rafmacalaba/data-use-mentions-extended huggingface dataset:
assume imports from different countries are imperfect substitutes, and that the importing country is a small country in world markets. These assumptions ensure that a change in tariff policy, whether it applies to all goods in a particular tariff line or only those from a particular source country, will have no effect on the world prices of any of the imported goods. The domestic production data is assumed to contain data on imports in IM product categories, which are used as intermediate inputs in domestic production and/or as consumption goods. The problem is to aggregate the NxH import values to IM composite imported goods.

The aggregation problem for a particular product category i is illustrated in Figure 1. The nodes at the top (denoted by a I) of the Figure represent the value of imports in industry i, denoted Mi, that are utilized as intermediate inputs or consumption goods in industry i in the domestic data. The entries at the bottom of the Figure correspond to the trade data, which is the value of imports by tariff line and source country. We will develop a quantity index, Z1, and a price index, j, for each sector i such that Zioi = Mi. In addition, we will obtain an index of the average tariff rate, **tj** , such that tariff revenue for each sector i is ti Zi. The effect of tariff policy changes on 4, and **t,** will then be utilized in the general equilibrium model to calculate the effect on domestic output, employment, and welfare.

Nested CES aggregation is used to allow for different degrees of substitution within product categories than between product categories. First, we allow for the possibility that goods in the same industry group have a different degree of substitutability for each other than for goods in another industry group. In the example illustrated in Figure 1, this is done by dividing each of the industry groups into J; subgroups. Each tariff line is then classified into an industry and a sub-industry. This results
in one of the world’s largest cash transfer programs, Bolsa Fam ́ılia (BF). BF is a Brazilian anti-poverty program that started in 2003 and gave benefits to around 14 million households as of 2014 (Gazola Hellmann, 2015). Eligibility for BF benefits is based on self-reported household income, likely generating substantial scope for misreporting given that approximately 50% of economically active individuals work in the informal sector (Henley, Arabsheibani and Carneiro, 2009). Importantly, the BF schedule features a pronounced notch that was expanded in June 2014. Prior to June 2014, households reporting a per-capita income below the extreme-poverty threshold of R$70 per-month were eligible for an unconditional monthly benefit of R$70 per-month, whereas households reporting an income above this threshold were not.<sup>3</sup> In June 2014, both the eligibility threshold and benefit were raised by 10%: the eligibility threshold increased from R$70 per-capita, per-month to R$77 per-capita, per-month, while the unconditional benefit increased from R$70 per-month to R$77 per-month.

We have access to administrative data spanning December 2011 to September 2016 from Cadastro Unico,<sup> ́</sup> which is the Brazilian government’s national registry used to determine eligibility for all federal social welfare programs. Using this data, we seek to estimate the number of original bunching households who move towards the new notch and the number of households jumping down to the new notch as a result of the reform. The number of bunching households is simply equal to the reduction in the bunching mass at the old notch (as a result of reform), while the number of jumping households is equal to the increase in the bunching mass at the new notch less the reduction in the bunching mass at the old notch (as a result of the reform). However, when looking at the raw data, there are clear time trends in the number of households locating at the old and new notch in the pre-reform
5

- Based on project experience, household statements, and multivariate analysis, what are the

constraints to greater biogas uptake and sustained use?

The findings from the baseline survey suggest that biogas is delivering on its potential. User satisfaction is high; there is evidence of fuel switching, especially away from fuelwood and crop residues, and lower fuelwood collection time, benefiting women in particular; and farmers report they are able to save on fertilizers and insecticides by using biogas residues. The quality of equipment and support structures appears adequate: breakdowns are few and quickly fixed, and most suspension of biogas after initial uptake appears to be temporary and related to, for example, families migrating or having too few animals at some point during the year.

The paper proceeds by describing the sampling and data collection, followed by a broad description of fuel use patterns in the sampled households (section 2). Preliminary indications of the welfare impacts of biogas are presented in section 3, while section 4 identifies the key factors associated with biogas uptake, use, and suspension. Section 5 presents some concluding remarks.

