rafmacalaba/datause-encoder-data huggingface dataset:
through (4). We also repeat the analysis separately for urban areas and for rural and estate areas (grouped together) to check if prediction accuracy varies across sectors. Finally, focusing on the 55 sub-districts, we reduce the training sample size by half and quarter, to examine if the prediction accuracy is sensitive to the size of the training sample. As a robustness check, we also implement a flexible random forest algorithm using population density as the response variable, using the four sets of satellite indicators described above. For each model, we report out-of-bag _R_<sup>2</sup> , RMSE, and MAE.<sup>17</sup> Next, we focus on an overlapping sample between the sampled villages in the HIES and those in the 55 sub-districts for which proprietary features are available. These amount to 1,360 villages. In 414 villages of this sample, we model the HIES population density as a function of the all public and proprietary satellite indicators, and apply the procedure described above. Since this is a survey-based model, we use the inverse of village population for village _v_ as weights, which is given by _V illage Population_ <u>1</u> _v_<sup>.Inaddition,therecouldbefactors</sup> correlated with satellite-derived indicators that affect the probability of selection of the village into the HIES. We therefore adjust the weights based on the predicted probability of the village being sampled in HIES, based on satellite indicators available for all villages (Horvitz and Thompson, 1952; Wooldridge, 2002). This probability is obtained from the following probit model: where _INHIESv_ is a binary indicator for whether village _v_ is sampled into HIES, and _lassov_ represents all LASSO-selected variables from the Poisson model. We estimate this model using data for all 1,360 villages in the 55 sub-districts. The predicted probability of the � village selection into the HIES, _INHIESv_ is used for correcting the weights in the following <u>1</u> way : � _V illagePopulation X INHIESv_<sup>.</sup> Using these above two sets of weights, we estimate the model in HIES-villages. Then, we predict out-of-sample densities for the remaining 946 non-HIES villages and report outof-sample accuracy measures by comparing them with the actual census density. To provide additional context, we compare the accuracy of this model to predictions from a similar censusbased model and with density estimates from the publicly available “top-down” population products. Further, we calculate population count predictions by multiplying these density predictions with village-level area, and report their accuracy with respect to the census population.

> 17 Out-of-bag _R_ 2 is an out-of- sample statistic that refers to the average variance explained by the random forest predictions, across all villages, when averaging over the bootstrapped samples that did not contain each village.

9
# **Table 8 Questions 4–7: Panel Regression**

This table shows the panel regression results with regard to questions 5 through 8, as in equation (2). The dependent variable is _X_ , the proportion of the mopnthly payment that will make the respondent indifferent in each question.

||Specifc|ation 1|Specifc<br>N=2|ation 2<br>404|Specifc|ation 3|
|---|---|---|---|---|---|---|
||Coef.|Std. Error|Coef.|Std. Error|Coef.|Std. Error|
|**Treatment**|**-0.0397****|**0.0189**|**-0.0431****|**0.0184**|**-0.0448****|**0.0183**|
|Question 4|0.782***|0.0147|0.786***|0.0163|0.621***|0.0649|
|Question 5|-0.0976***|0.0139|-0.0959***|0.0139|-0.0960***|0.0139|
|Question 6|-0.0554***|0.0111|-0.0553***|0.0111|-0.0555***|0.0112|
|Question 7|-0.0578***|0.0111|-0.0577***|0.0111|-0.0576***|0.0111|
|Mexico|||0.0607***|0.0231|0.0124|0.0290|
|Chile|||-0.153***|0.0210|-0.195***|0.0251|
|Age|||||0.00602**|0.0026|
|Male|||||0.0014|0.0182|
|Student|||||0.0173|0.0224|
|Children|||||-0.0642|0.0465|
|Married|||||-0.0851|0.0569|
|Mother Education|||||0.0251|0.0253|
|Father Education|||||0.0361|0.0244|

* 10% Significance Level

** 5% Significance Level

*** 1% Significance Level

28
13 Table 9: Tax Arrears To Taxes Due (%)

||1<br>1990<br>|11991|1992|
|---|---|---|---|
|l AAA|1.8|3.3|3.7|
|AA|0.2|17.4|26.8|
|A|_5.0_|42.7|50.8|

# II.4.A Evolution of Interest Costs

The preceding evidence suggests that A firms kept themselves afloat by financing growing inventories through increased working capital loans. This shows up dramatically in the ratio of working capital interest to sales for the three groups of firms (Table 10). A firms have consistently exhibited the highest interest ratio. Not only this, but the gap in relation to AAA firms has widened with time, from a ratio of almost 2 for the year 1990 to 3.5 for the first six months of 1992.

