rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Summary of results**

This paper describes the methodology and findings for the extreme hazard exposure analysis of the Chinese wastewater sector to flooding and earthquakes hazards. This analysis is undertaken for changing flooding, under climate scenarios, and earthquake exposure of a spatially accurate wastewater treatment plants (WWTP) database for China, covering a total of 1,346 assets with additional attributes quantifying the treatment capacity and users dependent on each plant.

For flooding, we apply a global river routing (CaMa‐Flood) model that quantifies the change degree of flood exposures from the present time‐period (1980‐2005) to the near future time‐period (2016‐2035) to a far future time period (2036‐2055). These changes are evaluated for all floods exceeding 1 in 30, 1 in 50 and 1 in 100 events as estimated through an ensemble of 11 climate models under RCP4.5 and RCP8.5 climate scenarios.

For earthquakes, we use Peak Ground Acceleration (PGA) hazard maps for return periods of 250‐, 475‐, 950‐, 1500‐, and 2475‐years, and group PGA into five discrete categories: Very High **VH** (>0.65g), High **H** (0.34g ‐ 0.65g), Medium **M** (0.18g ‐ 0.34g), Low **L** (0.092g – 0.18g), and Very Low **VL** (<0.092g). Similar categories are assumed for liquefaction susceptibility: VH, H, M, L, VL. These are based on a readily available dataset created for use at the global scale using a calibrated geospatial prediction model.

# **_Flood exposure results_**

Our analysis shows that more numbers and users of WWTPs will witness an increase in extreme flooding exposures, in comparison to the numbers of WWTPs that will witness a decrease.

- Under RCP4.
times larger in the IV estimations. We also find high values for both the Cragg-Donald Wald F-Statistics and the Kleibergen-Paap rk LM statistics. We are thus confident that, for the model of column (3), weak instruments or underidentification are not an issue. The first stage regression (column 1, Table C.5) shows that there is a significant correlation between the IV and FDI inflows, and the IV is

> 14Jaimovich and Panizza (2007) build the real external shock by using the weighted average of GDP growth in country _i_ ’s export partners. Formally: _SHOCKit_ = _EXPGDPii_ � _j_<sup>_φij,t−_1</sup><sup>_GRj,t_.Wherewhere</sup><sup>_GRj,t_measuresreal</sup> GDP growth in country _j_ in period _t_ , _φij,t−_ 1 is the fraction of exports from country _i_ going to country _j_ , and

> _EXPi_<sup>country</sup><sup>_i_’saverageexportsexpressedasashareofGDP.JaimovichandPanizza(2007)findthat</sup> _GDPi_<sup>measures</sup> in emerging and developing countries _GRi,t_ = 1 _._ 7 _× SHOCKi,t_ .

> 15We adjust by the relative standard deviations so that a one standard deviation shock to the instrument

> ( _σIV_ = 1 _._ 1) is equivalent to one standard deviation shock of the real shock ( _σSHOCK_ = 0 _._ 7).

23
**Table 9 Breakdown of Education Programs by Area**

|_Area_|_Number of programs_|
|---|---|
|Social sciences|2,953|
|Education|744|
|Humanities, arts|648|
|Services|455|
|Agriculture, forestry|383|
|Computer sciences|358|
|Health care|269|
|Engineering, industrial vocations|194|
|Otherprograms|40|

_Source:_ European Trend Chart on Innovation, 2001

- Government R&D spending is small, both as a percent of GDP (0.48%), in absolute terms, and relative to other NMS’s.  For example, such countries as Finland and Denmark with twice the population generate 100 times as much R&D spending as Latvia.

**Figure 29 Gross domestic expenditure on R&D (GERD) as % of GDP, 2001**

<!-- Start of picture text --> Romania<br>Latvia<br>Bulgaria<br>Lithuania<br>Estonia<br>Slovakia<br>Poland<br>Turkey<br>Hungary<br>Czech Rep.<br>Slovenia<br>EU<br>Ireland<br>Finland<br>Denmark<br>US<br>0,00 0,50 1,00 1,50 2,00 2,50 3,00 3,50 4,00<br>Public R&D expenditures Business expenditures on R&D<br><!-- End of picture text --> _Sources_ : EUROSTAT, R&D statistics and Trendchart.