# **2 Household Fuel Use Patterns in Rural China**

Following the adoption of its 11<sup>th</sup> 5-year plan in 2006, the Government of China (GoC) rekindled its national biogas program, which the World Bank joined in 2009 through a 5-year US$440 million Ecofarming project, of which US$120 million to be provided by a World Bank loan. In addition to expanding biogas provision to an additional 400,000-500,000 rural smallholder households, the project seeks to test an enhanced project design which complements the subsidized provision of biogas digesters to smallholders with the simultaneous construction of an improved kitchen, by connecting the toilet to the digester, and providing technical assistance for productive use of the biogas slurry as fertilizer. This enhanced project runs alongside the national biogas project, which only provides subsidized biogas
# **C Additional results**

**Table (C.1)** Impacts of the Program on Earnings, Labor, & Occupational Choice

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Total Earnings in Last 30 Days (PPP, IHS)|-0.51|(0.38)|[0.33]|1.59|(2.83)|1,422|
|Total Labor Hours Last Week|-3.29|(2.35)|[0.33]|7.72|(18.73)|1,422|
|Total Labor Hours [Monthly Average]|-0.65|(8.20)|[0.78]|24.19|(67.80)|1,422|
|Net Wage-Employment Income Last Month (PPP, IHS)|-0.46|(0.37)|[0.40]|1.56|(2.81)|1,417|

**_Notes_** : The table shows the regression results on pre-specified labor market outcomes of the Focus Respondent using the 2021 in-person data. Each row is its own dependent variable. The outcomes that require definitions are: Total Earnings is defined as the total of business profits and net wage salary in all jobs and is transformed using the inverse hyperbolic sine transformation. Total Labor Hours [Monthly Average] is defined as average weekly wage-employment hours multiplied by the numbers of months worked in last 12 months multiplied by 52
also suggest that this easing of demand-side constraints to growth increases e-commerce sellers’ incentives to formalize and undertake complementary changes to business practices and technologies. Strikingly, micro and small-sized firms from the informal sector report these benefits to the same extent as do medium to large-sized formal firms.

The case study approach used in this paper has helped assess the potential mechanisms through which firms are able to reap benefits from using e-commerce platforms. But being based on the subjective reports of e-commerce sellers and lacking a control group, this approach is not suitable for causal impact evaluation. Our findings thus highlight the need for more quasi-experimental or experimental research into estimating the impacts of e-commerce on firms in developing countries.

The selective use of e-commerce by firms in South Asia is also worth stressing. This issue is not limited to e-commerce platforms. Interviews with other types of digital platforms that facilitate business to business (B2B) transactions also suggest that for a number of reasons, their usage is concentrated at the upper end of the spectrum of small firms. For example, Power2SME, a B2B platform in India that aggregates orders of raw material for small firms, requires firms to have a turnover of at least 50 million Rupees, a threshold well above the revenue of most small informal firms. This is because aggregating orders when each order size is very small is prohibitively costly even with the digital platform technology. In addition, a key reason highlighted for the lack of use of the platform is that a majority of small firms prefer to purchase their raw materials through informal networks. This would remain a (transactional) barrier even if a platform such as Power2SME were to offer subsidized logistics, delivery services and access to financing. In fact, Power2SME has an active strategy of outreach to SMEs and microenterprises: it identifies industrial belts in the country

> 20The World Bank COVID Module of the Center for Monitoring the Indian Economy (CMIE) Consumer Pyramids Household Survey (CPHS), January-April 2021 wave.

26
. 2019), while our focus is on SME lending.

Second, our paper relates to the literature on the consequences of information sharing through credit registries on bank competition and credit supply (eg. Jappelli and Pagano 2002, Djankov et al. 2007, Hertzberg et al. 2011, Liberman 2016, Dobbie et al. 2016, Foley et al. 2018, Sutherland 2018). As noted above, our principal contribution to this literature is to document that information spillovers between lenders can also occur through loan approvals rather than loan issuance. Because loan approvals are not encoded in the Peruvian credit registry, our results imply that information spillovers can occur even before information is encoded in the credit registry, and before the initial lender has derived any benefit from screening new borrowers. And our results imply that information 4
p�,�,�,� , the median price of item k in the reference strata in the reference month. Two sets of price deflators were calculated, one for food and another for nonfood items, the nonfood price deflator was used to deflate the consumption flow of durable goods.

