rafmacalaba/data-use-mentions-extended huggingface dataset:
_Sources_ : Arroyo Marioli and Vegh (2023); International Monetary Fund; PRS Group (database). _Note_ : Bars show average correlation between the (Hodrick Prescott-filtered) cyclical components of real GDP and real government spending within groups. Vertical lines show 25th and 75th percentiles.

A. Countries with high (low) political risk are defined as those with political risk above (below) the sample median. Sample has 49 countries with high political risk and 24 countries with low political risk. *** indicates that the difference between the means for the two country groups is statistically significant at the 10 percent level or better.

B. Sample has 48 countries with low bureaucracy quality and 25 with high bureaucracy quality. The difference between the means for the two country groups is not statistically significant.

C. Sample has 45 countries with low control of corruption and 28 with high control of corruption. *** indicates that the difference between the means for the two country groups is statistically significant at the 10 percent level or better.

D. Sample has 32 countries with high government stability and four with low government stability. The difference between the means for the two country groups is not statistically significant.

E. Sample has 24 countries with high scores (above sample median) for the law-and-order index and 49 countries with low (below sample median) scores. *** indicates that the difference between the means for the two country groups is statistically significant at the 10 percent level or better.

F. For commodity exporters, the sample includes 28 countries with fiscal rules and 20 countries without fiscal rules. The entire sample includes 58 countries with fiscal rules and 47 countries without fiscal rules. The difference between the means for the two country groups (with and without fiscal rules) is statistically significant (indicated by ***) for the entire sample but not for the sample of commodity exporters.

32
IKK|30.00%|30.00%|30.00%|30.00%|30.00%|30.00%|
|INDEKS INVERS IPM|10.00%|10.00%|10.00%|10.00%|11.00%|10.00%|
|INDEKS INVERS PDRB|15.00%|15.00%|15.00%|15.00%|15.75%|16.50%|
|**VARIABEL KAPASITAS FISKAL**|||||||
|PAD|100.00%|75.00%|75.00%|70.00%|93.00%|93.00%|
|DBH PAJAK|100.00%|75.00%|75.00%|73.25%|100.00%|100.00%|
|DBH SDA|100.00%|50.00%|50.00%|100.00%|100.00%|63.00%|

Notes: Provinsi: Province; Variabel Kebutuhan Fiscal: Fiscal Need Variables;Jumlah Penduduk: population share; Luas Wilayah: area share ; Indeks Kemalhalan Konsruksi (IKK): Construction price index; Inverse Indeks Pembangunan Manusia (IPM): Inverse of Human Development Index; PDRB – Regional/local GDP ; Variabel Kapasitas Fiscal: Fiscal capacity variable; PAD – own source revenues ; DBH Pajak: revenue sharing from taxes; DBH SDA: Natural resources revenue sharing.

Source: Ministry of Finance, Government of Indonesia

# **DAU – An Evaluation**

In the interest of tax harmonization, limiting differentials in sub-national fiscal capacities and lowering tax administration costs, Indonesia has adopted a highly centralized tax system with the Central Government raising 95% of total revenues. This creates a large vertical fiscal gap (almost 90%) that is filled by revenue sharing and transfers. Revenue
Our results suggest that farmers in Vietnam can reduce their individual risks by employing lower-hazard pesticides and using protective items. However, the strong provincial effects may indicate significant externality problems as well. Pervasive contamination from others’ pesticide use may poison individual farmers, even if their own practices are prudent. Future research on pesticide poisoning should test this proposition by including measures of contamination of local surface water, ground water, air and soil.

# **6.  Summary and Conclusions**

In this paper, we have assessed the incidence and determinants of pesticide poisoning among rice farmers in Vietnam’s Mekong Delta. We believe that our results are of particular interest because they rely on explicit medical tests for poisoning, rather than conventional self-reporting of symptoms by farmers. The medical tests suggest that the incidence of poisoning from exposure to organophosphates and carbamates is quite high in Vietnam. Using the medical test results as benchmarks, we find that farmers’ selfreported symptoms have very weak associations with actual poisoning. Both the incidence of poisoning and farmers’ own apparent inability to distinguish its symptoms from other health problems suggest that regular checkups and blood tests should be conducted for those who load, mix, or apply highly or moderately-toxic organophosphate and carbamate pesticides.

