rafmacalaba/datause-encoder-data huggingface dataset:
# **Box 2: Small Island States and Subsidy Agreements with Air Operators**

Small island states with intercontinental capacity airports have been engaged in negotiations with airline operators to develop some type of bilateral commitment in support of air-transport based tourism.

The most common arrangement is for countries to pay airlines a “financial incentive” to fly into a country. This practice has been accepted since the mid 1990’s even in popular Caribbean destinations like The Bahamas. The Caribbean Hotel and Tourism Association conducted a study in 2009 assessing the use of airline subsidies. Of 35 countries queried, 9 responded, and reported a total of US$ 50 million being paid to airlines.

Countries take different approaches as how to negotiate these agreements. In the case of St. Kitts, the Minister of Tourism and International Transport (who is also the Minister of State) personally engages in the negotiations. In St. Lucia, the agreements are negotiated by the CEO of the Tourism Board, but become effective only upon approval by the Cabinet. Generally, the agreements are in the care of either tourism boards or authorities, with (in some cases) involvement of the finance ministries. In St. Kitts, the maximum limit is US$ 50 per passenger, though mostly per passenger rates are kept far below this level. The arrangements are of two types: Marketing Support Agreements (MSA) and Minimum Revenue Guarantees (MRGs).

When engaging in _Marketing Support Agreement_ (MSA) a country makes funds available to an airline for marketing purposes. In some cases, the country may use the same funds for marketing, with the airline’s permission. These agreements are generally managed with each country’s tourism board or authority, and typically amount to US$ 750,000 per year per airline, though this can vary. The funds do not have to be guaranteed and locked up in some form of an escrow. There are times when even though a fee has been determined, neither party feels obliged to exercise the agreement.

MSAs are usually signed between the small islands and their European counterparts, as a way of avoiding direct subsidies, which the European Union does not permit. European carriers (mainly British Airways and Virgin Atlantic) receive marketing support.

_Minimum Revenue Guarantees_ (MRG) are structured, confidential agreements between a government and an airline, often expressed in an Memorandum of Understanding (MOU), guaranteeing a specified load factor or volume over a given period. The agreements can act like a _future_ contract: If targets are not met, governments have to pay up (i.e. “take delivery”) of the unwanted capacity being provided. One complication with MRGs is that funds have to be locked up. In other words, the agreed upon amount has to be kept in escrow until the contract terminates regardless of whether the agreement ever triggers a payment. In addition, airlines have chosen to add compensation for fuel increases into the agreements, which can make MRGs even more unpredictable and open-ended. This was particularly difficult for the small islands during the fuel spike in 2007/8.

26
9 series available from Agricultural Wazs in India (AWI). However, these are currently only available up to 1986/7, so if we used that data as an independent variable in the model we would have missing values to deal with for the last two years, and we would have to forecast the series for 1988/89, prior to forecasting attendances. This creates complications, and it is probably better to give our model a reduced form interpretation, in which an equation for the agricultural wage rate has been "solved out". Monthly agricultural wage data for Maharashtra over the last few years ara available from a different source (though their co.nparability with AWI data is unclear). These are reported in Figure 1 and are used in section 4. They do no suggest that the drop in EGS attendances after mid-1988 could piausibly be attributed to an increase in the agricultural wage rate, which remained fairly constant. Our later inferences based on the post-sample forecasts are likely to be robust to the omission of agricultural wages from our cmdel.

Our model of EGS labor attendances can be written as where Lt is the recorded labor attendance on EGS for month t, Wt is the real EGS wage rate for the year including month t, Qt is the output of foodgrains for that crop year, Mjs are the monthly dummy variables fcr j-l,...11 (j-l represents July), t is a time trend, REj and RDj are the month-specific excess and deficit rainfall variables (as defined above) respectively for j-1,.. .12, and Et is an i.i.d. error term, Lt, Wt. and Qt are measured in natural logarithms. The above model was estimated by ordinary least squares over the period July 1975 to June 1988.12 13 After pruning the model to eliminate
(codes 111, 121, 21, 22, 42 and 53), supplemented with unfinished textile products in division 65 of the SITC classification.