Table 10: Working Capital Interest/Sales (%)

|_**_**_|1990 1<br>_<br>___<br>_<br>___<br>___|1991<br>__<br>___<br>___|1992<br>(1-6)J|
|---|---|---|---|
|AAA<br>|3.2|_3.5_|2.7|
|AA|2.3|2.4|1.9|
|A|6.1|11.3|9.6|

Table II contains remarkable information on the ratio of total interest on working capital and investment loans to sales. The evolution of these numbers captures the rapid borrowing for investment by A firms combined with declining real sales (hence low capacity utilization). The gap here between AAA and A firms widens even more rapidly than above.

Table 11: Total Interest/Sales (%)

|<br>_|11990<br>__I_<br>|1991<br>__<br>___|1992<br>~~~(1-6)|
|---|---|---|---|
|AAA<br>|4.2<br>|5.8<br>|6.3<br>|
|AA<br>|3.8<br>|3.2<br>|2.7<br>l<br>|
|A|6.3|19.7|27.5|

Figure 7 plots the amount of profits before interest and taxes being absorbed by interest payments (cumulatively within-years, by quarter). As expected, interest payments exceed such profits for A firms after mid-1991. Beyond this point, A firms as a group were unable to service their debt.

# _11.5_ Wage Setting and Decapitalization

SOEs entered the ETP with at least two disadvantages: excess labor; and inefficient materials and energy consumption owing to distorted pre-ETP prices. The first would require labor shedding,
51. On the one hand, local governments’ fiscal income was negatively influenced by VAT reform. On the other hand, the wind power industry in China experienced rapid development in the last decade, and VAT revenue in most wind resource-rich places increased remarkably. Actions taken by local governments may have brought about local protectionism, which can generally lead to market distortions, installation of substandard equipment as developers try to absorb the costs of administrative measures, and harm the overall quality of projects.

52. The administrative measures adopted by local governments, to require the procurement of locally produced equipment and other fee-charging schemes that are aimed to compensate for VAT losses on equipment, should not be encouraged. Local governments seem to have disregarded the outcome that the new VAT policy will have, along with other government initiatives, of inducing wind development at a larger scale. VAT revenues expected by local governments from large-scale wind power projects would at least be equal to or higher than the prereform level if wind power development were increased by 50 percent. Moreover, some of these practices can threaten good supply chains and increase the overall costs of wind power generation, which are important for the long-term, sustainable development of the industry.

# **Conclusions**

- The government’s large-scale wind power development objectives should be twinned with a clear objective to deliver electricity at a minimum cost. Efficiency is contingent upon ensuring that wind farms are built in places where crucial requirements for success are present: the best resources, adequate project designs, use of proven turbines, regulatory clarity, adequate incentives, transmission capacity to deliver the electricity and, last but not least, appropriate operation and maintenance practices carried out by skilled and trained staff.

- The grid is crucial. International experience has been good in planning wind farm operational integration. However, grid connection and stability issues for gigawattscale wind bases, as envisaged in China, have no precedent anywhere in the world. Comprehensive connection studies with special attention to the optimum connection size and connection circuit layout should be undertaken with the involvement of all stakeholders.

- As large capacity operates at low efficiency in resource-rich Northern China, and because planning and construction of the grid network to off-take the electricity will take time, the government needs to consider readjusting the incentive mechanisms to guide the developers to invest in regions with high efficiency and as close as possible to load centers with an objective of achieving the lowest incremental cost.

