rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Globally, the number of forcibly displaced people is unprecedented. At the end of 2020, the number of people forced to flee had reached 82 million, including 26 million refugees and 48 million internally displaced people (IDPs)<sup>1</sup> (UN High Commissioner for Refugees [UNHCR, 2021]).<sup>2</sup> Forcibly displaced women and men face specific barriers to taking up economic opportunities and accessing services compared to their host communities, which create higher risks of being trapped in poverty than many of their hosts (World Bank, 2017). Qualitative research underlines that women and men experience and respond differently to forced displacement (Forced Migration Review, 2018). However, little is known about how gender inequality influences poverty rates of forcibly displaced people, largely due to the scarcity of representative data adequate to investigate these differences.

We use the 2017-18 Somalia High-Frequency Survey to examine the role of gender-based disadvantage in poverty rates experienced by IDPs and non-IDPs. Three metrics of gender disadvantage are investigated: first, we use the traditional approach to measuring the impact of gender on poverty, via household headship; second, we classify households according to their demographic characteristics; and third, by the number and sex of earners in the household. To our knowledge, this is the first study to go beyond headship in analyzing the gender dimensions of poverty in situations of internal displacement.

> <u>[†]</u> Lead Economist, Gender Group, World Bank Group.

> <u>[‡]</u> Economist, Poverty and Equity Global Practice, World Bank Group.

> <u>[§]</u> Economist, International American Development Bank, Washington, D.C.

> 1 Internally displaced persons (IDPs), are “persons or groups of persons who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a
and the nutritional status of the child.<sup>6</sup> In regions in which agriculture is the dominant economic activity, food prices, through their effects on incomes and revenues for the state and local authorities, can also affect both the demand and provision of health, education and sanitation.

Studies examining the association between changing food prices and child nutritional outcomes are limited. Arndt _et al.,_ (2016) use household survey data and food price inflation rates to study the relationship between shifts in food prices and child nutrition status in Mozambique. Using propensity score matching, the study shows that nutrition measures (weight for height and weight for age) improve significantly when the inflation rate for food products is low. However, their analysis focuses on general price inflation and covers changes between quarters within one year and so does not allow for longer term impacts.

Torlesse, Kiess, and Bloem (2003) find that in the 1990s the price of rice was correlated with the percentage of underweight preschool children in rural Bangladesh. This seems to reflect a lack of substitution away from rice as the amount of rice consumed per capita did not change in response to price changes. As a result, household expenditure on rice varied with the fluctuations in price and less was spent on non-rice foods when rice expenditure increased. Expenditures on non-rice products were found to be negatively correlated with the percentage of underweight children.

Vellakkal _et al._ , (2015) investigate the association between food price hikes in the 2000s and child undernutrition in India. They first look at the association between changes in food prices and food consumption, and then the relation between these consumption changes and child nutrition. The study suggests that rising food prices were associated with an increased risk of undernutrition among children.

Miller and Urdinola (2011) investigate how child survival in Colombia responds to fluctuations in world coffee prices. They argue that the health of children depends on time consuming tasks such as breastfeeding, bringing clean water from far away or taking a
# Table 1. Contributions of global shocks to global CPI inflation factor

## A. Contributions for the full period

|||Struc<br>|tural Shocks<br>||
|---|---|---|---|---|
|Forecasting horizon|Oil<br>Price|Global<br>Supply|Global<br>Demand|Global<br>Interest rate|
|On impact|37.3|20.4|21.5|20.8|
|1 year|38.7|17.7|27.1|16.5|
|2 year|38.4|17.6|27.8|16.2|

## B. Contributions for sub-periods

|Sub-periods|Oil Price Shock|Global Supply Shock|Global Demand Shock|Interest Rate Shock|
|---|---|---|---|---|
|1970-1985|20.5|25.1|35.9|18.6|
|1986-2000|19.8|24.8|35.9|19.5|
|2001-2022|43.7|12.5|21.4|22.4|

Note: This table reports variance decompositions of global inflation factor (in percent), based on the global FAVAR model that consists of oil price growth, and the global factors of inflation, output growth, and interest rates extracted from the dataset of G7 countries.

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workers are released from agriculture and the labor market moves closer to the optimum as defined by _pf_ l (l _f_ , _k_ , _n_ ) = _w_ .

