rafmacalaba/data-use-mentions-extended huggingface dataset:
at https://doi.org/10.6084/m9. figshare.25560351 [52]. The only exception is the raw asset wealth data, obtainable directly from Atlas AI. However, we provide in figshare the asset wealth values for each protected area and year in our study, along with the code used to derive these values from the raw data.

**Code Availability.** All replication code is available in figshare at https://doi.org/ 10.6084/m9.figshare.25560351 [52].

**Peer-Reviewed Version of Article.** This article was also published in the _Proceedings of the National Academy of Sciences_ : https://doi.org/10.1073/pnas. 2401814121

**Author Contributions Statement.** SD contributed conceptualization, methodology, investigation, and writing. GE contributed conceptualization, methodology, investigation, visualization, funding acquisition, and writing. PH contributed conceptualization, methodology, visualization, investigation, and writing.

**Competing Interests Statement.** SD, GE and PH declare that they have no competing interests.

**Declaration of Generative AI and AI-Assisted Technologies in the Writing Process.** During the preparation of this work the authors used GPT-4 in order to

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The authors go further by attempting to explain the cross-country variation in efficiency and find that the degree of urbanization and the quality of bureaucracy are the most relevant variables. To capture possible non-linearity, the authors introduce these variables squared. This stage of their work poses several problems. First, it is possible that the (non-linear) quadratic terms reflect heterogeneity across countries and dynamics across time. As shown by Haque, Pesaran, and Sharma (1999), this would produce inconsistent estimates. Second, the authors do not adjust for the fact that the dependent variable (efficiency scores) is censored, given that it can adopt only values between zero and one. And third, the authors do not consider the serial correlation of the efficiency scores (Simar and Wilson, 2004) Greene (2003a) combines the previous two papers in the sense he concentrated on health efficiency only using the WHO panel data and explained inefficiency scores variation across the sample of counties. Greene’s stochastic frontier estimation is much more general and flexible, as it allows for time variation of the coefficients and heterogeneity in the countries’ sensitivity to the explanatory variables. The author first estimates a health production function using expenditure (public and private together) and education as inputs, and then explains inefficiency with a set of explanatory variables of which the only significant ones are the income inequality measure, GDP per capita and a dummy variable for tropical location.

Afonso, Schuknecht and Tanzi (2003) examine the efficiency of public spending using a non-parametric approach. First, they construct composite indicators of public sector performance for 23 OECD countries, using variables that capture quality of administrative functions, educational and health attainment, and the quality of infrastructure. Taking the performance indicator as the output, and total public spending as the input, they perform single-input, single-output FDH to rank the expenditure efficiency of the sample. Their results show that countries with small public sectors exhibit the highest overall performance.

Afonso and St. Aubyn (2004) address the efficiency of expenditure in education
Past research has demonstrated the use of survey paradata for (i) monitoring survey progress and informing adaptive survey designs; (ii) analyzing and adjusting for survey non-response; (iii) computing granular interview duration statistics during the survey fieldwork and as an input into the design and costing of future surveys, (iv) tracking answer modification patterns and compliance with the intended interview flow; (v) identifying falsified data, (vi) verifying compliance with the intended visits to sampled enumeration area and household locations, and (vii) studying respondent behavior and predicting future survey participation (Choumer-Nkolo et. al., 2019; <mark>Couper and Kreuter, 2013;</mark> Gordeev et al., 2021; Jans et al., 2011; <mark>Kreuter et al., 2010; Kreuter and Olsen, 2013; M</mark> urphy et al., 2019; Virgile, 2016). While these research efforts have been informed by the surveys conducted in high-income countries, the comparable applications are scarce in low- and middle-income countries, where building NSO technical capacity in the use of paradata for survey design, management, and quality control has been identified by the United Nations Intersecretariat Working Group on Household Surveys as one of the technical priorities for positioning household surveys for the next decade (Carletto et al., 2022).

