rafmacalaba/datause-encoder-data huggingface dataset:
# **1.  Introduction**

Recurrent cyclonic storms in the Bay of Bengal inflict massive losses on the coastal regions of Bangladesh and India. Extensive research has investigated the incidence, power and impacts of cyclones in Bangladesh (Ali 1999; Dasgupta et al. 2014; Hoque 1992; Khalil 1992), India (Mishra 2014; Srivastava et al. 2000) and the Bay of Bengal more generally (Bhaskar Rao 2001; Dash et al. 2004; Mandke and Bhide 2003; Mooley and Mohile 1983; Mooley 1980; Muni Krishna 2009; Rao 2002; Yu and Wang 2009). This paper attempts to extend the previous work in several ways.

First, we focus on the historical frequency and locations of cyclone strikes to inform household- and community-level research on adaptation to past and expected future cyclones. Previous studies of coastal community adaptation in the region have focused on responses to specific cyclones or generally-defined coastal hazards (Brouwer et al. 2007; Khalil 1993; Khan et al. 2015; Mallick et al. 2011; Mallick and Vogt 2013; Shameem and Momtaz 2014; Siddiqui et al. 2013; Sultana and Mallick 2015). With the notable exception of Dasgupta et al. (2016), these studies have seldom used household-level data to assess the impacts of past cyclones on communities and households, as well as their role in the formation of expectations about future impacts.

Such research requires construction of a georeferenced panel database that specifies the dates of cyclonic storms, their coastal landfall points, subsequent interior paths, and measures of relative power. Construction of an appropriate spatial panel has therefore been a primary objective of this exercise. To support ongoing research, we focus on the coastal regions of Bangladesh and two contiguous Indian states -- West Bengal and Odisha. However, our approach could easily be extended to cover an arbitrarily-large set of coastal regions.

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The first-order welfare impacts of a marginal reform of indirect taxes can be computed similarly. According to Roy’s identity (a manifestation of the envelope theorem), the Marshallian demand function of a commodity is equal to the negative of the first-order derivative of the indirect utility function with respect to the commodity price, divided by the marginal utility of income. Thus, the welfare impact of price changes _m_ induced by a tax reform is equal to: Δ _v_ = − _v y_ ∑ _qk_ Δ _pk_ . In terms of equivalent variation _k_ = 1 _m_ in income, we have: Δ _y_ * = −∑<sup>_q_</sup> _k_ Δ _pk_ . In other words, an increase in an indirect tax rate _k_ = 1 on commodity **k** induces a price change that leads to a loss in real disposable equal to the change in price times the observed level of consumption.

These equivalent variations in income can then be used to estimate the changes in the original income distribution induced by a fiscal policy reform. Incidence analysis can then proceed along the lines discussed earlier. As noted in section two above, one can also assess the progressivity of an indirect tax system on the basis of the price elasticity of a poverty measure. In particular, a price increase (reduction) for a given commodity is considered pro-poor if it causes an absolute increase (reduction) in poverty smaller (greater) than a benchmark case. By analogy to the Lorenz framework, one can choose as benchmark, a situation where everybody assigns the same proportion of real income to the purchase of the commodity in question. The ratio of the observed poverty elasticity to the benchmark case is an indicator of pro-poorness or a measure of the distributional characteristics for the given commodity (Essama-Nssah 2007). If this indicator is less than one, an increase in the price of the commodity would hurt the poor less than the nonpoor. This can be used in an ex ante assessment of indirect tax policy options.

Table 4.1 contains results from an application of this method to data for Guinea. They are estimates of the distributional characteristics for 19 components of food expenditure based on the Watts index of poverty and two measures from the FosterGreer-Thorbecke (FGT) family, the poverty gap and the squared poverty gap. On the basis of these results, there are 11 food items or groups that might deserve special consideration in the context of marginal commodity tax reform. These include palm oil, smoked fish, rice (both local and imported), sugar, cereals, roots, grains, vegetables, oils 44
# **Figure 2: Interest rates and spreads trends in EMDEs**

