rafmacalaba/datause-encoder-data huggingface dataset:
in freight rates across destinations. It stems from the fact that trade in commodities is highly imbalanced. As a result, countries with large net imports face much higher ‘incoming’ than ‘outcoming’ freight rates, with many ‘outcoming’ vessels traveling without a cargo, i.e., ballast traveling (Brancaccio et al., 2021).<sup>10</sup> Leveraging the Clarksons SIN spot freight rates, we demonstrate this fact with Table 3 (Column 5) and summarize it as follows.

**Stylized Fact 8.** _There is a pronounced dispersion in the normalized dry-bulk freight rates across destinations._

Using Clarksons Research data from 2010 to 2016, Brancaccio et al. (2020) demonstrated that the asymmetry and endogeneity in dry-bulk freight rates have important implications for international trade and beyond. First, it weakens the strength of comparative advantage and reallocates production from exporters to importers. Second, there is a network effect which can be demonstrated as follows. If one of the large importers experiences a slow-down in its imports, some ships reallocate to other countries and regions, thus reducing their freight rates and increasing trade in those regions.

Over time, the freight rates are affected by the demand volatility and uncertainty and by the sluggish response in supply due to time to build and convex operating costs (Kalouptsidi, 2014).<sup>11</sup> To construct the trend of dry-bulk freight rates, we utilize Jacks and Stuermer (2021) data and estimates, who draw on a bevy of historical sources to construct an impressive 170 year-long time series (see Appendix A in their paper for specifics). We present the resulting trend in Figure 8. When explaining the long-run trend in real dry-bulk freight rates, the authors appeal to Harley (1988), Mohammed and Williamson (2004), and Tenold (2019) who suggested the long-run productivity gains as the major contributing factors.<sup>12</sup> Instead, for the short-run variation, Jacks and Stuermer (2021) found the _demand_ shocks to account for approximately 50% of short-run boom/bust historical freight rate

> 10The magnitude of the effect is very large. According to Brancaccio et al. (2021), a staggering 42% of dry-bulk vessels are travelling without cargo.

11Kalouptsidi (2014) described pronounced “echo effects” in this market: entry spikes are likely to repeat with intervals equal to the ship’s lifetime. Government interventions potentially may smooth the supply and the volatility in this market.

12For example, a significant drop in the freight rates between 1850 and 1914 can be attributed to switching from wind sailing to steam engine shipping and the opening of the Suez Canal in 1869 (Pascali, 2017).

12
**Table A3: Descriptive Statistics - Before Delegated Randomization**

|Before delegated|randomizat|ion of inspec|tor assign|ment|||
|---|---|---|---|---|---|---|
||**Othe**|**r declaratio**|**ns**|**Excess in**<br>**(corr**|**teraction de**<br>**uption susp**|**clarations**<br>**ected)**|
||**Average**|**Std. dev.**|**Obs.**|**Average**|**Std. dev.**|**Obs.**|
||**Excess int**<br>|**eraction**<br>|||||
|Excess interaction share|0.00|0.02|40,397|0.11|0.06|4,661|
|Excess interaction share - inspector logits|0.00|0.02|40,397|0.11|0.06|4,661|
||**Cont**|**rols**|||||
|Risk characteristics|||||||
|Risk score|6.51|2.87|39,876|7.46|2.08|4,646|
|Tax rate|0.30|0.14|40,397|0.36|0.11|4,661|
|Red channel dummy|0.28|0.45|40,397|0.31|0.46|4,661|
|Mixed shipment dummy<br>|0.34|0.47|40,397|0.48|0.50|4,661|
|Diferentiated share|0.68|0.46|40,397|0.78|0.39|4,661|
|Valuation advice dummy|0.08|0.27|40,397|0.21|0.41|4,661|
|Additional characteristics|||||||
|Log initial weight|10.00|1.73|40,397|10.21|1.17|4,661|
|Log port authority weight|10.55|1.08|23,314|10.51|0.94|2,982|
|Log initial value|10.08|1.18|40,397|10.13|0.87|4,661|
|Log initial unit price (relative to internal prices)|-0.09|0.53|40,380|-0.16|0.52|4,653|
|Initial hypothetical tax revenue losses (internal<br>|0.08|0.50|40,380|0.16|0.51|4,653|
|prices)|||||||
|High potential tax yield dummy|0.26|0.44|28,547|0.50|0.50|2,855|
||**Customs o**|**utcomes**|||||
|Main outcomes|||||||
|Time|2.99|1.66|37,337|3.06|1.76|4,272|
|Fraud|0.08|0.27|40,397|0.13|0.34|4,661|
|∆log value|0.02|0.08|40,322|0.03|0.09|4,648|
|∆log tax|0.02|0.09|36,196|0.03|0.10|4,429|
|Hypothetical tax revenue losses (internal prices)|0.08|0.49|40,380|0.15|0.51|4,653|
|Additional outcomes|||||||
|Weight gap (port authority weight)|0.01|0.30|22,983|0.04|0.36|2,933|
|Hypothetical tax revenue losses (internal prices,<br>port authority weight)|0.09|0.55|21,168|0.21|0.60|2,797|
|Hypothetical tax revenue losses (external refer-<br>ence prices)|0.57|0.95|28,547|1.11|1.09|2,855|
|Hypothetical tax revenue losses (external refer-|0.36|1.13|14,742|0.95|1.17|1,733|
|ence prices, port authority weight)|||||||
|Hypothetical tax revenue losses (valuation ad-|0.10|0.18|3,280|0.23|0.26|978|
|vice)|||||||

