rafmacalaba/datause-encoder-data huggingface dataset:
particular indivilual; instead, it mu:st be measurt i in statistical terms-as the costs incurred by society to avoid a greater probability of a deadly accidenw or as an individual's willingness to accept a risky activity at a higher payment. Tne human capital approach, which values an individual's life according to the net present value of his/her productivity, is subject to wider technical and ethical criticism but requires substantially less data.

(2) Though the conceptual framework exists to evaluate in money terms the consequences of loss of biodiversity, this aspect is not included in the analyses. The main reason is lack of knowledge on the effects of loss of biodiversity (oss of known and unknown pharmaceutical products, soil erosion and so on). Economic valuations thus involve many subjective considerations and great uncertainty. The information available is extremely limited, even for developed countries.

As to uncertainties, °they range from questions about the existence of an effect, to statistical uncertainties about the values of coefficients, to issues about what dolar values should be assigned to various benefit categories" (NERA, 1990). 'Currently, the usual techiique for dealing with uncertainty is sensitivity testing" (Bojo et al., 1990). We think that addressing uncertainties more rigorously would be beyond the purpose here. Most references present sensitivity analyses for major parameters and assumptions. We make a few sensitivity exercises for the discount rate.

(3) Because it is not possible to make an assessment of environmental costs without information on th- levels of environmental degradation, the estimates hiere are restricted to those problems where data are known or reasonably estimated. Coastal management and deforestationmajor environmental problems in Mexico-are not included. And because the effects are largely the same, the environmental costs of water pollution and of solid and toxic waste disposal are not analyzed individually, but together.

We describe in some detail the steps taken in applying the U.S. methodology to the Mexican data. Because data are limited, a few assumptions and simplifications had to be made, and they are explicitly mentioned. We are convinced that the exercise provides a guide for prioritization and could be replicated elsewhere.

6
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16
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21
Table 2: Country-pair correlation regressions

||(1)<br>GDPD|(2)<br>GDPD|
|---|---|---|
|log index davax tot|-0.126<sup>_∗∗∗_</sup><br>(0.016)||
|log index davax fn||-0.048<sup>_∗∗_</sup><br>(0.022)|
|log index davax int||0.061<sup>_∗∗∗_</sup><br>(0.025)|
|log index DVARF||-0.137<sup>_∗∗∗_</sup><br>(0.021)|
|N|25692|25403|
|R2|0.575|0.575|
|Time windows|2|2|
|Country pairs|14823|14648|
|Time FE|YES|YES|
|CP FE|YES|YES|

Standard errors clustered by country-pair in parentheses Note: observations with non-overlapping 10-year windows. _∗ p < ._ 1, _∗∗ p < ._ 05, _∗∗∗ p < ._ 01 positive and significant association between bilateral inflation correlation and crosscountry production, as proxied either by total gross trade in intermediate inputs, or by value-added trade in intermediate inputs that are ultimately absorbed by the trade partner. The negative association between re-exported import and domestic inflation reveals that inflation dynamics and the association between two countries’ inflation does not only depend on how much they trade, but also on who is the ultimate consumer for those trade flows. Table 3 shows that the same results hold for the correlation of CPI inflation.

Table 4 comes back to the use of gross trade indicators but this time constructed using data from Borin and Mancini (2019) based on EORA data. The goal of separating the (gross) export flows that are directly absorbed from the share that is re-exported further. Results in column (1) show that the correlation of CPI inflation increases when two countries trade more intermediate inputs, which confirms the insight from 15
<!-- Start of picture text --> 50<br>40<br>30<br>20<br>10<br>0<br>MEXPERBOLHNDCOLARG NICUSAGRBOECD EU<br>Tax Revenues (% GDP)<br><!-- End of picture text --> Source: OECD (2002) and Martner and Tromben (2004).

