rafmacalaba/datause-encoder-data huggingface dataset:
interviews, the first few surveys introduced a self-administered component (containing sensitive questions) besides the main in-person interview. However, this method ran into logistical problems at the data entry stage. In the later surveys, sensitive questions were asked towards the end of the interview by which time greater rapport would have developed between the surveyor and respondent. Even if this made the respondent suspicious in any way or unwilling to answer further questions, it did not bias the whole interview.

Some less direct questions were asked, such as "How serious is corruption in your organization?" instead of the more direct "What percent of officials in your organization take bribes?" Interestingly, in all surveys analyzed so far, officials admitted that their. own organization was corrupt, but less corrupt than the rest of government.

**Figure 4. Officials say that the whole government is much more corrupt than their own organization.**

<!-- Start of picture text --> 100'/<br>80V.<br>60%!<br>94% 98%<br>20%' 40 444%<br>0°/.- ___ _____<br>my organiz?.ion government my or anization government<br>Armenia Bolivia<br><!-- End of picture text -->

# _3.2 Sampling methodology_

Probability sampling was the method employed in Bangladesh, Bolivia and Guyana. Stratified random sampling was utilized with the strata being the types of organizations described in Section 2.4.3 In East Caribbean countries, quota sampling was the method chosen because the number of employees at each level was known accurately from the 1999 budget estimates available for these countries.<sup>4 0</sup> Focus groups were mostly utilized to pretest draft questionnaires.

**39** However, in Bolivia, the strata employed were not the types of organizations but the hierarchical levels in each organization. The number of different types of organizations chosen was "purposive" to maintain some representativeness, and to ensure that certain agencies were included. **40** Fink (1995).

15
should occur almost simultaneously. Third, our analysis of heterogeneity is consistent with corruption mediating the effect of aid on wealth in havens: we find larger estimates in more corrupt countries although the difference is not statistically significant. In any case, since money is fungible we cannot distinguish between direct diversion of the funds disbursed by the donor and diversion of other public funds freed up by the aid disbursement.

Some alternative interpretations exist, but we find them harder to reconcile with all the patterns in the data. Most importantly, firms directly involved in aid-sponsored projects may receive payments in quarters with aid disbursements and deposit some of these funds with foreign banks. One set of results provides some support for this explanation: we find larger estimates in countries with less private credit suggesting that foreign banks serve as a substitute for inefficient domestic banks. However, this mechanism does not explain our finding that money only flows to places like Zurich, the global center for bank secrecy and private wealth management (Zucman, 2017), and not to other international banking centers like New York, London and Frankfurt. The dynamics in haven deposits, a permanent level shift at the time of aid disbursements, also seems more consistent with personal wealth accumulation than firm cash management.

There are other mechanisms that we can more confidently rule out. First, firms in developing countries have been shown to engage in aggressive tax avoidance by shifting profits to low-tax affiliates (Johannesen et al., 2016), but such profit shifting cannot explain our results because of the way the BIS statistics are constructed: deposits belonging to the Bermuda subsidiary of a Tanzanian company are assigned to Bermuda rather than Tanzania. Second, aid may increase income more broadly in the economy by stimulating aggregate demand and may therefore indirectly increase evasion of personal income taxes through havens; however, our model accounts for aggregate income shocks by conditioning on GDP growth and the sharp increase in haven deposits in the disbursement quarter does not mirror the typically protracted expansionary effect of economic stimulus (Kaplan and Violante, 2014). Finally, we can exclude that our results reflect portfolio adjustments by commercial or central banks as our deposit variables only include foreign deposits belonging to non-banks.

It is natural to express the estimates as a leakage rate: the dollar increase in haven deposits associated with a 100 dollar aid disbursement. Our model does not deliver this parameter directly and we therefore rely on the following transformation. Our key estimate suggests that aid corresponding to 1% of GDP increases deposits in havens by around 3.4%. While there is considerable variation over time and across the highly aid dependent countries in our main sample, the stock of deposits in havens stands at around 2.2% of GDP at the sample mean; hence 3
accelerated, rising, for the LMICX, to nearly 9 percent in 1991-93 from only 3.7 percent in the preceding decade.

