rafmacalaba/data-use-mentions-extended huggingface dataset:
Thus, in terms of financial savings, Egypt is about average across other countries with listed company data available.

**Figure 3.  Cash to Assets, Listed Firms Data**

<!-- Start of picture text --> Cash to Total Assets (Mean)<br>0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>Source: Datastream, staff calculations<br>Figure 4. Savings to Assets, Listed Firms Data<br>0.25<br>Savings to Total Assets (Mean)<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>-0.05<br>Iceland Slovenia Czech RepublicZimbabwe Chile Greece PortugalIndia Mexico Venezuela, RBSlovak RepublicMorocco TurkeyLithuania ArgentinaPeru Thailand Colombia Estonia SpainPakistan Saudi Arabia Russian Federation HungaryIndonesia New Zealand ItalyMalaysiaSri Lanka Poland Finland Netherlands BelgiumBrazil Korea, Rep.Austria South Africa PhilippinesLuxembourgEgypt, Arab Rep.Jordan France Switzerland Sweden QatarUnited Arab Emirates GermanyBermuda Denmark SingaporeNigeriaChina Taiwan Bahrain United KingdomKuwait JapanIreland Virgin Islands (UK)Canada Hong Kong, ChinaCayman IslandsIsrael Australia<br>Estoni a Iceland QatarUnited Arab Emirates Slovenia Czech RepublicJordan Slovak RepublicMorocco Chile Bahrain LuxembourgItalyMexico Thailand Saudi Arabia MalaysiaJapanTurkeyVenezuela, RBNetherlands New Zealand Lithuania Finland Korea, Rep.ArgentinaSpainVirgin Islands (UK)Austria Peru Greece PortugalBelgiumEgypt, Arab Rep.Pakistan Colombia Sri Lanka India Sweden GermanyTaiwan Kuwait France Ireland Switzerland Israel Brazil Indonesia United KingdomSingaporeHungaryNigeriaPhilippinesRussian Federation China South Africa Bermuda Canada Poland Denmark Hong Kong, ChinaAustralia Cayman IslandsZimbabwe<br><!-- End of picture text --> Source: Datastream, staff calculations Figures 5 and 6 report cross-country comparison of Egypt with respect to investment measures. For listed firms data we have an approximate investment to capital ratio. We see that according to this ratio Egypt is somewhat below average in the sample. Figure 6 shows the 14
bread consumed by the households. Next, following Abdalla and Al-Shawarby (2018), we assume a 30 piaster per loaf of subsidy received by households. Finally, HIECS 2015 also provides information on the items purchased using bread points.<sup>31</sup> Using an estimate of the value of the items purchased using a market reference price, we also impute such value as a transfer of the food program to the beneficiary household.

# **Energy subsidies and indirect taxes**

Energy subsidies for electricity and fuels and indirect taxes (sales taxes) are imputed based on household consumption expenditure records. In other words, when households record purchases of energy or goods or services that attract the sales tax, the subsidy or indirect tax payment implicit in this purchase is imputed based on the relevant subsidy or tax schedule. For example, if a household records US$50 in kerosene expenditures over a month, and the known subsidy rate on kerosene is 10 percent, the household is imputed to have purchased US$55.55 of kerosene, US$5.55 of which was actually an expenditure made by the government (via its subsidy policy) on behalf of the household.

The electricity subsidy (and the electricity tariffs) in Egypt is worth describing in further detail. In FY15, electricity tariffs were based on seven consumption brackets (Table 3). Using a cost-recovery rate estimate obtained from the Ministry of Petroleum, we first simulate for each bracket what would have been the corresponding “full recovery” or no subsidy tariff. The difference between the two provides an estimate of the per kilowatt hour (kWh) subsidy received by the household. Next, using HIECS 2015 data it is possible to estimate the electricity consumption volume from household expenditure records and the imposition of Egypt’s 2015-era block-tariff structure. For each household we have information on the bimonthly expenditure on electricity. After subtracting connection fees, we can estimate the corresponding volume that would be feasible based on the potential tariffs faced by households.
joint ventures, and among joint ventures, may be significant. It is suggestive that if "domestic" firms are excluded from the sample, FDI has a positive effect on firms with joint ventures, although this is not statistically significant (the t-statistic is 1.42, possibly reflecting the small sample size).

