rafmacalaba/datause-encoder-data huggingface dataset:
impact of the treatment.

In addition to the counterfactual that is missing,<sup>16</sup> another issue is that export and import activities can be carried by traders. Indeed, promoting the relationship between producers and traders might be one of the targets of EPAs,<sup>17</sup> but traders are accounted as services that are not directly related to the firms that produce the goods that are exported, so results could be misleading. I concentrate on the manufacturing sector where I assume that trade (export and import) is more likely to be accounted for producers, though manufacturing firms also may trade goods they do not produce (see Bernard et al. (2012) and Crozet et al. (2013)). I use the sectoral definition of CNAE 2.0<sup>18</sup> (Portuguese acronym for National Economic Activity Classification) that is equivalent to the International Standard Industrial Classification of All Economic Activities (ISIC, Rev.4.).

RAIS provides yearly information for approximately 250,000 manufacturing firms per year, which can work as an annual census for the formal manufacturing sector in Brazil. This is critical because we can observe all formal firms over time, disregarding their export status. Since my identification uses lagged variables in the first stage, I first impose a restriction in the dataset according to which firms should appear in RAIS at periods _t −_ 1 and _t_ .<sup>19</sup> For example, there are 257,473 manufacturing firms in RAIS in 2007. Among these firms 224,232 also appear in 2006 and so on.<sup>20</sup> The basic services provided by Apex-Brasil are related to matching domestic sellers and foreign buyers (e.g. through participation in trade fairs) and providing standard information on foreign markets, including standard qualification for exporting. There are two alternative ways to have access to these services: a) through an industry association (this scheme, called “Sectoral Project,” is more common if a firm is going to participate in trade fairs) or b) directly with Apex (this is more common for special projects related to participation in sports and cultural events, such as Formula Indy, Carnaval and the Peiex<sup>21</sup> project). Further details about how Apex’s assistance works are available in section 7.1 of the appendix.

There are spatial and sectoral heterogeneities among treated firms. Table 1 shows the regional distribution of firms according to their treatment and export status from 2007 to 2010. The

> 16We can observe only one status of the firm as being treated or untreated.

> 17According to Apex, this is among their goals.

> 18I used CNAE 2.0, 2 digits, from 10 to 33, which defines manufacturing.

> 19I am assuming that this is an existence condition for the firm.

> 20This number for the other years is 267,303 firms in 2008, 273,582 firms in 2009 and 292,680 firms in 2010.

> 21The Peiex (Portuguese acronym for Industrial Extension Project for Exporting) is an export capacity-building program program focusing on enhancing Brazilian SMEs competitiveness by providing coaching and consultancy on best management practices.

7
# Figure 9. Business and financial sector reforms

# A. Business regulatory environment

<!-- Start of picture text --> Score<br>70<br>65<br>60<br>55<br>50<br>45<br>40<br>DB2006 DB2013 DB2020<br><!-- End of picture text -->

# C. Financial regulatory environment

<!-- Start of picture text --> Score<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>DB2006 DB2013 DB2020<br><!-- End of picture text -->

# E. Capital account openness

<!-- Start of picture text --> Index<br>1996 2006 2016<br>1.0<br>Advanced economy average<br>0.8<br>0.6<br>EMDE average<br>0.4<br>0.2<br>0.0<br>LAC SSA MNA EAP ECA SAR<br><!-- End of picture text -->

# B. Business regulatory environment

<!-- Start of picture text --> Number of reforms Percent of population<br>Improving Deteriorating Difference<br>1000 50<br>800 40<br>600 30<br>400 20<br>200 10<br>0 0<br>-200 -10<br>DB08-14 DB14-20 2006 2013 2017<br>Reforms Internet Access (RHS)<br><!-- End of picture text -->

# D. Financial regulatory environment

<!-- Start of picture text --> Number of reforms<br>590 Improving Deteriorating Difference<br>490<br>390<br>290<br>190<br>90<br>-10<br>DB2008-14 DB2014-20<br>F. Capital account openness<br>Number of countries<br>100 Less restrictive capital account<br>More restrictive capital account<br>80 Difference<br>60<br>40<br>20<br>0<br>-20<br>-40<br>1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016<br><!-- End of picture text -->

# F. Capital account openness

Source: Chinn and Ito (2006), World Bank.

