rafmacalaba/data-use-mentions-extended huggingface dataset:
on _self_ -earned income. Gifts and humanitarian assistance are a source of income but are not self-earned. The same holds for income from remittances. Poverty measurements, by contrast, include consumption derived from gifts, or items purchased from remittances or on credit.

Provided only refugee self-earned income is considered, FGT1, also known as the poverty gap, can be interpreted as the ‘self-reliance gap’, which measures by how much, expressed as a fraction of the poverty line, refugee incomes would have to rise to make all refugees self-reliant.

Measuring income precisely through household surveys is notoriously difficult (Deaton 1997; Carletto 2022), particularly in low- and middle-income countries where much of refugee income is earned informally or through self-employment in, for instance, agriculture, services or trade. Despite this, income measures are the only ones that are available in some countries, most notably those in Latin America, where there is a tradition of using income approaches for measuring poverty. However, in most instances in this paper, a different approach is pursued, in which refugee earned income is approximated by deducting humanitarian assistance from refugee consumption. For the latter to proxy income, one needs to assume that refugee (net) savings and borrowing are negligible, while remittances are considered part of self-earned income.

The measurement of income and consumption of refugees using surveys is an evolving field, and microdata sets are becoming increasingly available. For this paper, 11 microdata sets were brought together. To our knowledge, these 11 datasets present the universe of microdata available for low- and middle-income countries suited for estimating refugee poverty and income. Nine of these datasets also permit the estimation of poverty and income among host communities, with this data not available for Jordan and Bangladesh only. In a subset of economies (Ethiopia, Jordan, Kenya, the West Bank and Gaza, Uganda) the data can be broken down into refugees living in camps and those living elsewhere, which further enriches the analysis.

> 3 When income is invested in an
The survey for the first wave of the dataset (Malawi’s Integrated Household Survey – IHS3) was conducted from March 2010 through March 2011, and covered 12,271 households in 768 enumeration areas (i.e. 16 households from each EA). A sub-sample of the households considered in IHS3 were re-surveyed in 2013 to create the second wave of the dataset (Integrated Household Panel Survey -IHPS). IHPS tracked and re-interviewed 4000 households (3,247 original households, and 753 split-off households) from 204 of the 768 enumeration areas. An overwhelming majority, 76.80%, of the 3104 baseline households did not split over time; 18.49% split into two households; and the remainder (4.70%) split into 3-6 households. Considering the 20 baseline household that died in their entirety between 2010 and 2013 and the fact that 4,000 households could be traced back to 3,104 baseline households, the dataset has an overall attrition rate of only 3.78% at the household level.

The households that were not resampled in IHPS were dropped from the dataset used in the analyses. Households from the urban enumeration areas were also dropped because farming in Malawi is predominantly rural. Among the rural households, the study focused on farm sub-plots on which maize is the main crop. In the end, the sample size for the analyses consisted of 4,688 households (2,175 from IHS3 and 2,513 from IHPS) and 6,619 maize plots (3,070 from IHS3 and 3,549 from IHPS). Attrition bias in the data could not be explicitly tested because there are no direct regression-based tests for attrition when FE estimators are used in a panel of only two waves (Wooldridge, 2010; Mason and Smale, 2013). That notwithstanding, we are confident that attrition bias is not likely to be a concern because, as indicated earlier, the attrition rate is only 3.78% at the household level.

# **
36 However, considering that in 1993-1995 the share of non-Muscovites in the overall number of households changing apartments decreased somewhat and the amount of residential movement leading to worse living conditions significantly rose, the analysis cannot be confined to the dissatisfaction of newcomers to Moscow with their apartments in Moscow.

The analysis of this trend by income level (Fig. 2.19) shows that the dlecision to move into an inferior apartment is much more often made by poor households, who are forced to part with their spacious apartments under present financial constraints.