In a regression analysis of blood cholinesterase test results, we find a lower incidence of poisoning for farmers who avoid the most toxic pesticides and use protective items. However, our results are either perverse or insignificant for other individual factors that are conventionally believed to reduce risk: Total pesticide applications, use of IPM techniques, and training for safe handling of pesticides. In contrast, we find very large provincial differences in poisoning incidence after we control for individual factors. Our results highlight the potential importance of negative externalities, and suggest that future research on pesticide-related damage should include information on local water, air and soil contamination. The potential importance of these factors suggests that collective 11
Policy Research Working Paper 7552

# **Abstract**

Fiscal indicators for resource-rich and resource-poor lowand middle-income countries are compared using annual data from 1996 to 2012. Resource richness is defined by export composition: fuel greater than a 25 percent share and/or ores and metals greater than a 10 percent share. Fuel exporters have a significantly better general government fiscal balance than the rest of the sample, and higher revenues and expenditures, which are approximately evenly split between extra consumption expenditure and extra capital expenditure. Only about a quarter of their extra revenue goes into extra consumption expenditure, and this proportion has been lower since 2005. Fuel exporters’ expenditure reacts with a lag to oil price fluctuations. There are no significant differences between ores and metals exporters and resource-poor countries, or between new and old resource exporters, in aggregate expenditures and revenues. Ores and metals exporters spend more on investment and less on government consumption. Some individual country cases are briefly discussed.

This paper is a product of the Governance Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at hhalland@ worldbank.org.

_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 views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
These values are less realistic based on evidence from other empirical studies (see Kilian, 2020, for a survey). However, I pick these values to demonstrate how the results vary as the oil supply elasticity restriction becomes less binding.

Figure 17 in the Appendix VII.1 presents the impulse responses of the global GDP factor and the real price of oil to U.S. oil supply and U.S. energy efficiency shocks with different upper bounds on the short-run price elasticity of oil supply. The results indicate that as we relax the elasticity constraint, the impact of a U.S. oil supply shock on the real price of oil and the global GDP factor becomes larger, while the impact of a U.S. energy efficiency shock on the same variables decreases. When the upper bound is 0.14, the effect on the global GDP factor of U.S. oil supply and U.S. energy efficiency shocks is almost identical after four quarters. However, the response at impact and during the first two quarters is always larger when triggered by a U.S. energy efficiency shock.

**_Fossil Fuel Data._** Next, I also check the robustness of the empirical results by using fossil fuel consumption data to identify U.S. energy efficiency shocks. Note that the historical decomposition results presented in Section IV.3 are obtained based on an identification of the U.S. energy efficiency shock using U.S. oil consumption data. However, it can be argued, for example, that a positive shock that satisfies the restrictions in Table 1, column 5, does not capture U.S. energy efficiency shocks because the decrease in U.S. oil consumption might be accompanied by an increasing the consumption of other fossil fuels such as coal and natural gas. Examples of such shocks include the increase in coal mining in the United States after the 1970s oil crisis and similarly a surge in natural gas production following the application of hydraulic fracturing
,236|6,939,667|14,337,250|32,820,500|83,057,000|824,328,000|
|Financial<br>cooperatives<br>**_Total active accts_**|429,409<br>**_306,791_**|6,775,080<br>**_6,577,066_**|13,558,793<br>**_12,987,120_**|27,872,800<br>**_27,912,376_**|68,375,462<br>**_55,461,244_**|242,573,867<br>**_1,042,339,394_**|

_Source: Own calculations based on Superintendencia Bancaria data Note: Inactive accounts are those which have not been used in the past six months_ 22. **Following the banking crisis, the relative supply of loan services has changed.** The total amount of gross loans in the period 1998-2003 has remained flat at around COP53 trillion. However, commercial loans increased by 18% over this period and now account for over 60% of available credit. In contrast, mortgage loans suffered a reduction of 38% between 1998-2003. The decline in mortgage lending is a direct result of the crisis, as asset problems in the mortgage sector and the closing of

- 17 Exchange rate of 1US$ = COP2,585 (as of October 27, 2004).

> 18 Minimum monthly salary in Colombia for year 2004 is COP358,000, or US$138.

25
20

# **Annex** A. **The** Data Sets

The national and urban data sets used in the current paper are essentially the same as those used in Ingram and Liu (1997). The primary data sources are explained in the Appendix of Ingram and Liu (1997, p. 35). The national data are from 50 countries with full data for 1970, 1980, and 1990. The urban data include 35 cities with data in two years (mostly in 1960 and 1980). The summary statistics for developing and high-income countries at both national and urban levels for the year of 1980 are shown in Table A-I below. Data for 1980 are used for the comparison because it is the only common year for the national and urban data sets.

When we carried out the empirical analyses presented in the current paper, a database of world infrastructure stocks for 1950-1995 compiled by Canning (1998) became available to us. Canning (1998) identified inconsistencies in the definition of paved road among countries and adjusted the official paved road data to fit a more consistent definition. Based on the Canning data, we made a few corrections to our national paved road length data. We also used the corrected data to re-estimate the paved road regressions presented in Ingram and Liu (1997) and found that the minor data corrections did not alter the empirical findings summarized there. The national data used in the current analyses are presented in Appendix Tables 1O, 1 1, and 12.