Note: EAP = East Asia and Pacific, ECA = Europe and Central Asia, LAC = Latin America and the Caribbean, MNA = Middle East and North Africa, SAR = South Asia, and SSA = Sub-Saharan Africa.

A.C. The score is measured on a scale from 0 (weakest) to 100 (best/the frontier). “DB” before the year indicates the related Doing Business publication. Average performance of four indicators (A: Starting a Business, Paying Taxes, Registering Property, and Dealing with Construction Permits; C: Access to Credit, Protecting Minority Investors, Enforcing Contracts, and Resolving Insolvencies). Indicators are spliced backwards where methodological changes affected the level.

B.D. The number of reforms is calculated using the business reforms by year and by country as listed in the World Bank’s Doing Business reports. These are codified from the text list of business reforms as reported by the Doing Business survey. B. Business regulatory reforms include those business reforms categorized under Starting a Business, Paying Taxes, Registering Property and Dealing with Construction Permits. Unweighted averages for Internet access.
# **APPENDIX B: RESULTS OF SIMULATIONS**

**Table B1: Increase of international prices of soy, soybean oil and biodiesel (20%)**

||**y1 **<br>|<br>|**y2 **<br>|<br>|**y3 **<br>|<br>|**y4 **<br>|<br>|**y5 **<br>|<br>|
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|5.9|8.1|10.1|12.3|14.5|16.7|18.9|20.9|23.0|
|Trade Balance|5.1|7.6|10.4|13.0|15.9|18.6|21.4|24.3|26.9|29.7|
|Fiscal Result|2.1|3.7|4.3|5.9|6.5|8.1|8.6|10.3|10.7|12.2|
|Rate of Unemployment|9.2|9.0|6.9|6.7|4.6|4.4|2.2|2.0|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|6.0|8.0|10.2|12.1|14.5|16.4|18.9|20.5|22.9|
|Richest Household|3.6|5.1|7.2|8.8|10.9|12.6|14.7|16.5|18.4|20.2|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|9.9|8.4|14.5|12.8|19.1|17.3|23.9|21.8|28.6|
|Manufactures|4.4|2.0|8.8|6.4|13.4|11.0|18.1|15.7|22.7|20.0|
|Services|3.9|4.0|7.9|8.1|12.1|12.2|16.3|16.5|20.4|20.3|
|Soy|4.1|57.4|8.3|63.5|12.6|69.9|17.0|76.4|21.5|82.8|
|Maize|4.1|-18.7|8.4|-15.2|12.7|-11.6|17.2|-7.9|21.7|-4.3|
|Sugar Cane|4.4|-6.5|8.9|-2.4|13.5|1.9|18.3|6.2|22.9|10.2|
|Rest of agriculture|4.2|-21.1|8.5|-17.7|12.9|-14.1|17.5|-10.5|22.1|-6.9|
|Soybean oil|4.9|88.7|9.9|97.1|15.0|105.7|20.2|114.4|25.4|122.5|
|Diesel|5.5|4.1|11.1|9.7|16.8|15.4|22.7|21.3|28.7|27.3|
|Gasoline|5.6|4.1|11.4|9.9|17.3|15.8|23.4|21.9|29.6|28.0|
|Biodiesel|10.0|43.7|26.5|78.6|55.9|152.2|116.3|358.8|277.8|1500.0|
|Bioethanol|10.0|7.3|26.4|22.6|55.8|49.7|116.0|104.4|276.7|242.4|