These various measures have advantages and disadvantages, which is the reason why we chose to employ a broader set of indicators rather than focusing on a single one. In particular, measures based on value added trade are more precise as they allow to directly deal with the problem of double counting in gross trade data and account for the input‐output relationships in production. WIOD data also have the advantage of covering trade in both goods and services. The data on (goods) trade in parts and components are available for a large set of countries and years,<sup>13</sup> thus allowing us to rely on a broader panel which includes many more developing countries than WIOD. The correlation between gross and value‐added trade variables for the sub‐sample of WIOD countries and years is however large and ranges between 0.75 and 0.88 (see first column of Table 1).

# 3. Depth of trade agreements and GVC integration

In this section, we present the empirical strategy and the analysis of the impact of deep agreements on value added trade. We also investigate whether the impact of deep trade agreements is heterogeneous for industries with high and low value added incorporated in their production.

## a. Empirical strategy

To assess the impact of deep agreements on GVC integration, we estimate the following structural gravity equation for the time period 1995‐2011 with a Pseudo‐Poisson Maximum Likelihood (PPML) estimator. The regression equation is: where ����������� is a measure of value added trade between country � and � at time � and is captured either by different components of value added trade or by gross trade in intermediates; �������� is one of the three measures of the depth of PTAs and are defined in section 2a above. ������ controls for the presence of a Bilateral Investment Treaty between country � and country

> 13 Regressions using gross trade data are estimated for 184 countries between 1995‐2014.

10
Table 8: Regression results from baseline specification for post‐crisis year, i.e. post‐2008 (continued)

||(13)|(14)|(15)|(16)|(17)|(18)|(19)|(20)|(21)|(21)|(22)|(24)|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐ TFPQ Hsie|<br>h and Klenow|<br>using countr|<br>y specific IO‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐ TFP|<br>Q Hsieh and K|<br>lenow using|<br>EU‐wide IO‐‐‐‐|<br>‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Overallt‐2|‐1.855**||||||‐3.575*||||||
||(0.025)||||||(0.060)||||||
|Entryt‐2||‐0.039|||‐0.341|‐0.392||1.057|||1.165|1.037|
|||(0.944)|||(0.619)|(0.566)||(0.429)|||(0.452)|(0.500)|
|Conductt‐2|||‐1.125***||‐1.219***|‐1.192***|||‐2.463***||‐2.393**|‐2.351**|
||||(0.001)||(0.002)|(0.003)|||(0.006)||(0.012)|(0.014)|
|FDI Overallt‐2||||0.708*||0.654*||||0.631||0.456|
|||||(0.052)||(0.072)||||(0.243)||(0.407)|
|FE firm|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE c‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|FE s‐y|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐YES‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐‐|
|Observations|4133795|4464783|4213758|4848904|4133795|4133511|4116776|4447758|4196738|4825498|4116776|4116492|
|R2A|0.816|0.808|0.817|0.819|0.816|0.816|0.867|0.862|0.868|0.872|0.867|0.867|
|R2W|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|RMSE|0.410|0.420|0.409|0.423|0.410|0.410|0.561|0.573|0.560|0.579|0.561|0.561|

Note: * p<0.10; ** p<0.05; *** p<0.01. The dependent variable is in log. Robust standard errors clustered at the country‐industry‐year level.

37
Finally, it is interesting to notice that there are no statistically significant effects neither on wages nor employment of informal wage earners. One hypothesis is that they are part of a segmented branch of the labor market in Brazil, shielded from changes in the labor market.

We have ignored corruption so far. Increased enforcement may indicate more frequent corruption opportunities (not stricter regulation), especially for firms breaking the law. One way to model this is as an increase in the costs of hiring either formal or informal workers. However, corruption by itself cannot explain out data since it would imply a decline in wages in both sectors, and possibly a decline in employment in both sectors (because of higher labor costs).

## **6.4.  Inequality**

The empirical findings suggest that income inequality declines when enforcement increases. The second panel of table 7 shows a compression in the distribution of wages both in the formal and in the informal (self-employment) sectors. Moreover, there is a decline in wage differentials between formal and self-employed workers. However, we also have to consider that the nonemployment rate increases with enforcement and this could lead to higher levels of city income inequality.

