rafmacalaba/data-use-mentions-extended huggingface dataset:
# **6.2 Predicted changes in overfishing status**

Today, 16 of the 18 FAO Regions experience overfishing (Figure A11). We find that a 10% reduction in Chinese distant water fuel subsidies would not eliminate overfishing in any FAO Region in the sense of reducing _F/Fmsy_ from above 1 to 1 or below (Figures 6 and 7). At best, our 10% reform scenario could reduce overfishing by up to 14% in some regions (e.g., the Southeast Pacific [FAO Region 87]). In contrast, consider our high reform scenario where Chinese distant water fuel subsidies are reduced by 50%. This would move two FAO Regions (the Southeast Pacific [87] and the Antarctic Atlantic [48]) below the overfishing threshold of _F/Fmsy_ = 1 and result in a further reduction in overfishing status in other regions by up to 20% (East Central Pacific [77] and West Central Pacific [71]). Figure 6 shows the changes in overfishing status by FAO Region for the three scenarios of subsidy reduction. We predict the largest decreases in overfishing in FAO Regions in the Pacific Ocean, where Chinese vessels account for large shares of fishing (Figure A10 and Table A5). Using the stock-level data, we can also calculate the proportion of stocks that would be subject to overfishing under each scenario. Currently, 64.5% of stocks (N = 3,042 stocks) are estimated to experience overfishing (Costello et al., 2016). A 10% reduction in fuel subsidies to China’s distant water fishing fleet would see this fraction decrease to 63.8%, while a more ambitious 50% reduction in subsidies to China’s distant water fishing fleet would result in 61.6% of stocks experiencing overfishing. While few stocks would no longer experience overfishing (N = 29 to 132 stocks [0.65% - 2.8% of total], depending on the level of the
_Table 2: Water Quality Data Sets_

|**Location**|**Mek**<br>**(Cambodia**<br>**an**|**ong River Bas**<br>**, Lao PDR, T**<br>**d Vietnam)**|**in**<br>**hailand,**|**In**|**dia**|**Gl**|**obal (36 cou**|**ntries)**||
|---|---|---|---|---|---|---|---|---|---|
|Source|Mekong Riv|er Basin Com|mission<br>C|entral Water<br>In|Commission f<br>dia|or|GEMStat|||
|Number of Monitoring<br>Stations Included||121||4|25||1,124|||
|Frequency of<br>Observations|Approxim|ately monthl<br>gaps|y, with|Varies b|y station||Varies by sta|tion||
|Years of Observations||2000‐2013||2000|‐2013||2000‐201|3||
|_Table 3: Impact of Electric_<br>Dep Var: Δ log(net primary|_al Conductivit_|_y on Net Prim_|_ary Productiv_|_ity, Mekong R_|_iver Basin_|||||
|productivity)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|Electrical Conductivity ><br>100 mS/m|-0.0550<br>(0.0138)
0.4|-0.8|-0.5|

Sources: Authors’ calculations using the turning points for rate cycles listed in Appendix Table 1 and data ending on May 31, 2024. Notes: This table shows averages across all tightening or easing phases within the given time period, The number and pace of rate adjustments only include "in-sync" rate adjustments. Velocity and amplitude are the total change in rates (in any direction) over the first six months of the phase or entire phase, respectively. Averages are calculated for each economy, and then over the sample as a whole. Members of the euro area are included as individual countries through 1998, and then the ECB cycle is included from 1999.

# Appendix Table 4 Variance Contributions of Global Factors

(Percent of total variation, averages across countries)

|Global factors|70-23|70-98|99-24|70-84|85-98|99-07|08-19|20-24|
|---|---|---|---|---|---|---|---|---|
|Interest Rates|13.4|11.4|38.1|9.6|10.7|30.8|29.3|38.4|
|Inflation|21.5|9.6|32.0|11.2|13.1|22.0|23.7|34.3|
|Output growth|23.6|6.5|39.7|7.1|10.7|15.7|13.2|46.9|

Sources: Authors’ calculations based on data from January 1970 – March 2024. Notes: The table presents the average contributions of the global rate factor, the global output factor, and the global inflation factor to the variance of country-specific policy rates, inflation, and output growth, respectively, over the periods as noted in
analysis was shortened by one year. Fifth, whereas the CTNS data set only covers protests and strikes, MEC also includes all non-institutional acts of civil disobedience such as sit-ins and nonviolent occupations, as long as they include at least 1,000 participants and aim to replace the government. In 79.45% of the cases where MEC identifies an ongoing nonviolent uprising, the CNTS data set also records at least one demonstration or strike.

