rafmacalaba/data-use-mentions-extended huggingface dataset:
assume that book values of net loans equal market values of gross loans. In most cases, however, there is a discrepancy between the market and book value of loans. A particular case is when loan data are distorted by inadequately reported nonperforming loans. Application of such distorted data to efficiency models might lead to incorrect conclusions, because banks with excessive loan growth will show high technical efficiency (ceteris paribus), while they take on excessive risks. Inadequately reported nonperforming loans are most likely to be a large factor of data distortion in emerging markets with weak enforcement of banking regulation. In such countries a bank efficiency model might estimate a bank to be in better shape that they actually are. In fact, the most efficient banks of a banking system as measured by a bank efficiency model are not necessarily the least risky banks. In the case of East Asia it seems likely than inadequate loan loss provisions were a large factor of data distortion as well, because, until 1997, loans were not classified as nonperforming loans until no payments were made for over one year, leading to underreporting of bad loans to regulators.

The extremely high loan growths in the five East Asian countries during the period 199296 (up to 151% for the Philippines - see Appendix, Table 4) suggest excessive risk taking behaviour of the East Asian banks. Since the technical efficiency measure does not take into account that excessive loan growth can indicate excessive risk taking instead of increased bank performance, technical bank efficiency measures are not a perfect indicator for bank performance, but are merely an indicator for how well banks are in transforming their inputs into loans, both bad and good loans. Especially in countries where poor regulatory frameworks and weak enforcement of banking regulations enable banks to take excessive risks, technical efficiency measures will provide a misleading insight in the performance of banks.

The earlier mentioned studies by Leightner and Lovell (1997), Leightner (1997), Gilbert and Wilson (1998) and Hao et al. (1999) on Thai and Korean banking
# **4.5 Determinants of private transport**

## **4.5.1 Extensive margin: the determinants of vehicle ownership**

In this section, we investigate the empirical determinants of vehicle ownership at the household level. We include cars as well as other vehicles including motorcycles and bicycles. A bicycle is often the first private transport asset poorer households can afford to acquire. Motorcycles can be a cheaper and more available substitute of cars in many countries. For example, less than 5% of households own a car in Mali, Burkina Faso, Togo and Benin, but more than 30% own a motorcycle (World Bank SSAPOV harmonized survey database). While the acquisition of a bicycle does not contribute to the growth of CO2 emissions, motorcycles do but to a lesser extent than cars. This section therefore looks at the determinants of first vehicle acquisition and ensuing substitution between bicycles, motorcycles, and motor cars.

In order to quantify the determinants of vehicle ownership, we estimate the following equation: The ordinal dependent variable _Pr_ ( _ownershipi_ = _k_ ) is the probability for a household to own a vehicle of type k, but not of higher order.

If a household owns more than one type of vehicle, we classify this household in the highest vehicle ownership category. For instance, k=4 if a household owns a bicycle and a car. The explanatory variables are defined as follows: _xi_ is total expenditure of the household i, _ni_ household size, and _zi_ a vector of other household socio-demographic characteristics, including age, gender, and educational level of household head.