The results in table 8 show that changes in non-employment are not uniform across the distribution of income. We consider 6 groups of individuals, according to their position in the distribution of household per capita income: 0-10 percentile, 10-25, 25-50, 50-75, 75-90, and 90100. Enforcement affects non-employment mostly for the poorest individuals in society, so it is not surprising that, in spite of the decline in inequality, we observe small decreases in poverty. Table 8 also shows that losses in employment are large among females, low skilled workers, and young workers, which are especially vulnerable groups (see also Heckman and Pages, 2004).

# **7. Conclusion**

This paper studies the effect of an increase of enforcement of labor regulation on unemployment and inequality, using city level data from Brazil. We explore variation in the enforcement of labor market regulations using a new administrative dataset with information on the intensity of enforcement activity for all cities in Brazil. We interpret our findings in light of standard multisector models of the labor market in developing countries, which integrate formal and informal sectors and unemployment in a single framework.

27
# **<u>Table 3.2: DALYs for Different Health Endpoints</u>**

|**Health Effect**|**DALYs lost per**<br>**10,000 cases**|
|---|---|
|Mortalityadults|75,000|
|Mortalitychildren under 5|340,000|
|Chronic Bronchitis(adults)|22,000|
|Respiratoryhospital admissions|160|
|EmergencyRoom visits|45|
|Restricted activitydays(adults)|3|
|Lower respiratoryillness in children|65|
|Respiratorysymptoms(adults)|0.75|

Note: DALYs are calculated using a discount rate of 3% and full age weighting based on WHO tables.

Urban air particulate pollution is estimated to cause around 109,000 premature deaths among adults and 7,500 deaths among children under 5 annually. Adult mortality estimated above is consistent with Cropper et al’s (2012) estimate of the annual mortality associated with coal electricity generation in India (about 60,000 people calculated as about 650 deaths per year with 92 coal burning power plants in India). Electricity generation is responsible for a fraction of PM pollution analyzed in this study<sup>8</sup> . Estimated new cases of chronic bronchitis are about 48,000 per year. Annual hospitalizations due to pollution are estimated at close to 370 thousand and emergency room visits/outpatient hospitalizations at 7,300 thousand per year. Cases of less severe health impacts are also presented in the Table. In terms of annual DALYs lost mortality accounts for an estimated 60 percent, chronic bronchitis around 5 percent, restricted activity days (RADs) for 7 percent, and respiratory symptoms for 25 percent.

.

# **<u>Table 3.3: Estimated Health Impact of Urban Air Pollution</u>**

|**Health end-points**|**Total**<br>**Cases**|**Total**<br>**DALYs**|
|---|---|---|
|Prematuremortality adults|109,340|820,049|
|Mortalitychildren under 5|7,513|255,431|
|Chronic bronchitis|48,483|106,663|
|Hospital admissions|372,331|5,957|
|Emergencyroom visits/Outpatient hospital visits|7,303,897|32,868|
|Restricted activitydays|1,231,020,030|369,306|
|Lower respiratoryillness in children|16,255,360|105,660|
|Respiratorysymptoms|3,917,855,052|293,839|
|TOTAL||1,989,773|

_Source:_ Staff estimates.

The estimated annual cost of urban air pollution health effects is presented in Table 3.4. The cost of mortality is based on the human capital approach (HCA) as a lower bound and the value of statistical life (VSL) as an upper bound for adults and HCA for children (see Annex I).

> 8 Cropper et al (2012) analyses direct emissions from coal burning power plants and applied annual average intake PM2.5 fractions. Ambient concentrations of PM2.5 are analyzed in this report.

13
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27
negative effects on agricultural production (Warszawski et al., 2013). The list of climate scenarios is summarized in Table 2.

_Table 2 Climate change scenarios setting_

|**Scenario name**|**GHG concentration**|**GCM**|**CO2 concentration**|
|---|---|---|---|
|**Reference**|1980‐2010 average|None|Constant at 2005 level<br>(380ppm)|
|||HadGEM2‐ES||
|**RCP26**||IPSL‐CM5A‐LR||
|**.**<br>**(5 scenarios)**|450 ppm CO2‐eq|GFDL‐ESM2M|Increase at 450 ppm)|
|||MIROC‐ESM‐CHEM||
|||NorESM1‐M||
|||HadGEM2‐ES||
|**RCP 85**||IPSL‐CM5A‐LR||
|**.**<br>**(5 scenarios)**|1370 ppm CO2‐eq|GFDL‐ESM2M|Increase at 1370 ppm)|
|||MIROC‐ESM‐CHEM||
|||NorESM1‐M||
|**RCP 8.5***|1370 ppm CO2‐eq|HadGEM2‐ES|Constant at 2005 level<br>(380ppm)|