Finally, account should also be taken of the short time frame on which the study has focused. Spillovers may require more time before they show up in TFP growth rates. And, as mentioned earlier, the absorption of new techniques requires significant in-house technological effort which may not be captured adequately by the production function methodology used. Clearly further research is required.

# **VI. Concluding Remarks**

Firm-level data for the Czech Republic during 1992-96 suggest that foreign investment has the predicted positive impact on the TFP growth of recipient firms. This result is robust to corrections for the sample selection bias that prevails because foreign investment tends to go to firms with above average initial productivity performance. This result is not surprising, given that there is a presumption that foreign investors should be transferring new technologies and knowledge to partner firms. With some lag, this is likely to be reflected in greater TFP growth. FDI appears to have a greater impact on TFP growth than joint ventures, suggesting that parent firms are transferring more know-how (soft or hard) to affiliates than joint venture firms obtain from their partners.

Taken together, joint ventures and FDI appear to have a negative spillover effect on firms in each industry that do not have foreign partnerships. This effect is relatively large and statistically significant. However, if the focus of attention is restricted to the impact of foreign20
to generate revenues annually on all the land available to them. Insecure property rights further discourage collected at the crop level (see table A.2 for description of available data), making it difficult to attribute costs to a particular crop. Importantly, the gross income ratio used here remains unbiased, under the assumption that the share of net over gross income is the same for income from trees on farms as for overall agricultural or total income. Put differently, to the extent that the share of expenditures on tree crop production to income from trees on farms is smaller than the share of expenditures on all agricultural production to income from agricultural production, the gross income from trees to gross agricultural income ratios reported here will be underestimates. A similar reasoning holds for the ratio of gross income from trees to overall gross income.

> 5 Under this method, seven basic categories of household income are considered: _(i)_ crop production; _(ii)_ livestock production; _(iii)_ agricultural wage employment; _(iv)_ non-agricultural wage employment; _(v)_ non-agricultural self-employment; _(vi)_ transfers; and ( _vii_ ) when available, other income sources like rental income, fishing or saving accounts. See Davis et al. (2010) for details.

> 6 In a recent study, Angelsen et al. (2014) show that environmental and forest incomes can also be important in certain communities, especially those with closer access to forests. As in most standard household budget surveys, such income is not appropriately recorded in the LSMS-ISA data and has not been accounted for here either. A forestry module for LSMSISA and other national surveys has recently been developed to address this information gap (Bakkegaard et al., 2016).

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measures, or highly correlated multidimensional measures. Cognitive ability is measured using a test inspired by Raven’s matrices (Raven, 1936). For each of six pictures, individuals are given a score of 1 for a correct response and 0 for an incorrect response, then divided by a total of six questions such that the final average score falls between 0 and 1.

The social desirability index (SDI) utilized is based on the 8-item impression management dimension of a short form of the Balanced Inventory of Desirable Responding (BIDR; Hart et al. (2015)) with a 5-point Likert response scale. The items used to assess SDI generally include a series of actions that are deemed socially desirable but uncommon, or socially undesirable but common. Here, the team excluded the dimension of the longer-form BIDR that is focused on self-deceptive enhancement, primarily due to time limitations. An average of the responses to the eight included items is taken, resulting in a score falling between 1 and 5.

Finally, a variable on equitable beliefs is constructed based on the reversed Likert response to the statement _“By nature, men are better at problem solving and decision making than women.”_ . The question focused on this particular skill since gender-biased beliefs about this skill were a common finding in qualitative data collection during the SES measure — development. The score for each individual is an integer between 1 and 5 and by its reverse nature, a higher score represents a more gender-equitable belief.

