rafmacalaba/data-use-mentions-extended huggingface dataset:
after the current conflict breakout in 2015, that is the period 2015-2021.<sup>5</sup> To construct the conflict indicator, we focus on 3 types of events, namely battles, explosions and violence against civilians, summing up the number of occurrences at the district level. The reason for choosing these events lies in the fact that throughout our period of analysis, these events have scored the highest number of occurrences compared to protests, riots, and developments (see Figure IV.4 and Figure IV.5). Even though the latter type of event has witnessed a remarkable increase in 2019 and 2020, we exclude it because they often refer to brokered deals or advances that are likely to affect households differently to conflict events. We also choose to focus on the number of events rather than the number of fatalities, since the latter is deemed to suffer from underreporting.<sup>6</sup> ACLED data is collected from reported conflict events in news outlets, and it is more likely that the number of events are better documented compared to the number of causalities which tends to be less accurate.

After constructing the conflict intensity at the district level, the sample is split into three equal groups of increasing conflict intensity: low, medium, or strong conflict intensity.

_Figure IV.4: Total number of conflict events by year. Source: ACLED_ <!-- Start of picture text --> 1400<br>1200<br>1000<br>800<br>600<br>400<br>200<br>0<br>2015 2016 2017 2018 2019 2020 2021<br>Battle Explosion Protest Riot Developments Violence civilians<br><!-- End of picture text --> 5 ACLED data is not available prior to 2015 to be able to detect trends in the period before the study.

6 As a robustness check we control for the number of fatalities and find no difference in the main results reported in the paper
nor do they account for counterfactual forest loss in the absence of the refugee population’s arrival. Two previous studies estimate camp impacts in Sub-Saharan Africa using a quasiexperimental approach. First, Maystadt, Mueller, Van Den Hoek, & Van Weezel (2020) examine the land characteristics of large grid-cells that contain camp populations over the subcontinent. They find that larger camp populations increase the probability that the grid-cell experiences both forest loss and agricultural expansion, but they also find that larger camp populations lead to slightly higher vegetation density. Building on this study, Salemi (2021) examines higherresolution grid-cells around refugee camps and finds that forest losses in response to camp openings are statistically significant but extremely small in magnitude. Our paper builds on this recent discourse by examining a case study with characteristics quite different from Sub-Saharan Africa, where land is relatively abundant and population densities are low. The environmental impact of camps may be quite different in Bangladesh, which is characterized by high population densities and low levels of in-tact natural lands.

Our case study focuses on the most recent arrival and encampment of Rohingya refugees in Cox’s Bazar, Bangladesh. In late 2017, more than 740,000 Rohingya citizens of Myanmar fled violent persecution in their home villages and crossed into the Cox’s Bazar region. Upon arrival, UNHCR, the Bangladeshi government, and their partners constructed and began servicing several large refugee camps, all within the southern half of Cox’s Bazar. Some of the unanswered questions regarding the impact of this migration and the establishment of the refugee camps are: how much forest cover was lost within and outside the refugee camps? To what extent is the forest cover change around refugee camps comparable to other locations across Cox’s Bazar district? What are the primary drivers of deforestation near the camps? Are Rohingya refugees directly causing forest cover loss in the region? Or has increasing economic activity associated with the refugee camps’ presence driven forest change?

Remote-sensing data opens up new opportunities to investigate whether the areas near camps face higher
: World Bank calculations using data from TUIK We also looked at the contribution of different expenditure categories to examine the sources of variation in the incidence of inflation across the population. Figure 4 shows a breakdown of the overall inflation experience of the 1st and 10th decile across the seven mains groups of expenditures plus a category that combines the remaining items consumed (labeled as “others”). Considering the large weight of food in the total consumption of poor households, it does not come as a surprise to observe that inflation episodes with a heavy burden on this group of the population –such as those that took place in 2013, 2015 and 2019– are chiefly driven by substantial increases 14
|_R_-squared|0.1115|0.1239|0.1603|0.5538|0.2042|0.1767|0.2024|0.2306|0.5558|
|N. of cases|442|442|442|442|442|442|442|442|442|