To gain paradata-powered insights on survey respondent burden, survey costs, and interviewer effects for the first time in low- and middle-income contexts, this paper uses paradata generated as part of the national household surveys implemented by the NSOs in Cambodia, Ethiopia and Tanzania between 2018 and 2020 using the Survey Solutions CAPI platform. These surveys were supported by the World Bank Living Standards Measurement Study (LSMS+) project and included (a) a multi-topic socioeconomic household questionnaire, and (b) a cross-country comparable individual questionnaire that was administered to the adult household members in private interviews to collect self-reported information regarding their work and employment, as well as ownership and rights over physical
Income Group**||||||
|LIC|15.0|15.8|16.0|15.9|16.4|
|LMIC|15.7|16.7|16.3|16.0|15.7|
|UMIC|16.0|15.5|14.9|14.9|14.5|
|HIC|13.1|13.2|13.1|12.7|12.6|
|**Region**||||||
|AFR|14.8|16.2|16.6|16.7|16.1|
|ECA|11.9|12.2|12.2|12.4|12.2|
|LCR|16.2|15.7|16.1|17.3|17.4|
|SAR|15.5|16.3|14.1|13.5|15.0|
|EAP|16.1|17.2|15.4|14.5|14.0|
|MNA|17.3|16.5|15.4|12.9|13.6|

_Source:_ World Bank calculations using UIS and IMF online databases. _Note:_ Income groups are defined by country income group classification in 2017. LIC = low-income country, LMIC = lower-middle-income country, UMIC = upper-middle-income country, and HIC = high-income country. AFR = Africa, ECA = Europe and Central Asia, LCR = Latin America and Caribbean, SAR = South Asia, EAP = East Asia and the Pacific, and MNA = Middle East and North Africa.

# **_Low-Income Countries Have Spent More on Primary Education, Richer Ones on Post-Primary_**

Wealthier countries have devoted a greater share of government spending to post-primary education than less well-off countries. In 2014 **–**
## **2.6 Description of the index insurance products**

Table 2 describes the products offered in each study. Three products are based on rainfall, two on area-yields, and one is hybrid (that is, based both on rainfall and area-yields). In four studies, the products are explicitly targeted towards particular crops: Elabed and Carter (2014) and Stoeffler et al. (2022) target cotton production; Karlan et al. (2014) target maize production; Bulte et al. (2019) target four crops (soya bean, sorghum, sunflower, and maize). In other studies (Cole et al., 2017; Hill et al., 2019), the products are not crop-specific. Large differences exist in sale prices, both within and across studies. Within study differences are due to the price experiment design of four studies. Differences across studies reflect only to some extent differences in actuarially fair prices. Insurance purchase was generally an individual decision, except in Elabed and Carter (2014) and Stoeffler et al. (2022) where farmer groups collectively decided to purchase the insurance. In most studies, the amount of insurance that farmers could purchase depends on the amount of land they cultivate. A notable exception is the study by Cole et al. (2017) in India, which offered insurance products as stand-alone policies (treated households received 10 policies). Take-up of the different products vary from 29% in Mali to 100% for the products offered for free. Payoff triggers depend for example on the amount of dry/wet days, on the duration of dry spells, on cumulative rainfall, or on area-yields averages. While products based on rainfall typically use data from weather stations, area-yield products rely upon data from crop-cutting exercises or from purchasing companies.

# **3 Data**

## **3.1 Primary outcomes**

We focus on five outcomes: the amount of cultivated area, fertilizers, pesticides, seeds, and a risk
sup>3</sup> Sales tax and excise tax accounted for a combined 80 percent of indirect taxes (table 1). Among the different types of taxes whose incidence can be estimated from the household survey, PIT and import duties are particularly overestimated compared to the amounts reported in the national accounts. In the case of PIT, this may be partly due to the fact that the entire value of durable goods is accounted for in the construction of the consumption aggregate, resulting in an overestimation of the consumption flow from durable goods. Indeed, dropping durable goods from the consumption aggregate yields a PIT allocation of 102%, much closer to the value reported in national accounts. A second possibility is that our methodology overestimates the number of formal workers. Our classification of workers (detailed in section 3.3) is closely aligned with the tax/contribution system, and while there may be some informal workers within each category of formal workers, it is impossible to identify them with the available information. The overallocation of import duties is likely because of an assumption related to the condition of the car— specifically, the tax rates differ for new and used cars, but the surveys do not collect information on the condition of the car. The modeling is based on the assumption that all cars were purchased new, likely leading to an overestimation of consumption and therefore, income.