# **2.4 Sample description**

The sample was selected using a community sampling frame of streets (a local administrative unit) which met the following criteria: they had at least 120 eligible young men and 120 eligible young women; community leaders confirmed interest in the program; and there was an existing venue suitable for hosting a training. 40 communities were randomly selected by Stata from this list, stratified by city. Within selected communities, we conducted a — listing to create a sampling frame of all eligible individuals defined as
<!-- Start of picture text --> July 2019 Feb 2020<br>public public<br>works works<br>start Nov 2019 end Mar 2020 Oct 2020<br>creches creches creches<br>open close due reopen<br>to COVID<br>Dec 2019 Feb 2021<br>Baseline Follow up<br>in-person phone<br>survey survey<br><!-- End of picture text -->
# **II. Prospects of Intra-Regional Trade: a Product-Level Assessment**

The success of the regional trade agreement may depend crucially on the extent to which the core market, India, given its relative size, becomes more accessible to its periphery and vice versa. In this context, the prospects of increasing regional trade may depend more on the existence of product complementarities and export efficiencies (defined by comparative advantage) and other characteristics such as the degree of concentration and diversification of trade profiles amongst the regional partners, particularly between India and the other countries of the region. These are evaluated in detail in the following section. A useful beginning to the analysis is a review of trade structures at a broad level.

An analysis of product-level trade data, at the SITC heading level in Table 6 suggests that agriculture and primary materials have dominated intra-regional trade in South Asia for the past two decades. For example, in 1998 the percentage of regional exports that originated in food and live animals was over 60 percent for Pakistan, 41 percent for India, 35 percent for Sri Lanka, and 34 percent for Nepal. For Bangladesh, crude materials were its major regional exports, with a share of 60 percent of total exports. In contrast, South Asia’s exports to the rest of the world are dominated by manufacturing products.

The data in Table 6 also reveal that, although food and live animals constitute a major share of regional exports, there have been large fluctuations over different periods. Such fluctuations are due to the import demand for basic food products such as rice, vegetables, fruits, pulses, onions, potatoes, and sugar, which vary with domestic supply conditions and, which, in turn, strongly impact the trade policies of respective member countries. The SACs often exercise arbitrary policies to maintain stable domestic prices in these “essential commodities.” When shortfalls occur domestically imports are encouraged and when the domestic supply is stable, restrictions are re-imposed, explaining the large swings in export shares.

A further insight into
farming sector as explained below, characterized by perfect competition and endogenous input prices. Aggregate land supply is fixed but land can move across uses according to relative returns. The yield response to prices is a much needed addition. Keeney and Hertel make the yield response explicit. It depends on the substitution between land, labor, capital, and other factors. Land supply follows a CET structure as described in Figure 2. However, the differentiated bilateral trade response in Keeney and Hertel is a potential drawback. Bilateral export demand assumes differentiation for commodities that are essentially homogeneous across countries (e.g., corn, soybean). The assumed trade substitution elasticities for grains are low (- 2.6). These elasticity estimates are based on econometric work by Hertel et al. (2007). Keeney and Hertel assume that U.S. imports of ethanol from Brazil increase proportionally to the total U.S. ethanol demand, irrespective of price levels, a questionable assumption as explained later in the modeling section and which has important consequences for land allocation.

