rafmacalaba/datause-encoder-data huggingface dataset:
# **4 Research Design**

In this section we describe the study’s data sources, the key variables and how we construct them, and our empirical strategy. Our study relies on spatial and temporal variation of refugee settlement in Uganda from neighboring countries.

## **4.1 Parish-Years as Units of Analysis**

Our main unit of analysis is parish-years. Parishes (N = 5,133) are the smallest administrative unit above the village level; they typically encompass an average of five villages. Our study period covers five years: 2001, 2006, 2011, 2016, and 2020. Since the arrival of large numbers of South Sudanese refugees began in 2014, we have several prior years to compare periods when parishes hosted moderate numbers of refugees with the much larger numbers in 2016 and 2020.

Over the past two decades, the number of parishes in Uganda has increased from 5,238 in 2002s census to 7,241 in 2014s census to over 10,000 today (2016). This administrative fragmentation means that our units are not stable over our study period. In a major contribution, we constructed an original parish dataset to facilitate research throughout this time period.<sup>12</sup> We use shapefiles, census data, matching techniques, and manual corrections to ensure that we have the same administrative units over time, using 2001 parish boundaries as the constant unit throughout.

We do not include all parishes in the main specification. Given that we use a continuous measure of refugee presence, it is not _a priori_ clear whether a greater refugee presence matters beyond a certain radii. We therefore test the sensitivity of our findings to different sample radius cutoffs: parishes within 100km, 150km, 200km of any refugee settlement as well as all Ugandan parishes. Here we present results with the 150km cutoff as our main specification; SI Section S4 reports the results with the other cutoffs.

## **4.2 Main Independent Variable: Refugee Presence**

For our main independent variable of interest, we create a measure of refugee presence for each parish-year. We obtained shapefiles of refugee settlements over time, and collected population data for each settlement in our study years from UNHCR reports and with the help of UNHCR Uganda country staff. Figure S4 in SI Section S1.2.1 shows which settlements are open and their population levels across the study years.

> 12For details, see Section S1.1 in the Supplementary Information (SI), https://osf.io/sv92q/.

13
# **"RENT OBRING" IN TEE ULTI-FIBR3 ARRANGUNENT.**

# Theory and Evidence fro- US Apparel<sup>**!mpJrts**from Bong</sup><sup>**Kong**</sup>

# **by**

# Refik Ersan, Kala Krishna **and** Ling<sup>**Hui**Tan*</sup>

# TABLE OF CONTENTS

|||PA|
|---|---|---|
|1.|INTRODUCTION|1|
|2.|THE BASIC MODEL|6|
||2.1<br>Perfect Competition<br>2.2<br>Market Power onthe<br>Seller's Side:<br>"Rent Appropriation"<br>2.3<br>Market Power on the Buyer's Side:<br>"Rent Sharing"|6<br>6<br>10|
|3.|THE DATA|15|
|4.|HONG KONG'S TEXTILE QUOTA SYSTEM|17|
|5.|TESTING FOR RENT SHARING: HOMOGENOU5 GOODS|20|
|6.|ALLOWING FOR A COMPOSITION EFFECT|29|
|7.|ALLOWING FOR DIFFERENTIATED<br>PRODUCTS|36|
|S.|DETERMINANTS<br>OF THE US-HONG KONG PRICE DIFFERENTIAL|42|
|9.|CONCLUSIONS|45|
|END N|OTES|48|
|REFER|ENCES|51|
|DATA|APPENDIX<br> <br>|i|
||1.<br>Data Source and Product Group Definitions||
||2.<br>Estimations<br>for Missing Observations<br> <br>|xv|
||2.1<br>Production data|xv|
||2.2<br>Import data|xv|
||End Notes For Data Appendix|xvii|

*Ref ik **Ersan, the** World Bank; Kala<sup>Krishna, Harvard University</sup><sup>**and**RBER; and Ling</sup> Bui Tan, Harvard University. We would like to thank **Ronald Chan,**<sup>**Carl Hauilton,**</sup> Paula Bol-es **and** Yun-Wing Sung for providing<sup>us with valuable data; Philip</sup><sup>**Iwagel**</sup> for helpful coments; **and Monique** Skrusany **and** Christopher<sup>**Holmes**for</sup><sup>**excellent**</sup> research assistance.
Before we discuss our findings in greater detail, we briefly summarize major findings from our econometric analysis. Later, we compare our findings in this section with our projection of country-level CO2 emissions based on different subsidy and tax scenarios, using the WB-IMF tool (CPAT). From the econometric analysis, the following are the key findings:

