rafmacalaba/data-use-mentions-extended huggingface dataset:
increased access to electricity in the country. Such experience could be replicated for developing other infrastructure that the country needs, provided that transparency and good governance rules are followed. Guinean authorities should also pursue efforts for diversifying the economy. Indeed, primary commodities account for about 90 percent of merchandise exports, which is a very high level of concentration, exposing the country to external shocks and limiting the possibility for structural change. Agribusiness with

> 19 According to 2019 Doing Business data. It is only recently that data on labor market conditions have been collected for the Doing Business report; thus, available data does not allow comparison over the analysis period.

> 20 For instance, of 140 countries around the world, the _2015-16 Global Competitiveness Report_ ranked Guinea the least performing country in terms of quality of infrastructure.

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not to evaluate hard close designs since a significant body of literature already suggests that hard close endings lead to suboptimal prices.

# 4. Data

## A. Data preparation

For the purposes of our analysis, we obtained three years (2015-2017) of detailed transactional data from Comprasnet’s online reverse auction system provided by the Brazilian Ministry of Planning, Budget and Management. For each year, we were provided with the following data sets: (i) a bidder-level data set containing general information about each auction, representing a total of 7 million observations across the three years; (ii) an item-level data set containing detailed information about each bid received during the auction, representing a total of 112 million observations; and (iii) a data set holding the milestone dates and times of all the electronic reverse auctions processes carried out through Comprasnet (0.9 million purchased items).

The data preparation phase consisted of three main steps. The first step involved merging the raw data provided by the government into a single data set by linking data from the same auctions across the different tables. It also required summarizing the data to obtain a final data set in which each observation represents an auction conducted for a specific item.

The second step was to clean the data to ensure its consistency as well as the coherence of the final sample used for the analysis. This involved removing observations with inconsistent dates and times, duplicated identifiers or missing values. Following the advice of the Brazilian Ministry of Planning, Budget and Management, we also removed all the data from agencies that are not part of the SISG ( _Sistema de Serviços Gerais_ ). All agencies that are part of SISG must use Comprasnet and SIASG for all their procurement purchases, while non-SISG agencies can choose to use or not to use these systems. When the use of the Comprasnet system is voluntary we do not observe the full purchasing activity of a buying organization potentially biasing the sample.

The third step was to generate new aggregates used in our models. The main
) data are from the General Statistics Office of Vietnam; _Brazil_ —For the period 1950–2005, value-added and employment data are from the Groningen Growth and Development Centre. For the period 1993/1995–2007/2008, data are from Pesquisa Nacional por Amostra de Domicílios. Note: _Botswana_ —Data are disaggregated at 10 sectors, as in McMillan and Rodrik (2011); _Ghana_ —Data are disaggregated at 9 and 14 sectors; _Nigeria_ —Data are disaggregated at 9 sectors; _Zambia_ —Data are disaggregated at 9, 10, or 3 sectors; _India_ —Data are disaggregated at 10 or 9 sectors. _Vietnam_ —Data are disaggregated into 19 economic sectors; _Brazil_ —Data are disaggregated at 10 sectors, as in McMillan and Rodrik (2011).

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.

Other studies have instead focused on the impact of uncertainty on consumer behavior specifically during downturn periods. In a study closely related to this one, Romer (1990) looks for evidence of the uncertainty hypothesis in the 1930’s recession, using data for the United States. The paper assumes the existence of an inverse relationship between consumer spending on durable goods and uncertainty about future income. The paper also suggests that one should expect a positive wealth effect on non-durable goods: consumers who are not buying durable goods will have more wealth to spend on perishable goods. She establishes that uncertainty is a positive function of stock market volatility. On this basis, stock market volatility and consumer spending on durables should be negatively related. The choice of a stock market volatility measure as a proxy of uncertainty opens, however, the possibility for alternative explanations. Therefore, she also explores alternative sources of the nexus between stock market volatility and spending on durables, namely 1st order stock market adjustment and financial constraints. However, even controlling for those effects, the uncertainty hypothesis remains valid. Carroll and Dunn (1997) analyze the recession of 1990s that was characterized by a spontaneous decline in consumption, particularly in durables. The recession came after a period of strong build-up of household debt, in part driven by high spending on durable goods, which leads them to explore the link between uncertainty and balance sheet deterioration. They find that unemployment expectations influence spending beyond any information those expectations contain about future levels of income. When uncertainty increases consumers postpone durable purchases until balance sheet conditions improve.