In 2001 Latvia invested $81million in R&D, four times as much as in 1995, but roughly equal to what Ford, General Motors, Siemens or Ericsson spend in one week and a fraction of what they spend per researcher.<sup>**37**</sup>

> 37 Another interesting comparison is with
taxes and direct transfers); and indirect interventions, such as value-added tax (VAT), excise tax, and other indirect taxes; and the monetized value of public education and health services. Additional details on the application of the CEQ in Niger can be found in Appendix A.

# **4.1 Fiscal Impoverishment**

In presenting the findings on fiscal losses, we contrast the conclusion Higgins and Lustig (2016) made, with the conclusions we have drawn. Table 2 reports fiscal impoverishment effects as defined by Higgins and Lustig in Niger at the national level as well as at some subgroup levels, for example, gender of the head of household, and area of residence.

> 6 _Enquête nationale sur les Conditions de Vie des Ménages et Agriculture_ (ECVMA).

> 7 A World Bank‐wide collaborative effort launched in 2010 to facilitate access to budget data.

13
# **Risk Shifting and Long-Term Contracts: Evidence from the Ras Gas Project**

Mansoor Dailami World Bank Institute The World Bank Washington, DC 20433 and Robert Hauswald Kelley School of Business Indiana University Bloomington, IN 47405-1701 The authors would like to thank Peter Christoffersen, Tim Crack, Bim Hundal, Lutz Kilian, Gregory Randolph, John Strong, Haluk Unal and Volker Wieland for stimulating discussions, and Panos Kogkalidis for excellent research assistance.
of night light density of origin while conditioning on destination fixed effects. The result reported in column 4 indicates a significant positive correlation implying a longer reach of origins with a larger size of the economy.

# 3.1 **Transport costs during the COVID-19 pandemic**

Our trips data set covers trips that took place during the COVID-19 national lockdown period (March 24May 30, 2021) and various localized lockdown periods that lasted till November 30, 2020. It also has information on trips during post-lockdown up to February 24, 2021. The pandemic period covered in our 10
dynamic eects of public expenditure shocks in the United States, Journal of Macroeconomics 56: 340360

- [32] Ramey, Valerie A. 2019. "Ten Years after the Financial Crisis: What Have We Learned from the Renaissance in Fiscal Research?" Journal of Economic Perspectives, 33 (2): 89-114.

- [33] Ramey, Valarie A. and Sarah Zubairy. 2018. Government spending multipliers in good times and in bad: Evidence from US historical data. Journal of Political Economy 126(2): 850901

- [34] Romer, Christina D., and David H. Romer. 2010. "The Macroeconomic Eects of Tax Changes: Estimates Based on a New Measure of Fiscal Shocks. American Economic Review, 100 (3): 763-801.

- [35] Romer, Christina D., and David H. Romer. 2016. "Transfer Payments and the Macroeconomy: The Eects of Social Security Benet Increases, 1952-1991." American Economic Journal: Macroeconomics 8 (4): 142.

- [36] Sala-i-Martin, Xavier. and Jerey Sachs. 1991. Fiscal Federalism and Optimal Currency Areas: Evidence for Europe from the United States, NBER Working Paper No. 3855

- [37] Wilcox, David. 1989. Social Security Benets, Consumption Expenditure, and the Life Cycle Hypothesis, Journal of Political Economy 97 (2): 288304.

41
# **1  INTRODUCTION**

Compared to the vast empirical literature on policies affecting trade in goods, the empirical analysis of services trade policy is still in its infancy.<sup>3</sup> A major constraint has been inadequate data on policies affecting services trade. Significant progress has been made over the last decade, at the World Bank and the OECD, in developing cross-country databases on services regulations and policies, and methods to quantify them.

This paper describes a new initiative: the Services Trade Policy Database (STPD) developed jointly by the World Bank and the WTO Secretariat. The Database contains comparable information for 68 economies on services trade policy in five main services sectors – financial services, telecommunications, distribution, transportation, and professional services – further broken down into 23 subsectors. It is based on a new comprehensive classification of services trade policies jointly developed by the World Bank and the WTO Secretariat, consistent with both the past World Bank classification and the recent OECD classification. Hence, it offers recent information comparable across a large number of developing and industrial economies and backwards compatible with information for the years 2008-11 in the previous World Bank database.

