rafmacalaba/datause-encoder-data huggingface dataset:
**Table 12. Difference between true IOO and median estimated IOO in percentage terms: Baseline scenario**

|||**E**|**xcluded**|**circumstanc**|**e**<br>**’**|**’**|
|---|---|---|---|---|---|---|
|**Observed**<br>**population**<br>**scenarios**|**None**<br>**(1)**|**Female**<br>**(2)**|**Urban**<br>**(3)**|**Region**<br>**(4)**|**Fathers**<br>**education**<br>**(5)**|**Mothers**<br>**education**<br>**(6)**|
||||True IO s|hare = 0.97|||
|all|0.00|‐31.99|‐5.87|‐49.55|‐1.46|‐7.52|
|Top 1% truncated|‐0.18|‐34.18|‐6.42|‐50.97|‐1.72|‐8.17|
|Top5% truncated|‐0.92|‐42.50|‐8.78|‐52.50|‐2.88|‐10.96|
|<br>|||<br>True IO s|<br>hare = 0.60|<br>||
|all|0.00|‐32.01|‐5.92|‐49.58|‐1.52|‐7.58|
|Top 1% truncated|‐3.56|‐36.71|‐9.67|‐51.28|‐5.08|‐11.37|
|Top5% truncated|‐13.76|‐50.54|‐20.58|‐53.64|‐15.45|‐22.45|
||||True IO s|hare = 0.43|||
|all|0.00|‐31.46|‐5.19|‐49.17|‐0.77|‐6.88|
|Top 1% truncated|‐4.77|‐37.61|‐10.83|‐51.22|‐6.30|‐12.53|
|Top5% truncated|‐18.15|‐52.07|‐24.44|‐54.95|‐19.75|‐26.21|
||||True IO s|hare = 0.27|||
|all|0.00|‐31.74|‐5.54|‐49.38|‐1.15|‐7.23|
|Top 1% truncated|‐7.09|‐36.71|‐9.67|‐51.28|‐5.08|‐11.37|
|Top5% truncated|‐22.50|‐50.54|‐20.58|‐53.64|‐15.45|‐22.45|
||||True IO s|hare = 0.14|||
|all|0.00|‐31.03|‐4.58|‐48.86|‐0.16|‐6.33|
|Top 1% truncated|‐7.39|‐38.65|‐13.30|‐52.08|‐9.08|‐14.96|
|Top5% truncated|‐23.92|‐51.92|‐29.22|‐58.68|‐25.37|‐30.70|
||||True IO s|hare = 0.08|||
|all|0.00|‐32.88|‐7.19|‐50.26|‐2.94|‐8.86|
|Top 1% truncated|‐10.27|‐40.25|‐16.04|‐53.76|‐12.03|‐17.59|
|Top5% truncated|‐26.46|‐52.55|‐31.47|‐60.63|‐28.00|‐32.96|

Notes: Results based on 1,000 simulations. Baseline scenario refers to data‐generating process described by equation (3), modified as described in robustness checks section.

30
# Figure 8.D. Economic activity during global recessions, by country group

## A. Output per capita, advanced economies

## B. Output per capita, EMDEs

<!-- Start of picture text --> Index, t-1 = 100 Index, t-1 = 100<br>110 120<br>1975 1982<br>1975 1982<br>105 1991 2009 110 1991 2009<br>Average Average<br>100 100<br>95 90<br>90 80<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>C. Trade, advanced economies D. Trade, EMDEs<br>Index, t-1 = 100 Index, t-1 = 100<br>120 120<br>1975 1982 1975 1982<br>110 1991 2009 110 1991 2009<br>Average Average<br>100 100<br>90 90<br>80 80<br>70 70<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br>E. Credit, advanced economies F. Credit, EMDEs<br>Index, t-1 = 100 Index, t-1 = 100<br>120 160<br>1975 1982<br>1975 1982<br>110 1991 2009 140 1991 2009<br>Average Average<br>120<br>100<br>100<br>90<br>80<br>80 60<br>t-4 t-3 t-2 t-1 t t+1 t+2 t-4 t-3 t-2 t-1 t t+1 t+2<br><!-- End of picture text --> Sources: Haver Analytics; International Monetary Fund; Kose and Terrones (2015); Organisation for Economic Cooperation and Development; World Bank.

Note: Year “t” denotes the year of the respective global recessions (shaded in gray). Average refers to an average of four global recessions with available data. Variables are index numbers equal to 100 one year before year “t” (i.e., t- 1 = 100). Aggregates for output per capita are market-weighted. For details, see Appendix 2.

