rafmacalaba/datause-encoder-data huggingface dataset:
and all expenditure modules used a recall approach. The 2017 sample counts 48,639 individuals representing a population of 692,895, while the sample size in 2022 is 52,822 individuals, out of a population of 650,118.<sup>1</sup> Both surveys are representative at the district level (there are 20 districts, or _Dzongkhags_ , in Bhutan) (Bhutan National Statistics Bureau 2017, 2022).

The intertemporal analysis of poverty requires the welfare measure of choice – in Bhutan’s case, household consumption – to be computed in a consistent way across surveys. The comparable consumption aggregate is constructed following the principles outlined in Deaton and Zaidi (2002) and Mancini and Vecchi (2022), and includes all required expenditure components, save for the consumption flow from durable goods, for which consistent information is not available across BLSS waves (Amendola et al. 2023). Finally, it is worth remarking that the 2017 and 2022 questionnaires do have a couple of discrepancies that affect the analysis presented in this paper: some variables that would be relevant in explaining households’ living standards and mobility, namely household income sources and individual labor market characteristics, were unfortunately not collected in both waves, and therefore cannot be used within the synthetic panel framework.

# **3 Methodology**

The study of the dynamics of poverty in Bhutan between 2017 and 2022 poses two distinct challenges. The first one is virtually unavoidable when performing this kind of analysis, and it concerns comparability: even when survey instruments are sufficiently similar, one cannot assume that poverty comparisons are accurate in the absence of consistent poverty lines, that is, monetary amounts that correspond to a reference level of utility that remains constant in the two periods (Ravallion and Bidani 1994, Ravallion 1998). This issue turns out to require special attention in the case of Bhutan: the process used to obtain consistent poverty lines is summarized in section 3.1. The second challenge concerns the absence of panel data: section 3.2 discusses the methodology employed for the construction of synthetic panels, following recent developments in the literature.

> 1 The 2017 sample counts 11,660 households (of 164,011 in the population), and the 2022 has 13,416 households (of 164,331).

4
# **1 Introduction**

Violence against women (VAW) is a global epidemic, with 35 percent of women worldwide having experienced physical or sexual violence in their lives (WHO, 2013). Its adverse effects range from physical and mental health issues for women and their children to broader social and economic losses (Raghavendra et al., 2019).<sup>1</sup> Permissive attitudes towards VAW are widely accepted, with four in ten women (and three in ten men) justifying VAW in about 50 low and middle-income countries (Sardinha and Catalán, 2018). Such widespread acceptance of domestic violence is a risk factor for its incidence (Abramsky et al. 2011; Flood and Pease 2009).

Most VAW prevention programs are delivered through resource-intensive grassroot mobilization campaigns (Green et al., 2020). Evidence on these interventions is mixed (Abramsky et al. 2014, 2016; Bourey et al. 2015; Dhar et al. 2022; Jewkes et al. 2020; Kerr-Wilson et al. 2020; Wagman et al. 2015) and their scaled implementation may be prohibitively costly in low-resource settings. Social and behavior change communication campaigns (SBCC) may provide a cheaper alternative. Theoretically, their messaging can reshape individuals’ attitudes and behaviors directly (individual channel), or indirectly through a social channel, by reaching other community members and eventually updating individuals’ perceptions of prevalent social norms (Akerlof and Kranton 2000; Mackie 1996).

– – Entertainment-education also known as edutainment is the use of entertainment media to increase audiences’ knowledge about an educational issue, create favorable attitudes, shift social norms, and change overt behavior (Brown and Singhal 1999; Singhal and Rogers 2012). Recent field experiments demonstrate that even low doses of edutainment programming (stand-alone programs that last between 20 minutes and 3 hours) can effectively reshape gender norms and reduce the social acceptability and incidence of VAW (Arias 2019; Banerjee et al. 2019 _a_ ; Green et al. 2020). This research tested the delivery of edutainment in community settings (e.g., public screenings), with two studies experimentally showing that the social channel greatly drove these impacts, and that private viewings of edutainment media had limited effects on VAW outcomes (Arias 2019; Green et al. 2020). A natural question, then, is whether these findings can be generalized to social media campaigns, where the influence of individual and social channels is less clear.