# **3.  CGE Model**

The CGE model of China used in this study has been developed at the Development Research Center of the State Council in Beijing with the explicit objective of modeling inequality and the rural-urban labor market. The model has its intellectual roots in the group of single-country, applied general equilibrium models used over the past two decades to analyze the impact of trade policy reform (Dervis, de Melo and Robinson, 1982; Shoven and Whalley, 1992; de Melo and Tarr, 1992). It began as a prototype CGE model developed for the Trade and Environment Program of the OECD Development Center in the mid-1990s (Beghin, et al., 1994). However, since that time, significant modifications have been made to capture the major features of the tax system in the Chinese economy (Wang and Zhai, 1998), differentiate China’s two separate trade regimes (Zhai and Li, 2000), address demographic issues (Zhai and Wang, 2001), as well as disaggregating the coastal economy (Li and Zhai, 2002). A variety of policy issues have been examined using variants of this model, including the economy-wide implications of China’s WTO accession and the income distribution consequences of trade and tax reform.6 The model used in this study represents the latest advance in the evolution of this Chinese CGE model. It is calibrated to the social accounting matrix complied from the most recent I/O table of 1997, incorporates highly disaggregated households based on detailed households survey data and introduces a novel approach to the modeling of rural-urban labor market linkages. A comprehensive algebraic description of the model is provided in the

> 6 See Development Research Center (1998),and  Wang and Zhai, (1998) .

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averaged only over the most recent (reform) years. For our purposes, however, the alternative of employing panel estimation has several advantages. First, our question is inherently a time series question: how did growth change when policy changed? Second, because the variables of interest vary significantly over time, their time series provide a considerable wealth of information ignored in cross-sectional averages. The gain in degrees of freedom by employing panel data is particularly important when a relatively large number of explanatory variables are used, as will be necessary to characterize the muliple dimensions of reform in Latin America. Third, the use of panel data allows us to control for, and assess the importance of; time-specific effects (in the form of world economic conditions) as well as country-specific effects. Fourth, the likely endogeneity of some explanatory variables can be accounted for by using previous observations of the variables in the panel as instruments. For these reasons, the estimates of policy (and non-policy) effects on growth obtained using panel data will be more consistent and efficient than those using only cross-sectional data.

At the same time, the use of the time-series dimension presents some problems for growth regressions. The effects of policies on growth are likely to exhibit complicated dynamics, which may be obscured by temporal effects emanating, for example, from the business cycle. Cross-section regressions avoid these problems by focusing on long-run effects. We will attempt to do the same here, while exploiting the time-series variation in the data, by using panels based on five-year averages.

Our estimation strategy will be based on the following regression equation: _GRi, = Y'RVI,_ + _CVI, + TEt +_ y _YCE_ + _rIl +_ + E, j (1) where the subscript i denotes a given country; time periods are normalized so that the subscript t 4
due to using population estimates as PSU size depends on the size of the discrepancy between the actual population and population estimates and whether the difference is systematic.

Our findings clearly show that the pre-EAs’ total populations vary, ranging from zero to a certain population size. In the preEA tool, the user has the option to define various constraints. The maximums of population and geographic areas are the two main hard constraints. These maximum thresholds may differ depending on the goal of the work or from one country to another. The primary goal of establishing maximum thresholds for both population and area is to strike a balance between the two limits to prevent the creation of unmanageable pre-EAs in areas with sparse populations. When one of these constraints is met, the aggregation will cease during the merging procedure. The size of pre-EAs in uninhabited areas (indicated by gridded population areas) is simply determined by the maximum geographic area; if the maximum area is reached, the aggregation will cease. As a result, several preEAs with zero or low population values might have been created. The main benefit of this strategy is that it eliminates the need to include uninhabited areas in your sampling surveys, which will result in significant time and cost savings. However, as the method primarily depends on gridded population data, a few inhabited areas may be overlooked if the data is unreliable. This can be resolved in the tool by ignoring the geographic area constraint, but doing so creates enormous pre-EAs that may be difficult to enumerate, especially in rural areas.

Model estimates of gridded population data can be enhanced using a reliable approach and sufficient resources and data to identify non-residential buildings. Despite significant efforts, this still presents a challenge because of the complexity of the problems (such as the structures' resemblance and the coexistence of residential and commercial tenants in the same building). As a result, non-residential areas are not excluded in the population prediction of several gridded population datasets. Consequently, certain pre-EAs that are located within non-residential areas exhibit positive population values.

It
markets improved with the adoption of IT. Historically, evaluations of transmission in India have been mixed. Mishra, Montiel, and Sengupta (2016) examined the strength of transmission using a structural VAR methodology. They found that a tightening of policy is associated with a significant increase in bank lending rates. Although passthrough to lending rates is only partial, they conclude that their result for India compares favorably with those for other developing countries. Consistent with these findings Acharya (2017) and Dua (2020) argue that transmission to money market and long-term interest rates is rapid and relatively complete, but that bank deposit and lending rates adjust more slowly and less completely.

We collated monthly data on government bonds yields of 1, 2, 5 and 10 year maturities, treasury bill rates, and average lending rates on new and outstanding loans. We used the repo rate as the relevant policy rate. Table 13 confirms that transmission is greater for treasury bill and short-tenure bonds. Transmission to government bonds yields and bill rates improved somewhat following the adoption of inflation targeting. Transmission to bank lending rates is relatively weak, as other authors have shown, and has not improved with the adoption of IT.

