rafmacalaba/data-use-mentions-extended huggingface dataset:
GARCH models by utilizing high-frequency data, focusing mostly on volatility (Engle 1982). Following decomposition, the logarithm of the price was regressed on each component to measure its relative contribution to price variability. For example, the from [8,20] the regression of the price on the medium term cycle, , gives the RR<sup>2</sup> relative contribution of the medium term cycle to total price variability. Note that the sum tt tt tt pp = μμ+ ββTTPP + εε of the respective s adds to unity since the sum of the four components adds to the price. Monthly data for 27 commodity prices covering the period January 1970 to DeRR<sup>2</sup> cember 2020 (612 observations) were used in the analysis. They come from the World **8** Even after removing the linear trend before detrending, as in equation (4), the cycle still contains a timevarying component, , leading to inconsistent results. Corbae and Ouliaris (2006) show that, in addition to consistency, (5) has no finite sampling error, has superior end-point properties, with a much lower TT pp /√TT mean-squared error compared to popular time-domain filters and other band-pass filters such as Baxter and King (1999) and Christiano and Fitzgerald (2003).

_— 8 —_
across digital occupation levels.<sup>38</sup>

> 36 Namely, relationship skills are less important for very low digital occupations in the other three countries, as compared to Malaysia, while personal growth is more prevalent among highly digital occupations.

> 37 These associations should not be interpreted as causal but rather as conditional correlations for at least two reasons. First, there are factors beyond cognitive and socioemotional skills that could influence the digital occupation level. Second, subsets of cognitive and socioemotional skills may be correlated with each other to some degrees, especially those in the cognitive subsets, and thus may bias the conditional correlations with digital occupation levels (see Table 7).

> 38 Similar results emerge from LPM estimates using the unweighted occupational skills profile sample and the countryspecific weighted occupations for Vietnam and Thailand. The communications, relationships, and personal growth variables play a greater explanatory role in the Cambodia -and, to some extent, the Vietnam - estimates, with the coefficient signs and magnitudes were similar to the Malaysia-weighted sample (Annex Table A.3.2).

25
lower than in countries at a similar level of development, including Bulgaria, Colombia, Ecuador, Peru and South Africa (Figure 18 ).

# **Figure 18 Herfindahl index and share of top 1%, 5% and 10% of exporters**

<!-- Start of picture text --> Herfindahl Index Share of Top 1% firms in Total Exports<br>1<br>0.2<br>0.9<br>0.18<br>0.16 0.8<br>0.14 0.7<br>0.12 0.6<br>0.1 0.5<br>0.08 0.4<br>0.06 0.3<br>0.04 0.2<br>0.02 0.1<br>0 0<br>2004 2005 2006 2007 2008 2009 2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br>Share of Top 5% firms in Total Exports Share of Top 10% firms in Total Exports<br>1 1<br>0.95 0.95<br>0.9 0.9<br>0.85 0.85<br>0.8 0.8<br>0.75 0.75<br>0.7<br>0.7<br>0.65<br>0.65<br>0.6<br>0.6<br>0.55<br>0.55<br>0.5<br>0.5<br>2004 2005 2006 2007 2008 2009<br>2004 2005 2006 2007 2008 2009<br>PAK BGR COL ECU PER ZAF PAK BGR COL ECU PER ZAF<br>Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank
390|2013|No|24|6.15|3|0.77|
|Venezuela<br>VEN|LAC|UMIC|320|2010|No|50|58.82|3|3.66|
|Vietnam<br>VNM|EAP|LMIC|996|2015|No|498|50.20|73|7.43|
|West Bank And Gaza<br>PSE|MENA|LMIC|434|2013|Yes|139|32.10|20|4.66|
|Yemen<br>YEM|MENA|LIC|353|2013|Yes|184|52.12|6|1.73|
|Zambia<br>ZMB|SSA|LMIC|720|2013|No|529|73.88|78|11.11|
|Zimbabwe<br>ZWE|SSA|LIC|600|2016|Yes|192|32.00|24|4.11|

Note: The percentages of pioneer firms presented in this table are calculated using the total number of firms that reported if they have innovated or not by introducing either new products or services; new products or services for the establishment's main market; or new and new products or services for the establishment's main market; or new and significantly improved process during the last three years. Firms with missing values in the three variables used to define pioneer firms are not considered. The note considers a country as in a FCS if it was a FCS during _(i)_ the year when the WBES was carried out or _(ii)_ the three years prior to the survey. This is an important aspect aimed at capturing the potentially long‐lasting impact of conflicts.

