rafmacalaba/datause-encoder-data huggingface dataset:
We recruit 363 Brazilian women commuters to crowdsource data on _≈_ 22,000 of their rides and proceed in three steps.<sup>3</sup> First, we show that women commuters are willing to pay to ride in the reserved space, but this demand is concentrated among a minority of riders. Second, we show that avoiding harassment partially explains women’s demand for the reserved space. Riders who use the reserved space perceive a higher risk of harassment and are most likely to be harassed in the public space. Riding in the reserved space allows women to avoid harassment; other factors such as crowding and non-sexual crimes do not explain demand for the reserved space. Third, we document that commuters are likely to associate women in the public space with higher openness to sexual advances. We show that perception of this norm predicts higher demand for the reserved space among women commuters.

We start by eliciting women commuters’ willingness to pay to ride in the women-reserved space. This demand is concentrated: twenty percent of riders place a positive value on accessing a women-reserved space as high as twenty percent of the transit fare. Moreover, demand is highest at times when the reservation rule is well followed, with few men in the reserved space. Demand for the reserved space does not go to zero when presence of men equalizes across the reserved and the public space, suggesting women attach a fixed-amenity value to riding in the reserved space. In addition, a third of riders make up eighty percent of the demand for the reserved space and are ten times more willing to pay for the reserved car than the rest of the sample. We show that these high-level users also perceive a higher risk of being harassed, and we show that this risk perception is associated with a higher demand for the reserved space. We use rich data on ride conditions to rule out alternative demand mechanisms such as crowding and fear of property crime.

Second, we ask: How much of women riders’ valuation of the reserved space comes from a reduction in harassment vs. other amenity values women may place in the reserved space? We document the incidence of sexual harassment in the public space and the impact of

> 3The analysis dataset is available on the World Bank’s Microdata Catalogue under the reference code BRA ~~2~~ 015-2016 ~~D~~ SS ~~v~~ 01 ~~M~~ (Kondylis et al., 2020)

3
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# **4. Official lending in wars, financial crises and natural disasters**

In this section, we match our data on official sovereign lending with data on the occurrence of disasters, more specifically of wars, financial crises and severe natural catastrophes such as earthquakes. This allows us to study the international response to crises across different eras of the international financial system. We begin by compiling a comprehensive database on disaster and crisis events in the past 200 years and then match this with our novel dataset on official lending.

# **4.1 Identifying disasters and defining crisis events**

To identify financial crises, military conflicts and severe natural disasters, we make use a variety of sources. This section provides an overview, while details are described in Appendix I. Our approach focuses on actual disaster dates and events, in contrast to a related macroeconomic literature that defines “macroeconomic disasters” as drops in consumption or GDP beyond a specific threshold (see Barro and Ursua 2010). The advantage of using event data is that it allows for much broader geographic and time coverage, given that long-run consumption and GDP data is only available for advanced countries and a few select emerging market countries. Furthermore, event-based data allows for a more granular analysis, e.g. by differentiating between different types of shocks and disasters.

To identify _financial crises_ , we rely on the data of Reinhart and Rogoff (2009), and the data update by Meyer et al. (2019). We consider banking, currency and external sovereign debt crises and follow the crisis definitions brought forward by Reinhart and Rogoff (see Appendix I).

Our definition of _wars_ follows the Correlates of War (CoW) project, namely a sustained combat, involving organized armed forces and resulting in a minimum of 1000 battle-related fatalities over the course of a 12 months period (Sarkees and Wayman 2010). The CoW data collection distinguishes interstate and intra-state conflict, both of which are included as disaster events here. International or interstate conflicts are defined as wars between two or more territorial states that are members of the international state system. Intra-state (or civil) wars are defined by sustained military combat within a state and with active participation of the national government. In the CoW database, war begins with the onset of sustained military combat ́, irrespective of a formal declarations of war.

Measuring _natural disasters_ and the resulting damages and casualties over 200 years is challenging. To build a long-run dataset we draw on a large number of sources. First and most importantly, we draw on the EM-DAT International Disaster Database, which contains information on several thousand natural disasters around the globe since 1900, including geophysical (earthquakes and volcano eruptions), meteorological (extreme temperatures and storms), hydrological (floods and landslides), climatological (droughts, famines and wildfires) and biological disasters such as epidemics.