We estimate Equation 4.6 using an ordered logistic model whose results are presented in Table B.16 in the Appendix. Belonging to a wealthy, educated, and large household increases the chances of being in a higher vehicle’s ownership category. As households become richer, they progress in the vehicle ownership hierarchy by switching from a low to a higher level. These results are consistent across the two
database based on industries’verbal descriptions.<br>Natural resource intensity<br>Indicator variable for natural resource intensive sectors in the EO RA database (wood and pap er;<br>p etroleum , chem icals and non-m etallic m inerals; and m etal products)<br>Contract intensity<br>Share of sp ecialized and custom ized interm ediate inputs used in the production of the ...nal good for each industry<br>3-digit ISIC revision 2 industry based on a U.S. input-output table. Sp ecialized and custom ized interm ediate<br>inputs are those not sold on an organized exchange nor reference-priced according to Rauch (1999) s<br>relationships. The 3-digit ISIC revision 2 level is concorded to the eight sectors in the EO RA database based on<br>industries’verbal descriptions.<br>Technology intensity<br>Indicator variable de...ning whether an ISIC sector is technology-intensive or not, based on research<br>and developm ent exp enditure incurred in the production of m anufactured goods.<br>Average output tari¤<br>Tari¤s im p osed at hom e on im p orts of the sector are constructed by ...rst aggregating tari¤s (taking the<br>sim ple average) and im p orts (taking the sum ) to the EO RA sector level. Then im p orts are sum m ed across<br>countries of origin to obtain im p ort weights which are used to com pute tari¤s im p osed at hom e on im p orts<br>of a sector as a weighted-average tari¤ at the rep orting country-EO RA sector-year level.<br>Average input tari¤<br>Tari¤s im p osed at hom e on im p orts of interm ediate inputs used are constructed by ...rst keeping tari¤ data<br>and im p ort data only for HS 6-digit products classi
fuel type in the column heading. Consequently, magnitudes of the own-price elasticities of demand are smaller in this table than in the previous table, which uses price changes of individual vehicles to compute elasticities. In Table 7, the cross-price elasticities of demand indicate that hybrid vehicles are closer substitutes for electric vehicles than are gasoline vehicles. Intuitively, consumers regard electric vehicles as closer substitutes for hybrid vehicles than for gasoline vehicles. The reasonableness of the consumer substitution patterns helps validate the demand model.

Table 7: Own- and cross-price elasticity of demand by fuel type

|Fuel Type|Electric<br>Hybrid|Gasoline|
|---|---|---|
|Electric|-4.0<br>0.0|2.6|
|Hybrid|0.1<br>-3.9|2.5|
|Gasoline|0.0<br>0.0|-2.1|

Note: This table shows the own- and cross-price elasticities by fuel type. Elasticities are the percent change in market share of the fuel type caused by a 1 percent increase of the prices of all vehicles belonging to the fuel type in the column heading.

# 4. Policy Simulations

## 4.1 Scenarios

We compare a baseline no-policy scenario that removes the preferential sales and import taxes against scenarios that include one or both of the preferential taxes. We use data from 2019 and 2020 for the simulations and report results separately for each year. Simulating outcomes for 2019 allows us to characterize the equilibrium before the COVID-19 pandemic, which likely disrupted Colombia’s new vehicle market as the economy contracted. The market shares of hybrids and electrics increased threefold between 2019 and 2020 (see Figure 1), and it is interesting to consider the effects of the tax policies when market shares reached historical highs. The baseline or ‘no-policy’ scenario includes the pre-reform status quo of 19 percent sales tax and a 30 percent import tariff for all vehicles. In the
# **Introduction**

This paper develops a conceptual framework related to the political economy of fuel subsidies, with a view to subsequent empirical applications. The paper seeks to provide systematic explanations of why and how fuel prices, including subsidies to or taxes on fuels, vary between countries. A key question in this context is how formal models can be fruitfully specified, and subsequently parameterized and estimated, to give more substance to empirical relationships between fuel prices, political variables, and other variables.

This research grows out of empirical work currently ongoing in the World Bank dealing with energy subsidies and related themes. For previous broader discussions of such issues see World Bank (2011), and Commander (2011). Current work focuses on building, estimating and testing empirical models for fuel (gasoline, diesel and kerosene) pricing across countries over a 20-year period, based on a much more extensive data set for such pricing integrated with a wide range of economic and political variables; see Strand (2011); Beers and Strand (2012); and Kotsogiannis (2011). Recently it has also become possible to match these with data for car ownership by country for a large set of countries (matching many albeit not all of the countries for which we have fuel price and consumption data).<sup>2</sup> Some empirical political economy aspects of fuel pricing have been treated in earlier papers, including Hammar, Löfgren and Sterner (2004), and Fredriksson and Millimet (2004), albeit on the basis of far more limited data sets than those we have available. Most existing work has focused on high-income countries. Our improved data situation also opens up the possibility to meaningfully include a range of new political variables, representing democratic or autocratic governments, length of regime or regime type tenure, and democratic system (such as presidential versus parliamentarian).

