax is progressive and the most inequality-reducing, while agricultural income tax is regressive and does not contribute to reduce inequality.

Relative to its peers, the equalizing effect of direct taxes in Turkey is similar to Chile, Poland and Mexico, but lower than Russia, Croatia and the United States (panel (b) of Figure 8). Most countries’ direct taxation is more progressive than Turkey’s. It is also the case that, with the exception of Mexico and Croatia, all peer countries collect more direct taxes than Turkey, as a share of GDP.<sup>15</sup>

**Figure 8. Progressivity and redistributive impact of direct taxes**

# **a. Direct taxes in Turkey, 2016**

<!-- Start of picture text --> 0.5 Kakwani Coefficient Marginal Contribution 0.020<br>0.015<br>0.3 0.010<br>0.005<br>0.0 0.000<br>-0.005<br>-0.3 -0.010<br>EQUALITY<br>KAKWANI INDEX<br>(CHANGE IN GINI POINTS)<br>MARGINAL CONTRIBUTION TO<br>REGRESSIVE <-------------> PROGRESSIVE (property) Payrol Inc. Tax All direct taxes contributions<br>Agricultural Inc. Tax Private Pension Inc. Tax Residence Prop. Tax Bank Profit Inc. Tax Rent Tax Inc. from Store Personal Inc. Tax Motor Vehicles Tax All Contributions (SSI) All direct taxes and<br><!-- End of picture text -->

**b. Direct taxes in selected countries (excluding contributions)**

> 15 Revenue from direct taxes in percent of GDP: Turkey 5.8, Chile 6.6 Poland 6.3 Mexico 5.1 Brazil 9.5 Russia 9.1 Croatia 5.4 Argentina 8.4. Source: CEQ Data Center on Fiscal Redistribution.

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in both figures. Figure 1 is in some sense a best case scenario. Variability is very large for the first two years of the sample but becomes quite a bit more stable after this initial uncertainty. Figure 2, on the other hand, shows high sampling variability throughout the entire period. We will assume that the signal of each series can be well approximated by its average and use the average when referring to the series for a single keyword in what follows unless otherwise indicated.

The second thing to note in figures 1 and 2 are that many of the observations for a single draw of the Google Trends data are exactly zero. These zero observations present two difficulties in particular – one conceptual and one practical. First, conceptually, these zeros suggest a lack of signal where presumably there should be some. As we collect more daily samples of the data, this problem becomes less and less, again assuming that the signal is well approximated by the mean. However, this problem does not disappear. Looking at the early parts of both series, there are still observations which are zero even at the mean.

Second, as a practical problem, some of the Google Trends data contain strong seasonal components. Studies such as Carri`ere-Swallow and Labb ́e [2013] alleviate the effects of seasonality in the trends data by using year-over-year percent changes for them as well as the series to forecast. However, if the base year is zero, we would lose this entire year of data.

We employed several techniques in an attempt to overcome these problems, which we will now describe. The Google trends data can be written more formally as _Xi,j,t_ – where _i_ represents the vintage a downloaded sample on a particular day, _j_ represents a particular keyword, and _t_ represents the weekly, bi-weekly, or monthly observation of each keyword. The first task is to deal with the _i_ vintage, or sample, index. We took the mean and the median of all the samples. This leaves us with either for the mean, where _I_ is the total number of samples taken or for the median.

After handling the sampling dimension, we apply transformations to smooth the data for each keyword and attempt to better identify the signal from the noise, given the 9
# **4.2 Empirical strategy**

We rely on the latest empirical gravity literature developments to specify our econometric model as follows:<sup>11</sup> Here, _Xij_ , _t_ denotes bilateral trade flows from country _i_ to country _j_ at time _t_ in levels. Importantly, _Xij_ , _t_ includes both international and intra-national trade flows, which is critical to get theory-consistent estimates (Yotov, 2022; Larch and Yotov, 2023). Our analysis uses trade data in total goods (agriculture and manufacturing sectors) as a baseline.<sup>12</sup> We also use sector-specific data for a more disaggregated analysis.

_RTAij_ , _t_ captures the average world effect of trade agreements and will serve as a reference point to the effect of CAFTA-DR. Equation (4), as is standard in the literature, uses exporter-time fixed effects _γi_ , _t_ and importer-time fixed effects _ωj_ , _t_ to control for the unobservable exporter and importer multilateral resistances. These fixed effects will also absorb/control for any other country-time-specific characteristics that may impact bilateral trade on the exporter and importer sides. In addition, following the recommendations of Baier and Bergstrand (2007), we also employ country-pair fixed effects ( _μij_ ), which control for all time-invariant bilateral trade costs and will mitigate endogeneity concerns with respect to our trade policy variables of interest. Finally, we follow Bergstrand et al. (2015) and account for common globalization effects that could bias our trade agreements variables, with a set of time-varying border dummy variables _BRDRij_ , _t_ , for which a parameter _βt_ is estimated for each year _t_ separately. We estimate our model using the Poisson pseudo maximum likelihood (PPML) estimator, which allows accounting for heteroscedasticity in trade data and takes advantage of the information that is contained in zero trade flows (Santos Silva and Tenreyro, 2006).