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for expenditure data, in particular for the lowest income quintiles who presumably have low or no savings accumulation. An adaptation of the RIM methodology is used by micro finance institutions (MFIs) and affordable housing finance companies (AHFCs) in India as they work with the low-income segment in providing micro-finance loans, including for housing. Loan officers work with community groups and individuals, often leveraging technology, to collect housing and non-housing expenditure data that is used to assess a household’s capacity to pay (Bhanot, et al. 2019).

# 3.3 House Price-to-Income Ratio (PIR)

Another simple and commonly used indicator of affordability is the price-to-income ratio (PIR), which is used to measure the affordability of for-sale housing at both the _household_ and _aggregate levels_ . Decades ago, a common rule of thumb used by real estate agents and financial advisors in the United States was that a family could afford to purchase a home priced at about 3 times its gross annual income. A 2019 study of first-time homebuyers in 50 metropolitan areas, however, found that this ratio was actually closer to 3.4 on average, and as high as 5.2 in some markets (AEI Housing Center 2019). Also referred to as the _median multiple_ , at the aggregate level it is the ratio of median house prices to median annual household incomes. It is widely used by the real estate and lending industry and allows for comparison across markets at various geographical scales.

That said, the PIR can produce some conflicting results as compared to the EIR. Consider, for example, the case of two households who plan to purchase a USD 50,000 house. A 30-year loan with a 5 percent annual interest rate requires a monthly loan repayment of USD 215. For a household with an annual income of USD 10,000, the PIR would be 5 (typically considered unaffordable), but the EIR ratio is 26 percent (typically considered affordable). However, if the annual interest rate is 20 percent and the repayment
individuals who purchased a test at the same price of BDT45 were also given a metal placard of a color depending on the arsenic level: blue for arsenic up to 10 ppb (parts per billion or micro-grams per liter), the World Health Organization guideline for arsenic in drinking water, green if above 10 and up to 50 ppb, and red if ‘unsafe’, that is, above the national government standard of 50 ppb. Similar metal placards have been used before in some testing campaigns (Opar et al. 2007, van Geen et al. 2014), as a more durable alternative to the routine strategy—adopted also during past nationwide testing campaign in Bangladesh—of applying to the well spout red or green paint that often becomes invisible within a year (Pfaff et al. 2017). Such visible indicators are a reminder about the status of specific tube wells with respect to arsenic and can spread this information throughout the village. In different contexts, other researchers have found large impacts of reminders on health-related behavior, for instance through the use of SMS messages, see Pop-Eleches et al. (2011) and Raifman et al. (2014). However, the cost of the placards (about BDT80) is high enough to increase significantly the total cost of testing. It was therefore important to determine whether the placards made any difference relative to the alternative solution (adopted in the two experimental arms described earlier) of informing the household with a less expensive laminated card indicating the test result and encouraging the household to keep the card in the house.

Despite much progress on numerous health indicators (Chowdhury et al. 2013), Bangladesh remains in the midst of a severe health crisis due to the widespread presence of naturally occurring arsenic (As) in shallow aquifers (see Ahmed et al. 2006, Johnston et al. 2014, and Pfaff et al. 2017). The problem, due to the widespread presence in the country of geological conditions conducive to accumulation of arsenic in groundwater, is compounded
and education, initial child development, preferences, availability of informal care, and location. Child care providers are heterogeneous in their marginal costs–capturing, for instance, heterogeneous managerial ability.