40
Average rate progression =<sup>1</sup> 1−0 0) 1 (MM −AA 1 0 YY 1−0 1 where 1−0 is the tax rate on marginal income, 1 0 . This shows that for a tax to be locally progressive, the marginal rate must be higher than the average tax rate. MM YY −YY In addition to average rate progression, Musgrave and Musgrave (1989) propose two further measures of structural progressivity (originally proposed by Musgrave and Thin, 1948<sup>25</sup> ): liability progression and residual income progression. Liability progression is defined as the elasticity of the tax liability with respect to pre-tax income (i.e., the ratio of the percentage change in tax liability to the percentage change in pretax income): 0 Liability progression =<sup>TT1 −TT0</sup> ∙ 0 1 0 YY Musgrave and Thin (1948) also show that average rate progression can be written as the ratio of marginal TT YY −YY Musgrave and Thin (1948) also show that average rate progression can be written as the ratio of marginal to average tax rates: Liability progression =<sup>MM1−0</sup> 0 A progressive tax, with marginal rate higher than average rate, will have a liability progression coefficient AA A progressive tax, with marginal rate higher than average rate, will have a liability progression coefficient greater than 1, while a proportional tax will have a coefficient equal to 1, and a regressive tax will have a coefficient less than 1.

Unlike liability progression, a progressive tax will have a residual income progression coefficient less than 1, a proportional tax will have a coefficient again equal to 1, while a regressive tax will have a coefficient greater than 1.

# 3.2 Examples

While examples of structural progressivity metrics are not readily available for IDA countries, OECD (2014) presents a relatively recent example of a structural progressivity indicator for OECD countries. OECD (2014) presents an average rate progression metric for both the PIT and for the tax wedge. The tax wedge is a broader measure of the labor tax burden incorporating both PIT and SSC. It is defined as: (PIT + employee SSC + employer SSC) / (gross income plus employer SSC). Figure 2 presents both these metrics for four different family types. In each case, the metric is calculated over five different income intervals

> 25 A further indicator proposed by Musgrave and Thin (1948), marginal rate progression, is not detailed here.

18
# **Table 6: Actual and theoretical transfers**

||FPM tr|ansfers|All Tr|ansfers|
|---|---|---|---|---|
|Thresholds 1-7|0.804***|0.773***|0.989***|0.984***|
||[0.010]|[0.012]|[0.040]|[0.028]|
|Obs.|21168|21168|21168|21168|
|F-statistic|59336.48|54124.90|12366.89|13070.67|
|Thresholds 1-4|0.753***|0.710***|0.853***|0.816***|
||[0.012]|[0.013]|[0.037]|[0.024]|
|Obs.|18365|18365|18365|18365|
|F-statistic|29612.69|10949.45|5042.12|8357.66|
|Municipality fixed-effects|No|Yes|No|Yes|
|State fixed-effects|Yes|No|Yes|No|

_Notes:_ Estimates from reduced-form regressions relating theoretical transfers to actual FPM transfers and all federal transfers. All regressions include a three order population polynomial and year fixed-effects. Robust standard errors clustered at the municipal level in brackets. *, **, *** represent significance at the 10%, 5% and 1% level, respectively.
<!-- Start of picture text --> Yield(9)[I+ 480SorghumAttisanal553 - - 57 5 37sites 2008 (Kg/ha) ) ne— r 4g<br>(0) 757 - 889<br>[Ey 220 - 949<br>[EE 949 - 1070<br>HE 1070- 1232<br><!-- End of picture text --> <!-- Start of picture text --> (__)YieldIndustrial© millet 496Attisanal2008 stes sites (Kg/ha) b =re. |<br>(7) 702 -- 880 702<br>(HS 220 - 964<br>HEB 96+ - 1082<br>HE 1062<br><!-- End of picture text -->
empirically determined. The optimal hedge ratio depends upon the hedger's level of risk-aversion. Hedging is useful if the reduc':ion in risk is sufficient to compensate for the reduction in returns. We report calculations of the optimal hedge ratios for FPA cotton at different levels of risk aversion later in the paper.