Notes. Receptive English, Swahili, and Luo vocabulary scores are age-adjusted _z-_ scores for children ages 2 to 6 years, measured using separate assessments based on the British Picture Vocabulary Scale (BPVS). Expressive vocabulary _z-_ scores were measured using a tool developed from the PPVT. Baseline and follow-up were conducted approximately 5 weeks apart. * _p_ < 0.1, ** _p_ < 0.05, *** _p_ < 0.01, **** _p_ < 0.001.
non-farm enterprise work by 9.5 percentage points (columns 1 and 4).<sup>21</sup> Conflict is not associated with changes in labor activities.

Next, we investigate the relationship between parents’ mental health with children's well-being. Table 4, columns 5 and 6, evaluates how parents’ depression status is correlated with investments in children's education. Parental depression is significantly negatively correlated with educational expenditure for children (who are in school) (column 6), while it does not seem to be related to the probability of their children being in school (column 5).

We observe the differential effect of parent’s depression on children’s educational outcome by children’s age. If children are young (11 years old or younger), parents’ depressive symptomatology is not associated with whether the child is enrolled in school or not, but it is significantly and negatively correlated with educational expenditures among those who go to

> 18 Despite this, we believe that the story supported by this analysis would not be further supported by the use of quasi-experimental methods.

> 19 Note that the relationships between depression and the labor-related outcome variables are negative (results not shown); these negative relationships hold and grow stronger when adding in additional covariates such as adverse events, indicating that they are not necessarily mechanisms.

> 20 We find that the measure of depression is not correlated in any significant way with measures of work (both extensive and intensive) from wave 1 of the survey.

> 21 In addition to this impact on agricultural work and participation, we find a negative correlation of depression with yields. Because the direction of causality is difficult to determine in this case, we will not discuss this extensively, but results are provided upon request.

14
. Households who consume less than 99 kwH per month are exempt from paying the VAT, while households who exceed this consumption pay the VAT on the excess over the limit. Similarly, the water consumption limit is 2,900 gallons per month, with households paying the VAT on the excess consumption over the stated limit.

Because the VAT is already incorporated in the price, a pretax rate is first calculated for each E <u>E</u> good using the equation P = and then the rate is applied to the pretax value. v jj 1+rr vv Although the VAT regulations require companies to have a special registration document showing that they offer taxable products for EC$12,000 per year or operate for at least 12 months, this information is not available in the data source consulted. Therefore, it is difficult to know whether households buy in stores exempt from the VAT registration requirement or sell informally. Likewise, the application of the incidence of indirect taxes does not consider cascading effects, given the difficulty of imputing household deductions to reduce the net payment of these taxes. Consequently, there is an overestimation of the effect of the VAT and excise taxes.

# **_Transfers received by households_**

Direct transfers in the model are mainly associated with the SEED program and the School Feeding Programme. The SEED transfer is identified in the SLCHBS through a direct question to the households. Based on administrative records, we identify the amounts of program transfers to children, youth, and elderly individuals. Finally, we aggregate the amounts at the household level. The SEED amounts are reduced for households to ensure that base/market 10
of ninis over this period; in fact, the share of male ninis increased marginally during those years (for both age groups). And over the same period, the increase of 14 percent in the total number of ninis (1.8 million individuals) is entirely explained by the increase in the number of male ninis. Simultaneously, the number of female ninis has fallen in the case of those aged 15 to 18 (decreasing by 5.6 percentage points, or over 400,000 individuals) and stayed relatively constant for those aged 19 to 24. These trends, which are likely explained by the general increase in women’s education levels and labor-force participation rates in the region, combine to produce a shift in the gender composition of ninis towards men.