_Table 5 Alternative dependent variables_

||(1)<br>Ongoing<br>uprisings<br>(MEC)|(2)<br>Riots<br>(CNTS)|(3)<br>Violent<br>conflict<br>(UCDP/PRIO)|(4)<br>Battle-related<br>deaths<br>(UCDP/PRIO)|
|---|---|---|---|---|
|Suffering (%)|0.125**<br>(0.064)|0.013ꝉ<br>(0.007)|0.025<br>(0.104)|-0.012<br>(0.011)|
|Struggling<br>(%)|0.113*<br>(0.065)|0.008<br>(0.006)|0.114<br>(0.12)|0.008<br>(0.008)|
|Constant|-122.665***<br>(41.705)|-0.686<br>(4.749)|30.467<br>(30.469)|9.964<br>(8.605)|
|Country FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Control Var|Yes|
, a matched sample is constructed based on "propensity scores," that is, the probabilities that the individuals receive the treatment given a set of observed covariates. As stressed by Rosenbaum and Rubin (1983), propensity scores are balancing scores in the sense that they allow both treated and control groups to exhibit on average no significant differences in their characteristics. These characteristics are materialized by a set of explanatory variables used in the estimation of the propensity scores. In our case, the outcome variable is Participation ( ). We define two treatment variables, that is, one for Provision and another for Protection. The Provision ( ) treatment PP3 variable named is equal to 1 if and 0 otherwise. Similarly, for PP2 Protection ( ) the treatment variable, namely , takes value 1 if and PPrrPPpp_ddiim dd_1 PP2 > 0.5 0 otherwise. The treatments are and , respectively. We are interested in the PP1 PPrrPPffppmm_ddiim dd PP1 > 0.5 impact of each of these treatments on Participation. For Provision, the treated group includes PP2 > 0.5 PP1 > 0.5 the countries for which and the control group contains countries that exhibit PP2 > 0.5 PP2 ≤

> 46 For a discussion on the advantages of PSM over regression, see Grier and Grier (2021) and Ichino et al. (2008).

27
The HEPR provides the institutional means for standardizing poverty lines and ensuring consistency of poverty comparisons and hence policy across provinces. Ideally, this will involve some rethinking of current definitions of absolute poverty and the establishment of a national consensus on minimum norms. For example, the current criteria need to better allow for spatial cost of living differences and varying levels of access to public and publiclyprovided private goods.

# **How much decentralization is optimal?**

As in the past, under the current system the poor are identified at the local level, by the commune chairman and mass organizations such as the women's union, the veterans group, and farmers' associations. These local authorities decide who qualifies for programs and dispense aid, including micro-credit. There appears to be little checking of the outcomes of this process and the scant evidence there is suggests that provincial lists of poor are not reliable.

There are clear advantages to relying on local knowledge. The chairman and mass organizations are likely to know commune households intimately and to be able to detect changes in levels of living standards over time. But there may be drawbacks as well. Even when national criteria are established, incentives are for localities to stack the lists of qualifying individuals in order to maximize transfers. Bias may occur from the preferences and agendas of those-including the commune chairman and mass organizations-who determine the lists at the local level. It is presumably in the interests of the women's union, busy aggressively campaigning for family planning, for example, to favor households practicing 25
# 4. LAND TRANSACTIONS AND STATE RESPONSES

# **4.1 Land sale transactions**

Land can be transferred from one party to another through sale/purchase, gift, inheritance, mortgage and tenancy. The last two are of a temporary nature and are excluded from discussion in this section. Data are not available from which to assess the relative volume of each type of transaction in Orissa. Discussions with revenue inspectors suggest that sale/purchase transactions constitute nearly 80 percent of all annual landrelated transactions in a village, while gifts comprise 10 percent, and inheritance the remaining 10 percent (around a half of which result in partitioning of a land holding).

