rafmacalaba/data-use-mentions-extended huggingface dataset:
weight-for-weight z-score (WHZ) and height-for-age z-score (HAZ).<sup>78</sup> Comparing across the two groups of households, we see that children in coffee-producing households have slightly better anthropometric outcomes than those in non-coffee-producing households.

It is worth discussing the distribution of some key variables in detail. First, table 2 provides summary statistics of the share of farmland allocated to coffee for different sub-samples of households. The first row presents the distribution of the share of land allocated to coffee across _all_ farmers in the ESS data (rural sample). An average farmer allocates about 5% of its farmland to coffee. However, because the majority of farmers are not coffee producers (the 75th percentile farmer allocates zero fraction of its farmland to coffee), the distribution of the share of land allocated to coffee is highly positively skewed. The second row provides the summary statistics of the share of land allocated to coffee among farmers who allocate at least 5% of their farmland to coffee. This sample accounts for nearly 15% of the total sample. Among these farmers, the average farmer allocates about one-third of its farmland to coffee, while the median and the 75th percentile farmers, respectively, allocate a quarter and half of their farm land to coffee. The third row reports similar summary statistics for sub-sample of farmers who allocate at least 10% of their farmland to coffee. These farmers account for slightly above 10% of the total sample, and they allocate about 40% of their farmland to coffee, on average. Rows 4-6 report similar summary statistics for sub-samples of framers who allocate at least a quarter, one third and half of their farmland to coffee production, respectively. This summary statistics for sub-samples of the households is useful later when we interpret the magnitude of the estimated regression coefficients.

The second important variable is household consumption expenditure. The average adult equivalent consumption in 2011 per day is 14.5 Birr (about 2.75 in current PPP $). The median adult equivalent consumption per day
|The Banker Database|The Financial Times|Firms in the financial sector in more than<br>190 countries covering 5,000+ public,<br>private, government and subsidiary banks<br>accounting for more than 90% of total global<br>bankingassets|Yes|No|
|---|---|---|---|---|
|Fitch Connect|Fitch Solutions|Financial data on 36,000+ banks, 12,000+<br>insurers, 117 sovereigns, and 3,000+<br>corporates globally across developed and<br>emergingmarkets.|Yes|No|
||IJGlobal (part of the<br>Euromoney<br>Institutional Investor<br>PLCgroup)|Firms in the global infrastructure finance<br>sector, including 636 SOEs globally|||
|Uniworld online|Uniworld|Firms with headquarters in over 200<br>countries and 20,000 industries|Yes (revenue,<br>employees)|No|
|Bloomberg|Bloomberg Finance<br>L.P.|Data for more than 5 million legal entities,<br>including public & private companies,<br>sovereign entities, funds, governments,<br>agencies, and municipalities|Yes|Yes|
|Banker's Almanac|LexisNexis Risk<br>Solutions|Firms in the financial sector covering<br>200,000 financial institutionsglobally|Yes|Yes|
|CB Insights|CB Insights|Global database on private firms and<br>investor activities.|Yes|Yes (investment and acquisition<br>data only)|
|S&P Capital IQ|S&P Global Inc.|Data on more than 62,000 public firms and<br>18.2 millionprivate firmsglobally.|Yes|Yes|
|Extractive Industries<br>Transparency Initiative<br>(EITI)|EITI|Firms in the extractive industries
each case.

The insights from our surveys advance our understanding of the behavioral consequences of displacement in a number of ways. The overwhelming majority of existing research on the political and social ramifications of displacement focuses on refugees in the global North,<sup>5</sup> despite the fact that 85% of refugees were hosted in developing countries (UNHRC 2019). Out of 79.5 million total forcibly displaced persons in the world in 2019, 45.7 million were IDPs (UNHCR 2019). We bring to bear original survey evidence from countries and subnational regions with especially large and consequential clusters of IDPs. In doing so, we have amassed an usually large and diverse body

> 3We define social cohesion, in line with De Berry & Roberts (2018), as “the set of relationships between and individuals and groups in a particular environment and between those individuals and groups and the institutions that govern them in a particular environment” (28).

> 4Additionally, we discuss a survey we conducted in Colombia in supplemental material that is not included here due to differences in the survey design and content that limit comparison, but nevertheless provide additional insights into our research question.

> 5For a discussion on the imbalance between displacement loads and the geographic focus of research on the consequences of displacement, see Alrababa’h, Dillon, Williamson, Hainmueller, Hangartner & Weinstein (2021).

2
county, in the second one, at the level of a specific bilateral trade relation and in the third one, at the level of a single exporting sector within a bilateral flow. We also derive the accounting methods for other relevant perspectives. We argue that each perspective is conceptually suited to addressing a different class of empirical issues. For instance, the country-level approach is the proper one when allocating a country’s GDP across the different trade flows, whereas the bilateral perspective is best suited to evaluating the extent to which the GDP of a country is involved in the commercial interchange between two economies.