The nominal dollar direction of trade statistics compiled in Table 2 shows that developing countries continued to grow exports to industrial countries, comprising 60 percent of their total exports, by 5-6 percent a year; but that intra-developing country exports grew at twice that rate. The share of intra-developing country trade in world trade rose from 10 percent in 1990 to 13.3 percent in 1994. Within intra-LMIC trade, the intra-Asia segment has been growing strongly since the 1970s. Intra-LAC trade, after languishing for decades as a result of inward-oriented policies and the debt crisis, also grew quickly, confumirng the success of recent regional trade-opening initiatives such as Mercosur. The table also shows a collapse in intra-developing Europe trade as countries of this region (including FSU) undertook reforms. However, this trade segment has seen over 50 percent growth in nominal terms in 1993, after averaging negative growth of 28 percent a year over the preceding 3 years, as many of the Eastern European economies resumed growth. Further, trade between developing country regions, for example between East Asia and Latin America, also strengthened (see lower panel in Table 2). The emergence of developing countries as an autonomous influence in world trade is illustrated by the fact that exports to developing countries-whether from developing countries or from industrial countries-accounted for a major 3.3 percent of the 5.6 percent increase in world export values in 1991-94 (2.8 percent out of a nominal 3 percent increase in 1991-93).

Finally, there was a surge of private capital flows to developing countries, motivated in part by successful policy reforms and export success. Flows totaled $325 billion during 1991-93 and an estimated additional $173 billion in 1994, and provided, for the recipient countries, much more financing for imports than they were able to use. During the oil price hikes of 1973-74 and 1979-80, purchasing power was transferred to oil exporting countries through changes in terms of trade (and eventually reached other LNIC regions, notably Latin America, in the form of private capital flows). These transfers were translated into higher import demand by direct and indirect recipient regions. A surge in capital flows-targeted to highly absorbent regions such as East Asia and Latin America-has been creating heightened import demand among recipients in a more direct manner, supporting the growth of world trade.

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# **Annex: Forestry**

In considering country-specific efforts to protect, one needs to control for a country’s natural forest propensity, as well as account for forest ‘consumption’ as part of a country’s development trajectory. One also needs to consider economic geography in terms of the density of settlements.

The regression model outlined here (Table 4) seeks to identify a measure of “ _excess_ forestation” for every country _i_ . Excess forestation is the difference between the observed forest cover in 2019 ( _forest19i_ ) (sq kms) and a hypothetical share in 2019 predicted by the regression model. Hence, excess forestation for country _i_ is simply the residual, _ui_ , of the regression model: _forest19i_ = α + X _i_ β + _ui_ (1) where α is the intercept and β is a vector of coefficients for controls of interest.

The key question is on the choice of control variables that should be included in X _i_ . What are the main drivers of (de)forestation?

- Log of per-capita GDP ( _incomei_ ): Richer countries are expected to have smaller forest shares if they exploited forests during earlier stages of development. Yet this relationship is non-linear with faster deforestation in low-income countries (which may rely on forest resources) than in high-income countries. Hence, the specification also includes this variable with a squared term. The variables are averages over the last 5 years to flatten out business cycle fluctuations.

- Population density ( _population_densityi_ ): Countries with higher population densities may need to expand into the forest frontier to accommodate its residents – the evidence from the urban literature suggests that cities around the world expand at the extensive margin as incomes and populations increase (Lall et al 2021).

- Precipitation ( _Annual_precipi_ ) and temperature ( _Annual_tempi_ ): They both capture climatic properties. While it may be preferred to use measures reflecting agroclimatic zones, considerable heterogeneity within large countries makes it difficult to assign specific zones to a country.

- Share of agricultural land ( _agricultural_landi_ ): Agriculture is a main driver of deforestation.

Variants of the specification have also considered urban density, education, real effective exchange rates, and the share of services as a proxy for structural change. Yet, these variables were statistically insignificant and did not change R<sup>2</sup> considerably. Hence, they are excluded from the final specifications. All data have been obtained from the World Bank’s World Development Indicators.