> 2 The fiscal year in Bhutan runs from July to June of the following year. 3 Ministry of Finance. Annual Fiscal Statement, FY2016-2017.

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housing. A similar pattern is observable the Democratic Republic of Congo, Madagascar, Mozambique, and Zambia where housing adequacy is also unequally distributed across top and bottom income groups. Even though the Democratic Republic of Congo enshrines the right to decent housing, access to drinking water as well as to electric energy in its 2006 Constitution (Art. 48), 62 percent of HHs in the Democratic Republic of Congo do not have access to adequate housing. This is most pronounced in the area of access to electricity: in the richest income decile only 70 percent of HHs have access to electricity and in the lowest income deciles less than 1 percent of HHs have access to electricity.

In countries such as Angola (housing adequacy Gini 0.25) and India (housing adequacy Gini of 0.19), the pattern is slightly different. In both countries, in the lowest income deciles, every third household is likely to live in an adequate house (about 36 percent). In the highest income decile, almost every HH (94 percent in India and 90 percent in Angola) is likely to have adequate housing. While the housing inadequacy is still very pronounced in the lowest income group, particularly compared to the richest HHs, housing adequacy gradually improves with growing incomes.

Iraq’s access to adequate housing is rather equal across income deciles (housing adequacy Gini of 0.09). About 80 percent of low-income HHs and almost 90 percent of high-income HHs are likely to live in adequate housing. Iraq has a large water and sanitation network and almost all HHs according to the latest household survey have access to safe water (91 percent) and sanitation (99 percent) services. This closely ties in with the latest estimations by JMP, outlining that about 99 percent of households in Iraq have access to water and sanitation (UNICEF & WHO 2019). However, frequent power shortages, a continuing lack of qualified personnel, illegal water tapping, or failures to empty pit latrines contribute to system failures. For instance
should be to allow the parties to freely agree on the conditions of the transaction in the credit agreement. Currently, a number of MENA countries require unnecessary formalities to create and enforce security interests in movable property.

- **Modernizing movable collateral registries.** The collateral registry is the cornerstone of a functioning and efficient secured transactions system. Without a functioning registry, the best secured transactions law in the world would be completely ineffective and useless. The registry fulfills an essential function of the system which is to notify parties about the existence of a security interest in movable property (of existing liens) and to establish the priority of creditors vis a vis third parties.  None of the countries in MENA have modernized their secured transactions registry in line with standard internationally accepted practices. Therefore, collateral registries in MENA usually lack the critical features of a modern registry: centralization in a single database, electronic web-based system, registration of notices, accessibility to the general public for real time consultations, unified registry for all types of movable assets, cost effectiveness, restrictions on the authority of registrar, security of data, etc.

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# **3. An Application for Irrigated Rice Production**

The CH4 database has been designed to complement the CO2 database developed in Dasgupta, Lall and Wheeler (2022 a,b). Both are intended to assist global stakeholders with readily-usable, internationally-comparable and objectively-verifiable information about greenhouse gas emissions. We believe that 25-km resolution strikes a reasonable balance between robust estimation of cell means, given the available data, and the resolution required for analysis at subregional scales.

We illustrate with a global analysis of emissions from irrigated production of rice, the staple crop for the majority of the world’s population (Adhya et al. 2014). In 2013, rice was harvested on 165 million hectares of land in 100 countries, with 90 percent of global production in Asia. Lowland irrigated fields occupy about 80 million hectares and produce 75 percent of the global crop (FAO 2014; Fischer et al. 2014).

