rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Refugees and Humanitarian Aid: The Rohingya Impact on Bangladesh’s Development**<sup>*</sup>

Alvin Etang Ndip<sup>†</sup> Sandra V. Rozo<sup>‡</sup> Mar ́ıa Jos ́e Urbina<sup>§</sup> Christina Wieser<sup>¶</sup>

**Keywords:** Refugees, Deforestation, Labor Market **JEL Classification:** J1, J6, O1

*We thank Zaeem Al Ehsan for his work as field coordinator of the Cox’s Bazar Panel Survey (CBPS) and for cleaning and preparing the data for analysis, Data International for undertaking the data collection, and Roberto Mendoza for his excellent work processing the remote sensing data. We also thank our partners at Yale University including Mushfiq Mubarak, Paula Lopez Pena, and Ellen Bates-Jefferys, and George Washington University including Jennifer Seager, for the excellent collaboration on the CBPS. We also appreciate the comments and suggestions of Ximena Del Carpio, Abdoulaye Seck, Gayle Martin, Souleymane Coulibaly, Ayago Wambile, and Rumana Islam. Several Bangladesh Country Office World Bank staff also provided useful comments on preliminary findings that helped to strengthen the paper. This project was approved by IRB protocol #2000033048. The CBPS data collection was generously funded by the World Bank-UNHCR Joint Data Center on Forced Displacement. Rozo acknowledges support from the Research Support Budget from the DEC Vice Presidency. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own.

> †World Bank, Poverty and Equity Global Practice. E-mail: aetangndip@worldbank.org

> ‡World Bank, Dev. Research Group. Corresponding author, e-mail: sandrarozo@worldbank.org

> §World Bank, Dev. Research Group. E-mail
(0.014)|(0.011)|
|Pure Control Mean|0.907|0.913|0.964|0.903|0.881|0.939|
|Sample Size<br>Notes: Robust Standar<br>*, **, and *** indicate<br>Data on weekly sales a<br>be closed (since then s|3537<br>d Errors in P<br>significance<br>nd profits av<br>ales and pro|3537<br>arenthese<br>at the 10,<br>ailability c<br>fits are kn|3537<br>s, Clustere<br>5, and 1 pe<br>odes data<br>own to be|3537<br>d at the M<br>rcent leve<br>as availab<br>zero).|3537<br>arket Level<br>ls respectiv<br>le if firm is|3537<br>.<br>ely.<br>known to|

46
|-0.016*|-0.000|
||(0.012)|(0.017)|(0.009)|(0.004)|
|Eligible Child|0.029|0.014|0.042|-0.008|
||(0.043)|(0.062)|(0.040)|(0.018)|
|FDR q-value|0.987|0.987|0.987|0.434|
|Observations|20,193|17,804|37,523|48,537|
|Mean|442.201|467.009|457.837|37.890|

Notes: Sample is restricted to females aged 19-45 in at least two IFLS rounds. We infer preschool data in-between PODES years. Each column regresses the dependent variable indicated in column heading on the density of public (Panel A) or private (Panel B) preschools, defined as the number of preschools divided by the population of children aged 3-6 in each district, a dummy for having a preschool-aged eligible child, and their interaction. All regressions include mother’s age fixed effect, urban dummy, district, year, and individual fixed effects. Salary, net profit, income are per month and adjusted for inflation using national consumer price index (CPI) with 2010 base year obtained from FRED. Income is defined as the sum of salary and net profit. Work hours are per week. We apply log transformation to all dependent variables so that estimates can be interpreted as percentage changes; zero values are imputed with log(0.1). Robust standard errors clustered at district level. Stars denote statistical significance at 1, 5, and 10 percent levels based on unadjusted p-values. FDR q-values for the interaction coefficient of preschool density and eligible child are computed over all 4 outcomes and are shown in the last row of each panel. FDR q-values indicate the probability
**Figure 7.  Share of youths aged 15-18 considering migrating, by sex and intended destination.**

