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

According to UNHCR (2020), 85% of the displaced population worldwide live in developing countries, where economic shocks are frequent, and a global crisis can damage more than the developed world (Gourinchas & Obstfeld, 2012). Previous studies suggest that migrants are disproportionately hit by economic downturns, and they might be used as a cyclical buffer to alleviate the consequences of recession (Dustmann et al., 2010). The unequal effect of economic shocks can be more severe for refugees as their employment relationships are commonly informal and in poorly regulated activities. Turbulent economic times may also escalate tensions between host and displaced communities by increasing competition over scarce resources.

The primary focus of forced displacement literature has been on the consequences of forced migration – for either receiving, sending, or displaced populations – and the disproportionate impact of economic shocks in the host country on indigenous and displaced communities is an understudied field of inquiry. This paper will contribute to the forced displacement literature by exploring the unequal impact of economic fluctuations in Iran on Afghan refugees and host communities.

Using representative household-level surveys, we study how the economic shocks due to Iran’s conflict with the United States in the 2010s affected inequality and social cohesion between Afghan refugees and Iranians. We examine how these shocks affect inter-community and intra-community inequalities and whether bad and good shocks have asymmetric effects on welfare and social cohesion. To measure social cohesion between the two communities, we utilize information on aid received by refugees from other households. For estimation, Afghan refugees are compared once with all Iranians and once with the matched sample of Iranians with similar characteristics.

We find that during turbulent times in Iran, Afghan refugees exit the country more often, spend less for consumption, and disproportionately receive less aid from other households than similar Iranians. The last evidence suggests that economic shock weakens trust as a dimension of social cohesion between refugees and host communities. Our findings show that the effects of bad and good shocks are not symmetric, and the economic
Figure 4: Language requirements and share of migrants per occupation and average by type of migrants Panel a. Language requirements and share of migrants Panel b. Language requirement by type of migrant <!-- Start of picture text --> 911 0.1<br>0<br>941<br>912 932 -0.1<br>921 816 512 -0.2<br>933 513<br>713 521931818515832711 532 343 141 221 -0.3<br>815752753834721751962712814523812821731813 524 961722754611514313723432531833741 441 312522732516742351315511133431352142325541342321311422215252332341333322331250112265132251334412216111214241143121226134413211243242232222122213263235 264231 -0.4-0.5-0.6<br>831 421 411 261233<br>612 314410335 234 -0.7<br>910 750810710930830720613520610 510740430310340540 140330130 210120240220 260 230 Natives Migrants Migrants Migrants<br>high low<br>-2 -1 0 1 2 3 language language<br>Standardized language intensity per occupation command command<br>.4<br>.3<br>.2<br>.1 in average occupation<br>Share of foreign workers per occupation<br>Standardized language requirements<br>0<br><!-- End of picture text --> Source: Own elaboration based on the EU-LFS 2018 and the ad-hoc migration module of 2014. Note: The size of the circles represents the share of each occupation in the total employment in the EU. 95% confidence intervals are included for comparison. Panel b shows language intensity of in the average occupation in standardized terms, that is, the original language intensity minus the mean, divided by the standard deviation.

37
). Zimbabwe, on the other hand, has a higher income level than Madagascar, but is observed to have implemented at most one household consumption survey during the period 1990-2012; the corresponding figure for Botswana is two

> is that, since the SPI is the average of a larger number of indicators than the SCI, it is more likely to have a normal distribution by the central limit theorem (see, e.g., Casella and Berger (2002)).

> 27 Unless otherwise noted, we use the World Bank’s (2018) income classification for all countries.

32
set of 20 household accounts integrated into the social accounting matrix. Of these 20 households, ten are rural and ten are urban, ranked according to income. Due to some problems with the consistency of the household data, however, we employ one representative household in this model.

.

