rafmacalaba/datause-encoder-data huggingface dataset:
Global Investment Costs for Coastal Defense through the 21st Century Report to the World Bank 21

# **Table 10. The range of total undiscounted protection costs from 2015 to 2100 across all uncertainties under the five protection strategies (US dollars 10**<sup>**3**</sup> **billion). The main uncertainty is the unit cost.**

|||**Adaptation s**<br>|**trategy cost (c**<br>|**apital and m**<br>|**aintenance)**<br>||
|---|---|---|---|---|---|---|
|**Scenarios**||**1. CP**|**2.**<br>**CA**<br>**FR**|**3.**<br>**CRF**<br>**R**|**4. RI**|**5.CBA**|
||**SSP2**|**4.6-13.2**|**5.5-15.6**|**4.8-13.5**|**6.1-17.3**|**6.8-16.0**|
||**SSP3**|**4.4-12.5**|**5.2-14.8**|**4.5-12.7**|**5.7-16.3**|**6.2-13.6**|
|**RCP8.5**<br>|**SSP5**|**4.9-14.0**|**5.8-16.6**|**5.2-14.6**|**6.4-18.3**|**7.9-18.2**|
||**SSP2**|**3.5-10.0**|**4.2-11.9**|**3.5-10.0**|**4.8-13.6**|**5.0-11.4**|
||**SSP3**|**3.3-9.4**|**4.0-11.2**|**3.2-9.2**|**4.5-12.8**|**4.4-10.0**|
|**RCP4.5**<br>|**SSP5**|**3.7-10.5**|**4.5-12.6**|**3.8-10.8**|**5.1-14.4**|**5.7-13.2**|
||**SSP2**|**3.1-8.8**|**3.8-10.7**|**3.1-8.8**|**4.5-12.8**|**4.3-9.8**|
||**SSP3**|**2.9-8.3**|**3.5-10.1**|**2.8-8.0**|**4.3-12.1**|**3.8-8.8**|
|**RCP2.6**<br>|**SSP5**|**3.3-9.3**|**4.0-11.4**|**3.3-9.5**|**4.7-13.4**|**5.8-11.5**|

**Table 11. The range of total undiscounted protection costs from 2015 to 2030 across all uncertainties under the five protection strategies (US dollars 10**<sup>**3**</sup> **billion). The main uncertainty is the unit cost.**

||**Adaptation st**|**rategy cost (c**|**apital and ma**|**intenance)**||
|---|---|---|---|---|---|
|**Scenarios**|**1. CP**|**2.**<br>**CA**<br>**FR**|**3.**<br>**CRF**<br>**R**|**4. RI**|**5.CBA**|
|**SSP2**|**0.7-2.4**|**1.1-3.6**|**0.7-2.4**|**1.7-5.3**|**2.3-5.7**|
|**SSP3**|**0.7-2.4**|**1.1-3.6**|**0.7-2.4**|**1.7-5.3**|**2.0-4.6**|
|**RCP8.5**<br>**SSP5**|**0.8-2.5**|**1.1-3.7**|**0.8-2.6**|**1.7-5.5**|**2.8-6.7**|
of informal employment takes place within the formal sector as opposed to the informal sector? Furthermore, there is a lack of knowledge regarding how the characteristics and outcomes of informal workers in formal enterprises differ from those of informal workers in informal enterprises. For instance, do informal workers in formal enterprises earn higher incomes and experience better working conditions than informal workers in informal enterprises?

# **Emerging trends and recent developments**

As in many other countries, the COVID-19 pandemic has caused significant disruption to the Indonesian economy. To contain the spread of the virus, the Indonesian government imposed large-scale social restrictions _(Pembatasan Sosial Berskala Besar)_ at the end of March 2020, which limited people’s movements and activities (Temenggung et al., 2021) _._ These measures contributed to a GDP contraction of 2.1 percent in 2020 – the country’s first since 1998 (OECD, 2021). The associated job losses, particularly in the formal segment of the economy, pushed many workers towards informality (Octavia, 2020; Temenggung et al., 2021). Consequently, informal employment increased between 2019 and 2020.<sup>13</sup> The months that followed the first case of COVID-19 and subsequent declaration of a public health emergency in March 2020 were challenging for all Indonesians, but most especially for those in the informal economy. Many informal enterprises faced a steep drop in domestic demand, which in turn, resulted in a sudden and substantial loss of income (Shinozaki, 2022). Informal enterprises – particularly MSMEs – were forced to lay off workers, reduce hours, and suspend wages to survive (Shinozaki, 2022). Working capital quickly emerged as the top concern of MSMEs at the time.<sup>14</sup> Their informal nature constrained them from accessing credit from

> 13 Using self-employment as a proxy for informality, the OECD (2021) estimates that informal employment increased by 4.6 percentage points from 55 percent in 2019 to 59.6 percent in 2020.