For this exercise I find it useful to start with a rather coarse classification of government types and population groups, to make a preliminary investigation of what
established production rules using spectral, temporal, and spatial constraints to identify the main agricultural systems and natural vegetation types in the study area (Cohen and Shoshany 2002). The production rules were not necessarily conclusive, but they provided a degree of evidence in favor of some class labels (Richards and Jia 2006, 342–55). These rules were defined from the analysis of the spectral profiles during the vegetative cycles of the different land-use types. Specific temporal windows were selected to maximize the separability between classes using expert knowledge in the seasonal dynamics of the land surfaces in the study area (figure 2, panel a). Similar strategies have been previously used for LCLU in tropical environments (Hansen et al. 2008; Sedano, Gong, and Ferrao 2005). The numerical values in the spectral rules were provided as thresholds that were empirically generated from observed data (Hodgson et al. 2003). These spectral thresholds were defined based on a training data set of 1,750 points of known land-use types randomly searched using visual interpretation of a combination of Landsat and very high-resolution imagery (Google Earth) for the period of study. The thresholds for each land-use class were calculated from the statistical distribution of pixel values in the training data set. The spectral rules were built based on Normalized Difference Vegetation Index (NDVI) images (Tucker 1985) calculated from each image in the original data set. The NDVI was chosen because, besides data compression, it facilitates the interpretation of land surface dynamics over time and the definition of spectral thresholds. The presence of burnt areas in natural vegetation surfaces within the three-year imagery epoch was interpreted as a sign of land-cover transition, supporting the labeling as nonstable natural vegetation pixels.
and the consumer price index in country _m_ is equal to

# **B.1 Data sources and description**

This appendix provides further details about the data used in the empirical analysis. The set of developed countries included in our estimation sample is: Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Netherlands, Poland, Portugal, Romania, Slovak Republic, Slovenia, Spain, Sweden, United Kingdom and United States. For each of these countries, the estimation sample includes all years for which the IFR data are available during the period 1995-2015. We examine bilateral trade flows between these countries and all non-OECD countries included in BACI. Table A1 reports the final list of sectors used and their correspondence with the sectors defined in the original data sets.

**Industrial robots** : The stock of industrial robots by industry, country and year comes from the International Federation of Robotics (IFR). These data are based on yearly surveys of robot suppliers, and currently cover 75 countries (about 90 percent of the industrial robots market). The IFR measures deliveries of “multipurpose manipulating industrial robots” based on the definitions of the International Organization for Standardization. The data are disaggregated roughly at the three-digit level for manufacturing and roughly at the two-digit level for non-manufacturing. We focus only on tradable sectors, which are listed in Table A1.

To calculate a measure of the robot stock per sector, we use the perpetual inventory method assuming a depreciation rate of 10%, following Graetz and Michaels (2018).<sup>18</sup> We initialize the series using the IFR measure of the stock of robots per sector in 1993. For countries for which the 1993 sectoral breakdown of the stock of robots is not available, we initialize the series using the initial robots stock for the first year for which it is available.<sup>19</sup> The IFR data are regarded as
refugees in Addis Ababa and Tigray regions are either urban assisted refugees or those under the OCP, while those in the Oromia region are refugees living in semi-formal and informal settlements.

> 20About 24% (19%) of the sample household/individual-round observations reside in EAs that are within a 70-kilometer (50-km) buffer from the closest refugee settlement sites. How does this compare with related studies? For instance, the smallest distance between the markets (for the price analyses) and the corresponding closest refugee camps in the Alix-Garcia and Saah (2009) is 70 kilometers. Moreover, only 6% of their sample households (for the welfare analyses) reside within a 100-kilometer buffer from the closest refugee camps. On the other hand, I also show that the main findings are robust to restricting the sample to the aforementioned (70- and 50-kilometer) buffers in section 7.5.