In this paper, we experimentally test the effectiveness of edutainment at reshaping gender norms and reducing social acceptability of VAW when delivered individually through Facebook

> 1For instance, Duvvury et al. (2013) find that VAW’s annual economic costs can reach up to 1-2 percent of GDP due to healthcare costs, productivity loss, and losses in future human capital formation.

1
(2) **A measure of** **_extinction risk_** that adds the insights of the international scientific community. We convert IUCN Red List status codes to extinction probabilities using the methodology of Mooers et al. (2008).<sup>7</sup> For species indicator construction, we normalize these probabilities so that a weight of 1.0 is assigned to species in the highest category (Critically Endangered). Table 3 tabulates conversions from Red List codes to normalized species weights, using four probability assignments. Three employ IUCN estimates to derive measures of extinction probability over the next 50, 100 and 500 years. The fourth draws on Isaac et al. (2007), who combine a direct extinction risk measure with a measure of each species' isolation on a phylogenetic tree.<sup>8</sup>

**Table 3:  Normalized species aggregation weights**<sup>a</sup>

|||Normal|ized Extin<br>IUC|ction Proba<br>N: Future Y|bilities<br>ears|
|---|---|---|---|---|---|
|IUN||||||
|<br>Code|Status|Isaac<sup>b</sup>|50|100|500|
|CR|CriticallyEndangered|1.00000|1.00000|1.00000|1.00000|
|EN|Endangered|0.50000|0.43299|0.66770|0.99600|
|VU|Vulnerable|0.25000|0.05155|0.10010|0.39000|
|NT|Near Threatened|0.12500|0.00412|0.01000|0.02000|
|LC|Least Concern|0.06250|0.00005|0.00010|0.00050|
||Rounded Weight Ratios|||||
||CR:EN|2|2|1|1|
||CR:VU|4|19|10|3|
||CR:NT|8|243|100|50|
||CR:LC|16|20,000|10,000|2,000|

a Data source: Mooers et al. (2008).

b From calculations by Mooers et al., based on Isaac et al. (2007).

> 7 The IUCN's current classification categories are Critically Endangered, Endangered, Vulnerable, Near Threatened and Least Concern.

> 8 A phylogenetic tree is a branching tree diagram that traces the evolutionary descent of different species from a common ancestor.  Species in sparse (isolated) branches of a phylogenetic tree are relatively unique, since they share common descent patterns with fewer other species.

21
spread across Asia, Africa, the Middle East, and Latin America and have a gross national income (GNI) per person (Atlas method) ranging from US$1,710 to US$9,960 (World Bank 2023A).<sup>4</sup> According to the latest available data, they were collectively responsible for spending almost US$200 billion on explicit energy subsidies in 2022 (and more than US$550 billion on implicit energy subsidies), which is around a quarter of all energy subsidies provided by middle-income countries (IMF 2023). On average, they provide 4 percent of GDP in energy subsidies (IEA 2023), which is equal to or larger than health spending in 10 of the 12 countries. The IMF (2023) estimates that on average the negative externalities in terms of climate change and air pollution alone have an indirect cost of another 7 percent of GDP. Further, the richest 20 percent of households in these countries receive on average around 11 times more in energy subsidies than the poorest 20 percent of households. The sample of survey respondents is broadly representative of the population with internet access, and the data were collected by a leading global online research firm (this is a similar approach to that in seminal work by Alesina et al. [2018]). Results are produced for the sample of respondents (i.e., the sample average treatment effects); moreover, to maximize generalizability, survey weights are applied to match the general population in each country (i.e., the weighted average treatment effects).

The results of the survey reveal that respondents have limited knowledge about energy subsidies and are far more supportive of reducing energy subsidies if accompanying policies are immediately implemented. Just over half of respondents were aware that energy subsidies existed in their country; and even after being informed of their existence, less than half of respondents were aware that energy subsidies have a negative impact on efficiency, equity, or the environment (only around one-third of respondents in the case of the environment). In the control group, around 30 percent of respondents were supportive of energy subsidies being reduced without accompanying policies (i.e., respondents expressed unconditional support); less than a quarter were supportive of energy subsidies being eliminated without 4Several countries that provide substantial energy subsidies, such as Iran and India, could not be included due to an absence of data about the distribution of energy subsidies.

