rafmacalaba/data-use-mentions-extended huggingface dataset:
resources that armed groups rely on for revenue generation can erode political stability (SI A.6.1 and Figure S17). Rebel groups with extensive resource endowments are capable of mounting complex attacks on vulnerable targets [85], and government forces also threaten civilians’ safety in resource-rich regions [86]. The subset of APmanaged protected areas in active conflict zones, such as Garamba National Park in the Democratic Republic of Congo and Pendjari and W National Parks in Benin, likely are driving the increase in civilian targeting we estimate (Figure S14).

Our findings principally underscore the need to strengthen local communities’ involvement in protected area management. Recall that we find suggestive evidence of transference to AP coinciding with lower levels of decision-making inclusiveness in protected area management. If increased insecurity is one cost of transferring protected areas to private organizations, then the normative argument for bridging the gap between communities’ safety and private organizations’ conservation activities is even stronger. Doing so also may have instrumental value: higher levels of insecurity could undermine communities’ perceptions of protected area management, reducing rangers’ ability to gather information critical to anti-poaching efforts [87]. Forming what some call “inclusive” anti-poaching units—whereby rangers are accountable to local communities instead of external organizations—may help safeguard wildlife without placing nearby communities’ safety at risk [88, 89].

Yet our analysis of AP’s effect on economic development provides some optimism that privately-managed protected areas can benefit people and wildlife. For example, we find that tourism increases in protected areas after they are transferred to AP. This hints at the economic potential of private sector involvement in protected area management. Of course, our results rely on tourism proxy data, and actual visitor numbers should be obtained to confirm these findings. Moreover, the distribution of tourism benefits within local communities remains unknown, warranting future exploration.

It also appears that AP’s positive conservation impact does not impair economic development, in contrast to the traditional view that conservation and development are competing objectives [
, who has given birth to _b −_ 1 children by _t_ and whose last birth took place _r_ years ago, we estimate the following OLS regression: The outcome variable, _Birthit_ , equals one if the mother gives birth in year _t_ and is zero otherwise. _Post_<sup>1</sup> _t_<sup>,</sup><sup>_Post_2</sup> _t_<sup>,and</sup><sup>_FirstbornGirlj_aredefinedasearlier.Thevector</sup><sup>**Xi**comprisesindicators</sup> for household wealth quintiles, educational attainment of the mother and her husband, caste, religion, residence in a rural area, and mother’s year of birth. We include fixed effects for year ( _ωt_ ), state ( _δs_ ), mother’s age ( _φa_ ), parity ( _ψb_ ), and years since last birth ( _σr_ ), state-specific firstborn-girl fixed effects ( _νsFirstbornGirlj_ ), and state-specific year fixed effects ( _θst_ ).<sup>31</sup> Additionally, we estimate the effects of ultrasound availability on the “stock” of children a

> 28Our results do not change if we instead use robust standard errors without clustering.

> 29Since our empirical strategy relies on the sex of the first birth, the sample excludes the 11 percent of women in the data who had never given birth by the time of the survey.

> 30Out of wedlock birth in India is rare (0.06 percent in 2005 National Family Health Survey).

> 31We also modify this specification by including mother fixed effects to test if ultrasound availability delays time to the next birth for a given mother, conditional upon the time since last birth, in the post-ultrasound (relative to the pre-ultrasound) period and whether this delay is on average greater in firstborn-girl (relative to firstborn-boy) families. The results from this specification are available upon request.

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# **Figure S17. Share of providers who provide accurate diagnosis and treatment conditional on availability of equipment and drugs**

# A. Medical equipment

<!-- Start of picture text --> 40<br>30<br>20<br>10<br>0<br>Facilities without equipment Facilities with equipment<br>No condition One condition Both conditions<br>Share of providers<br><!-- End of picture text -->

**Note** : Mean number of conditions (0,1,2) diagnosed and treated correctly by the most knowledgeable provider in the facility conditional on the facility having access or not to a minimum set of functional medical equipment (thermometer, stethoscope, and sphygmomanometer). Two conditions: diarrhea and pneumonia. The estimates are (unweighted) mean outcomes across countries, with the country means calculated using country-specific sampling weights. The error bars represent the 95-percent confidence interval. Data are from clinical vignettes and visual inspections of storage facilities and consulting rooms from Kenya (2018), Madagascar (2016), Mozambique (2014), Nigeria (2013), Niger (2017), Senegal (2010), Sierra Leone (2018) Tanzania (2016), Togo (2014), and Uganda (2013).