_Notes:_ excess interaction declarations are those that are handled by an inspector whose excess inspection share (the difference between the share of given broker’s declarations handled by the inspector in question and the hypothetical share she would be expected to handle if the allocation of declarations to inspectors was conditionally random as prescribed by official rules) for which we can reject the null hypothesis of conditional random assignment using simulation methods (as described in section 4). The sample covers the period Jan 1, 2015 to Nov 17, 2017, i.e. the period before delegated random assignment.

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Understanding the factors determining RER volatility and the role of openness in either mitigating or amplifying the shocks to real exchange rates is crucial due to the effects of the variability of RERs on economic performance. Aghion _et al._ (2006) show theoretically and empirically that rising exchange rate volatility can hamper growth, especially in countries with shallow financial markets and macroeconomic volatility is mainly driven by financial shocks. The negative impact of the RER volatility on growth can be transmitted through declining investment (Servén, 1998; Bleany and Greenaway, 2001) and by lower foreign trade —particularly in differentiated products (Broda and Romalis, 2003). This paper also relates to the literature that investigates the role of RERs as shock absorbers. Although more flexible exchange arrangements have greater ability to mitigate the impact of real shocks (say, terms of trade shocks) on economic performance — especially, negative terms of trade shocks (Broda, 2004; Edwards and Levy-Yeyati, 2005), this line of research goes beyond our scope.

Our paper complements existing evidence that higher trade and financial linkages between debtor and creditor countries would render a lower volatility of the bilateral exchange rate of the debtor country vis-a-vis its creditor (Dereveux and Lane, 2003). In general, deeper trade linkages between two countries would dampen their real exchange rate volatility and may encourage them to join a currency union (Broda and Romalis, 2003). It also complements evidence on the role of openness in mitigating or amplifying the impact of real shocks in the economy. For instance, Calderón, Loayza and Schmidt-Hebbel (2005) find that trade openness tend to amplify the impact of terms of trade shocks on output volatility while financial openness seem to attenuate the impact of trade and financial shocks. Di Giovanni and Levchenko (2006) use data at the industry level to show that trade leads to deeper specialization and increasing output volatility in more outwardoriented industries. Finally, Buch _et al._ (2006) use the firm-level data in Germany to show that rising trade openness affects the volatility of firm’s output by modifying the exposure to shocks as well as the response of firms with the net effect being ambiguous.

In addition, our paper improves upon the evidence presented by Hau (2002) in the following dimensions: first, it presents evidence for a larger sample of countries (82) and sample period (1975-2005).<sup>2</sup> Second, we present panel data evidence (instead of crosssection). We work with 5-year period observations on the volatility of RER such as openness and fundamentals. Third, we instrument for trade and financial openness using external instruments recommended by recent existing literature. Fourth, unlike Hau (2002), 4
from a non-linear logistic diffusion model, it is under the suspicion of relying on the nonlinearity instead of homogeneity to isolate the impact of faster internet.