The roots of this apparently incongruous behavior reside to a great extent in the high levels of income inequality pervading all over Latin America. As Latin American countries are much more unequal than OECD and EU countries, to support similar levels of spending the richest income quintile must be more heavily taxed, at least in absolute terms. To be sure, in _relative_ terms (i.e., as a percentage of its income) the richest quintile does not necessarily contribute more than in OECD or EU countries, and therefore it should potentially be possible to increase taxation. Nonetheless, as in Latin America the rich contribute to government revenue a much higher share than in OECD or EU countries (see Figure 6), increasing their contributions even further may impose some strain on the social contract, as the rich may feel to excessively contribute to a welfare state that gives them little back. This argument may acquire increased relevance in light of the poor quality of public services in Latin America, and of recent research finding that willingness to pay taxes is strongly related to the ability of the government to provide efficient public services (see, for instance, Becker _et al._ , 1987, Brosio _et al._ , 2002, and Hanousek and Palda, 2004).

**Figure 9: Net transfers of the richest and poorest income quintiles.**

<!-- Start of picture text --> 0.8<br>Net amount received by the poorest quintile<br>Net amount paid by the richest quintile<br>0.6<br>Difference<br>0.4<br>0.2<br>0<br>BOL GRB USA MEX COL NIC HND PER ARG<br>% GDP per capita<br><!-- End of picture text --> 17
Table 6: LATEs of Short-cycle (SC) Programs vs. the Next-best Option

||Prob(Working)|Log Mont|hly Wage|
|---|---|---|---|
||No-interaction<br>Interaction|No-interaction|Interaction|
||[Other HEI=1]||[Other HEI=1]|
||**A. Full sample**|||
|SC vs. next-best|0.068<br>0.014|-0.534<sup>_∗∗∗_</sup>|-0.234<sup>_∗_</sup>|
||(0.121)<br>(0.112)|(0.154)|(0.125)|
|N|328358<br>328358|156823|156823|
|Kleibergen-Paap F-stat|115.912<br>124.939|97.900|115.894|
|Cragg-Donald F-stat|601.700<br>726.542|309.562|399.201|
||**B. Female**|||
|SC vs. next-best|0.501<sup>_∗∗∗_</sup><br>0.311<sup>_∗∗_</sup>|-0.460<sup>_∗_</sup>|-0.154|
||(0.182)<br>(0.156)|(0.265)|(0.214)|
|N|176651<br>176651|31872|31872|
|Kleibergen-Paap F-stat|67.811<br>80.459|22.143|35.639|
|Cragg-Donald F-stat|235.768<br>305.882|46.495|68.561|
||**C. Male**|||
|SC vs. next-best|-0.237<sup>_∗_</sup><br>-0.243<sup>_∗∗_</sup>|-0.434<sup>_∗∗∗_</sup>|-0.228<sup>_∗∗_</sup>|
||(0.122)<br>(0.123)|(0.128)|(0.114)|
|N|151707<br>151707|124951|124951|
|Kleibergen-Paap F-stat|100.798<br>101.022|108.271|113.189|
|Cragg-Donald F-stat|373.543<br>426.592|300.575|358.374|

* ** *** Note: _p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Standard errors in parentheses, clustered at the high school level. The next-best options refers to a combination of enrollment options different from short-cycle programs (that is, choosing a bachelor’s program or choosing not to enroll in higher education). The "No-interaction" columns present TSLS estimates without interacting the variables of supply of higher education ("HEI only offers SC" and "Other type of HEI"); the "Interaction [Other HEI=1]" columns present TSLS estimates interacting "HEI only offers SC" and "Other type of HEI" and evaluating the effects at "Other type of HEI=1".

