rafmacalaba/datause-encoder-data huggingface dataset:
16

**Table 9: Share of public expenditures in total environmental expenditures, selected industrial countries, mid-1980's**

|-(percent)-|
|---|
|**Autrbia**<br>50|
|**Fimahnd**<br>45|
|Norway<br>67|
|Sweden<br>72|
|Gernany<br>51|
|<br>Japan<br>94|
|<br>US<br>41|

Source: OECD (1990).

The exact amount of actual subsidies in this expenditure is difficult to assess. In countries where utilities, for example, are owned and run by governments their expenditures on pollution control would count on the public side, whereas the opposite would hold in countries with privately run utilities. The actual existence of subsidies would depend on whether these entities operate under market principles and how the costs are included in prices to users etc. General subsidies are included in the data only to the extent that governments classify them as "environment-related".

Depending on how potential subsidies are implemented, they may carry the risk of reducing the incentives for internalization of pollution-control expenditures and the incentives to innovate. This hypothesis does not seem to hold for Japan. In Japan, much of the public money was spent on supporting joint research projects with industry to develop new processes and technologies (Fukasaku, 1992). The measures appeared to have reduced pollution drastically (Annex Table 3) as Japan now has one of the lowest levels of major pollutants among the industrial countries. But despite the fact that the government bore the bulk of the environmental expenditures, Japan lost markets in most of the environmentally sensitive industries during the past two decades.

Despite the difficulties in extracting subsidies from the data, it suggests that private costs of abatement have been highest in the United States. Lower private costs in Europe than in the United States can reflect different laws, differences in absorptive capacities, or the higher share of public expenditures in total expenditure. For Japan, complete industry-level data for private
# **A Supplemental material [online version only]**

## Table A1: Variable descriptions

|Variable|Description|Source|
|---|---|---|
|_Panel A: Delegati_|_on-age-education {d, e, a} level variables_||
|Age group<br>(dummies)|Dummy variables indicating which age group _a_ individ-<br>uals in a given _{d, e, a}_ cell belong to. There are 10 dif-<br>ferent groups, notably: (i) 15-19 years, (ii) 20-24 years,<br>(iii) 25-29 years, (iv) 30-34 years, (v) 35-39 years, (vi) 40-<br>44 years, (vii) 45-49 years, (viii) 50-54 years, (ix) 55-59<br>years, and (x) 60 years or more.|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Education group<br>(dummies)|Dummy variables indicating which level of education _e_<br>individuals in a given_{d, e, a}_cell have completed. There<br>are 3 diferent groups, notably: (i) primary or below, (ii)<br>secondary, and (iii) tertiary.|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Internet access<br>(rate)|Share of men in the _{d, e, a}_ cell that used the internet<br>in the last 3 months|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Married<br>(rate)|Share of men in the _{d, e, a}_ cell that are married|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Number of<br>Daesh recruits|Number of individuals in the _{d, e, a}_ cell who joined<br>Daesh in Syria or Iraq between 2013 and 2014.<br>Note<br>that the personnel records only present information on<br>male recruits.|Daesh personnel records|
|Population|Number of men in the _{d, e, a}_ cell|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Radio ownership<br>(rate)|Share of men in the _{d, e, a}_ cell that own a radio|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|TV ownership<br>(rate)|Share of men in the _{d, e, a}_ cell that own a TV|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|Unemployment<br>(rate)|Share of men in the _{d, e, a}_ cell that are unemployed|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|
|_Panel B: Delegati_<br>Area|_on d level variables_<br>Geographical area of each delegation _d_ in km<sup>2</sup>.<br>Douz<br>North and Douz Sud are combined into one administra-<br>tive division.|Author’s calculations based on<br>HDX-OCHA<sup>[</sup><sup>_b_]</sup>|
|Convicted<br>terrorists|Number of individuals indicted for terrorist crimes com-<br>mitted between 2004 and 2018 from delegation _d_. The<br>records include their addresses, which allows us to geolo-<br>cate them.|National List of Persons, Or-<br>ganizations<br>and<br>Entities<br>As-<br>sociated with Terrorist Crime<br>(CNLCT, 2021)<sup>[</sup><sup>_c_]</sup>|
|Emigration<br>(rate)|Share of men from delegation _d_ that emigrated abroad.|The 2014 Tunisia Census of Pop-<br>ulation and Housing (INS)<sup>[</sup><sup>_a_]</sup>|

24
represent the percentage of firms out of the year‟s population that make it to the following period. We show results for the 2006 cohort, which offers three year-on-year transitions. This way of presenting the data would reveal the “learning” process, whereby firms that survive up to a certain date have a higher chance of hanging on in the future, so the yearly rates should increase towards 100 percent.