The difficulty in creating such a database is well known but the task has large potential benefits. Our main reason for presenting a product that is inevitably imperfect and still a work-in-progress is to enhance policy transparency and to facilitate further research in this important area. Even in its present form, we see this Services Trade Policy Database as playing an important role: in advancing policy reform by facilitating the analysis of services policies; in informing international negotiations by providing data on actual policies; in assisting businesses by creating a first port-of-call for information on the state of access to markets; and in fostering dialogue among relevant stakeholders by making information on policies publicly available. We see this database as providing not a definitive picture of services trade policy, but a step in enhancing the availability of information which might hopefully evolve – through feedback from various interested parties –into a collectively created
pressure on prices of other goods and services to rise as a result of the increase in gasoline prices. The indirect impacts aim to capture these effects and the model used is called “costs-push”.<sup>4</sup> We estimate the impact of the gasoline price reform on producer prices by combining the Input-Output (I/O)<sup>5</sup> table of the Islamic Republic of Iran for 2011/12 with a price-shifting model of price formation in the productive sectors. Since all petroleum products are aggregated together in the Iranian I/O table, we estimate the price hike in petroleum products to be 57.9 percent (details of the estimation are provided in Annex 1). Using the I/O table and a price-shifting model, we estimate the increase in prices of productive sectors due to the reform. We then use the same forecasted HEIS 2018/19 micro-data for expenditure records of households and map all household consumption expenditure to economic sectors for which we have estimated producer price changes. The estimate of the indirect impact is empirically driven in precisely the same way as the direct impact estimate: it uses household-level consumption expenditure records to compile a household-specific indirect impact.

The negative impact on consumption from increased prices of goods and services other than the gasoline sector is shown in Table 6. The average loss in welfare from indirect impacts is about 3.4 percent and it is higher than the direct impact for all deciles. Thus, due to the higher cost of other goods and services, the poorest 10 percent will lose more than twice as much as they lose due to their direct consumption of more expensive gasoline. For the richest, direct and indirect impacts are closer in the magnitude of impact on the overall consumption per capita.

In contrast to the direct impact, the indirect impact hits the poor the most. For instance, population from the poorest decile lose about 5.2 percent of their expenditures through indirect impacts while the richest lose only 2.7 percent
38 Elbers, C. J. Lanjouw, and P. Lanjouw (2003), “Micro-Level Estimation of Poverty and Inequality," _Econometrica_ , pp355-364 Elbers, C. and Lanjouw P. (2001) Intersectoral Transfer, Growth and Inequality in Rural Ecuador , _World Development_ , 29/3, 481-496.

Estache, A. (2004), “Argentina Privatization: A Cure or a Disease?” in Hirschhausen, Christian, Thorsten Beckers and Kay Mitusch, **Trends in Infrastructure Regulation and Financing: International Experience and Case Studies from Germany** , Edward Elgar, Cheltenham, UK and Northampton, MA., USA Estache, A. V. Foster and Q. Wodon (2002 **), Accounting for Poverty in Infrastructure Reform – Learning from Latin America’s Experience** , World Bank Institute Publications, Studies in Development Series Foster, V., A. Gomez-Lobo, and J. Halpern (2000a), Better Household Surveys for better Design of Infrastructure Subsidies, Public Policy for the Private Sector, Note 213, available at <u>http://rru.worldbank.org/Documents/PublicPolicyJournal/213gomez.pdf</u> Foster, V., A. Gomez-Lobo, and J. Halpern (2000b), "Designing Direct Subsidies for Water and Sanitation Services. Panama: a Case Study," World Bank, Policy Research Papers 2344, Washington, DC Foster, V. and O. Irusta (2003), “Does Infrastructure Reform Work for the Poor? A Case Study on the Cities of La Paz and El Alo in Bolivia”, World Bank, Policy Research Papers, No3177, Washington, DC Foster, V. and M.C. Araujo (2004), “Does Infrastructure Reform Work for the Poor? A Case Study from Guatemala”, World Bank, Policy Research Papers, No3185, Washington, DC Galasso, E. and M. Ravallion (2004), Decentralized targeting of an antipoverty program **,
**government;** and private barnks are less likely than state-owned banks to be covered by government guarantees. Thus, an additional **credit squeeze** may hit those **customers borrowing from smaller banks,**<sup>**8**</sup> **private banks, or domestic banks which are suffering from the deposit flight,** and typically SMEs depend more than other firms on small, private and domestic banks' lending.<sup>9</sup> The institutions which receive new flows of funds often have no established relationship with the borrowers of those institutions losing resources, and are thus less likely to make loans to those customers.

