rafmacalaba/datause-encoder-data huggingface dataset:
# 1. **Introduction**

While many empirical studies, surveyed subsequently in this paper, have investigated and affirmed the role played by lobbies in influencing farm policy, especially in the United States, few have examined the structure of lobbying at a level of detail sufficient to reveal patterns about who lobbies, who are lobbied, and whether lobbies accomplish their goal of influencing policy. The objective of the paper is to fill this shortcoming in the literature.

As we will see, the term lobby takes on different meanings depending on the theoretical context. Thus, informational lobbying means just that – providing information rather than money, while _quid pro quo_ lobbying implies an exchange of money for services in the form of favorable policy. The paper also presents direct and indirect evidence on whether agricultural lobbying in the US is better characterized as informational or quid pro quo lobbying.

Multilateral negotiations that seek to implement freer trade in agriculture must recognize the political-economic nexus that has led to continuous subsidization and protection of agriculture in developed countries, for example in the U.S. since the 1930s. To the extent that lobbies significantly influence agricultural policy, implementing freer trade in agriculture requires designing incentive schemes that take into account the status quo political-economic equilibrium. Accomplishing free trade in agriculture requires effective bargaining at the level of diplomacy. Effective bargaining at that level must be performed over policy options that are politically viable domestically, since governments must serve their constituencies first. And governments do listen to their politically active constituents disparately more than others.

Despite the fact that agriculture accounted for less than 5% of GDP and employment in developed countries, the farm trade dispute held up the Uruguay Round of negotiations. Agricultural protection was virtually ignored in the first four rounds, by design due to the sector’s political sensitivity. Even regional trade agreements routinely exclude agricultural products, without which the agreement might not succeed. The task of multilaterally negotiating reductions in agricultural protection in the Doha round is therefore a challenging one. The extent of trade liberalization in agriculture is already being used as a barometer for the Round’s success. To this end, with the objective of designing implementable agreements in mind, this paper investigates lobbying and its role in influencing the structure of agricultural protection in the U.S.

1
# **B DATA BASE CONSTRUCTION**

# **B. A Mayoral elections**

The database was constructed with original data from the Colombian electoral authority, . _Registradur ́ıa Nacional del Estado Civil_ Our database begins after the 2000 elections, since after that year the electoral data has information on the electoral roll and the total votes received for all candidates. To begin, we identified the political party of each candidate or the political movement that endorsed the candidate’s campaign<sup>20</sup> and then classified its ideology as left, center, or right following the methodology proposed by Fergusson et al. (2020).<sup>21</sup> The classification for each candidate’s ideology includes three steps.

1. Check party names, mottos, and slogans for words that identify the candidate’s party clearly as left-leaning or right-leaning (e.g., communist or socialist for leftwing, and conservative or Christian for right-wing).

2. In the event that the previous step did not work, we checked the party statutes (when available) for policy stances that clearly leaned either to left or right. We coded a party as left-wing if the party statutes included at least three of the following five leftist policy positions: (1) pro-peasant, (2) advocates greater market regulation, (3) thinks workers should be defended against exploitation, (4) advocates state-owned or communal property rights, and (5) anti-imperialist. We coded a party as right-wing if its statutes included at least three of the following five rightwing policy positions: (1) economic growth is emphasized over redistribution; (2) advocates free market, orthodox policies, and privatization; (3) believes that family and religion are the moral pillars of society; (4) appeals to patriotism and national-

> 20In Colombia, a mayoral candidate may register for the elections with the endorsement of a political movement or party, with legal status recognized by the National Electoral Council, or with the support of a significant group of citizens, in which case it must provide the total of the corresponding signatures.

> 21Fergusson et al. (2020) demonstrate the credibility of their classification by linking electoral victories of left-wing parties to increases in right-wing paramilitary violence, a relationship that aligns with theoretical expectations from Acemoglu and Robinson (2008). Similarly, Rozo and Vargas (2021) applied this method to classify candidates in Colombian local elections, further validating its relevance.