Throughout rural India, land markets are incomplete, imperfect and often (though decreasingly) interlinked, resulting among other things in the persistence of marginal and sub-marginal operational holdings which can neither be easily added to nor disposed of (Meams, 1998). The situation in Orissa is consistent with this trend. A recent study reported that only about seven percent of farm land changed hands (through 88 sale/purchase transactions) over the period 1955-95 in a village in Sambalpur district (Sarap, 1998). There were wide fluctuations but the maximum area sold in any year was three acres. Based on a longitudinal survey of two villages in Cuttack and Dhenkanal districts over the period 1965-95, Swain (1998) also reported that around 5- 7 percent of village land was bought and sold. Thus, in general, the land market in Orissa is thin, with wide fluctuations in levels of activity.

A land sale has first to be registered before mutation of the RORs of the transacting parties can be effected. This section analyses the various processes in executing land sale transactions to identify the limiting constraints on land transfers, and specifies and attempts to quantify the transaction costs involved. The section also examines, in light of recent research, whether removal of these constraints or reduction of transaction costs would indeed help to facilitate land sales and increase access to land by the
correlates of how much schooling students are doing.

# **4. Time-Use**

Our surveys took place during a period when schools were closed, and a nationwide shutdown (with exceptions for certain types of workers and businesses) was in place. When asked, 82 percent of students said they had not left their house in the past day. To examine how they spend their time during the shutdown, we used a detailed 24-hour time use questionnaire based on the American Time Use Survey (Bureau of Labor Statistics, 2019). Our module asked the students to recall their activities from 4 a.m. the previous day to 4 a.m. on the interview day. This is recorded in half hour increments, using 24 pre-coded activities developed during pre-testing, plus a code for other activities.

7
data we use.

Our data are drawn from the 2019 version of the World Bank’s Health Equity and Financial Protection Indicators (HEFPI) database (Wagstaff et al. 2018a; Wagstaff et al. 2019). In generating the database, we identified potentially suitable surveys by searching the above-mentioned microdata catalogs and multi-country survey collections. In some cases, the surveys were not accessible to us. In others, the data were accessible, but the surveys turned out to lack key information. Sometimes the data sets we analyzed were different adaptations of the same survey – for example, as mentioned above, Romania’s HBS has been harmonized by Eurostat and the World Bank’s ECAPOV exercise. We identified 1,948 potentially suitable surveys or adaptations thereof from 182 countries. We analyzed and obtained out-of-pocket expenditure estimates for 1,000 of these surveys, covering 154 countries.

Our out-of-pocket expenditure estimates vary in how they are computed. In some cases, the survey asks only in the consumption or expenditure section about out-of-
<!-- Start of picture text --> Stunting prevalence (%)<br>60%<br>50 0-5 completed months<br>6-18 completed months<br>19-60 completed months<br>40<br>30<br>20<br>10<br>0<br>Poorest 10% 2 3 4 5 6 7 8 9 Richest 10%<br>Income decile<br><!-- End of picture text --> Source: Author's calculations using 2015 NNS anthropometry data and WHO's 'igrowup' software for R. Notes: Income deciles are based on monthly household income from the 2015 FIES matched to households in the NNS. The line represents a non-linear prediction of the stunting rate by income decile fitted with a generalized additive model (gam) using all observations. The 95% confidence interval around the predictions from the gam smoothed line is shown as a grey area.
**Figure 7: Cost-effectiveness of targeting**

<!-- Start of picture text --> 16<br>Children<br>12 Elderly<br>8<br>4<br>0<br>Cost effectiveness of targeting<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases The heterogeneity in efficiency gains also suggests that targeting methods should vary by country. For instance, in low income countries with widespread poverty, implementing a sophisticated proxy means test (PMT) may lead to high costs and strong implementation challenges without being necessarily much more effective than simpler targeting based on categorical and geographic exclusion restrictions. In contrast, in wealthier countries, unless pockets of poverty show strong geographical concentration, implementing a more sophisticated targeting mechanism (such as proxy-means or means-tested mechanisms) could lead to large efficiency gains. In these countries, stronger implementation capacity could also better resolve some of the technical implementation challenges that lead to exclusion errors.

