rafmacalaba/datause-encoder-data huggingface dataset:
||**Tabl**<br>|**e 2: Testin**<br>|**g for rever**<br>|**se causality**<br>|||
|---|---|---|---|---|---|---|
||(1)<br>_ΔPB_<sup>_30min_</sup>|(2)<br>_ΔPB_<sup>_30min_</sup>|(3)<br>_ΔPB_<sup>_30min_</sup>|(4)<br>_ΔPB_<sup>_30min_</sup>|(5)<br>_ΔCP_<sup>_30min_</sup>|(6)<br>_ΔREG_<sup>_30min_</sup>|
|_Δemp _|-0.2157|||-0.1328|0.0487|-0.1816|
|_(t-1)_|(0.237)|||(0.275)|(0.073)|(0.255)|
|_Δwae _||0.0036||0.0034|0.0001|0.0033|
|_g(t-1)_||(0.003)||(0.003)|(0.001)|(0.003)|
|_Δhours _|||-0.0054|-0.0039|-0.0003|-0.0036|
|_(t-1)_|||(0.005)|(0.006)|(0.002)|(0.006)|
|_Δfat _|0.0034|0.0026|0.0017|0.0027|0.0134|-0.0107|
|_(t-1)_|(0.034)|(0.034)|(0.034)|(0.035)|(0.009)|(0.032)|
|Locality effects|YES|YES|YES|YES|YES|YES|
|Time effects|YES|YES|YES|YES|YES|YES|
|Observations|1,347|1,327|1,347|1,327|1,327|1,327|
|R-squared|0.499|0.497|0.499|0.498|0.474|0.446|
|Nr. of localities|282|282|282|282|282|282|

_Robust standard errors (Huber-White method) in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. The regressors are measured as changes in the variables between the third and the fourth quarter of the preceding year (see main text for more details)._

**Table** **<u>3: The effect of closures on labor market variables</u>**

||(1)<br>Emp|(2)<br>Emp|(3)<br>Wage|(4)<br>Wage|(5)<br>W_days|(6)<br>W_days|(7)<br>W_hours|(8)<br>W_hours|
|---|---|---|---|---|---|---|---|---|
|_PB_<sup>_30min_</sup>|-0.0008***||-0.0036||-0.0029||0.0033||
||(0.000)||(0.003)||(0.002)||(0.002)||
|_CP_<sup>_30min_</sup>||-0.0045***||-0.0521***||-0.0192**||0.0346***|
|||(0.001)||(0.013)||(0.008)||(0.010)|
|_REG_<sup>_30min_</sup>||-0.0004*||0.0017||-0.0011||-0.0002|
|||(0.000)||(0.003)||(0.002)||(0.003)|
|Basic contr.|YES|YES|YES|YES|YES|YES|YES|YES|
|Ind. eff.|YES|YES|YES|YES|YES|YES|YES|YES|
|Time eff.|YES|YES|YES|YES|YES|YES|YES|YES|
|Obs.|96,318|96,318|85,532|85,532|93,865|93,865|88,845|88,845|
|R-sq. within|0.082|0.082|0.027|0.028|0.015|0.015|0.008|0.008|
|Nr. workers|45,584|45,584|42,652|42,652|44,560|44,560|43,838|43,838|

_Robust standard errors (Huber-White method) clustered at individual level in parentheses; *significant at 10%; **significant at 5%; ***significant at 1%. Basic controls include years of schooling, age, age squared, marital status, tenure, urban area / refugee camp residence dummies, a set of work place dummies and Hebron district-time interaction._ 38
the legal system faced by firms located in different regions. Third, the existing empirical literature focuses mostly on data from large, publicly traded firms. Our data are from a census of all firms, giving more weight to small and medium-sized firms.

We adopt an approach that draws on the work of Kumar, Rajan and Zingales [henceforth, KRZ] (2002), who examine the determinants of firms size across 13 European countries. As in their work, our key dependent variable is the average size of firms in a given region and industry. KRZ find that more efficient legal systems are associated with larger firm sizes across countries in Western Europe, an effect especially pronounced in industries characterized by low levels of capital intensity. They posit that the reason for this is that all legal systems in Europe are of high enough quality to protect investment in physical capital. Variation among the European countries, therefore, shows up in the more challenging area of intangible assets such as intellectual property. Intangible assets are more important in less capital-intensive industries such as services. Our paper complements KRZ (2002) in that the legal environment in Mexico varies from bad to less bad, while the legal environment in Western Europe varies from good to very good.<sup>3</sup> Hence, protection of more basic financial contracts is less certain in Mexico. Additionally, we examine explanations for firm size distribution other than legal systems, which have been offered in the development literature.