# **3.3. Mitigation policies**

The Integrated Assessment Modeling (IAM) community has put substantial efforts over the past years into quantifying the alternative SSP reference scenarios, as well as the stabilization scenarios corresponding to the RCPs’ radiative forcing levels. Unfortunately, this work has not yet been finalized and therefore we have decided to anchor the mitigation part of this report rather in the set of scenarios representing the backbone of the IPCC WGIII 5<sup>th</sup> Assessment Report on mitigation (IPCC, 2014b). These scenarios are the outcome of the Stanford Energy Modeling Forum Study 27 (EMF27, (Kriegler _et al._ , 2014). The scenarios are constructed along two dimensions: _i)_ climate policy assumption, and _ii)_ technology assumption. Out of the five alternative policy assumptions, we have decided to consider the only one which was compatible with the climate change impact scenario RCP2.6, and that was an idealized policy scenario where all regions and all sectors participate starting in the year 2010 and implement a uniform carbon price ensuring a cost‐effective achievement of atmospheric concentration of 450 ppm CO2e by 2100.

Similar to the uncertainty with respect to the climate change effects on agricultural markets, the level of the mitigation efforts required from the AFOLU sector and bioenergy substitution for attaining a specific level of climate stabilization is also subject to substantial uncertainties. The cost competitive mitigation efforts demanded from the land use sectors will depend on the technical and economic potential of AFOLU and bioenergy assumed in each integrated assessment model, on the assumptions about developments in other economic sectors, on societal preferences and also on new knowledge and technology which may become available over the long time horizon over which climate change mitigation is planned.

EMF27 explored in total nine technology assumptions and their effects on carbon price and bioenergy demand. The estimated AFOLU contribution to the mitigation efforts compatible with cost effective achievement of the 450 ppm target goes from ‐2% to 69% in the period 2010‐2030. A comparable range of uncertainty exists also for energy from biomass, which depending on the model is required to 13
respectively) but categorizing the neighborhoods by informality quartiles (using as proxy the proportion of individuals in the neighborhoods affiliated to the _Seguro Popular_ ). Neighborhoods located in the lowest quartile are neighborhoods with the lowest level of informality, while neighborhoods in the highest quartiles are neighborhoods with the greatest proportion of informal workers.

**Figure 4: Relationship between housing prices and private accessibility* by quartiles of informality****

<!-- Start of picture text --> Price and private accessibility by level of informality- 2009 Price and private accessibility by level of informality- 2013<br>2<br>1<br>1<br>2<br>3<br>4<br>4 3<br>0 500 1000 1500 2000 0 500 1000 1500 2000<br>Private accessibility Private accessibility<br>1400000 2000000<br>1300000<br>1500000<br>1200000<br>Median Price Median Price<br>1100000<br>1000000<br>1000000<br>900000 500000<br><!-- End of picture text --> Note: * Private accessibility measured by accessible employment opportunities in employment subcenters, discounted by the distance between each property and each subcenter. **Informality measured by the proportion of informal workers in the living in the neighborhood Figure 4 shows an inverse relationship between the proportion of formal workers in the neighborhood and accessibility to employment subcenters. Households located in neighborhoods with lower informality (quarter one) appreciate more than households located in neighborhoods with greater levels of informality (quarter four). On the other hand, private accessibility is lower in neighborhoods with greater level of informality. Since the employment subcenters were estimated using information of formal employment, these results are not surprising: informal workers do not capture proximity to employment subcenters as an amenity in their housing location choices.

17
Figure 6

# PUBLIC **DEFICIT**

<!-- Start of picture text --> 025-<br>0.3<br>025-<br>02-<br>0.1-<br>~0.06-<br>-0.05-<br>-0.1<br>2 7071 7273 7445 7A TT 7879808182838485868788<br>(FicaI Year)<br>PLANNED -- ACTUAL<br><!-- End of picture text --> 31