rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Classifier Options I

|**Method**|**Description**|**Hyperparameters (Options)**|**Hyperparameters (Used)**|
|---|---|---|---|
|LogisticRegression|This is a linear classifer<br>that<br>uses<br>a<br>logistic<br>function<br>to<br>predict<br>the<br>probability<br>of<br>a<br>sample<br>belonging<br>to<br>a particular class.<br>It<br>is<br>commonly<br>used<br>for<br>binary<br>classifcation<br>tasks,<br>but<br>can<br>also<br>be<br>used<br>for<br>multi-<br>class<br>classifcation<br>by<br>implementing<br>a<br>one-<br>versus-rest approach.|• C:<br>The<br>inverse<br>of<br>the<br>regularization<br>strength,<br>with<br>higher<br>values<br>indicating<br>less<br>regularization.<br>• penalty:<br>The<br>type<br>of<br>regularization<br>to<br>use,<br>either<br>L1 or L2.<br>• ft_intercept:<br>A<br>boolean<br>fag<br>indicating<br>whether<br>to<br>ft<br>an<br>intercept term.<br>• tol:<br>The tolerance for stopping<br>criteria.<br>• intercept_scaling: The scaling of<br>the intercept term, if it is being<br>ftted.<br>• class_weight: The class weights<br>to use for unbalanced classes.<br>• max_iter:<br>The<br>maximum<br>number<br>of<br>iterations<br>for<br>the<br>optimization algorithm.|• penalty:<br>The<br>type<br>of<br>regularization<br>to<br>use:<br>L1<br>or<br>L2.<br>• C:<br>Inverse<br>of<br>regularization<br>strength. [0.00002, 10000]|
|SGDClassifer|This is a linear classifer<br>that<br>uses<br>stochastic<br>gradient<br>descent<br>to<br>learn the parameters of<br>the model. The modifed<br>huber loss function is a<br>smooth<br>approximation<br>of the hinge loss, which<br>is<br>commonly<br>used<br>for<br>linear<br>classifcation<br>tasks.|• loss:<br>The loss function to use,<br>with<br>options<br>such<br>as<br>"hinge",<br>"log",<br>"modifed_huber",<br>"squared_hinge",<br>and<br>"perceptron".<br>• penalty:<br>The<br>type<br>of<br>regularization<br>to<br>use,<br>with<br>options<br>such<br>as<br>L1,<br>L2,<br>"elasticnet", and "none".<br>• alpha:<br>The<br>regularization<br>strength,<br>with<br>higher<br>values<br>indicating<br>stronger<br>regularization.<br>• l1_ratio:<br>The<br>proportion<br>of<br>L1 regularization to use in the<br>elasticnet penalty.<br>• tol:<br>The<br>tolerance<br>for<br>the<br>stopping criteria.<br>• learning_rate: The learning rate<br>for the optimization algorithm,<br>with options such as "constant",<br>"optimal", and "invscaling".<br>• eta0: The initial learning rate for<br>the "constant" and "invscaling"<br>learning rate schedules.<br>• power_t:<br>The<br>exponent<br>for<br>the<br>"invscaling"<br>learning<br>rate<br>schedule.|• loss:<br>The loss function to use.<br>("modifed_huber")<br>• penalty:<br>The<br>type<br>of<br>regularization<br>to<br>use:<br>L1<br>or<br>L2.<br>• learning_rate: The learning rate<br>schedule to use. ("optimal")<br>• alpha:<br>The<br>constant<br>that<br>multiplies<br>the<br>regularization<br>term. [0.00002, 1000]|