# **Trade Data by Regional Partner and Sector**

To obtain the shares of imports and exports from the different regions of our model, we used trade data for 2007 obtained from WITS access to the COMTRADE database. The regions of our model are Kenya, the European Union, the East African Customs Union plus COMESA and the Rest of the World. For the European Union, we took the 27 member countries as of 2007. In appendix A, we calculate and report data for the East African Customs Union and COMESA separately. For the East African Customs Union, we took Tanzania, Uganda, Rwanda and Burundi. Excluding those East African Customs Union countries that are also COMESA members, COMESA includes Comoros, Democratic Republic of Congo, Djibouti, Egypt, Eritrea, Ethiopia, Libya, Madagascar, Malawi, Mauritius, Seychelles, Sudan, Swaziland, Zambia and Zimbabwe.<sup>22</sup> Trade shares for the ―Africa‖ region in our model are the sum of East Africa Customs Union plus COMESA. Rest of the World is the residual.

We mapped two digit sectors from the COMTRADE database into the sectors of our model. The exact mapping is defined in appendix A. We used Kenya as the reporter country for both exports and imports. Results for both exports and imports are reported in tables A2 and A3 of appendix A.

# **Tariff Data**

**Tariff and Sales Tax Data** .  We started with MFN tariff rates at the eight digit level taken from

the website of the Kenyan government: <u>www.kra.go.ke/customs/customsdownloads.php. These tariff</u> rates were then aggregated to the sectors of our
# 1   Introduction

How do feedback mechanisms affect users’ demand for services? Empowering users to demand better services has become a keystone of social programs. International development organizations such as the World Bank (WB) advocate the use of participatory users’ surveys to enhance service provision in their projects.<sup>1</sup> The idea is that users are best placed to monitor service delivery, and that their involvement in giving feedback will lead to higher empowerment, thus closing the accountability loop (Banerjee et al., 2010; Björkman and Svensson, 2009; Ringold et al, 2012). The spread of users’ feedback tools is not restricted to public interest programs; customer surveys are widely used by the service industry as a way to retain customers and boost satisfaction.

Despite this enthusiasm for beneficiary monitoring, little is known as to whether establishing feedback loops can in fact affect users’ demand for services. Even less explored are the channels through which feedback mechanisms increase demand for services. Typically, theories of change associated with users’ feedback loops assume that satisfaction and demand for the service improve through both supply-side effects, resulting from improved accountability, and demand-side effects, as users are empowered to demand better service (World Bank, 2003). However, there is little evidence on the respective roles of pure monitoring relative to users’ empowerment in improving service delivery.

> * Development Economics Research Group, World Bank. Maria Jones, <u>mjones5@worldbank.org, and Florence</u> Kondylis, fkondylis@worldbank.org. We thank Jasmeen Batra and Cindy Sobieski for superb management of all field work, and Paula Gonzalez for excellent research assistance. We are grateful to the Ministry of Agriculture and Animal Resources in Rwanda and the staff from the Land Husbandry, Water Harvesting, and Hillside Irrigation Project for their leadership in this work. We thank One Acre Fund Rwanda for their hard work in implementing the feedback interventions and granting us unlimited access to their data. The Global Agriculture and
Table 4: Determinants of Migration Flows Across Libyan Provinces

||**Migration**<br>(Binary <br>|**flows in 2018**<br> Variable)<br>|
|---|---|---|
||**STERGM**<br>**Formation**<br>**(1)**|**STERGM**<br>**Dissolution**<br>**(2)**|
|Night lights|1.7785|-1.0517*|
|(1 = moving to a province with equal or more night lights)|(1.2099)|(0.6437)|
|Log distance|-0.3475<br>(0.7568)|-|
|Conflicts|-1.5842*|-0.2631|
|(1 = moving to a province with equal or more conflicts)|(0.9157)|(0.6216)|
|Number of migrants|0.4434|0.1486|
|(1 = moving to a province with equal or more migrants)|(0.9509)|(0.6029)|
|Intransitivity<br>(province_i_and_j_send migrants to province_z_, but they do not<br>share any migrant flow with each other)|0.2910<br>(0.2245)|-0.6251*<br>(0.2504)|
|Edges|-2.6633<br>(5.0537)|1.5719<br>(0.4939)|
|Number of Observations|342|342|
|R2|-|-|
|Akaike Information Criterion|63.26|97.39|

Source: International Organization for Migration Displacement Tracking Matrix.