53. Shifting part of the focus to the central and eastern regions in China by offering a higher feed-in price _can balance the wind power development pace in China and minimize the_ 24
Rodriguez, Sanzo-Pérez and Suárez Pandiello, 2005; Molero and Pujol, 2005; Alm and Torgler, 2006; Alm and Gomez, 2009) and as a novelty Martinez-Vazquez and Torgler (2009) focus on the inter-temporal evolution of tax morale in a country using data from different WVS survey waves.

The last 30 years represent a period of radical transformation for Spain, which could have profoundly affected the way citizens view the state and ultimately, their tax morale. The state of tax morale was quite low at the end of Franco‘s regime. As reported by Comin (2007), at the end of the 1970s, tax evasion in Spain was estimated at 40 percent of tax receipts and above 90 percent of all Spanish taxpayers recognized the existence of fiscal fraud. In addition, at the time the fiscal system provided ample tax evasion opportunities for the wealthy, and there was no political will to prosecute fiscal fraud. In particular, tax evasion was not a criminal offense and evaders only received small administrative sanctions. After Franco‘s death, the first democratic elections took place in 1977 and were won by a center-right coalition (the UCD — Unión de Centro Democrático). The highlight of this period was the coalescence of all political forces in the country in what became known as the Moncloa Pacts of 1977. These Pacts set the foundations for political reform, with a new Constitution being approved in 1978. These Pacts also set the blueprints for deep fiscal reform, including legislation making tax evasion a crime. The goals of the Moncloa Pacts were to introduce policy and institutional reforms in the public and private sectors, that would bring Spain up to par with the system Spain‘s European neighbors had put in place almost three decades earlier following the end of World War II (see Comin, 2007). The period subsequent to the Moncloa Pacts was one of political indecision, poor economic performance and political turmoil - including a failed coup by a small group of security forces in 1981. It appeared that the government had withdrawn from its commitments made in the Moncloa Pacts in terms of policy reforms, including the modernization of the tax administration and the fight against tax evasion.

Fiscal and other institutional reforms gained momentum with the new Socialist Party: the Partido Socialista Obrero Español (PSOE) government after their election victory in 1982. The PSOE would stay in power until the general elections of 1996. Several important changes and reforms took place during this period. Perhaps the most 34
mentors in Chiapas, and given these constraints, mentors are allocated across communities on a rotating basis.

# **6.1 The Exposure Effect of the Program**

We start by analyzing the initial transition of the schools in the evaluation sample from the experimental situation to the policy at scale. We do this by estimating the following regression model: where 1 � _ExpPlusj_ ( _i_ ) = _k_ � _∈{_ 0 _,_ 1 _}_ represents an indicator variable for whether school _j_ is exposed to _k ∈{_ 0 _,_ 1 _, . . . , K}_ years of API _Plus_ program, while **_X_** _j_<sup>_Criteria_</sup> ( _i_ ) is a vector of indicator variables for the program eligibility criteria. Our outcome of interest ( _Yj_ ) represents the 2016–2017 school-level transition rates to seventh grade. This variable is constructed from the same administrative source we used for our experimental evaluation. The sample includes 207 schools of the 224 that were part of the experiment. Beyond a school that permanently closed, the sample attrition is caused by schools not having sixth graders during that school year. This fact is consistent with the multi-grade nature of the CONAFE system. Attrition is balanced with respect to the total years of exposure to API _Plus_ ( _p_ -values = 0.467, 0.812, and 0.568).

We exploit the change in situation to analyze the exposure effect to the program one year after the government rollout, when school closures were still minimal (only one school closed among our 224 schools by the fall of 2017). After the first year of the rollout, schools started to close more intensively (11 additional schools closed during the second year); hence, outcome variables based on survey or administrative school data during this period would suffer from endogenous censoring.<sup>18</sup> By the spring of 2017, the total years of exposure to API _Plus_ range from zero to three depending on whether a school received the _Plus_ modality for two years during the second experiment, as well as on the API _Plus_ government assignment in the year after the experiment. The underlying assumption of this approach is that, once controlling for the official government criteria, the remaining variation in the program’s

> 18For this reason, in Section 6.2 we take advantage of the 2019 Census information that is not subject to this issue. The Mexican Census is available only every ten years, and hence we cannot use this information to estimate the model in equation (4).