**Figure 8. Share of youths aged 19-25 considering migrating, by sex and intended destination.**

<!-- Start of picture text --> 100% Ethiopia Malawi 100% Ethiopia Malawi<br>Nigeria Average Nigeria Average<br>90% 90%<br>80% 80%<br>70% 70%<br>60% 57% 60% 56%<br>53%<br>50% 50% 46%<br>44% 44%<br>40%<br>40% 36% 40%<br>31%<br>30% 25% 30% 26% 26%<br>20% 14% 15% 13% 13% 20% 14% 14% 15%<br>9%<br>10% 10%<br>0% 0%<br>Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female<br>Capital city City/town Another country Rural No preference Capital city City/town Another country Rural No preference<br><!-- End of picture text --> Note. Each respondent was able to select more than one destination. T-tests of equality of means were conducted across men and women within each country. Only average shares are reported for the sake of clarity. Source: Own calculations based on Source: Own calculations based on Ethiopia - High Frequency Phone Survey (2020-2023); Malawi - High-Frequency Phone Survey 2020-2024; Nigeria COVID-19 National Longitudinal Phone Survey 2020-2021. Datasets downloaded from https://microdata.worldbank.org/index.php/catalog/hfps/?page=1&ps=15&repo=hfps on January 2, 2024

# **5. Empirical analysis and discussion**

# **5.1
provided by a subsidiary located in the borrower country, while Third-country subsidiary/total volume is the share of loans provided by a subsidiary located in a third country. These two variables have means of 0.018 and 0.077, respectively; implying that loan provision through third-country subsidiaries is relatively common.

Our main independent variables are indices of the quality of bank regulation taken from the World Bank’s Bank Regulation and Supervision Surveys (Barth et al., 2004 and 2006). The available information is from five consecutive surveys. Following Houston, Lin and Ma (2012), we take values of the regulatory variables for the years 1995 to 1999 from the first survey (measuring regulation in 1999); for the years 2000 to 2003, we use the second survey reflecting the quality of regulation as of the end of 2002; for the years 2004 to 2007, the third survey’s results are used (reflecting regulation at the end of 2005); for the years 2008 to 2012, we take the results of the fourth survey documenting regulation at the end of 2012; finally, for the years 2013 to 2016 we use the results of the fifth survey measuring regulation at the end of 2016.<sup>9</sup> Among the regulatory variables, Capital regulation (borrower) is an index of the stringency of capital adequacy standards in the borrower country, with higher values indicating greater stringency. Capital regulation (borrower) ranges between 1 and 10 and has a mean of 6.333. Overall activity restrictions (borrower) is an index of the extent to which banks can engage in securities, insurance and real estate activities in the borrower country, with higher values indicating more restrictions. This variable ranges between 3 and 12 and has a mean of 7.121. Official supervisory power (borrower) is an index of the power of the supervisory authorities in the borrower country to take specific actions to prevent and correct

> 9 <mark>One of the subcomponents of Capital regulation
as follows. The flow originates from country _i_ if it is adjacent to the segment circle of country _i_ and it has the same color as the segment; the flow ends in country _i_ if it is adjacent to the segment circle of country _i_ and does not have the same color as the segment.<sup>19</sup> Similar colors are used to identify larger geographical areas: pink for East Africa, violet for North Africa, green for West Africa, yellow for Asia, red for the Americas, and blue for Europe.

The investigation of the origins of migrant flows shows that West African countries (green) are the origin for most migrants. Outside Africa, Asia, specifically Bangladesh (yellow), plays a major role. Analysis of the destination of migrant flows reveals that Europe (blue) is the preferred destination for migrants. The second-most-preferred destination in 2017 is Western Asia (yellow: Kuwait, Israel, Saudi Arabia, Turkey), but this almost disappears in 2018.

Comparing 2017 and 2018, changes in the direction of flows originating in a specific area can be identified. For East Africa, namely Ethiopia, Eritrea, and Somalia, we observed an increase in the destinations reached, as shown by the increase in the number and width of the pink flows.

Finally, the figures allow the composition of migrants’ nationality at destination to be analyzed by examining the colors of incoming flows. For all receiving countries, most migrants originated in West Africa (green), but we also observed that East Africa played an increasing role, with a significant increase in the number of pink flows reaching Italy, the Netherlands, and Sweden. Less notable, but still relevant, is the increase in the number of yellow flows, representing the Asian countries, reaching France and Germany.