**Table 13: Transmission of policy rate**

||1 year govt bond<br>yield|91 day T bill rate|Bank lending<br>rate on<br>outstandingloans|Bank lending<br>rate on new loans|
|---|---|---|---|---|
|Repo Rate|0.94<sup>***</sup>|1.15<sup>***</sup>|0.61<sup>***</sup>|0.64<sup>***</sup>|
||(20.25)|(20.23)|(12.56)|(17.77)|
|IT
<!-- Start of picture text --> Vietnam<br>2<br>Correlation: 0.74<br>ia correlation: 0. ssp4 |. SSP5 o °°<br>TM 8 i i 0 2g cs)<br>Cc 1 i a<br>= H H Fo fe]<br>nm 7 i i o ae a °<br>= Boo. o %<br>£ o, 9 oH 8<br>as 6 9 %5 ~28"o o<br>up S65 ee8 _<br>£3 of is) G08 o<br>35°a 7 “aahGiteoS eReobsE ED.Wo. BeO00900oO0<br>Sso 4 &.ReoNsers:hace ceeaiiieereelen Ma Foa oO8 ° 6:<br>) °° RgREE ORROSEoS Ro pe.Ler GU CO”& SEo<br>3g 2 of3 BSBs BtOE ofPog | ‘t 2<br>< a? ; :<br>1 i<br>0.02 0.04 0.06 0.08 0.10 0.12<br>Average income growth between 2007 and 2030<br><!-- End of picture text -->
the female employment rate in all transition economies.<sup>2</sup> This is due to the difference in the labor force participation rate, which is much lower for women, rather than the unemployment rate which tends to be similar for men and women (Figure 3).

**Figure 3 Labor force indicators by gender, 2002**

<!-- Start of picture text --> A. Employment/populaion Ratio, 2002<br>80<br>70<br>60<br>Men<br>50<br>Women<br>40<br>30<br>20<br>10<br>0<br>CEE EU15<br>percent<br><!-- End of picture text -->

> 2   This statement is based on data for CEE countries and Russia.  However, it is highly unlikely that the common pattern of higher male than female employment rate does not apply to other ECA countries.

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# Figure 20. SAR: Policies to raise potential output growth

A. Potential output growth and contributions

# B. Reform scenarios

<!-- Start of picture text --> Percent Percent Social benefit reform<br>7 Baseline Demographic trends Other factors 8 Labor market reforms<br>Education and health improvements<br>Investment surge<br>6 6 Baseline<br>5 4<br>4 2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>3 impact impact<br>2011-21 Factor 2022-30 2011-21 Factor 2022-30<br>EMDE SAR<br>EMDEs SAR<br>C. Climate change scenarios D. Female labor force scenarios<br>Percent Investment boost (climate change) Percent<br>8 Disasters (climate change) 8 Baseline Female LFPR improvement<br>Baseline<br>6 6<br>4 4<br>2 2<br>2011-21 2022-30 Reform 2011-21 2022-30 Reform<br>impact impact<br>0<br>EMDE SAR<br>2011-21 2022-30 Reform<br>E. Share of agriculture sector F. Climate risk<br>Percent Occurences per year Millions 2021 US$<br>60 Share of GDP Share of employment Occurences<br>50 250<br>Cost per event (RHS)<br>50<br>40 200<br>40<br>30 150<br>30<br>20 20 100<br>10<br>10 50<br>0<br>0 0<br>Pre-2000s Post-2000s<br>Sources: EM-DAT database; Penn World Tables; UN Population Prospects; World Bank; World Development<br>Indicators database.<br>2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020<br><!-- End of picture text --> Note: EMDEs = emerging market and developing economies; SAR = South Asia. GDP weights are calculated using average real U.S. dollar
# and widespread use of new technologies.<sup>9</sup>

In addition to supply effects, due to the many unknowns, epidemics and pandemics can weigh on productivity through demand-side channels, by raising uncertainty, eroding consumer and business confidence, weakening investment and depressing demand (Dieppe, 2020).

Table 1: Number of episodes by type of events

||AEs|EMDEs|LICs|World|
|---|---|---|---|---|
|**Natural disasters**|**1031**|**4699**|**1098**|**5730**|
|Climate disaster|843|3054|651|3897|
|Biological disaster|50|953|369|1003|
|Geophysical disaster|138|692|78|830|
|**Wars**|**45**|**191**|**55**|**236**|
|Intra-state war|45|191|55|236|
|External war|45|68|9|113|
|**Financial crises**|**54**|**390**|**83**|**444**|
|Banking crisis|34|113|27|147|
|Currency crisis|18|208|44|226|
|Sovereign debt crisis|2|69|12|71|

Note: Natural disasters include climate, biological, and geophysical disasters (EM-DAT). Wars include intra-state and external (extra-state and inter-state) wars (COW and PRIO). Financial crises include banking crisis, currency crisis, and sovereign debt crisis (Leaven and Valencia 2018). Sample is restricted to the observations where labor productivity growth data exist for the period 19602018. For each country-year pair, the episode dummy of a specific type of event is 1 if the event occurs at
the household sampling weight. Statistically significant differences between men and women are indicated by asterisks (***p<0.01, ***p<0.05, * p<0.10).