# **Imperfect targeting and administrative costs**

These basic simulations miss an important feature. Actual poverty targeting, means or proxy-means tested, is costly and far from being perfect. As a robustness exercise, we simulate therefore the impact of an “imperfect” poverty-targeted program to children. To do so, we presume that the targeted program spends 15 percent more than the categorical program in administrative costs (so that 15 percent less resources are being transferred to the beneficiaries), and that exclusion errors lead to missing 30 percent of the extremely poor (randomly selected among potential beneficiaries). For comparison purposes, we also explore the possibility that categorical targeting is also subject to a degree of imperfectness, 15
indicators can predict income gains during the pandemic, in addition to income loss as shown in Table 4.<sup>13</sup> The three columns in Table 5 show the predictive power for farm income gain, nonfarm income gain, and wage income gain respectively. The predictive power for non-farm and farm income gain is highest (R<sup>2</sup> = 0.17 and 0.16), followed by wage income gain (R<sup>2</sup> = 0.12), and total income gain (R<sup>2</sup> = 0.06). The signs of the statistically significant correlations are generally opposite to those for income declines, as one would expect. For example, searches for the “Quran” and “Bible” are negatively correlated with gains in total and nonfarm incomes, while searches for “gym” are negatively correlated with non-farm income. Increases in pollution are also positively associated with wage income gains.

As shown in Table 6, predictive models for adults skipping meals as the dependent variable have similar explanatory power as those for income gain. Column 1 shows that a higher level of NO2 is associated with a smaller incidence of skipping meals, while additional searches for “bible” are positively associated with skipping meals. The average explained variation over the 100 imputations is 0.16. When evaluating the model using data from another question on food security (“adults going a whole day without food”), the explanatory power is slightly lower (R<sup>2</sup> = 0.13). In that model, higher vegetation is associated a greater tendency to skip meals for a full day, as are Google searches for “Milk”, which is consistent with the latter as an indicator of economic distress.

The real-time indicators can also predict people’s social distancing and schooling behaviors during the pandemic, as reported in Table 7. The predictive power for social distance
, using quotations from The Economist and Financial Times from 1930 to 1980. For illustration, Figure B1 shows an example of how this source looks like. The bond price data from the NYSE was coded from the “Bank and Quotation Section” of the Commercial Financial Chronicle (from 1905 to 1927 and 1954 to 1978) and from the Bank and Quotation Record (from 1927 to 1954).

Our baseline is to use price data of the last trading or business day of the month. We deviate from this rule only for a few dozen bonds before 1850, when markets were not as developed and liquid. For that period, we use the last available price of a month and, rare occasions, also the average between low and high prices of a month.

Beyond prices, we draw on additional sources to collect the financial and legal characteristics of each bond, in particular amount issued, issue date, maturity, coupon rates, coupon frequency, repayment schedule, guarantees or type of issuer. These are taken from bond manuals such as Fenn’s Compendium of the English and Foreign Funds (1837-1838, 1855, 1857, 1863, 1867, 1869, 1874, 1876, 1883, 1889, 1893); Fortune’s Epitome of the Stock and Public Funds (1800, 1810, 1820, 1824, 1826, 1833, 18381839, 1850-1851, 1856); Kimber's Records on Government Debts and other Foreign Securities (1918, 1919, 1922, 1934), Moody's Manuals on Foreign and American Government Securities (yearly 1920196ex0), and the London Stock Exchange Yearbooks (1877-1878, 1880-1881, 1883-1888, 1890, 1894A4
# Winning Hearts and Minds through Development? Evidence from a Field Experiment in Afghanistan<sup>1</sup>