59
23 related to market entry or regulatory constraints that prevent the financial system from deepening. Analysis of the market structure and degree of competition in the financial system might be useful in that context.

If the actual level of financial development is above the predicted level, this can also be due to several reasons, which can be gauged with different data sources. First, a sound and flexible institutional framework might allow the financial system to move beyond its structural depth line. If this movement beyond the predicted level has been a gradual one and in line with improvements in policy and institutional indicators, it might be indeed sustainable. If on the other hand, there is a rapid increase in financial depth indicators such as Private Credit to GDP, concentrated in specific sectors, such as household or mortgage credit or in foreign currency rather than local currency, this might indicate an unsustainable expansion. Finally, bailout expectations as gauged from banks’ credit ratings and funding cost differences between systemically important banks and nonsystemic institutions might give additional indications of overheating.

The benchmarking exercise can also be used to gauge the differential development of different segments of the financial system, as done by De la Torre, Feyen, and Ize (2013) who apply a quantile regression framework to a global panel database to explain the variation in a large set of financial indicators. The model’s independent variables consist of structural country factors including demographic factors (population size and density) and dummy variables for specific country circumstances (fuel exporter status, transition country indicator, and an offshore center indicator) and a set of year indicators. In addition, the model includes economic factors that are decomposed into an initial income effect, a contemporaneous economic growth effect, and the interaction between
Overall, these descriptive findings suggest that the impact of DSM on seed purchases and yields may be greater for maize than for wheat and more evident in the 2019 survey round than in 2016. However, it is essential to account for changes in the comparison groups relative to the treated cohorts, rather than focusing solely on the progression of the outcome variables for a cohort over time. We explore this in the next section.

# **4 Empirical Model**

The ACC survey covers districts introduced to DSM at different times: five in 2013, eight in 2014, seven each in 2015 and 2016, nine in 2017, and 20 each in 2018 and 2019. This staggered rollout allows us to use the Callaway and Sant’Anna (2021) Difference-in-Differences method (CSDID), which accommodates variation in treatment timing, a defining characteristic of our sample. CSDID also allows the parallel trends assumption to hold, conditional on a vector of pre-treatment covariates that explain the evolution of outcomes without DSM treatment.

Following Callaway and Sant’Anna (2021), the basic CSDID framework is presented as follows. Let i denote the outcome variable (such as seed purchases or yield) for household _i_ at time _t_ , for _i=1, 2, ..., N_ and _t=1, 2, ..., T_ . Define i as the treatment status, taking a value of 1 if household YY _i_ resides in a DSM district at time _t_ , and 0 otherwise. i indicates treatment cohorts or groups, DD equaling 1 if the household lives in a district first treated to DSM at time _g_ , and 0 otherwise. _C_ GG represents a “never-treated” comparison group, identifying households that were never treated between 2012 and 2019. Once treated, districts remain under DSM, satisfying the CSDID assumption of treatment persistence. denotes pre-treatment control variables (fertilizer use, oxen ownership, household size, gender, and age of the household head) that are either time-invariant or, in the case of continuous variables, ii XX refer to the pre-treatment period. The parallel trend assumption under CSDID will be conditional on , and the use of pre-treatment levels as controls ensures that their values are not affected by the treatment. This conditional parallel trend assumption adjusts for the fact that outcome variables ii XX are likely to evolve differently across households with different observed pre-treatment characteristics in the absence of DSM. With the availability of the variables, several options exist to estimate average treatment effects. The first is the regression adjustment procedure, in which the difference in the outcome variable ii XX 12
Edmonds and Shrestha (2012) investigate the impact of MEA on child labor and schooling using micro-data from around 60 countries, mostly low-income countries. They found no short-term effects of enforcement of MEA in all countries but one.

The evidence of long-term consequences of child-labor laws is also very limited. Most of what is known has used CSL as an instrumental variable to estimate returns on education (Angrist and Krueger 1991; Oreopoulos 2006a and 2006b). As far as we are concerned, this is the first study to look at long-term effects of a child-labor ban. Depending on how the problem is framed—either as an earlier or later exposure to the labor market—some of the findings discussed in this paper can contribute specifically to the literature on returns to experience (Imbens and van der Klaauw 1995; Connolly and Gottschalk 2006; Grooger 2009; Angrist 1990; Angrist and Krueger, 1994; Angrist et al. 2011).