**Fig** 2.19. Change in mover households' living conditions in Moscow (by quintiles based on per capita income.)

<!-- Start of picture text --> 26-<br>20<br>,6 : /~~~~~~~~~~~~~~<br>16 El~~~~~~~~~~~~ improve<br>Ono change<br>/ 1became worse<br>10.Wf<br>i.'2' 2V.6 3 V7< °-<br>Note: 1 - lowest quintile, 5 - highest quintile<br>Source: The Moscow Loitudinal Household Survey, January 1996.<br><!-- End of picture text --> On the whole, the processes of receipt, sale and purchase, and exchange of housing have evolved in one direction: households mainly moved to better apartments or managed to get apartments of their own and stopped sharing with relatives. On the other hand, the opposite trend of households who were forced to move into inferior housing, also consolidated. It is especially noticeable when housing is exchanged in the secondary market.
We calculate a Market Accessibility Index (MAI) as another agglomeration measure. Treating the population size within the town administrative boundaries could mismeasure agglomerations. For instance, the presence of economically vibrant, small satellite towns surrounding major towns could result in underestimating our estimates. To take account of such connectivity between towns, we calculate the MAI, following Donaldson and Hornbeck (2016) and Berg, Blankespoor, and Selod (2018). It is calculated as the sum of its own population and surrounding towns’ populations that are discounted based on its travel time from the town (see Appendix A for the details).

# **3.2 Labor Outcomes**

Table 1: Employment Characteristics of Whole Sample and Among Employed

||Total|Size 1|Size 2|Size 3|Size 4|Addis<br>Ababa|Min|Max|
|---|---|---|---|---|---|---|---|---|
|**Employment Status**|||||||||
|Employed|0.62|0.72|0.66|0.61|0.60|0.55|0|1|
|Unemployed|0.13|0.08|0.11|0.13|0.14|0.17|0|1|
|Non-Labor Force|0.25|0.20|0.23|0.26|0.25|0.28|0|1|
|Observations|124,354|9,240|16,727|43,732|26,774|27,486|||
|**Among Employed**|||||||||
|Sector of employment|||||||||
|Government|0.23|0.24|0.22|0
climate environment at the country level. For each country, the performance on individual attributes relative to the rest of the world and best performers is being recorded. The Doing Business Indicator series, which was prepared annually by the World Bank Group between 2003 and 2020 measures 11 different areas using 41 indicators for 180 economies.<sup>6</sup> For each area, each country’s performance is measured by a score indicating the country’s level relative to best practice (Distance to Frontier indicator).<sup>7</sup> Another widely used set of indicators are the Global Competitiveness Indicator (GCI) series prepared annually by the World Economic Forum since 2007. The WEF strategy is to gather existing indicators (including Doing Business ones) alongside own perception surveys of executives. Its 2017-18 edition covers 12 areas of

> 6 The areas are: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, resolving insolvency and labor market regulations.

> 7 The methodology of the Doing Business indicators was updated in 2015 to adopt the DTF measure, which measures progress over time over the previous ordinal ranking measure.

3
LSMS-ISA data from Malawi (2010/11), Tanzania (2010/11), Niger (2011) and Uganda (2009/10), identify a common trend in the magnitude of measurement bias, defined as selfreported minus GPS area for plots for which both measures were available and taken independently. The smallest of plots (less than 0.5 acres) are systematically over-reported. The degree to which these are over-reported varies but in all countries the mean self-reported area is overestimated by at least 90% of the mean GPS area of plots in that particular class. With increasing plot size the degree of over-estimation decreases and, eventually, converts to under-estimation for the largest plots. Carletto et al (2013) find the same trend in 2005/6 data from Uganda while Dillon et al. provide evidence of similar systematic trends in data from Nigeria (2016). In a methodological study conducted in Southern Mali comparing area estimates with compass and rope measurement on larger plots (average 0.816 ha, maximum 8.78 ha) De Groote and Traoré (2005) find that the same is true: farmers (while aided by expert observers) are inclined to over-estimate the area of plots less than one hectare, while the degree of area under-estimation increases with plot size.

7
addressing the needs of the middle class for (i) more and better formal-sector jobs in a competitive economy, driven by innovation and entrepreneurship, (ii) financial security through social safety nets appropriate for an economy with lots of informality and <mark>complementary programs for reemployment assistance and reskilling,</mark> and (iii) good governance, strong and effective institutions, able to deliver quality services to both the lower and middle classes.