The regression model above seeks to explain the forest coverage by various explanatory variables. Yet, if countries are rich today, have a high population density, or have a high share of agriculture, they could have reduced their forest shares many decades, even centuries ago. While it may be useful to consider natural propensity for a country to have forest cover, such historic data are challenging to collect for a large sample of countries. Further, since much of the work in the paper 16
underlying riders’ demand for the reserved space. Section 7 tests for norms against women riding in the public space as a mechanism underlying a fixed-amenity value of the reserved space. Section 8 discussed robustness to alternative mechanisms, and Section 9 concludes.

# **2 Study Context**

We study sexual harassment on the public transit system of Rio de Janeiro, Brazil. Sexual violence on the transit system is pervasive in Brazilian cities. A recent survey in S ̃ao Paulo suggests that public transport is the most common place where women suffer harassment and 35 percent of women respondents reported ever being sexually harassed while using public transport (Datafolha, 2015).

Issues of sexual harassment on the transit system have led Rio de Janeiro state government to pass legislation to reserve a space for women in its rail system. The 2006 law requires the train and metro operators to reserve one carriage in each train for women during rush hours (6-9AM and 5-8PM).<sup>5</sup> Rio de Janeiro’s public transit system connects many low-income families to economic opportunities: most low-income households reside in the periphery, while jobs are concentrated in the city center (Motte et al., 2016). Rio’s metropolitan area has an extensive public transport system that includes bus, metro, a suburban rail, bus-rapid-transit and ferry system. Commutes are long, with a 95-minute average transit time (Moovit, 2018).

In order to capture the behavior of households living in the periphery, and for whom commuting to opportunity is particularly critical, we focus on Rio’s suburban rail system, the SuperVia. This system comprises seven lines that connect downtown Rio with its outskirts, including many low-income areas. All lines radiate out of the central station, Central do Brasil (cf. Figure A1 for a map of the SuperVia network). The SuperVia carries around 700,000 passengers a day, or 10 percent of all public transport trips in the Rio metropolitan

> 5Lei No 4.733, de 23 de Mar ̧co de 2006.

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Palmer and Truong (2017) argue, based on findings of Dangelico and Pujari (2010) and Berrone et al. (2013), green technologies lead to increased profitability for firms.<sup>14</sup> Using 1,020 press releases on green technology products introduced by 79 global firms between 2007 and 2012 and their operational profitability, Palmer and Truong (2017) show a positive relationship between green firm profitability. Palčič and Prester (2020) argue that investments in green technologies or innovations benefit firms as it creates a positive image, which eventually leads to an improvement to their market valuation. Using content analysis of corporate disclosures and financial data of 162 companies listed on the Frankfurt Stock Exchange during the 2007-2016 period, Przychodzen et al. (2018) show firms with green information technology receive higher subsequent returns on assets and the market-to-book values of assets ratios. Saha et al. (2017) suggest that investment in green operations and preservation technology can significantly improve the retailer's financial performance.

Some studies report that green/clean investments do not necessarily improve the financial performance or market values of firms. Investors, such as venture capital firms, would be interested in financing green/clean technologies. But Gaddy et al. (2017) find the other way around. Using the investment data from venture capital firms for the 2006-2011 period, they find clean technologies poorly suited for venture capital investment. They find that materials and hardware industries have a longer payback period than venture capital firms normally expect. The size of capital requirement is normally big for large-scale green/clean technology development or adoption. Venture capital firms see the green/clean technology market as risky and yield low returns because the potential acquirers of these technologies (e.g., utilities and large industrial corporations) are reluctant to buy risky startups. Table 6 provides selected empirical studies on the relationship between green investment and the financial performance of firms or industries.

**Table 6. The selected empirical literature on indicating the relationship between clean/green investment and financial performance of firms**

||Method|Findings|
|---|---|---|
|Chen & Ma (2021)|Regression analysis is|Green investment has a significant and positive correlation|
||applied to 90 Chinese|with financial performance. The green investment helps|
|||reduce environmental violations and promote|

> 14 Dangelico and Pujari (2010) show new products based on green/clean technologies result in higher commercial values due to a higher visibility of these products to market actors. Berrone et al. (2013) report new products based on green/clean technologies help attract more customers at higher premiums as a result from a long-term investment on R&D.

