rafmacalaba/data-use-mentions-extended huggingface dataset:
maximum resident populations are the same in more than one cell, the algorithm makes a random selection.

> 3 We compute average protected area size from WDPA (2024) and country area from the World Bank’s country shapefile (World Bank, 2024).

> 4 The t-statistic is the estimated regression coefficient divided by its standard error.  Classical significance with 95% confidence generally requires a t-statistic above 2.0.

> 5 We could compute mean PA size directly for individual countries, but across the 212 countries and political units we observe great variation in both the numbers and sizes of PAs.  We believe that our logarithmic regression provides more robust and stable results for the computation of appropriate grid scales.

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obtain the other coefficient in this way. The tables suggest that Collier et al. (2016) regressed the log of cost on the population density (in 100 people/ _km_<sup>2</sup> ), but applying the coefficient to the levels density yields peak construction costs above $ 10M/km near large cities, which I regard as unrealistic. Thus, I use the natural log of the population density and add 1 to avoid negative values in remote regions. This effectively reduces the relevance of this term and remains close to Fajgelbaum & Schaal (2020). I extract ruggedness from the original 30 arc-second layer by Nunn & Puga (2012) within a 3km buffer around existing and proposed links, and do the same for population from the WorldPop 2020 population layer at 1km resolution.<sup>22</sup> Figure 17 shows ruggedness and population density thus estimated for each link.

> 22It is possible to track the terrain around existing roads more closely, but being more realistic with existing links complicates comparison with potential new links. Thus, I opt for a simpler approach of considering terrain within a broad 3km buffer in the direction of travel for both existing and potential new links.

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variability in expenditure changes than male headed households. However, in this sample their mean expenditures is much higher and their poverty rate is much lower and, hence, their headcount vulnerable rate is much lower than male headed households. In data set as well, however, the ratio of vulnerable to poor households is slightly higher for female than male headed households (1.56 to 1.50).

This means that both data sets agree that female-headed households tend to have higher mean per capita expenditures and, at the same time, greater variability in expenditures changes than male headed households. However, the two data sets do not agree on the relative headcount poverty and vulnerable rates between the two groups of households, but agree on the ratio of both rates. Hence, these results give some light to, but stop for short of resolving, the quantitative vs. qualitative puzzle for female headed households.

_Educational level._ The analysis of vulnerability by education level using both data sets in general gives expected results. The higher the level of education of household head, the higher mean of per capita expenditures and the lower the poverty rate. There is no particular pattern on the variability of expenditure changes with respect to the education level, but they seem to be roughly the same across the level of education. Therefore, the average vulnerability and headcount vulnerable rate are lower the higher the education level of household head. However, in relative terms, the ratio of vulnerable to poverty rate is increasing with education level. So, in the Mini Susenas data even though the poverty rate of the most educated is only 4.2 percent, 17.7 percent are vulnerable, while the result is less dramatic in the 100 villages data, it is still 12.7 percent vulnerable even if the highest education group.

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13 the government. Therefore, who has the authority in deciding and monitoring the specifics of the fiscal rules and debt limits will depend on the country’s constitutional and legal framework.

**III­B Subnational debt indicators – Further considerations for subnational government general budgets**

Subnational government general budgets could be monitored using indicators (i), (ii), and (iii), which are in line with international practices (see Box 1 below), and the guarantees indicated in (v) should be adequately quantified and monitored. Some further considerations on the definition and operationalization of the indicators are in order, concerning measures of repayment capacity, fiscal risks associated with land financing, arrears, and guarantees.

# **Box 1: International practice on debt indicators and limits**

**US** states mainly follow golden rules constraining the operating balance to be zero, thus debt must be used to finance capital expenditures. Rules governing subnational borrowing vary across states and depend on the type of debt issued, the revenues used as collateral, and the type of government entity issuing debt. Markets play a key role in fiscal surveillance.

**Brazil** ’s Fiscal Responsibility Law sets a limit of 200 percent on the debt-to-net current revenue ratio (current revenue net of certain national government transfers), so there is a legal limitation on the amount (not the use) of new debt flows. It also imposes a limit of 11.5 percent on the debt service-to-net current revenue. Debt restructuring agreements between federal and state governments established a comprehensive list of fiscal targets.

**Colombia** ’s extensive legislation on government debt established a traffic-light system to regulate subnational borrowing. States in the red-light zone, with the debt-to-current revenue ratio above 80 percent and the interests-to-operational savings ratio exceeding 40 percent, are prohibited to borrow.

