rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 10884

# **Abstract**

This paper examines the impact of refugee camp hosting on local communities, specifically the Rohingya crisis in Cox’s Bazar, Bangladesh. It uses remote sensing measurements and panel data to compare areas and individuals at varying distances before and after the Rohingya arrived. The results highlight the complex dynamics of areas that host displaced populations. The paper finds that when the proximity of a grid to the refugee camps increases by 30 km (18.6 miles), night light density rises by 1.7 percent and deforestation expands by 0.02 percent. Land use results align with these findings, showing a decline in dense-open forest and an increase in land covered by grass and crops. The analysis of individual-level data suggests that the Rohingya’s presence manifests in higher job formality, better access to aid, and more food consumption—all largely attributable to the activities of humanitarian organizations. However, their presence is also associated with heightened safety concerns and a higher prevalence of viral diseases such as diarrhea, fever, and cough.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at sandrarozo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
1980s. In contrast, the paper reported statistical results which suggest no meaningful, stable broad money demand relationship in Australia over the sample period. These results implied that while financial deregulation has made the broader monetary aggregates more volatile, given the increasing availability of financial assets with competitive returns, there have been only steady changes in transaction technology in Australia since the early 1980s; therefore, the demand for narrow money has remained stable. An implication is that money remains important for Australia in its use of monetary policy to target price stability. Thus, narrow monetary aggregate can be used to measure excess liquidity for forecasting or predicting its impact on inflation, asset prices, and the output gap.

Capasso and Napolitano (2012) investigated the consistency of the stability of money demand in Italy, before and after the European Monetary Union (EMU) and tried to determine the effect of a change in the currency regime on the monetary aggregates. They estimated the money demand function in Italy using quarterly data from 1977 to 2007, using the bounds testing co- integration procedure proposed by Pesaran et al. (2001). In order to compute the short- and long-run elasticities of the demand for money, they implemented Cumulative Sum (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) stability tests proposed by Brown et al. (1975). The analysis is performed on broader money M2 and M3 also. Their findings revealed significant stability of money demand coefficients<sup>10</sup> in the short- and long-terms. This result applied to both M2 and M3. In order to further investigate long-term dynamics in the coefficients of money demand, Pesaran, et al. also employed Kalman filtering (Kalman 1960). They found that the introduction of the Euro has significantly increased the stability of money demand parameters particularly for M3, and the effects occurred before the introduction of the new currency in 1996 when the Lira was strongly linked to the Euro in preparation to the EMU.

# **_ii. Literature on Money Demand in Egypt_**

With regard to Egypt, ten research papers studied money demand during the past three decades; the most recent was issued four years ago. As part of my research plan, I conducted a

> 10 Income, interest rate and nominal effective exchange rate.

12
|_d = 3_|46.1|38.3|53.2|-14.9*|30.6|24.7|36.0|-11.4*|
|---|---|---|---|---|---|---|---|---|
|_d = 4_|19.9|13.8|25.6|-11.8*|15.4|10.4|19.9|-9.4*|
|_d = 5_|4.3|2.5|5.9|-3.4*|3.8|2.2|5.3|-3.0*|

(*) and (**) mean that the differences are significant at 5% and 10% level respectively.

By contrast, in Togo, inequalities seem to be more considerable for lower levels of cutoff ( _d_ < 3). In fact, unlike in Burkina Faso, deprivation rates decrease more strongly when increasing the cutoff. These findings are consistent with gender inequality in monetary poverty since women appear poorer than men in both Burkina Faso (43.7% versus 40.6%) and Togo (53.6% versus 47.3%), which are equivalent respectively to statistically significant values of -3.1 and -6.3 points of percentage in term of gender difference.