**1. Oil subsidies among MENA oil producers strongly and positively impact higher CO2 emission.**

**2. This effect seems to work through the energy consumption path.**

**3. Even after the consumption effect is controlled for, there remains a direct net effect of subsidies on CO2, likely arising from other sources such as manufacturing.**

**4. Comparing three groups; all 41 countries, only MENA countries, and only MENA oil producers, the adverse effect of oil subsidies on CO2 emission matters** **_only_ for MENA oil producers and not the other two groups.**

**5. Flaring contributes to CO2 emission for MENA oil producers.**

**6. Oil subsidies do not have a significant effect on short-run or long-run economic growth.  Thus, reducing subsidies does not adversely impact economic growth. This is true for all countries, for MENA, and for MENA oil exporters. Single country projections from CPAT (below) are consistent with these findings in the long run but differ in the short-run, probably because of the select nature of our single country analysis and differences in the methods used.**

# **_4b. Summary of Country Projections (Section 4)_**

**1. Focusing on several individual MENA countries and comparing the policy of removing fossil fuel subsidies with imposing a carbon tax, in terms of projected CO2 reductions, we find that in general when the carbon tax is modest, removing fossil fuel subsidies is more effective, but when the carbon tax is large, it may dominate or tie with removal of subsidies, depending on the country.**

**2. In general, when subsidies are larger, it takes a larger carbon tax to yield equivalent CO2 reduction. Thus, in general, oil producing MENA countries that experience large subsidies have equivalent carbon tax levels that are larger than those of non-oil MENA countries.**

14
a technology, such as 2G, 3G or 4G, by intersecting the estimated site locations with the coverage map polygons from the global Mobile Coverage Explorer (Collins Bartholomew, 2019).

To ensure the analysis is logical, certain areas need to be excluded to avoid overestimation. Firstly, all statistical areas with a name identifier containing ‘lake’ are excluded, to prevent the model overbuilding infrastructure on large water bodies. Secondly, for the vast Saharan desert (covering over 9 million km<sup>2</sup> ), existing statistical boundary products often diverge in their zoning of these regions, with some statistical areas having diameters over 1 million kilometers. Therefore, for the northeastern part of Mali, where statistical boundaries differ considerably in size from other regions being assessed, nine statistical areas are excluded, constituting only 0.4% of the modeled population. If these areas are not excluded, terrestrial investment costs are overestimated. Other analyses exclude the hardestto-reach 10% to 20% of the population, assuming satellite will be the only option to connect in these areas (International Telecommunication Union, 2020), so despite these exclusions this analysis still assesses universal broadband reaching 99.6% of the total population.

To estimate baseline capacity both current and future spectrum bands are used for the assessment period. Average downlink spectrum portfolios for a hypothetical MNO are identified for Côte d’Ivoire (3G: 15MHz@2100MHz and 4G: 10MHz@ 800MHz), Mali (3G: 10MHz@2100MHz and 4G: 10MHz@700MHz), Senegal (3G: 10MHz@1800MHz and 2100MHz, and 4G 10MHz@800MHz and 1800MHz), Kenya (3G: 20MHz@1800MHz and 4G: 10MHz@700MHz and 800MHz), Tanzania (3G: 10MHz@1800 MHz and 2100MHz and 4G: 10MHz@ 700MHz and 1800MHz) and Uganda (3G: 10MHz@1800 MHz and 2100MHz, and 4G: 10MHz@800MHz and 1800 MHz) using available country information (Frequency Check, 2021). To reiterate, these bands constitute an average MNO spectrum portfolio, therefore representing a fraction of the total nationally available spectrum portfolio for each country.