**India** imposes a ceiling on budget deficit-to-GSDP ratio of 3 percent for all states and requests them to gradually close the current deficit. In addition
# **Abstract**

Four West African nations have demanded that the WTO’s Doha Development Agenda include a Cotton Initiative that involves two issues: cutting cotton subsidies and tariffs, and assisting farm productivity growth in Africa. This paper provides estimates of the potential economic impacts of (a) complete or partial removal of cotton subsidies and import tariffs globally and (b) cotton productivity growth through the adoption of genetically modified (GM) cotton varieties. Use is made of the latest version of the GTAP database and model. Our results confirm that – unlike for other agricultural subsidies and tariffs – for cotton it is subsidy reductions rather than tariff cuts that would make by far the largest impact. For Sub-Saharan Africa the potential gains are huge relative to the effects on that region of reforming other merchandise trade policies. And they could be more than doubled if that reform provided the cash for farmers to take advantage of the biotechnology revolution and adopt GM cotton varieties. But those potential gains, and the affordability of switching to costly GM seed, depend crucially on the extent to which high-income countries are willing to lower domestic support to their cotton farmers.

## **JEL codes: D58, F17, Q16, Q17**

**Key words:** subsidy and tariff reform, computable general equilibrium modeling, economic welfare, GMOs, cotton biotechnology

## **Contact author:**

Kym Anderson Development Research Group The World Bank Mailstop MC3-303 1818 H Street NW Washington DC 20433 USA Phone +1 202 473 3387 Fax +1 202 522 1159 kanderson@worldbank.org
_Segmentation_ . In the terrain analysis, road characteristics are calculated by segment, which is defined somewhat arbitrarily according to the network geometry. While the start and end of a segment may not necessarily define a homogenous feature, errors in network geometry only exacerbate the problem.

## **Scale difference between Spatial resolution of Digital Segmentation example gROADS and Open Street Map Elevation Model**

_Figure 11 Examples of Challenges with Input Datasets_

# 6.3. The Highway Development and Management Model, HDM-4

Road user costs (RUC)<sup>10</sup> and speed (km/hour) are calculated for roads of different attributes using the World Bank’s Highway Development and Management Model (HDM-4). This software package was built to evaluate and assess road maintenance, improvement, and investment projects. Inputs into HDM-4 include both road characteristics (network type, terrain, surface, and pavement condition) and vehicle fleets information (traffic classes). For the purposes of this study, the vehicle fleet data were collected at the country level with a heavy truck taken as the representative vehicle. For each of the possible combinations of road characteristics and traffic class (6,804 in total), we calculated in HDM-4 a unique road user cost and travel speed estimate.

## **Road conditions definition in HDM4**

(http://siteresources.worldbank.org/EXTAFRSUBSAHTRA/Resources/SSATPWP89A-RONET.pdf) For paved roads, the road condition classes are defined as follows:

- Very Good: Roads in very good condition require no capital road works.

- Good: Roads in good condition are largely free of defects, requiring some minor maintenance works, such as preventive treatment or crack sealing.

- Fair: Roads in fair condition are roads with defects and weakened structural resistance, requiring resurfacing of the pavement (periodic maintenance), but without the need to demolish the existing pavement.

> 10 RUCs are defined as the unit cost of using a road expressed in dollars per ton-kilometer and consist
global (free trade) prices exceed the domestic price. For this reason, three measures of coupled subsidies are used in the analysis. In one variant, market price support is removed to focus only on the portion of the subsidy that amounts to direct producer support. In the second variant, only support on inputs is included, and in the third variant, only market price support is included. This country-level data is then combined with water quality data and the following equation is estimated: 1 1share (c c c c 2 c c c c where ihs(WWQQccii) = αα is total nitrogen in water in country c and year t measured by taking the mean value of + ββ cc ssc ssttccii) + ββ sshaasscc (c cc ssc ssttccii) + θθii + γγcc + εεccii water quality for all grid cells in a country in a given year. Coupled supports and decoupled supports are ccii WWQQ estimates of annual Producer Support Estimates from the OECD, separated into those tied to production incentives and those decoupled from output levels. The equation includes country fixed effects, to account for time-invariant differences between countries, such as geography and climate, as well as year cc γγ fixed effects, , to account for common time trends. Standard errors are clustered at the regional level to account for spatial and temporal correlation. ii θθ Although this analysis provides insight into the association between producer support and water quality, it is not clear whether the estimated effects can be interpreted as causal because levels of producer support are not randomly assigned but rather endogenously determined in response to conditions facing producers and consumers. To partially address this concern, a placebo test is conducted that estimates the effect of future producer support (i.e. PSE in time t + 1) on total nitrogen in water. If 7
income from PSNP in 2010|0.066|0.248|
|Household net consumer of food|0.481|0.5|
|Household owns land|0.913|0.283|
|Household has own fush toilet/pit latrine<br>|0.388|0.487|
|Household uses fush toilet/pit latrine|0.5|0.5|
|Household has corrugated iron roof|0.382|0.486|
|Household has electricity|0.1|0.3|
|Dummy: urban household that rents|0.085|0.278|
|**Geographic characteristics**|||
|Rural Household|0.849|0.358|
|Ln Distance to market (km)|3.726|1.229|
|Close to market (less than median minutes)|0.49|0.5|
|Distance to town (minutes)|288.226|201.758|
|**Shocks**|||
|Household sufered death of member|0.043|0.204|
|Household sufered job loss of member|0.007|0.086|
|Household sufered self-reported food price shock|0.121|0.326|
|Infation rate (3-month moving average)|1.545|1.412|
|Drought: proportion lost crops|14.848|18.928|
|N|3|7,964|

Notes: Descriptive statistics for 2005 and 2011 pooled.