20
effect after 1999. Hence we compare FEMA deforestation rates from periods prior to program implementation (1996-97; 1998-99) with post-program FEMA data (2000-01; 2002). Since FEMA enforcement targets private rural properties, our analysis extent was limited primarily to non-protected areas of Mato Grosso. Because the program prioritized enforcement on large landholders (FEMA 2001:21), we compare the change of large-scale deforestation (deforestation clearings greater 200 hectares) versus small-scale deforestation (clearing below 200 hectares)<sup>8</sup> .

# **3.5 Data**

The original and derived data sets (digitally coded maps) that are used in the analysis are summarized in Table 1. All data is projected to Universal Transversal Mercator (UTM), Zone 21 South, South American Datum 1969.

An important variable in our study distinguishes high/low natural vegetation cover. Ideally, we would like to distinguish between deforestation potentially consistent with the RL requirement versus deforestation on properties that are already out of compliance with the requirement. Lacking a map of property boundaries, we constructed a proxy. Using the SEPLAN land cover map, the proportion of natural vegetation cover within a radius of about 1,250 meters – an area of 625 hectares that corresponds to the mean property size in Mato Grosso (IBGE 1998) – was computed for each 100 meter pixel. Based on the biome specific legal reserve requirement, the mean natural vegetation cover in non-protected areas in Mato Grosso was classified into areas of “high” (in concordance with the RL-definition) and “low” (below the RL-requirement) proportion of natural vegetation cover. We defined high versus low cover based on the legal reserve requirement of the revised Forest Code 1965, i.e. 35% in the Cerrado, 80% in Amazon

> 8 Our criterion based on size of _incremental_ clearing corresponds to the criterion of 200 ha _incremental deforestation size_ which distinguishes FEMA oversight (large-scale deforestation) from IBAMA oversight (Mato Grosso – Ordem Serviçio No. 26/00). However, it differs from the criterion of 300 ha _property size_ which distinguishes FEMA oversight (larger properties) from IBAMA oversight (MMA/FEMA Pacto Federativo 2003-2004).

16
# **Table 9**

# **VWAP spreads between foreign and domestic institutions: Investor style correlates**

This table reports the estimates of daily Value-Weighted-Average-Price (VWAP) of foreign investors relative to domestic institutions. The dependent variable in columns (1)-(4) is the VWAP for purchases. VWAP for sells is reported in columns (5)-(8). Each model include stock fixed-effects and t-statistics, presented in parenthesis, are calculated from standard errors clustered at the fund level. Note: ***/**/* indicate that the coefficient estimates are significantly different from zero at the 1%/5%/10% level.