# B. Antibiotics

<!-- Start of picture text --> 60<br>50<br>40<br>30<br>20<br>10<br>0<br>Facilities without antibiotics in stock Facilities with antibiotics in stock<br>Cannot manage condition Can manage condition<br>Share of providers<br><!-- End of picture text -->

**Note** : Share of the most knowledgeable provider in the facility that can provide correct diagnosis and treatment for pneumonia, conditional on the facility having access or not to antibiotics. The estimates are (unweighted) mean outcomes across countries, with the country
allows us to include China and Mauritius for example. Data on access to water is missing for India. The latest available data in World Development Indicators indicates that access is 93 percent in India. Including India in our measure of vulnerability without this information on who is missing water may underestimates vulnerability in India by up to 7 percent. Again, future work requires developing methods to impute data for this measure, or whatever infrastructure measure is used, when it is missing.

• **Electricity** : whether a household has access to electricity. This is available for 139 countries of 168 for all years and 120 for the period 2015-present. Again, when this is missing but the World Development Indicator data indicates universal coverage, we assume no household is vulnerable on this dimension.

# Social Protection

The Atlas for Social Protection (ASPIRE) database provides the coverage of social protection and labor programs at the national level by household income quintile in each country.<sup>19</sup> Specifically, coverage is defined as the ratio between the number of individuals in the quintile who live in a household where at least one member is a direct or indirect beneficiary of any social assistance, social insurance, and labor programs, and the total number of individuals in that quintile.<sup>20</sup> In order to avoid using data that is too old, when the survey is outside of the range of +/- 5 years from the reference year we do not use the survey. For social protection we used 2018 instead of 2019 to maximize coverage because currently estimates of social protection coverage in 2020 and 2021 are not included in ASPIRE as they reflect the COVID-19 response rather than regular social protection coverage. If no data is available within the range 2013-present social protection data is counted as missing. This results in data being available for 92 countries, such that the inclusion of a social protection indicator limits global coverage. Similarly for 2010, data for the closest year to 2010 is used as long as
Our results are obtained from the data covering approximately a 20-year period from 1980 to 2000. The data come from three large and relatively recent data bases of occupational inequality (Occupational Wages around the World), inter-industrial inequality (University of Texas Inequality Project) and tariff rates (World Bank data). Although all three databases are rich in terms of the number of observations and do represent a major improvement in data availability, a user cannot escape the impression that there is still a non-negligible noise in the data, perhaps less because the data supplied by different countries and in different periods are wrong, but because the coverage of sectors and occupations and the definitions of wages are uneven and vary not only between countries but within countries as well. Thus the data issues still represent an important obstacle to our ability to draw stronger conclusions regarding the effect of import liberalization on wage inequality in a cross-sectional setting.

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**Figure 1. Vulnerability Status by Department**

<!-- Start of picture text --> Chuquisaca La Paz Cochabamba Oruro Potosi Tarija Santa Cruz Beni Pando<br>Mean<br>2003 2007<br>4<br>3<br>VAM 2<br>1<br>0<br><!-- End of picture text --> Source: Authors from World Bank (2010) Agricultural Public Expenditure Database (APER).

At the municipal level, 62 percent of the municipalities were categorized in 2007 as having a moderate to very low vulnerability to food insecurity (VAM 1 to 3), while 38 percent were in the high to very high vulnerability (4 and 5) categories. The percentage of municipalities with high or very high levels of vulnerability to food insecurity decreased from 51 percent in 2003 to 38 percent in 2007 (Figure 2).

**Figure 2. Percentage of Municipalities by Vulnerability Status**

<!-- Start of picture text --> 60<br>40<br>20<br>0<br>1 2 3 4 5<br>VAM<br>2003 2006 2007<br>Percentage<br><!-- End of picture text --> Source: Authors from World Bank (2010) APER database.