In fact, this paper relates more broadly to the growing literature on long-term effects of interventions designed for the youth groups. A great deal of these studies look at educational policies such as Dustmann et al (2012) on high-school quality, Deming et al (2013) on high-school accountability, Chetty et al (2014) on teacher quality, Lavy (2015a) on school choice, and Lavy (2015b) on teacher pay-for-performance. Others assess the impact of either youth training or vocational education on labor-market outcomes (Card et al. 2011; Hicks et al. 2013; Hirshleifer et al. 2013; Ripani et al. 2015; Attanasio et al. 2015) or ‘remedying’ interventions targeted at disadvantaged children (Angrist et al. 2006). The evidence that comes out of these studies point to a long-term effect on education attainment or labor-market outcomes. Our study adds new evidence to this body of the literature by evaluating the long-term effects of an active labormarket policy aimed at young people that acts through a channel underexplored so far— restricting the choice-set of time allocation of the youth.

# **3. The Institutional Setting and the Intervention**

The Brazilian Constitution of 1988 set the minimum legal age of entry into the labor market at 14. In 1990, a federal rule named ‘The Statute of Children and Adolescents’<sup>9</sup> established children and youth rights beyond regulating the conditions of formal labor-market entry. Complementing the Constitution of 1988, the statute is

> 9 _Lei do Estatuto e do Adolescente_ , Law No. 8069 from July 13, 1990.

8
recovery scenarios. The total potential short-term costs associated with privatization are equal to the sum of any capitalization costs borne by the province and residual entity liabilities, minus the price paid for the privatized entity (column 5). Final privatization costs will depend on the extent to wlhich provinces recover residual assets. Column 6 lists total privatization costs assuming that 20% of residual assets are recovered; Column 7 assumes a 50% recovery rate. Clearly, total costs will be quite sensitive to the success of the recovery effort. The total cost estimates in Table 7 assume that the province pays off all of the residual liabilities. To the extent that they are able to negotiate their way out of some liabilities, the total cost figures in columns 5-7 should be reduced. The bulk of the short-term fiscal implications will, thierefore, derive from the terms on which the residual entity is liquidated. The Fondo Fiduciario loan data, moreover, should be some guide as to the post-privatization financing assistance that provinces might expect.

# **IV** PRIVATIZED PROVINCIAL BANKS: INDICATORS OF FUTURE SOLVENCY

Owners of privatized provincial banks should face the same incentives as other private bankers in Argentina. To the extent that prudential regulations and bank supervision are adequate -- and all indications are that both have improved substantially in the 1990s -- neither privatized nor private banks should be so crisis-prone that they pose a substantial bail-out risk, especially when one considers that Argentina's present system of deposit insurance has a disciplining effect on member banks.29 In a country with a strong private deposit insurance

> 2') That system is partly privately managed and imposes high premia (.36-.72% of deposits, depending on the class of deposit) by international standards on member banks. In Denmark, another country with privately managed deposit insurance, premia payments are only .2% of deposits. In Colombia premia are .15% of deposits; in Mexico they are .3% -- and coverage per depositor is unlimited. Membership in Argentina's program, moreover, is compulsory. Details regarding deposit insurance programs are drawn from, Alexander Kyei, "Deposit Protection Arrangements: A Survey," IMF Working Paper No. WP/95/134, Monetary and Exchange Affairs Department, Dec., 1995, and Samuel Talley and Ignacio Mas, "Deposit Insurance in Developing Countries," World Bank Policy Research and External Affairs Working Paper No, 548, 1990.