_Median nominal and real lending rates have declined across all regions. However significant heterogeneity within and between regions persists. The median interest rate spread however barely fell, but has declined significantly in various regions._ A. Nominal lending interest rate <!-- Start of picture text --> %<br>22<br>20<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>75 th and 25 th percentiles in dotted lines.<br>C. Real lending interest rate<br>%<br>15<br>10<br>5<br>0<br>-5<br>Note: real interest rate=nominal interest rate-inflation rate<br>75 th and 25 th percentiles in dotted lines.<br>E. Lending-deposits spread<br>%<br>15<br>13<br>11<br>9<br>7<br>5<br>3<br>75 th and 25 th percentiles in dotted lines.<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br>2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br><!-- End of picture text --> B. Nominal lending rate by region 2003-2017 <!-- Start of picture text --> %<br>23<br>21<br>19<br>17<br>15<br>13<br>11<br>9<br>7<br>5<br>EAP ECA LAC MENA SA AFR<br>03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17<br><!-- End of picture text --> Median value in black line. 75<sup>th</sup> and 25<sup>th</sup> percentiles box top and bottom. D. Real lending rate by region 2003-2017 <!-- Start of picture text --> %<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>-2<br>EAP ECA LAC MENA SA AFR<br>Median value in black line. 75 th and 25 th percentiles box top and bottom.<br>F. Lending-deposit spread by region<br>2003-2017<br>%<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>EAP ECA LAC MENA SA AFR<br>Median value in black line. 75 th and 25 th percentiles box top and bottom.<br>03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17<br>03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17 03-07 08-12 13-17<br><!-- End of picture text --> Source: World Bank Finstats 2019 36
|**Table A2**<br>|**: Growing Season**|**SPEI in G5 Sa**<br>|**hel Countries**<br>|||
|---|---|---|---|---|---|
|Dependent Variable:||Any|Conflict during|Year||
||(1)|(2)|(3)|(4)|(5)|
|SPEI|-0.00225|-0.00533|-0.00341|-0.00369|-0.0139|
||(-0.00745)|(-0.00662)|(-0.00677)|(-0.0072)|(-0.00986)|
|SPEI in Growing Season|-0.171***|-0.116***|-0.0986***|-0.0973***|-0.0728**|
||(-0.0303)|(-0.0265)|(-0.0248)|(-0.026)|(-0.029)|
|N|6090|6090|6090|6090|6090|
|R-sq|0.021|0.068|0.072|0.08|0.107|
|Controls|N|Y|Y|Y|Y|
|Country FE|N|N|Y|Y|Y|
|Year FE|N|N|N|Y|Y|
|Country-Year FE|N|N|N|N|Y|
|The dependent variable is a dummy equal to one if<br>evapotranspiration index. Controls include the foll<br>cell area, presence of roads, distance to river, pres<br>fractionalization. Standard errors in parentheses cl<br>* p<0.1, ** p<0.05, *** p<0.01|any conflict event occur<br>owing time-invariant cell<br>ence of a country border,<br>ustered at the grid-cell le|red in a grid-cell in<br>characteristics (als<br>presence of any m<br>vel|a given year. The SP<br>o used by HF in thei<br>ineral resources an|EI is the standardize<br>r specification): terr<br>d a measure of ethn|d precipitation-<br>ain, elevation,<br>o-linguistic|

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# **3. Human Capital and Migration**

A striking feature of most FSU countries is that their human capital is much greater than is typical for countries with similar levels of per capita income, or conversely their per capita incomes are much lower than is typical for countries with similar endowments of human capital, which is likely to be a legacy of the strong educational and health systems which existed during the Soviet period and, in some countries, notably the Kyrgyz Republic, Tajikistan and Uzbekistan, continued high levels of public expenditure on education as a share of national income. It may also reflect other constraints, common to all the FSU countries, which prevent human capital generating the same level of returns than is the case in other regions of the world.