38
except for El Salvador, whose exports are Cost, Insurance and Freight (CIF) figures. Finally, we consider the universe of export transactions in agricultural, mining, and manufacturing sectors but we exclude exports in HS Chapter 27 (hydrocarbons such as oil, petroleum, natural gas, coal etc.). Cebeci, Fernandes, Freund, and Pierola (2012) argue that the raw exporter-level datasets are reliable based on comparisons of export totals (excluding HS 27) with export flows in the WITS/COMTRADE database (excluding HS 27) for each country and year.

Three remarks on the data are worth mentioning. First, to filter out potential noise and focus on true entrepreneurial ventures in export markets (rather than for example the shipping of samples or other small shipments) we drop from the sample in each country exporting firms whose total export values are lower than 500 USD in all their years in the sample. Second, in the case of Costa Rica we exclude the largest exporting firm (INTEL) from the sample in each year, following Lederman, Rodriguez-Clare, and Xu (2011).<sup>11</sup> Third, in the case of Nicaragua our dataset includes firms located in Export Processing Zones even though exports reported to WITS/COMTRADE by that country exclude the exports from those firms.<sup>12</sup> The analyses cover two time periods: (i) the period 2005-2007 which has the greatest coverage across LAC countries and represents a growth period or ‘normal times’ and (ii) the period 2008-2009 which covers the global financial crisis that started in 2008 and allows us to present for the first time evidence on the effects of a major crisis on export entrepreneurship in emerging economies.<sup>13</sup>

# **_3. Methodology_**

The first objective is to describe recent patterns of export entrepreneurship in LAC countries using the list of indicators defined in Table 1. Those indicators defined at the countryyear level are analyzed in Section 3 while indicators defined at the country-year-product level are analyzed in Section 4.<sup>14</sup>

> 11 Lederman, Rodriguez-Clare and Xu (2011) excluded the products that were probably exported by INTEL and Abbot Labs during 1997-2007, because these multinational firms began operations in 1998 and thus dominate any analysis of the contribution of export entrepreneurship. Since the story of these cases is relatively well known, our interest is to assess entrepreneurship among the rest of the firms in Costa Rica.

> 12 Indicators for Nicaragua in the _Exporter Dynamics Database_ are calculated excluding firms located in Export Processing Zones given the criterion of data quality used which is the match between the total exports calculated based on the exporter-level customs data and the total exports reported in WITS/COMTRADE.

> 13 For Ecuador in the period 2005-2007 only years 2006 and 2007 are available.

> 14 In addition to the indicators listed in Table 1, Section 4.1 mentions the average number of HS 6-digit products per type of firm (exporter, entrant, incumbent) and the average number of destinations per type of firm (exporter, entrant, incumbent).

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Table 4: Estimations of GQ improvements for total size of manufacturing organized sector