Before a determination of the optimal hedge ratio is made it would be of interest to check the relationship between FPA cash prices and Cotlook A Index prices. This is because a recently introduced cotton futures cont:ract based on the Cotlook A index may make this contract a more appropriate hedging instrument than the New York No. 2 futures contract.1 3 Table 4 reports the results of regressing Cotlook A Index price changes on FPA cotton price changes. 14 The R-Square indicates that 80% of the variation in FPA cotton prices is explained by changes in the Cotlook A Index. This reasonably good fit is not surprising given that FPA cotton prices form one of the thirteen components of the Cotlook A index. The fact that FPA prices and Cotlook prices were significantly correlated"<sup>5</sup> implies that the Cotlook futures contract may prove a better hedging instrument for FPA cotton than the New York No.2 "<sup>3</sup> For the definition of the Cotlook A index see note #2 under Table 4.

1<sup>4</sup> we use spot-to-spot regression rather t'lan spot-to-futures because there is not sufficient data on Cotlook futures prices. We, therefore, assume a close relationship between Cotlook index prices and Cotlook futures prices.

"<sup>5</sup> The correlation between SSA export price changes and Cotlook price changes is 0.89. The correlation coefficient turned out to be significant at the 99% level.
Belém region hosts the most and has the fastest growth of poor headsof households. The share of household heads earning less than one minimum salary also varies across regions. The Belém region is the region with the largest number of poor heads of households, 22 percent of the 280,314 total in Pará. Bragantina, Guamá, and Santarém each host around 8 percent of household heads with income less than one minimum salary (Table 4.5). Only the latter experienced negative growth in the number of poor, while the former two regions, poverty increased by 12 and 24 percent respectively. Hence, these numbers show that poverty in Pará is highly urban and, therefore, not only a rural phenomenon.

# **POVERTY DEPTH**

Although Pará has made substantial progress in reducing the share of the population living in extreme poverty, the problem remains broad and deep. The P0, analyzed above, measures the proportion of people below a certain poverty line but takes no account of how far they are below that line - the degree of poverty - or whether they are becoming even poorer. To address the situation of the poorest and to evaluate whether their economic situation has improved, the squared poverty gap measure is used. This takes into account the degree of poverty, because it gives more weight to the poorest and most vulnerable.

Poverty is deep in Pará **.** The squared poverty gap measure (P2) reveals that the extreme income poverty in Pará reached 15.7 percent in 2000. Poverty was deepest in rural areas, where P2 reached 24.7 percent compared to 11.6 percent in urban areas in 2000.

The depth of urban poverty increased since 1995. P2 reveals that the extreme poverty in Pará’s urban areas fell 26 percent to 9.3 percent during 1993-1995 (Figure 4.4). During 1995-2001, the trend reversed and P2 increased by 14 percent in Pará to 10.3 percent in 2001, while the Amazon region as a whole only experience an increase of 8.5 percent in the depth of urban poverty. In 2001, the depth of urban poverty in Pará was one and three percentage points higher then in the Amazon region and Brazil as a whole. As a matter of fact, P2 reveals that urban poverty was much deeper in 2001 than in 1981 (122 percent), implying that the poorest became much poorer during the past two decades. In fact, Pará has performed worse than Brazil as a whole where P2 increased by 52 percent.

23
**Table T3.CU:  Father's Education and Educational Expenditure on Children**

|||**URBAN**<br>**CFPS**<br>|**CHINA**<br>**2010**<br>||
|---|---|---|---|---|
||**(6-22) Yea**|**rs Children**|**(8-23) Yea**|**rs Children**|
|Father's Sch.|236.9***|162.8***|225.4***|138.3***|
||(35.0)|(34.1)|(32.9)|(30.5)|
|**Father's Sch. * Daughter**|**36.8**|**18.6**|**27.5**|**13.5**|
|**Dummy**|(48.2)|(46.9)|(49.2)|(47.4)|
|**Daughter Dummy**|**-71.2**|**181.1**|**22.4**|**267.5**|
||(400.2)|(386.8)|(413.7)|(397.5)|
|Intercept|827.4***|4137.0***|1034.3***|4592.9***|
||(278.8)|(337.8)|(264.5)|(330.1)|
|No. of Children||-2222.0***||-2401.2***|
|||(132.7)||(154.4)|
|No. Observations|2784|2784|2435|2435|

Notes: (1) The data used are CFPS 2010. (2) Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.
A large medical and epidemiological literature documents that poor sanitation continues to cause death and disease, particularly among children in developing countries today. Ingestion of fecal pathogens as a result of living near poor sanitation is well-known to cause diarrhea (Esrey et al., 1991). Checkley et al. (2008) use detailed, high-frequency longitudinal data from five countries to demonstrate effects of childhood diarrhea on subsequent height. Humphrey (2009b) posits that chronic but subclinical “environmental enteropathy,” caused by ingestion of fecal pathogens, may also lead to slowed growth. Lin et al. (2013) find associations among fecal environmental contamination, enteropathy and child height in Bangladesh. Several papers in economics have also identified large effects of sanitationrelated diseases on early-life mortality ( _e.g._ Galiani et al., 2005; Cutler and Miller, 2005; Watson, 2006), as well as effects on subsequent human capital accumulation ( _e.g._ Bleakley, 2007; Baird et al., 2011).