The backhaul for the sites in each region is estimated based on statistics reported by GSMA on the existing composition of technology types by global region (GSMA, 2019b). For Sub-Saharan Africa the 16
**MONGOLIA'S INFORMAL SECTOR -**

**Appendix B:** Methodologies and Descrptive Statistics

**46**

# **Generalized Informal Sector Survey Instrument**

<!-- Start of picture text --> Name of Researcher:<br>a. District surveyed ] b. Gender M F [2] c. Activity [3]<br>d. Day surveyed () © (D (a) i) © (7)[41 e. Age [5]<br>f. Hour surveyed [6] g. Married? y N [7]<br>h. Education: OD (Z (a ) [8]<br>primary secondary vocational higher<br>18. Do you have another job or source of income in addition to this activity? Y N 19]<br>If yes is the other job in [10)<br>O) state<br>(a) state enterprise, private company, cooperative, partnership, or public organization<br>(3) another form of self- or family-employment<br>If yes, is the job mentioned above from () active work, ( pension, ( leave? (I) ) (M [11]<br>If yes, what is your average monthly income from your other job? [121<br>If yes, how many hours per week do you work at your other job? [13]<br>19. Is your activity registered with the duureg? Y N [14]<br>20. When the duureg administration asks if you are employed, how do you answer? Y N [15]<br>If you respond that you are not employed, what do you give as the reason? (D M GD 0 ® [t6]<br>(I illness, or watching a | because of | not finding | fired, or the organization | other<br>child, sick or old person seasonal work professional work doesn't work any more l<br>If you respond that you are not employed, have you actively looked for a job in the past month? Y N 117)<br>If you respond that you are not emploved, which of the following is correct? (0) ® [18]<br>(D I am currently registered at the employment regulation office.<br>CD I was registered in the past, but I am not registered now.<br>CD I've never been registered at the employment regulation office.<br>21. How much money do you eam on an average day after subtracting all of your costs? [19]<br>22. How many hours do you work on an average day? (Same day as the previous question.) [20]<br>23. How many days do you work in an average week? (I) (a) () @) ) 1211<br>24. How many months of the year do you engage in this activity? (1- 12) [22]<br><!-- End of picture text -->
Figure A1 plots the sector composition and the respective sales and purchase channels of firms covered in these administrative records. Manufacturing and wholesale and retail firms together account for almost half of the sales in the tax records. The graph on the left in Figure 2 shows the geographical dispersion of formal firm headquarters, which shows that many firms concentrate in Nairobi and Mombasa.

Figure A2 plots the share of total observed firm-to-firm links attributed to different groups of firms, distinguishing by size, sector, and location. Panel A shows that aside from retail, wholesale, and manufacturing, business services account for a substantive proportion of supplier linkages. Mining firms account for the least number of both supplier and buyer links. Panel B shows that firms in Nairobi and Mombasa account for more than three quarters of all buyer and supplier links. Panel C shows that firms in the top sales quartile account for a disproportionately large share of total links, especially as suppliers. Aside from the statistics plotted in Figure A2, 61% of total links are formed among firms located in the same county, 17% of links with firms within the same sector, and 45% among firms in the same sales quartile.

# **2.2 Data on Informal Activity**

Informality in firm-to-firm transaction data can either arise because firms themselves are not registered (extensive margin) or because transactions of registered firms are not recorded (intensive margin). Not being registered as a firm or not recording a transaction in turn can either be the result of non-compliance or because of exemptions (e.g. for small firms).<sup>12</sup> To obtain an updated distribution of overall (formal and informal) economic activity that we can compare to the administrative data, we draw on a series of data sources listed in Table 1. Importantly, we not only measure informality as the gap between overall economic activity and what is captured in the administrative data, but additionally rely on measures generated independently of the administrative records. This helps us to rule out the possibility that our measures of informality capture idiosyncratic patterns that are specific to the VAT system.

Throughout this paper, we draw on three types of measures for economic activity: employment figures, the number of firms, and value added (i.e. the difference between sales and purchases). Our preferred measure of informality uses formal sector employment as a share of total employment, drawing on a comprehensive labor force module in the 2019 population census (KNBS, 2019). Alternative measures of informality based on the number of firms rely on estimates of the universe of businesses in KNBS (2016),<sup>13</sup> while a value-added based measure utilizes estimates

> 12See Appendix A.2 for a detailed discussion of these margins and why administrative data may not fully capture economic activity.

> 13KNBS (2016) obtains information on the number of licensed businesses from county governments and estimates the number of unlicensed businesses based on household survey data.