Our primary purpose is to estimate the effects of the CAFTA-DR agreement. For this, we introduce the variables _CAFTAij_ , _t_ to capture the specific impact of this trade

> 11Larch and Yotov (2023) survey the methods and data for estimating the effects of trade agreements. 12We focus on total goods data for our baseline due to data quality issues in the services trade flows.

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13 employed to measure export concentration.<sup>9</sup> The first (the global diversification index) is based on the divergence of a country's export trade shares from the product's share in world trade. The second (the Hirschman index) reflects the relative importance of individual products in a country's overall export structure.<sup>10</sup> Table 4 shows these concentration indices values in the base and end periods for each of the regional groups. As was the case with the competition and demand factors, Africa's experience regarding export diversification differs sharply from that of the other regions. First, while the concentration ratios for the other exporters were either essentially static or declining, those for Africa show the region was becoming increasingly dependent on a relatively smaller number of commodities. Specifically, in 1962-64 the three largest three-digit SITC products accounted for about 36 percent of total exports, yet three decades later this share rose to 62 percent. Both the middle East and North Africa had higher concentration ratios in 1992-94, but neither region showed the marked increase evident in the sub-Saharan African indices. For all non-OECD countries as a group, the three product concentration ratios fell by about five percentage points to a level that was half that for Africa. Each of the other two indices in Table '<sup>9</sup> UNCTAD (1993 and other issues) utilizes a measure of diversification which is a count of the number of threedigit products that are exported by a country. A practical problem relating to application is that some specific level of exports must be attained before the items is considered an established export. This is due to the fact that minimal exports of some goods may occur due to random or irregular developments when a country has not yet fully established itself in export markets for the product. UNCTAD does not consider a product to be an export item unless it accounts for at least 0.3 percent of total trade).

'"Specifically, the global diversification index for country j (G) is defined as, where hi, is the share of commodity i in the exports of country j, and h, is the share of the commodity in world trade. The Hirschman index for country j (H1) is measured by,

# Hi = -V(xj,/X)2

where xi, is the value of j's exports of commodity i and X is j's total exports. Both indices range between 0 and 1 with the higher values reflecting increased concentration. The Hirschman index discriminates more finely between countries which are relatively more concentrated in their export structure while the global index discriminates more finely between countries which are relatively more diversified.
# _2.3.1 GHG footprints_

Life cycle assessment (LCA) is a widely used technique for environmental impacts evaluation of products and processes over a full life cycle. However, life cycle inventory data are often decentralized and very difficult to harmonize (Green et al. 2020). Hence, we used data that represent averaged production methods in a given country or in the world instead of using LCA data that are specific for a production method. In the absence of LCA data, the less ideal GHG footprints data should be both item specific and country specific. However, such information was not available for all items in our analysis. Thus, we categorized our items into country specific (CS) items and non-country-specific (NCS) items. Within each of the two categories, the items were further grouped into animal food items, and plant-based food items plus other non-animal food items. CS footprint data come from GLEAM-i<sup>4</sup> tool as made available in Kim et al. (2020) which only reports animal food items. Therefore, CS items are all animal food items whereas NCS items are mostly plant-based food plus several insects and aquatic products. For NCS items, as CS footprints were not available and unapplicable, we used items’ footprints distribution (Kim et al. 2020) and substituted item specific footprints with world weighted average footprints. These imported NCS items are deemed to be imported from the “world” in terms of GHG footprints, even though they might have trade records indicating their country of origins (COOs) in FBS or TM data sets.

# _2.3.2 Trade patterns_

To account for varying GHG footprints in different production systems, we traced food items back to their COOs. We used the two-step method (Kim et al. 2020) described below to decompose consumptions into quantities imported from all COOs. The imported quantities are proportional to COOs’ shares of total domestic supply, which can be obtained from FBS and TM data sets.<sup>5,6</sup> STEP 1. Using FBS trade data to allocate the share of each FBS item _f_ consumed in country _c_ (Bangladesh) between imports and domestic production. i i i <u>f</u> %i i i i = i i i i + i + i i i i f,cc ii ff,cc STEP 2. Using the share of TM items iidd i ppddi _t_ exported from iippff,cc iiddss ddhaappaai _e_ into _c_ to allocate the imported share of ff,cc iiff,cc − iieei iiff,cc _t e_ into _c_ FBS item _f_ among exporting countries.

