rafmacalaba/data-use-mentions-extended huggingface dataset:
:** ADB calculations using data from Association of Southeast Asian Nations Secretariat. ASEANstats Database. https://www.aseanstats.org/ (accessed July 2019); and United Nations Conference on Trade and Development. World Investment Report 2018 Statistical Annex Tables (accessed June, 2020) http://unctad.org/en/Pages/DIAE/World%20Investment%20Report/Annex-Tables.aspx

In fact, intraregional investment linkages have continued to strengthen since the Global Financial Crisis – in 2007 the intraregional share of FDI inflows to Asia was around 30 percent. In 2018, over
**Imputing Country of Residence** One limitation of the PATSTAT dataset is the absence of location information. For example, for patents applied to Chinese patent office, around 37% of patent applicants/inventors have missing information on their country of residence. To address this gap, we utilize the methodology proposed by De Rassenfosse et al. (2013) and Menon and Tarasconi (2017) to recover and assign country codes to each inventor/applicant with missing location information using the following steps.

First, we use the location information associated with each inventor/applicant id to recover their country of residence. Next, we utilize the standardized names by PATSTAT. Due to variations in how names are recorded across different patent offices, the same inventors/applicants may appear under different names. PATSTAT regroups name that likely represents the same individual under a unique standardized name and assigns them a unique PSN ID. Inventors/applicants linked by the same PSN ID are assumed to reside in the same country within a specific year. If some inventors/applicants id lack country information, but others under the same PSN ID have, we infer the missing country information based on the available location information. In some rear cases, multiple country codes are associated with a single PSN ID, the country code corresponding to the highest number of patents is selected for all linked inventors/applicants.

Second, we use the priority patent information, when, for example, a patent filed in China lacks the location data of its applicants/inventors, but its priority filings in EPO contain the location information. The location information recorded by EPO is used to infer the country of residence of applicants/inventors that lack location information in China. In some cases, where the first filings lacks location information, whereas the subsequent filings contains the location information, we use the country code in the subsequent filings to impute country information for applicants/inventors.

Lastly, for the rest of applicants/inventors with missing country code, we use the information on
observed EC. Consequently, the results can be interpreted as conservative predictions of the risk of high salinity.

Using the coefficients shown in figure 5 (based on 30 percent cropland), annual changes in NPP, and the EC data set described above, we estimate annual losses in agricultural productivity due to EC for each 0.5x0.5‐degree gridcell where cropland exceeds 30 percent of landcover, globally. We then convert the resulting loss of NPP to kilocalorie equivalents, following Strobl and Strobl (2011) and Imhoff et al. (2014). Globally, average annual losses from 2001‐2013 due to saline water total 124 trillion kilocalorie‐ equivalents. In total, assuming 2,000 calories/person/day, these calories represent enough food to cover the food budgets of 170 million people, annually.

The distribution of these losses is shown in Figure 7. With the exception of Sub‐Saharan Africa, where most food production is rainfed, all continents have large hotspots. Very wet regions, such as the Amazon basin, Southeast Asia, and the southeastern United States also avoid losses from salinity, despite being important agricultural zones. Figure 8 aggregates losses by countries and shows further that the impact is generally not correlated with development level. The United States is the country with the largest loss of agricultural production, losing the equivalent of 32 trillion calories per year, more than double that of Argentina, the country with the next largest losses.

# 7. Conclusions

Addressing water quality needs for agriculture is a tremendous challenge. With agriculture responsible for 70 percent of freshwater abstractions globally, and the need for an additional 20 percent increase in water withdrawals by 2050 to feed a population of 9 billion, the problems of water scarcity often overshadow those of water quality. Nevertheless, the results in this paper demonstrate that yield losses due to saline water are significant and deserve more attention.