Recent econometric studies suggest an interaction between sanitation and population density in predicting health outcomes in developing countries. Spears (2012) finds that infant mortality improved by more and child height increased by more in Indian districts where a government rural sanitation program was more intensively implemented, and that this effect was larger in districts with greater population density. Spears (2013) observes that heterogeneity across developing countries in open defecation can account for a large fraction of international differences in average child height, and that this fraction is further increased when the density of open defecation is used as the explanatory variable. However, unlike this paper, neither of these studies focuses on understanding the interaction of population density with sanitation; instead, interactions have been used as supporting evidence that sanitation indeed has important implications for health.

7
# **N. Regulatory substance- market entry index for Vietnam and comparators, 2015**

||**Colombia**|**Peru**|**Philippines**|**Vietnam**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Market Entry Regulation**|**50%**|**50%**|**100%**|**100%**|**77%**|
|**Permitting New Entrants**|50%|100%|100%|100%|90%|
|Responsible for monitoring compliance<br>with the terms of the license orpermit|||||88%|
|Authority to impose penalties for<br>violatinglicense orpermit terms|NAP||||100%|
|Penalties are formally written and<br>publiclyavailable|NAP||||80%|
|Provisions to force companies to<br>relinquish licenses or permits for<br>violation|||||100%|
|**PPA Approvals**|50%|0%|100%|NAP|57%|
|Legally required to approve all power<br>sales contracts either directlyor indirectly|||||59%|
|Approve or refuse a proposed PPA in a<br>legallyspecifiedperiod of time|NAP|NAP||NAP|60%|
|Authority over the process by which<br>utilities can select or procure power from<br>IPPs|||||50%|

Satisfactory result Unsatrifactory result NAP Not applicable NAV Not available 52
In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador.

# 4.1.1 Variation of the NSE as a percentage of total employment

The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points.

Figure 1: Prevalence of NSE among salaried employees. (Mid-90s/Mid-2010s) <!-- Start of picture text --> 80%<br>70% Starting point<br>Ending point<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Argentina Brazil Peru Dominican El Salvador<br>Republic<br><!-- End of picture text --> Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “Elementary Occupations” are the category where the prevalence of NSE is higher. The “Professionals” and “Skilled agricultural, forestry and fishery workers” are also types of occupations characterized by a higher prevalence of NSE. On the other hand, the categories of “Managers”, “Technicians” and “Plant and machine operators and assemblers” are the types of occupations with a lower incidence of NSE in the region (see Figure 2).

The observed distribution of the prevalence of NSE among types of occupations suggests a strong heterogeneity across non-standard employees. Actually, the productivity and task profile of professionals and elementary workers, as we will discuss later in this paper, are very different, even though both types of occupations are characterized by a higher prevalence of non-standard employment arrangements. It is important to highlight that the distribution of NSE among categories of occupations does not present a significant change in the considered period. Annex II presents the prevalence of NSE by categories of occupations for the starting point of the analysis.

Page 8 of 51
# _<u>Geography</u>_

The physical attributes of shared rivers have long been recognized as important to understanding conflict and cooperation over transboundary water (e.g. Le Marquand, 1977; Toset et al., 2000). In particular, empirical studies considering the river basin as the unit of analysis find that the greater the significance of the particular basin to the respective countries the higher the cooperation evinced. In addition, the more control a country has over a given river basin the less cooperation (or more conflict) is evinced because that country perceives more benefits from unilateral action (Espey and Towfique, 2004; see also Gleditsch et al., 2006).