Birur et al. (2008) use the GTAP database developed by Lee et al. (2005) and (2008) to analyze the impact of biofuel production on global agricultural markets. The latter authors decompose land into 18 agro-ecological zones (AEZs). Birur et al. consider 3 biofuels, ethanol from grains, ethanol from sugar-crops, and biodiesel from vegetable oils. They treat the 2 ethanol productions as imperfect substitutes. The crops considered are coarse grains, oilseeds, sugarcane, other grains, and other agriculture. Crops, grazing, and forestry use the total land endowment. Composite land supply is made of land in the 18 AEZs, which are treated as highly substitutable (CES with an elasticity of substitution of 20). Within any AEZ, land shifts between forest, pasture, and crops with some CET value of -0.11. Within crops, land shifts moving with a CET elasticity of -0.5. Land owners maximize
1: Data collection rounds and sample sizes**

|**Date of data collection**|**Feb-May 2017**|**Nov 2017-Jan 2018**|**April-Jul 2018**|**Nov-Dec 2018**|
|---|---|---|---|---|
|Round Name|PAB|PDM2|PDM4|PDM6|
|Non-beneficiary households|4,193|2,801|2,530|1,924|
|Beneficiaryhouseholds|4,497|3,739|3,652|3,046|
|Total observations|8,690|6,540|6,182|4,970|

The reader will note the large and increasing attrition from the study sample, an issue for phone-based surveys, which is discussed in great detail in Section 4. Of the 8,690 households interviewed in the PAB, 24.7% could not be reached in the first follow-up surveys, with this figure reaching 28.9% and 42.8% at the 12- and 18month follow-ups, respectively (Table 1). Some of the main reasons for attrition include the households simply not picking up their phone (after a maximum of three attempts), phone numbers going out of service, or phone numbers belonging to someone else. At the 18-month follow-up, for the 8,690 households included in the original baseline sample, the most common reasons for being lost to follow-up included: households not

> 17 A fourth and final follow-up panel survey was started in December 2019, continuing until March 2020, but data collection was paused due to the outbreak of the COVID-19 pandemic. It has not resumed as of August 20, 2020.

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commonly in poorer countries), ii) providing an alternative to conducting new surveys that are prohibitively expensive or for which technical and administrative capacity is unavailable, iii) overcoming issues of non-comparability in existing surveys or to side-step the non-availability of reliable price deflators, and iv) back-casting consumption from a more recent to an older survey for better comparison with older surveys.

# **Additional references**

<mark>Casella, G., & Berger, R. L. (2002).</mark> _<mark>Statistical inference</mark>_ <mark>. 2</mark><sup>nd</sup> Edition. California: Duxbury Press. <mark>Dang, H. A., & Nguyen, M. (2014). POVIMP: Stata module to provide poverty estimates in the absence of actual consumption data.</mark> _Statistical Software Components S457934_ . Boston College, Department of Economics.

- <mark>Dang, H. A., Jolliffe, D., & Carletto, C. (2019). Data gaps, data incomparability, and data imputation: A review of poverty measurement methods for data‐scarce environments.</mark> _<mark>Journal of Economic Surveys</mark>_ <mark>,</mark> _<mark>33</mark>_ <mark>(3), 757-797.</mark>

- <mark>Gourieroux, C., & Monfort, A. (1996).</mark> _<mark>Simulation-based econometric methods</mark>_ <mark>. O</mark> xford University Press.

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is possible that FLFP rates drive some of the correlations and not necessarily the other way around, making it very difficult to exclude “reverse-causality” biases. This would make it difficult to interpret the estimated coefficient as a causal impact. Take for instance the fact that certain locations have a larger number of jobs suitable to women’s competencies and skills. This could raise women’s bargaining power within the household and thus impact their degree of participation in household decision making (Majlesi, 2015). We mitigate these concerns by controlling for a wide set of observable variables at the individual, household, and municipality level. We also show the robustness of our findings to the inclusion of department fixed effect. We minimize concerns of reverse causality by exploring lagged values when data is available. Despite all efforts, our findings should be interpreted cautiously. Ultimately, the analysis makes a strong case for the simultaneous importance of the four set of proposed factors –individual and household characteristics, social norms and attitudes towards women, social policies, and local labor market factors-- as drivers of the current levels of FLFP in Guatemala.