**Table B2: Increase of international prices of maize and bioethanol (20%)**

||**y1 **||**y2 **||**y3 **||**y4 **||**y5 **||
|---|---|---|---|---|---|---|---|---|---|---|
|**Indicators**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|**Base**|**Sim**|
|**_Macroeconomic Indicators_**|||||||||||
|GDP|4.0|4.3|8.1|8.5|12.3|12.7|16.7|17.1|20.9|21.3|
|Trade Balance|5.1|5.4|10.4|10.8|15.9|16.2|21.4|21.9|26.9|27.3|
|Fiscal Result|2.1|2.3|4.3|4.4|6.5|6.6|8.6|8.8|10.7|10.8|
|Rate of Unemployment|9.2|9.1|6.9|6.9|4.6|4.5|2.2|2.1|0.0|0.0|
|**_Welfare Indicators_**|||||||||||
|Poorest Household|3.9|4.3|8.0|8.4|12.1|12.5|16.4|16.9|20.5|20.9|
|Richest Household|3.6|3.9|7.2|7.6|10.9|11.4|14.7|15.2|18.4|18.9|
|**_Sectoral Activity Level_**|||||||||||
|Agriculture|4.2|5.3|8.4|9.6|12.8|14.1|17.3|18.6|21.8|23.2|
|Manufactures|4.4|3.9|8.8|8.4|13.4|13.0|18.1|17.8|22.7|22.2|
|Services|3.9|4.0|7.9|8.0|12.1|12.2|16.3|16.4|20.4|20.4|
|Soy|4.1|-1.1|8.3|2.9|12.6|7.1|17.0|11.3|21.5|15.6|
|Maize|4.1|97.7|8.4|105.3|12.7|113.1|17.2|121.2|21.7|129.2|
|Sugar Cane|4.4|2.6|8.9|7.1|13.5|11.7|18.3|16.4|22.9|21.4|
|Rest of agriculture|4.2|-1.3|8.5|2.9|12.9|7.1|17.5|11.5|22.1|15.9|
|Soybean oil|4.9|4.3|9.9|9.3|15.0|14.5|20.2|19.7|25.4|24.8|
|Diesel|5.5|4.9|11.1|10.5|16.8|16.3|22.7|22.2|28.7|28.2|
|Gasoline|5.6|5.0|11.4|10.8|17.3|16.7|23.4|22.8|29.6|29.1|

33
the true parameter estimated in this way are reported at the bottom of each of the columns of Table 5. In all cases both bounds have the same sign of the estimated coefficient and, although the bounds are not tight, the baseline parameter lies close to the lower bound for its magnitude, indicating that is most likely underestimating the true degree of correlation between our measure of prevalence and the extent of pro-incumbent regulation.

In this way, in countries where there is more connection between politicians and bankers, bankers are (to a greater extent) allowed to conduct more activities, to be monitored by private firms or themselves instead of being more heavily regulated by the government, are required to have less capital, and are granted higher barriers of entry. This piece of evidence, then, supports the private interest-capture view in the sense that, at least, it seems to be the case that bank owners would be getting a sweet deal in exchange for just a seat on the board.

# **_C. Rents and Rent Allocation_**

Private interest stories differ markedly from public interest ones on the issue of rents. Higher rents where the banker-politician arrangement is more prevalent would be _prima-face_ evidence that at least some private interest component is at work. Who, bankers or politicians, then gets the rent determines the flavor of private interest theory: tollbooth or capture.

We define total rents as net interest revenue to assets, and the share of rents accruing to bank owners as the return on assets (net income to assets) to total rents. The complement of that share is how we measure what goes to politicians under the assumption that what they gain is somehow accounted in the books as costs to the bank (over employment, higher salaries, taxes, etc). We measure these using _Bankscope_ data for each bank, and then aggregate by taking the mean value for each country and year, and then average out over the years for each country. In what follows we regress the different measures on _PREVALENCE_ and controls as in (3). Results are reported in Table 6.

We first check whether a higher prevalence of the phenomenon is associated with higher rents in the banking system. The idea being that, no matter who gets the rents it is going to be interested in making them grow. The evidence here is inconclusive. _PREVALENCE_ and total rents are indeed positively and significantly correlated, both when all the banks are considered and when prevalence is measured only on private banks (columns two and three). Furthermore, the effect is large: increasing _PREVALENCE_ from the 10<sup>th</sup> to the 90<sup>th</sup> percentile is associated with a 40% increase in total rents (63% for private banks only). However, the effect and its significance drop when controlling for per capita GDP and total population.