We show how the main methodologies proposed in other contributions fit into this framework. Then, on the one hand, we reconcile a large part of the existing literature under a unique comprehensive scheme; on the other hand, we show the main shortcomings and limitations that affect some of the techniques that have been proposed in the literature.

In general, the detailed breakdowns of bilateral/sectoral exports we present find a broad scope for empirical investigations on global production networks both at macro and micro level. Indeed, there is a fast expanding empirical literature that uses tools similar to those proposed in this paper. Our contribution to this literature is twofold: i) we extend the set of possible measures, including a new indicator of GVC-related trade, in order to address a wider range of empirical issues; ii) we improve the accuracy of some of the existing measures. These improvements are likely to become increasingly relevant from a quantitative point of view since the inter-country input-output data will become more and more detailed, eventually relaxing some of the simplifying assumptions that characterize current databases (de Gortari, 2018).

Finally, an aspect that is currently debated in the literature is how to measure the impact of trade policies on countries’ productions by taking all international supply linkages into account. The ‘sectoral-bilateral’ perspective of value-added accounting proposed in this paper offers a promising conceptual framework to address this issue, since it provides a precise measurement of
_Table 2: VHI coverage by consumption quintile_

|Quintile|Mean|Std.Dev.|# cases|
|---|---|---|---|
|1|0.0911|0.2878|3656|
|2|0.1225|0.3279|3754|
|3|0.1690|0.3748|3586|
|4|0.2041|0.4031|3473|
|5|0.2709|0.4445|3355|
|Total|0.1693|0.3750|17824|

Source: VLSS data.

_Figure 2: Enrollment in 1997/98 by age in 1992/93_ <!-- Start of picture text --> 60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>0 10 20 30 40 50 60 70 80 90<br>Age in 1992/93<br>% enrolled in 1997/98<br><!-- End of picture text --> Source: VLSS data.

On the face of it, VHI enrollment ought to be almost perfectly predictable on the basis of employer type, pensioner status and schooling status. In the event, our probit enrollment equation (Table 3) gives a pseudo R-squared of only 0.19, pointing to considerable variation across similar individuals in the probability of being enrolled. As expected, being in school (around 25% of our sample is currently in school) has a significant positive effect on VHI enrollment. Age has no significant effect on enrollment, once being in school and pensioner status are controlled for. Unsurprisingly, too, a person’s job has a marked impact on insurance status. The equation includes the employer of individuals with one or more formal sector jobs in the week prior to the VLSS interview. The omitted category from the employer dummies is
sup>24</sup> <mark>Developing consistency of interest among young children may, paradoxically, require diversification (sampling) of interests earlier in life.</mark><sup>25</sup> In sum, we find that our intervention induced students in both treatment arms to update their beliefs about deliberate practice relative to students in the control condition, with stronger effects when teachers deliver the curriculum as opposed to student self-learning. Impacts on grit are mixed: while students in either treatment arm report more perseverance-of-effort aspects relative to students in the control condition, impacts on consistency-of-interest aspects are negative, pointing towards the possibility of unintended consequences, and in particular, the possibility of crowding out <mark>of long-term goals-orientation.</mark>

# _Index of Socio-Emotional Skills_

As discussed above, we collected survey data on various other measures of socio-emotional skills. As all of them focus on some element of self-regulation, in our main analysis, we look at a single index (S/E skills) that combines the measures of deliberate practice beliefs, grit, frustration reaction, motivational frameworks, locus of control, and present bias, to reduce dimensionality and multiple hypotheses testing.<sup>26</sup> But before proceeding to this analysis, we first have a look at the _relative_ importance of different sets of skills, in particular deliberate practice beliefs and grit _versus_ all others. Table 4a shows the impacts of our intervention on our S/E skills index on average (Column 1), when excluding deliberate practice beliefs and grit (but including all other skills) (Column 2), and when including only deliberate practice beliefs and grit (excluding all others) (Column 3).

> 24 See, for example, Sturman and Zappala-Piemme (2017).

> 25 See, for instance, Gopnik (2020).

> 26 Appendix 1 Table A1.2 replicates Table 3 and shows findings for frustration reaction, motivational frameworks, locus of control, and present bias.
2

# **I Introduction**

This paper develops a statistical model of graduation from International Development Association (IDA) assistance and uses it to analyze the factors that affect countries’ graduation as well as the effects of the shift in graduation policy which occurred in FY1999.<sup>1</sup> IDA provides concessional financing (credits, grants, and guarantees) to the world’s poorest countries to help reduce poverty and improve living standards. IDA’s eligibility criteria include (a) absence of International Bank for Reconstruction and Development (IBRD) creditworthiness and (b) gross national income (GNI) per capita below the IDA operational cutoff (US$1,175 for FY2020).