There is no one‐size‐fits‐all solution to reducing salinity in irrigation waters. Solutions vary greatly by location and
# **Table A 3: Balance Table for the Village Selection Model: Jharkhand**

|**Variables**|**Me**|**an**|||
|---|---|---|---|---|
||**Treated**|**Control**|**Percent**<br>**Bias**|**P-**<br>**value**|
|Agricultural Credit SocietyStatus – Census 2011|0.07|0.04|17.40|0.24|
|Tube Wells/Borehole Status – Census 2011|0.24|0.21|7.30|0.63|
|Proportion of Scheduled Tribe Population – Census 2011|0.77|0.76|2.20|0.86|
|Proportion of Scheduled Caste Population – Census 2011|0.04|0.05|-17.70|0.19|
|Logof Total Geographical Area – Census 2011|5.75|5.76|-1.90|0.89|
|Proportion of Forest Area – Census 2011|0.14|0.17|-19.70|0.11|
|Proportion of Government SectorJobs Households– SECC 2013|0.02|0.01|10.90|0.42|
|Proportion of Public SectorJobs Households– SECC 2013|0.02|0.00|45.50|0.00|
|Proportion of Private SectorJobs Households– SECC 2013|0.07|0.00|45.10|0.00|
|Proportion of Manual Scavengers Households– SECC 2013|0.00|0.00|19.50|0.14|
|Proportion OwningMobile OnlyHouseholds– SECC 2013|0.71|0.61|31.80|0.
[loi.Ic( Rl-ISFARCH W(ORKING PAPER 2036

# **Summary** findings

Ingram and Liu extend their earlier empirical work by exploring alternative specifications for the relationship between vehicle ownership, road length, and the ratio of vehicles to road length, on the one hand, and income, population, population density, and other variables, on the other.

For the constancy or variability of cross-section income elasticities for each of these variables, they find that:

* The income elasticity of vehicle ownership decreases slightly with income at the national level but is constant at the urban level.

* The income elasticity of road length is constant at the national level but increases with income at the urban level.

* The income elasticity of the vehicle to road ratio decreases with income at both the national and urban level and, at high income levels, becomes negative for urban roads and for national paved roads.

Comparisons of income elasticities from cross-section and time series data indicate that they are generally similar for vehicles, road length, and the velhicle to road ratio at the national level.

But at the urban level the time series income elasticitv of road length (0. 1) is mlich smaller thaln the crosssection elasticity (0.7), and the time series income elasticity of the vehicle to road ratio (0.9) is much larger than the cross-section elasticities (0. I for low income cities and -0.5 for high income cities).

This means that the vehicle to road ratio (a proxy for congestion) is increasing over time in cities, almost in step with per capita income - a trend with dire consequences for urban transport.

Although the urban results are somewhat alarming, the national results indicate that the vehicle to road ratio nationally (especially on paved roads) is relatively low and not rising rapidly. It also shows remarkable stability across country income levels.

This contrast between the national and urban results reinforces the view that urban decentralization is likely to remain the major mode of adjustment to
on local governments fueled the pace of large-scale privatization in the late 1990s. In 1997, the central government officially announced its policy to restructure the state sector by allowing local governments to experiment with different ways to restructure SOEs. To facilitate economic turnaround, local governments were encouraged to privatize SOEs. In the Annual Survey of Industrial Firms (ASIF) used in this study, which accounts for the vast majority of Chinese industrial firms in terms of value added and employment, roughly 80% of local SOEs had been privatized by 2009.

Given the extraordinary scale of Chinese privatization and its potential implications, the number of studies on the impact of Chinese privatization is surprisingly small (Bai, Lu and Tao ``` 9 ```
<!-- Start of picture text --> 2<br>©<br>o$$Boo<br>222o$$Boo<br>&s<br>cooooo<br>“<br>°<br>Feb'20 Apr dun Aug Oct Dec Feb'21 Apr Jun<br>—®— Estimated mean for refugees<br>——®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> nN<br>©<br>o<br>Boo<br>£<br>cososososos<br>”<br>“<br>Jun'20. Aug Oct Dec Feb'21 Apr Jun<br>—e— Estimated mean for refugees<br>—®— Mean of urban nationals<br><!-- End of picture text --> <!-- Start of picture text --> 2<br>nN<br>© ©<br>222o$$Boo<br>&s £<br>==22cooooo cososososos<br>“<br>”<br>° “<br>Feb'20 Apr dun Aug Oct Dec Feb'21 Apr Jun Jun'20. Aug Oct Dec Feb'21 Apr Jun<br>—®— Estimated mean for refugees —e— Estimated mean for refugees<br>——®— Mean of urban nationals —®— Mean of urban nationals<br>2<br>i"&& 2<br>rey&&<br>Be2o 32°o<br>££<br>2ccSo 2c8c<br>So 8c<br>= 20<br>ry<br>° nt<br>Jun'20 Aug Oct Dec Feb'21 Apr Jun Feb'20 Apr Jun Aug Oct Dec Feb'21 Apr
Instead, we explore effects around school entry months, which proxies for specific entry dates. An advantage of the PISA data is that it codes a variable for the gap between the grade a student is in and the expected grade given that country’s school entry age laws. For example, in Finland where school starts at age 7, students are expected to be in grade 9 by age 15. In Mexico, students start at age 6 and are expected to be in grade 10 by age 15. This means a PISA-taking