Learning is evident in all the six types of units, even though they have different starting points and slopes. FPDs have the lowest starting point, as they are at the finest level of detail in the export space, where churning is at its highest, but after three transitions their resilience rate almost doubles, from 32 to 63 percent. Destinations start very high and reach 100 percent by the third year, which seems to show that there is much less geographical exploration, and firms that make it to a foreign market will do their best to remain there.

**Figure 8:** Yearly survival rates for firms, products, destinations, firm-products, firm-destinations, destinationproducts and firm-product-destinations in Laos

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>2007 2008 2009<br>firms products destinations firm-prod<br>firm-dest dest-prod FPD<br><!-- End of picture text --> _Source:_ Authors‟ calculation based on Lao PDR Customs data In order to put this phenomenon into perspective, we compare Lao rates with those available for Senegal, Tanzania, Mali and Malawi (comparable data is from Cadot et al. (2010)). Figure 9 presents the evolution of yearly survival rates for the five countries, synchronized despite the different years which the data represent (and thus ignoring any year-specific effects such as global recessions, etc.). Senegal and Tanzania have longer data spans – the Senegal data captures seven transitions and displays an almost asymptotic trend of improvement which stabilizes around 80-90 percent.

_16_ _Exporting from a small landlocked economy_
TANZANIA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# b. Water

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>piped water standposts<br>wells/boreholes surface water<br>% of population<br><!-- End of picture text --> _Source:_ Banerjee and others 2009.

Assuming that households can afford to spend 5 percent of their budget on power and 5 percent on water, subsistence consumption bundles of water and power at cost recovery prices would be affordable for the vast majority of Tanzanian households (figure 19). For power, with a cost recovery tariff of $0.16 per kilowatt hour for power and a subsistence consumption of 25 kilowatt hours per month (enough to power two 100 watt light bulbs for four hours per day), the monthly power bill would be $4. For water, with a cost recovery tariff of $0.68 and a subsistence consumption of 4 cubic meters per month (25 liters per person per day for a family of five), the monthly water bill would be $4. Based on analysis of household income distribution, monthly utility bills of $4 would be affordable to all but the poorest 20 percent of Tanzania’s population. Given that access to these services is currently confined to the more affluent 10 percent of the population, affordability is unlikely to become a real issue for some time to come until service access is much more widely available than at present.

Even if all inefficiencies could have been eliminated, an infrastructure funding gap of $0.7 billion per year (5 percent of GDP) remained as of 2006 (table 12). Looking across sectors, the overall funding gap was mainly associated with water (55 percent of total) and to a lesser extent power (30 percent of total).

Tanzania could further reduce the infrastructure funding gap by adopting lower cost technologies and seeking cross-border finance for regional investments. Adoption of more appropriate technologies for the paving of roads could save $220 million annually. In the power sector, where $150 million per year of spending needs relate to power export projects, cross-border finance could be sought from neighboring countries that could benefit from importing lower cost power from Tanzania. Overall, if these measures were adopted, the infrastructure funding gap could be reduced by $0.3 billion per year, lowering it to only $0.4 billion per year.

Alternatively, Tanzania could spread the requisite spending over a longer time period, although this would entail delaying the achievement of the targets. If Tanzania were unable to increase current levels of infrastructure spending, address the associated inefficiencies, or take any of the other proposed cost 31
<!-- Start of picture text --> 25,000 +<br>20,000 +<br>@ Uganda<br>15,000 @ Tanzania.<br>| Rwanda<br>10,000 @ Kenya<br>@ Burundi<br>~ |<br>Maize Rice Bananas Cassava<br><!-- End of picture text -->

<!-- Start of picture text --> 600 -<br>5007<br>400 + - @ Uganda<br>@ Tanzania‘<br>300 +<br>| Rwanda<br>200 +— y Kenya<br>@ Burundi<br>"| o =I<br>Coffee Tea Cotton Tobacco<br><!-- End of picture text -->