3
The results, reported in Table 3, show that the coefficient of the interaction between the change in 2-year Treasury yield and dummy for tight policy is positive and significant for exchange rate, negative and significant for equity prices, and indistinguishable from zero for bond yields. The results convey that the spillover to exchange rates and equity prices is larger for a surprise tightening of the US policy than for a surprise easing; while the spillover to bond yields is symmetric for surprise policy tightening and for surprise easing; and seemingly equity prices in emerging economics do not react to surprise policy easing.

**Table 3: The Differential Effect of Surprise US Monetary Policy Tightening on Emerging Economies’ Financial Variables**

||E<br>k=1|xchange rates<br>k=2|<br>k=3|k=1|Equity prices<br>k=2|k=3|k=1|Bond yields<br>k=2|k=3|
|---|---|---|---|---|---|---|---|---|---|
|2yr Treasury yield|0.028***|0.030***|0.028***|0.018|0.000|-0.032|0.068**|0.083***|0.070***|
||[4.15]|[4.40]|[6.03]|[0.97]|[0.02]|[-1.65]|[2.66]|[3.51]|[3.36]|
|2yr Treasury yield*||||||||||
|<br>Dummy Tight|0.034***|0.039**|0.040***|-0.166***|-0.196***|-0.143***|0.007|0.020|0.053|
||[3.02]|[2.75]|[3.49]|[-5.12]|[-5.09]|[-3.58]|[0.19]|[0.46]|[1.24]|
|Constant|-0.167***|-0.152***|-0.180***|0.996***|1.134***|1.084***|-0.215|-0.303*|-0.420**|
||[-4.01]|[-2.98]|[-4.53]|[7.65]|[7.47]|[7.00]|[-1.39]|[-1.81]|[-2.61]|
|Observations|1,140|1,140|1,140|1,048|1,056|1,052|921|928|926|
|R-squared|0.098|0.084|0.071|0.083|0.099|0.084|0.063|0.078|0.072|
|# of countries|20|20|20|19|19|19|17|17|17|

_Notes:_ Dependent variables are percent change in exchange rate, equity prices, or bond yields, between the day of FOMC announcements and k days after that, as per Equation 1. The independent variable is percent change in 2-year Treasury yield between t and t-1, t being the day of FOMC announcements. Dummy variable Tight takes a value 1 when 2-year Treasury yield increases between t and t-1, and 0 otherwise. The 8 FOMC announcement dates when 2-year Treasury yields remained unchanged are exclude from the regression. Regressions include country fixed effects. Robust t statistics are in parentheses. *, **, *** indicate that the coefficients are significant at 10, 5, and 1 percent level.

The more pronounced market reaction to tightening announcements is consistent with the financial literature, which shows that financial markets tend to react more strongly to negative news than to positive news (e.g. Brown, Harlow and Tinic (1988), and Veronesi (1999)). In terms of our analysis here, a policy tightening in the US is “negative news” when it comes to the asset price valuations in emerging economies. In addition, investors may react more sharply to policy tightening by rebalancing their portfolios away from a few large emerging markets, the kind we have 12
**Table 4: Probability a product is traded and intensive margin – Results in 2007**

||(1)<br>|(2)<br>|(3)<br>|(4)|(5)<br>|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Pro|bability to exp|ort||Log of export||
|Water|||-0.0139*<br>(0.00781)|||-0.523***<br>(0.0819)|
|Tariff (log)|-0.315***|-0.268***|-0.317***|-3.344***|-2.570***|-3.356***|
||(0.0351)|(0.0357)|(0.0352)|(0.167)|(0.193)|(0.166)|
|Bound rate (log)||-0.0534***<br>(0.0117)|||-0.851***<br>(0.119)||
|Observations|11,764,033|11,738,708|11,738,708|3,430,113|3,423,406|3,423,406|
|R-squared|0.375|0.375|0.375|0.332|0.332|0.332|
|Importer-|||||||
|Industry-2dig FE<br>xporter-Industry-|Yes|Yes|Yes|Yes|Yes|Yes|
|2dig FE<br>|Yes|Yes|Yes|Yes|Yes|Yes|
|Country pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Robust SE in brackets clustered by country-pair. The estimates are based on the subsample of importers that acceded the WTO after 1995. *** p<0.01, ** p<0.05, * p<0.1