> 21 See, for example, Angrist and Krueger 1991; Dobkin and Ferriera 2010; Fredriksson and Ockert 2014; Smith 2009; Robertson 2011; Puhani and Weber 2007; Kawaguchi 2011; McEwan and Shapiro 2008; Bedard and Dhuey 2006; Elder and Lubotsky 2009; Pehkonen et al. 2015; and Crawford et al. 2010.

> 22 This is similar to work being conducted by Singh (2017) using data from the Young Lives project.

30
These results are not a trivial exception to past findings. Floods were the most common natural disaster between 1985 and 2009, accounting for 40 percent of the total number of events, more than half of the number of people affected (including more than a tenth of those killed), and the bulk of economic damages (Table 1). Of all natural disasters over the last 25 years, their frequency has increased most rapidly (Figure 1). This rapid increase has been linked to climate change and is expected to become more pronounced over this century (IPCC 2001; Wetherald and Manabe 2002; Emanuel 2005; Swiss Re 2006; IPCC 2007a; Table SPM2 IPCC 2007b). There is general agreement that the impacts of climate change will be larger in poorer countries (Tol 2008), which have greater exposure to climate change, particularly in agriculture and water resources, and lower adaptive capacity (Adger 2006; Smit and Wandel 2006; Tol and Yohe 2007). Already during the last 25 years, over 95 percent of the deaths caused by large floods were recorded in developing countries (DFO 2010), which was higher than their shares of flood events and population (77 and 84 percent, respectively).

Nearly all previous economic studies on disasters have drawn data from the Emergency Events Database (Cavallo and Noy 2010), which is affiliated with the World Health Organization and several other international organizations (EM-DAT; www.emdat.be). Though widely used, EM-DAT has shortcomings that include multiple, separate events being recorded as a single one and underreporting of smaller events in developing countries (Jonkman 2005, p. 153). Our flood data originate instead from the Dartmouth Flood Observatory‘s (DFO) Global Archive of Large Flood Events, which is housed at the University of Colorado (floodobservatory.colorado.edu). The DFO is funded by NASA and the European Commission. It uses a collection of tools to detect and locate 3
Hill, B.M. (1975). A simple general approach to inference about the tail of a distribution. Annals of Statistics, 3: 1163– 1174.

Hummels, D., Ishii, J. and Yi, K.-M. (2001). The nature and growth of vertical specialization in world trade’, Journal of International Economics 54(1): 75–96.

Ioannides, Y and S. Skouras. (2013). US city size distribution: Robustly Pareto, but only in the tail. Journal of Urban Economics, 73(1): 18–29 Ioannides, Y., H. Overman, E. Rossi-Hansberg and K. Schmidheiny. (2008). The effect of information and communication technologies on urban structure. Economic Policy, 54(23):201-242 Ioannides, Y.M., and H.G. Overman (2003). Zipf Law for Cities: An Empirical Examination. Regional Science and Urban Economics, 33:127-137.

Koopman, R., W. Powers, Z. Wang, S. Wei (2010). Give Credit Where Credit Is Due: Tracing Value Added in Global Production Chains”, NBER Working Paper No. 16426.

Krugman, P. (1991a). Geography and Trade. Cambridge, Mass.: MIT Press.