30
are substitutes for improved nutrition.

We investigate how some of these mechanisms contribute to the overall impact of cash and in-kind food transfers on children’s learning in the context of the _Programa de Apoyo Alimentario_ (PAL), one of Mexico’s flagship anti-proverty transfer programs. To identify impacts, we leverage the randomized controlled trial that was implemented during the program’s rollout in 2003, in which 208 villages in southern Mexico were randomized into three groups: program-eligible households received either cash transfers, in-kind food transfers, or no transfers (a control). In-kind food transfers were of a similar value to the cash transfer, and both represented around 12% of household consumption pre-program. We follow approximately 4,000 children whose families were part of this experiment into primary school by merging individual-level experimental data with a nationwide census of standardized test scores from 3rd to 6th grade. Test score data spans the years 2007 through 2013, which allows us to study children’s learning 4 to 10 years after transfers began. Unlike Mexico’s other wellknown anti-poverty program, _Progresa/Oportunidades_ , PAL transfers were not conditional on school attendance.

Previous research (Cunha, 2014) has shown that, in the short-run (approximately one year after transfers began), in-kind and cash PAL transfers both increased total household consumption by similar magnitudes, a result attributable to the fact that the cash transfer was largely inframarginal. However, certain in-kind food items were extramarginal and binding which induced children receiving in-kind transfers to consume more micronutrients than children who received cash transfers. Many children were deficient in some key micronutrients that have been shown to support brain development (Black, 2003) - namely, vitamin C, iron, and zinc - and the in-kind treatment induced more children to consume above the Recommended Daily Allowance (RDA) of these micronutrients. By linking individual-level information on consumption from the post-experiment survey in 2005 with test score results in primary education, we can thus directly assess whether improvements in the nutritional intakes translate into improvements in subsequent performance in
smaller in Ghana at this date than what has been observed in many other countries (cf. Kenya, Mazumdar, 1993).

**Table 6.3: Rural -Urban Differences in Expenditure, 1991-92**

_(Thousand cedis)_

||Accra|Other|Rural|
|---|---|---|---|
|||Towns||
|Medians||||
|Expenditure per Eamer|660|620|447|
|Expenditure per capita|256|184|144|
|Expenditure per AEU|358|276|228|

_Source GLSS 1992_

**Table 6.4: Rural -Urban Differences in Income, 1991-92**

_(Thousand cedis)_

||Accra|Other<br>Towns|Rural|
|---|---|---|---|
|Means||||
|Household Income|571.4|498.1|462.2|
|Income for Earners|520.8|438.3|370.9|
|Income per capita.|206.5|143.1|129.5|
|Income for AEU|520.8|438.3|370.9|
|Mediums||||
|Household Income|388.9|328.3|322.1|
|Income for Earner|348|276.9|256.7|
|Income per capita|136.2|84.2|81.3|
|Income per AEU|186.3|127.2|125.6|

_Source: GLSS 1987_ Measurement of income in terms of individual welfare can be refined by considering income per adult equivalent units (measured according to the scales worked out by Deaton) to take account of varying proportions of household members of different ages. When we compare this measure among the three sectors, the rural areas emerge with a significantly higher level of
We have also calculated a variety of other NPC indicators which aggregate the NPCs of various groups of crops. These include aggregations for cereals (wheat, rice, maize and sorghum), foodgrains (cereals and pulses), oilseeds (groundnut, mustard, copra, soybean and sunflower), non foodgrains (oilseeds, cotton, sugar and rubber) and aggregate agriculture (all thirteen crops). These aggregations have been made alternately treating all the crops as import substitutes i.e. using the importable hypothesis NPCs only, and treating some of the crops as exportables. In particular the NPC series for all 13 crops, which is an estimate of the NPC for Indian agriculture as a whole, has been done in two ways. In the first aggregation, all the crops are treated as import substitutes with their NPCs based on import reference prices. In the second aggregation, the NPCs over the entire period ( 1965 -95) for rice and cotton use the exportable hypothesis, the NPCs for wheat use the importable hypothesis up to 1973 and the exportable hypothesis in 1974 and after.