However, this reduction masks important variations in the vulnerability situation across municipalities. Evidence suggests that mobility across vulnerability levels is limited and asymmetric. Transition matrices (Table 1) indicate that 80 percent of municipalities did not change either their low/moderate or high/very high vulnerability status between 2003 and 2007. For each municipality that worsened its status from low to high vulnerability, more than two improved from high to low vulnerability. This is also true comparing 2003 to 2006.

**Table 1. Vulnerability Transition Matrix by Municipality**

|||**2006**<br>Low vulnerability|High vulnerability|**2007**
of Rohingya outside camps. The IPA population count consisted of a census of unions in seven upazilas of Cox’s Bazar district, which accounted for 76 percent of all unions in Cox’s Bazar district.<sup>12</sup> According to the IPA count, 57 percent of unions in Cox’s Bazar reported a refugee prevalence of less than 10 percent, with 21 percent of unions reporting a prevalence of more than 10 percent. Unions reporting a positive refugee prevalence tended to be larger, covering about 11 villages on average. Figures 1 and 2 present figures on refugee prevalence outside camps at the union and upazila levels. Prevalence is reported starting from the union level (instead of the village), as this was the lowest unit for which complete refugee and host information was available.

**Figure 1:** Distribution of unions in Cox’s Bazar by Rohingya prevalence

**Figure 2:** Prevalence of Rohingya displaced by upazila

<!-- Start of picture text --> 45% 80.00%<br>40% 70.00%<br>35% 60.00%<br>50.00%<br>30%<br>40.00%<br>25%<br>30.00%<br>20%<br>20.00%<br>15%<br>10.00%<br>10%<br>0.00%<br>5% Chakaria Cox's Pekua Ramu Teknaf Ukhia Cox's<br>Bazar Bazar<br>0% Sadar District<br>0% prevalence >0%-10% >10% Unknown<br>Prevalence: Old Rohingya Prevalence: New Rohingya<br>prevalence prevalence prevalence<br><!-- End of picture text --> Source: 2011 census for host population count; IPA population count for Rohingya displaced.

Source: IPA Population Count,
demand is larger amongst poor households. This estimate is statistically significant at the 10% level in column (5), and at the 1% level in column (6).

# _B. Gujarat: Video experiments_

Amongst the 20 Gujarat villages where video treatments were implemented, we randomized the content of the video viewed and the size of the discount coupon the household received. Correspondingly, we regress insurance purchase on the discount amount in rupees and the randomized video features: (i) whether the video featured a strong SEWA brand emphasis, (ii) whether a peer rather than authority figure endorsed the product, (iii) whether the policy is framed positively as paying in 2 of 10 years (rather than not paying in 8 of 10 years), and (iv) whether the product is framed in terms of “safety” rather than “vulnerability”. We also include a dummy for whether the household was part of the 2006 baseline survey.

Results are presented in Table 6. Columns (1) and (2) report basic results with and without village fixed effects, respectively, while (3) and (4) include additional interaction terms. As shown in the table, the overall take-up rate is 29%.

The size of the discount has a large effect on take-up. The coefficient on discount size is positive and statistically significant at the 1% level. The coefficient of 0.005 implies that raising the discount from Rs. 5 to Rs. 30 increases the probability of insurance purchase by 12.5 percentage points (or 43% of the sample average take-up rate). In contrast, none of the framing effects are significant at even the 10% level, and they are also jointly insignificant.

In columns (3) and (4) we interact the size of the discount with each framing effect. While in some cases the price sensitivity of demand does vary with framing treatments, we are unable to reject the null that these interaction terms are jointly zero. Finally
SENEGAL’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Table 8. Benchmarking air transport indicators for Senegal and other select countries**

|Country|Senegal|Côte d’Ivoire|Ghana|Nigeria|
|---|---|---|---|---|
|**Traffic (2007)**|||||
|Domestic seats (seats per year)|130,000|—|144,183|1,199,572|
|Seats for international travel within Africa (seats per<br>year)|1,260,000|851,003|909,819|1,373,745|
|Seats for intercontinental travel (seats per year)|1,230,000|297,891|832,895|2,437,702|
|Seats available per capita|0.23|0.06|0.08|0.09|
|Herfindahl-Hirschmann Index—air transport market<br>(%)|11.64|9.75|6.28|11.28|
|**Quality**|||||
|Percent of seat-km in newer aircraft|98|90.9|96.8|71.42|
|Percent of seat-km in medium or smaller aircraft|39.3|52.3|15.7|29.6|
|Percent of carriers passing IATA/IOSA audit|50.0|0|0|28.6|
|FAA/IASA audit status|No audit|Fail|Fail|No audit|