**Figure 10: Total number and percentage of youth who are ninis in LAC by gender and age, 1992-2010**

Panel A: Percentage of youth who are ninis in LAC by gender, (15-18 years) Panel B: Number of ninis in LAC by gender, (15-18 years) <!-- Start of picture text --> 25.4 24.9 5.8 6.0 5.9 6.0 5.9<br>23.3<br>20.5<br>19.8<br>18.4 18.3 17.6 4.1 4.1 4.0 3.8<br>3.7<br>15.6 15.5<br>11.1 11.7 12.0 11.0 11.5 1.9 1.9 2.1 2.2<br>1.7<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Year Year<br>Total Men Total Men<br>Women Women<br>Panel C: Percentage of youth who are ninis in LAC by Panel D: Number of ninis in LAC by<
b</sup> Nine major groups of occupations according to ISCO 88.<sup>c</sup> 15 sectors (A, B,...,O) according to NACE. _Sources:_ Calculation based on LFS data.

Can segregation, human capital, or the longer hours worked by men explain inequality in pay between the sexes? While females are better educated and over-represented among professionals (see Appendix Table 27), a well-paid occupation, they are heavily concentrated in education and health care. These two sectors are found at the bottom of the wage ranking.

36
# **2. Methodology**

## **Overview**

The rapid approach being proposed here applies a split-questionnaire design to the consumption module of a household survey, thereby generating systematically absent data that can be conveniently imputed.<sup>3</sup> Instead of having all households report on all consumption items, important items are assigned to a core module and the remaining items are split into two or more optional modules.<sup>4</sup> Each household then answers the questions in the core module and in only one of the optional modules. This approach reduces average interview time considerably, down to 45 to 60 minutes per household for a standard household consumption survey. The cost of this efficiency gain is that data are deliberately absent for those optional modules that were not administered to certain households. We can however offset this cost by estimating the deliberately absent data for each household based on the data collected from other households for that module. While this approach utilizes a structural model for the imputation of the deliberately absent data, the model is estimated within the survey rather than between two surveys, thereby circumventing the problem of biased structural parameters due to having different sample populations or considering the same population at different points in time.

The rapid approach starts by defining the number of core items and the number of optional modules for the non-core consumption items. The smaller the number of core items and the greater the number of optional modules used, the faster the questionnaire can be administered, as fewer items need to be asked for each household. However, having fewer core items and more optional modules also increases the uncertainty in the estimation as less consumption information is available. Thus, the choice of these two parameters can be informed by simulations on a previous or similar survey to gauge the performance of the estimation vis-à-vis the time savings in administering the questionnaire. Another consideration is that it is beneficial to balance the number of households for each optional module, ideally at the cluster level of the survey
# **Relative Deprivation, Reference Groups and the Assessment of**

# **Standard of Living**

_Jacques Silber_ Department of Economics, Bar-Ilan University, 52900 Ramat-Gan, Israel _Paolo Verme_ The World Bank and Department of Economics, "S. Cognetti de Martiis", University of Torino

**Abstract**

This paper proposes two new indices of relative deprivation, derived from an extension of the concept of generalized Gini to the measurement of distributional change.

Population- and income-weighted relative deprivation indices are then defined and, using panel data from the Consortium of Household Panels for European SocioEconomic Research (CHER), we check which of the various ways of defining individual deprivation best fits the answers given by individuals on the degree of their satisfaction with income. We find that the deprivation indices proposed are consistently and negatively correlated with income satisfaction as reported by respondents, that income weighted measures fit better than population weighted measures and that this fit improves with countries that experienced deep institutional changes such as the transitional economies of Eastern Europe.