15
volumes and 600 reader seats (Tolosa, 2012). Information services not only include books and magazines, but also children’s rooms with specialized staff, programs for babies and their parents, activities for teens, workshops in literature, puppets, etc. The intention is to attract the public with these activities while integrating education into them. One of the main projects occurs over holidays, when _BibloRed_ implements _Bibliovacaciones_ , a program with the activities mentioned plus cost-free art, history and literature exhibitions such as theater plays and films. In this context, it is evident that these libraries have many activities which enhance the quality of life, particularly through their integration of culture; thus, the possible effect on the educational performance of children and young people is just one of the multiple benefits that libraries bring to society.

Since it is not possible to have information on which of the test-takers actually use the libraries, we propose to use the distance of libraries to their schools as an alternative indicator for treatment status. As discussed in the previous section, this rests on the assumption that the use of libraries is likely to be higher for those living closer than for those who live far, supported by travel costs to libraries incurred by the latter which reduce students’ incentives to visit them frequently. According to Table 1, 77% of students in Bogot ́a live less than 20 minutes from the school they attend. As a result, it is a fair assumption that distance from school to the library approximates the distance from the library to students’ residence and, therefore, the likelihood that they live in an environment affected by libraries.

The Euclidean distance between the school and the local library is shown in Figure 1.We calculate it based on the information on the spatial location of each school as specified by Bogot ́a’s Department of Education. Alternatively, we use road-based distances as shown in Figure 2.<sup>4</sup> Figure B.1 presents the link between both distances. As expected, the roadbased distances all fall above the blue line corresponding to the 45-degree line. The black dotted line is the predicted linear relationship between both measures, which captures up to 80% of the total variation. As a robustness check, the main estimators are repeated using the fitted distance.<sup>5</sup>

> 4These calculations were made using ESRI ArcMap 10.2 Closest Facility Analysis. The road network was obtained from Open Street Map project (OSM).

> 5More explicitly: _AdjustedRD_ = _RD_ ˆ _−β_<sup>ˆ</sup> <u>0</u> , where _β_<sup>ˆ</sup> come from the OLS regression between road distance _β_ 1 _RD_ and euclidean one _ED_ : _RD_ = _β_ 0 + _β_ 1 _ED_ + _u_

7
scores across countries, the differences between men and women within countries is small. However, these seemingly small differences are statistically significant in several cases. For instance, in the ECA countries, we find that men and women show significant differences with regard to conscientiousness (women show higher average scores), extraversion (women score higher), and emotional stability (women’s scores are higher in Armenia and Georgia but not in Ukraine). Gender differences in emotional stability (or neuroticism) are observed across all seven countries, with women showing higher emotional stability scores than men. Scores on decisionmaking are significantly different in five of the seven countries (here again, women have higher average scores), while gender differences in “openness to experience” are observed only in Colombia, Kenya, and Vietnam, where men score higher than their female counterparts.

# **Table 7. Average Scores on Noncognitive Skills among Men and Women Ages 25 to 64, by Country**

Panel 1

|||Ar<br>|menia<br>|Bo<br>|livia<br>|Col<br>|ombia<br>|
|---|---|---|---|---|---|---|---|
|||Men|Women|Men|Women|Men|Women|
|Openness|Mean|3.23|3.21|3.22|3.16|3.22|3.17|
||SD|0.49|0.50|0.56|0.61|0.50|0.53|
|Conscientiousness|Mean|3.23|3.26|3.18|3.19|3.32|3.34|
||SD|0.50|0.49|0.50|0.51|0.53|0.48|
|Extraversion|Mean|2.98|3.03|3.00|2.88|3.09|2.94|
||SD|0.54|0.59|0.70|0.73|0.64|0.69|
|Agreeableness|Mean|3.20|3.27|3.08|3.18|3.26|3.26|
||SD|0.51|0.54|0.61|0.66|0.53|0.57|
|Emotional|Mean|2.44|2.26|2.62|2.31|2.78|2.33|
|stability|SD|0.61|0.61|0.64|0.69|0.70|0.69|
|Grit|Mean|3.15|3.15|3.04|3.03|3.08|3.05|
||SD|0.57|0.57|0.63|0.65|0.56|0.58|
|Decision-making|Mean|3.20|3.19|3.04|3.07|3.02|3.17|
||SD|0.52|0.55|0.63|0.64|0.62|0.58|