> 3 Levine (1998) uses data from LLSV (1998) and shows that Mexico scores a 5.95 on a scale ranging from 1-10 in a measure of the efficiency with which contracts are enforced. Of the 15 European countries included in the KRZ (2002) sample, 11 score above 9, and an additional 3 between 8 and 9 on the same scale. Only Greece scores below 8, and its score is 6.40. The data in LLSV are averages over the period 1982-1995. More recent data on the LLSV measures show a similar, if not more pronounced, difference between Mexico and the Western European countries. The median rating of the Political Risk Services Group data averaged over the 1990-1999 period on rule of law for the KRZ (2002) sample of countries is 9.72, compared with a rating of 4.73 for Mexico. Even the lowest average score over the period 1990-1999 for the European countries on rule of law, 7.82 for Greece, is well above the score for Mexico.

4
<!-- Start of picture text --> .8<br>.6<br>.4<br>.2<br>0<br>North Plains West South East Northeast<br>Urban areas Rural areas<br>Share of energy poor<br><!-- End of picture text --> Source: IHDS, 2005.

**Figure 5: Share of energy poverty among India’s income non-poor by geographical region**

Estimation of energy poverty depends only on the amount of energy consumed by the household but not on the energy sources. But understanding the constituent energy sources is important to analyzing the energy poverty. In particular, the extent of modern energy consumption can give us important insight into the formulation of policy directives aimed at reducing energy poverty. For this, it is necessary to understand the composition of energy used by both energy poor and non-poor households. Table 7 presents interesting contrasts in the energy use pattern of poor and non-poor between rural and urban households.

First, in rural areas energy non-poor households consume more energy than energy poor households regardless of the source, whereas in urban areas non-poor households consume more energy than poor households only from modern sources (LPG and electricity). In urban areas, both types of households consume the same amount of biomass at per capita level. In fact, the non-poor households consume less kerosene than the poor households. There is a clear transition from traditional to modern sources as the energy poverty situation of the urban households improves.

22
- 21 -

that some firms are about 2,000 km away from the nearest border point. On average it costs about US$35 per ton.

# **<u>Table 2. Summary statistics: Firm registry data</u>**

|Variable|Abb.|Obs|Mean<br>|Std. Dev.|Min|Max|
|---|---|---|---|---|---|---|
|Cost analysis:|||||||
|Total production or operating costs|_C_|41,410|<br>342,164|3,939,146|1.00|<br>257,000,000|
|(1,000 tenge)|||||||
|Average wage (total payroll expenses|_Wl_|41,410|<br>1,451|6,412|0.02|<br>833,377|
|divided by the average number of<br>employees during the year) (1,000 tenge)|||||||
|Energy costs divided by total asset value|_We_|41,410|<br>0.9|99.1|0.00|<br>19,744|
|Fuel costs divided by total asset value|_Wf_|41,410|<br>0.3|6.2|0.00|<br>566|
|Financing costs divided by total asset|_Wi_|41,410|<br>6.7|551.9|0.00|<br>62,446|
|value|||||||
|Other costs divided by total asset value|_Wm_|41,410|<br>16.1|839.3|0.00|<br>146,091|
|Total revenue (1,000 tenge)|_Y_|41,410|<br>510,205|6,484,125|1.00|<br>410,000,000|
|Transport costs to the nearest city ($/ton)|_CITY_|41,410|<br>1.29|1.91|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|41,410|<br>35.83|17.73|5.36|<br>80.98|
|point ($/ton)|||||||
|Market Access Index based on transport<br>costs|_MAI_|41,410|<br>0.25|0.34|0.01|<br>1.00|
|Amount of inventories at the end of the|_Q_|41,410|<br>83,400|1,056,314|0.50|<br>104,000,000|
|period (1,000 tenge)|||||||
|<br>Number of firms located in the same|_N_|41,410|<br>2,260|2,719|1|<br>9,317|
|district|||||||
|Number of firms in the same industry<br>that are located in the same district|_M_|41,410|<br>186|281|1|<br>1,891|
|Inventory analysis:|||||||
|Amount of inventories at the end of the|_Q_|17,475|<br>113,447|1,290,961|0.00|<br>104,000,000|
|period (1,000 tenge)|||||||
|Transport costs to the nearest city ($/ton)|_CITY_|17,475|<br>1.37|2.02|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|17,475|<br>35.61|17.85|5.36|<br>80.98|
|point ($/ton)|||||||
|<br>Market Access Index based on transport|_MAI_|17,475|<br>0.22|0.32|0.01|<br>1.00|
|costs|||||||
|Interest rate (financing costs divided by<br>total|_I_|17,475|<br>6.43|507.44|0.00|<br>62,446|
|Total revenue (1,000 tenge)|_Y_|17,475|<br>740,949|8,362,113|1.00|<br>410,000,000|
|Number of employees|_L_|17,475|<br>33.49|29.72|1|<br>999|
|Share of the major business revenue in<br>total revenue|_diverse_|17,475|<br>0.07|0.44|-38.58|<br>1.00|
|Agglomeration analysis:|||||||
|<br>Number of firms located in each district|_N_|1,104|<br>239.28|744.48|1|<br>9,317|
|Transport costs to the nearest city ($/ton)|_CITY_|1,104|<br>2.00|2.69|0.05|<br>14.31|
|Transport costs to the nearest border|_BP_|1,104|<br>36.64|14.81|5.36|<br>80.98|
|point ($/ton)|||||||
|<br>Market Access Index based on transport<br>costs|_MAI_|1,104|<br>0.08|0.11|0.01|<br>1.00|
**-30-**