16
Table 2a: Win rates in selected Fundamental Rights before the Indian Supreme Court, Claimants from Advantaged and Disadvantaged social classes

|Social class|1961-1989|1990-1999*|2000-2008*|
|---|---|---|---|
|Advantaged|57.9%|68.1%|73.3%|
|Disadvantaged|71.4%|47.1%|47.2%|

* probability of χ<sup>2</sup> < 0.05

Table 2b: Probit estimation of win rates for advantaged classes in Fundamental Rights cases before the Indian Supreme court, by decade

||1961-1989|1990-1999|2000-2008|
|---|---|---|---|
|Advantaged class|-0.37|0.54*|0.69*|
||(0.29)|(0.25)|(0.32)|
|Constant|0.57*|-0.07|-0.07|
||(0.21)|(0.15)|(0.21)|
|Observations|80|114|66|
|Pseudo R<sup>2</sup>|0.02|0.03|0.05|

Standard errors in parentheses; * significant at p < 0.05 16
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**Table 2. Estimated global demand and supply of health workers, by WHO/World Bank regions and World Bank income group, 2013 and 2030**

||**201**|**3 (165 countries)**||**203**|**0 (165 countries)**||**# Countries**|
|---|---|---|---|---|---|---|---|
||**Demand**|**Supply**|**Need**|**Demand**|**Supply**|**Need**|**ineach group**|
|WHO Region||||||||
|Africa|1,106,183|1,874,830|5,891,071|2,404,807|3,066,666|8,910,473|43|
|Americas|8,826,933|8,385,480|5,439,623|15,288,610|12,742,856|6,246,463|28|
|Eastern Mediterran|3,057,524|2,690,443|3,797,769|6,201,515|4,611,408|5,055,625|15|
|Europe|14,178,009|12,692,401|5,628,533|18,158,772|16,803,264|5,786,268|50|
|South‐East Asia|5,964,318|5,772,250|12,433,083|12,206,786|10,168,591|14,712,987|8|
|Western Pacific|15,133,290|10,294,627|11,538,553|25,894,849|17,261,342|12,270,476|21|
|WB Region||||||||
|East Asia and Pacific|15,481,985|11,141,638|13,692,899|26,546,027|18,250,702|14,734,499|23|
|Europe and Central Asia|14,007,183|12,594,176|5,578,223|17,844,850|16,640,618|5,722,567|48|
|Latin America|4,526,235|4,140,233|3,287,004|8,374,987|5,784,767|3,825,876|26|
|Middle East & North Africa|2,517,001|2,570,885|2,354,695|4,913,419|3,846,948|3,032,910|15|
|North America|4,300,699|4,245,248|2,152,619|6,913,623|6,958,089|2,420,587|2|
|South Asia|6,494,350|5,357,579|11,745,586|13,459,980|10,293,688|14,248,390|8|
|Sub Saharan Africa|938,804|1,660,273|5,917,606|2,102,453|2,879,315|8,997,462|43|
|WB Income group||||||||
|Low|637,584|692,757|4,861,904|1,400,074|1,384,576|7,049,048|29|
|Lower‐middle|10,897,535|9,867,919|17,605,293|21,682,581|17,958,943|21,940,256|44|
|Upper‐middle|19,040,552|13,764,139|14,617,189|33,291,730|21,362,033|15,934,777|46|
|High|17,690,584|17,385,217|7,644,247|23,780,953|23,948,576|8,058,211|46|
|**World**|**48,266,256**|**41,710,032**|**44,728,633**|**80,155,338**|**64,654,127**|**52,982,292**|**165**|

Notes: Health worker refers to physicians, nurses/midwives, and other health workers.

1Region and income group aggregates are summed for only countries that have a health worker shortage; countries without a shortage are not included.

2Demand for nurses/midwives was calculated assuming a ratio of 2.517 nurses/midwives to one physician.

3Demand and supply of other health workers was calculated assuming a ratio of 3.517 doctors and nurse/midwives times a World Bank income group‐specific multiplier.

4Supply of physicians and nurses/midwives was projected based on the country‐specific linear growth rates of physicians and nurses per 1,000 population.