**Table 5 Ranking of FSU countries by HCI and GDP at PPP per worker**

|Country|Human<br>Capital Index<br>(0-1, a higher<br>score denotes<br>more human<br>capital)|HCI ranking<br>(1 = highest<br>ranking<br>country)|GDP PPP per worker<br>(average 2015-17, in<br>constant 2011 US<br>dollars|GDP PPP per<br>worker ranking<br>(1 = highest<br>ranking<br>country)|
|---|---|---|---|---|
|Armenia|0.572|75|22,915|83|
|Azerbaijan|0.597|66|33,257|64|
|Georgia|0.614|58|22,522|84|
|Kazakhstan|0.746|29|46,968|50|
|Kyrgyz Republic|0.580|73|11,142|108|
|Moldova|0.580|72|18,549|89|
|Russian Federation|0.729|32|49,663|44|
|Tajikistan|0.533|83|13,786|99|
|Ukraine|0.647|47|18,734|88|

The total number of countries is 141 Sources: HCI, world Bank, GDP PPP per worker, Penn World Tables and WDI The World Bank has recently published a Human Capital Index (HCI) which is a composite of indicators of the educational attainment and health status of the population, for more than 150 countries. In general, a higher HCI correlates with higher productivity and thus higher GDP per worker. The cross-country correlation coefficient of the HCI and GDP at Purchasing Power Parity per worker is 0.72. A labor force with more human capital will, ceteris paribus, be more productive, but the relationship also runs in the opposite direction, in that a more productive labor force generates more resources for investment in human capital, whether by the state or households.

6
**Exports of goods and services (% of GDP)** : Exports of goods and services represent the value of all goods and other market services provided to the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, license fees, and other services, such as communication, construction, financial, information, business, personal, and government services. They exclude labor and property income (formerly called factor services) as well as transfer payments. Source: WDI.

**Openness (% of GDP)** : Exports of goods and services (% of GDP) plus imports of goods and services (% of GDP).

**Bureaucracy Quality Index** : Indexed series between 1 and 4 measuring the quality of bureaucracy. Higher numbers indicate higher quality. Source: _International Country Risk Guide_ .

# **2. Initial Condition Variables**

**GDP per capita in constant 2000 U.S. dollar** : GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant U.S. dollars.

Source: WDI.

**Initial Human Inde** x: Following Bose, Haque, and Osborn (2007), we construct the initial human capital variable as the weighted sum of the initial enrolment ratios (%) in primary and secondary schools and in higher education. The weights are 1 for primary school enrolment ratio, 2 for secondary school and 3 for enrolment in higher education. The weights are approximations to the relative values of three types of education. The initial year is 1970.

Source: Barro and Sala-i-Martin (1995, 1999).

# **3. Government Budget Items**

**Tax Revenue** : Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties, and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue. For central government. Source: GFS.

**Total Government Revenue** : Revenue is cash receipts from taxes, social contributions, and other revenues such as fines, fees, rent, and income from property or sales. Grants are also excluded here. For central government. Source: GFS.

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the same or higher in rural areas than in Abidjan (except in Savannah). This' may reflect higher production costs for these items in rural areas.

Table 4: Regional Price Index by Expenditure Categorv

||Abidjan|Other<br>Cities|East<br>Forest|West<br>Forest|Savamnah|
|---|---|---|---|---|---|
|Food|100.0|86.7|85.5|66.7|72.6|
|- grains&bread|100.0|96.6|111.1|74.4|64.8|
|<br>- roots & vegetables|100.0|75.4|82.7|43.1|82.1|
|- other food|100.0|88.6|80.9|87.2|71.6|
|Non-Food|100.0|99.8|89.2|101.9|87.6|
|- clothing|100.0|105.8|80.2|106.2|66.0|
|- home & furniture|100.0|93.9|106.4|92.9|79.1|
|- transport & communications|<br>100.0|108.3|*76.2|114.4|109.6|
|<br>- education & health|<br>100.0|89.4|116.2|99.6|79.1|
|- other non-food|100.0|95.4|100.2|90.4|90.4|
|Tradeable Food|100.0|86.2|104.8|76.1|62.6|
|Non-Tradeable Food|100.0|87.0|80.8|62.5|79.6|
|Tradeable Non-Food|100.0|99.8|79.5|97.8|84.8|
|Non-Tradeable Non-Food|100.0|99.9|115.5|110.4|94.7|

As we mentioned earlier, the Government of Cote d'Ivoire produces two series for the CPI, depending upon the type of occupation of the head of household. This reflects the existence of differences in expenditure patterns across socio-economic categories in the population. With the CILSS, it is possible to distinguish more than two groups. Table _5_ shows the cost-of-living index recalculated for eight socio-economic groups, taking into account the expenditure pattern of each group. In general, the differences are fairly minor (at least, if one ignores the cells with a small number of observations). The main differences are that the regional cost-of-living differential appears somewhat larger for farmers. The other groups, which are primarily urban based, display smaller regional variations.