(2) 442 men respondents and 319 women respondents engaged in more than one activity across (A)-(D).

(3) The results include 188 men and 111 women that reported by proxy.

Figures 1 and 2 shed light on the total work burdens that women with and without an off-farm or agricultural occupation face, compared to men. Figure 1 presents locally weighted regressions of minutes spent in the last 24 hours across unpaid work, agricultural or off-farm work, and leisure by age and occupation as reported in the labor module (NFE work, wage work, agriculture, or not working in these 21 Average weekly hours worked from the labor module is consistent with average daily hours reported in the time use diary, under the assumption that individuals work similar hours for 5 business days.

> 22 Off-farm earnings were calculated by adding annual wage earnings with annual non-farm enterprise earnings collected in the survey.

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than one household member, for instance all plot managers in an agricultural household, increases the response and time burden on the household and may carry the risk of greater attrition.

When it comes to collecting individual-level data, for instance on nutrition or individual food security, selecting a sample of individuals representative of the population of individuals is preferrable. For phone surveys, the challenge for individual-level data collection again comes down to the pattern of phone ownership whereby some segments of the population are less likely to own or have access to a mobile phone and thus will not be adequately represented in phone survey samples. Such issues can be magnified in recontact surveys that use the household (rather than the individual) as their unit of observation. Brubaker et al. (2021) show that the main respondents in High Frequency Phone Surveys on COVID-19 in four African countries are predominantly household heads, and also better educated and more likely to own a non-farm enterprise than adults overall, so that individual-level estimates are not representative of the population at large. In turn, the scope for a ‘proxy respondent’ answering on behalf of other household members depends critically on the kind of information collected and has been linked to measurement error (Kilic et al., 2020). For these reasons, a randomized (probability-based) respondent selection protocol—whereby a targeted respondent within the roster of eligible household members is randomly selected for interview—is the preferred strategy to achieve unbiased individual-level data.

# 3.3. Post-survey adjustments

When mobile phone ownership is not complete, the phone-owning population and the general population that the phone survey seeks to represent are different, often resulting in coverage bias. Similarly, if nonresponse is systematic, then the responding sample will not be representative of the population of interest. There are a range of post-survey reweighting techniques that can be used to attempt to counteract these differences and improve the representativeness of the estimates obtained (Valliant et 12
performance. Third, the extent of convergence weakens over time; the rate of improvement experienced by a cohort declines within the second decade. The fourth finding is that country specific attributes impact on the improvement in immigrants’ occupational performance occurs mainly through their effect on initial placement: thus, they lose significance when initial occupational levels are controlled for in the estimation.

Most papers in the literature focus on wages to assess performance, assimilation and convergence in destination labor markets. In this paper, we use measures of occupational placements instead of earnings because the former are less likely to vary by city or state within the US. This is a significant advantage when measuring performance of people spread over a large geographic area where costs of living and other local factors strongly influence wages. For example, the wages in metropolitan areas like new York City and San Francisco for medium skilled occupations tend to be higher than wages of high skilled occupations in more sparsely populated areas. In addition, characteristics of occupations are less likely to be volatile relative to wages as labor market conditions change over time. In short, biases due to geographic and time effects are less of a concern when occupational placement indices are used as measures of performance.

One main concern is that the magnitude and patterns of return migration can potentially bias the results in this paper. Although prior literature acknowledged this problem and some researchers made inferences about the nature of the selectivity in return decisions, the bias stemming from out-migration is typically left unresolved in studies that rely exclusively on census data. We evaluate to what extent attrition of cohort size due to return migration affects the samples by country. We conclude that estimates are not significantly affected by patterns in return migration. Furthermore, working with data from several Censuses raises serious methodological concerns because the samples are based on responses of different individuals. To check whether results change significantly when correcting for the potential biases, we rerun the individual-level regressions using pseudo-panel techniques. The patterns are robust to these methodological changes.

Occupational status has important implications for understanding economic
Bihar Survey sample as a whole and the Bihar Survey for un-rationed households only. Figure 2 shows a kernel estimation of the distribution of Wi⁄Ci and Table 2 gives corresponding summary statistics for the four datasets.

> 13 To help interpret this number, imagine a household that derived its consumption solely from casual manual work. Then a value of 0.5 for Wi/Ci means that 2 days of casual labor were supplied per week per household member, which paid for the weekly consumption of one household member.

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