29
**<u>Table 7: Subjective certification effects at household level</u>**

||**Total**<br>**Sample**|**Tigray**|**Amhara**|**Oromia**|**SNNPR**|**Richest**<br>**quintile**|**Poorest**<br>**quintile**||
|---|---|---|---|---|---|---|---|---|
|**Land redistribution and takings**|||||||||
|Experienced changes in landholding 5 yrs?|50.32|39.67|56.99|53.58|40.92|52.83|49.35||
|Don't expect changes of land holdings next 5 yrs|71.58|69.20|70.38|73.15|70.80|66.59|77.01|*|
|Expect increase due to land redistribution|8.34|12.40|7.53|9.46|4.80|7.39|8.84||
|Expect decrease due to land redistribution|6.13|11.98|10.22|3.19|4.80|9.13|3.66|*|
|Expect compensation if land taken|86.29|86.78|90.32|87.23|79.33|90.43|83.19|*|
|Compens’n now much more likely than 5 yrs ago|32.88|18.64|21.08|39.51|39.45|35.81|31.37||
|Compensation now more likely than 5 yrs ago|40.39|36.02|46.67|41.27|33.33|41.05|37.25||
|**Land management**|||||||||
|Certificate reduces no. of inheritance conflicts|84.67|87.19|88.71|82.82|82.67|84.57|84.70||
|Wanted to undertake land related investments|77.93|83.47|92.83|77.32|59.08|77.83|77.37||
|Certificate encourages soil & water conservation|86.31|84.71|95.16|90.54|67.64|88.48|79.74|*|
|Certificate encourages planting trees|87.52|80.17|92.29|91.89|76.20|90.43|80.82|*|
|Certificate improves position of women|85.05|81.40|90.14|83.88|83.51|86.52|81.03||
|Certificate increases confidence to rent out land|84.96|87.29|90.84|85.08|76.68|87.77|82.00||
|Certification encourages CPR soil conservation|66.13|82.64|77.06|60.91|56.37|68.91|63.36||
|Certification will reduce encroachment on CPRs|75.64|92.15|85.13|68.24|72.23|79.35|74.78||

_Source:_ Own computation from 2006 EEA/WB Land certification survey. Stars indicate the significance of differences between poorest and richest quintile: * significant at 5%; ** significant at 1%.

27
compelling review of how policy interventions (or lack thereof) can distort resource allocation; three main policy causes can be listed: statutory provisions that vary with firm characteristics,<sup>5</sup> discretionary provisions favoring specific firms<sup>6</sup> and market frictions.<sup>7</sup> Against this backdrop, this paper aims at shedding light on the potential gains from trade liberalization in Brazil while discussing the (policy induced) stringencies that have been limiting domestic integration and distorting markets. The paper is organized in five sections besides this introduction. Section 2 shows how closed to trade the Brazilian economy is and highlights the key policy interventions that lie behind these results. Section 3 presents the potential gains accruing from trade liberalization and points to opportunities to boost productivity through trade integration. Section 4 sheds light on domestic market integration and discusses the (policy created) stringencies that have been affecting resource allocation in Brazil; three main areas are highlighted: infrastructure, use of distortive and ineffective business support policies and product market regulation and competition law enforcement. Section 5 shows the potential productivity benefits accruing from stronger competition. Section 6 concludes.

# **_2. How closed to trade is the Brazilian economy and which policies lie behind this result?_**

Openness to trade in Brazil is limited. Trade openness (measured as trade of goods and services over GDP) in Brazil, considering the level of per capita income, is below the predicted line, with no signs of improvement (Figures 1 and 2). In 2017, the ratio of exports plus imports over GDP was 24.11 percent relative to the world average of 56.1 percent. Although larger economies do tend to be more dependent on their domestic markets and export less, Brazil is the least open country and significantly below its benchmarked openness based on different econometric specifications and even after controlling for country size and distance to main partners (Lederman et al, 2014).

<!-- Start of picture text --> Figure 1: Trade to GDP: 2000-2011(%) Figure 2: Trade to GDP: 2012-2017 (%)<br><!-- End of picture text --> <!-- Start of picture text --> Brazil<br>Brazil<br>3 3.5 Log GDP per capita (PPP adjusted -avg 2012-17)4 4.5 5<br>3 3.5Log GDP per capita (PPP adjusted -avg 2000-11)4 4.5 5<br>Source: World Bank staff elaboration using WDI data Source: World Bank staff elaboration using WDI data<br>400 400<br>300 300<br>200<br>200<br>Trade to GDP(%)avg2012-17<br>Trade to GDP(%)avg2000-11<br>100<br>100<br>0<br>0<br><!-- End of picture text -->

> 5 For example, provisions of the tax code that vary with firm size; employment protection measures; product market regulation limiting size or market access; and tariffs applied to specific categories of goods.

> 6 Discretionary provisions made by the government or other entities (such as banks) that favor or penalize specific firms; for instance, subsidies, tax breaks, low interest loans granted to specific firms.