Before FY1999, the two major criteria for graduation from IDA were creditworthiness for IBRD for several years and a level of GNI per capita that exceeded the operational cutoff set in 1987 and adjusted since then annually based on GNI per capita calculations in accordance with the Atlas conversion factor methodology. However, the experience of eight reverse graduations which occurred in the 1990s, including that of Indonesia following the Asian financial crisis, led to a change in policy in FY1999, intent on limiting future reverse graduations. The set of factors affecting graduation decisions broadened to include other factors to account more precisely for country-specific circumstances. The only reverse graduation after this policy change was that of Syria in 2017.

Similar to the investigation of IBRD graduation policy conducted by Heckelman, Knack, and Rogers (2012), this paper takes a positive approach to IDA graduation policy, analyzing factors that influenced IDA’s decisions on graduations and reverse graduations over the past three decades through a pooled logit estimation using panel data covering IDA-eligible countries for 1987–2016. We find that throughout the sample the probability of being a graduate is positively and statistically significantly associated with income per capita, creditworthiness, and country size. We account for the shift of policy in FY1999. Using an interaction dummy variable to capture the pre-FY1999 period, we find that life expectancy
, family size, religion, and the educational background of the respondent (Annex Tables D1 and D2). Specifically, past and current malaria incidence rates in households living in solid dwellings (i.e., those with walls and roof made of bricks, stones and cement) were 21% and 43% lower compared to those living in non-solid and semi-solid houses (i.e., made of mud, tin and straw), respectively.<sup>20</sup> For our analysis, we combined the latter two dwelling categories.

Understanding the most important risk factors enables us to estimate conditional treatment effects for the most vulnerable populations, addressing the research question of _whether social media campaigns reach and impact these high-risk groups_ . In our initial recruitment, however, people in non-solid dwellings were significantly less likely to be reached and to respond to our survey than those in other dwelling types. In fact, the vast majority of respondents lived in solid dwellings. For this reason, we decided to stratify our recruitment by district and dwelling type (160 strata in total, 80 for the districts and 2 for the dwellings). Besides being a strong predictor of malaria risk, dwelling type captures the dimensions of poverty and rurality (this variable is correlated with education,

> 18Annex Figure C2 shows examples of ads used to recruit survey participants.

> 19Following the India DHS survey, respondents were asked which kind of house they lived in among three options: non-solid houses (made of mud, tin, straw); solid houses (built with durable materials like stones, bricks, cement, or concrete, encompassing the entirety of the structure, including the floor, roof, and outer walls); semi-solid houses, which occupy an intermediate position between the previous two housing types.

> 20This is in line with previous work in India (Yadav et al., 2014) and elsewhere (Wanzirah et al., 2015).

12
Retai|l trade||
||Zoom|Skype|delivery|mall|shopping||
|IHS (cases)|0.07<sup>***</sup>|0.05<sup>***</sup>|-0.01|-0.09<sup>***</sup>|-0.03<sup>**</sup>||
||(0.02)|(0.01)|(0.02)|(0.01)|(0.01)||
|R-squared|0.87|0.75|0.88|0.92|0.91||
|Observations|8,208|8,208|7,872|7,776|7,968||

Note: the data used cover January-April of 2018-2020. For every country where Google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. IHS stands for inverse hyperbolic sine transformation. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

The magnitude of the impact we find is quite large. And it is important to put our results in perspective. Unlike previous pandemics, COVID-19 has been spreading quickly, increasing by over 100 percent weekly, on average. The sheer magnitude of the pandemic and the speed at which it transmitted translates into substantially large reductions (increases) in demand for services. Figure 3 presents the weekly marginal (Panel (a)) and cumulative (Panel (b)) impact of COVID19 on demand for selected services. At the margin, the pandemic has
DOTs were banned implies that the refiners earn lower profit after the reform, because per unit profit of refiners does not depend on the quantity sold (see equation (17) above).<sup>32</sup> The evidence in Table 4 that the effects of the reform on the intercept and slope of the wholesale margin equation became small or statistically insignificant after first 9 months of the implementation of the reform supports the conclusion that the market was reverting back to the old DOT system after the first 9 months of the reform. Interviews with the refiners by the authors in 2013 and 2016 confirm that they were unhappy with the reform, and surreptitiously went back to the DOTs to offload their accumulated inventory, as the dealers were reluctant to take oil delivery. A retrospective survey of 50 wholesale traders in Dhaka by the authors conducted in February of 2016 also corroborates this conclusion. According to the survey, the wholesale traders gradually went back to the DOTs starting from after 3 months of the implementation of the reform, and two of the smaller refiners Nurjahan Group and Bangladesh Edible oils limited were unable to survive the market disruption caused by the reform and had to exit. Taken together, different pieces of evidence strongly suggest that the refiners’ profit was adversely affected by the reform, consistent with the prediction of the credit rationing model.