38
|**Annex Table 1. Available Tax Relief by Per Taxpayer (in PL**|**N thousand)**|||||
|---|---|---|---|---|---|
|_|l<br>19941|19951|19961|1997|1998|
|**1 Losses from previous year**|**782.3**|**986.5**|**561.2**|**1,486.9**|**746.1**|
|Tax bracket 1|349*31|400.2|283.4|278.8|183.3|
|Tax bracket II|303.2|1,068.4|993.0|1,091.2|524.0|
|Tax bracket III|1,708.11|<br>1,802.4|1,283.2|10,901.7|5,772.7|
|2 Donation|**54.1**|**248.8**|**287.8**|**129.8**|**104.1**|
|Tax bracket I|13.2|142.6|195.0|37.7|36.2|
|Tax bracket II|25.0|367.5|549.5|167.7|143.1|
|Tax bracket III|397.9|1,914.2|2,149.5|2,145.9|1,178.0|
|3 Social insurance premiums of taxpayer and his|349.8|415.9|534.7|573.8|587.3|
|employees||||||
|Tax bracket I|199.8|252.1|353.4|382.9|402.6|
|Tax bracket II|337.7|444.7|595.4|680.3|673.4|
|Tax bracket III|611.5|855.4|1,115.3|1,399.4|1,445.0|
|4 Pensions, permanent burdens, alimony;|..|303.8|265.5|1,028.4|1,557.3|
|Tax bracket I|..|139.9|193.2|261.9|300.7|
|Tax bracket II|..|715.2|731.1|1,255.4|1,138.9|
|Tax bracket III|_|<br>..|518.9|6,287.9|10,104.0|
|**5**Membership fees for organizations the affiliation<br>to which is**obligatory for taxpayer**|..|..|..|..|67.9|
|TaxbracketI<br>=||..|..|..|31.2|
|Tax bracket II|||..|..|70.1|
|TaxbracketIII<br>-<br>__|||..|_||692.4|
|**6 Rehabilitation expenditures**|**78.1**|**139.7**|**135.0**|**172.1**|**210.4**|
|Tax bracket I|61.8|72.3|128.4|166.7|197.3|
|Tax bracket II|111.0|73.9|209.5|255.3|349.9|
|Tax bracket 111|277.2|2,601.6|83.3|280.4|654.6|
|7 Purchase of equipment and research aids and|..|84.3|98.8|112.9|139.4|
|professional publications||||||
|Tax bracket I|.|62.2|75.2|98.9|130.5|
|Tax bracket 11|..|90.3|120.6|139.5|149.2|
|Tax bracket III||197.6|198.8|225.7|202.2|
|8 Investment expenditure in areas of high structural|..|6,459.0|6,939.2|11,940.3|16,335.0|
|unemployment||||_||
|Tax bracket I|..||||2,576.3|
|Tax bracketII|..|||||
|Tax bracket III|..|6,459.0|6,939.2|11,940.3|17,211.7|
|9 Investment relief|6,112.3|6,354.9|6,743.3|6,155.7|7,681.7|
|Tax bracket 1|105.1||287.7|308.8|691.8|
|Tax bracket II|410.6||1,009.4|893.0|967.9|
|Tax bracket III|7,335.1||11,446.0|9,204.5|10,285.7|

17
years around the time of reform to the level recorded in the year immediately preceding patent reform. This figure suggests that, relative to the pre-reform period, patenting grows for nonresidents after reform. However, patenting by domestic residents is flat. This pattern is broadly consistent with the findings of Lerner (2001, 2002). It also suggests that at least one component of the observed increase in licensing payments is connected to an increase in the volume of technology transferred.

To study patterns in patent data more carefully, the analysis presented in Table VI measures the impact of IPR reform on patent filings by both domestic and foreign innovators. The log of the counts of patent filings is regressed on the post reform dummy, an interaction term between the post reform dummy and an overall time trend, country and year fixed effects, country specific time trends, and some additional controls. Columns (1)-(4) present specifications where the dependent variable is the log of resident patent filings. In column (1), the coefficient on the post reform dummy is indistinguishable from zero, indicating that resident patent filings do not increase after reforms occur. Column (2) includes two additional controls: the log of host country GDP per capita and the log of host country net inward FDI. The post reform dummy remains insignificant. As the results in the next two columns show, the post reform dummy interacted with a time trend is also insignificant, indicating that the growth rate of resident patent filings does not increase after reforms. These findings are consistent with Lerner (2002).