> 21As discussed, the ESS data has information on the location (GPS coordinates) of each of the sample EAs, permitting linking the refugee data from UNHCR to the ESS EAs and thereby constructing refugee intensity for each round-EA.

> 22Due to incompleteness of the records in December 2008 and December 2009, I have the records in the following month (January) for each of these cases.

> 23I do not have the locations of these three semi-formal and informal settlements, since refugees are settled spontaneously over a large geographical area. I assigned locations as follows. For Addis Ababa, I use the location of the center of the city. In Tigray, most of the refugees are urban refugees under the OCP. I use the location of the center of Mekelle city, the largest and capital city of the Tigray region. Most of the refugees in the Borena zone of the Oromia region are Kenyan Borenas settled in the Megado settlement site located close to the Ethiopian-Kenyan border. As a result, I use the location of this settlement site for Kenyan Borena refugees.

13
but are otherwise similar. In this setting, a large home market for goods of a given quality confers a competitive advantage to ...rms located there, which renders them as net exporters in the trade equilibrium.

Our model yields predictions about the pattern of trade that are consistent with the empirical evidence. For example, we ...nd that, among countries of similar size, the richer countries export goods of higher average quality. This is in keeping with the empirical ...ndings by Schott (2004) and Hummels and Klenow (2005). When trading costs are small, a country imports higher-quality goods from richer trading partners, as Schott (2004), Khandelwal (2010) and Hallak and Schott (2011) have found to be true in the U.S. bilateral trade data. When a country exports varieties of a given quality to two di¤erent markets of similar size, it exports a greater volume to the country whose income ranking is more similar to its own.

Our framework lends itself readily to welfare analysis. We can decompose the welfare impact on a particular income group of, for example, reductions in trading costs into a cost-savings e¤ect and a composition e¤ect. The former tends to bene...t all consumers, whereas the latter— re‡ecting the induced change in the relative numbers of low- and high-quality products— often bene...ts consumers at one end of the income distribution at the expense of those at the other. We ...nd that, in the absence of supply-side determinants of comparative advantage, trade between countries at di¤erent levels of income tends to bene...t on average the poorer consumers in the richer country and the richer consumers in the poorer country. These income groups gain from integration not only because imports provide a wider variety of choices, but also because trade shifts the composition of available products toward those goods that they are most likely to buy.

Our nested
7|13.1|
|Increasing|453|58.5|57.1|

# **6.2 Sectoral Results**

Table 11 explores the sectoral distribution of results weighted by EDGAR emissions. Soils and Livestock exhibit enormous disparities between Increasing and Decreasing cases (90.2 % vs. 0.6% for the former; 63.6% vs. 4.6% for the latter). The fuels sectors reveal striking differences, with a huge imbalance toward Increasing for Coal (66.7% vs. 5.1%) while Gas and Oil are both dominated by Decreasing (44.7% vs. 25.3% for the former; 23.8% vs. 10.6% for the latter). The two waste sectors have large imbalances toward Increasing (52.4% vs. 18.0% for Landfills; 61.0% vs. 18.3% for Wastewater). In summary, Oil and Gas exhibit more Decreasing results but the two sectors only account for 27.2% of EDGAR emissions. The remaining sectors all exhibit huge dominance for Increasing results.

26
|Dehradun|No impact on revenues.|
|---|---|
|Surat|Yes, it impacts revenues but not to a large degree. As the discount value is only 1% of the<br>transaction value, the total loss would be to the tune of 5 to 6%.|
|Delhi|Officials do not know the amount foregone in benefits to females and, likewise, cannot tell if<br>this has impacted revenues.|
|Pipili|Yes, it has impacted revenues, but this is twofold; there’s a loss of 1% on every transaction<br>due to the discounts, but the number of registrations is increasing, which could be offsetting<br>this loss.|
|Gurugram|Benefits to females lead to 2% of foregone revenue in every such transaction, but overall, it<br>has not impacted on revenues. This is because more citizens are coming forward to utilize<br>these discounts, and the number of registrations has thus increased. Technically, revenues<br>have increased.|
|Pune|From total revenue collection, an estimated 18% of the amount is foregone in benefits to<br>females. It has therefore impacted revenues negatively.|

# 6. Main conclusions

Property ownership among females in India remains low, although some evidence indicates an increase in the proportion of female homeowners over the last decade (both single and joint ownership). Genderdisaggregated data collected during the study from government departments across various states suggest home ownership for females currently ranges between 20 percent and 30 percent of total homeowners.