> 14 A 2015 survey of micro and small manufacturing firms by BPS revealed that the median cash buffer of MSMEs in Indonesia could only cover about 11 days of operational costs. The survey also showed that about 80 percent of firms would not be able to survive for more than 30 days without income from operations (Temenggung, et al., 2021).

18
18 discrepancies. Indonesia is much closer to international standards, but there are some differences still.

The last column of Table 5 reflects the market’s rating of the overall corporate governance culture—as manifested by the degree to which institutional and retail investors are engaged in actively promoting corporate governance. Here again, there is room for further strengthening, particularly in Indonesia, China and the Philippines.

# **6.2. Enhancing market and institutional infrastructure**

Well-functioning market infrastructure and supporting infrastructure—such as repo markets, margin trading, securities lending and derivatives markets—can reduce transactions costs.

Although in our empirical results, market infrastructure does not turn out to be significant, it is important to note that the only proxy that we have to capture market infrastructure is the number of days to settlement.

In any case, in terms market infrastructure, countries in EA are well placed, with almost all jurisdictions having fairly advanced clearing and settlement systems with recommended features to minimize the various risks associated with pre-settlement and settlement of securities. This is borne out by the GSCS benchmark clearance and settlement ratings and by the Thomas Murray post settlement scores for countries in the region (see Table 6).

Table 6. Scores on pre-settlement and post settlement risks

|**Economy**<br>|**GSCS benchmark clearance**<br>**and settlement score**<br>|**Post settlement**<br>**score**|
|---|---|---|
|China<br>|92.5<br>|A-<br>|
|Indonesia<br>|68.5<br>|A<br>|
|Korea<br>|97.3<br>|A+<br>|
|Malaysia<br>|93.3<br>|A+<br>|
|Philippines<br>|92.4<br>|A<br>|
|Thailand<br>|93.6|A<br>|
|Hong Kong<br>|n.a|A+<br>|
|Singapore|n.a.|AA-|
|_Memorandum:_<br>|||
|Greece<br>|85.0|A+<br>|
|Japan<br>|n.a.<br>|A+<br>|
|Mexico|90.5|A+|
|Peru|97.8|A-|
|Turkey<br>|98.3<br>|A|
|Venezuela, RB|72.6|BBB|

Source: GSCS and Thomas Murray. GSCS compares the settlement efficiency of markets, incorporating average trade size, local market interest rates, the proportion of trades that fail, and the length of time for which they fail. Thomas Murray produces ratings of post-trade risk exposures according to various criteria of clearing and settlement, safekeeping, and asset servicing. The ratings follow a standard alpha scale from AAA to C.
_5_ reduced thus causing the social rate to increase at a given private rate; (ii) the private rate of return is reduced thus having the oppcs.ce effect on the social rate. The net effect of reducing distortions on the social rate is thus an empirical rnatter. If microdistortions actually reduce the social efficiency of investment then there would be additional dynamic welfar" losses for countries where increasing the aggregate capital/labor ratio is an important source of grow. as is the case for practically all LDCs.7' It is surprising that despite the large number of empirical studies devoted to measuring the welfare losses originated in economic distortions, the evaluation of the possible losses due to decreased investment efficiency has never been performed.

The purpose of this paper is to conceptually and empirically evaluate the two premises just discussed. Specifically, we intend to: (i) analyze and empirically estimate the magnitude of the social loss caused by the combination of tariff distortions and real wage distortions; (ii) reconsider the conventional models used to analyze the relationsbips between tariffs and the marginal social efficiency of capital by incorporating the existence of both trade and labor market distortions and by relaxing the "even' assumption so pervasive in the theoretical literature on immiserizing growth; and (iii) empirically measure the dynamic welfare losses that may exist if distortions reduce the social return of capital.