|||Bu|ys|||Se|lls||
|---|---|---|---|---|---|---|---|---|
|Variables|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|**Firm-stock vari**|**ables**||||||||
|lsize|0.052***<br>|0.038**<br>|0.031*<br>|0.016<br>|0.001<br>|0.014<br>|0.029**<br>|0.026<br>|
||(2.996)|(2.291)|(1.813)|(0.787)|(0.059)|(1.009)|(2.102)|(1.498)|
|BMratio|0.019<br>|0.005<br>|0.004<br>|0.015<br>|-0.013<br>|0.003<br>|0.002<br>|-0.026<br>|
||(1.072)|(0.253)|(0.211)|(0.682)|(-0.722)|(0.166)|(0.079)|(-1.122)|
|ctc5|-0.387***|-0.380***|-0.419***|-0.482***|-0.084|-0.118|-0.132|-0.162*|
||(-4.528)|(-4.390)|(-4.619)|(-4.924)|(-1.049)|(-1.469)|(-1.568)|(-1.914)|
|cto|0.323|0.372|0.404|0.658*|0.685*|0.688*|0.837*|0.381|
||(0.861)|(0.993)|(1.026)|(1.716)|(1.655)|(1.654)|(1.863)|(0.874)|
|otc|8.235***|8.273***|8.675***|9.493***|8.375***|8.368***|8.820***|9.454***|
||(8.257)|(8.271)|(8.223)|(8.390)|(7.301)|(7.268)|(7.278)|(7.250)|
|avgps|-8.298|-8.193|-7.710|3.779|-18.254***|-16.429***|-15.701***|-19.274***|
||(-0.725)|(-0.734)|(-0.698)|(1.157)|(-3.634)|(-3.817)|(-3.686)|(-6.213)|
|**Market variable**|**s**||||||||
|colret|-0.442|-0.528|-0.575|-0.217|-1.392**|-1.555***|-1.686***|-1.447**|
||(-1.334)|(-1.551)|(-1.614)|(-0.548)|(-2.453)|(-2.861)|(-2.930)|(-2.220)|
|mkt25|-0.031***|-0.034***|-0.034***|-0.032***|0.030***|0.029***|0.029***|0.031***|
||(-5.476)|(-5.891)|(-5.798)|(-5.721)|(4.376)|(4.387)|(4.277)|(4.497)|
|**Investor variabl**|**es**||||||||
|Active Share|-0.185***|-0.116***|-0.108***|-0.095***|0.112***|0.048**|0.033**|0.121***|
||(-6.727)|(-3.558)|(-3.618)|(-3.871)|(4.796)|(2.300)|(2.205)|(5.430)|
|Diferent Stocks||0.006***|0.006***|||-0.006***|-0.005***||
|||(4.726)|(4.877)|||(-4.781)|(-4.649)||
|Turnover|||-0.010***|-0.010**|||0.007**|0.009**|
||||(-3.060)|(-2.557)|||(2.505)|(2.213)|
|Net Flows|||0.015***|0.018***|||-0.004**|-0.002*|
||||(-2.624)|(-2.597)|||(-2.175)|(-1.978)|
|Trading Interval||||0.099***<br>(10.907)||||-0.035***<br>(2.937)|
|Constant|-1.378**|-1.014**|-0.786|-0.359|-0.190|-0.519|-0.969**|-0.967*|
||(-2.560)|(-1.985)|(-1.507)|(-0.570)|(-0.434)|(-1.237)|(-2.312)|(-1.826)|
|Observations|104,321|104,321|101,178|95,873|71,471|71,471|69,034|64,906|
|R-squared|0.051|0.053|0.057|0.067|0.052|0.054|0.058|0.064|
|FE|stock|stock|stock|stock|stock|stock|stock|stock|
|Cluster|Investor|Investor|Investor|Investor|Investor|Investor|Investor|Investor|

34
|15|Sfax|4,698.13|133.87|2.85%|1.03|1.72|
|---|---|---|---|---|---|---|
|16|Kairouan|2,269.21|62.51|2.75%|1.01|1.70|
|17|Kasserine|2,542.98|92.31|3.63%|1.01|1.69|
|18|Sidi Bouzid|2,663.65|75.74|2.84%|1.04|1.74|
|19|Gabes|3,043.21|83.40|2.74%|1.01|1.69|
|20|Mednine|3,318.50|91.06|2.74%|1.05|1.76|
|21|Tataouin|3,538.70|106.89|3.02%|1.08|1.81|
|22|Gafsa|3,154.77|106.11|3.36%|1.01|1.70|
|23|Tozeur|3,191.66|106.38|3.33%|1.01|1.70|
|24|Gbeli|2,834.20|72.02|2.54%|1.03|1.74|
||**Tunisia**|**3,871.94**|**53.00**|**1.37%**|**1.30**|**2.19**|

Source: our elaborations of the EBCNV 2015.

During the first planning phases of the new EBCNV 2021, the INS initially took into account the same three aspects for determining the sample size: i) the information of the population at that time; ii) the _explicit stratification_ of the PSU in the design, and iii) the expected maximum error of 10% at the governorate level. These would have resulted in a sample size of about 29,000 households and a total cost of about 6.9 million dinars.

Given the comparative benchmark of 6.9 million dinars and the benchmark of expected maximum error of 10% at the governorate level, the aim of the new sampling design has been to reduce both costs and maximum errors. Table 5 reports results from three scenarios, with different survey designs in terms of design effect and sample sizes needed to reach specified maximum percentage errors (left column). The three scenarios are as follows:

- a) <u>Explicit stratification only: PSUs are stratified in the 24 governorates, and</u> then selected with simple random sampling; this is an unrealistic scenario nowadays, even in household surveys in Africa. However, the expected standard errors are usually calculated hypothesizing this scenario so that the implemented sample sizes are overestimated for the purposes of the survey with very high costs.