**Key Words:** generalized Gini index, income satisfaction, relative deprivation

**J.E.L. Classification** : D31 – D63 – I32 – O15

Sector Board: Poverty (POV) <mark>1</mark>
or, conversely, that<br>there is a specific, quantifiable form for the distributional change. Changes in employment distribution<br>are accommodated by reweighing the sample households.|
|**Data/information:**<br>**ments**|This program requires unit record household-survey data. Also, a poverty line, survey year, and<br>forecast horizon are parameters that must be provided by the user. Macroeconomic variables at the<br>nationally aggregated or sectorally disaggregated level and growth rates of income, employment, and<br>population are also required. In addition, the user can input changes in the CPI and GDP deflator,<br>changes in the relative prices of food and the shares of food in CPI, and changes in the poverty line<br>consumption bundle. This allows one to generate different types of forecasts and optional projection<br>parameters such as employment shifts across sectors. The software can be adapted for grouped data.|
|**Time:**<br>**equire**|One or two days to format the household survey data, collate and check exogenous economic variables,<br>and enter everything into PovStat.|
|**Skills:**<br>**R**|Familiarity with Excel and appropriate household data-handling software (such as Stata). Also,<br>familiarity with PovCal if synthetic data from a grouped distribution are to be used.|
|**Supporting**<br>**software:**|Excel.|
|**Financial cost:**|**–**|
|**Limitations:**|PovStat does not capture second-round effects. These may be captured by CGE models.|
|**References and**<br>**applications:**|�<br>For an overview of the technique, see Datt and others (2003).
variables with a dummy indicating whether the flow involves a PowerSeller or not. The only statistically-different coefficient at the 5 percent level is the distance one as shown in Table 4.

To examine whether eBay reduces search costs associated with product information as suggested by Rauch (1999), we use three measures of information asymmetries at the product level. First, we use Broda and Weinstein’s (2006) estimates of elasticity of substitution.<sup>18</sup> The median of their HS-6 digit estimates measures the need for information, or the level of product differentiation, within each category. Indeed as substitution among import sources is smaller there is a stronger need for product information. Next, we construct two measures of trademark intensity that also capture the presence of product information in each sector. Our first measure of trademark intensity uses data from the WIPO Global Brand Database which contains around 660,000 records relating to internationally protected trademarks. We base our trademark intensity measure on the number of registered brands per keyword search, where the keyword is the eBay category. For example, there are 605 registered brands that match the keyword ’music’, and 284 that match ’electronics’. We suggest that the lower this number, the higher the need for information gathering and diligence by importers. If the search costs lowered by eBay are related to product information, we should find eBay to reduce the role of distance most in categories with low trademark intensity for which asymmetric information regarding product characteristics is stronger. Our second indicator of trademark intensity comes from our eBay data and measures the intensity of complaints by trademark owners to eBay about potentially-illegal transactions. We take the share of companies who complain per category as an indicator of trademark intensity. We then suggest that the more complaints there are, the more branded the product category should be. We then interact these product-information indices with distance in our gravity regressions. The results are summarized in Figure 6 (drawn from coefficients found in Table 5). It shows that the distance-effect
times less expensive per question asked.<sup>11</sup> Cost per question ranges from 10 cents in Malawi to 25 cents in Tanzania for in-person interviews, and from 3 cents in Uganda to 6 cents in Malawi for phone surveys (Table 3).

One should note, however, that the scope of data collection in the in-person surveys is far more extensive than that of the HFPS surveys, so these figures should be considered as illustrative of the cost differential with acknowledgment that the surveys are not comparable in terms of data collected. A truly meaningful comparison needs to qualitatively discount these numbers by also looking at the information that can be collected, and the discussion in earlier sections showed how some key objectives of agricultural sample surveys cannot be fulfilled via phone.

_Table 3. Comparison of in-person and phone surveys (US$)_

||**_By country_**|||
|---|---|---|---|
|_Country_<br>_Survey_|_Mode: phone vs._<br>_in-person_|_Cost per_<br>_interview_|_Cost per_<br>_question_|
|Burkina<br>EHCVM-1 2018/19|In-person|230|0.20|
|<br>HFPS|Phone|9|0.05|
|Ethioia<br>ESS4 2018/19|In-person|245|0.17|
|p<br>HFPS|Phone|10|0.04|
|Mli<br>IHPS 2019|In-person|269|0.10|
|aaw<br>HFPS|Phone|12|0.06|
|Nieria<br>GHS-Panel 2018/19|In-person|351|0.21|
|g<br>HFPS|Phone|12|0.05|
|Tanzania<br>NPS 2021<br>HFPS|In-person<br>Phone|409<br>8|0.25<br>0.04|
Mexican states and from all the different regions of the country. Although these states vary in the percentage of land cultivated under rainfed technologies, in most at least 75% of the land is rainfed (Table 5). Also, in most at least 50% of the land cultivated in the wet season is in corn and in all the area cultivated in the wet season is greater than the area cultivated in the dry season. These figures suggest that we can expect for an average rural household in our sample the income, as well as production for self consumption, to be relatively highly dependent on the weather and especially on the weather during the wet season. Also, given the relative importance of corn, the pre- _canícula_ period is of interest.