||A|ll areas of dist|ricts|Ur|ban areas of d|istricts|Ru|ral areas of dis|tricts|
|---|---|---|---|---|---|---|---|---|---|
||Plants|Employment|Output|Plants|Employmen|t<br>Output|Plants|Employment|Output|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|||A. Base s|patial horizon|measuring ef|fects relative|to districts 50+|km from the|GQ network||
|Post GQ upgrades *|0.560|0.560|0.654|0.249|0.328|0.377|0.309|0.572|0.643|
|Nodal district|(0.501)|(0.462)|(0.453)|(0.184)|(0.225)|(0.278)|(0.324)|(0.312)|(0.322)|
|Post GQ upgrades *|0.094|-0.039|0.166|0.046|-0.141|0.030|0.176|0.012|0.226|
|District 0-10 km from GQ|(0.112)|(0.131)|(0.176)|(0.135)|(0.149)|(0.225)|(0.137)|(0.152)|(0.199)|
|Post GQ upgrades *|-0.023|-0.078|-0.006|0.001|-0.164|-0.170|-0.011|-0.095|0.042|
|District 10-50 km from GQ|(0.129)|(0.135)|(0.191)|(0.181)|(0.217)|(0.323)|(0.196)|(0.174)|(0.239)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1248|1248|1248|1112|1112|1112|1164|1164|1164|
|||B. Extended|spatial horiz|on measuring e|ffects relative|to districts 20|0+ km from t|he GQ network||
|Post GQ upgrades *|0.513|0.576|0.744|0.183|0.281|0.379|0.283|0.657|0.845|
|Nodal district|(0.505)|(0.468)|(0.458)|(0.199)|(0.240)|(0.298)|(0.331)|(0.319)|(0.332)|
|Post GQ upgrades *|0.047|-0.022|0.257|-0.019|-0.189|0.031|0.149|0.097|0.429|
|District 0-10 km from GQ|(0.128)|(0.144)|(0.177)|(0.156)|(0.172)|(0.249)|(0.152)|(0.162)|(0.203)|
|Post GQ upgrades *|-0.071|-0.062|0.084|-0.065|-0.212|-0.169|-0.037|-0.011|0.244|
|District 10-50 km from GQ|(0.144)|(0.153)|(0.202)|(0.196)|(0.234)|(0.341)|(0.207)|(0.187)|(0.252)|
|Post GQ upgrades *|-0.209|-0.108|-0.018|-0.248|-0.165|-0.138|-0.023|0.118|0.278|
|District 50-125 km from GQ|(0.157)|(0.136)|(0.183)|(0.174)|(0.192)|(0.256)|(0.195)|(0.180)|(0.242)|
|Post GQ upgrades *|0.014|0.163|0.361|-0.017|-0.027|0.146|-0.076|0.206|0.496|
|District 125-200 km from GQ|(0.140)|(0.155)|(0.200)|(0.170)|(0.197)|(0.278)|(0.176)|(0.178)|(0.247)|
|District and year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|1248|1248|1248|1112|1112|1112|1164|1164|1164|

Notes: See Table 2.

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# **V.2 Alternative detection probability**

In the baseline calibration, we use the same variable to capture both institutional quality and the detection probability. As argued previously, the level of institutional quality may send an equivalent signal of the level of monitoring and auditing, i.e., the detection probability. However, this assumption can raise questions as the latter may drive our findings. In this section, we check the sensitivity of the results using the perception of the control of corruption as an alternative measure of the detection probability. The latter is from the World Governance Indicators’ database and is defined as the perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. We use the percentile ranking of the index which is ranged between 0 and 1. The simulation procedure is the same as previously, and the model is calibrated to the United States economy. Figure 7 plots tax evasion and output per worker from the model against the data for African and Latin American countries. As previously, the model’s prediction is strongly correlated with the data. The model still explains around 20% of the variation in tax evasion and 49% of the dispersion of GDP per worker across African and Latin American countries.

32
transition; the strength and vibrancy of civil society throughout the 1980's-were all elements which did not exist in Azerbaijan. Moreover, transition in Azerbaijan occurred in the context of the break-up of the Soviet Union. The local elite was now free to appropriate the rents which, in a context of a unified country, it could not. For sure, Azerbaijan might represent an extreme example of rent-grabbing simply because there was a lot to grab thanks to large oil revenues. But substantially the same appropriation by the local elites happened in all the new countries created after the break up of the Soviet Union. This is, of course, an additional _sui generis_ factor which might explain large inequality increases in the former Soviet republics.

We thus run two additional specifications of the inequality equation (on the sample of transition economies only). The first, where we replace current level of political and civil democracy with the cumulative level over the last 9 years. In the second regression, we add a dummy variable for the former Soviet republics. In both cases, we control for income levels using the 1988 values of country's GDP per capita in PPP (equal purchasing power) dollars.. The results show that cumulative liberalization enters the regressions with the correct (negative) sign. However, once the CIS dummy variable is introduced, its effect, in view of large increases in inequality in CIS countries, swamps the effect of political variables, and in neither of several formulations, are democracy variables any longer significant nor do they have the predicted sign. _13_ 26