# _5.2.5 International migration flows through Libyan provinces_

Using data about origin and destination countries registered at the FMPs, we track migration flows connecting migrants’ origin countries to Libyan provinces and Libyan provinces
weights given **by** the structure of intra-OECD FDI flows; we set .at 4 percent. For portfolio equity liabilities **EQYL,** we set 6at **0** and measure **_Q_ by** the domestic stock market price index (in **US** dollars), when one is available; otherwise, we use the same valuation as for FDI liabilities. In turn, for portfolio assets **-** whose breakdown across debtors is unavailable **-** we take **_Q_** to equal the Morgan-Stanley world stock market index.

So far we have ignored the problem of mis-measurement of capital flows and stocks. To attempt to capture unrecorded (net) assets, we augment our measure **of** recorded non-equity assets **_LA_ by** adding to it the cumulative errors and omissions of the Balance of Payments, starting from the earliest date for which the information is available.' Finally, we complement our information on foreign asset positions with data on wealth stocks. For each country and year in our sample, we define wealth as the sum ofthe country's net foreign asset position, plus its central bank's gold holdings and the value of its physical capital stock:

**_W(j,t) = NFA(j,t)+qK(j,t)+ G(j,t)_**

(3.4) where **G** and K respectively denote gold and capital stocks, and **q** is the unit value of the latter.

To calculate these wealth stocks, we first construct estimates of each country's physical capital stock. In order to do this, we extrapolate in the time-series and cross-section dimensions the Summers-Heston PPP-based investment and **GDP** series, on the basis of available constantprice investment and per capita **GDP** data. We next construct initial capital stock estimates for those countries lacking
increased, suggesting population aging. However, Mexico’s population and the number of childcare facilities grew at similar rates, since the average number of government childcare facilities per 1,000 people in each municipality remained almost stable (decreased less than 1 percent).

Concerning labor market characteristics, states’ average unemployment rates for both males and females increased.<sup>10</sup> Surprisingly, the share of part-time employees did not increase from 2007 to 2017, despite structural labor reforms implemented in 2012 to promote labor market flexibility. Agriculture sector employment share declined by more than a percentage point, while the industrial, white-collar, and services sectors all expanded, with the latter—where 49 percent of all women work—experiencing the biggest increase.

Estimation results from our model to explain women’s employability are presented in Table 2. These represent the average marginal effects of probit model (2) and show the change in the probability of a woman being employed with every unit change in the explanatory variables.

The year dummy of “2017” has a negative and significant coefficient, but its magnitude and sign should not be interpreted as a decrease in women’s employment at the end of the decade; the effect varies for each population group due to the interaction terms. Therefore, no simple interpretation of this coefficient is possible. In any case, this variable captures unaccounted factors beyond the explanatory variables in our fixed-effects model.

> 10 Labor market characteristics averages in Table 1 might not coincide with those observed for the national aggregates. We first computed the states’ industry employment shares, part-time employment shares, unemployment rates, and average real hourly wages using the full sample of the ENOE and frequency weights. We then restricted the sample to women aged 18 to 65 and generated the labor market characteristics indicators. Small differences between the sample and population averages are explained by differences in people’s distribution across states.

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2005 Pula)<br>Volume of production (million carats)<br><!-- End of picture text --> Sources: Department of Mines Annual Reports (various issues) and author’s calculations. 1/ 2-year moving averages of the price of rough diamonds, estimated from the volume of production and value of diamond exports.

In line with the decline in mineral revenue, total government revenue has fallen over time. Non-mineral, non-SACU revenue (excluding foreign grants) has grown recently following the introduction of VAT (2002) and improvements in tax compliance and collection (2003/04). However, its contribution to overall revenue is still low, accounting for only about 30 percent (10 percent of GDP). With 70 percent of revenue outside the control of the government, Botswana’s revenue structure is vulnerable to external conditions. For example, in 1994/95 government revenue fell due to sluggish diamond sales and low BoB transfers associated with adverse conditions in the international capital markets. In 1998/99 when the Asian crisis led to a drop in diamond exports, government revenue plunged even more sharply by 10 percent of GDP, turning Botswana’s fiscal position into a deficit for the first time since 1982/83.