The approach to calculating the aggregate NPCs can be illustrated by the aggregation for cereals. As regards each of the four cereals ( rice, wheat, maize and sorghum), we first assumed that the weighted average NPC of the estimated NPCs in the states included in the study, is equal to the all-India NPC for that crop. Secondly, we divided the gross value of the all-India output of each crop (obtained from the National Accounts or estimated from other sources) by its all-India NPC to give an estimate of the all-India value of each crop's output at reference prices. Finally, we divided the all-India aggregate gross value of the output of the four cereal crops by the aggregate value of the output of the same crops valued in reference prices. The same procedure was followed in the other aggregations. That is: NPCc = yi (GVPDi) / Xi (GVPDi/NPCi) where GVPD1 = (Gross value of production
Source: Authors’ calculations based on the World Bank’s January _Global Economic Prospects_ , the International Monetary Fund’s January _World Economic Outlook_ , January Consensus Forecasts, January Focus Economics Forecasts, the World Bank's _Statistical Capacity Indicator,_ the World Bank’s _World Development Indicators_ , the Export Commodity Price Shocks data from Gruss and Kebhaji (2019), and the UCDP-PRIO Dataset for Conflict.

Note: Statistical significance level 0.01 - ***; 0.05 - **; 0.1 - *. Robust standard errors clustered at the country level. All regressions include year fixed effects, and the reference year is 2015. Columns (1) and (4) samples refer to same-year World Bank GEP January Forecasts sample from 2010 to 2020, covering 126 countries. This common sample is used to define the columns (2) and (5) samples, for same-year IMF-WEO January Forecasts from 2010 to 2020. Columns (3) and (6) samples refer to same-year Consensus January Forecasts and the MENA region Focus Economics January Forecasts, from 2015 to 2020, covering 56 countries, and are not forced to be a common sample with WB-GEP and IMF-WEO. Log of SCI (lagged) is the one-year lagged log of Statistical Capacity Indicator. Growth Volatility (lagged) is the one-year lagged rolling 10-year standard deviation of realized growth rates. Log of GDP Per Capita (lagged) is the one-year lagged log of GDP per capita in constant 2010 US dollars. Absolute Value of Export Commodity Price Shocks (T) is the same-year absolute value of the log change in export commodity prices, it captures both exposure to commodity exports and the fluctuations of the international prices. Internal Conflicts Shocks Dummy (T) is the same-year dummy variable for internal conflict country-year pairs - as defined using the UCDP-Prio Dataset. Log of Total Population (lagged) is the one-year lagged log of population and is considered a proxy for the country size. Boom Dummy if Above Median Growth (lagged) is a
agricultural research, and beyond (Gourlay et al. 2021; Zezza et al. 2022; Glazerman et al. 2023). In this regard, phone surveys, such as those we study, address two common criticisms of the usefulness of (in-person) survey data for health policy: their low temporal frequency and sparse coverage in conflict-affected or hard-to-access areas. Our study explores the reliability of phone survey data for vaccine research on COVID-19 and beyond.

The remainder of this paper proceeds as follows. Section 2 describes our data and Section 3 our empirical strategy. Section 4 presents our results. Section 5 discusses the implications of our results for health policy and research. Section 6 concludes.

# 2 Data

We use data from three sources: phone surveys, in-person surveys, and administrative records.

Phone surveys have become key tools to fill information gaps when in-person data collection came to a near complete halt during the COVID-19 pandemic (Wollburg, Contreras, et al. 2022). As a result, they have become widespread and enabled repeated experimentation at high frequency for the purpose of this analysis (Gourlay et al. 2021; Glazerman et al. 2020).