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# **5.8 Limits to Pro-Local Policies**

One interesting question is how much discretion would county level cadres have in choosing pro-local policies in an authoritarian regime? It is reasonable to hypothesize that there are limits to what local leaders can do to promote local growth; and that the pro-local policies that local leaders may implement are limited to those not easily observed by upper-level leaders, especially if they only have loose tie to upper-level leaders. As in our theory, the local leaders’ behavior relies on two factors: connection with upper-level authority (the factional connection) and connections with the grassroot (local accountability). Since the higher level government holds the power of promotion/demotion or even political survival, the local leaders dare not overtly disobey the center. Therefore, we implicitly assume that YRD cadres are likely to take policies favorable to local residents only when doing so does not openly defy their superiors. In other words, two types of cadres will perform in the same way when they implement tasks that are easily monitored by the upper-level authority.

<!-- Start of picture text --> YRD<br>FA3<br>0 .5 1 1.5 2<br>Population Growth Rate<br>1.5<br>1<br>Density<br>.5<br>0<br><!-- End of picture text --> Figure 5: Kernel Density Estimation of Population Growth Rates Among Counties Led by YRD- and FA3affiliated Cadres, 1984-1998.

## **5.8.1 One Child Policy**

We use the population growth after One-Child policy to test the above hypothesis. First, the One-Child Policy was launched as a national policy by the central government in 1979; it is a highly visible policy. Second, at all levels of the government hierarchy, Population and Family Planning Commissions were established after this policy, which made enforcement of the policy easily observable. Due to these reasons, even though taking
_groupement_ level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can.

# **5.2 Individual Level Relationships**

Two survey waves posed additional questions on local displacement dynamics. While these additional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only samples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d’Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all _territoires_ in the three provinces.<sup>11</sup> These survey waves are labeled as “Cities” and “General” samples in the individual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion.

Figure 5 plots the coefficients from series of logistic regressions to account for the binary nature of the dependent variables. The regressions include Province fixed effects and _groupement_ clustered standard errors to account for unmeasured context-specific dynamics. Responses are weighted by the inverse proportion of selection at the _territoire_ in each regression. The relationships between displacement dynamics and perceptions of each manifestation of social cohesion are analyzed separately by running 12 models for each independent variable (6 regressions for each sample).

The regressions are correlations and should not be interpreted causally. Hosting status and displacement flows are likely related to perceptions of social cohesion in indirect ways and the structure of the survey data limit the ability to specify the channels through which these relationships run. Each regression controls for characteristics that may influence respondents’ perceptions of social cohesion outside of the presence of IDPs or refugees in the local community such as province, gender, age, marital status, level of education, employment, and exposure to violence. 11Poll numbers correspond to the number wave in our larger project, as described and shown in Table 2.
Setting out this approach has also highlighted four areas of measurement work that would benefit a global measure of vulnerability. First, generating global spatially disaggregated measures of poverty. This work has used administrative data, but there is room to experiment with how to disaggregate further. Second, further innovations on the method used to overlay dimensions from different data sources. A simple method was used for social protection and financial inclusion data, but further work could be undertaken on this to implement survey to survey methods across a wider range of dimensions of vulnerability. Third, developing a method to impute and line-up estimates of non-monetary vulnerability as is done for the income measure using methods developed for the global poverty measurement work. Finally, we have relied on historical datasets to define the probabilities of extreme events. As such this analysis provides a picture of who is vulnerable today. In order to say something about the future, the impact of a changing climate on the risk faced will need to be taken into account (see Baquie and Foucault, 2023).

# REFERENCES

Abay, K. A., Yonzan, N., Kurdi, S., & Tafere, K. (2023). Revisiting Poverty Trends and the Role of Social Protection Systems in Africa during the COVID-19 Pandemic. _Journal of African Economies_ , _32_ (Supplement_2), ii44-ii68. https://doi.org/10.1093/jae/ejac041

- Abdulai, A., & CroleRees, A. (2001). Determinants of income diversification amongst rural households in Southern Mali. _Food Policy_ , _26_ (4), 437-452. https://doi.org/10.1016/S0306-9192(01)00013-6

- Abiona, O., & Foureaux Koppensteiner, M. (2022). Financial inclusion, shocks, and poverty: Evidence from the expansion of mobile money in Tanzania. _Journal of Human Resources_ , 57(2), 435-464.