A large body of literature that has started since Kraft and Kraft (1978) examined the causal relationship between energy consumption and output. However, the results are mixed. Review studies by Oztruck (2010) and meta-analysis by Bruns et al. (2014) conclude that economic growth drives energy consumption instead of the other way around. There are some exceptions though, which suggest that energy consumption drives economic growth.<sup>5</sup>

# **4. Infrastructure and Income Inequality**

Is there any empirical evidence to demonstrate that increased infrastructure investment or service provisions increase the incomes of poor households (i.e., reduces poverty) and thereby reduces income inequality? A few studies examine this issue (Démurger 2001; <mark>Calderón</mark> and Chong 2004; <mark>Calderón & Servén</mark> 2004, 2010; Chatterjee and Turnovsky 2012; Chotia and Rao 2017a, 2017b; Hooper et al. 2018). Table 3 summarizes the methodologies used and the findings of these studies.

<mark>Calderón</mark> and Chong (2004) investigate the relationship between physical infrastructure stocks and income inequality using data from more than 100 economies around the world for the period 1960-1997. Relationships of different infrastructure stocks – telecommunications, energy, roads, and railways – with income distribution were investigated individually and in aggregation. Using alternative empirical techniques (e.g., cross-country and GMM on dynamic panel data), they found that infrastructure stock is negatively linked with inequality (i.e., higher the infrastructure stock, the lower is the income inequality); the relationship is more prominent in low-income countries. Calderon and Serven (2004) expanded the study in terms of the number of economies (121 instead 101 before) and time horizon (1960-2000 instead of 1960-1997 before) using the same methodology. They show that not only the increased quantity of infrastructure stocks reduces income inequality but also the improved quality of infrastructure services does the same. <mark>Calderón & Servén</mark> (2010) conducted a similar analysis focusing on Sub-Saharan Africa, where the level of economic development is low and where the trade-off between the investment on immediate

> 5 Please see Oztruck (2010) and Bruns et al. (2014) for more details.

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Figure 3.20 Probability of going to museums, films, exhibitions, or sports events during the past year

|Family economic condition-well off (student response)<br>Mother's education sr. sec|||||||
|---|---|---|---|---|---|---|
|Student performance ranking - high (self assessment)|||||||
|school is in city<br>Student standardized test score|||||||
|-0.100|-0.050|0.000<br>0.050|0.100|0.150|0.200|0.250|

_Source_ : Authors’ calculation based on the CEPS.

# **Curriculum reforms for the academic track**

There have been continued curriculum reforms in China, and one of the primary goals of the reform in recent years is to emphasize holistic development of students (普普普普 ), shifting away from the exam orientation. The Eighth Curriculum Reform is one of the most ambitious reforms. The reform covered the entire basic education system including preschool, compulsory education (primary school and middle school), and high school. The high school part of the reform was enacted in 2004. It was first formally implemented in four provinces (Shandong, Hainan, Guangdong, and Ningxia) in China. Other provinces and municipal cities joined the reform in succession in the following eight years until 2012.

There are six specific implementation goals of the reform as stated in the MOE’s “Basic Education Curriculum Reform Outline” (MOE 2001).

- (a) Change the existing curriculum, which puts too much emphasis on imparting knowledge to students, to a new curriculum to emphasize helping students form positive and active attitudes toward learning.

- (b) Reduce the number of subjects and form a more balanced and comprehensive curriculum. Adjust the curriculum to satisfy different needs of students in different areas in China.

- (c) Reduce the workload of students by removing the “hard, sophisticated, seldom-used, and outdated” contents from courses. Pay attention to students’ interest, and select the most fundamental knowledge and skills that could provide lifelong benefits.

- (d) Change the current style of teaching that only requires students to memorize and accept what is taught in class, and cultivate students’ abilities in collecting and processing information, acquiring knowledge, analyzing and solving problems independently, as well as in communicating effectively and cooperating with each other.

- (e) Align curriculum with student development rather than a tool for screening.