Columns (5)-(8) present similar specifications for non-resident patent filings. There is robust evidence that these filings experience large increases and that these filings grow at an increased rate after reforms. The coefficient on the post reform dummy in 25
12 partly into an increase in margins. The imperfect correlation between domestic and foreign prices reflects to some degree the existence of quotas in the automobile sector and the preferential tax treatment in Tierra del Fuego, though the Argentine government has significantly reduced the overall level of protection over the last few years.13 The second term reflects the non-neutrality of inflation on price-setting, the equation implies that a decrease in inflation of 1 % eventually increases markups by 3.36 % (although the long-run effect is estimated very imprecisely). While surprising, the nonneutrality of inflation on markups has been found in empirical studies in the US (Benabou (1992)) and France (Blanchard and Muet (1993)). It suggests that consumers react to more noisy prices by gathering more price quotations, forcing down markups, as the increased search pressure implies more competition in the market.1<sup>4</sup> An alternative popular explanation for the non-neutrality of inflation is that the credibility of commitment to established parities has improved only slowly. Forward-looking Argentine price setters used to believe that Argentina was, and would remain, a high-inflation country. The increasingly aggressive commitment of the authorities to a fixed dollar parity continually surprises them. As a result, expected inflation has been falling, but at a slower pace than costs. 1<sup>5</sup>

# _Agriculture_

The results are summarized in Table 3. Agricultural wages were proxied by the wages in the industrial sector because the former series are unavailable in Argentina. In addition, a dummy variable for the period 1987(II)-1988(l) was introduced to account for the dual exchange rate applied to agricultural products during this period.

In the short-run, the inflation rate in the agricultural sector has been affected positively and significantly by the changes in foreign prices, labor costs, and the nominal interest rate. The influence of foreign prices is explained by the high level of exports of

> '3 To account for the trade liberalization initiated since 1989, we introduced a dummy variable in the regressions. However, the estimated coefficient was not statistically different from 0.

> ' This argument, however, contradicts the usual view that sellers may use the inflationary noise as a cover behind which to increase their markups

- l See Blanchard and Muet for a stylized model of this mechanism..
classified by the World Bank from the list of countries benefiting from the US Generalized System of Preferences (GSP) schemes that grant preferential duties access.

A further use of the classification is for setting and monitoring policy targets. A number of countries, for example, use the “graduation” from one grouping to the next as a mechanism for setting a time-bound policy target. For example, the Government of Ghana set a goal of reaching middle income status by 2015, while the Government of Bangladesh adopted the goal of transitioning to a middle income country by 2021 under its “Vision 2021” plan (Gimenez, Jolliffe, and Sharif, 2014).

A number of users within the World Bank were interviewed to obtain their views on the classification, focusing on whether (and why) there is a need to change or introduce modifications to the Bank’s current classifications and to establish what issues are relevant for different regions. The views of commentators and users outside the Bank have been obtained through blogs and other online discussions, and the team has reviewed a number of papers. Issues and concerns raised by external users are similar to those of many World Bank staff.

# **Figure 2. Population in each income group, latest year of data availability during each fiscal year (FY00–FY16), millions**

<!-- Start of picture text --> 1,399<br>High<br>886<br>588<br>2,361<br>Upper middle<br>886<br>Lower middle<br>2,880<br>3,536<br>Low<br>622<br>1998 2014<br><!-- End of picture text --> _Source_ : _World Development Indicators_ database (accessed November 30, 2015), and print editions 2000 to 2015.