8
than 0.5% of Chinese overseas lending since 2000 and their average volume is just US$ 12.9 million, with a median of US$ 6.3 mn (Horn et al. 2021). The loans are canceled regularly as part of China’s development aid program, often in the context of high-level diplomatic visits and largely independent of the economic cycle of the debtor country (Gardner et al. 2020; Acker et al. 2020; Appendix I).

To place Chinese debt restructurings in the broader context of debt workouts, we compare our data with a chronology of credit events involving private or other official creditors. For private external creditors we use the updated database of sovereign debt restructurings with private external creditors (bondholders and banks) by Asonuma and Trebesch (2016), which covers 192 restructurings between 1975 and 2021. To add restructurings with other official creditors, we use data on the Paris Club deals from Cheng et al. (2019), which we update to bring the total tally of Paris Club restructurings to 460 cases from 1956 to 2021.<sup>6</sup> It is important to note that there is no systematic database that covers debt restructurings with official creditors outside of Paris Club umbrella. To date, restructurings of “South to South” loans remain largely undocumented, despite evidence that such lending is on the rise (Cerutti et al. 2020; Broner et al. 2020). More details are provided in Appendix I.

# **3. China’s overseas loans and “hidden defaults”:  Stylized facts**

**_Frequency and incidence._** Figure 1 reveals that restructurings of China’s overseas loans now account for a major fraction of sovereign credit events in developing countries. Since 2008, we record 71 restructuring operations involving Chinese loans, but only 21 on external bonds or loans involving private creditors. The high number of recorded Chinese loan restructurings poses an important knowledge gap, given that relatively little is known about these events. Defaults on Chinese creditors seem to fall through the cracks of global credit reporting. Schlegl et al. (2019) conclude that sovereign bond defaults are “loud” and often trigger headline news and sharp credit rating downgrades. In contrast, defaults on banks, particularly Chinese state-owned banks, typically occur “silently", without much international coverage.

The lack of reporting by rating agencies is puzzling, not least because China’s lending amounts are so large and China Development Bank (CDB) or ICBC emphasize that their overseas activities are commercial in nature. If we take CDB and ICBC self-declarations at face value, recent Chinese restructurings with Angola, Ecuador, Venezuela and Zambia were

> 6 As with Chinese restructurings, we exclude 33 Paris Club restructuring agreements under the DSSI with countries not classified to be “at high risk of” or “in debt distress” (World Bank and IMF in 2020-21).

6
# **ENHANCING THE OUTREACH AND QUALITY OF THE RURAL REMITTANCE INFRASTRUCTURE**

## **I. Introduction**

_Migrant labor remittances into Sri Lanka are significant and rising._ At the end of 2004, recorded remittances had increased to US$ 1.3 billion (Table 1). Together with Bangladesh (US$ 3.4 billion), India (US$ 23.0 billion), and Pakistan (US$ 2 billion), Sri Lanka was among the 20 largest recipients of remittances, collectively making the South Asia region the second largest regional recipient of remittances in the world after Latin America and the Caribbean.

**Table 1  Estimates of Workers’ Remittances Channeled through the Formal Financial Infrastructure**

**(US$ Billion)**

||1999|2000|2001|2002|2003|2004|2003<br>(per capita US$)|
|---|---|---|---|---|---|---|---|
|Total|10|16.0|15.9|21.4|25.9|31.9||
|Bangladesh|1.8|2.0|2.1|2.8|3.2|3.4|23.2|
|India|11.1|11.7|11.1|13.7|17.4|23.0|15.8|
|Pakistan|0.1|1.1|1.5|3.6|0|2|27.0|
|**Sri Lanka**|**1.0**|**1.2**|**1.2**|**1.3**|**1.3**|**1.3**|**67.7**|
|Sri Lanka: as percent GDP|6.8|7.1|7.5|7.9|7.1|7.1||

_Source: World Bank (2005)_ Significantly, remittances are larger<sup>_1_</sup> and more stable<sup>_2_</sup> than foreign direct investment (FDI), and on a per capita basis, workers’ remittances to Sri Lanka are the highest in South Asia (Figure 1). In recent times, remittances to Sri Lanka have exceeded FDI inflows by 2 to 3 times; more than doubled net receipts of foreign assistance; and reached close to 25 percent of export earnings, second only to the garment industry and ahead of tourism and tea -- the island’s traditional export industry. They account for 7 percent of gross domestic product. At the microeconomic level, about 10 percent of households are recipient of overseas and domestic remittances. Overseas and domestic remittances constitute as much as 20.7 percent and 9.1 percent, respectively, of total income of recipient households, according to the 2001/2002 household survey.<sup>3</sup> 1
proxy for a firm’s capital-intensity. Including the firm’s age serves as a proxy for its experience and strength of its business network.