**Table 5: Interaction with institutions: extensive and intensive margins**

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|VARIABLES|Pro|bability to exp|ort||Log of export||
|Water|-0.0425***|-0.0497***|-0.0413***|-0.463***|-0.477***|-0.435***|
||(0.0119)|(0.0129)|(0.0121)|(0.115)|(0.113)|(0.114)|
|Tariff (log)|-0.560***|-0.563***|-0.560***|-3.608***|-3.613***|-3.608***|
||(0.0543)|(0.0544)|(0.0543)|(0.208)|(0.208)|(0.209)|
|Water*Rule of law|0.0318**|||0.0251|||
||(0.0156)|||(0.124)|||
|Water*Regulatory|||||||
|quality||0.0718***<br>(0.0169)|||0.0986<br>(0.150)||
|Water*Control of<br>corruption|||0.0366***<br>(0.0142)|||0.152<br>(0.110)|
|Observations|9,767,065|9,767,065|9,767,065|2,945,477|2,945,477|2,945,477|
|R-squared|0.376|0.376|0.376|0.340|0.340|0.340|
|Importer-Industry-|||||||
|2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Exporter-Industry-|||||||
|2dig FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Country pair FE|Yes|Yes|Yes|Yes|Yes|Yes|

Robust SE in brackets clustered by importer-product. ** p<0.01, ** p<0.05, *<0.1 14
and levels of equipment. In contrast, the _private_ western facilities that are bypassed are those with more doctors, nurses, services, and levels of service. While this might seem paradoxical, this is consistent with sophisticated health seeking behavior on the part of individuals. Since prices tend to be lower in the public facilities they will bypass sophisticated, but expensive, private for public if the condition is not serious or quality is not important. That is, they will bypass "expensive relative to the disease condition' private western facilities. However, for serious conditions or when quality of service is important, individuals will be willing to pay-both in terms of time and fees-for higher quality care.

Bypassing leads to low utilization of available public facilities. A recent survey of a rural area of Punjab province, Pakistan, found that although the physical infrastructure of rural PHC was in place "[o]nly about 5 percent of the sick children were taken for treatment to primary health care facilities; half were taken to private dispensers, and another quarter to private MBBS doctors. Around 95 percent of deliveries took place at home." (PIEDR, 1994, P.vi).

Roughly the same percentage of respondents sought treatment from a public rural health facility (5.2 percent) as a "quack" (4.9 percent) (PIEDR, 1994, p35). This was not because individuals were deterred from public facilities because of queues--on the contrary, the typical rural health center was seeing only about 30 patients a day, and the typical "basic health center" only 11 patients a day which was far below capacity as rural health centers had on average eight workers, and basic health units five. Two atypically busy rural health centers attracted and serviced an average of over 450 visits per day.

Low utilization is attested in a recent study of health center use in Indonesia. Even 22
# **Bridging Conflicts and Biodiversity Protection: The Critical Role of Reliable and Comparable Data**

## Brian Blankespoor,<sup>a</sup> Susmita Dasgupta,<sup>b</sup> and David Wheeler<sup>c</sup>

- a. Senior Geographer, Development Economics Data Group, World Bank, bblankespoor@worldbank.org

- b. Lead Environmental Economist, Development Research Group, World Bank, <u>sdasgupta@worldbank.org</u>

- c. Consultant, World Bank, wheelrdr@gmail.com

_Corresponding author:_ Brian Blankespoor, Senior Geographer, Development Economics Data Group, MC9-204, The World Bank, 1818 H Street, Washington, DC 20008, USA. Tel: 1-202-473-1546. Email: <u>bblankespoor@worldbank.org</u> _Keywords:_ Biodiversity Conservation; Non-determined legal status Territories; Fragile and Conflict-Affected Situations, Transboundary Ecosystems; Marine Joint Regimes; International River Basins _JEL Classifications:_ Q34; Q25; Q57 _Funding_ : This research was funded by the Global Environment Facility (GEF).