The empirical part of the analysis uses data from Chile for the period 1974-89. Rather than using parameters from different sources, we proceed with a compret ve econometric estimation of all the required parameters within a unified empirical framework. This goes a long way in solving the problem of consistency so prevalent in analyses of this nature when parameters obtained from different sources are incorporated into CGE models. We choose the case of Chile to illustrate the analysis mostly because

- 7/ The fact that the capital/labor ratios rapidly increase through time is a justification for focusing on the social efficiency of capital rather than of labor. Distortions that reduce the social efficiency of capital may at the same time increase that of labor. These distortions would cause dynamic welfare losses due to investment efficiency reductions but also dynamic gains if employment is also increasing through time. However, if the capital/labor ratio is increasing through time, the net dynamic welfare effect under constant returns to scale will still necessarily be negative.
# **Wage equation for testing segmentation hypothesis (skilled)**

Number of obs = 16669698 F( 47, 7228) = 357.69 Prob > F = 0 R-squared = 0.4503 Root MSE = 0.75121

||_Coef._<br>_R_|_obust Std._<br>_Err._|_t_|_P>|t|_|_[95% Con_|_f. Interval]_|
|---|---|---|---|---|---|---|
|dprim1|0.200|0.040|5.050|0.000|0.122|0.277|
|dprim2|0.239|0.038|6.290|0.000|0.165|0.314|
|dprim3|0.425|0.037|11.490|0.000|0.352|0.497|
|dsec1|0.490|0.037|13.140|0.000|0.417|0.563|
|dsec2|0.766|0.035|22.190|0.000|0.698|0.834|
|dter|1.484|0.036|41.620|0.000|1.414|1.554|
|exp|0.067|0.002|43.160|0.000|0.064|0.070|
|exp2|-0.001|0.000|-26.140|0.000|-0.001|-0.001|
|d1|-0.417|0.152|-2.740|0.006|-0.714|-0.119|
|d2|0.499|0.176|2.840|0.005|0.154|0.843|
|d3|0.216|0.211|1.020|0.306|-0.198|0.630|
|d4|-0.319|0.065|-4.870|0.000|-0.447|-0.190|
|d5|0.035|0.052|0.690|0.493|-0.066|0.136|
|d6|-0.048|0.183|-0.260|0.792|-0.407|0.311|
|d7|0.345|0.073|4.720|0.000|0.202|0.489|
|d8|0.195|0.033|5.970|0.000|0.131|0.259|
|d9|0.133|0.037|3.630|0.000|0.061|0.205|
|d10|0.214|0.035|6.140|0.000|0.146|0.282|
|d11|0.280|0.036|7.670|0.000|0.208|0.351|
|d12|0.135|0.034|3.940|0.000|0.068|0.203|
|d13|0.066|0.034|1.980|0.048|0.001|0.132|
|d15|0.264|0.031|8.630|0.000|0.204|0.324|
|d16|0.185|0.015|12.670|0.000|0.157|0.214|
|d17|0.110|0.015|7.260|0.000|0.081|0.140|
|carteira|-0.059|0.012|-4.940|0.000|-0.083|-0.036|
|seasonal|-0.705|0.213|-3.320|0.001|-1.122|-0.289|
|dse|-0.254|0.025|-10.000|0.000|-0.304|-0.204|
|arrend|0.627|0.191|3.280|0.001|0.252|1.002|
|poss|-0.085|0.341|-0.250|0.802|-0.754|0.583|
|cess|-0.014|0.230|-0.060|0.952|-0.465|0.437|
|prop|0.391|0.062|6.350|0.000|0.270|0.512|
|nnonrem|0.039|0.017|2.340|0.019|0.006|0.072|
|gend|-0.473|0.009|-50.400|0.000|-0.492|-0.455|
|preta|-0.257|0.024|-10.910|0.000|-0.303|-0.211|
|amarela|0.255|0.069|3.680|0.000|0.120|0.391|
|parda|-0.209|0.011|-18.640|0.000|-0.231|-0.187|
|dun|-0.168|0.022|-7.470|0.000|-0.212|-0.124|
|drn|-0.635|0.102|-6.230|0.000|-0.835|-0.436|
|dune|-0.396|0.018|-21.520|0.000|-0.432|-0.360|
|drne|-0.784|0.040|-19.360|0.000|-0.863|-0.704|
|drse|-0.368|0.055|-6.730|0.000|-0.475|-0.261|
|dus|-0.161|0.017|-9.470|0.000|-0.194|-0.127|
|drs|-0.414|0.055|-7.580|0.000|-0.521|-0.307|
|ducw|-0.153|0.023|-6.510|0.000|-0.199|-0.107|
|drcw|-0.224|0.096|-2.330|0.020|-0.413|-0.035|
|isourban2|-0.028|0.078|-0.360|0.723|-0.180|0.125|
|_cons|5.007|0.040|123.880|0.000|4.928|5.086|

Source: Authors’ calculations. Note: The grey-shaded dummies are dummies for agricultural sectors.