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13 The charts illustrate the need to look at both spending and outcomes, particularly in education where for the most part higher quality entails on average higher spending per pupil. The PISA data reveal, for example, that Uruguay fares relatively badly in terms of test scores, but also spends relatively little; in fact, it ends up on our frontier. Sweden, by contrast, does much better in terms of test scores, but spends considerably more and ends up some way above our frontier. Finland does well both in terms of its test score results and its efficiency in translating resources into outcomes; it ends up on or close to the frontier depending on the year.

Figure 2 shows the least efficient units in each of the four studies. These are all units that achieve similar outcomes to other units but at a considerably higher cost. Interestingly six of the seven least efficient countries in the education rankings are among the least efficient seven in the health system rankings. For the California schools example, the majority of the least efficient school districts listed, such as Shoreline Unified and San Pasqual Valley Unified, tend to be tiny districts located in sparsely populated remote areas, a feature similar to the Pacific Unified that Freedberg and Doig (2011) noted.

Figure 3 compares our hybrid approach with the stochastic frontier model (SFM) where inefficiency is assumed to be half-normal and time-invariant (in all four examples, we have panel data) and the relationship between expenditure and output or quality is assumed to be double logarithmic. (Interestingly, in all these applications the cross-section SFM concluded that there was no cross-sample variation in inefficiency.) The charts show the hybrid frontier and the actual expenditure-output combinations along with the deterministic section of the SFM and the efficient expenditure levels emerging from the SFM. These differ from the expenditure corresponding to the deterministic section of the frontier because the SFM approach allows for statistical noise and random shocks. In each example, the deterministic section of the SFM and most of the efficient spending levels lie well below our hybrid frontier. The SFM typically finds one or at most a few units to be efficient; the inefficiency components of the residuals of the rest are adjusted accordingly. By contrast, in our approach, we find an efficient unit _for each output range_ . The fact that the SFM imposes a specific functional form also results in differences between the efficiency scores emerging from the two approaches; these are particularly large in the case of the California schools example, but are also evident in the PISA and OECD examples particularly at the upper quality levels. Of course a different functional form in the SFM could
<!-- Start of picture text --> US growth by percentile: North-East<br>&<br>8<br>fadx<br>=<br>2<br>°s<br>©<br>S<br>0 20 40 60 80 100<br>petile<br>—+*— 1960-70 —*— 1990-00<br>US growth by percentile: South<br>8<br>rel<br>o:<br>az3<br>2<br>On<br>S<br>N<br>S<br>S<br>0 20 40 60 80 100<br>petile<br>—+*— 1960-70 —*— 1990-00<br><!-- End of picture text --> <!-- Start of picture text --> US growth by percentile: Mid-West<br>3<br>So<br>8<br>a<br>£o=<br>2<br>“3<br>wo<br>S<br>S<br>0 20 40 60 80 100<br>pctile<br>—*— 1960-70 —*— 1990-00<br><!-- End of picture text --> <!-- Start of picture text --> US growth by percentile: West<br>é<br>oo<br>S<br><=38<br>2<br>oO<br>3<br>o<br>0 20 40 60 80 100<br>pctile<br>—*— 1960-70 —*— 1990-00<br><!-- End of picture text -->
By contrast, the results show no difference between stock‐listed and unlisted MNEs (column 5). Figure 1 confirmed that reporting instruments target both listed and unlisted companies equally. We also interact the MNE variable with sector, region, and income dummies, but none of the individual or joint effects is statistically significant with the exception of MNEs operating in the computer industry whose interaction term with MNE status is positive. In other words, the relationship between MNE status and the average disclosure rate does not vary across sectors, regions, and income levels in our data sample.