ANNEXES

# Annex A: Preparation of Enterprises for Privatizatlon

An inventory of all state property was conducted in 1991 and information **wa** published to solicit privatization proposals. Enterprisa were divided into two 'waves depending on their rediness for privatization and a small third category consisted of fim that would remain In stoa hands. Many of the more complex privatizations -such **as** onrgy, health services and qrlculture - were postponed to the second wave. The inventory of enterprises for the first wave of lae scale privatization, consisting of 2744 enterprises, was published on November 20, 1991 with a deadline for submitting projects of January 20, 1992. Tbe list of enterprises for the second wave was published on April 16, 1992 with a deadline for submissions of June 16, 1992.

Privatization projects included proposals on what proportion of shares would be sold through which means, but founding ministries also made recommendations about the use of different methods of privatization. In general, there was a strong preference for competitive processes - **such** as public auctions, tendors, or vouchers - over direct sales to predetermined buyers.> In the **case** of privatization by direct sale, the decision also had to be approved by the Economic Council of the government, to insure transparency and then implemented by the National Property Fund, the executing agency for privatization. In the vast majority of cases, domestic and foreign buyers were treated identically. All privatization projects also had to insure that all restitution claims have been met or that resources have been set aside to meet future restitution claims. Three percent of the shar of all joint stock companies undergoing privadzation were set aside in a National Restitution Fund. Where restitudon claims were clear, the property was returned to the original owner and resulted in one of the quickest forms of privatization. But resdtution claims also slowed down the
_29_ _Private sector coverage of thefamily planning market_ TABLE 6