- b) <u>Serpentine: PSUs are first stratified in the 24 governorates, and then</u> further stratified (implicitly) with systematic sampling by ordering the lists

17
factor is likely to be cost - it is cheaper to move goods by sea than overland. We take freight forwarders’ choices of longer sea routes over faster routes involving more overland travel to be indicative of a preference for lower transport cost over faster shipping times.

**<u>Table 6. Seaport or Air Transport Use for Imports from East Asia and North America</u>**

|Number of<br>freight|Share|of firms indica|ting share o|Nth<br>f shipments usin|g each seaport|or using air tra|Nt<br>nsfer|
|---|---|---|---|---|---|---|---|
|forwarders|Thessaloniki|Rotterdam|Bar|orern<br>Adriatic ports|Air transfer|Amsterdam|o<br>applicable|
|For East Asian Imports||||||||
|Small<br>69|10%|1%|17%|67%|0%|1%|3%|
|Medium/Large<br>8|0%|0%|0%|75%|13%|0%|13%|
|Total<br>77|9%|1%|16%|68%|1%|1%|4%|
|For North American Imports||||||||
|Small<br>23|4%|4%|9%|65%|9%|0%|9%|
|Medium/Large<br>4|0%|0%|0%|75%|25%|0%|0%|
|Total<br>27|4%|4%|7%|67%|11%|7%|0%|

Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders.

# **_Freight costs are a substantially more important factor affecting route choice than borderrelated costs or delays_**

Many factors can determine route choice for a given shipment by a freight forwarder, some related to freight costs, others related to time or uncertainty in clearance procedures along the route. Freight costs are the main factor affecting route choice for the large majority of freight forwarders but the relative importance of ocean versus overland differs by size, as seen in Table 7. Ocean freight costs are important for more of the small freight forwarders while overland freight costs are important for more of the medium/large freight forwarders. Border costs/delays in countries other than Serbia are the main determinant of route choice for just a minority of freight forwarders, more so for larger ones. Border costs or delays in Serbia play no meaningful role as determinants of route choice.

**Table 7. Main Factor Determining Route Choice by Freight Forwarder Size**

||Number of<br>freight<br>forwarders|Ocean freight<br>costs<br>Share of firm|Overland<br>freight costs<br>s indicating th<br>route|Border<br>costs/delays<br>outside<br>Serbia<br>is as major fac<br>choice|Border<br>costs/delays<br>in Serbia<br>tor affecting|
|---|---|---|---|---|---|
|Small|130|48%|38%|9%|5%|
|Medium/Large|11|36%|45%|18%|0%|
|Total|141|47%|38%|10%|5%|

Source: Authors’ calculations based on data from the Survey of Serbian Freight Forwarders.

15
**Table 14: Rice, farm-level indicators**

||**Yield**<br>**(MT/ha)**|**Kg**<br>**Fertilzer***|**Price**<br>**(USD/MT)**|**%**<br>**Foreign**|**Tax as %**<br>**DVA**|**Total SV**<br>**(USD/MT)**|**Net Profit**<br>**(USD/ha)**|
|---|---|---|---|---|---|---|---|
|**Malawi (2007/08)**||||||||
|Rainfed||||||||
|FAM-low (rain)|1.00|0|250.00|16%|7%|163.10|119.05|
|FAM-high (rain)|2.30|200|250.00|45%|13%|153.87|257.53|
|Irrigated||||||||
|FAM-low (irrig)|2.00|100|250.00|36%|15%|222.71|239.40|
|FAM-high(irrig)|3.00|200|250.00|41%|15%|177.05|405.83|

74. The per ton composition of rice production costs as shown in Figure 14, include fertilizer, labor, irrigation and marketing costs. Figure 16 further shows that unlike in maize, higher input levels are more profitable and improve competitiveness both under rainfed and irrigated conditions.