63
# **Attitude: Everybody should/does pay taxes**

Respondents believing that all businesses should pay taxes and that most of them actually do (this category is combined from two statements about should everybody pay tax and if they actually do) are much more likely to register for tax, and the difference is highly significant.<sup>19</sup>

# **Attitude: Information about registration is available**

It seems that attitude towards availability of information about tax registration and knowledge about it does not significantly influence the likelihood of registering for tax. (The level of significance is 10.4%)

# **Figure 18: Attitude: “Everybody should (and does) pay taxes” and likelihood for tax registration**

# **Figure 19: Attitude: “Information about registration is available” and likelihood for tax registration**

<!-- Start of picture text --> Agree 77 Agree 69<br>Disagree 54 Disagree 63<br>Total 66 Total 65<br>50 55 60 65 70 75 80 50 55 60 65 70<br>Percentage of informal businesses likely to register Percentage of informal businesses likely to register<br>Attitude: Everybody should (and do) pay taxes<br>Attitude: Information about registration is available<br><!-- End of picture text -->

# **Attitude: Tax authority/officers are unfair / corrupt**

Chi square analysis did not find the difference outlined in Figure 20 as significant at the 5% level of risk (the level of calculated risk is about 7%), but it still seems that there is at least a tendency showing that the belief that tax authority officers are unfair or corrupt may influence one’s decision to register for tax.

# **Figure 20: Attitude: “Tax authority/officers are unfair / corrupt” and likelihood for tax registration**

The survey of registered SMMEs did not include a similar question. It was excluded based on previous surveys which found little evidence of corruption in tax administration in South Africa (SBP, op cite).<sup>20</sup> <!-- Start of picture text --> Agree 61<br>Disagree 68<br>Total 64<br>50 55 60 65 70<br>Percentage of informal businesses likely to register<br><!-- End of picture text -->

> 19 Chi square = 42.657, significant on 1% level; correlation between the two variables is 0.291.

20 See also Transparency International, where South Africa is ranked second-best in Africa (after Botswana) and on par with South Korea.

15
Figure 5: Growth Incidence Curve: Income per capita, 2002-2012/13 <!-- Start of picture text --> A. Total Income B. Labor Income<br>9 9<br>8 8<br>7 7<br>6 6<br>5 5<br>4 4<br>3 3<br>2 Growth Incidence 2 pr_growth<br>gr_in_mean<br>1 Mean growth rate 1 pg_ci_u<br>95% Confidence Interval<br>0 0<br><!-- End of picture text --> Source: Own estimates based on HIES 2002, 2009/10 and 2012/13, annualized growth.

Figure <u>6: Sri Lanka. Remittances, Subsidies and Transfers (percentage of GDP) , 2002-2012/13</u> <!-- Start of picture text --> 12<br>10<br>8<br>6<br>4<br>2<br>0<br>Personal remittances received (% GDP)<br>Subsidies and other transfers (% GDP)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013<br><!-- End of picture text --> Source: World Development Indicators.

18
.

Table 1: Social Indicators, 1993 — 1998

|Indicator<br>7<br>~|| 1993|1998|
|---|---|---|
|Human Development|||
|--Education|||
|Primaryenrollment rate (net)|||
|Female|87.1|90.7|
|Male|86.3|92.1|
|Lowersecondary enrollment rate (net)*|||
|Female|29.0|62.1|
|Male|31.2|61.3|
|Uppersecondary enrollmentrate(net)*|||
|Female|6.1|27.4|
|Male|8.4|30.0|
|—-Child Nutrition|||
|Incidence ofStuntingAmongChildren 0-59Months.|51|34|
|Female|51|33|
|Male|50|35|
|-- Adult Nutrition|||
|Incidence ofmoderate and severe malnutrition in adults (Body mass<br>index less than 18.5)|32|28|
|Female(nonpregnant)|32|30|
|Male|32|25|
|Access to infrastructure|||
|%ofruralpopulationwithpublic health center within thecommune|93|97|
|% ofruralpopulationwithaccessto cleanwater**|17|29|
|% ofurbanpopulation withaccessto cleanwater**|60|75|
|% ofpopulationusing electricityasamain sourceoflighting|48|77|
|Ownership rates ofConsumerDurables|||
|%households owingaradio|40|47|
|%householdsowingatelevision|25|58|
|%householdsowingabicycle|67|76|

- Footnote: * The rapid increase in secondary enrollment rates reverses a decline in enrollments in the 1987-1992 period.

*%C]ean water is defined to include piped water, deep wells with pumps and rainwater.

Source: World Bank estimates based on VLSS93 and VLSS98.