|CSM com|modity|sales (i<br>|n thousa<br>|nds) by|<sup>country,</sup><br>|<sup>1988 a</sup><br>|<sup>nd 1989</sup><br>||
|---|---|---|---|---|---|---|---|---|
|||CONDOM<br>|SALES<br>LO|AL|ORAL C<br>DON|ONTRACEP<br>TED|TIVE SA<br>LOC|ES<br>AL|
||DON<br>|TED<br>|<br>|<br>|<br>|<br>|||
||1988|1989|1988|1989|1988|1989|1988|1989|
|_LatinAmerica_|||||||||
|Bolivia|||||14|48|||
|Brazil|||||||404<br>|487<br>|
|Colombia|5474|6588|||696|587|4815|4856|
|Costa Rica|1905|2186|||||||
|Dominican|26|290|||||250|434|
|Rep.|||||||||
|Ecuador|||||||536|551<br>|
|El Salvador|1739|1864|||155<br>|180<br>|14|43|
|Guatemala|651|939|||67|122|||
|Haiti||252|||||||
|Honduras|420|491|||203|218|||
|Jamaica|1699|2105|||510|500|||
|Mexico|2183|1542|||||||
|Peru|||||||594|714|
|Trinidad||142|||||||
|TOTAL|14096|16400|||1644|1655|6613|7 0 8|
|_Africa_|||||||||
|Ghana|3501|3214|||351|431|||
|Kenya||250|||||||
|Nigeria|607|5944|||43|191|||
|Zaire|943|4135|||||||
|Zimbabwe|50|301|||5<br>|32<br>|||
|TOTAL|5101|13844|||398|654|||
|_Asia/North Afri_|_ca_||||||||
|Bangladesh|116711|115152|||4704|6692|||
|Egypt|17165|15772|||2129|1783|||
|India|||13152|41853|||||
|Indonesia|||7206|7868|||46|338|
|Morocco||280|||||||
|Nepal|2825|2817|||118|161|||
|Pakistan|34030|44314|||||||
|Sri Lanka|4833|9501|||||574|610|
|TOTAL|175564|187836|20358|49722|6951|8636|620|948|

Source: The Futures Group, SOMARC<sup>project</sup> IMS data are available from the Dominican Republic and<sup>demonstratethe</sup> impact of the CSM project on the total commercial market for<sup>pills in that country. In 1986-90, the commercial</sup> market grew by approximately 390 percent. As<sup>can be seen in Figure 3, the increase came from</sup> CSM sales; the other commercial brands remained<sup>constant. Since pre-1985 data are not available,</sup> market expansion in the absence of CSM cannot<sup>be projected; however, it is unlikely that it would</sup> have accelerated so rapidly.

CSM projects usually do not include explicit prevalence<sup>targets, largely because of the</sup> inherent difficulties in attributing changes in prevalence<sup>to specific interventions, and because the</sup> commercial market is more concerned with sales.<sup>Nevertheless, from the donor perspective,</sup> evaluating project impact on prevalence or market share<sup>is essential to justifying continued support</sup> to CSM efforts.

Consumer intercept studies are used to assess impact<sup>of CSM on source switching and</sup>
<!-- Start of picture text --> 0.5<br>2 047sSa<br>z3 03 ea cee<br>x)<br>=<br>BS 02<br>s<br>[3<br>a<br>0.1<br>—_—— Male<br>——— Female<br>0<br>1 2 3 4 5 6 € 8 9 10<br>Round<br><!-- End of picture text --> <!-- Start of picture text --> 0.5<br>0.4<br>2—<br>B$ 03 = a<br>=<br>s ~<br>2 0.2<br>Fs2<br>2<br>a 0.1<br>0<br>1 2 3 4 5 6 7 8 9 10<br>Round<br><!-- End of picture text -->
-20-