To see the relevance of the equilibrium mechanisms in our model, consider a policy that provides child care vouchers to all households. Such policy would raise the demand and use of child care and, if the supply of child care centers did not change, it would raise prices by a given amount. Our model, however, allows incumbents to alter their supply and also allows for the entry of new providers, leading to a lower price increase (or even a decrease) and a greater expansion of child care use than if supply did not change. These equilibrium effects are crucial when evaluating the large-scale policies often proposed to increase access to center-based care.

We estimate the model’s structural parameters by Simulated Method of Moments (SMM) using data from the Early Childhood Longitudinal Study (ECLS). We focus on the ECLS birth cohort (ECLS-B), which is a nationally representative sample of children born in 2001 and followed from birth through kindergarten. We use the second wave, conducted between January 2003 and December 2003, when the children were two years old. The data are well suited for our study because they contain information parental labor supply, child development, child care modes, and providers’ quality. We focus on households with two-year-old children because the child care market for this age features mostly private providers and has a large expansion potential.<sup>5</sup> The estimated model fits the data well. It replicates the patterns of child care quality and price across households of different parental income, education, labor supply, and marital status. It also replicates the variation of child care markets across locations, which we define based on size (children population) and income (median family income). The point estimates show that parents value child development and mothers’ time inputs are very effective in producing
# **_Abstract_**

Levels of child malnutrition in India fell only slowly during the 1990s, despite significant economic growth and substantial public spending on the Integrated Child Development Services (ICDS) program, of which the major component is supplementary feeding for malnourished children. To begin to unravel this puzzle, we assess the program’s placement and its outcomes, using NFHS data from 1992 and 1998. We find that program placement is clearly regressive across states. The states with the greatest need for the program ⎯ the poor Northern states with high levels of child malnutrition and nearly half ⎯ India’s population have the lowest program coverage, and the lowest budgetary allocations from the central government. Program placement within a state is more progressive: poorer and larger villages have a higher probability of having an ICDS centre, as do those with other development programs or community associations. We also find little evidence of program impact on child nutrition status in villages with ICDS centers.

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expenditures.

The relative declines in per capita total budget revenue were greater than for total budget expenditures. As a result, the regional budget surplus, which was quite large in 1992, almost disappeared. From 1992-95, pre-transfer revenue dropped by about 27% on average across the country, and average transfers declined by 28%. Transfers dropped by 2.3 times in 1995 after three years of growth.

The only non-budgetary category shown in Table 5, household income, is also the only category that has steadily grown in real terms from one year to the next, rising by 48% from 1992 to 199520, which reflects the gradual recovery of household incomes after their dramatic decline in 1992 following price liberalization.

> _19_ In fact that trend stopped in 1995, when the relative share of local budgets dropped compared to 1994 mainly due to a drastic decline in federal transfers to the regions. See also Table 2 in the previous section. 20 There are some inconsistencies between reported trends in household incomes in Table 1 and those in traditional statistical reports by Goskomstat. The difference is the largest for the change from 1992 to 1993. Table 5 suggests that real incomes increased by 22% while regular Goskomstat publications report a 10% increase. This difference should be attributed to some inconsistencies between Goskomstat's reports on regional CPI and regional nominal incomes and their aggregate national equivalents, which reflects general weaknesses of the existing Russia income statistics and underlying household surveys (World Bank, 1995b)
to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan’s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important for female entrepreneurs in both host and refugee communities (Slimane et al, 2020; Turner, 2019).

# **3. Data and descriptive statistics**

We use two rounds of household-level data collected by UNHCR in 2013-14 and 2017-18.<sup>6</sup> These data come from two sources, namely, the Profile Global Registration System (ProGres) and Jordan Home

> 6 For simplicity, from here on we refer to the first wave as the 2013 wave, and to the second wave as the 2018 wave.

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In the 19 data sets with an answer scale, having “some difficulty” is more common than “at least a lot of difficulty”. The prevalence of at least a lot of difficulty among adults ranges from under 1% in Vanuatu to 6% in Papua New Guinea and the median stands at about 2%. The median prevalence for some difficulty is at about 9%. There is more variation in prevalence when it is measured based on functional difficulty questions other than the WGSS compared to the WGSS. The data sets with other functional difficulty questions tend to be at the lower end of the distribution (e.g. Philippines, Panama, Indonesia) and also at the maximum (Papua New Guinea). Prevalence estimates based on the WGSS range from 7% in Nigeria to 19% in Namibia.