11
|**Table 13: Supply Side Proxim**<br>|**ate Determin**|**ants – Pop**<br>|**ulationgro**|**wth 1970-2**|**000**<br>||
|---|---|---|---|---|---|---|
|**Variables**||**1970**|||**1991**||
||**1**|**2**|**3**|**1**|**2**|**3**|
|Intercept|0.023|-0.285|-0.290|-0.01|-0.461|-0.403|
||(2.01)|(1.72)|(1.70)|(0.74)|(3.6)|(3.1)|
|Income per capita|0.0005|0.0005|-0.010|0.005|0.012|0.018|
||(0.19)|(0.17)|(0.28)|(2.20)|(3.42)|(4.13)|
|Population market potential||0.043|0.450||0.070|0.059|
|||(1.63)|(1.65)||(3.44)|(2.78)|
|Income market potential||-0.04|-0.418||-0.069|-0.587|
|||(1.51)|(1.53)||(3.37)|(2.73)|
|Access to electricity|||0.007|||-0.0001|
||||(0.67)|||(0.36)|
|Piped water|||-0.002|||-0.0002|
||||(0.23)|||(1.21)|
|adj. R|0.00|0.06|0.05|0.03|0.12|0.14|
|Number of observations|123|123|123|123|123|123|

t-values in parentheses.

Assuming a mobility constraint posed by migration distance, people who live in neighboring areas of a city are potential immigrants to the city. Also residents in the city are potential emigrants out of the city. Their migration decisions in and out of cities depend on the relative differences in city endowments. We therefore assume that net migration, and consequently city population growth, as determined by the size of neighboring city populations discounted by geographical distances, the average per capita income of the city and per capita incomes of neighboring cities discounted by geographical distances. To test these assumptions, we calculate two measures of market potential, one which is essentially an inverse distance weighted sum of neighboring areas’ population and the other with average per capita income similarly discounted by distance.<sup>8</sup> We exclude a city’s own population and per capita income from its market potential calculation.

> 8 Market potential of city i is defined as

31
educating girls at higher levels is considered pointless, is due to the high levels of dowry in South Asian cultures. While this is a singularly un-Islamic practice, it is widely prevalent in Bangladesh and from recent accounts, also on the rise (World Bank, 2007). Thus, a more educated girl requires a groom who is even more educated and accomplished, thus inflating the amount of dowry her family would have to pay for the marriage.

In other ways too, women and girls are expected to behave in “appropriate” ways. One of the key attributes of a “good Bengali girl” for instance is the notion of “shyness” or “lojja”, where girls seldom speak their minds before elders and outsiders. In many conservative parts of South Asia, higher education is considered to liberate girls so much that they would have problems “adjusting” to their marital home. During focus group discussions we have found that rural populations perceive the impact of girls’ education most strongly in the ability of the latter to “speak” and to shed inhibitions. This is variously considered one of the positive or negative effects of education, depending on who we are talking to (Das and Hossain, forthcoming; World Bank, 2007).

Norms of seclusion or “purdah” in Bangladesh is yet another reason often cited for low demand for girls’ education. Thus, pubescent girls traveling to neighboring villages to seek secondary education is considered unacceptable and a risk to the chastity and purity of girls, who may then have problems finding suitable, respectable husbands. Other research has recently shown that not only is the “purdah mentality” prevalent in non-Islamic populations of South Asia (Lateef, 1991; Das, 2004), but even when it is practiced, it is so amorphous a concept and so tied up with acceptable notions of safety and security than when appropriate conditions exist, it is a scant constraint on girls' 8
period, a fraction of the old capital depreciates. Population and productivity growth are exogenous drivers of the model’s dynamics. The former is taken from the projections of the United Nations Population Division, where labor force growth corresponds to growth of the population aged 15-64 years. Productivity growth is modeled as exogenous and factor neutral for agricultural sectors and labor augmenting for industrial and service sectors. Productivity of energy follows an autonomous energy efficiency improvement (AEEI) path so that there is no endogenous technological change in the model. To ensure equilibrium in the model, three sets of market clearing conditions are met. First, total production of each commodity equals the sum of domestic consumption and export so that the goods and services markets clear. Second, total investment equals total saving, where savings are composed of private (household) savings, public (government) savings and exogenously fixed foreign savings. Third, factor markets clear, which implies full employment.

The model is calibrated with GTAP version 7.0 data. However, not all data needed for the model are available in the GTAP database. For example, biofuels are not a proper sector in the original GTAP 7.0; therefore, we modified the database in a way that allowed us to introduce biofuels sectors in our CGE model. For this purpose, we collected detailed information on production, consumption and trade, a total of seven new biofuel sectors, which have been created by splitting existing GTAP sectors. The land data are also based on the GTAP 7.0 database.