Note: Separable Temporal Exponential Random Graph Model (STERGM) estimated coefficients and standard errors (
# **3.2 Empirical Facts**

In this section, I discuss three empirical findings that show a negative relationship between informality rates and the accessibility of formal jobs in Mexico City: 1) informal workers are more sensitive to commuting costs and spend less time commuting; 2) informal workers are located in areas in which they have poor access to formal employment; and 3) informality rates decrease with transit improvements that connect informal workers to formal employment.

## **3.2.1 Cross-sectional Variation**

**_Finding 1:_** _Informal workers spend less time commuting and work closer to their home relative to formal workers._

To reach the first finding, I use the 2015 Intercensal Survey. With this data, I observe the residence and workplace and average commuting time of each worker at the municipality level. Exploiting cross-sectional variation, I compare the average commuting time and the workplace decisions of informal vs. formal workers. I adopt the standard definition of informality based on the contractual relationship of the worker, I also restrict the sample to individuals who worked the week before the census interview. I run the following linear probability model to test whether informal workers spend less time commuting: where _yi_ is a dummy variable that takes the value of 1 if individual _i_ commutes to a different municipality than the one in which he/she resides, whether he/she works in the central business district (CBD) of Mexico City, or whether their average commuting time is within some window of time (i.e., 16 to 30 minutes); _Xi_ is a vector of individual characteristics that includes age, education, gender, relationship to head of household, and a dummy variable indicating whether the individual has an African or indigenous background; _γl_ ( _i_ ) and _γn_ ( _i_ ) are origin and destination fixed effects; _γm_ ( _i_ ) is a transportation mode fixed effect to compare informal vs. formal workers that use the same transportation mode; and, _εi_ is the
# Household Use of Bottled Gas for Cooking: Evidence from Sub-Saharan Africa*

Masami Kojima<sup>1</sup> and Xin Zhou<sup>2</sup> Keywords: LPG, household energy, fuel stacking, abandonment JEL Codes: D12

* The authors are grateful to Koffi Ekouevi, Bryan Koo, and Alisha Pinto, all of the World Bank, for useful comments; Franck Adoho, Nadia Belghith, Tom Bundervoet, Habtamu Fuje, Wendy Karamba, Zakaria Koncobo, Martin Mba, Carolina Mejia-Mantilla, Moritz Meyer, Alvin Ndip, Utz Pape, Marco Ranzani, Aly Sanoh, Manohar Sharma, Emmanuel Skoufias, Victor Sulla, Tomomi Tanaka, Aibek Uulu, and Ayago Wambile, all of the World Bank, for providing household survey data; and the Energy Sector Management Assistance Program of the World Bank for funding the study.

> 1 Corresponding author, The World Bank. Email: mkojima@worldbank.org. 2 Consultant to the World Bank.
Table S2: Effect of Extending DMSP-OLS Baseline with More Recent VIIRS Data.

||ln(GDP)<br>(1)|ln(GDP)<br>(2)|ln(GDP)<br>(3)|ln(GDP)<br>(4)|ln(GDP)<br>(5)|ln(GDP)<br>(6)|
|---|---|---|---|---|---|---|
|ln(lights/area)|0.055***<br>(0.017)|0.169***<br>(0.050)|0.218***<br>(0.049)|0.228***<br>(0.030)|0.124***<br>(0.028)|0.109***<br>(0.027)|
|PWT Version|10.0|10.0|10.0|10.0|10.0|10.0|
|Stray Light-Corr. VIIRS|✓|✓|✓|✓|✓|✓|
|Top 3 Clusters (City Core)||✓|||||
|Stable Lights (VIIRS, ‘14-‘20)|||✓|✓|✓|✓|
|Inter-Calibrated (OLS, ‘92-‘13)||||✓|✓|✓|
|Satellite Average (OLS)||||||✓|
|Satellite Fixed Efects||||✓|✓||
|Observations|1,194|1,188|1,194|7,363|3,399|2,752|
|Countries|180|179|180|180|180|178|
performance variable that enters the financial constraints regressions that use data from all three years. Following Brown et al. (2011), we also include a proxy for the availability of internal finance (a dummy variable indicating that the firm did not apply for a loan within the past year because it had no need for external finance).<sup>19</sup> We expect