_Acknowledgments:_ This research was funded by a grant from the Global Environment Facility to a World Bank program managed by Nagaraja Harshadeep Rao, Susmita Dasgupta and Brian Blankespoor. Georeferenced species occurrences reported by GBIF were accessed from Google BigQuery on 2024-0217. We are thankful to Ms. Polly Means and Mr. Pritthijit (Raja) Kundu for their help with the graphics.

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.
**Table 1. Donor Fragmentation and Bureaucratic Quality**

|Equation|1|2|3|4|5|6|
|---|---|---|---|---|---|---|
|Period||1982-|2001||1990|-2001|
|Sample|Aid/GNI<br>> .03%|Aid/GNI<br>> 7%|Aid/GNI<br>> 7%|Sub-<br>Saharan<br>Africa|Aid/GNI<br>> 7%|Sub-<br>Saharan<br>Africa|
|Constant|2.757|3.834|2.229|6.053|4.150|6.471|
||(0.647)|(1.757)|(1.517)|(1.843)|(1.522)|(1.637)|
|Initial bureaucratic|-0.851**|-1.123**|-1.100**|-1.056**|-1.179**|-1.059**|
|quality|(0.085)|(0.171)|(0.188)|(0.130)|(0.162)|(0.134)|
|No. of years covered|0.067*|0.013|0.070|-0.104<sup>#</sup>|-0.018|-0.085|
||(0.027)|(0.094)|(0.085)|(0.057)|(0.080)|(0.054)|
|Population change/|-0.176|0.150|0.029|0.898*|0.264|0.303|
|initial population|(0.155)|(0.400)|(0.320)|(0.319)|(0.285)|(0.528)|
|GDP p.c. change/|0.090<sup>#</sup>|0.156|0.159|0.188*|0.173<sup>#</sup>|0.094|
|Initial GDP p.c.|(0.052)|(0.128)|(0.139)|(0.071)|(0.103)|(0.080)|
|Aid/GNI|-0.042**|-0.027|-0.005|-0.046**|-0.022|-0.042**|
||(0.015)|(0.019)<br>|(0.020)|(0.012)|(0.015)|(0.012)|
|Fragmentation (AiDA|-0.017*|-0.026<sup>#</sup>||-0.056**|-0.028**|-0.044*|
|activities counts)|(0.007)|(0.013)||(0.015)|(0.010)|(0.019)|
|Fragmentation (DAC|||-0.019*||||
|(disbursements)|||(0.009)||||
|N|96|30|30|30|32|30|
|R<sup>2</sup>|.58|.67|.64|.82|.73|.84|
|Std. error of est.|0.92|1.02|1.07|0.72|0.96|0.72|
|Mean,dep. var.|0.05|-0.17|-0.17|-0.73|-0.33|-0.75|

Robust standard errors are in parentheses.<sup>#</sup> , *, and ** , respectively, indicate significance at the .10, .05 and .01 level for two-tailed tests.

For many aid recipients in this 96-country sample, foreign assistance is sufficiently modest that there is little reason to expect the fragmentation of aid to matter much. For samples consisting of high-aid countries, fragmentation should matter more. This result is confirmed in several tests (not reported in the table) that progressively eliminate countries with increasing thresholds of aid/GNI, which show coefficients for fragmentation progressively increasing (in absolute value).<sup>24</sup> Equation 2 includes only 30 high-aid countries with aid/GNI exceeding 7 percent. Some control variables significant in the larger sample are not in this smaller sample. Aid level, in particular, is less likely to be significant because its variation is reduced by the way the sample is constructed. Fragmentation remains marginally significant, with a coefficient half again as large as in equation 1.

> 24 An alternative approach is to test in the full sample an interaction between aid/GNI and fragmentation. The coefficient for this interaction has the anticipated (negative) sign but is not significant.