# **3.3 DISCLOSURE RATE BY INDICATOR CATEGORY**

In a next step, we assess if the results change for different development categories. Table 12 shows the baseline regressions using different types of disclosure rates as dependent variables. They are computed based on the average disclosure across all indicators in a specific development category. The four broad development categories are (i) economic (column 1), (ii) labor and social (column 2), (iii) environmental (column 6) and (iv) governance (column 10). The labor and social category is further subdivided into labor, social, and human rights impacts (columns 3 to 5). Environmental impacts can be further classified into energy‐related, water‐related, and other impacts (columns 7 to 9). For a list of indicators included in each of the categories, see Tables 13 to 16.

As for MNE status, we do not find a significant impact on the disclosure rate of any of the development categories. Large companies, by contrast, show higher disclosure rates across all development categories. Firm size has a larger explanatory power for environmental (columns 6, 7, 8) and governance indicators (column 10) compared to other indicators.

**Table 12: Regressions by indicator category, OLS**

|Dependent variable:|_DRisc_<br>||||||||||
|---|---|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Development<br>category:|Econ|Lab&Soc|Labor|Social|Human|Environ|Env_Ener|Env_Water|Env_Oth|Govern|
|_mneisc_|0.0275|0.0065|0.0013|‐0.0353|0.0281|‐0.0073|‐0.0081|‐0.0315|0.0085|0.0081|
||(0.264)|(0.757)|(0.953)|(0.291)|(0.272)|(0.717)|(0.743)|(0.227)|(0.696)|(0.741)|
|_largeisc_|0.0811**|0.0928***|0.1004***|0.0841**|0.0833**|0.1361***|0.1817***|0.1181***|0.0985***|0.1276***|
||(0.019)|(0.003)|(0.002)|(0.042)|(0.018)|(0.000)|(0.000)|(0.001)|(0.001)|(0.000)|
|_constantisc_|0.3915***|0.6375***|0.5347***|0.9512***|0.7219***|0.6104***|0.7153***|0.5134***|0.5596***|0.4892***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000|
|Country‐ sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|R2|0.52|0.52|0.50|0.55|0.54|0.58|0.58|0.58|0.55|0.48|
|Observations|2,008|2,009|2,007|2,006|2,005|2,005|2,005|2,004|2,004|2,009|

Source: Own calculations. p*<0.1, p**<0.05, p***<0.01 (p‐values in parentheses). Note: GRI variables refer to 2015, while GVC variables refer to 2011. All regressions control for country‐sector fixed effects. Standard errors are robust to heteroscedasticity.

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liberalization among East Asia countries. While that model has broad regional coverage, it does not model the production structure explicitly: There are no primary factors or inter-industry linkages, so that the structural effects of trade policy change are not easily analyzed. Wang (1994) developed a seven-region, six-sector model for world production and trade to estimate the impact of economic integration among East Asia Chinese economies, but did not address policy issues related to APEC or ASEAN.

Contemporary efforts to build multi-regional CGE models for the Asia-Pacific region are underway by participants in the Purdue Global Trade Analysis Project (GTAP).'<sup>7</sup> Among recent GTAP applications, Young and Chyc (1994) conduct experiments on a 10-region, 3-sector version of the basic GTAP model and compare three scenarios for APEC regional trade liberalization. They found that a trade reform package that extends MFN status to the rest of the world, but does not require reciprocity, is not desirable for the APEC member countries, and suggest instead that APEC members would be better off to withhold tariff reduction with the rest of the world until reciprocity can be agreed to. This policy suggestion is somewhat at odds with that of Martin, Petri, and Yanagishima (1994), who argue the case for a MFN-based liberalization in East Asia _without_ requiring reciprocity from non-Asian regions.

# **The APEC CGE Model**

The model developed in this paper consists of a multi-regional CGE framework containing a tensector, six-region, general equilibrium model, where the regional CGE models are inter-connected through trade flows."<sup>5</sup> For the purpose of describing the model, it is useful to distinguish between the individual "country" models and the multi-region model system as whole, which determines how the

> " The primary reference to these efforts is Hertel (1995). The GTAP project is the source of much of the data used in our APEC model as well.

18 The model also permits regional interactions through endogenous rmigration of capital and labor, but for all experiments presented in this paper, this feature is not used. See Hinojosa-Ojeda, Lewis, and Robinson (1994) for analysis of a Greater North America Free Trade Area (GNAFTA) using a similar model with labor migration permitted.

27 _Lewis, Robinson, and Wang_