> 19 We use a different variable than in Brown et al (2011), who use the percent of working capital financed with internal funds.  We cannot use the same variable as in Brown et al (2011) because this question was not included in the 2008 BEEPS survey.  A separate variable was included in all three rounds asking about the percent of  fixed investment financed with internal funds.  This second question, however, was only asked of firms that did invest. Therefore  a large number of firms (about one-third) are excluded.  Moreover, this exclusion is not random.  That is,

15
Informel, which was collected between May and August 2016 (ENSESI 2016). Both surveys include detailed agricultural modules, with production and input data at the plot-crop level, allowing for comparability across the two years.

The third contribution of our paper is the decomposition of gender gaps along the productivity distribution, offering insights into the drivers of the gender gap for both poorer and richer farmers. We first use the traditional Oaxaca-Blinder technique employed in the gender wage gap literature, based on the gender of the household head, to estimate: (i) the proportion of the gender productivity gap that results from differential characteristics of household heads, land, and unequal access to productive inputs (endowment effect); and (ii) the proportion that results from unequal returns to the previous components (structure effect) (Oaxaca 1973; Blinder 1973). This method allows us to identify the main drivers that explain the gender differences in productivity, and changes in these drivers between 2008 and 2016. We then use Recentered Influence Function (RIF) regressions proposed by Firpo et al. (2009) to identify how the previous results change along the productivity distribution.

A large and significant unconditional gender difference in productivity of 21 percent favoring male-headed households is found in 2008. The gap increases to 31 percent once introducing region fixed effects and controlling for household and farm characteristics. It is attenuated – dropping by 4 percentage points – when we control for whether any export crops are farmed by the household. When the analysis is conducted separately for food and export crops, the productivity gap within food crops is over twice as large as that within export crops (40 vs. 17 percent) suggesting that the gender gap in agricultural productivity is closely related to occupational segregation between crop types.

Between 2008 and 2016, we note a large reduction in the gender gap between female- and male-headed households. The unconditional gender gap drops to 18 percent in 2016, while the conditional gender gap drops to 21 percent: a 32 percent reduction over 8 years. Further
Consistent with other studies, adequate housing seems to be a higher priority for female-headed HH, particularly if incomes rise beyond the median income (e.g., UN-Habitat 2014). Femaleheaded households may also be more likely to use their house as an informal business, a contributing factor explaining their better housing adequacy outcome.

Next, we disaggregate by education of the head of household.<sup>17</sup> Within the sample of 64 emerging economies, the difference in terms of housing adequacy between high educated and low educated HHs is most pronounced in Burkina Faso, where 89 percent of educated HHs have adequate housing compared to only 41 percent of HHs with heads having below secondary education. Burkina Faso HHs with low educated HH heads have a 40 percent likelihood to live in adequate housing.

We also find housing adequacy to vary by occupation of the head of HH. HHs where HH heads are employed in the agriculture sector are significantly more likely to have inadequate housing compared to their peers in the services or industry sector.<sup>18</sup> The difference between employees in the agriculture sector – a highly informal sector – and the service sector is most pronounced in Angola, Zambia, and Nicaragua.

Differences in terms of likelihood for adequate housing are also observable within the three biggest cities / provinces in Brazil, India, Indonesia, and Nigeria. Differences are most pronounced in Nigeria. Overall, only about 55 percent of HHs live in adequate housing conditions. About 87 million people in Nigeria live in inadequate housing. Within Nigeria’s three biggest cities, 7 million people in Kano, 3.8 million in Lagos, and 4 million in Kadua live in inadequate housing conditions. In contrast, in Brazil, there are almost no differences between the three largest cities. Overall, about 10 million people across the country are currently in need for adequate housing. Brazil’s HH survey is one of the few surveys that provides information on ethnic origin. The difference between indigenous groups, Afro-Americans
PPA that was hailed as one of the cheapest tariff rates in Sub-Saharan Africa, at US$2.526 cents/kilowatt hour (kWh) over 25 years (Ayele et al. 2021). However, none of the projects have yet become operational due to a range of macroeconomic issues including risk of foreign currency availability.