19
|**Table 3.**_Consortia Bidding for the Flumitrens_|_Concession_|
|---|---|
|_Company_|_Bid (in millions of R$)_|
|Bolsa 2000 (Brazilian)<br>Caf (Spanish)<br>Rede Nacional De Los Ferrocarriles (Spanish)|279.6|
|Rio Express (Brazilian)<br>Andrade Gutierrez<br>Auto Viacao 1001 (Brazilian)<br>Opportunity (Brazilian)<br>RATP (French)|164.7|
|Pem T'trans (Brazilian)<br>Pem Engenharia (Brazilian)<br>Trans (Brazilian)<br>ATM (Italian)<br>Breda Construziones Ferroviarias (Italian)|46.3|
|Canari (Brazilian)<br>Canari Participag6es (Brazilian)<br>MPE (Brazilian)<br>Emepa (Argentine)<br>Transurb (Belgian)|44.4|

_Note:_ IUS$=1.03 R$

# **The CONERJ Concession**

60. The concession for CONERJ, the ferry boat company, was won by Andrade Guitierrez (Brazilian), the only consortium to bid on the project, for the minimum bid price (R$28.4 million). The two other prequalified consortia decided not to bid at the last minute raising accusations of possible collusion and preventing competitive bids. This incident was a good lesson for the State which became more vigilant during the Flumitrens concession process. Andrade Guitierrez is also the concessionaire of the toll bridge (Rio-Niter6i), which is the main link for automobile traffic between the two sides of the bay.

# **The CTC/SERVE Liquidation**

61. Both CTC and Serve were liquidated and their routes and equipment auctioned to private sector operators. The move guaranteed that service would be provided by private operators, which do not receive subsidies from the State.

# **The CODERTE** Sale

**62.** CODERTE's main asset, its parking garage in downtown Rio de Janeiro was auctioned for R$80 million.

15
implies that prices received by coffee growers in 1999/2000 were 65 percent higher than in 1992/93 when world market prices were at roughly the same level.<sup>4</sup> The data hence show clear evidence that the share of the world price received by Ugandan coffee farmers has increased significantly in the course of the 1990s. When prices are measured in current US cents (as in Figure 2), our data suggest that this share increased from approximately 15 to about 25 percent between 1992/93 and 1995/96 possibly reflecting the transition away from “guaranteed” prices and the successful adjustment to liberalization. It appears that the share has stabilized, as it remains constant in the period of falling world prices, thus confirming the symmetries in the way positive and negative international price changes are transmitted to the domestic market after liberalization (Krivonos 2004).

> 4 These findings are largely consistent with those in Fafchamps et al. (2003). In fact, the prices reported by Fafchamps et al. (2003) are estimated from a survey explicitly designed for tracking coffee prices and thus their estimations are more accurate than those computed from household surveys. However, Fafchamps et al. (2003) only consider a single year period.

9
**Figure 3: Product-space for China, 2006**

<!-- Start of picture text --> 0 5 10 15 20<br>invdensity<br>Electronics and Electricals Other, High-Technology<br>Automotive Process<br>Engineering Textile, garment & footwear<br>Other, Low-Technology Agro-Based<br>Other, Resource-Based Primary Products<br>20000<br>10000<br>0<br>-10000<br><!-- End of picture text --> Source: authors’ calculation This is inevitable given that the variety of products exported from Penang is smaller relative to that from Malaysia and China (see Table 6). In each year, there were about 780 products exported by at least one country in the world. China and Malaysia exported almost as many products. Penang exported significantly fewer.

**Table 6: Number of Products Exported by Penang, Malaysia, and China**

||1995|2000|2006/7|
|---|---|---|---|
|China|766|763|763|
|Malaysia|740|740|746|
|Penang|582|608|601|

Source: authors’ calculation Furthermore, the lower number of individual export items also reduces the number of products in which Penang has a revealed comparative advantage (see Table 7), and on which the product space analysis is based. The small number of products with a revealed comparative advantage implies lower export capability and consequently, this moves the distribution farther away from the origin. Remarkably, China has revealed 11