An important piece of evidence comes from a survey the authors conducted in 2013 in the edible oils market in Bangladesh (Emran et al. (2015)). Using data on 6176 transactions between DOTs and wholesalers in Dhaka and Chittagong markets, an estimated 30 percent of the transactions between DOTs and wholesalers is on credit. In terms of quantity, the supplier credit from the DOTs accounted for 32 percent of the volume. Thus the market seems to have reverted to the relational credit arrangements by 2013, and credit provided by the DOTs to the wholesale traders played a vital role. Although 32 percent of the quantity at the wholesale level are financed by the DOTs, we would expect the
Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181.

> 8 For quality road we followed the definition of the Rural Access Index: “ _Good roads are paved roads that look dark and are not eroded. Fair roads are either paved roads in fair condition or good gravel roads that are motorable_ ” (Iimi, et al., 2016).

> 9 10% of the Kenya Population and Housing Census (KPHC), randomly selected.

> 10 The Kenya Integrated Household Budget Survey (KIHBS) conducted between 2015 and 2016.

> 11 The restricted sample works with the facilities that merges with the reviewed Master List. Moreover, an initial inclusion of “Distance to the nearest Facility” made it necessary to work with facilities in the 10,131 list.

8
# **7. Role of Firm Heterogeneity**

This section investigates the role of firms in the reduction of wage inequality. Equally skilled workers, even if working in the same occupation and sector, may differ in a key dimension: they may work for different firms. The “firm component” of wage differentials has been shown to be large for developed countries (see Abowd, Kramarz, and Margolis 1999; Goux and Maurin 1999; Abowd, Creecy, and Kramarz 2002; Gruetter and Lalive 2009).<sup>21</sup> Due to data constraints, the evidence is more limited in developing countries, but to the extent that firms differ in attributes such as entrepreneurial skill and management practices, these differences are expected to exist.<sup>22</sup> Although these factors are more structural, demand shifters, such as trade shocks, can affect the productivity (and employment) distribution within a sector and the pass-through from firm characteristics to pay. Thus, the firm component is not static, and changes in this component could affect growth in wage inequality. This section focuses on the role of changes in inter-firm pay differences in generating changes in wage inequality. More specifically, it tackles two questions. First, did inter-firm wage differentials fall over time in those countries where inequality fell? Second, if so, what was the contribution of this fall to the reduction in cross-sectional wage inequality?

The previous sections of this paper rely on individual-level data from household surveys, which are likely representative of the formal and informal workforce. However, these surveys do not identify the employing firm of each worker and do not allow us to follow individual workers over time (except for short periods of time in some countries). This section answers the questions above using longitudinal matched employer-employee data from Brazil, Costa Rica, and Ecuador. These data sets are based on social security administrative records. Thus data coverage is restricted to formal workers. As discussed in the previous section, formal workers accounted
A vast literature in the field of bibliometrics uses data on scientific publications and citations.<sup>10</sup> While peer review has traditionally been the main form of evaluating academic research, quantitative methods have become more important in the past 20 years (Hicks _et al_ . 2004). Within the field of economics, until recently most empirical studies of innovation used patent data, rather than publication data, as the measure of research output. While the use of publication data within the economic research community has recently increased, the focus has primarily been on using publication data to measure knowledge flows across institutions, rather than evaluating the effectiveness of research inputs.<sup>11</sup> Other options for measuring knowledge creation are also available to researchers. Some studies focus on the effects of innovation. For instance, Newell _et al_ . (1999) demonstrate a correlation between energy prices and the energy efficiency of home appliances available for sale between 1958 and 1993. Surveys are also used, such as in Lanoie _et al_ . (2007). In a survey of firms in seven OECD countries, they find that greater regulatory stringency induces a firm to perform more environmental R&D. Surveys are particularly useful in the case of process innovations, which are more difficult for the researcher to observe with secondary data.

The above data focus primarily on innovative activity. Focusing on the diffusion of technology is more difficult. In some cases, data are available at the macro-level for crosscountry comparisons. For instance, studies such as Popp _et al_ . (2011) and Brunnschweiler (2010) compare investments in renewable energy capacities across countries. However, for more micro-level studies, detailed data on the adoption of specific technologies is required, such as in Kemp (1997), Blackman and Bannister (1998), or Gallagher and Meuhlegger (2011). Data

> 10 Moed, Glänzel, and Schmoch (2004) provide a good introduction and overview of this literature.

> 11 Examples of the former include Adams