33
**Figure 7 Translation of Monopoly Equilibrium and Profit Variation in the Scenario of Higher Dwell Time and Cost-Plus Pricing Strategy**

<!-- Start of picture text --> p<br>AC2<br>p1<br>π2<br>AC1<br>π1<br>p2<br>D<br>Q<br><!-- End of picture text --> _Source:_ Authors.

The second conclusion is therefore that a monopolist who opts for a cost-plus pricing strategy when demand is inelastic to price will not be affected in the short term by higher logistics costs and will make no effort to reduce dwell times in case of a punctual congestion or punctual inefficiencies of port operators. Such a scenario is likely to happen for cyclical patterns of demand that are elastic to price only in the long term (food supplies, drugs, equipment).

A third pricing behavior derived from this situation of inelastic demand and observed among monopolistic companies is opportunistic pricing. Such traders use, for example, the pretext of higher logistics costs to increase substantially their selling prices. It is especially the case for category C (landlocked countries traders) during rainy seasons or port congestion periods. For example, a 10 to 20 percent increase in total logistics costs might translate into a 30 percent increase in price. If we refer back to Figure 7, traders would charge price _p_ 2 as soon as any difficulty is noticed in the port or along the transport corridor.

Another example of opportunistic behavior is when shippers prefer leaving their cargo in the port until the price peaks in an upward season. They create an artificial shortage in the local market and delay early deliveries until market prices rise. For example, in a situation similar to the one depicted in Figure 8, deliveries will be postponed for at least six or seven days. This is a very particular situation, where rising costs do not constitute a sufficient incentive to accelerate the clearance of goods because expected profits more than balance the extra costs.

15
school.<sup>52</sup> Overall, these results contribute to the existing body of research that shows the positive effects of mindfulness on an array of behaviors and outcomes of adults (Keng et al., 2011). The main feature of the _Mindful_ intervention is that it is low cost, which is an attractive feature of public policy. Based on the impacts estimated from the ASP, an ASP should also include an evidence-based curriculum oriented toward improving student outcomes. We find that the _Mindful_ intervention is the best curriculum to add to _Clubs_ to help vulnerable youth in poor and highly violent countries.

# **5.5 ASP Attendance**

We first explore the differences in ASP attendance by ASP variation using data obtained from reports from volunteers. Table 8 shows the results from estimating a version of specification (2), which excludes the interactions with the _Post_ variable. Column (1) presents the effects on attendance (as a percentage of the total sessions scheduled), column (2) shows the effects on an indicator of whether the student dropped out of the ASP, and column (3) presents the effects on the number of sessions scheduled. We find that, on average, students assigned to _Clubs_ attended 61% of the scheduled sessions. The attendance rate was greater for the _Virtue_ intervention relative to both _Clubs_ and _Mindful_ (column (1)). Moreover, students in the _Virtue_ ASP are 11 pp less likely to abandon the intervention relative to students assigned to _Clubs_ (column (2)). We show that these differences in attendance are not driven by differences in number of sessions scheduled in each type of ASP variation (column (3)). Considering the ASP attendance and dropout results together, we show that the _Virtue_ ASP was more attractive to the students assigned to it since they attended a greater share of sessions relative to both _Mindful_ and _Clubs_ and they were less likely to abandon the intervention relative to _Clubs_ .

> 52 Overall, these results are robust to the inclusion of control variables selected using a Double-LASSO procedure, as we show in Table A10.