This study is in part limited by data availability since we do not have access to good data that combines SWB and voting behavior. Only the Latinobarómetro includes voting behavior, but data was collected a few months before the elections and surprisingly a small number of people stated that they voted for Partido Social Liberal. This study also raises questions about the different mechanisms that give rise to voting the incumbent out of office. Particularly, it can be hypothesized that a loss in SWB translates into a vote for the non-incumbent when people hold the incumbent party responsible for their loss in SWB. This hypothesis should be examined in future research.

# **References**

Abi-Nasif, C., Islam, A.M., & Lederman, D. (2021), Perceptions, Contagion, and Civil Unrest. Paper presented at the ABCDE Conference 2021.

Ahn, N., Mochón, F., & de Juan, R. (2007). Expectations and happiness. Documento de trabajo, 2007-01. Fedea Alston, L. J., Melo, M. A., Mueller, B., & Pereira, C. (2013). Changing social contracts: beliefs and dissipative inclusion in Brazil. _Journal of Comparative Economics_ , 41(1), 48-65.

Alves, J. A., & Vargas, J. C. (2020). The spectre of Haiti: structural antiblackness, the far-right backlash and the fear of a black majority in Brazil. _Third World Quarterly_ , 41(4), 645-662.

Amaral, O. E.
6.2%|5.3%|4.9%|1.9%|

_Source: Superintendencia Bancaria; Encuesta de Calidad de Vida 2003_ 13. **Real interest rates for small savings amounts are frequently negative** . The average interest rate paid by banks on savings accounts ranges from 2-7%, depending on the bank and the type of service provided (see Figure 2.3). The majority of banks do not pay interest below a certain minimum balance. For example, BanColombia, the largest bank in the country, starts paying interest on savings accounts on a minimum balance of COP300,000. Factoring in inflation of 7% in 2003, the real interest rate for small deposits is therefore negative. This provides limited incentives for savers to convert informal savings into savings with the formal banking sector. Focus groups confirmed that low income people generally do not trust formal financial institutions as they feel that they do not receive any remuneration on savings accounts, and fees are too high<sup>31</sup> .

> 31 Marulanda, 2004.

36
severity of food safety standards expressed in maximum allowable contamination in their econometric analysis. The severity of standards was therefore comparable across countries and results better suited as input to policy discussions.

We use an econometric approach to determine the effect of European aflatoxin standards on African exports. The framework in our empirical study follows the-gravityequation model that was developed in Otsuki, Wilson and Sewadeh. A gravity-equation model is a widely used method to explain trade patterns between countries using each country's measures of 'mass' and geographical distance between countries. In most countries, aflatoxins standards on foods, for example, are specified for both aflatoxin B 1 alone, and total level of aflatoxins B1, B2, GI and G2 (See Table 1). In practice passing the B 1 standard is more difficult than passing the standard for the total level of aflatoxins. This is the standard that is more likely to affect trade flows.

Our specification of gravity equation is as follows: _ln(M, k) = bo+bok+b,fln(PCGNPi)_ + _b2 kln(PCGNPj)_ + _b kln(DIST j)_ + _b4YYEAR_ + _b5COLij_ + _b6ln(STik)+ £k_ (1) where _M-k_ denotes value of trade from African country j to EU country member i. It is obtained from trade data of the United Nations Statistical Office. Trade data includes bilateral trade value across time. We use data for the time period between 1989 and 1998. Parameter b's are coefficient. _PCGNP_ is real per capita GNP in 1995 US dollar. _DIST is_ geographical distance between country i and j, and _YEAR_ is a year. _COL_ is colonial tie dummy. It equals one if a colonial tie between country i and j, exists, and is zero otherwise.

_STi_ is maximum aflatoxin level imposed on import of food product, k, by EU importing country i. It is obtained from FAO survey of mycotoxin standards on food and 12
average crosscountry decline in GDP growth during 2009 was almost twice as large as that during tranquil

> 11 Alternative measures capturing the length of the recession, displayed in the bottom panel of Table 1, confirm that emerging economies started to recover earlier than advanced economies.

> 12 In making this comparison, we use the emerging economies’ geometric trend over the period January 2002-April 2008.

> 13 The crisis years are defined using a composite crisis indicator that takes into account banking, currency, and domestic and external debt crises on an annual basis. The beginning of a crisis event is considered as the first year with any of these crisis events, provided that no other crisis has been observed in the preceding two years. For more details, see Broner et al. (2011).