Following Espey and Towfique (2004), we derive two sets of geographic measures. The first set proxies for the level of control a given country (within the dyad) has over the basin as well as the level of control the entire country dyad has over the basin. The second set proxies for the importance of the basin to a given country (within the dyad) as well as the importance of the basin to the entire country dyad. The measure for control is estimated by calculating the percentage of the total river basin within the boundaries of each country, while the proxy for importance is derived from the size of the river basin within the country, expressed as a percentage of the total area of the country. Two numbers are then obtained for that dyad in order to gauge asymmetry and country-wide nuances: a ratio derived value and a sum-derived value ( **_Basin Control Sum; Basin_**

**_Control Ratio; Basin Importance Sum; Basin Importance Ratio_** ). The variables are calculated from data in OSU’s International River Basin Register. The geography variables are time invariant since physical attributes of a basin, generally, do not change drastically over time. We are able to account for the changes that took place during the collapse of the Soviet Union as well as other major geopolitical changes using the International River Basin Register.

# _<u>Governance and Democracy</u>_

Based on the Democratic Peace Theory, scholars have claimed that political regime type should also matter for explaining conflict and cooperation over the environment. Neumayer (2002a), for example, finds that democracies tend to exhibit higher environmental commitment. Gleditsch (1998) suggests that democratic countries sharing a river should be more peaceful in their hydropolitical relations in comparison to non-democratic countries. Brochmann and Hensel (2009), who consider conflictual river claims and their subsequent settlement, find that river claims are less likely to 9
13 non‐poor? As explained on page 6, this paper uses five measures to calculate access. Access statistics by location, quintile, and poverty status using the most expansive definition (definition 5) are shown in Table 1; the statistics using the first definition (grid connection) are reported in Table A.3 in the annex. The results are consistent with the general observations made elsewhere that access rates in rural Africa are significantly lower than in urban areas, the poor are far less likely to have access to electricity than the rich, and in some countries access of the rural poor to electricity is essentially non‐existent. Overall, the poor in nine out of 22 countries had an access rate of less than 5 percent.

Table 1: Percentage of people with access to electricity according to definition 5

||||All people<br>|||Peop<br>|le classified<br>|poor<br>|
|---|---|---|---|---|---|---|---|---|
|Country|Urban|Rural|Total|Q1|Q5|Urban|Rural|Total|
|Angola|75|14|47|8|85|40|6|16|
|Botswana|63|23|46|15|79|42|10|25|
|Burkina Faso|47|3|13|2|38|14|2|3|
|Côte d'Ivoire|88|31|57|41|38|14|2|3|
|Ethiopia|96|12|23|7|45|86|6|10|
|Ghana|89|47|68|37|91|74|32|41|
|Madagascar|38|6|12|1|44|12|3|4|
|Malawi|38|4|9|1|31|8|0|1|
|Mali|92|57|65|49|80|86|50|52|
|Mozambique|47|2|16|1|51|11|1|2|
|Niger|61|6|15|2|47|20|3|4|
|Nigeria|93|48|64|33|88|88|38|48|
|Rwanda|48|6|12|1|46|6|1|1|
|São Tomé and Príncipe|69|48|59|49|72|62|44|53|
|Senegal|93|32|59|36|84|84|26|44|
|Sierra Leone|42|2|17|3|43|25|1|8|
|South Africa|94|81|89|78|99|87|77|81|
|Swaziland|70|30|40|4|83|45|16|19|
|Tanzania|52|9|20|4|58|7|4|4|
|Togo<sup>a</sup>|80|10|37|6|76|65|6|19|
|Uganda|39|7|15|3|42|6|4|4|
|Zambia|59|16|31|8|78|19|11|12|
|Median|66|13|34|7|65|33|6|11|

_Source_ : World Bank staff analysis of household surveys a. The statistics for Togo are not for those who are connected to the grid because that question was not asked, but for those who reported positive expenditures on electricity utility bills.

Differences in access rates according to the metrics as defined on page 6 are shown in Figure 3. The difference in the rate of access between definition 1 and definition 5 represents largely people who use electricity other than grid electricity and people who failed to report grid connection (possibly those connected to neighbors’ grid and did not consider themselves connected to the grid). The difference was striking in Mali, especially in rural areas and among the poor where the differences reached 50 percentage points. Two factors accounted for this difference. For rural residents, the most important cause of the difference was extensive use of solar panels. For the urban poor, it was non‐zero expenditures on electricity by those who did not cite electricity as the main source of lighting or cooking. These expenditures were small in urban areas. However, in rural areas, spending on electricity by those who did not cite electricity as the main source of lighting or cooking, did not indicate connection to the grid, or did not own a generator or a solar panel was much higher when expressed as a share of total household expenditures, and significantly