# **2.4 The Shocks Causing the Credit Crunch in East Asia**

The key factors leading to the specific credit crunch in East Asia can be divided into two broad categories: external factors stemming from the crisis itself and domestic factors predominantly associated with initial policy responses.

_A major reversal of international capital movements_ took place in 1997. According to data from the Institute of International Finance (IIF), net international inflows to the ASEAN 5 countries produced increasingly strong inflows reaching $93 billion in 1996. In 1997, there was an outflows of $12 billion, associated with the loss of confidence in some of the EA countries. Drawing on data from the Bank of International Settlements and from IIF, Radalet and Sachs (1998) estimate a capital outflow of $34 billion in the second half of 1997 -- a negative shock of 3.6 percent of GDP for the five countries under consideration. The short-term structure of their foreign borrowing was also an important element behind the drastic reversal (Chart 1).

_Unprecedented exchange rate devaluation_ (Chart 2) provided a major unanticipated shock (at least in its magnitude) to the corporate and financial sectors of the EA crisis-countries. This, in turn, multiplied the debt service burden and gave rise to

- s Kashyap and Stein (1994,
|Sofala & Zambezia-rural|3.30|14.35|0.29|3.12|14.52|0.27|
|---|---|---|---|---|---|---|
|Sofala & Zambezia-urban|5.39|19.07|0.42|4.15|17.85|0.31|
|Manica & Tete-rural|4.21|19.39|0.29|4.34|19.54|0.28|
|Manica & Tete-urban|5.93|21.47|0.40|4.97|20.57|0.32|
|Gaza & Inhambane-rural|5.31|18.37|0.43|5.07|18.17|0.39|
|Gaza & Inhambane-urban|6.32|20.31|0.47|5.78|19.88|0.41|
|Maputo Province-rural|7.45|24.84|0.43|6.62|24.52|0.39|
|Maputo Province-urban|10.25|30.86|0.50|6.25|26.95|0.31|
|Maputo City|12.34|33.14|0.59|7.05|27.95|0.35|

**Official:** 2008/09 PLs and shares (MPD-DNEAP, 2010) **; Set A:** Consumption poor/ Wealth index non-poor households.

Columns 5, 6 and 7 of Table 2 report the results obtained by eliminating the underestimated observations identified above (Set A) from the calculation of the non-food PL. After eliminating observations that were singled out as suspicious we ran the estimation procedure, and once the final results were obtained we compared these with the baseline scenario, including the
firm-level data covering both advanced economies and EMDEs, our panel regression found that the declines in statutory corporate income tax rates that occurred in the period 2000-17 can explain a considerable portion of the rise in corporate savings. R&D spending appears to have had significant but limited effects on corporate savings, contrary to past studies that identified R&D as a key driver of corporate savings. Additionally, a propensity scores matching analysis shows that the reductions in statutory corporate tax rates contributed significantly to the increases in corporate savings.

16
1.36|
|**1994 data**|(2.8)|(4.2)|(4.8)|
|**Constant**|43.61|33.4|15.3|
||(1.0)|(1.4)|(0.9)|
|**Adjusted R**<sup>**2**</sup>|.14|.35|.48|

Table 16 demonstrates the familiar positive relationship between pre-transfer regional revenue and regional expenditures on subsidies. Another factor that has a positive influence on agriculture expenditures is the percentage of people living in rural locations in the region, which is a demand factor. Holding constant the other independent variables in each regression, for every 10% increase in the rural population compared to the average level, per capita expenditures on agriculture went up by 14-18 rubles or by 20% of the average spending on agriculture. The share of the rural population is the demand factor identified in our analysis which has the most substantial impact on subsidy financing.