19
|Risk taking (Z score)|-0.021|0.013|0.000|-0.001|0.052|0.028|0.005|-0.019|-0.007|
|---|---|---|---|---|---|---|---|---|---|
||(0.987)|(1.031)|(1.014)|(1.026)|(1.006)|(1.015)|(1.041)|(1.014)|(1.027)|
|Time preference (Z score)|-0.049|-0.050|-0.050|-0.131|0.046|-0.034|--|--|--|
||(0.925)|(0.990)|(0.966)|(1.010)|(0.998)|(1.007)|--|--|--|
|Metropolitan area of residence|0.555|0.604|0.586|0.623|0.613|0.618|0.344|0.377|0.360|
||(0.498)|(0.490)|(0.493)|(0.485)|(0.487)|(0.486)|(0.475)|(0.485)|(0.480)|
|Observations|247|412|659|491|600|1,091|579|589|1,168|
|||**Georgia**|||**Ghana**|||**Kenya**||
||(1)|(2)|(3)|(1)|(2)|(3)|(1)|(2)|(3)|
|VARIABLES|Male|Female|All|Male|Female|All|Male|Female|All|
|Log of hourly earnings in USD (trimmed)|1.239|0.984|1.079|0.689|0.298|0.519|0.720|0.540|0.642|
||(0.764)|(0.750)|(0.765)|(1.062)|(1.229)|(1.153)|(0.968)|(1.044)|(1.005)|
|Female|--|--|0.628|--|--|0.435|--|--|0.433|
||--|--|(0.484)|--|--|(0.496)|--|--|(0.496)|
|Age, years|39.102|40.128|39.746|35.133|34.297|34.770|33.599|33.264|33.454|
||(8.831)|(7.894)|(8.263)|(8.033)|(7.106)|(7.651)|(7.430)|(7.031)|(7.260)|
|# of years of education <=9 years|9.000|9.000|9.000|8.688|8.724|8.704|7.544|6.927|7.277|
||(0.000)|(0.000)|(0.000)|(1.361)|(1.219)|(1.300)|(2.582)|(2.931)|(2.754)|
|# of years of education 10-13 years|3.636|3.757|3.712|2.387|1.989|2.214|2.138|1.700|1.948|
||(0.768)|(0.675)|(0.713)|(1.964)|(2.003)|(1.990)|(1.735)|(1.741)|(1.751)|
|# of years of education > 13 years|2.809|3.251|3.087|1.135|0.795|0.987|0.677|0.424|0.567|
||(2.230)|(2.111)|(2.164)|(1.816)|(1.515)|(1.699)|(1.266)|(1.036)|(1.178)|
|Literacy assessment (plausible values Z score)|0.009|0.195|0.126|0.381|0.041|0.233|0.126|-0.070|0.041|
||(0.691)|(0.685)|(0.692)|(0.926)|(0.970)|(0.959)|(0.863)|(0.891)|(0.881)|
|Extraversion (Z score)|-0.035|0.160|0.087|-0.018|-0.019|-0.019|0.053|-0.002|0.029|
||(0.919)|(0.987)|(0.966)|(0.974)|(0.983)|(0.977)|(1.013)|(1.017)|(1.015)|
|Conscientiousness (Z score)|0.152|0.267|0.224|0.223|-0.105|0.080|0.122|0.063|0.096|
||(0.887)|(0.888)|(0.889)|(0.920)|(0.946)|(0.945)|(0.974)|(0.998)|(0.985)|
|Openness (Z score)|0.006|0.203|0.130|0.061|-0.176|-0.042|0.060|-0.147|-0.029|
||(0.994)|(0.851)|(0.911)|(0.994)|(0.974)|(0.992)|(0.988)|(1.014)|(1.004)|
|Emotional stability (Z score)|0.225|0.063|0.123|0.136|-0.166|0.004|0.107|-0.015|0.054|
||(0.914)|(0.992)|(0.966)|(0.926)|(1.035)|(0.986)|(1.013)|(0.971)|(0.996)|
|Agreeableness, cooperation (Z score)|-0.021|0.129|0.073|0.013|-0.076|-0.025|0.022|0.053|0.035|

26