Figures 3 and 4 display the global distribution of irrigated rice production by field extent and yield, respectively, estimated by a team affiliated with the International Food Policy Research Institute (IFPRI) (Yu et al. 2020). Aside from the vast irrigated areas in South, Southeast and East Asia, Figure 3 reveals smaller areas under particularly intense cultivation in the United States (California and the Mississippi River Valley), southern Brazil and Uruguay, northern Italy, The Gambia, Mali, the Nile Delta and Madagascar. Figure 4 displays a yield pattern that is somewhat different, with evident high-yield areas in the United States, southern Brazil and Uruguay, southern Spain, northern Italy, the Nile Delta, central and northeastern China, and eastern Australia. South and Southeast Asia offer the most striking contrast, with widespread cultivation intensity in Figure 3 but relatively low yields in Figure 4.

The georeferenced IFPRI data have higher spatial resolution (0.083 degrees) than our spatial grid for CH4 anomalies (0.25 degrees). To incorporate them, we map each IFPRI pixel to a grid cell. Then
to either have no dropouts at all, or have a lot of dropouts, relative to the status quo. These results suggest that, in the presence of a feedback mechanism, farmers’ decision to stay or leave the service was more coordinated across members. Demand effect spillovers.

We next test whether providing users with feedback tools had any impact on non-users in the vicinity. The underlying hypothesis is that the lack of opportunity to influence the delivery of OAF’s agricultural services presents a significant barrier to farmers’ take up.

Again, we are interested in capturing both the extensive and intensive margins of expanded membership across treatment and control. Using the administrative data, we regress both a dummy for having new members join the group and the number of number of new members on our treatment assignment variables, as described in equation (1). We find evidence of large, positive spillover effects to non-users in the vicinity of treated groups (cols 6 and 7, Table 4). Groups that were offered any channel of feedback are 26-28 percentage points more likely to attract new members (col. 6, Table 4); these are very large effects relative to an 8 percent chance in the control group. Looking at the intensive margin, we find logbooks have a larger, more precise effect than scorecards. Logbooks add on average 1.12 new members to a group (significant at the 1% level), or 1.35 of a standard deviation, relative to 0.61 for scorecards (not significant). However, we cannot statistically tell these coefficients apart in this single equation.<sup>23</sup> Taken together, these results show that providing a simple feedback tool such as a logbook positively affected users’ sustained demand for the service and had large, positive demand spillovers for non-users. This indicates that our intervention addressed a significant barrier to adoption of this service—a lack of accountability. Neighboring client farmers

> 23 A Wald test of joint significance indicates that most of these
61 Interjurisdictional equalization of personal incomes<sup>may be deemed</sup> even more important by higher level policy makers<sup>than equalization</sup> of tax bases if much of the public rhetoric concerning<sup>grant objectives can be</sup> believed. The lack of adequate local income data in<sup>many countries greatly</sup> hinders analyses of these outcomes;<sup>however, there are a few instances of</sup> such research. The 1977 allocations of<sup>grants in the Philippines revealed</sup> no statistically significant relationship<sup>between grant allocations and</sup> local per capita incomes, suggesting<sup>that the systems there were neither</sup> income equalizing nor counterequalizing. 5 In their analysis of grants in Columbia, Slack and<sup>Bird used multiple</sup> regression analysis rather than simple correlation<sup>techniques to relate tax</sup> allowance grant allocations to a variety of local<sup>government (department)</sup> characteristics, including income. 4<sup>6</sup> When total real tax allowance allocations were regressed (using pooled cross<sup>sectional data) on</sup> population shares, number of primary students,<sup>mortality rates and real</sup> incomes in 22 departments over the period<sup>1974-1977, the results indicated</sup> that there was a significant, albeit small,<sup>negative relationship between</sup> income and grant size. This suggests<sup>a slight tendency for the Columbian</sup> system to redistribute in favor of lower<sup>income areas, even after</sup> accounting for the other variables.<sup>The two more explicit measures of</sup> 4Ibid.