Unemployment, which is similar to wage arrears as an indicator of regional distress (i.e. a demand factor) and which is so far mainly an urban phenomenon in Russia, shows a negative relationship: growth in registered unemployment leads to a drop in agriculture subsidies.

- 29 For instance, subsidize public investments in rural areas (in the form of matching grants) instead of current financing of subsidies on agricultural inputs.
medium run until any inefficient costs associated with formality such as high registration costs, restrictive product licensing requirements and harassment/corruption costs are removed, with more productive growing informal firms likely to choose formality over time as the benefits to them become more valuable.

Policy design should therefore be informed by an assessment of the private benefits and costs of formality and informality to influence the cost-benefit calculations of firms. If unnecessary regulatory costs are a main component of firms’ cost benefit calculations, addressing these would be a priority.<sup>5</sup> If informality has generated an informality trap, support programs increasing access to resources that are critical for productivity improvements could induce firms to become more productive, grow and formalize over time. Policy design should also be informed by an assessment of the social costs of informality and the welfare benefits of formalization, i.e., those that are not captured by private business calculations. The main social benefits include a reduction of foregone tax revenues, an increase in allocative efficiency (i.e. reducing the degree to which low productivity informal firms have larger market shares than would be warranted by their level of productivity while enabling the growth in earnings and jobs of those informal firms willing and able to upgrade their productivity), an elimination of “unfair competition” facing formal firms, and strengthening the prevalence of rule based norms of behavior that would make compliance with welfare improving regulations more widespread. The relative weight of each of these sources of potential benefits will depend on country circumstances.

# **3. Formal versus informal firms and their relationship with performance variables**

# **_3.1 Formal versus informal firms_**

The Senegal data were collected through a Business Survey of microenterprises compiled by Research ICT Africa (RIA) in 2017-18.<sup>6</sup> Formal firms are defined as firms that have legally registered either at the national registrar general or country-level revenue authority, at the local authority or municipality, or both. This definition aligns with
the ASP between each HM group compared to the HT group and present the results in Appendix Table A9. Due to an increase in the standard errors, I find a small but not significant reduction in club attendance by both HM groups. Despite this lack of statistical significance, this result can shed light on preferences for diversity.

Further evidence to support the preference-for-diversity mechanism is presented in our work (Dinarte and Egana (2019)). For the same sample, we use data from spillovers and find different effects regarding proximity to misbehavior between nonenrolled and treated students. The results are higher for students whose bad behavior at school is between 1 and 2 standard deviations from the average misconduct of treated students from their classroom. Notably, the effects of this intermediate proximity are more significant on bad behavior reports.

The last mechanism that may underlie the group composition results is that tracking can strengthen the possibility of _creating violence networks_ , which has been previously analyzed in the literature (Billings et al., 2016; Bayer et al., 2009). The implementation of interventions for groups comprising only highly or less violent students can generate unintended effects on both groups, particularly for the most violent children. These results also match those of Pekkarinen et al. (2009), who find benefits from ending school tracking on the performance of students from lower-ability backgrounds in Finland.

Such a limited effect of tracking is an unexpected finding, especially because the scope is large for targeting the learning experience of school children. In this particular context, qualitative evidence collected from focus groups with instructors indicate that there was no difference in adjusting the content of the club to the specific composition of the groups. Only 42% of instructors indicated that they employed different methods or activities in the clubs. Most of those activities were not related to particular needs of the participants, but with lack of materials, adequacy of spaces to implement the activities, reorganization of the games, and others. This may indicate 33
. Despite the extensive discussion of these U.S. sanctions in the media, there is little research exploring their effects on Chinese firms’ innovation.

This paper examines how inclusion in the U.S. Entity List affects the innovation activities of Chinese firms. Data from PATSTAT, a patent database, is used to measure firms’ innovation output and its quality by examining patent applications and citations. The key finding is that inclusion on the Entity List reduces the quantity and quality of a firm’s patent output.

> 1Based on data from WIPO IP Statistics Data Center.

1