_Strong regulators:_ Based on the Global Power Market Structures database, majority of countries established an energy regulator between 1991 and 2010 (Table 3). During this period 125 countries transitioned 20
similar with an average absolute difference between the modalities of 4.75 percentage points. This is less than one-third of the average difference between the modalities in the questions that were more sensitive.

Furthermore, the attrition patterns are also consistent with the respondents in the internet-based surveys believing in the anonymity of the modality. Respondents were offered one question at a time and had to provide a response before being posed the next question. Overall attrition rates were not systematically different for sensitive and nonsensitive questions, which we might expect if respondents did not believe their responses were anonymous. Furthermore, attrition rates at each question were nearly identical in regions controlled by the de facto authorities in Sana’a (DFA) and the internationally recognized government (IRG) despite the fact that some of the questions were potentially more sensitive in DFA-controlled regions.

However, the ability of novel internet-based surveys to effectively elicit sensitive information relies on two critical issues. First, it is important to characterize how internet users differ from the general population to properly analyze and caveat the results. The results illustrate that the internet surveys are biased in exactly the ways one would expect- the internet respondents are slightly more educated and better off economically than mobile phone respondents, who themselves are slightly better educated than the general population. However, all results are qualitatively identical when restricting the sample in both the internet and the mobile phone surveys to be more comparable.

Second, it is important to investigate the degree to which the low response rate in the internet survey and the potentially different motives for non-response than in other modalities might be biasing the results. Although we cannot fully reject the possibility that there is unobserved sample selection, the results are consistent with the sample selection not being different from more commonly used modalities. The answers to nonsensitive questions are similar between the internet and mobile phone survey- both overall and when making the internet and mobile phone samples more comparable demographically; and the internet surveys are also able to identify many of the major ways in which the conflict has impacted
however, give an incomplete and potentially biased picture of the organization of GPNs since many multinationals outsource a large portion of their manufacturing activities to external firms (Gereffi, 1999; Gereffi, Humphrey and Sturgeon, 2005; Swenson, 2006; Bonham, Gangnes and Van Assche, 2007; Desai, 2009). If these outsourced activities are more dispersed geographically than the assets owned by the multinational firms, the existing estimates on the dispersion of upstream activities will be biased.

In this paper, we will exploit a unique data set that allows us to overcome some of the shortcomings in the existing literature. Specifically, we will exploit a data set collected by the _General Administration of Customs of the People’s Republic of China_ on China’s 7
.2|71.4|
|Exporter|74.9|37.3|25.8|27.3|55.8|70.4|48.6|
|Two-way|72.3|100.0|46.6|74.6|100.0|89.3|80.5|
|**Foreign Status**||||||||
|Domestic|52.6|21.4|23.3|23.2|44.3|72.3|39.5|
|Foreign|88.3|60.7|16.1|20.9|67.7|76.9|55.1|

Source: Author’s own elaboration from enterprise surveys _Younger firms are not more connected to the internet compared to older firms._ Regarding firm characteristics, internet use and computer use are highly correlated: larger and more internationalized firms are more likely to adopt internet. One small difference is that middle-aged firms tend to adopt more internet use. In general, we expect that there is a propensity for younger firms to adopt more often new technologies such as internet; however, this pattern is not observed in these African countries. One possible explanation might be the higher cost of internet access. Even for more developed countries like OECD members, costs remain an important barrier for internet adoption as reported in OECD (2004). When looking at firms’ size,

> 13 More than 90% of Mexican (2008) and Turkish (2010) businesses with over 10 employees has access to internet, according to OECD (2012).

17