The bottom panel of Figure 3.1 gives the prevalence of any difficulty at the household level from the lowest to the highest. The median household level prevalence is at 23%. It ranges from a low of 9% in the Philippines to a high of 55% in Papua New Guinea.

Figure 3.2: Distribution of functional difficulties across domains among adults 15+ with any difficulty (%) for datasets with the WGSS <!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Seeing Hearing Moving Cognitive Selfcare Communication<br><!-- End of picture text --> For countries with two datasets under study (Rwanda (Census and LFS), South Africa (Census and GHS), Tanzania (Census and LSMS)), prevalence rates differ across datasets, perhaps due to different disability questions under use. For instance, in Rwanda, the prevalence rate is 9% with
cooking and mud-wall construction (BIMK, BIML). In these cases, mean PM10 concentrations for kitchens and living areas are 515 and 467 ug/m<sup>3</sup> , and the differences from the benchmark (BIOL) mean are highly significant. Outside kitchens with mudwall construction also have much higher pollution (351 ug/m<sup>3</sup> – a highly-significant difference from BIOL). However, the mean concentration for mud-wall living rooms with outside cooking (BOML) is no higher than the BIOL benchmark. This reflects the symmetric effect of mud walls, which act as a sealant against airflow in or out of the living area. For other building materials, outside cooking generates a kitchen concentration (BOOK) that is not significantly different from the benchmark concentration. However, the living room concentration in the same case (BOOK) is significantly lower than the benchmark case. As expected, clean-fuel households have the lowest mean concentration (133), which is significantly (90 ug/m<sup>3</sup> ) lower than the benchmark case. Our clean fuel results are quite similar to those obtained by earlier studies in Chile (Pino, et al., 1998; Caceres, et al., 2001) and India (Balakrishnan, et al., 2002; Parikh, et al., 2001).

Replication of this pattern in the firewood-only case highlights an important implication of the results: Two ventilation factors – kitchen location and mud-wall construction – account for very large differences in PM10 concentrations across households. This is as true for a single biomass fuel (firewood) as it is for all fuels combined.

# **7.  PM10 Concentrations by Geographic Area and Income Group**

We have analyzed the determinants of indoor air pollution using a stratified sample of urban and peri-urban households in the Dhaka region. Our stratification has been 25
The phase data is netted from humanitarian impacts, the values indicate the five phases of food insecurity: (1) minimal/none, 350 300 2 250 2 3 5 200 - ° ® 150 a = =] Zz 100 50 0 Tm MMM OO ORHRDDAHAA SeooNagagagaaqgqgggqgngqygqyganquqjnqygnqygnqygnquyugqygnqyngqyaguagngeaaqagqggqggqgaagaaaen ooclUmcrmUCcUclmUCOCOUCOUCOUCcOUUCcOUUCcOUCee|CDC OCCTOOUCOOUCOCOOCOCOOCOOCOOCOCOCAH TCCOH HH ANNA COC COCOYM EY ~~~~SauriaAcooeo~~~rtO ~~~AFOFR~~~AtOF—~~~GNQAKBCCoOGCCOKAGCKBoKAKECCKAKTCSCAKCSCAKCSAtoO~~Oo~At ~~~~atOoOO~~~~atTF ~~~~etOCUCOCUCUCOCUCUCOCUCUCNDCOCCUOCUCOCUCOCUCOUCUCCCOCUCOCUCOCUCOCUCOUCOlCloO ~~~aAFO~~~~rt ~~~~AOo —~F~At ~~FCO~tt ~~~ttGo ~~~~TtFO~~~~tt ~~8St ~~~~FTOt ~~~~Oohet wc~~CG —~~FOa ~~~~oo ~~~~ot8 —~~otFt A~~~oSao GGBOo~~~ox Date