18
<!-- Start of picture text --> 2 200<br>2 150<br>Fy<br>= 100<br>=<br>o 50<br>—<br>—<br>© 0<br>=<br>4 -50<br>an<br>£ -100<br>3.<br>2<br>aSu —“ cSwe o& iS)xs Na RSs> <3<br>RS) > Os e RNG & rs<br>Ss x» NS Ne we “vy<br>ov Se wv?<br>aS S<br>@ maintenance norm Omaintenance and rehabilitation norm<br><!-- End of picture text -->
Table A7: Predictability of work schedule does not impact well-being or preferences

||(1)<br>Stability|(2)<br>Dist. Mon.|(3)<br>Risk Tol.|(4)<br>Time Pref.|
|---|---|---|---|---|
|Received Schedule|-0.046|-0.165|0.104|0.040|
||(0.068)|(0.076)|(0.091)|(0.096)|
|Shrp. q-val Schedule|0.988|0.143|0.620|1.000|
|Observations|403|403|403|403|

**Notes:** This table reports the treatment effect of receiving a schedule of two months of work relative to those who received no such schedule. Sample includes only those in the employment treatment arm. The columns show OLS estimates of a regression of the outcome on a dummy for assignment to the work treatment, as well as the baseline measure of the dependent variable, camp and enumerator fixed effects, and sociodemographic controls as determined through a double-selection LASSO procedure (Belloni, Chernozhukov, and Hansen, 2014).

All outcomes are standardized. ‘Received Schedule’ are those who received the calendar the complete two month work schedule marked. ‘Dist. Mon.’ is a prespecified revealed preference question on whether the respondent is interested in joining a committee to determine how funds will be distributed to the community, used as a proxy for ‘agency.’ Standard errors are clustered at the block level. The row labeled “Shrp. q-val Work” reflects p-values, referred to as ‘sharpened q-values,’ adjusted for multiple hypothesis testing using Anderson (2008) for all outcomes in this table.

51
born of a particular circumstance of disadvantage may induce despondency, helplessness and a fatalistic lack of belief in one’s agency to achieve something and this may lower effort which may accumulate over the life cycle to affect outcomes in a meaningful way. Instead of treating differences in circumstanceinduced effort as a matter of individual responsibility, some may be of the view that they should be accounted into the circumstances themselves in order to get a fuller picture of the true inequality of opportunity.

In order to do that, we will need to purge the effort variable from any component that is correlated with circumstances. We begin by first implementing the following regression: i i (14) ii ii which is a pure fixed effect regression of wages. From this regression we extract an estimate of individual yy = uu + uu + εε effects, � . In the next step, we run a regression of the effort on the estimated individual effects to extract the component of effort that is orthogonal to individual circumstances. To be precise, we run: ıı uu i i (15) ii ii EE = uu� + uu + ee and collect the residual of that regression. In the final step, we then run the following regression: i i (16) ııii ii ii yy = ββee�+ cc + uu + εε which is a regression of log wages on an estimate of the component of effort that is orthogonal to circumstances and the individual fixed effects. Note that the difference between (12) used in the calculation of the first upper bound and (16) which is being used to calculate the second upper bound is that any component of effort that is likely to be correlated with circumstances is going to be captured by the individual effects in the latter.

# **3. Data**

In this section, we discuss some key characteristics of the data. We use the Russia Longitudinal Monitoring Survey (RLMS) for this analysis. The RLMS is a nationally representative survey that is widely used to monitor the health and economic welfare of households and individuals in the Russian Federation. We focus on the rounds between 2006 and 2010. The survey contains detailed information about the demographic and economic characteristics of the surveyed individuals. Variables of interest include age, education, nation of birth of the individual, as well as labor force participation, wages and fringe benefits for those working. Given our focus on circumstance variables for inequality of opportunity, we use a key question included in the 2006 round that refers to the parents of the interviewee. In particular, we use the questions on father’s education and the father’s affiliation to the communist party before 1991.

7