# **3. Impacts on Intensities and Absolute Emissions**

The study simulates various level of carbon tax ranging from US$10 to US$250 per ton of carbon dioxide. Per ton of carbon, the range corresponds to US$37 to US$917, the upper range is indeed a very high level of carbon tax. This section presents key results from simulations of the model.

# **3.1 Impacts on emission intensities**

Figure 5 illustrates that emission intensities of Annex I and Non-Annex I countries are moving closer as rate of carbon tax increases. Under the business as usual scenario the average emission intensity of the Annex countries would be 13.2 tCO2 per capita in 2030. This is 3.62 times as high as the average emission intensity of Non-Annex I countries in the same year. A uniform carbon tax of US$10/tCO2, US$50/tCO2 and US$ 100/tCO2 in the Annex I countries 8
|**Table 6: Heterogeneity of Experim**|**entQuestionnaire Treat**<br>|**ment Impact on Pooled Binary **<br>_Sample Comparison: E_<br>|**Poverty Proxies by Selected H**<br>_xperiment vs. Standard_<br>|**ousehold Attributes**<br>|
|---|---|---|---|---|
||**All**|**Food**|**Non-Food**|**Shocks**|
||(1)|(2)|(3)|(4)|
|Eit*Fl Hd|-0.011|-0.009|-0.017|-0.007|
|xpermenemae ea †|(0.011)|(0.020)|(0.014)|(0.011)|
|i*d A|0.001**|0.001***|0.000|-0.000|
|ExpermentHea ge (Years)|(0.000)|(0.000)|(0.000)|(0.000)|
|Experiment*Highest HH Education:|0.020**|0.011|0.022|0.022**|
|No Education †|(0.010)|(0.019)|(0.015)|(0.011)|
|Experiment*Highest HH Education:|-0.000|-0.031|-0.012|0.039***|
|Primary †|(0.012)|(0.023)|(0.020)|(0.014)|
|Exeriment*Household Size|0.010***|0.012**|0.020***|0.001|
|p|(0.003)|(0.005)|(0.005)|(0.004)|
|Ei*# f Dd|-0.009*|-0.009|-0.019***|-0.000|
|xperment o epenents|(0.005)|(0.008)|(0.007)|(0.005)|
|*|-0.022**|-0.052***|-0.032**|0.004|
|ExperimentRural †|(0.010)|(0.017)|(0.016)|(0.016)|
|**Observations**<br>Note: † indicates a binary variable. The r|197,443<br>eported coefficients and|62,060<br>standard errors are marginal eff|70,504<br>ects associated with the interac|64,879<br>tions between the selected|

Note: † indicates a binary variable. The reported coefficients and standard errors are marginal effects associated with the interactions between the selected household attributes and the binary variable identifying whether a household was subject to the experiment questionnaire treatment. The estimations are based on Logit regressions, using the pooled data at the household level for all 70 binary poverty proxies from food, non-food and shocks modules. The control variables, as specified in Equation 1, are included but not reported. The regressions are weighted and take into account clustering and stratification. The results are robust to varying the set of control variables and/or including interviewer fixed effects. ***/**/* indicate statistical significance at the 1/5/10 percent level.

22
We examine these dynamics in Ho Chi Minh City, using high‐resolution local‐scale flood maps designed specifically for HCMC (Lasage et al. 2014) and proxy for poverty using the spatial location of potential slums from the Platform for Urban Management and Analysis (PUMA) data set (World Bank 2015). The PUMA data set also has information on locations of urban expansion from 2000 to 2012. We therefore examine exposure to flooding in all three locations – urban areas as a whole, potential slum locations, and areas of urban expansion. The results we present below are for all districts in HCMC; results for only districts with slum areas are similar and thus not reported.