_Source_ : Bofinger 2009. Derived from the AICD national database (www.infrastructureafrica.org/aicd/tools/data). _Note:_ Herfindhal-Hirschmann Index (HHI) is a commonly accepted measure of market concentration. It is calculated by squaring the market share of each firm competing in the market and then summing the resulting numbers. All data as of
<!-- Start of picture text --> a,<br>Ahmedabad - Built-up densities:in 2001 ‘<br>Peg ons* Wyaty oei -<br>ty - a- we<br>"th Rye<br>‘ 3 Wis a ce ee<br>Legend ; a ” * reatPes aah oo7¥ ae<br>LandLand use 2000 heeutshet -. ge “ a ae. é° A sar,4<br> use ad ¥v J?‘ _ wit ae Y Fo é<br>Airport ay my . :. ‘ \ >‘ oya ct ssps *- %<br>ON OpenParks or agriculture _ a it aeae acaira2 Fe 7: A ¥ —"ae<br>I) Utilities eos) o q Get i te sg Bs ; ee<br>River and water bodies 5" Y sa ; “h der ~ A e " . rt<br>Densities p/ha in 2001 . : 0, = p<br>N_DENS oe Pah a CBD | - , -<br>WO 26 - 132 : i 2 x. 3°“ ? : i o -, i<br>GN 133 - 234 a oF ’ a ‘ —.<br>HE 235- 350 + 2p a a me<br>HEE 360 - 567 a’; : > om”cS 2 apes4° d : y \ té= ww<br>i<br>= : : aa . or 2 “~ or -<br>a” rear f aN te’ . Tv:<br>” f Be oe 4 ae ee e .<br>. : ‘ {P<br>Sad ( i. TM ii:Me 4. ane° is<br>ji =f “pi : i ‘ r ** i<br>/ y & a -— / ae = £<br>Kilometers<br>
tender preparation time for electronic tenders. The number of bids had the tender been manually administered is predicted using the same two models as used for the main results and have been given in the Table 3, Models 1a and 1b.

Based on the survey of bidders, we find that the average staff-time required to prepare a bid is found to be dependent on the contract value only for OTM tenders, as shown in the table. This is expected as 74
3. Impute or not impute monetary outcomes into DHS and MICS, and how? Imputing monetary outcomes into a non-monetary survey is one way of avoiding “wasting” some of the data present in separate surveys, like LSMS and DHS. However, imputation methods have limitations, mainly that their underlying assumptions may not hold in practice. One open empirical question is whether they still improve (inter-households and/or cross-regions) well-being comparisons when their underlying assumptions do not hold. Hence, is it the case that using data from two separate surveys more than compensates for the errors made on their joint distribution? Another empirical question is which imputation method performs best to impute monetary outcomes into non-monetary surveys. The paper should study these questions and quantify how different are the well-being comparisons based on these imputations from currently used comparisons and from “ideal” comparisons made when the joint distribution is known (see sections 3.4.1 and 3.4.2).

4. Accounting for local public services through the total consumption approach

- LSMS consumption surveys often provide very limited information on households’ outcomes in dimensions such as health, education or security. One pragmatic solution is to construct a household’s “total consumption” from monetary valuations of the public services provided in their area. The paper should investigate the best approach to make these monetary valuations at scale given prevailing data constraints. It should then quantify the impact of such valuations on well-being comparisons. Conceptually, it remains an open question how to aggregate consumption aggregates with monetary valuations of public services. Indeed, households cannot freely allocate the monetary valuations from these services. As a result, assuming perfect substitution (i.e., summing these valuations) may not be conceptually ideal. The paper should provide a conceptual discussion of alternative aggregation methods.