**Figure 16: Rice, composition of farm-level SV**

<!-- Start of picture text --> Rice, Composition of Farm-level SV (USD per MT)<br>Rice, Composition of Farm-level SV (USD per Ha)<br>300 Irrigation<br>600<br>Irrigation<br>250<br>Depreciation<br>500<br>Depreciation 200<br>400 Marketing<br>Marketing 150<br>300 Labor<br>Labor 100<br>200 Chem<br>Chem 50<br>100<br>Fert<br>Fert -<br>- Low High Low High<br>Low High Low High Seed<br>Seed<br>Rain Fed Irrigated<br>Rain Fed Irrigated<br><!-- End of picture text -->

# **A. Rice Competitiveness**

75. When we compare Malawi’s total SV for paddy rice with other countries, Malawi’s is not competitive except with Nigeria where prices are protected by trade policy (see Figures 15 and 16). Farm-gate price for paddy in Malawi is however higher (estimated at about USD250 per ton) compared to other countries except Nigeria. These high prices are a benefit to farmers, but eventually cause problems for trade competitiveness when the final SV for milled rice is compared with import parity. Given such high domestic price, there is little scope for further improvement in farm-gate price if Malawi is to achieve competitiveness in rice.

33
A standard deviation in the three significant sectoral specialization indicators is associated with a three to 16% increase in the standard deviation of total volatility. A standard deviation in the four sectoral specialization indicators is associated with one to seven percent decrease in the standard deviation of the franchise value. Finally, a standard deviation in the four sectoral specialization indicators is associated with a one to 18% increase in the standard deviation of MES.

Table 8 reports the results for the lending specialization indicators based on the hand-collected reported exposures and are organized in a similar fashion as Table 6. We add each of the four accounting-based sectoral specialization indicators (as specified in Subsection 2.2) one-by-one to the regression setup reported in the introduction to Section 3, resulting in 16 different specifications. Unlike in Table 6, we no longer include bank fixed effects due to the very limited within variation in each of the lending specialization indicators.

# < **Insert Table 8 around here** >

turns, which is consistent with the pairwise correlation results (see panel B of Table 5) and the results in Table 7. None of the four lending specialization variables enters significantly at conventional levels. Lending specialization is thus not reflected in stock returns. We find a significant relationship between three out of four lending specialization indicators and the volatility of stock returns. Specifically, banks with a higher dispersion and a higher concentration of sectoral portfolio shares, as measured both by the CR3 and the HHI, show a higher volatility of stock returns. On the other hand, we do not find any significant relationship between lending specialization and market-to-book values of banks. Banks with sectorally more specialized lending portfolios are thus not traded with a discount. Hence, the results in the pairwise correlation table (panel B of Table 5) might have been spurious and disappear after properly controlling for other factors that affect the franchise value. Finally, accounting-based sectoral specialization is significantly and positively 21
44

**Table 2**

# **Thresholds for Small and Medium Enterprises, GCC banks, Non-GCC banks and the EU**

||**Turnover-- Thousands of US Doll**<br>|**ars**<br>|
|---|---|---|
||**Minimum Small Maximum Small**|**Maximum Medium **|
|**GCC**|365<br>5,456|18,390|
|**Non-GCC**|61<br>897|4,757|
|**Memo: EU**|2,816<br>14,085|70,422|
||**Turnover-- Multiples of Per Capita I**|**ncome**|
||**Minimum Small Maximum Small**|**Maximum Medium**|
|**GCC**|8<br>144|601|
|**Non-GCC**|26<br>342|1,719|
|**Memo: EU**|85<br>427|2,134|
||**Number of Employees**||
||**Minimum Small Maximum Small**|**Maximum Medium**|
|**GCC**|3<br>24|90|
|**Non-GCC**|4<br>17|58|
|**Memo: EU**|20<br>50|250|

(*) GCC and Non-GCC: Average of individual bank thresholds EU: Official definitions
37 too early to judge the ultimate impact of the Rule. The resale market for privately placed issues has the potential to evolve into a global trading market closely linked to the Eurobond market.

5. For a review of some likely reforms in the U.S.A., see Goldman Sachs, Economic Research: Financial Market Perspectives, December 1990.

6. "Korea Sets Timetable for Financial Market Reform," Financial Times, London, November 14, 1988, at page 27 and "Liberalization Hints Give Boost to South Korean Stock Market," Financial Tines, London, October 18, 1988.

7. Indeed, the pace of financial market liberalization often occurs faster in international markets than in domestic markets. R.egulatory liberalization appears to have facilitated the rapid growth of the non-U.S. dollar currency secrors of the international bond markets during the 1980s. See Chuppe, T. H. Haworth, M. Watkins, "Public Policy Toward the International Bond Markets in the 1980s," Advances in Financial Planning and Forecasting, Volume IV, Part B, JAI Press, Inc., forthcoming 1990.