Krugman, P. (1991b). Increasing Returns and Economic Geography. Journal of Political Economy 99: 483-99.

Lanz, R. and A. Maurer. (2015). Services and global value chains – some evidence on Servicification of manufacturing And services networks. WTO Working Paper ERSD-2015-03 Mia, I. and E. Austin. (2008). Assessing the Foundations of Mexico's Competitiveness: Findings from the Global Competitiveness Index 2007-2008. White Paper, World Economic Forum.

Nitsch, V. (2005). Zipf Zipped. Journal of Urban Economics, 57:86-100.

Pham, M. D. Mishra, K. Cheong, J.
real GDP per capita<sup>7</sup> under the assumption that recent spurts in growth could have additional effects on overall quality of health and mortality rates in the country. The variable is taken from WDI.

Next, we control for the level of urbanization in the country and the density of population as these factors have often been linked to the availability of health services and disease transmissions. For urbanization, we use the percentage of population in the country that lives in urban areas. Population density is defined as total population divided by the total land area (in square kilometers) of the country. The data source for both these variables is WDI.

Participation in the labor market provides women with financial freedom, income and an outside option that improves their bargaining power within the household. Hence, such participation is likely to improve women’s standard of living, leading to lower mortality rates. To filter out this factor from affecting our results spuriously, we control for the ratio of women’s to men’s labor force participation rate, where participation rate is defined as the percentage of women and men above 15 years of age that are part of the labor force The variable is taken from WDI.

While women’s participation in the labor market is an important factor increasing women’s empowerment, it is by no means the only determinant. Greater education and political representation are also likely to be important in this regard. To this end, we use two controls. These include number of years of education among women aged 25 or above (lagged by one year) relative to men, and the proportion of seats in the lower house of the parliament that are held by women. The data source for the education variables is Gakidou et al. (2006) and Inter-Parliamentary Union (IPU) for women in parliament. We also include the quality of democracy as measured by Polity 2 variable taken from Marshall et al. (2013). One possibility here is that electoral compulsions may

>
|
|How many enforcement actions have you taken over the past 5 years (2006-2010)<br>based on a breach of anyof the above bankgovernance requirements?|0|0|0|
|What is the limit on related party exposure as a percentage of a bank's regulatory<br>capital(%)?|20<br>(100)|25<br>(100)|20<br>(100)|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey, which reflects values as of 2010. Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey, which captures data as of 2005. ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries).

34
- 20 -

returns are anticipated. The annual rental value definition, reflecting only the current returns on property, will not make this distinction.

It is unlikely that this difference in impact would be apparent in a comparison of actual experience under the two systems. In practice, values under both ARV and capital value systems borrow freely from the techniques of the opposite camp. Elasticities and land use impacts depend far more on the peculiarities of administration than on characteristics inherent in either system. In choosing between capital value on ARV, the overriding consideration is administrative convenience. As discussed in Section II.B., **the** definition that is used should be the one which exploits the best market **data.** If rental tenure is widespread and accurate rental data is readily available, ARV should be used. If owner-occupancy is common, and accurate data on sales prices are readily available, capital value should be used. In practice, neither definit.on is going to completely meet the test of "accurate data, readily available." Rent controls, though not necessarily obeyed in the market, distort the rental prices reported to officials of government. High capital gains taxes, similarly, result in inaccurate sales data reported to official sources.

# 2. Rate Structure and Exemption Policies

Progressive Rate Structures Many countries employ progressive rate structures or assessment ratios in fixing tax liabilities. Th*ir objective is to increase the progressivity of the property tax. They do so, but imperfectly. In most progressive structure countries, the rate is
GDP per capita at approval stood at 5,625 USD in PPP terms, and the average annual economic growth rate over the life of the project was 3.8%.

In the qualitative data set, selected as a stratified random sample from the larger groups of projects, the mean project characteristics are virtually the same as the full set even though the rate of project success is only 50%. This is because projects were selected to ensure relatively equal numbers of low- and highrated projects.