<u>Table 6: Multidimensional poverty rates in Togo</u>

||||Headco|unt H(%)|||MP|I(%)||
|---|---|---|---|---|---|---|---|---|---|
|Level|Cutoff|All|Men|Women|Gender<br>diff.|All|Men|Women|Gender<br>diff.|
||_d = 1_|94.4|91.9|96.4|-4.5*|41.5|38.0|44.4|-6.5*|
|**nal**|_d = 2_|70.1|62.2|76.8|-14.5*|35.5|30.9|39.5|-8.6*|
|**tio**|_d = 3_|34.7|28.0|40.5|-12.5*|21.0|16.9|24.5|-7.6*|
|**Na**|_d = 4_|7.6|6.0|9.1|-3.1*|5.6|4.4|6.7|-2.3*|
||_d = 5_|0.5|0.3|0.7|-0.3*|0.4|0.3|0.6|-0.3*|
||_d = 1_|99.8|99.8|99.9|-0.1|49.5|47.6|50.9|-3.3*|
|**al**|_d = 2_|84.5|80.5|87.5|-7.0*|45.3|42.4|47.5|-5.1*|
|**ur**|_d = 3_|50.7|45.6|54.6|-9.0*|30.9|27.6|33.4|-5.8*|
|**R**|_d = 4_|12.4|10.5|13.9|-3.4*|9.1|7.7|10.2|-2.5*|
||_d = 5_|0.9|0.6|1.1|-0.5**|0.8|0.5|0.9|-0.4**|
||_d = 1_|86.3|82.1|90.6|-8.5*|29.6|25.8|33.5|-7.6*|
|**n**|_d = 2_|48.8|39.4|58.5|-19.2*|21.0|16.3|25.8|-9.5*|
|**rba**|_d = 3_|11.2|6.0|16.5|-10.4*|6.3|3.4|9.4|-6.0*|
|**U**|_d = 4_|0.6|0.3|0.9|-0.6*|0.4|0.2|0.7|-0.5*|
||_d = 5_|0.0|0.0|0.0|-|0.0|0.0|0.0|-|

(*) and (**) mean that the differences are significant at 5% and 10% level respectively.

# **_4.2 Regional decomposition of gender inequalities_**

Gender differences vary from one region to another (see Figures 1 and 2). If we refer to Figure 1, which shows the regional distribution in Burkina Faso, it is clear that the value of the gender gap fluctuates in general between -5 and -15 in terms of percentage points for either the headcount or the MPI. In addition, differences seem relatively higher in least deprived regions such as Centre, Hauts-Bassins, Centre-Ouest and Cascades. Also, inequality is greater for MPI because not only women are poorer than men, but their average deprivation intensity is higher. This intensity seems to be important for poor 22
estimation results from the quantile regressions for sales and employment. We explore one such measure of the intensity of exposure to the invasion shock next.

In addition, the results in Table 4 suggest that manufacturing and other services firms in the 25th percentile of the employment distribution maintained higher employment levels than other firms throughout the study period. As we move up the size categories, smaller, medium-sized, and large firms consistently exhibited higher employment levels than micro firms across the entire employment distribution. This observation suggests that, on average, firms in the manufacturing and other services sectors, as well as firms of larger sizes, were able to maintain relatively higher employment levels compared to micro firms. This finding hints at potentially greater resilience and the ability of these sectors and larger firms to sustain employment during the studied period.

**Table 4: Effect of Russia’s Invasion of Ukraine on Firms’ Employment in Central Asia**

||(1)<br> **mean**|(2)<br> **25**<sup>**th**</sup>**percentile**|(3)<br> **median**|(4)<br> **75**<sup>**th**</sup>**percentile**|
|---|---|---|---|---|
|Effect of the invasion|0.136|-0.112|0.077|0.425***|
||(0.154)|(0.149)|(0.117)|(0.094)|
|Manufacturing|0.082|0.320***|0.077|-0.041|
||(0.086)|(0.072)|(0.104)|(0.124)|
|Retail|-0.098|0.054|-0.146|-0.062|
||(0.086)|(0.065)|(0.101)|(0.125)|
|Other Services|0.001|0.208**|0.077|-0.041|
||(0.087)|(0.098)|(0.092)|(0.120)|
|Small (5-19)|1.110***|1.067***|1.099***|1.301***|
||(0.036)|(0.063)|(0.031)|(0.024)|
|Medium (20-99)|2.306***|2.245***|2.303***|2.505***|
||(0.048)|(0.063)|(0.048)|(0.036)|
|Large (100+)|4.064***|3.498***|4.212***|4.296***|
||(0.298)|(0.526)|(0.055)|(1.058)|
|Constant|1.301***|0.916***|1.309***|1.448***|
||(0.079)|(0.108)|(0.089)|(0.118)|
|Observations|1767|1767|1767|1767|
|R-squared|0.724|0.490|0.507|0.534|

_Robust standard errors are in parentheses. *** p<.01, ** p<.05, * p<.1_

**Varied effect of Russia’s invasion of Ukraine on firms’ performance by firms’ pre-invasion exposure through exports to Russia**

Table 5 reports the estimation results for regression specification (2) using the exported versus notexported to Russia (1/0 firm-level) dummy as the exposure measure that we interact with the invasion effect to capture the potentially varied intensity of the invasion’s effect. We expect the invasion’s effect to be more detrimental for firms with export links to Russia before the invasion because of the sanctions imposed on Russia affecting trade, payment settlement, and financial flows.