<!-- Start of picture text --> 200<br>= Burundi<br>180 @ Kenya<br>160 TM Rwanda<br>Africa average --}---~ m Uganda<br>140 ®@ Tanzania<br>120<br>7]<br>= 100<br>= - -<br>2av 80<br>60<br>40<br>Maize Rice Cassava Bananas Coffee Tea Tobacco<br><!-- End of picture text -->
minus premium payments) associated with the predicted IBLI uptake volume, converted into TLU units at prevailing livestock prices, NI.<sup>16</sup> We therefore estimate the aggregate effect of IBLI on SWB as: The point estimate _β_<sup>ˆ ̃</sup> in equation (5) reflects the SWB benefit of a unit of free IBLI with ̃ no indemnity payment. Likewise, the coefficient estimate _σ_<sup>ˆ</sup> measures the SWB loss due to a unit of free IBLI that has expired without payout. Note, however, that _β_<sup>ˆ ̃</sup> , _σ_ ̃<sup>ˆ</sup> and _θ_<sup>ˆ ̃</sup> measure effects on SWB in log-odds scales while SWB is measured in ordinal Likert scale. It is necessary to harmonize the units in which these coefficients and SWB are measured before one can calculate the overall effect of IBLI on SWB. We use the fact that the logistic and Normal distributions are similar, except at the tails of the distribution, to convert the coefficients from log-odds units to Normal equivalent deviates. The effects measured in logodds and their corresponding standard errors can be converted to approximate effects in Normal equivalent deviates by dividing by the standard deviation of the logistic distribution _s_ = _π/√_ 3 (Hasselblad & Hedges, 1995; Chinn, 2000).

Given that during R2 and R3 there were no indemnity payments but respondents paid for IBLI, our estimates provide a lower bound, reflecting the SWB associated with insurance coverage in the absence of any payout, i.e., a period in which insurance represents an unambiguous financial loss. A finding that ∆ _SWB_<sup>�</sup> _ivt >_ 0 _|NIivt <_ 0 would therefore represent a strong finding with respect to the welfare effects of index insurance in this setting.<sup>17</sup>

# **6 SWB and vignette correction**

Subjective measures of welfare are becoming increasingly popular but pose methodological challenges (Krueger & Schkade, 2008; Ravallion, 2012). Respondents may have different reference points when answering a subjective question, making interpersonal comparisons

> 16 _NI_ =<sup>Indemnity</sup><sup><u>per</u></sup> Price<sup>TLU</sup> per<sup>_−_Premium</sup> TLU<sup><u>per</u>TLU</sup> _× TLUI_<sup>�</sup> is the TLU equivalent wealth gained or lost due to IBLI purchase.

> 17Estimates for ∆ _SWB_ � are obtained by evaluating equation (5) at the average TLUs insured and NI. The price per TLU is obtained by weighting livestock prices from Haro Bake livestock market (the largest livestock market in Borana zone) with the TLU conversion units of each species (Table A1).

15
share of profitable firms in the population varies from around 60% in Mexico to 85% in Senegal. Most countries feature statutory CIT rates between 25% and 30%, but Albania and Montenegro apply low statutory rates of 15% and 9% respectively.

# **2.2 Methodology**

**Objective** We aim to compute ETRs that are comparable across countries with different tax systems and tax reporting requirements. Thus we select concepts from the CIT returns that are consistently used across countries. To the best of our ability we aim to distinguish variables that measure ‘standard’ deductions—allowed in all countries and for all firms, and reflecting economic costs—and variables measuring tax expenditures, which can be country and firm-specific.

**Accounting concepts** We consider the concepts that can be consistently measured across countries’ tax returns and the accounting relations between them in Figure 1. Total revenue is composed of sales plus other incomes (e.g. non-operating incomes, rents, interests). The deductions that firms typically subtract from their revenue to calculate their profits include the cost of material inputs, labor, and capital costs, as well as financial costs, and depreciation of capital. We call the difference between revenues and costs “net profit” (or loss). This concept is not always directly reported in tax returns as a line item, but can always be reconstructed. We consider that net profit is the best proxy for economic profit that can be constructed from tax return data.