**Table A-1. Sample Summary Averages, 1980**

||Developing|economies|High income|economiesa|
|---|---|---|---|---|
||National|Urban|National|Urban|
|Item|(31 countries)|(9 cities)|(19 countries)|(26 cities)|
|PercapitaGNPin<br>1987US$b|546|1,589|14,247|15,037
Labor force participation rates by gender differ quite widely across the nine countries. In our regression samples, women’s share of the remunerated workforce ranges from 43% in Kenya to 64% in Ukraine.<sup>42,43</sup> The distribution of workers in formal wage, informal wage and selfemployment is starkly different across the nine STEP countries,<sup>44</sup> and there are key differences in the employment distribution of men and women. Men are more likely than women to be employed in the formal wage sector, although this is not the case in the former socialist countries in Europe where the share of women in formal wage employment exceeds that of men and a larger share of men than women are self-employed.<sup>45</sup> Women are more likely to be employed in the informal wage sector in the lower-income countries. Among those in the informal wage sector, men are more likely to be self-employed, a pattern that may be due to unequal access to savings and capital between men and women.

Education levels have risen significantly over the past two generations (1950-2010) in the STEP countries, but those levels have progressed differently for men and women.<sup>46</sup> The STEP data show not only gender gaps in years of schooling but also gender differences in cognitive and

> 42 Using cross-country data, men’s average participation rates are within 20 percentage points of each other (with Colombia falling far lower with men’s participation rate at just 60 percent); women’s labor force participation rates differ more widely, ranging from 32 percent in Colombia to 80 percent in Vietnam.

> 43 Women’s labor force participation rate is influenced by the pull on women’s time of marriage and caring for young children. While the mean singular age at marriage for women in these countries is similar at 22-24 years, their total fertility rate ranges from 1.4 in Serbia to 4.4 in Kenya.

>
~.~~

# = = <u>5.2.3.  Congestion (ζ 1) vs pure public good (ζ 0) and actual vs measured TFP growth</u>

Users of the LTGM‐PC must choose either a calibration with congestion (ζ=1) or without it (pure public good ζ=0). Unless users strongly feel that public capital in their country is not congested, we recommend using the congestion calibration. We use this ourselves as the default for the results in this paper and in the Excel‐based tool. The reason is that without congestion, _actual_ TFP in the model ( A� in equation 1) tends to depart from _measured_ TFP using standard growth accounting exercises. In any practical application of the LTGM‐PC, the actual TFP growth rate is one of the most important assumptions ‐ and also the most difficult to calibrate. When using the congestion specification, the actual TFP growth rate is similar to what one would get applying a standard growth accounting exercise. In contrast, without congestion the measured TFP growth rate is above the actual TFP growth rate, and so cannot be used to guide the calibration of the actual TFP growth rates without some kind of adjustment.

To see this, for each country we used the LTGM‐PC to generate a growth path by assuming that actual TFP growth A� was the same as that recorded in PWT 8.1 (over a 10‐year average).<sup>27</sup> Then we performed a standard growth accounting exercise on the generated growth rates (given growth rates of other growth fundamentals like human capital and the total capital stock), to generate _measured_ TFP growth. We then compared the measured TFP growth to the inputted actual TFP growth.

> 24 In the study, infrastructure is measured as a composite of several physical infrastructure indicators (as opposed to the monetary value of capital stock). A panel data set is used, comprising 88 countries that cut across
Figure 3 shows how the percentage of suffering depends critically on the income quintile in which respondents are located. Income quintiles are based on self-reported income in the Gallup World Poll and are calculated at the country level. For the bottom 20% of society, a median of 18% report to be _suffering_ , whereas for the richest 20% _suffering_ has a median of only 5%. This suggests that there is a positive relationship between welfare and well-being, at least at the within-country level.