## Poverty Line

Determining a household’s poverty status requires a poverty line against which to compare consumption. A poverty line serves as a reference point for what might be an acceptable minimum standard of well‐being, below which one could be considered deprived, or living in poverty (Ravallion, 2017, 1998). The choice of the poverty line considers what might constitute an acceptable minimum standard of living and the potential impact of resulting poverty estimates on policy decisions. Once a poverty line has been chosen, poverty analysis is then typically based on comparing the first three poverty measures of the Foster‐Green‐Thorbecke (FGT) class of poverty indicators. FGT measures consist essentially of variations of specification 0, where the parameter α takes the value of 0 for the poverty headcount, 1 for the poverty gap, and 2 for poverty severity (Foster et al., 1984).

Where y� denotes the consumption y of individual i , n denotes the total population, and z the poverty line.

Theoretically, a national poverty line could have been estimated for South Sudan in the year 2016 using the survey data. However, the international poverty line of US$1.90 PPP was chosen.<sup>32</sup> Given that the international poverty line was based on the predicted poverty line for the world’s 15 poorest countries, combined with the expectation that poverty in South Sudan was to be relatively high, the international poverty line was considered an appropriate metric, also offering the ability to make international comparisons. Hence, the $1.90 USD PPP (2011) poverty line was first converted into current SSP and adjusted to reflect South Sudanese purchasing power using the South Sudan PPP
where his caste identity would be known (e.g. Gupta 2000, Srinivas 2009). A low-caste boy could not move up, a high-caste boy could not move down.

At the bottom of the traditional caste hierarchy are the castes whose members were traditionally marked as “unclean.” They were called untouchables and are today called Dalits. Untouchability has several dimensions: exclusion from public spaces and water sources, humiliation (including prohibition from all but menial occupations), and exploitation by the high castes (Desphande, 2011, p. 9). Untouchability is illegal under the Constitution of India, and attitudes towards Dalits are radically different today from what they were in the recent past (Kapur _et al._ 2010). But the social division persists. Bros and Couttenier (2011) use official Indian crime statistics for 2001 to demonstrate the systematic use of violence across India to enforce untouchability rules. Two surveys give some indication of how untouchability plays out in schools: “One common example of social prejudice in the classroom is the disparaging attitude of upper caste teachers towards Dalit children. This can take various forms, such as telling Dalit children that they are ‘stupid,’ making them feel inferior, using them for menial chores, and giving them liberal physical punishment.” (PROBE, 1999, p. 51) “In one out of four primary schools in rural India, Dalit children are forced by their teachers or by convention to sit apart from non-Dalits. As many as 40 percent of schools practice untouchability while serving mid-day meals, making Dalit children sit in a separate row while eating.” (Shah et al., 2006, p.168, based on a 2001-02 national survey) Participants in our experiment were 288 junior high school boys drawn from the top of the caste hierarchy (the “General Castes”, hereafter “high castes”), and 294 junior high school boys drawn from the bottom of the caste hierarchy (the Dalits, hereafter “low castes”).
the two groups – priming and non – and find that it is marginally higher for the primed respondents (e.g. those who answered this module later in the survey).<sup>17</sup> In general, the priming results point to careful consideration when designing future surveys to capture depressive sypmtoms using the CES-D. Careful consideration is required of any potential priming quesitons prior to the CES-D.

Spillover effect of adverse events Neighbors’ experiences with deaths, shocks, and conflicts could also have an impact on one’s depression measure. This could be due to a variety of factors: via neighbors’ mental health outcomes spilling over into individual mental health, correlation of covariate adverse events and consequent lack of mutual insurance (Fowler & Christakis, 2008; Ray, 1998).