- (f) Improve the adaptation of the curriculum to students and schools by changing the centralized management of curriculum to a three-tier management at the national, the local, and the school levels.

The key changes included new compulsory courses such as arts, even though these are not covered by the university entrance examination. It also provided extensive elective courses and established a credit system. The elective content constitutes enriching extensions of the compulsory courses.

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<!-- Start of picture text --> Potential Investors<br>Payment of interest and<br>principal of the cat bond (at<br>Investment in the<br>maturity; no disaster)<br>dedicated cat bond<br>Contract with Special Purpose Vehicle BigCat (Issuer)<br>(principal invested in safe investments)<br>Disaster claims<br>(investors lose their investment)<br>Premiums<br>ProactiveCountry (Sponsor)<br>Provide the government with rapid capital post disaster<br>High value if rigid budget procedures<br>Fast financial aid to victims of disasters<br><!-- End of picture text --> _Source: Michel-Kerjan et al. (2011)_ **FIGURE 3. SIMPLIFIED STRUCTURE OF A GOVERNMENT CAT BOND** _How a government benefits from a cat bond._ There are several widely used ways the payment of a cat bond can be triggered. First, all the stakeholders can agree at the execution of the contract on an external trigger for the insurance payment, independent of the actual level of losses the country has suffered, but easily verifiable, similar to the rainfall trigger on index-based insurance. This is called a _parametric_ trigger. The data for this parameter can be collected at multiple reporting stations across a given geographical area. It is also possible to agree on a certain level of the actual economic losses incurred by Proactive from a disaster or series of disasters over the maturity of the cat bond. This is an _indemnity_ trigger.<sup>10</sup> The main advantage of an indemnity trigger is that the payment received by Proactive will be much closer to its actual loss but it could create moral hazard problem by having the country overstate the loss it has incurred. Parametric

> 10 This form of cat bond trigger is more analogous to a traditional insurance policy with its loss settlement process. Other triggers are on _modeled losses_ or _industry losses_ .  For _modeled losses_ , instead of dealing with Proactive's actual losses, an exposure portfolio is constructed for use with catastrophe modeling software. When there is a disaster, the event parameters are run against the exposure database in the cat model. If the modeled losses are above a specified threshold, the bond is triggered. For _industry losses_ , the cat bond is triggered when an entire industry loss from a certain peril for the insurance industry doing business in this country reaches a specified threshold.

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WPS4045

# **URBAN INFRASTRUCTURE FINANCE FROM PRIVATE OPERATORS:**

# **WHAT HAVE WE LEARNED FROM RECENT EXPERIENCE?**

**Patricia Clarke Annez**<sup>**1**</sup>

## World Bank Policy Research Working Paper 4045, November 2006

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> 1 Urban Advisor, World Bank. The author is indebted to Robert Buckley, Jonathan Halpern, Sonia Hammam, and Ioannes Kessides for comments and discussions of this paper.

1
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# **6. Numerical Example**

We present here a numerical example to evaluate the performance of some of the pricing schemes discussed above, regarding efficiency of water allocation. We consider a representative farm of 4 hectares (1 hectares = 2.47 acres) with a surface water quota of 1000 acre inches (or 102800 cubic meter). Two crops, cotton and wheat that require water and nitrogen, are considered. Nitrogen can be purchased in the market; water is provided by a water agency. Other inputs are assumed fixed. The farmer's decision problem is as described in Section 3.

= We use quadratic approximation for the per-hectare production functions, gj(q,x) +xj+jq+yjx+6jqq2 +jx 2 +ijqx, j = cotton or wheat, with the parameter estimates of Hexem and Heady (1978)

## **Table 3: Parameter estimates of the quadratic production functions for Cotton and Wheat are taken from Hexem and Heady (1978). Yield is measured in Lb/acre***

|Coefficient|Cotton|Wheat|
|---|---|---|
|Intercept: a|233.71|-10414|
|Water (acre-inch): ,B|23.65|852.01|
|Nitrogen (Lb/acre): y|0.438|11.6|
|Water*Water: 3|-0.182|-12.9|
|Nitrogen*Nitrogen:0|-0.0033|-0.032|
|Water*Nitrogen: q|0.0209|0.0925|
|Range of water input|8-40<br>|0-40|
|Experimental range of nitogen input|j0-120|0-200|

* Metric conversion: 1 acre-inch = 102.8 cubic meter; 1 Lb = 2.24 Kg; 1 Lb/acre = 1.102 Kg/hectare.
and age, and reporting on the intrahousehold inequalities in deprivation status for individuals in MPI poor households, following from the strategy set forth in Alkire, Ul Haq, and Alim (2019). This paper is the first such study to apply this MPI measurement strategy to adults.