5. Multidimensional poverty weights selected from information on preferences The “equal-weights” procedure is regularly used when designing national multidimensional poverty measures and always used for global measures. The use of this obviously ad-hoc procedure runs two risks.
Policy Research Working Paper 6096

# **Abstract**

This paper uses Ethiopian data to explore credit rationing in semi-formal credit markets and its effects on farmers’ resource allocation and crop productivity. Credit rationing—both voluntarily and involuntarily—is found to be widespread in the sampled rural villages, largely because of risk-related factors. Political and social networks emerge as key determinants of access to credit among smallholder, peasant farmers. Significant regional variation emerges as well. In high-potential, surplus producing areas where credit is largely used for agricultural production, eliminating credit constraints is estimated to increase productivity by roughly 11 percentage points. By contrast, in low-productivity, drought prone areas where loans were rarely used to acquire inputs for crop production, the authors find no relationship between credit rationing and agricultural productivity. To be effective, efforts to improve agricultural productivity not only need to increase credit supply, but also explore the reasons for credit rationing and the availability of productive opportunities.

This paper is a product of the Agriculture and Rural Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at dali1@worldbank.org or kdeininger@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._
municipalities seems very high and varies a lot for several reasons (commercial area, industrial etc.).

- (iv) Land area information was not available to estimate the annual rental value of the property.

- (v) Informality in the payment of property tax. To overcome this limitation, the model applied proportional downscaling to match the effective property tax rate in the administrative data (relative to consumption).

# **9.2.2 Indirect taxes**

# **The main limitations for the VAT model are:**

- (i) We assume households purchase goods and services in the same region where they live.

- (ii) There is no place of purchase variable nor a secondary study on consumption informality for the West Bank and Gaza.

- (iii) We only applied indirect effects from VAT to West Bank residents.

To address the second limitation, we used estimations of consumption informality by decile based on an average from 10 similar countries (available in Bachas et al. 2020).

**In the case of customs duties, the major challenge is that we do not have an “imported” dummy in the PECS (a limitation of most household budget surveys).** To address this limitation, we assume the “Law of One Price”—that is, that prices of domestic goods converge to prices of similar imported goods. In applying the Law of One Price for simulating customs duties, we could overestimate the incidence of this tax, particularly if there are price differences between similar domestic and imported products and if poor or rich households have different consumption patterns between domestic and imported products. However, sensitivity analysis conducted for other fiscal incidence studies shows that alternative methods are likely to yield similar results.<sup>79</sup>

**Lastly, we assumed that Gaza residents are not paying indirect taxes.** We could be underestimating the tax burden for Gaza residents if they do pay informal taxes to the _de facto_ authorities, or
, South Sudan. Retrieved from https://www.oxfamamerica.org/explore/researchpublications/born-be-married/ <mark>Buvinić, M., & Gupta, G. R. (1997). Female-headed households and female-maintained families: are they worth targeting to reduce poverty in developing countries?.</mark> _<mark>Economic development and cultural change</mark>_ <mark>,</mark> _<mark>45</mark>_ <mark>(2), 259-280.</mark> Buvinic, M., Das Gupta, M., Casabonne, U., and Verwimp, P. (2013). Violent Conflict and Gender Inequality: An Overview. _The World Bank Research Observer,_ 28(1): 110-138.

Chaaban, J. M., Seyfert, K., Salti, N. I., El Makkaoui, G. S. (2013). Poverty and Livelihoods Among UNHCR Registered Refugees in Lebanon. _Refugee Survey Quarterly_ , 32(1): 24-49.

Espinoza-Delgado, J. and Klasen, S. (2018) ‘Gender and multidimensional poverty in Nicaragua: An individual based approach’, World Development, 110: 466-491 Evans, M., Nogales, R., and Robson, M. (2020). Monetary and Multidimensional Poverty: Correlations, Mismatches, and Joint Distributions. _OPHI Working Paper 133_ . Oxford: University of Oxford.

Hanmer, L., Rubiano, E., Santamaria, J., and Arango, D. J. (2020). How Does Poverty Differ among Refugees? Taking a Gender Lens to the Data on Syrian Refugees in Jordan. _Middle East Development Journal_ , 12(2): 208-242.

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