The estimation results indicate that firms with export links to Russia had higher sales on average during the studied period. However, following the invasion, these firms experienced a significant drop in sales, with a decline of approximately 180 log points. This drop corresponds to a reduction of around 86 percent 9
38 Table 4: Contributions to Welfare Reduction Index for covered products by different policy instruments,<sup>a</sup> by region,<sup>b</sup> 1980-84 and 2000–04 (percent)

# (a) Production side of economy

||||1980-|84|||||200|0-04|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|
|**All measures**|**54**|**61**|**46**|**58**|**64**|**60**|**38**|**20**|**25**|**23**|**53**|**36**|
|**Border measures**|**48**|**44**|**38**|**43**|**61**|**51**|**33**|**17**|**18**|**19**|**45**|**31**|
|Export tax|25|29|23|28|0|15|16|2|7|4|0|3|
|Export subsidy|4|1|1|1|4|2|3|3|3|3|4|4|
|Import tax|11|12|7|11|57|31|8|12|7|11|41|23|
|Import subsidy|8|2|6|4|1|3|6|1|2|1|0|1|
|**Domestic taxes & subsidies**|**6**|**17**|**8**|**14**|**2**|**9**|**5**|**3**|**7**|**4**|**7**|**5**|
|Production tax on output|5|15|1|12|0|6|4|1|2|1|1|1|
|Production subsidy on output|1|0|1|0|2|1|0|0|2|1|6|3|
|Farm inputnet subsidies|0|1|7|2|0|1|0|2|3|2|0|1|
|(b) Consumption side of ec|onomy||||||||||||
||**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|**Africa**|**Asia**|**LAC**|**DCs**|**HIC**|**World**|
|**All measures**|**47**|**47**|**39**|**45**|**69**|**55**|**33**|**21**|**22**|**22**|**55**|**37**|
|**Border measures**|**46**|**41**|**37**|**40**|**69**|**53**|**32**|**20**|**22**|**21**|**53**|**36**|
|Export tax|14|25|17|23|0|13|12|2|4|3|0|2|
|Export subsidy|3|1|1|1|3|2|2|3|3|3|3|3|
|Import tax|15|13|11|13|65|35|11|15|13|14|50|29|
|Import subsidy|13|2|8|4|1|3|7|1|2|1|0|1|
|**Domestic taxes & subsidies**|**1**|**6**|**2**|**5**|**0**|**3**|**1**|**2**|**0**|**1**|**1**|**1**|
|Consumption tax|1|6|2|5|0|3|1|2|0|1|1|1|
|Consumptionsubsidy|0|0|0|0|0|0|0|0|0|0|0|0|

Source: Authors’ compilation based on data in Anderson and Valenzuela (2008).
<!-- Start of picture text --> Mechanisms - Baseline/Endline - Host<br>—___?<br>Change Baseline to Endline<br>-.2 ie} 2 4 6<br>© Current Needs © Future Needs<br>© Fair Employment © Fair Salaries<br>© Employment Tensions<br><!-- End of picture text --> <!-- Start of picture text --> Mechanisms - Treatment/Control - Host<br>Difference Treat and Control<br>—__»«—____<br>-5 0 aS) 1<br>® Current Needs © Future Needs<br>© Fair Employment © Fair Salaries<br>© Employment Tensions<br><!-- End of picture text --> <!-- Start of picture text --> Mechanisms - Baseline/Endline - Refugee<br>~<br>Change Baseline to Endline<br>-5 (0) 5) 71 1.5<br>® Current Needs © Future Needs<br>© Fair Employment © Fair Salaries<br>© Employment Tensions<br><!-- End of picture text --> <!-- Start of picture text --> Mechanisms - Treatment/Control - Refugee<br>—____»<br>Difference Treat and Control<br>2 te) ED} 4 6 8<br>® Current Needs © Future Needs<br>© Fair Employment © Fair Salaries<br>© Employment Tensions<br><!-- End of picture text -->
<!-- Start of picture text --> 100.0 Ferre one am ae oe eee ccece<br>90.0<br>= 80.0 ieri<br>g 70.0 Me<br>io Se<br>= 60.0 ms,<br>e 44.0 a=<br>S 40.0 a<br>~ .300 ao* =n<br>20.0 ie<br>10.0<br>0.0<br>1 #£ 4 4 5 6 F EF & 20 11 it 13 14 15 16<br>monthly utility bill (U5)<br><!-- End of picture text --> mee——Walawl cc middle-income counties ==" low-income countries <!-- Start of picture text -->
1.6<br>1.4<br>1.2<br>3 1<br>ss 0.8<br>= 0.6<br>0.4<br>0.2<br>0<br>Malawi low-income countries<br><!-- End of picture text -->