3
However, depending on the spot market is problematic. If the processor assigns the contract quantities necessary to meet the requirements of the factory, other farmers’ production decisions depend only on the expected cane price in the unregulated market, which is lower than the factory’s floor price. Thus, cane production by non-contracted farmers is expected to be lower. In the event that the processor has to depend on the spot market, there may not be enough cane available.

This alternative hypothesis was analyzed by examining factory capacity utilization. Similar to the ex ante contract system, cooperatives’ cane acreage allocation is associated with the factory’s capacity. If capacity utilization by private factories is significantly higher than that by cooperatives, this would support the alternative hypothesis that the probabilistic permit system increases cane supply to the factory.

The Andhra Pradesh Sugar and Cane Commission reports each factory’s capacity utilization and sugar recovery rates annually (APSCC various years). The difference in capacity utilization between private and cooperative factories is statistically insignificant. Hence, increasing the quantity of cane as the reason for the use of ex post permits is not supported by the data. In contrast, private factories achieved significantly higher sugar recovery rates than cooperatives did, consistent with the predictions of the theoretical model.

Finally, cooperative members represent an imperfect proxy for the ideal control group, namely farmers with an ex ante contract to sell to private factory processors. Such farmers, of course, were unavailable in AP. Both farmer types share the characteristic that is essential for the purposes of this study: neither has to compete through production practices to obtain an outlet for cane. However, there may be other aspects of cooperative membership for which this study cannot control and that could account for cooperative members having lower costs. Some of 25
Table 1: Satisfaction rate - January 2011.

|Total po|pulation|30.0|
|---|---|---|
|(a1)|Bottom 40 percent of income distribution|28.0|
|(a2)|Rest of income distribution|33.2|
|“Losers”|or “Winners”||
|(b1)|Consume more than 200 Kwh per month (losers)|26.9|
|(b2)|Consume less than 200 Kwh per month (winners)|30.5|

(see table 1)<sup>7</sup> .

Yet the puzzle faded over time, slowly but surely. The popularity of the reform improved substantially but only after many months of implementation. The evolution of satisfaction rates is shown in figure 2. While in January 2011 only 30 percent of people were satisfied with the upcoming reform by August 2012 it more than doubled to 66 percent. This pattern was observed also among the poor, who went from a 28 percent satisfaction rate in January 2011 to 68 percent in August 2012. In short, it took many months before the reform became popular among the majority of the population. In May 2011 the overall satisfaction with the reform was better but still low at around 40 percent, even though by then households had been receiving the benefit under the new scheme for two months. It appears that people’s negative priors about the reform were only slowly adjusted.

# **3. Data**

This study uses data from 6 waves of household surveys conducted by _’La Prensa Gráfica’_ , the largest newspaper in El Salvador. The survey reflects the regular practice of that institution of polling people’s views on political and social issues. As discussions about the proposed subsidy reform had become a contentious political issue the newspaper decided to start polling, devoting a module of its periodic survey to the reform.

> 3It is also worth noting that under the new scheme churches were not eligible for receiving the benefit.

> 4Agencia EFE, March 15, 2012.

> 5Agencia EFE, March 12, 2012.

> 6René Portillo, dean of the Universidad Tecnológica de El Salvador, as quoted by the newspaper La Opinión on March 13, 2012.

> 7A t-test fails to reject the null that the satisfaction rate among ’losers’ is statistically different from the one of ’winners’.

11
but economic and social equity criteria are also prominent; simple designs and standards offering a service to a high proportion of low income users and residents characterise these specifications, particularly for investments like water supplies, slum improvement, solid waste management or sanitation.

44. But the concern in a number of cases extends beyond the scope of the immediate project. Several MDFs such as those in Salvador and Santa Catarina, Brazil, Nicaragua, the Philippines or Tamil Nadu State,India, insist that projects can only be financed within the framework of medium term investment plans which municipalities must produce to qualify for assistance. BANMA, Honduras, requires municipalities to produce plans of action for improved operation and maintenance of services. Several of the Latin American MDIs provide assistance with surveys, physical zoning and traffic plans. The beneficiary municipalities in the Philippines must establish mayoral task forces to improve operation and maintenance of services. The Calcutta Metropolitan Development programme including the MDF also seconds technical staff from state government to all municipalities.

45. On the revenue side the most common focus is on cost recovery practices. Conditions for MDF assistance are fairly standard. Water supply schemes, markets, commercial refuse collection, slaughter houses and bus terminals must be fully financed by user charges, rents and fees covering both operation and maintenance and amortisation of debt. Investment in non-self liquidating