Furthermore, because several criteria are applied – e.g., an individual’s displacement status, gender, residence in an MPI poor household, and eligibility for the relevant indicator’s deprivation cutoff – the number of observations used for cell size comparisons can become small. Point estimates where the _n_ <25 is noted with ‘NA’, and if _n_ <=50, brackets ‘[]’ – this follows the reporting criterion established by DHS (Croft et al. 2018: p.1.65) and cautions against strong interpretation of those results.

# **4 Data**

# **_4.1 Data sources_**

The five household surveys conducted in Sub-Saharan Africa and selected for the purpose of analyzing the MPI constructed in Admasu et al. (2021) are presented in Table 2.<sup>8</sup> These surveys differ in population and geographic coverage, as well as sample design and size, limiting the possibilities for comparisons across countries. A full analysis of these data sources can be found in Admasu et al. (2021).

_Table 2: Summary of the datasets_

|**Country**|**Survey**|**Sample Design**|**Geographical**<br>**coverage**|**Population**<br>**coverage**|**Retaine**<br>**d**<br>**sample**|
|---|---|---|---|---|---|
|Ethiopia|Skills Profile<br>Survey<br>(2017)|Multi-stage<br>stratified random<br>sample|Refugee camps and<br>proximity in Tigray<br>Afar,<br>Gambella,<br>Benishangul<br>Gumuz,<br>and Somali regions|Refugees<br>from<br>Eritrean,<br>South<br>Sudanese,<br>Sudanese,<br>and<br>Somali and hosts|26,517<br>(96.5%)|
|Northeast<br>Nigeria|IDP Survey<br>(2018)|Multi-stage<br>stratified random<br>sample|Six Northeastern states<br>(Adamawa,<br>Bauchi,<br>Borno, Gombe, Taraba,<br>and Yobe)|IDPs and host<br>communities|17,543<br>(97.7%)|

10
# **Conclusions**

There have been significant changes in attitudes to poverty over the last three centuries, away from complacent acceptance, and even contempt for poor people, to the view that society, the economy and government should be judged in part at least by their success in reducing poverty. There are a number of possible explanations for this change. Greater overall affluence in the world has probably made it harder to excuse poverty. Expanding democracy has given new political voice to poor people. And new knowledge about poverty has created the potential for more well-informed action.

Word counts from _Google Books_ suggest that there was a pronounced “Poverty Enlightenment” in the latter half of the 18th century, with a seven-fold increase in the incidence of references to poverty between 1740 and 1790. The all-time annual record level of references to poverty (as a proportion of all words) was in 1787.

But attention faded after that. If the late 18th century gave birth to the modern idea of distributive justice, based on the notion that a minimum standard of living should be attainable by all members of society, then it seems that the idea died a slow death in the public consciousness for the next 170 years.

In both English and French, a second “Poverty Enlightenment” only emerged in the latter part of the 20th century. Within 10 years the incidence of the word “poverty” in Englishlanguage _Google Books_ had exceeded its level even in 1800. The latter half of the 20th century saw a doubling in the incidence of references to poverty. Averaging over a few years either side, the peak in the average incidence of references to poverty was around 2000. This Second Poverty Enlightenment came with a similar (proportionate) increase in references to economics, which provided a deeper set of models for understanding poverty. It also came with rising awareness of poverty in developing countries.

In the English language, the word “inequality” has been used far less than “poverty.” This was not true in the French language books. Until the late 20th century, “inégalité” was as prominent as “pauvreté” (more so in the late 18th century). And the French texts referred less to poverty and more to inequality than the English texts, though there are strong signs of convergence between the two languages toward the end of the 20th century.

43