We evaluate the spillover effect of neighbors’ experiences on CES-D scores. In particular, we examine the effect of the percentage of neighbors who experienced negative events on one’s depressive symptoms (while controlling for personal and household experiences).

First, we evaluate the effect of percentage of neighboring households affected by any type of adversity on one’s own likelihood of being over the cutoff (Table 3 Panel A). We find that there

> 17 The overall reliability for the entire sample is 0.76; the Cronbach’s Alpha is .7699 for primed, and .7473 for nonprimed. The reliability can indicate how well the module measured depressive symptoms, but alternatively could indicate issues such as yay-saying.

12
22 sterility (see Bongaarts and Potter (1983) for a comprehensive account). Data from the WFS show clear patterns, again according to how far a country has progressed into transition.

First, in all countries, prevalence of contraceptive practice increases monotonically with higher levels of education, while the fertility inhibition due to post-partum infecundability decreases (Casturline et al (1985)).

When comparing countries at different stages of transition, we find that the further a country has gone in the transition, the further down the "educational ladder" contraceptive use is diffused (Singh and Casterline (1985) pg 210, Casterline et al (1984) pg 27). This has implications for family planning policy which are considered below.

The effects of nuptuality have a similar pattern, except that they are relatively more important in Asia than in the Americas, and everywhere, apart from Africa (where the effects of contraception are much weaker), are more restricted to the higher levels of maternal education (Bongaarts, Frank and Lestahaeghe (1984), pg 518).

In summary, at the risk of severe over-simplification, the WFS evidence suggests that for countries firmly into the demographic transition, such as some in the Americas, educational fertility differentials are equally due to later age at marriage and the use of contraception (ealier termination of child bearing). In much of Asia, early and universal marriage persists for all but the highest educational subgroup, and differentials are more due to different contraception use rates. Finally, for African
in Armenia. The data has been collected by the National Statistical Service and the Ministry of Health of the Republic of Armenia and is co-funded by the United States Agency for International Development (USAID). The main goal of the survey is to collect demographic and health indicators, particularly from women of reproductive age, and in some questions from men as well. In total, 2015-2016 survey has collected data on 6,116 women and 2,755 men of the age range of 15 to 49. The data is representative on the national and rural-urban areas. The 2015-2016 survey has a total of 313 clusters, of which 121 are rural.

One of the advantages of the DHS survey is that the program collects the GPS location data of surveyed clusters. In order to protect the confidentiality of the respondents, the locations have been displaced. Each urban cluster has been displaced from the actual location up to 2 kilometers, and each rural cluster up to 10 kilometers.<sup>10</sup> The survey also provides a wide range of geospatial covariates, very useful for the purpose of the study.

|Occupation|Men|Women|Total|
|---|---|---|---|
|Not working|27%|62%|50%|
|Professional/technical|9%|25%|17%|
|Clerical|1%|2%|1%|
|Sales|6%|9%|8%|
|Agricultural|26%|45%|36%|
|Services|16%|6%|11%|
|Skilled manual|30%|6%|18%|
|Unskilled manual|9%|7%|8%|
|Other|4%|0%|2%|
|Total|1,233|2,571|3,804|
|Working|904|981|1,885|

Table 1: Rural employment by gender (
D D 2 × D D × T T T (1), where yy _m_ mm indexes a town or village close to the state border. = αα+ ββ<sup>BB</sup> mm + ωω DDT TTmm + ωω represents an outcome variable for all DDT TTmm mm + εεmm manufacturing and services sectors in town or village _m_ , including total employment and the number of mm yy firms. We measure the outcome using data from 2013, after most interventions had been fully implemented. _Target_ indicates whether the town or village is located in Uttarakhand, the targeted area (=1), or in Uttar Pradesh, the control area (= _0_ ). We are interested in its coefficient, , which indicates the average effect of all interventions of the New Industrial Policy in Uttarakhand relative to Uttar Pradesh at the border. _Distance_ measures the driving distance between each town or village and the border. Following the ββ<sup>BB</sup> literature, we incorporate it in the form of a linear function, to control for any additional differences near the border areas. How close to the state border should we select our sample places and conduct the analysis is an empirical question. Following the literature, we replicate the analysis for town and villages within several distance buffers, ranging from 40 to 100 kilometers.<sup>5</sup>

> 5 Based on Calonico, Cattaneo and Farrell (2020), we identify the optimal bandwidth for our analysis to be in this range.