> year, and recorded as government revenue. In addition, the BoB’s net income for its financial year, after accounting for administrative and interest expenses, as well as any appropriation to the BoB’s General Reserve, are paid annually to the government at the end of the fiscal year (March 31).

> 8 Time series data on the volume of diamond exports are not available.  It is said that during the Asian crisis, when demand for diamonds slumped, Debswana limited the supply of diamonds by stockpiling them to keep the price up.  Once the crisis was over and global demand recovered, it released the stock to the market.  If this was indeed the case, the volume of diamonds exported around 1999-2001 should have exceeded the volume
shale oil and nuclear in the 1980s, coal and natural gas from the 1990s until 2009, and now renewable energy – and proposals from ONEE to address short term concerns and avoid power shortage. These generation and transmission plans are however, not mandatory, the process for developing them is not transparent and participatory, and they do not generally lead to timely initiation of procurement (see annex). While there is a procurement framework for new generation plants, inclusive of public auctions, there is no procurement framework for transmission lines although it is subject to competitive tenders.

_Table 3: Institutional arrangements for power sector planning and procurement in Pakistan and comparators, 2015_

||**Egypt**|**India**|**Morocco **|**Pakistan**|**International**<br>**benchmark**|
|---|---|---|---|---|---|
|**Planning and Procurement**|82%|78%|61%|63%|71%|
|Generation Planning|71%|57%|43%|29%|56%|
|Procurement of Generation|100%|95%|100%|100%|85%|
|Transmission Planning|75%|75%|50%|25%|74%|
|Transmission Procurement|83%|83%|50%|100%|68%|

_Source: Rethinking power sector reform project, 2015_ _For the more detailed planning and procurement index, see Table A2 and A3 in the annex._ ONEE’s mandate over short-term considerations often overwhelm its coordination of various projects but does not undermine its implementation of the sector’s longer-term strategic objectives. ONEE’s 20 to 30-year master plans are used for annual reporting, rather than informing decisions on generation and transmission. These nominal plans are not very impactful since they are based on assumptions beyond
## **Table 1: Sample distribution by State**

||**Jharkhand**|**Madhya Pradesh**|**Maharashtra**|**Total**|
|---|---|---|---|---|
|Districts|4|12|7|23|
|Blocks|10|27|13|50|
|Villages|212|208|307|727|
|Self-help Groups|858|550|893|2301|
|Households|1260|1237|1819|4316|
|SC/ST Households|1051|946|1069|3066|

# **3.1 Sampling Strategy**

The survey followed a multi-stage sampling strategy at the block, village and household levels. We first identified potential treated blocks where the program takes up rate was at least 50 percent. Then we listed potential comparison blocks in consultation with the National and various state level rural livelihood missions as it involved identification of those blocks where the program had just entered in 2016 or was about to take off in the following year. The treatment and comparison blocks were randomly selected from the list of potential blocks.<sup>8</sup> This block selection strategy reduced the program placement bias to some extent at the sampling stage itself.

After block selection, propensity scores using standard logistic specification were estimated in order to construct the group of matched treatment and control villages using village-level variables from the pre-intervention period. Variables such as literacy, availability of infrastructure, composition of caste, access to land, remoteness and indicators associated with household amenities, were drawn from the Socio Economic and Case Census (SECC)2013. We used data on other village-level characteristics such as the Population Census 2011. Several variables were identified in consultation with state missions because implementation agencies have recently used these for identification and mobilization of target groups. The SECC data were given priority over the census data wherever data on a characteristic
whether the results are sensitive to the application of equal weights to individual soil properties in this context.