> 19 The correlation of the 6 different weather shock variables for the MxFLS sample is given in Appendix 1.  The rainfall deviations from mean for the various periods (annual, wet season and pre- _canícula_ period) are positively correlated with annual rainfall and wet season rainfall being very highly correlated.  The GDD deviations from mean are all very highly correlated.  Given the high correlations among the different time periods, we only include weather variables from one time period in each regression.

16
# Figure 7. International trade costs in EMDEs, by country characteristics

A. Trade costs by free trade agreements B. Trade costs by tercile of logistics performance index <!-- Start of picture text --> Percent Percent<br>200 200<br>150<br>150<br>100<br>100<br>50 50<br>0 0<br>Non-FTA FTA Bottom Top<br>C. Trade costs by tercile of liner container D. Trade facilitation<br>shipping index<br>Percent Index<br>200 3.0<br>150 2.5<br>2.0<br>100<br>1.5<br>1.0<br>50<br>0.5<br>0 0.0<br>Bottom Top Advanced economies EMDEs<br><!-- End of picture text --> Sources: Comtrade (database); Gurevich and Herman (2018); OECD; UNESCAP-World Bank Trade Costs Database; World Bank; World Trade Organization.

Note: EMDEs = emerging market and developing economies; FTA = free trade agreements; LSCI = Liner Shipping Connectivity Index; LPI = Logistics Performance Index. Orange whiskers indicate minimum and maximum range. Sample includes 52 EMDEs.

A. Average trade costs (unweighted) of countries based on their membership in free trade agreements as defined in Gurevich and Herman (2018).

B. Average trade costs (unweighted) for countries ranked in the bottom and top quartiles of the Logistics Performance Index.

C. Bars show average trade costs (unweighted) for countries in the bottom and top quartiles of the liner shipping index.

D. Unweighted average for 36 advanced economies and 122 EMDEs. Trade facilitation index is an unweighted average of 11 subindexes, all scored on a scale of 0-2. A higher index indicates greater trade facilitation. The indexes score countries on information availability, trade consultations, advance rulings, appeals procedures
less affected by the quantity of poor rural-urban migrants (who are more likely to become urban poor, at least in the short term) than the total urban population variable. Again, the difference between the standardized coefficients of the total urban population _β_ and of the urban nonpoor population _β’’_ provide a sense of the magnitude of the _location_ effects.

# **<<A>>Results**

The descriptive statistics for the main variables used in the analysis are presented in table S2.1 (available online at http://wber.oxfordjournals.org). Table 1 presents the results of specifications (2) and (2’) using fixed effects estimation. Our dataset includes observations of 363 districts for three different time periods: 1983, 1993, and 1999. We run the regressions applying a two-year lag to the measure of the urban population and to the other demographic controls for two primary reasons. First, the two-year lag reduced the risk of potential simultaneity bias **.** Second, the twoyear lag allows us to limit the use of interpolation for the Census variables (both population and socio-demographic variables), which are recorded in 1981, 1991 and 2001, to the last period (1999) only.<sup>21</sup> The standard errors are robust to heteroscedasticity (using the Huber-White correction) and are clustered at the district level.

## **<mark><<Table 1 about here></mark> >**

The estimate in column 1 indicates that the growth of the urban population exerts a significant poverty-reducing effect on the rural areas in the same district. This result is obtained by including district and state-year effects as well as the total rural population and should capture the overall effect of urbanization on rural poverty. Column 2 adds the set of controls, which, as we have argued above, should capture 17