11
Figure A1. The Progression of Severity: High Cases <!-- Start of picture text --> Belgium Ecuador Spain France<br>150<br>100<br>50<br>1 1 1 1<br>0<br>United Kingdom Italy Netherlands Sweden<br>150<br>100<br>50<br>1 1 1 1<br>0<br>Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1 Mar 1 Apr 1 May 1<br>Daily severity ratio (percent)<br><!-- End of picture text --> Source: Johns Hopkins University; World Health Organization. Note: The daily severity ratio is the daily equivalent of the cumulative severity ratio. Horizontal line shows nth cause of death above or around the maximum daily severity ratio. Cut-off date for all data is May 23.

30
AGI gives to low wage earners, and/or ii) increase benefit levels in targeted social transfers to provide more adequate support to existing beneficiaries.

In comparison to its peers, Turkey is below the median. Overall, the progressivity and inequality reducing nature of social transfers is relatively lower than in comparator countries (panel (b) of Figure 9). Turkey’s transfers show a better equalizing performance than Chile’s and Mexico’s, but are less equalizing than in the rest of the countries. However, Turkey allocates a smaller budget to social transfers than most of its peers, as a share of GDP.<sup>16</sup>

# **_6.2 Indirect Taxes and Their Effect on Inequality_**

The Fiscal Incidence Curve of Consumable Income shows how indirect taxes affect incomes across different deciles of the distribution (Figure 10). The negative incidence of these taxes is higher for lower income households. For the poorest decile, these taxes represent an income decrease of 22 percent, while for the richest decile these bring about an income decrease of 11 percent.

**Figure 10. Fiscal Incidence Curve of Indirect Taxes**

<!-- Start of picture text --> 0<br>-5<br>-10<br>-15<br>-20<br>-25<br>Poorest 2 3 4 5 6 7 8 9 Richest<br>Deciles of Market Income plus Pensions<br>share in per capita income (%)<br><!-- End of picture text --> Source: Own estimates based on Turkey 2016 HBS. Note: changes calculated from Market income plus pensions to Disposable income.

16 Spending in social transfers (non-contributory) in percent of GDP: Turkey 1.2 Chile 1.6 Mexico 1.0 Russia 5.3 Brazil 5.4 Argentina 5.8. Source: CEQ Data Center on Fiscal Redistribution.

28
to idiosyncratic shocks, households may also be exposed to covariate shocks that potentially affect all households in a community such as a town or state. In this section, we describe the exposure of Sudanese households to covariate shocks at the state level. We consider deviation of rainfall from historical averages to identify covariate flood/drought shocks and the number of conflict/crime events. Rainfall data at the state level were obtained from the Center for Hydrometeorology and Remote Sensing (CHRS)<sup>9</sup> . The conflict/crime data are obtained from the Armed Conflict Location and Event Database (ACLED)<sup>10</sup> . We focus on more recent data of these variables to better inform policy.

> 9 See http://chrsdata.eng.uci.edu/.

> 10 See https://www.acleddata.com/data/

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and how much they over or underperform similar private firms in the same sectors.

In this paper, we compare the size, cost structure, fiscal support, and financial performance of infrastructure SOEs across countries and relative to private firms, using data from the World Bank Infrastructure SOEs Database, which covers 135 SOEs in 19 countries between 2000 and 2018. We examine the role of state ownership in four sectors: power, airlines and airports, railways, and roads.

The analysis reveals that infrastructure SOEs are large and perform poorly, generating significant fiscal risk. We introduce new measures of performance net of fiscal transfers and examine previously uncovered patterns of subsidies by sector. We show that relative to comparable private firms, infrastructure SOEs are less efficient, represent a larger share of GDP, have larger liabilities as a share of GDP and larger employment costs as a share of revenues, and yield lower returns on assets.

3