4<sup>6</sup> N., Enid Slack and Richard M. Bird,
regimes<sup>26</sup> . Columns 3 and 4 of Table 7 display the results without and with controls. The estimates in both columns are positive, statistically significant and economically relevant. A one standard deviation increase in overrepresentation is associated with an increase in the dependent variable by between 0.35 and 0.50 standard deviations.

All in all, these results imply that the degree of political concentration, particularly in favor of parties that benefit from overrepresentation after transition to democracy, increases when overrepresentation increases, as suggested by our motivating theory.

# **4.4 Malapportionment and Government Transfers**

Our theoretical discussion and the previous literature also suggest that malapportionment could affect the distribution of public transfers across regions. We estimate the relationship between malapportionment and transfers per capita using within country data<sup>27</sup> and a model similar to Equation 3, where the dependent variable is transfers per capita from the central government to region _j_ . We use two alternative measures of transfers. “Total transfers” includes all transfers that the central government makes to a region, including transfers to state and municipal governments, social transfers, direct expenditures and investment by the central government, as well as transfers to public universities. The categories included vary from country to country, depending on availability. A more uniform variable is “transfers to sub-national government” which includes only transfers to state and/or municipal governments within a region.

The results in Columns 1 and 2 of Table 8 confirm the previous findings in the literature that overrepresentation translates into higher transfers per capita from the central government. In terms of economic significance, these results imply that a one standard deviation increase in malapportionment at the local level increases transfers per capita by about ten percent of a standard deviation.

Columns 3 and 4 of Table 8 check whether overrepresented areas are either poorer or more unequal than underrepresented areas. If this were true, then the higher transfers to these

> 26 The interaction term can be interpreted as the probability
Page 8 Table II. 2 Growth Rates of Employment and Population 1991-2001 <mark>Average Annual Growth 1991-2001</mark>

||Employment|Population|
|---|---|---|
|Gujarat|2.5%|2.1%|
|Gujarat Urban|3.8%|2.9%|
|Ahmedabad UA|4.0%|3.0%|

In order to understand the demand for housing in Ahmedabad in relation to the supply of formal housing, it is essential to examine the distribution of household income, not just examine averages.

The income distribution of households within the AMC current boundaries is shown for 2001 and 2011 on the two graphs of _<u>Figure II.1</u>_ . The population living in slums/challis is shown as yellow bars while the population living outside slums is shown as blue bars. Predictably, the slum population is mostly clustered at the left part of the graph for the 2 years measured. However, we can see an overlap between the income of the more affluent households living in slums and the income of the less affluent living in formal housing. The total number of households living in slums/challis in both years is derived from the slum mapping described in section III. Based on data for the distribution of household income, we were then able to determine how high up the income distribution are the households obliged to live in slums. We then cross check this information with a sample of housing prices using standard affordability measures of multiples of income.
**Figures 14, 14A and 14B: Maternal, infant and child mortality and GDP per capita across provinces, 2000 and 2008**

<!-- Start of picture text --> 500 Provincial<br>maternal Tibet<br>450<br>mortality<br>400 (per<br>100,000<br>350 live<br>births)<br>300<br>250<br>200<br>150<br>2000<br>100<br>2008<br>50<br>0<br>1500 15000<br>Provincial GDP per capita (RMB, log scale)<br>60<br>Provincial infant<br>mortality Xinjiang<br>(per 1,000 live<br>50 births)<br>40 2000<br>30<br>20<br>10 2008<br>0<br>1500 15000<br>Provincial GDP per capita (RMB, log scale)<br>70 Provincial under‐<br>five mortality (per Xinjiang<br>1,000 live births)<br>60<br>2000<br>50<br>40<br>30<br>20 2008<br>10<br>0<br>2000 20000<br>Provincial GDP per capita (RMB, log scale)<br><!-- End of picture text --> Source: DevInfo (UNICEF), a database system endorsed by the United Nations Development Group for monitoring human development. The mortality data in China used here is gathered through China’s official maternal and child mortality surveillance system which until 2008 covered 116 surveillance sites covering 73million people in 31 provinces at the time of the last national census.

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