The analytic approach used to identify significant predictors of project performance is a straightforward regression framework given by the following, i 0 i i i Where _Y_ is the rating of project _i_ YY approved in year ii = γγ + γγXXXX ii _t_ + γγand country ZZZZ ii + ffEE _c_ , + εε _X_ is a vector of considered predictors ii Where _Y_ is the rating of project _i t c X_ which will vary with the particular analysis, _Z_ is a vector of standard project covariates included in all specifications, and fE is a vector of evaluator fixed effects to control for possible rater bias. The standard set of project covariates ( _Z_ ) includes world region, sector of project, whether the loan is a standard (IBRD) or a concessionary (IDA) loan extended to low-income countries, the original intended size of the project (in 2015 constant dollars), the original intended project length, the country’s CPIA score in the year of approval, and the change in the CPIA score over the project’s lifetime.

The main outcome, the IEG rating, is an ordinal six-point scale and the main analytic results (Tables 4 to 9) are simply estimated from OLS with standard errors clustered at the country level. The results are robust to alternative specification choices such as an ordered probit, a probit with a dichotomized success/failure outcome, whether the observations are weighted by the constant dollar value of the intended loan volume, as well as the missingness of key explanatory variables (most notably the ‘value
|

**Notes:** Column 3 reports p-values from a test of equality of means carried out by OLS regression of each characteristic on a dummy for assignment to treatment. Regressions include strata (district) dummies and standard errors are clustered at the village level. Column 4 reports normalized differences are computed following Imbens and Wooldridge [2009]. Intimate partner violence is defined as the threat or use of physical violence from the respondent's partner. For the Empowerment Index, respondents were asked whether men, women or both should be responsible for the following activities: earn money for the family; have a higher level of education; do washing, cleaning and cooking; fetch water if there is no water pump or tap; feeding and bathing children; help the children in their studies at home; look after ill persons. The index is the share of questions to which the answer was both/same. Therefore, higher values represent more egalitarian gender norms. Time allocation data was collected both at baseline and endline. Respondents were provided a set of 25 beads and a board with six circles representing: "Education", "IGA", "Leisure", "Household Chores", "Sleep" and "Other". The Education category includes schooling, vocational training and study time. "IGA" includes paid and unpaid work of any kind. Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. Data on leisure time allocation was collected in a similar way. The recorded categories for leisure are: "Friends", "Men", "Alone", "Church", "Volunteer" and "Other". The exact phrasing for the "Men" category is "With boys or men you have a sexual relationship with". Respondents were then asked to allocate beads into each circle in a way that represents time allocation in an average week. The data points were later converted into weekly hours using recorded total leisure time
unconditional cash transfers and conditional cash transfer schemes which do not condition on MCH service use or employ soft conditions or co-responsibilities.

In terms of _outcomes_ , this review focuses on six indicators that either represent the official and supplemental RMCH indicators of the Millennium Development Goals (MDGs)<sup>11</sup> or are intermediate indicators critical to their achievement, namely the shares of (1) women of fertile age who use modern contraceptives, (2) pregnancies with four or more antenatal care checks, (3) pregnant women receiving tetanus vaccination, (4) births occurring in health facilities, (5) births with postnatal care, and (6) children receiving the full course of vaccinations recommended for the first year of life. Importantly, we only include impact estimates of outcomes whose provision is financially incentivized by the interventions under study. This incentivization may be direct, e.g. a fee the health facility receives for each birth taking place in it, or it may be indirect – e.g. maternal tetanus vaccination being incentivized in a CCT which rewards pregnant women for antenatal care visits during which maternal tetanus vaccinations are carried out. By contrast, we exclude impact estimates of outcomes without financial incentivization, e.g. those measuring an intervention’s unintended consequences. Finally, we only include impact estimates from samples representing the entire population of program beneficiaries – for instance, impacts on antenatal care content are included if based on a sample of all pregnancies and excluded if they come from the subsample of antenatal care users.

_Methodologically_ , we only include evidence from household survey data because of sample selectivity and reporting bias concerns in health facility and administrative data sets from LMICs.<sup>12-15</sup> Regarding study design, we include randomized controlled trials (RCTs) as well as evaluations of non-randomized interventions which identify impacts using regression discontinuity (RD), instrumental variables (IV), or double difference (DD) and triple difference (DDD) models. The parameter of interest in