## 6.1 Impact of incentives on property ownership

### 6.1.1 Stamp duty discounts may have a positive impact on female property ownership

**_Stamp duty and registration discounts positively impact female property ownership_** . The study found that 9 of the 12 cities surveyed had stamp duty discounts available to females registering a property in their names and that
, several empirical approaches were applied and yielded general findings applicable to all countries and some specific to STP. This paper constructs a database from different sources to provide a complete picture of tourism statistics and explanatory variables. Different statistical models were used to establish correlations and, whenever possible, to establish causation.

The first set of general findings confirms most of the results found in the literature. Gross domestic product (GDP) and exports of the host country are positively associated with tourism. Higher flight connectivity increases tourist arrivals but does not change average expenditures per tourist. Tourist decisions are influenced by relative price variations, which means that a real exchange rate depreciation has a positive effect on arrivals and a negative effect on expenditures. Finally, tourist arrivals and expenditures per tourist have a persistent effect, meaning that increases in one year tend to be maintained in future years.

The second general finding is that positive attitude towards LGBT population, as measured by decriminalization, is associated with an increase in tourist arrivals. A causal analysis was performed to evaluate the effect of decriminalization of LGBT and large positive effects were observed. Using

> 1 See Frankel, J. A., & Romer, D. H. (1999). Does trade cause growth? American Economic Review, 89(3), 379399 and Alcalá, F., & Ciccone, A. (2004). Trade and productivity. Quarterly Journal of Economics, 119(2), 613646.

> 2 See section II for a review of the literature.

> 3 Digital presence is how a business, in this case a tourism destination, appears online in all forms possible: website, social network, mobile, review sites, etc.

2
systems face the problem of how to pay providers. Japan, richer than the Tigers, has been most successful in devising a non-inflationary and non-distortionary fee schedule, but it is Korea and Taiwan (China) that are ahead in introducing prospective payment methods. Policymakers in many systems are conscious of the pitfalls of the shared approach (a legacy of Chinese medicine) of having the same person both prescribe and dispense medicines.

> *  In terms of GDP per capita, China today is roughly where Korea and Taiwan (China) were in 1960. Malaysia today is roughly where Taiwan (China) was in 1997, and Korea was in 1984. And Thailand today is roughly where Korea and Taiwan (China) were in the early 1970s.

> † Data are from the World Bank’s World Development Indicators. GDP per capita is at constant 2000 prices, expressed in US dollars, not purchasing power parities (PPP) due to the shorter time-series for PPP GDP per capita data.
5 to 8 show the same effect but using the BarOn instrument. Columns 3 and 7 combines, in a single group, students in treatment 1 and 2. Columns 4 and 8 controls for potential self-selection into the test. All specifications include school fixed effects.

The effect on the PLANEA score is large and statistically significant, about 0.17 and 0.18 standard deviations (sd), for the 10 and the 20 sessions respectively. Both effects, separately or combined are statistically significant at conventional statistical level and are robust to potential selection into the exam. When we look at the impact on the BarOn inventory, we find an effect that is small for both treatment groups and statistically not significant. When the two treatment effects are combined, the resulting effect is about 0.03 sd after accounting for possible selection into the exam (column 8 in Table 3).

Table 4 reports the intention to treat effects of PODER on math test scores, enrollment in 11<sup>th</sup> grade, re-taking COMIPEMS 2017 or a measure of school engagement combining both 11<sup>th</sup> grade enrollment or re-taking COMIPEMS. Overall, we find a negative but small and statistically insignificant effect on math test scores and the probability of enrolling in 11<sup>th</sup> grade. The effect is 17