34
**Figure 6:  Consumption and Growth in Mean Consumption by Percentiles/Quintiles, 1995-2002**

<!-- Start of picture text --> 1339 The Pen's parade:<br>7.1<br>consumption by percentile<br>1130<br>6.9 1994<br>953 1998<br>6.7<br>804 2002<br>6.5<br>679<br>572 6.3<br>483 6.1<br>407 5.9<br>344 5.7<br>290 5.5 percentile<br>0 10 20 30 40 50 60 70 80 90 100<br>PLN / month / eq. adult<br><!-- End of picture text --> <!-- Start of picture text --> Consumption growth for selected<br> quintiles<br>1<br>5%<br>2<br>4% 5<br>3%<br>2%<br>`<br>1%<br>0%<br>-1%<br>-2%<br>-3%<br>1995 1996 1997 1998 1999 2000 2001 2002<br><!-- End of picture text --> _Source:_ Staff calculations based on GUS data.

**Figure 7: Growth Incidence Curve**

<!-- Start of picture text --> Growth incidence: consumption<br> increase by percentile<br>15%<br>10%<br>5%<br>0%<br>-5%<br>1998/1994<br>-10% 2001/1994<br>2001/1998<br>-15%<br>0 10 20 30 40 50 60 70 80 90 100<br>Note : The 2002/1994 change is approximately a sum of 2002/1998 and<br>1998/1994<br>Source : Staff calculations based on GUS data.<br><!-- End of picture text --> It is straightforward to extend the idea of the Pen’s parade to show the change in mean consumption (rather than ITS level) for various percentiles of the consumption distribution during the period of interest. This approach, which uses the so-called growth incidence curve ( _gic_ ), is meant to show how gains from aggregate economic growth are distributed among the population.<sup>9</sup> Figure 7 shows growth incidence by percentile.

It should be noted that there was almost no change in the consumption level at the 15<sup>th</sup> percentile between 1994 and 2002. However, the consumption level at the 90th percentile and higher grew by over 10 percent over this period. These results are consistent with what has already been identified. For example, the curves are (almost) regularly increasing, meaning that inequality has risen at all levels of distribution.

During 1994-98 the curve was above zero everywhere, indicating that poverty has fallen, whatever the poverty line and whatever (reasonable) poverty measure is adopted. Although, 9 Note: The curves should not be interpreted as a growth in consumption of an average p-percentile Mr. X during that period. The cross-sectional character of the exercise implies that the graph rather depicts the difference in mean consumptions of two groups of individuals that happen to be in the same percentile at both ends of the period of interest. We are comparing the poor in year A with the poor in year B, not asking whether they are the same people, thus (leaving aside mobility within the distribution) we underestimate the improvement for individual poor people and overestimate the growth of rich people.

9
ing/distribution firms to have more market power vis-`a-vis smaller suppliers. At the same time, exporters in lower income countries may be less organized, and less adept, in holding their own against market power exercised by buyers. (Imagine WalMart negotiating supplier contracts in Jamaica, as opposed to in Canada.) In Table 3 we explore this issue by making the following splits in the data. The first split involves OECD trade with low-income countries (defined as having a per-capita income below $1000 in 2001 dollars), and all other trade. For the second split, we divide the sample into OECD trade where the importer is large (with a nominal GDP greater than $500billion) and the exporter is small (defined as having a nominal GDP below $100billion), versus all other trade. For the final split, we examine OECD trade where the importer is large and the exporter is both poor and small. In all cases, we find that the correlation in the data between exports to the OECD and competition is greater when there is likely to be greater market power, in the sense that it matters more for smaller and poorer exporters. The structure of the retail and distribution sector in the OECD countries is more of a trade barrier for small and low-income countries than it is for exporters from higher income and larger economies.

Finally, Table 4 is our attempt to convey a sense of the magnitudes involved, not so much statistically but rather economically. In the table, we have taken the tariff coefficient from Table 2, combined with sample values for EU competition indexes and a competition coefficient estimated for the intra-EU15 subset of our full sample. We have used these to calculate a trading cost- or tariff-equivalent from changing the degree of competition in the sample of EU countries, for intra-EU (i.e. duty-free) trade. Hence, for example, from the first column of numbers in Table 4, moving France to the average level of competition in distribution across the EU would be comparable to eliminating a 4.2 percent tariff against its EU partners. Moving to the most competitive level in the sample would correspond to the elimination of an 8.4 percent tariff. In the table, these trading cost equivalents range 17