Our results show that, between 2002 and 2018, FLFP increased 5.7 percentage points, from an average of 26% to 32% nationwide. At the same time, we observe an increase in women’s level of education, a reduction in fertility (as proxied by the number of children ages 0-12 living in the household), and an increase in the share of services in total employment . These changes explain part of the increase in FLFP over time. However, a large component remains unexplained. This suggests that factors not included in the model, such as social norms or attitudes towards women in the society or public policies, could be important drivers of changes in FLFP over time. Our analysis of the within-country variation exploring 2018 data is fully aligned with this conclusion.

The cross-section findings show very interesting, strong correlations across local social norms and FLFP. FLFP is higher when women
tally different set of challenges to social cohesion than the more commonly analyzed camp-based displacement or refugee flows into European countries.

Due to contextual differences, the salient dimensions of social cohesion may not match academic definitions derived mainly within western contexts. To address this challenge, this project employed participatory research methods to identify the elements of social cohesion considered relevant in eastern DRC. By adopting this design, the project iteratively built a set of research questions and methodological tools to ensure locally appropriate decisions to measure contextually appropriate concepts. The insights from the focus groups dictated our measurement strategy of social cohesion when analyzing a series of surveys conducted in eastern DRC between 2017 and 2021.

The findings contribute to a growing research agenda on how hosting forcibly displaced persons impacts perceptions of social cohesion. The results are consistent with findings from Zhou, Grossman & Ge (2021), who find that proximity to refugee settlements can improve goods provision to host communities and that the presence of displaced persons does not necessarily negatively affect host community attitudes towards the displaced persons they host. Similarly, Aksoy & Ginn (2021) also find that the arrival of migrants do not necessarily have negative effects on host community attitudes. Consistent with the results in this paper, Betts, Stierna, Naohiko & Sterck (2021) find that local context – including urban versus rural settings – matters in how host community interactions with displaced populations impact social cohesion.

# **2 Context: Eastern DRCongo**

For the past five decades, eastern DRC has experienced varying levels of conflict. The violence has been fueled by complex and interlinked domestic and foreign competition over access to resources and political power, deepening long-standing inequities and conflicts along ethnic lines.<sup>3</sup> Mobutu’s

> 3For details on the roots of the conflicts in eastern DRC, see Reyntjens (2001), Vlassenroot & Raeymaekers (2004), and Vlassenroot & Raeymaekers (2009).

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part of a household survey such as those of the LSMS-ISA collection, which are meant to be, by their very nature, multi-topic and nationally-representative, and not exclusively targeted to the study of migration. In this respect, the relatively small sample size of most LSMS-ISA surveys often makes them unsuitable for the study of migration, as the standard LSMS-ISA multi-stage cluster design is unlikely to sample a sufficiently large number of households with migrants (De Brauw and Carletto, 2012).

**_Respondents’ issues._** More broadly related to data collection, Lucas (2021) notes that collecting migration data is essentially limited to two approaches: asking individuals about their migration experiences or asking remaining household members about those who left, and both present limitations. The former approach, in fact, provides little information about the household that the migrant left; the latter assumes that the respondent knows the current whereabouts and activities of the migrated household members, and memory about the list of those who departed may prove selective.

In addition, as noted by Kirchberger (2022) in her recent review, even when panel household surveys aim to track respondents, household surveys can still suffer from high levels of attrition. Other general shortcomings are: i) questions about migrants need to be answered by a proxy, generally a family member, which may introduce many imperfections and substantial bias (Carletto et al., 2014); ii) the double-counting of migrants, especially those who can be claimed as members in other households’ rosters; iii) the difficulty in classifying the type of migration: temporary (short-term) migration and (longterm) permanent migration are usually distinguished by an arbitrary threshold or time criterion set by the analyst. Return migration, seasonal migration, and circular migration can also be difficult to distinguish from one another (De Brauw and Carletto, 2012).<sup>12</sup> Finally, specifically concerning climate-related hazards, it is unlikely that migration caused by a fastonset climatic disaster would be captured in an LSMS-ISA