**TABLE 2.3**

|**Czmpariaon**of Mexican<br>**Census Estimates**<br>**for Na**<br>|National<br>Accounts<br>**nufacturing**<br>**Gross**<br>|and Industrial<br>**Value Added**|**1975**|
|---|---|---|---|
|Census Estimate<br>(US Census)<br>Concept) of<br>Gross Value<br>Added<br>(million pesos)|Census<br>Estimate<br>(Present Nationa<br>Accounts Concept<br>of Gross Value<br>Added<br>(million pesos)<br>(|National<br>l Accounts<br>) Estimate<br> <br>(present<br>concept) of<br>Contribution<br>to GDP at<br>factor cost<br>million pesos|Ratio<br>of Value<br>Added (Present<br>National<br>Accounts Concept)<br>National Accounts/<br>Census Values<br>)|
|(1)|(2)|(3)|(4)|
|Food Products<br>28,963.6|22,111.8|50,794.0|229.7|
|Beverages<br>1 2<sup>,</sup>99 3<sup>.</sup>8<sup>a</sup>|8 <sup>,</sup>54 6 <sup>.</sup>3<sup>a</sup>|<br>12,635.4|147.8|
|Tobacco<br>1 <sup>,</sup>8 16 <sup>.8a</sup>|1 <sup>,</sup>32 3 <sup>.2a</sup>|1,348.2|101.9|
|Textiles<br>14,078.8|11,837.9|15,992.9|135.1|
|Clothing<br>5,598.8|4,412.1|10,946.3|248.1|
|Footwear&Leather<br>3,329.5|2.558.2|7,553.1|295.3|
|Wood Products<br>2,826.8|2,141.3|3,175.8|148.3|
|Furniture<br>1,994.1|1,605.0|4,618.3|287.7|
|Paper&Allied Products<br>6,980.0|5,385.3|6,605.1|122.7|
|Printing & Publishing<br>6,004.4|4,585.7|5,204.3|113.5|
|Chemicals & Allied||||
|<br>Products<br>40 <sup>,</sup>3 3 4 <sup>.</sup>0 <sup>ab</sup>|28 <sup>.</sup>1 8 5 <sup>Oac</sup>|30,201.1|107.2|
|Rubber and Plastic||||
|Products<br>8,275.8|6,459.1|6,967.7|107.9|
|Stone, Clay and Glass||||
|Products<br>11,930.2|9,486.9|13,605.0|143.4|
|Primary Metals<br>17,956.8|14,760.1|14,138.1|95.8|
|Metal Products<br>13,322.7|10,984.7|9.949.9|90.6|
|Machinery except||||
|Electric<br>10,532.7|8,518.7|7,676.1|100.2|
|Electrical Machinery &||||
|Equipment<br>13,429.5|10,053.3|12,532.7|114.8|
|Motor Vehicles &||||
|Equipment<br>14,534.6|11,016.1|11,374.2|103.3|
|Other Transport||||
|Equipment<br>1,675.5|1,523.9|1,507.5|98.9|
|Other Manufacturing<br>3,241.1|2,605.6|5,251.6|201.6|
|Totald<br>2 1 9 <sup>, </sup>8 1 9 <sup>.5ab</sup>|1 68 1 0 0 <sup>.</sup>4 <sup>ac</sup>|232,077.4|138.1|

a) excludes indirect taxes and subsidies, as taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981: 3,545.1 million pesos for alcoholic beverages, 2,598.0 million pesos for tobacco, and 4,836.2 million pesos for petroleum refining.

b) includes 7,148.2 million pesos (excluding indirect taxes and subsidies) for petroleum refining, which are not shown in the census, but taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981.

c) includes 4,545.1 million pesos (excluding indirect taxes and subsidies) for petroleum refining, which are not shown in the census, but taken from the detailed national accounts document Sistema de Cuentas Nacionales de Mexico, 1981.

d) excludes activities of head offices and auxiliaries with payrolls of 5,816 million pesos.

Sources: Column (1) and (2) (except figures mentioned under footnotes a) to c)) calculated from Resumen General (see text); column (3) from Sistema de Cuentas Nacionales de Mexico.
reduce their grazing by 10%, they would require an increased average payment of approximately 160 Birr (about $8.00). As already discussed, fuelwood collection reduction is not a significant determinant of REDD+ contract choice for this sample.

# **6. Conclusion**

Reducing Emissions from Deforestation and Forest Degradation (REDD+) is a payment for ecosystem services (PES) system tasked with creating markets for carbon sequestration services (Bluffstone 2013). Despite the acknowledged importance of local level opportunity costs and preferences (e.g. McKinsey & Co, 2010; Gregorsen _et al._ 2011), the literature on costs and preferences for programs to prevent deforestation like REDD+ is extremely limited. In this paper we use choice experiment surveys conducted in in Ethiopia to understand household preferences for REDD+ contracts and to estimate the opportunity cost to rural Ethiopian households of reducing fuelwood collection and grazing. We also shed light on respondents’ preferences for REDD+ institutional arrangements, such as the portion of payments going to communities versus households and the terms of REDD+ contracts.

The results show that respondents care how REDD+ programs are structured, particularly with regard to restrictions on grazing. A key REDD+ policy question is how to divide up REDD+ payments. Should they go to the community? To households? Or Part to households and part to communities? We find that respondents prefer that more of the payments go to households. This result may seem intuitive but similar ongoing work in Nepal finds that households prefer more of the payment to go to the community. It needs to be further investigated how the strength of the local institutions and the interactions between local, regional and government institutions may influence household preferences to how the payments are divided between households and the community.

16
<u>Figure 1:</u> Example of estimation without and with firms-specific time trend correction – Average Number of connections for Latin America.