Net profit differs from taxable profit as the latter concept excludes tax-exempt incomes and reintegrates non-tax-deductible costs. After this, investment incentives, capital allowances, and other deductions are applied to obtain the gross tax base, and loss carry-forward is accounted for to obtain the net tax base. Multiplying the net tax base by the statutory tax rate yields the gross tax liability. We validate the data cleaning and variable construction process by ensuring that when we divide the gross tax liability by the net tax base we obtain the statutory tax rate.

**Computing Effective Tax Rates** We define a firm’s ETR as the net tax liability divided by the net profit. The numerator, net tax liability, is the tax due net of any after-tax deductions and credits, but ignoring advanced payments and withholding of taxes already paid. Hence, any deduction that is subtracted either from the tax base (proxied in our methodology by net profit) or from the gross

6
70. In the Phase III of ICP, the number of pupils were introduced to be a furth_r dimension of output in the education sector, the result **s** howed that the low-income countries have higher quantity indexes because they have larger class sizes than the higher-income countries. Altemative approach is to take educational achievement as the indicator of output. The results of intemational test for several subjects across countries were used in some international comparisons (Kravis, et al, 1982, Pilat, 1993). It uill be worthwhile to use this approach to make an altenative assumpton on the productivity differential when the data for Chinese studente achievement are available in the future. 71. In order to test the sensitivity of the final resuts to the diiftf assumption on the international productivity differentiaL itwas assumed that the productivity of a Chinese doctor is equal to one half of that of an American doctor because American doctors have better facilities and asssnce, and longer training time, as well as other possible fictors influencing their efficiency (Kravis, et aL 1982). By this assumption coupled with the adjustmnent made in the two categories, the purchasing power parity for GDP changes from 0.8709 to 1.1805 and dollar GDP per capita of China in 1986 decreases from 1$1,044 to I$770. Using this new estimate for 1986, the 1991 China's per capita GDP in 1991 dolgars changes from I$1,663 to I$1 ,227. It is easy to see that if the same productivity adjustments were made for the education and the government sectors, the PPP over GDP woudd be much higher and real income level would be much lower. The results of applying alteatively the esimates of PPP before adjustment and after adjustment to China's per capita GDP in local currency (World Tables, 1993) for 1986 and 1991 are sununaized below in Table 1 1: Table I 1: DOLLAR PER CAP1TA GDP ESTIMATES FOR CHINA (IS)

||After ad=ustme|Before adjustment|
|---|---|---|
|1986 _|770|1.044|
|1991|1,227|1,663|

31
<!-- Start of picture text --> 0.43<br>0.41<br>0.39 :<br>0.37<br>0.35<br>433 i _$—<$<$<$<_—_—<br>ie,<br>0.27<br>0.25<br>circa 2003 circa 2007 circa 2009 circa 2011<br><!-- End of picture text --> —Tajikistan ———Moldova ———Georgia ———Armenia ———Kyrgyzstan
# **Indigenous Primary School Results**

Since indigenous students tend to be geographically isolated, SEP identifies indigenous education as a modality separate from rural or urban education. But the difficulty of forming an effective control group against which to compare indigenous students makes evaluation of indigenous education difficult. Every indigenous school but one had a p-score of over 0.9. Those scores are high in part because nearly all indigenous schools receive CONAFE support, and in part because indigenous schools are extremely disadvantaged. Because the schools with p-scores over 0.99 represent the highest tail end of disadvantage, it is likely that indigenous schools in this group have very different characteristics than do non-indigenous schools in this group. Thus, this section focuses on the indigenous school group with p-scores between 0.9 and 0.99. We use nonindigenous and non-CONAFE schools with p-scores between 0.9 and 0.99 as control groups. Data from 1998 in the 0.9 to 0.99 p-score range included only 55 indigenous students, so we excluded 1998 data from this analysis.

Indigenous students effectively learned much of the material they studied in primary school. Indigenous students with CONAFE support rapidly improved their test performance over the sample period. Indigenous global scores increased an average of 19 points per year, with an annual average math score increase of 26.8 points and an average Spanish score increase of 12.3 points. From 1999 to 2002, indigenous scores increased from 398 to 454 points in global scores, from 385 to 464 points in math scores, and from 407 to 447 points in Spanish scores (Table 7). Again, it is likely that much of this increase is attributable to cohort’s following of students through different grades in different years, so the appropriate conclusion to reach from this data is that indigenous students learn a significant amount of new material in each year of education.