> 4 All the GLEAM-i data in this work refer to the data acquired from FAO’s GLEAM-I tool version 2.0 accessed in December 2017 and made available in Kim et al.’ s supplementary data.

> 5 FAO. FAOSTAT Trade Matrix database. Available at: https://www.fao.org/faostat/en/#data/TM, accessed 24/12/2021.

> 6 FAO. FAOSTAT Food Balance Sheet. Available at: https://www.fao.org/faostat/en/#data/FBS, accessed 24/12/2021.

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<!-- Start of picture text --> soring atDistributionthe horizon<br>Assets of asset value @ 1. Current asset value<br>Possible 2. Distribution of asset<br>asset value value at time H<br>path<br>(@) 3. Volatility of future<br>asset value at time H<br><!-- End of picture text --> <!-- Start of picture text --> , a<br>@ DD of default point<br>5. Expected rate of<br>@ Default growth in asset value<br>MN EDF Point 6. Time horizon<br><!-- End of picture text -->
**Figure 3. Average MRL per Product**

<!-- Start of picture text --> 6<br>5<br>4<br>3 BRICS<br>non-BRICS<br>2<br>1<br>0<br>2006 2007 2008 2009 2010 2011<br><!-- End of picture text -->

**Figure 4. Average Restrictiveness Index per Product**

<!-- Start of picture text --> 0.06<br>0.05<br>0.04<br>0.03 BRICS<br>non-BRICS<br>0.02<br>0.01<br>0<br>2006 2007 2008 2009 2010 2011<br><!-- End of picture text --> 21
||Enterprise Survey in the country. In cases<br>where the fiscal year spanned more than one<br>calendar year, the figure used is the weighted<br>average over the calendar years covered (and<br>lagged by 2 years), where the weights used are<br>the proportion of months covered in the<br>calendar year.<br>Source: Doing Business, World Bank.|
|---|---|
|Firm-size fixed effects|Two dummy variables indicating the size<br>group of the firm. Firms are either small or<br>medium. Small firms have between 11 and 20<br>full-time permanent workers three fiscal years<br>ago (or 2 fiscal years ago in some countries)<br>and the rest are medium firms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|
|City fixed effects|A set of dummy variables indicating the city<br>where the firm is located.<br>Source: Enterprise Surveys.<br>ti|
||www.enerprsesurveys.org|
|Growth rate of GDP per capita (lagged)|Annual percentage growth rate of GDP per<br>capita lagged by 2 years from the fiscal year<br>covered by the Enterprise. In cases where the<br>Enterprise Survey spanned more than one<br>calendar year, the GDP per capita figure is the<br>weighted average over the calendar years<br>covered (lagged by 2 years), where the weights<br>used are the proportion of months covered in<br>the calendar year.<br>Source: World Development Indicators, World<br>Bank.|
|Employment Growth|Total number of permanent full-time workers<br>in the final year (last fiscal year) minus the<br>same in the initial year (beginning of the<br>growth period which is 2 fiscal years ago for<br>most countries and 3 fiscal years ago for other<br>countries) divided by the average (mean value)<br>number of permanent workers in the final and<br>initial year. The ratio is divided by the number<br>of years between the final and initial year and<br>multiplied by 100 to convert to percentage<br>terms.<br>Source: Enterprise Surveys.<br>www.enterprisesurveys.org|

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**Qr,t-1** + **Q&.t-j** + Q.,t_j + **At-Y. -** Qi + **Qf** + Q. + Q. + Qs + Q. including the supply-demand identity where

- **Q,** - quantity demanded (z - i for industry or food, z - f for feed, z - x - -

- for export, z r for farmer owned reserve, z g for government -

- stocks, z m for market stocks)

- P,,market price

- X, - exogenous variables which determine the relevant demand

- Y - actual average yield

- Y- - per capita consumer income

- T- - quarterly shift terms

- FJ - numbers of various types of livestock on feed

-

- P, prices of various types of livestock meat

- E - trade weighted exchange rate

Ps - support price

- Pr - release price

D - shift term reflecting the 1983 PIK program.

The demand system was not estimated in the form of (9) because a system that determines price through an identity equation tends to pzoduce erratic price estimates particularly when demands are inelastic. Alternatively, a demand equation in (9) can be solved for price, (10) Pm - Qi (Qi,Xi)I and then the identity can be used to determine Qi. This Approach suffers in practice because the coefficient estimates of exogenous variables in the inverted equation are susceptible to spurious correlations with other factors in the system. This can lead to an unreasonably large contribution of these variables relative to other exogenous variables in the system in determining price predictions in practice. The approach used in this study is to solve 6