Specifically, we use data from longitudinal and cross-country comparable national phone surveys implemented between March 2021 and January 2023. These multi-topic phone surveys were conceived in order to track the effects of the COVID-19 pandemic in the absence of in-person data collection (Himelein et al. 2020). Our experimentation with survey design choices draws on five of these surveys in Sub-Saharan Africa that were supported by the Living Standards Measurement Study (LSMS) team at the World Bank and implemented by the respective National Statistical Offices. These surveys are re-contact surveys, drawing their samples from the latest nationally representative, in-person LSMS-ISA household survey conducted in each country before the pandemic. As part of the LSMS-ISA surveys, phone contact numbers of all household members (where available) as well as from a reference contact such as a neighbor were collected (Gourlay et al. 2021). The list of households with
replacing these types of macro estimates at the first step of the chain with data and evidence from detailed household survey information, such as that of LSMS, would allow much more precision and nuance, but, to the best of our knowledge, has yet to be attempted.

- Through integration with other data sources, the analytical value and relevance of LSMS-type survey data can be considerably enhanced. To achieve this, however, efforts must be made to increase data accessibility (while at the same time finding solutions to maintain ethical standards and data confidentiality) and enhance interoperability in data collection methods. In operational terms, this means being able to link different data sources through, for example, the use of common identifiers for individuals, households, or administrative areas, geospatial coordinates, time stamps, and common classification standards (Carletto et. al., 2022).

# **4. Empirical guidance on existing surveys**

Based on both the conceptual framework and the methodological agenda set out in the previous sections, we now provide some practical empirical guidelines for interested researchers along with some simple illustrations of why and how to use household surveys to study climate migration. We start with descriptive statistics, drawing on a case study, and carry out some simple back-of-the-envelope calculations about the ‘rare event’ issue; then we provide an empirical checklist for undertaking the research effort.

# **4.1 An illustrative case study to assess ‘rare event’ issues and the migration-adaptation link**

The first step for a researcher interested in leveraging a specific household survey to study climate migration is to understand whether this is feasible in the first place. As we have repeatedly mentioned throughout the paper, in standard surveys which are not _ex ante_ planned for the _ad hoc_ tracking of migrants, there is often a ‘rare event’ issue which can undermine research efforts: small migrant samples compared to the overall sample size. But, since the ‘rare event’ issue depends on the unit of analysis, we argue that apparent ‘
migration, we examine the distance to their first destination of refuge. Theoretically, events and circumstances outside of people’s control largely determine where they end up under forced migration. We model how far refugees traveled by exploring the relationship between total driving time from refugees’ home districts to the districts where they initially settled in host countries, relating this to household characteristics, origin district, and timing of displacement.

On average, refugees reported the following travel times to reach their destinations: 7.5 hours to Kurdistan, 4 hours to Lebanon, and 2 hours to Jordan (constant estimate in columns Jordan, Lebanon, and KRI in Table 2). The distance traveled to the first destination is determined mostly by factors outside refugees’ control: location of origin, timing of displacement, and the direct effect of the conflict on household assets and the ability to capitalize them. In Lebanon and Kurdistan, for example, refugees who left Syria in 2014 traveled significantly farther than Syrians who left in 2012. On the other hand, in Jordan, refugees traveled significantly shorter distances in years other than 2012. It is likely that host-country settlement policies arbitrarily affect the distance traveled by Syrian refugees. If all Syrians crossing into Jordan were assigned to camps after 2013, that would explain the year-specific impact of distance traveled. Home destruction is negatively related to distance traveled. But, households that were able to sell assets traveled longer distances. Likewise, refugees in Jordan and Kurdistan who had no time to prepare traveled a shorter distance. In Kurdistan, economic opportunities at the destination as measured by expected monthly income is statistically significant, but the magnitude of the influence on distance traveled is small. All in all, the evidence on the first origin is consistent with the decisions being largely determined by external

> 9 The question was not included in the survey in Lebanon.

5
provided by the private sector, in contrast to a reformed public sector.

The estimated coefficients from the regression modeling can also be used to further evaluate the uptake of piped water connections. In this paper, we use the model to evaluate the uptake of private piped water connections under various scenarios – demand in sub‐groups, institutional decision to rely on private sector provision, and various financial incentives targeted to the poor.

> 2 In principle, you could construct a survey experiment that considers all the important features of demand. In reality, you would run into the curse of curse of dimensionality such that the individual cells will contain too few or zero observations to allow for reliable inference. Regression modeling gets around this curse by making some assumptions about the functional form of the model and the error.