> 7 For example. monopoly power, market frictions, and enforcement of property rights.

3
Extension workers with higher education and communication skills are often likely to be assigned administrative responsibilities. The system is characterized by a one-way transfer of technology to a fevy inadequately selected farmers. Women and small farmers are often ignored. Feedback from farmers is poor. In contrast to the system for crops, livestock forestry and fisheries are neglected. Nor is it fully integrated with water management extension -- a clear obstacle to improving the efficiency of water use.

Much effort was made in the 1980s to implement the training and visit extension system (T&V). It appears that the system had only modest success. Measures of farmer contact do show an increase, and extension services may have had impact on increasing pesticide use. However, Husain et al (1994) could find little evidence that T&V had improved the _q2uality_ of extension advice. An increase in extension contact does not necessarily show that extension has aided growth. In addition, contact is highly skewed towards large farmers. A survey of extension contact in Punjab in 19868 showed that 60 percent of farmers with over 10 hectares of land had contact with the extension service in the previous year, whereas only 24 percent of farmers with 5 to 10 hectares had contact c- er the same period.

Moreover, some success of the T&V system can be explained by the poor state of extension service before the system was introduced. By introducing organizational discipline and some additional funding, the T&V system was able to improve the previously deficient extension services. It is less clear, however, that the system was the most cost effective use of resources. It involves a centralized, hierarchical approach that linit;s feedback and adaptability to local conditions.

The quality of extension services does not compare favorably with India. Husain et al (1994)9 find that although the Training and Visit system has increased the quantity of extension advice (visits, messages etc.), it does not appear to have increased farmers technical knowledge or the rate of adoption of new technology. This contrasts with evidence from a comparable region of India. Two major problems specific to Pakistan are identified. First, implementation of T&V was lacking. For example, one survey found that 20 percent of contact farmers did not know that they were contact farmers, and hence the information flow never went beyond them. Second, messages were inappropriate to farners' circumstances, and ignored the rational and often location-specific tradeoffs that farmers often make. For instance, messages about wheat continue to assume that wheat will be planted on-time, whereas farmers often rationally delay wheat planting as a result of interactions in double-cropping systems.

# Public Expenditure

Pakistan is one of a group of countries that followed a policy of compensating for the discrimination against agriculture in price policy with heavy investment in nrral infrastructure (notably irrigation), agricultural institutions, and subsidies to water, credit, electricity, and fertilizers. We have s Byerlee (1994).

9 Cited in Byerlee (1994).

20
that any progressive income tax is equivalent to a flat tax of equal yield combined with appropriate rich-to-poor transfers.

Table 2.3. Ratio of Equivalent Variations to Total Expenditure

|Weekly Expenditure|Option A|Option B|
|---|---|---|
|200|0.075|0.078|
|400|0.098|0.102|
|600|0.116|0.118|
|800|0.128|0.129|
|1000|0.138|0.138|
|1200|0.147|0.145|
|1400|0.160|0.157|

Source: Creedy (2001) The impact of indirect taxes on individual welfare is channeled through changes in commodity prices. This suggests the use of welfare measures such as equivalent variation (EV) or compensating variation (CV) as indicators of tax burden. These two measures can be defined in terms of the expenditure function which represents the minimum level of expenditure required to achieve a given level of utility given the prevailing prices (Deaton and Muellbauer 1980). Denote this function by **e(p,u)** , where **p** is a vector of prices and **u** stands for utility. If a tax induces a price change from **p**<sup>**0**</sup> to **p**<sup>**1**</sup> , the equivalent variation is the maximum an individual would be willing to pay to avoid the tax and the associated change in prices. This can be formally written as: _EV_ = [ _e_ ( _p_ 1, _u_ 1) − _e_ ( _p_ 0 , _u_ 1)] . Analogously, the compensating variation is the minimum amount of income an individual should be given in compensation for the price change in order to keep her as well off as before the change. Formally, this is defined as: _CV_ = [ _e_ ( _p_ 1, _u_ 0 ) − _e_ ( _p_ 0 , _u_ 0 )] . Creedy (2001) uses the ratio of equivalent variation to total expenditure to assess the progressivity of alternative indirect tax structures in 12