From a methodological perspective, the experience of the MAPS study highlights the analytical value of integrating objective soil measurement into household surveys, while at the same time shedding light on the scalability of the current approach. The adoption of these methods in largescale household surveys, including those conducted by national statistical offices, will likely require, or at least benefit from, more scalable tools, such as in situ soil sensors (including handheld devices) that provide real-time measures of soil attributes during the fieldwork and increase the timeliness of data collection while reducing reliance on laboratories and overall costs. However, these tools require validation in the field, especially for use in smallholder farming systems. A related approach to facilitate the cost-effective scale-up of objective soil measurement in household surveys is through reliance on sub-sampling and imputation. In this case, soils can be objectively analyzed for an intelligently-selected sub-sample of agricultural plots and imputation methods can be leveraged to predict soil attributes for the remainder sample, with a model that is trained on the sample with objective soil measures, complementary survey data (including subjective assessment of soil characteristics) and publicly available geospatial soil data. The validation of this approach can too be a focus of future methodological research. To the extent that more scalable approaches are developed and integrated into recurrent household surveys in lowincome contexts, including longitudinal surveys, the resulting data would not only enhance the scope and accuracy of the research based on these data, but also inform downstream remote sensing applications, including on soil mapping, that would benefit from georeferenced, ground-truth measures of soil attributes.

29
unprocessed foods, supply of solar power, machinery or medical goods, among others, are exempt from VAT. Rates on excise taxes and custom duties vary depending on the good or service<sup>10</sup> . VAT collections in Uganda alone represent 4.3 percent of GDP, almost a third of total tax revenue and almost as much as direct taxes, at 4.7 percent (Figure 2).

_Figure 2. Tax revenue in Uganda_ <!-- Start of picture text --> 30%<br>25%<br>2.6%<br>20%<br>6.9%<br>15% 1.5% 1.9%<br>2.10%<br>3.3% 3.5% 1.9%<br>10% 4.8% 3.6% 5.6% 4.8% 2.9% 4.30%<br>4.3% 4.20%<br>5% 3.0% 2.9% 8.5% 1.8%<br>2.0% 2.7% 2.7% 2.3% 4.30%<br>0%<br>Ethiopia (2010) Ghana (2012) South Africa Tanzania Uganda (2016) Kenya (2015)<br>(2010) (2011)<br>Personal and payroll taxes Corporate taxes Other direct taxes VAT Excises Other indirect taxes<br>% of GDP<br><!-- End of picture text --> _Source: CEQ Institute database, World Bank WDI for GDP 2011 PPP per capita numbers. For Kenya, World Bank (2018a)_ _Note: The data for Ethiopia corresponds to 2010, for Ghana to 2012, for South Africa to 2010, for Tanzania to 2011, and for Kenya to 2015._

# 2.3. Direct taxes
regression for explaining the FFE stipend. This first stage regression included X and a dummy variable for village participation. To be a valid instrumental variable, village participation must not affect child labor or schooling controlling for the variables in the second-stage regressions. Under this identifying assumption and with normally distributed errors, we can consistently estimate the coefficient on the FFE stipend in the probits for child labor and schooling as long as we control for the residuals from the first-stage regression. '° The coefficient on the residuals also provides an exogeneity test.

Given the way the program is targeted, village-level participation is a suitable instrument for participation at the individual level provided one includes a set of geographic control variables for village placement (Ravallion and Wodon, 1998). The set of geographic controls include distances to school; the type of school (govermmental, private, NGO); a series of school quality variables reported in the community survey; land distribution; irrigation intensity; road quality; electrification; distance and time to thana and district headquarters and to Dhaka; distance to various facilities (health care, Banks, governnent agencies); incidence of natural disasters; attitudes to women's employment, education and family planning; average schooling levels of the head and spouse; majority religion of the village; and population size of the village. These were (jointly) very good predictors of program placement. A probit regression of whether the village had the program on the geographic control variables gave a pseudo-R<sup>2</sup> of 0.55 (Chisquare of 91.7 which is significant at the 0.5% level, with 166 observations).

> **10** Datt and Ravallion (1994, Appendix 1) prove consistency for a more general simultaneous tobit model (generalizing Smith and Blundell, 1986, to allow for a censored endogenous variable). The consistency proof for our case is a minor variation. If the FFE stipend was continuous (rather than censored) then our estimation method would be the