27
We also estimate Probit models for every natural hazard separately. Figure 3 presents the average marginal effects of each natural hazard of these estimations. Positive marginal effects indicate a positive relationship between household poverty and the hazard of the event. In Brazil, earthquakes and droughts show positive effects, _ceteris paribus_ . In the Dominican Republic, the number of earthquakes and landslides' potential damage positively correlate to poverty. The number of landslides and the earthquake's potential damage in Ecuador positively correlate to poverty. The droughts and the number of landslides matter in El Salvador. In Honduras, mainly earthquakes are associated with household poverty. In Mexico, cyclones are the natural hazards associated with household poverty. In Peru, the number of landslides positively associates with poverty. Owing to the differences in natural hazard effects, below, when we present the results of vulnerability that come from the disasters separately models, we are going to present the results from the models that only include earthquakes in Honduras, cyclones in Mexico, floods in Peru, droughts in Brazil and El Salvador, and landslides in the Dominican Republic and Ecuador. Additionally, it is important to highlight that we do not count on the potential compounding effects of these hazards by incorporating their interactions into the models (for example, including the interaction between floods and landslides). This is an aspect that remains to be studied.

**Figure 3.** Average marginal effects from the models with natural hazards separately.

<!-- Start of picture text --> TH index Floods Number of Floods TH index Landslides Number of Landslides<br>TH index Earthquakes Number of Earthquakes TH index Cyclones Number of Cyclones<br>TH index Droughts Number of Droughts<br>B R AZIL M E XIC O PE R U DOM INIC AN E L HONDUR AS E C UADOR<br>R E P. SALVADOR<br>0.027<br>0.026***<br>0.024*<br>0.019<br>0.015** 0.015<br>0.014 0.014<br>0.011<br>0.009<br>0.008*** 0.008<br>0.005** 0.007** 0.007<br>0.004<br>0.003 0.003 0.003 0.003<br>0.002** 0.002<br>0.001** 0.001 0.001<br>-0.001<br>-0.002 -0.002<br>-0.003 -0.003<br>-0.004<br>-0.005**<br>-0.008 -0.006** -0.008<br>-0.009**<br>-0.010***<br>-0.011***<br>-0.013*** -0.013** -0.015<br><!-- End of picture text --> Note: *** p<0.01, ** p<0.05, * p<0.1. Each family color bar corresponds to a specification that includes one type of natural hazard controlled by exposure, coping mechanisms, and region. Source: authors' calculations based on ThinkHazard!, EM-DAT, and SEDLAC database.

14
subsidized regime and persons from rural areas who are affiliated with the contributory regime receive the third best care. Persons from rural areas who are affiliated with the subsidized regime or who are not insured at all receive the worst care. One of the greatest difficulties mentioned by the users of subsidized EPSs is the frequent changes in service providers contracted by their EPSs, which leads to frequent problems in treatment continuity.

**Figure 9: Ranking in the Quality of HIV/AIDS Care in Colombia According to the Insurance Modalities of Patients and Area of Residence**

<!-- Start of picture text --> 1<br>RV<br>Big cities<br>2<br>RC – U<br>3<br>3<br>RS – U<br>RC– R<br>4<br>4<br>RS – R RV – U&R<br><!-- End of picture text -->

# **Legend:**

RV Big cities: “Régimen de aseguramiento vinculado” for people living with HIV in big cities; RC-U: “Régimen contributivo” in urbana reas; RS-U: “Régimen subsidiado” in urbana reas; RC-R: “Régimen contributivo” in rural areas; ” RS-R: “Régimen subsidiado” in rural areas; RV-U & RS-U: “Régimen vinculado” in urbana reas (excluding big cities) and rural areas.

# **Source:** Técnicas de Salud 2011.

In the specific case of diagnostics services (HIV tests), there are social and gender inequities in Colombia. The 2010 ENDS survey<sup>47</sup> shows that a substantial percentage of women in certain age groups are lacking in knowledge about where to take the HIV test. Only 19% of women below the age of 19 and 28% of women above the age of 40 have taken the test. Furthermore, within both of these age groups, women living in

> 47 MPS (February 2011) Encuesta Nacional de Demografía y Salud 2010, Bogotá: MPS.

46