Guided by the observation of Marques (2015) that the gender of an entrepreneur affects the firm’s decision whether and how much to export, we include a dummy for whether the firm’s owners are women. We control for whether a firm has foreign ownership, as this can be expected to influence export decisions. We control for whether the firm offers training, following Dixit and Pal (2010), who evaluated the effects of training, among others, on firm performance. We include whether the firm uses foreign technology and whether it imports inputs, as we would expect these to influence the firm’s propensity to export. To proxy innovation, we include an indicator for whether the firm has introduced a new product or service in the last 3 years (Srinivasan and Archana, 2011). While the ES data are restricted to formal firms, to include a past indicator of informality we control for whether the firm was registered at the time it began operations. We include a dummy to indicate whether the firm exports directly or if it instead sells to an intermediary that then sells abroad. Finally, we include year dummies. The other parameters are , the overall intercept parameter, is a time-invariant ii unobservable effect, and i is an unobservable time-varying error term assumed to be well αα μμ behaved. εε Next, we turn to the intensive margin to determine how a firm’s employment relates to its export sales. For this, we consider the following model of derived labor demand of the firm: i i i i (5.2) ii ii where, ln NNi is export sales of firm = αα+ ββln EE + XX _i_ ′γγ+ DDat time + μμ _t_ , and the remaining variables are as above. + εε EEFinally, to evaluate the relationship between the change in employment relative to a change Finally, to evaluate the relationship between the change in employment relative to a change in exports, we estimate the following regression: 19
**Table 10:** Endowments and emissions

|_L_|Aggregate labor endowment|
|---|---|
|_K_|Aggregate capital endowment|
|_Qi_|Endowment of natural resource _i_|
|_G_|Public good provision|
|_I_|Investment demand|
|_B_|Balance of payment defcit or surplus|
|_CO_2<br>|CO2 emission constraint|
|_a_<sup>_CO_2</sup><br>_i_|CO2 emissions coefcient for fossil fuel _i_|

48
O.A.M.D.G.

income from the drug trade and are therefore indirectly a target of the Drug Act<sup>9</sup> . - Note that our database measures the capture rate at departmental level (more aggregate), but violence at municipal level, which provides some control for potential endogeneity of the capture rate to violence.

||Public order<br>problem|Guerrilla<br>attack rate|Homicide<br>rate|Capture<br>rate|People in 2<br>lowest income<br>strata (%)|Quality of<br>Life<br>Index|
|---|---|---|---|---|---|---|
|Public order<br>problem|1||||||
|Guerrilla attack rate|0.16|1|||||
|Homicide rate|0.24|0.43|1||||
|Capture rate|-0.09|-0.17|-0.12|1|||
|People in 2 lowest<br>income strata (%)|0.28|0.28|0.11|-0.19|1||
|Quality of Life Index|-0.31|-0.15|-0.10|0.31|-0.40|1|

Data Table 1: Correlation matrix of violence and welfare We also assess to what extent violence is linked to other community characteristics. – Data Data Table 1 shows that correlations between welfare and violence indicators are small, but significantly different from zero and with signs as expected. There may be a weak positive link between inequality and violence, and a negative one between Quality of Life and violence. Likewise, better access to basic services (measured in the Quality of Life indicator) goes hand in hand with a higher capture rate.

# **Measuring Migration and Savings**

Our data allows us to measure how many people have already emigrated from each household and the characteristics of the remaining household members. From a second wave of data in 2003 we can also identify households, which eventually left completely.

> 9 Also see Fajnzylber, Lederman and Loayza (1998) for evidence of the reaction of violence to deterrence in Colombia.
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