FEWS NET IPC originalphase #2 m3 m4 m5 issuing alerts at two specific thresholds. These thresholds are adjusted to balance the trade-off between
For the phase-4 cutoff neighborhood sample (Columns 4–6), whose data are subject to a sharp RD design, we find evidence of significant positive impacts on the number of students, teachers, classrooms, and blackboards in marginal passers; furthermore, their significance is robust to the selected alternative bandwidths. The accompanying figures show a discernible structural change in the mean levels for these outcomes marginally above and below the cutoff. However, as with the partially-fuzzy RD estimates for the phase-3 cutoff neighborhood sample, the magnitudes of the impacts appear to be sensitive to bandwidth. In particular, the estimates drop fairly sharply when we increase the bandwidth from the optimal choice of 3 percentage points by 50% to 4.5 percentage points. The most conservative impact estimates at the cutoff on the number of students, teachers, classrooms, and blackboards are 85, 3.4, 4, and 2.8, respectively. Relative to the baseline means for these outcomes in the phase-4 cutoff neighborhood sample, the absolute impact estimates translate into percent impact estimates of roughly 37%, 37%, 47% and 27%, respectively. These impacts are substantial. They are particularly impressive given the short treatment exposure under investigation: the follow-up telephone interview data were collected only some ten months after phase-4 program schools received their first subsidy payment.

Notwithstanding, we do not find evidence of impacts at the cutoff on the number of toilets and student-teacher and student-classroom ratios. This finding is robust to the selected alternative bandwidths. The lack of evidence of a positive effect on the number of toilets is concerning given the large expansion in enrollment in marginal passers and its potential negative bearing on the use and maintenance of the facility. The finding may reflect that PEF does not encourage the adequate provision of this facility to the same degree as the provision of other types of physical infrastructure (such as classrooms). Given the expansion in enrollment in 33
SSCBT-related alliances, and coordinating their actions would present an additional layer of complexity.

# **5. Conclusion**

This study used survey data collected at the eastern borders of DRC with Burundi and Rwanda to analyze the new institution of SSCBT group trade. This institution emerged as a coping strategy among small-scale traders to overcome mobility restrictions and other sanitary measures imposed by the authorities to protect the population from COVID-19. Traders joined up to collectively ship their merchandise to the other side of the border in larger vehicles and thereby reduced the need for personnel movement and inter-person contact.

The econometric analysis finds that motorized transport, business registration, and trader association membership are important determinants of group trade participation among smallscale traders. This finding suggests that it is the socio-economically more advanced and institutionally better-established traders who have been engaged in group trade, while poor, politically less well-connected traders have been underrepresented. Group trade, thus, has not been inclusive and has tended to further increase the intra-trader income gap.

The analysis further investigated the factors that have made it possible for successful group traders to increase their income and found that getting access to new clients and obtaining higher prices for their products are significant explanatory factors. These group traders for whom the collective arrangement has proven income-enhancing will likely continue the practice once the COVID-19 restrictions that gave rise to the new group trade institution have been phased out. To achieve similar long-term viability for other group traders and possibly pull additional traders 24
131|6%|12%|10%|8%|64%|Expert self-<br>assessment|
|8|UN NQAF self-checklist|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|87|10%|16%|2%|7%|64%|Expert self-<br>assessment|
|9|Paris21 NSDS self-<br>assessment|Self-assessment/<br>N.A.|N.A.|N.A.|N.A.|149|14%|23%|1%|1%|61%|Expert self-<br>assessment|

**Note** : N.A. stands for not available. The number of countries shown under the column “Country coverage” are for the latest year for all the indexes. The SPI covers between 167 and 186 countries for 2016-2022. The SCI covers between 115 and 145 countries for 2004-2020. The ODIN covers between 125 and 192 countries for 2015-2022 but offers comparable data starting from 2016. The ODB covers between 30 and 115 countries for 2013-2017. The ODB only covers 30 countries in 2017 and is replaced by the GDB after this year. The GDB covers 109 countries for 2019-2021. The IIAG covers 54 countries for all the years for 2010-2019. All the indexes provide annual data, except for the GDB that provides a single data point for each country for 2019-2021.

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