We find that a relatively high percentage of the potential slum areas are exposed to floods, ranging from 68.9% (for a 10‐year return period) up to 83.3% (for a 1000‐year return period). When considering all urban areas of HCMC, exposure to flooding is lower: 63% (for a 10‐year return period) up to 68.3% (for a 1000‐year return period). A sea level rise of 30 cm increases the extent of flooded areas the most in slum areas and for a low‐probability but recurrent flood (10‐year flood). For a 10‐year flood and looking only within slum areas, we find an increase in exposure of 15 percentage‐points due to sea level rise, compared to a difference of 5.7 percentage‐points when looking at the entire urban area of HCMC. These results, as presented in Figure 1, suggest slum areas to be more exposed to floods than non‐slum areas.<sup>4</sup> Due to cognitive biases, it can be hypothesized that flood risk from frequent events (like the 10‐year return period event) are more likely to be remembered than a rare event (like the 1000‐year return period event) and thus more likely to be included in land values. If this logic holds, it is likely that potential slum areas should exhibit a higher exposure than other areas for frequent events. However, in our analysis we find the opposite: that the difference between slum and non‐slum increases as the return period event gets rarer.

When looking at the areas of urban expansion we find that a large share, 72.2% under a flooding event with a 10‐year year return period up to 74.4% in case of a 1000‐year return period, of these areas is located in areas prone to flooding (Figure 1).

> 4 Disaggregated results per district can be found in Appendix 2. Results using depth as an indicator for flooding is also presented in Appendix 2.

18
**Table 4: Impact of Internet job search among the unemployed on employability within 5 years**

|VARIABLES|(1)<br>Employed|(2)<br>Employed|(3)<br>Employed|(4)<br>Employed|
|---|---|---|---|---|
|Internet job search|0.109**|0.103*|0.103*|0.103*|
||[0.054]|[0.055]|[0.055]|[0.053]|
|Internet access at home|0.008|0.007|0.007|0.000|
||[0.028]|[0.028]|[0.029]|[0.029]|
|Male|0.033*|0.041**|0.041**|0.038*|
||[0.020]|[0.021]|[0.021]|[0.021]|
|Married|-0.008|-0.000|-0.001|0.006|
||[0.016]|[0.015]|[0.015]|[0.016]|
|Age||-0.002**|-0.002**|-0.002**|
|||[0.001]|[0.001]|[0.001]|
|Primary or preparatory education||-0.076*|-0.076*|-0.083*|
|||[0.045]|[0.044]|[0.044]|
|Secondary education||-0.028|-0.028|-0.025|
|||[0.045]|[0.044]|[0.043]|
|Above secondary education||-0.025|-0.025|-0.022|
|||[0.045]|[0.045]|[0.044]|
|Rural|||0.002|-0.035|
||||[0.013]|[0.039]|
|Observations|928|928|928|928|
|R-squared|0.034|0.041|0.041|0.052|
|Region dummies|NO|NO|NO|YES|
|Dependent variable mean|0.038|0.038|0.038|0.038|

Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1 _Notes._ Each cell represents a coefficient estimate using OLS regression. The analysis is restricted to unemployed individuals in 2012 that were also interviewed in 2018, aged 15 to 64 years old in 2012. Unemployed individuals are defined according to the market definition of labor force participation. The dependent variable is a dummy variable indicator equal to one if the individual is employed within 5 years after this unemployment spell in 2012 (between the years 2013 and 2017, included). The control variables refer to the year 2012 and they include: a dummy variable equal one for individuals who report that finding a job is one of the purposes of Internet use, a dummy variable for having Internet access at home, age, a dummy variable for being male, a dummy variable for being married, three dummies for the individual’s educational attainment (primary or preparatory education, secondary education either general or vocational and above secondary education either post-secondary institute or university and above, and the reference category is no educational degree either illiterate individuals or literate individuals without any diploma), a dummy variable for rural areas as well as dummies for the various geographical regions included in the last column (Greater Cairo, Alexandria, and Suez cities, Urban Lower Egypt, Urban Upper Egypt, Rural Lower Egypt, and Rural Upper Egypt). The dependent variable mean is reported in the last row.

29