5
709-716 Burke, M., Driscoll, A., Lobell, D., Ermon, S., 2020. Using Satellite Imagery to Understand and Promote Sustainable Development. National Bureau of Economic Research, Inc, NBER Working Papers: 27879 d'Agostino, A.L., Schlenker, W., 2016. Recent Weather Fluctuations and Agricultural Yields: Implications for Climate Change. Agricultural Economics 47, 159-171 D'Souza, A., Jolliffe, D., 2014. Food Insecurity in Vulnerable Populations: Coping with Food Price Shocks in Afghanistan. American Journal of Agricultural Economics 96, 790-812 d’Andrimont, R., Taymans, M., Lemoine, G., Ceglar, A., Yordanov, M., van der Velde, M., 2020. Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series. Remote Sensing of Environment 239, 111660 D’Souza, A., Jolliffe, D., 2013. Conflict, food price shocks, and food insecurity: The experience of Afghan households. Food Policy 42, 32-47 Dabalen, A.L., Paul, S., 2014. Effect of Conflict on Dietary Diversity: Evidence from Côte d’Ivoire. World Development 58, 143-158 Dara, A., Baumann, M., Freitag, M., Hölzel, N., Hostert, P., Kamp, J., Müller, D., Prishchepov, A.V., Kuemmerle, T., 2020. Annual Landsat time series reveal post-Soviet changes in grazing pressure. Remote Sensing of Environment 239, 111667 Deininger, K., Nizalov, D., Singh, S.K., 2018. Determinants of Productivity and Structural Change in a Large Commercial Farm Environment: Evidence from Ukraine. The World Bank Economic Review 32, 287-306 Dhillon, J.S., Figueiredo, B.M., Eickhoff, E.M., Raun, W.R., 2020. Applied
We characterize this phenomenon as the spontaneous reaction of firms to the dismantling of Soviet institutions and to the coordination failures and higher transaction costs which followed.

But, does this alternative way of coordinating activities improve significantly firm performance? In the following section, we explore the link between these informal institutions and firm's performance using our firm data set. The results of this empirical exercise are starting. Being a member of a business association significandy improves a firm's performance, by reducing the probability of a decline in production. Moreover, firms are more likely to join an association in regions where incentives for cooperation among firms are stronger. Hence, the results of this estimation procedure characterize the use of associations by firms as a way to limit the (negative) "disorganization shock", which followed the dismantling of Soviet coordinating institutions, by "self-organizing" their activities.

# **3.1** THE **STRUCTURAL MODEL**

An observable implication, which can be drawn from our characterization of business associations, is that the decline in output following the dismantling of Soviet institutions should be smaUler for firms which have organized their activities through business associations.

To explore the link between membership and fimns' performance, we use a data set compiled at the end of 1994 by the World Bank This survey gathered extensive information on 157 enterprises in five Russian provinces between January 1992 and October 1994. In particular, the data set contains infornation on firms' performance and membership in business associations.

10
its output. We can solve this modified IO system for the counterfactual vector of GDP values in each country-sector. The difference between the actual GDP of a country and this counterfactual GDP equals the domestic value added contained in a country’s exports.

The domestic value added content of trade can also be calculated on a bilateral basis, but we are only interested in each country’s total domestic value added, summing up across all of its export destinations. In practice, we start from the country-specific IO tables that we discussed earlier and collapse them one step further, combining all export destinations. This leads to the following IO system, that we represent here in simplified form: The _GO_ vector stacks the gross output vector of both countries, first the country under study and below the rest of the world aggregate. The number of rows are twice the number of sectors _S_ in the IO table. The _FDc_ vector similarly stacks the final demand of country _c_ for both types of products: the first _S_ elements is demand for output of country _c_ itself and the next _S_ entries is demand for output produced in the rest of the world. The _FDrow_ vector similarly stacks all final demand from the rest of the world, with again demand for output from country _c_ in the first _S_ rows of the vector. The matrix _A_ is the global IO matrix that has dimensions (2 _S_ ) x (2 _S_ ). Using the input coefficients in matrix _A_ , we can calculate value added once we know the gross output of a country-sector.

As discussed above, we can simply replace the actual final demand from the rest of the world for products from country _c_ , i.e. the first _S_ elements in the _FDrow_ vector, by zero. We then solve for a counterfactual _GO_ vector that will imply lower equilibrium output from all 30