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1.6<br>1.4<br>1.2<br>a 1<br>s 0.8<br>= 0.6<br>0.4<br>0.2<br>0<br>Malawi low-income countries<br><!-- End of picture text -->
**Figure 1: Contrast of IELFS 2019–20 and AWMS sociodemographic variables, various rounds**

<!-- Start of picture text --> IE-LFS 2019/20 AWMS - Round 3<br>80<br>54.6<br>70 49.8<br>60<br>50<br>25.1<br>40 17.4 20.0 20.3 20.6 21.7 17.4 21.9<br>30 13.8 13.9 8.4<br>20<br>10 25.0 20.0 20.0 20.0 20.0 20.0 67.0 9.4 15.2 7.8 49.0 20.2 7.3<br>0<br>Location Household welfare Household head education Individual characteristics<br>Urban share Poorest* Quintile 2 Quintile 3 Quintile 4 Richest Uneducated * Primary schooling * Secondary schooling Tertiary schooling * Female share Average Age* Members*<br><!-- End of picture text -->

**Note:** An asterisk (*) indicates that differences in means are significant at 1%. AWMS R2 only collected information on the household head. **Source:** Elaboration based on IELFS 2019–20 and AWMS round.

# The SWIFT-plus approach

Over the past decade, significant advances have been made in the research on survey-to-survey imputation techniques. Lack of household survey data during or after a crisis is not unique to Afghanistan. Not surprisingly, the research on how to fill knowledge gaps on welfare has been growing considerably over the past decade in response to a growing need for real-time welfare monitoring data and the cost and time challenges involved in implementing standard household expenditure surveys. The Survey of Well-Being via Instant and Frequent Tracking (SWIFT) methodology (Yoshida et al., 2015) provides poverty estimates in situations where no expenditure and consumption surveys are available.

SWIFT-plus trains a model to predict household expenditure using data from past household expenditure surveys. It was developed to address two major methodological challenges: model stability over time and model capacity to capture shocks. The model identifies key poverty correlates and uses the model coefficient and errors to impute household expenditures in a survey where no household expenditure data was collected. Additionally, the SWIFT-plus approach integrates Machine Learning (ML) and CrossValidation (CV) steps to improve the stability of the estimated model and expand the list of variables for poverty modeling/data collection to include dummies on consumption of key items.<sup>4</sup>

> 4 See Yoshida et al (2022). The Concept and Empirical Evidence of SWIFT Methodology. World Bank.

5
Table 2: Sources of Household Data

|Country|Survey Name|Years|Original _n_|Final _n_|
|---|---|---|---|---|
|Ethiopia|Ethiopia Socioeconomic Survey (ESS)|2011/2012|3,969|1,689|
|||2013/2014|5,262|2,865|
|||2015/2016|4,954|2,718|
|||2018/2019|7,527|1,996|
|||2021/2022|4,999|1,406|
|Malawi|Integrated Household Panel Survey (IHPS)|2010/2011|3,247|2,250|
|||2013|4,000|2,472|
|||2016|2,508|1,845|
|||2019|3,178|2,330|
|Niger|Enquˆete Nationale sur les Conditions de Vie des|2011|3,968|2,223|
||M ́enages et l’Agriculture (ECVMA)|2014|3,617|1,690|
|Nigeria|General Household Survey (GHS)|2010/2011|4,916|2,674|
|||2012/2013|4,716|2,768|
|||2015/2016|4,581|2,783|
|||2018/2019|4,976|920|
|Tanzania|Tanzania National Panel Survey (TZNPS)|2008/2009|3,280|2,001|
|||2010/2011|3,924|2,013|
|||2012/2013|5,015|1,889|
|||2014/2015|3,352|2,127|
|||2019/2020|1,184|312|
|||2020/2021|4,709|1,564|
|Uganda|Uganda National Panel Survey (UNPS)|2009/2010|2,975|1,883|
|||2010/2011|2,716|1,886|
|||2011/2012|2,850|2,020|
|||2013/2014|3,119|2,190|
|||2015/2016|3,305|1,868|
|||2019/2020|3,098|1,845|
|Total|6 countries|27 waves|105,945|54,237|

_Note_ : The table summarizes the household data details for each country, per LSMS Basic Information Documents.