- Afridi, F., Mahajan, K., & Sangwan, N. (
property size, etc.). A significant number of questions were also asked with the intention of measuring social capital for use in a complementary study (Alix-Garcia, Sims, Orozco-Olvera, Costica, Medina & Monroy 2018).

The household survey also began with questions about the PSA program, including the same referendum question asked to the leaders (though with different random prices) and household use of the PSA parcel. A basic household roster and demographic characteris-

> 7When we test within our sample for a difference in estimated WTA between those with above 50% indigenous and those below, we find a difference of 3 pesos on average, and a t-test of this difference across subsamples yields a p-value of 0.61. This assuages concerns about possible bias from a high presence of observations from majorityindigenous municipalities.

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hence raise the cost of borrowing. A one-point increase in the World Bank data transparency index conditional on a better ICRG reduces sovereign bond spreads by 0.49 ([5] in Table 7). In the same regression, a one-point increase in the data transparency conditional on greater trade and financial openness reduces borrowing costs by 0.109 and 0.00388, respectively. Consequently, opening-up markets in goods and financial assets brings more benefits in terms of lower borrowing costs as long as the country has a better level of institutional quality. Table 7 also shows robust estimations for the interaction terms between data transparency and public debt in [5] and between data transparency and external debt in [6]. Data transparency, conditional on higher public debt and conditional on higher external debt, is positively corelated with larger bond spreads. This implies that there are increasing marginal costs of borrowing to having higher (public and external) debt.

# **4.3 Calculating Impacts of Data Transparency by Country**

We calculate how much a country could directly benefit from improving data transparency. First, we calculate the impact of data transparency on sovereign bond spreads conditional on the level of external public debt by region and by country. Then we compute how much a country could gain in terms of lower spreads by improving their data transparency to levels determined by a certain benchmark. Finally, we also estimate how much a country could reduce their external debt burden by improving data transparency. We use the IV estimation from Table 5 [1] to calculate those impacts. Figure 5.a shows the impact of data transparency on sovereign bond spreads conditional on the level of external public debt for five different regions such as Africa, emerging Asia, Latin America, emerging Europe and the Middle East. We use the 2018 average bond spreads for each region to calculate the change of spreads in basis points. We take the coefficient of the World Bank data transparency index of 3.112 in Table 5 [1] and the interaction
Alcacer and Chung (2010) in our design of many these factors, and we refer interested readers to these papers for additional details on agglomeration metrics and their properties.

# _Population and Demographics_

Our initial explanatory measures naturally focus on basic traits of the district: population, age profiles of the population (demographic dividend), and population density. Our population control comes from the 2001 population census, and models several effects. First, and most important, it provides a measure of the size of local markets in terms of consumers. For some industries, especially in services, these local markets constitute the firm‘s primary product market. For other industries that are traded at a distance, this output feature of the local market is less essential. The overall size of the local district nevertheless represents an important measure for the overall surrounding economic activity (e.g., general availability of workers).

The population control will also reflect to some degree the supply of potential entrepreneurs. Most entrepreneurs start their businesses in their current local area: for example, Dahl and Sorenson (2007) document that over 70% of new firms in Denmark are founded in commuting regions where entrepreneurs were previously living. While some entrepreneurs move to new cities to start their businesses, this is mostly confined to niche, high-growth industries like biotech where a few dominant clusters form. Looking across all industries and types of firms, several studies have instead shown an underlying pattern where entrepreneurs are disproportionately located in their home regions compared to wage employees. This pattern has been shown in the United States, Italy, and Portugal (e.g., Figueiredo et al. 2002, Michelacci and Silva 2007). The population control will pick up some of this supply side effect.

We next consider the age structure of the district, which is also referred to as the ―demographic dividend‖ in the Indian context. The propensity to start firms changes over the lifetimes of individuals (e.g., Evans and Leighton 1989), and
Bangladesh by the International Center for Diarrhoeal Disease Research has been an important source of data

> 6 Details on the methodology can be found at <u>http://devdata.worldbank.org/hnpstats/pvd.asp.  The</u> estimates for various demographic and health variables are also available at that site, and (for a broader set of variables) at <u>http://www1.worldbank.org/prem/poverty/health/</u> .

> 7 Wagstaff (2003) also estimates child mortality and nutrition indicators at the $1 a day line for 32 developing countries.  However, his methodology does not allow one to calculate the _DRD_ .

> 8 I know of one large sample survey (in the field at the time of writing) that did collect recall data on mortality for both children and adults, namely the Malawi Integrated Household Survey.

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