**Andrew Beath**<sup></sup> **Fotini Christia**<sup>†</sup>

**Ruben Enikolopov**<sup>‡</sup>

1 We are indebted to Chad Hazlett, Hamid Gharibzada, and Maiwand Siddiqi for excellent research assistance, to Jason Lyall for generously sharing the data on security incidents, and gratefully acknowledge the generous cooperation and assistance provided by Tariq Ismati and Abdul Rahman Ayubi of the National Solidarity Programme; H.E. Wais Barmak, Minister or Rural Rehabilitation and Development; Ehsan Zia, Former Minister of Rural Rehabilitation and Development; staff of AfghanAid, C.H.A., InterCooperation, IRC, NPO/RRRAA, Oxfam UK, and People-in-Need; and Kamran Akbar, Josephine Bassinette, Ladisy Chengula, Philippe Dongier, Susanne Holste, Nicholas Kraft, Qazi Azmat Isa, Dean Jolliffe, Zishan Karim, Elliot Mghenyi, Norman Piccioni, Mariam Sherman, and Mio Takada of the World Bank. We would also like to thank Mike Callen, Rex Douglas as well as colleagues and seminar participants at Brown University, Georgetown University, MIT, NYU, and Princeton University’s NMACPIR conference for helpful comments. The study was financially supported by the Food and Agriculture Organisation (FAO), World Bank, and the National Solidarity Programme. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors and should not be attributed in any manner to the Government of the Islamic Republic of Afghanistan, FAO, World Bank, the World Bank’s affiliated organizations, members of the World Bank’s Board of Executive Directors, or the countries they represent. **_JEL Classification_** : H11; H12; H41; H43; H56; H84; D74; D78. **_Keywords_** : Civil Conflict; Government Legitimacy; Government Scope; Crisis Management
_e-Dhara_ records is considered highly unlikely for several reasons: electronic land records are centrally administered by an authority separate from the institutions keeping land records at the village level. There are several additional checks against manipulation and any changes in landholding status have to be cleared and verified by independent authorities.

## **B.1 Evidence from the Audit of Electronic Land Records**

To compare the landholding numbers reported by banks and survey respondents, we obtained official copies of the land records of 2,064 of 2,897 survey respondents or 71% of the sample. Table B.1 reports the land audit results. There are several legitimate reasons for electronic landholding records to differ from the landholding numbers reported by the banks. First, many banks accepted partial mortgages: to qualify for some loans, farmers were allowed to mortgage only a portion of their land. In these cases, the bank-reported landholding is less than the total land held by the farmers, and the smaller landholding amount will have been used to determine program qualification. This does not constitute manipulation, and does not affect the validity of the identification strategy.

Second, in a smaller number of cases loans considered the landholdings of multiple individuals. Most frequently, land held by multiple members of the same extended household is pooled in order to qualify for a larger loan. In many cases, the loan was recorded as having a single beneficiary, and the total landholding was listed –even though the beneficiary did not himself or herself own all of the listed 50
Incidence of COVID-19 and Connections with Air Pollution Exposure

# 6.2.6 Re-estimation using satellite-derived PM2 _._ 5

This section evaluates whether the relationship with PM2 _._ 5 generalizes to measurements from a different source. Table 9 compares the municipality-level data. Both measurements trend in the same direction but the levels according to the RIVM are roughly one-third below those of van Donkelaar.

Table 9: Comparison of PM2 _<u>.</u>_ <u>5</u> statistics: RIVM 2017 vs van Donkelaar 2016.

|**Variable**|**Mean**|**Min**|**q.25**|**q.50**|**q.75**|**Max**|
|---|---|---|---|---|---|---|
|RIVM 2017|10.22|6.95|9.43|10.69|11.23|12.04|
|van Donkelaar 2016|14.60|10.15|13.44|15.01|16.01|18.05|

Correlation: 0.70 Model 5g in table 10, estimates step-wise AIC with the new pollution data and uses the selected covariates in the specification of Model 5. Model 5h uses the same controls as Model 5, and Model 5i applies the Yeo-Johnson power transform. The analysis makes use of the confirmed cases from March 22. It is clear from the estimation results that the use of satellite-derived PM2 _._ 5 results in similar conclusions. The parameter estimate for PM2 _._ 5 remains significant in the presence of controls and is significant at the highest level when the dependent variable is first normalized.

Table 10: Dependent Variable: Confirmed COVID-19 cases <u>per</u> 100,000 inhabitants.

|**Variable**