_Figure 3 Distribution of the share of respondents categorized as suffering by income quintile over the period 2007-2014_ <!-- Start of picture text --> 1st quintile 2nd quintile<br>3rd quintile 4th quintile<br>5th quintile<br>.6<br>.4<br>.2<br>0<br><!-- End of picture text --> _Note: Dots indicate outliers as defined by Tukey (1977)._ Figure 4 shows the correlation between the average GDP growth per capita rate between 2006 and 2014 and the average change in the percentage of _suffering_ over the same period. Surprisingly, there is no significant correlation between these variables (ρ��0.07). Hence, at the between-country level there is little evidence of a relationship between GDP growth and subjective well-being. In several countries with an average GDP growth rate of more than 4%, most notably Sudan, Rwanda and India, there was a substantial increase in the percentage of people _suffering,_ consistent with the existence of the so-called “unhappy development” paradox 14
are often misdiagnosed because pesticide poisonings can mimic other common health problems (FAO, 2001).<sup>2 3</sup> To assess the actual extent of this problem, the World Bank collaborated with the Vietnam Association of Occupational Health to study pesticide poisoning among Mekong Delta farmers in the first quarter of 2004. With 482 farmers participating, the study collected both survey and clinical data. Structured questionnaires were used to collect information on farming systems, pesticide use and practices, applicator precautions, protective measures, and self-reported poisoning symptoms.<sup>4</sup> All participating farmers were examined by the doctors from the Vietnam Association of Occupational Health. Using clinical exam results and self-reported symptoms, the study team selected potentially-poisoned farmers for blood tests that detect acute or chronic contamination by pesticides (organophosphates and carbamates).

The medical survey covered the districts of An Phu and Chau Thanh (An Giang province), Thot Not and Vi Thanh (Can Tho province), Tan Thanh and Thu Thua (Long An province ), Cai Lay and Cho Gao (Tien Giang province), and Tra Cu and Tieu Can (Tra Vinh province) in the Mekong Delta. The 482 surveyed farmers are almost all male (3 females were examined). Their ages range from 15 to 70 years, with an average age of 39. The surveyed farmers average 8 years of education; 74% report that they smoke and 48% report drinking alcoholic beverages. To minimize possible reporting bias, the surveyed farmers were assured of anonymity.

# **4. Health Impairment: Self-Reported and Blood Test Results**

The survey asked farmers to report symptoms experienced after mixing and spraying pesticides. Figure 2 displays the most commonly-reported symptoms: dermal (skin irritation: 63%), neurological (headache: 60%; dizziness: 53%), ocular (eye

> 2 Hospital admission records in Vietnam attribute 11% of all poisonings to pesticide misuse: approximately 840 poisonings in 53 cities and provinces in 1999 (Poison
should expect more violence today. To test these hypotheses, we considered homicide rates during 2017 using police data, and victimization rates from 2004 to 2016 in LAPOP surveys.

Table D.25 in the Appendix presents the results. Consistent with the finding that violence during the conflict was not greater in guerrilla-controlled areas, the results largely suggest no differences in homicide rates between areas once under and outside FMLN control.<sup>35</sup> If anything, the estimates are negative, which suggests that the documented differences in long-term development did not arise from increases in conflict or violence. Additionally, there is evidence that residents of

> 34 We excluded all segments within an 80 m distance to shut off almost every segment close to the boundary and inside the guerrilla zone with an immediate neighbor outside it.

> 35Figures D.17 and D.18 confirm this result for different bandwidths.

40
# **2.3. Earthquake Hazards and Impact on WWTPs**

Direct earthquake impacts on WWTPs are largely due to ground shaking and ground failures, with individual components or stages of the wastewater treatment process responding in different ways. For example, non‐structural elements, such as scrapers, aerators, and mechanical equipment are largely be affected by excessive shaking. Whereas, ground deformation (in the form of fault rupture, landslides, and liquefaction causing settlement or lateral spreading), lead to breakages, lifting, and deformations of structural elements, such as tanks, pipes, or treatment pond bunds. The actual shaking properties required to initiate such processes vary significantly spatially – even at very local scales. This ensures predicting damage due to both shaking and ground deformation on a wider WWTP facilities remains a challenge.

Given the myriad of data that is required to carry out a detailed analysis at each WWTP site, we instead adopt two readily available measures to provide a proxy for estimating asset damage. These are: Peak Ground Acceleration (PGA) and Liquefaction Susceptibility. PGA is an important ground motion parameter used in earthquake engineering applications and design as structural damage is often closely correlated. Liquefaction susceptibility is a measure of how susceptible local ground conditions would be to liquefy given sufficient ground shaking is experienced. Combined these parameters can give a proxy for a combined likelihood of a WWTP facility experiencing damage in an earthquake via the two predominant causes: shaking and ground deformation.

As with the flooding exposure analysis above, PGA and Liquefaction Susceptibility parameters are assigned to each WWTP through a simple spatial intersection. These datasets are described in the following section.

# **2.3.1. Hazard Datasets**

PGA hazard maps are for return periods of 250‐, 475‐, 950‐, 1500‐, and 2475‐years (UNISDR, 2015). As these were created at the global scale, we note that they may underestimate the hazard in local areas for fault lines not captured in historical catalogues. As