<!-- Start of picture text --> 300,000<br>280,000 Original data<br>260,000<br>Pre-Transition Transition Post-Transition<br>240,000<br>220,000<br>200,000<br>180,000<br>-5 -4 -3 -2 -1 0 1 2 3 4 5<br>Year with respect to privatization<br>Number of Connections<br><!-- End of picture text --> <!-- Start of picture text --> 300,000 300,000<br>Without correction in trend With correction in trend (jump)<br>280,000 280,000<br>Original data Original data<br>260,000 260,000<br>Pre-Transition Transition Post-Transition Pre-Transition Transition Post-Transition<br>240,000 240,000<br>220,000 220,000<br>200,000 200,000<br>180,000 -5 -4 -3 -2 -1 0 1 2 3 4 5 180,000 -5 -4 -3 -2 -1 0 1 2 3 4 5<br>Year with respect to privatization Year with respect to privatization<br>Number of Connections Number of Connections<br><!-- End of picture text --> Source: Authors’ elaboration based on the data described in section IV.

Moreover, as will be described later, the transition period has some important effects on firms. Most of the literature reviewed uses a basic approach similar to that in equation (1), and in some cases accounts for specifications like those included in equation (2). In other words, the firm is evaluated immediately before and after the change in ownership. This approach does not account for changes that may occur in preparation for the change in ownership or in response to it, perhaps through one-time decisions (e.g. a reduction in personnel). In this paper, in order to isolate and identify the outcomes during the period around the change in ownership, specific dummies are defined for these transitional years. The transition period is defined the same way as in the first approach: from announcement of the change in ownership through one year after the privatization or concession is awarded. Since it is not clear for all the cases _when_ the process to change _8 of 28_
- Poole, Danielle N., Bethany Hedt-Gauthier, Shirley Liao, Nathaniel A. Raymond, and Till B ̈arnighausen. 2018. “Major depressive disorder prevalence and risk factors among Syrian asylum seekers in Greece.” _BMC Public Health_ 18 (1):908. URL `https://doi.org/10. 1186/s12889-018-5822-x` .

- Ridley, Matthew, Gautam Rao, Frank Schilbach, and Vikram Patel. 2020. “Poverty, Depression and Anxiey: Causal Evidence and Mechanisms.” _Working Paper_ .

- Schuettler, Kirsten and Laura Caron. 2020. “Jobs Interventions for Refugees and Internally Displaced Persons.” Working Paper, World Bank, Washington, DC. URL `https://openknowledge.worldbank.org/handle/10986/33953` . Accepted: 2020-0620T19:37:09Z.

- Semkovska, Maria, Lisa Quinlivan, Tara O’Grady, Rebecca Johnson, Aisling Collins, Jessica O’Connor, Hannah Knittle, Elayne Ahern, and Taylor Gload. 2019. “Cognitive function following a major depressive episode: a systematic review and meta-analysis.” _The Lancet Psychiatry_ 6 (10):851–861. URL `https://www.thelancet.com/journals/ lanpsy/article/PIIS2215-0366(19)30291-3/abstract` . Publisher: Elsevier.

- Singla, Daisy R., Brandon A. Kohrt, Laura K. Murray, Arpita Anand, Bruce F. Chorpita, and Vikram Patel. 2017. “Psychological Treatments for the World: Lessons from Lowand Middle-Income Countries.” _Annual Review of Clinical Psychology_ 13:149–181.

- Soffia, Magdalena, Alex J Wood, and Brendan Burchell. 2021. “Alienation Is Not ‘Bullshit’: An Empirical Critique of Graeber’s Theory of BS Jobs.” _Work, Employment and Society_ :09500170211015067URL `https://doi.org/10.1177/09500170211015067` . Publisher: SAGE Publications Ltd.

- Terkel, Studs. 1974. _Working; People Talk about What They Do All Day and How They Feel about What They Do_ . New York:Pantheon Books.

- UN. 2018. “Global Compact on Refugees.” URL `https://globalcompactrefugees.org/ sites/default/files/2019-12/Global%20compact%20on%20refugees%20EN.pdf` .

UNHCR. 2020. “Forced displacement passes 80 million by mid-2020 as COVID-19 tests refugee protection globally.” URL `https://www.unhcr.org/news/press/2020/12/5fcf94a04/ forced-displacement-passes-80-million-mid-2020-covid-19-tests-refugee-protection. html` .

32