32
Section J provides concluding remarks.

# **D Background**

In India, limited infrastructure and transport services restrict mobility for both men and women. Still, women frequently experience extra socio-cultural and economic factors that negatively affect their commute patterns (Srinivasan and Rogers, 2005; Tripathi et al., 2017; Alam et al., 2021). Given the sizeable gender wage gap (Duraisamy and Duraisamy, 2016; Deshpande et al., 2018) and the additional barriers in accessing the financial system compared to men (Khera, 2018), paid access to transportation is plausibly harder for Indian women. Besides, the low rate of female usage of public transport might raise a perception problem since more female presence in public transportation makes women feel safer (Sajjad et al., 2017). In a survey of 3,800 students at Delhi University, Borker (2021) found that women are willing to travel 27 minutes more per day or 40 % more than their daily travel time if they can use a perceived safer transport route. These factors put women at a disadvantage regarding access to transport services and infrastructure (Astrop et al., 1996; Dominguez Gonzalez et al., 2020), potentially affecting their participation in labor markets (Patacchini and Zenou, 2005; ILO, 2017; Sajjad et al., 2017; Martinez et al., 2020).

In light of these challenges, the Delhi government introduced a scheme offering free bus rides to all women in the city from November 2019 onward (Kejriwal, 2019). The scheme makes bus travel free for women in all Delhi Transport Corporation (DTC) and Cluster buses. On each ride, bus operators provide a pink ticket to each woman. Afterward, Delhi’s government compensates the bus operators with |10, the equivalent of $0.14 (all currency conversions use the 11/2019 exchange rate), per pink ticket ride (The Economic Times, 2019; Durai, 2021). The program showed an early response: just 20 days after the scheme’s launch, female daily ridership in DTC and cluster buses increased from 33% to 44%.<sup>26</sup>

Spurred by the good reception of the initiative in Delhi, on April 1 and May 7, 2021, Punjab and Tamil Nadu implemented free bus ride schemes for women in their states, allowing free travel in government-owned public buses.<sup>27</sup> From July 2021 to March 2022,

> 26https://shorturl.at/cAFMT, last accessed on May 13, 2024.

> 27In Punjab, these include PEPSU Road Transport Corporation (PRTC), PUNBUS, Punjab Roadways Buses, and City Bus Services, but did not include AC buses, Volvo Buses, and HVAC Buses (Express, 2021) In Tamil Nadu, the free ride scheme includes tickets for the Tamil Nadu State Transport Corporation (TNSTC) ordinary city buses.

4
capital were similar across income groups in 1970. Nearly half of total agricultural capital was composed of fixed capital. Treestock accounted for one-third, and the livestock share was around 20%. By 2000, the differences in the composition of capital were pronounced. In high-income countries, the share of fixed capital did not change, but livestock only accounted for 9% of total agricultural capital and treestock increased to 45%. In middle- and low-income countries, the share of treestock increased to 47% as well. Livestock still accounted for 17% of total agricultural capital, while the share of fixed capital fell to 37%.

# **Comparison of data on agricultural fixed capital with FAO data on tractors**

The new database on agricultural fixed capital has several advantages, in particular the sound theoretical basis for the construction of the series, as well as the broad coverage of fixed capital to include structures and all types of equipment and machinery. From the United Nations’ System of National Accounts (2008), fixed assets are defined as “produced assets that are used repeatedly or continuously in production processes for more than one year.” Tractors represent one component of fixed capital.

The obvious drawback to the new database is the limited availability of national accounts data on gross fixed capital formation needed to construct the stock of fixed capital.<sup>19</sup> In the absence of cross-country data sets on agricultural fixed capital, the most commonly used proxy is FAO data on the numbers of tractors in use. This is a partial measure, in that it does not include buildings, irrigation systems, local infrastructure, as well as other types of machinery, nor does it take account of the varying quality and horsepower of tractors.<sup>20</sup> We compute the correlation between the FAO data series on the numbers of tractors in use and our data on agricultural fixed capital (of which tractors are one component) for each country; the average of the country correlations is 0.43. The median correlation is 0.8, so for half of the countries in our sample, the data series are somewhat correlated. Only 30% of the sample have correlations greater than 0.9, while the correlation was actually negative for seven countries. In Figure 5, we plot the data on fixed capital in agriculture

> 19 As discussed, raw data needed to construct the livestock and treestock measures are available from the FAO for a larger number of countries.

> 20 In some studies, the data on tractors is converted into horsepower equivalents in an attempt to control for the quality differences in tractors (e.g. Craig, Pardey, and Roseboom (1997)).

10