14
self-employed workers (wage workers serve as the reference group). The above equation can be estimated separately for men and women to get estimates of returns to schooling for men and women, respectively.

We add skills measures to the model above to estimate the association between skills and earnings, controlling for completed years of education. The resultant model is expressed as follows: where _Skills_ represents scores on each of the selected personality traits that are used to estimate noncognitive skills’ associations with earnings. The standardized score on the reading proficiency assessment is then added to estimate the association between cognitive skills and earnings, 7
.... 23|
|Figure 13: UNHCR’s Population of Concern by Urban/Rural Location end-2015 ...................................... 24|
|Figure 14: Refugees by Urban/Rural Location end-2015 ........................................................................... 24|
|Figure 15: UNHCR Population of Concern by Urban/Rural Location end-2015 ......................................... 24|
|Figure 16: UNHCR’s Population of Concern by Accommodation end-2015 .............................................. 25|
|Figure 17: Durable Solutions Relative to Refugee Stock 2015 ................................................................... 26|
|Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 ......................
**Table 4: Duration of suspension and dynamic effects**

||lnEx<br>|ports<br>|Export Dummy|
|---|---|---|---|
||Coefficients|Marginal effect||
||(1)|(2)|(3)|
|Suspended×Country×Product×short|-0.141|-13%|-0.001|
||(0.239)||(0.012)|
|Suspended×Country×Product×long|-0.601*|-45%|-0.023*|
||(0.358)||(0.014)|
|Product-year Fes|Yes||Yes|
|Country-year Fes|Yes||Yes|
|Country-product Fes|Yes||Yes|
|Number of observations|584,434||584,434|

The marginal effects throughout this paper are calculated as exp(β)−1 if the dependent variable is lnExports. Robust standard errors in parentheses are clustered by country and product. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively.

As shown in columns (1) and (2) of table 4, the suspension appears to have a significantly larger negative impact on exports to the US for countries with _longer-term suspension_ of AGOA eligibility, exceeding two years; the AGOA suspension resulted in a statistically significant 45 percent drop in exports and 2.3 percentage points drop in the probability of exporting from AGOA eligible countries. However, the effect on both exports and probability of exporting is insignificant for short-term suspended countries.

In table 5, we consider how the suspension effect varies with pre-suspension utilization rates. Coulibaly, Kassa and Zeufack (2022) document a widespread underutilization of AGOA benefits. Table 2 also confirms that there is a huge disparity in AGOA utilization across countries prior to
Readiness” are two important lagging policy areas that have relatively few numbers of laws and decrees. In contrast, “Diversification”, also a lagging policy area, has received more laws and decrees. We test this hypothesis formally in Appendix A2 with different econometric specifications and confirm the robustness of this finding.

One possible explanation for the finding is that countries enter a 'virtuous cycle' whereby they have more capacity to implement reforms in areas of pre-existing strength. Another argument is related to the sequencing of reforms. The ‘right’ reforms are not necessarily on weak policy areas but the most logical next step (Edwards, 2009; <mark>Feltenstein and Nsouli, 2003</mark> ). More studies and thoughtful debates are needed to understand the countries’ current reform efforts. They will likely help policy makers boost the engagement and support for economic reforms, which we will discuss in the next section.

**Figure II.2: Number of Laws and Decrees Passed and Lagged of Distance to Policy Frontier**

<!-- Start of picture text --> Bahrain Kuwait Oman<br>Qatar Saudi Arabia UAE<br>0 20 40 60 80 0 20 40 60 80 0 20 40 60 80<br>Distance to Frontier, Lagged (100: farthest away from the frontier)<br>Diversification Financial Market Development Goods Market Efficiency Health and Primary Education<br>Higher Education and Training Infrastructure Innovation Institutions<br>Labor Market Efficiency Macroeconomic Environment Technological Readiness<br>150<br>100<br>50<br>0<br>150<br>100<br>50<br>0<br>Number of Laws and Decrees Passed<br><!-- End of picture text --> _Note_ : A higher ‘Distance to Policy Frontier’ implies being farther away from policy best practices. GCI data are from 2007 to 2017 due to data constraints from WEF’s GCI database (the GCI scores are only methodically
Business registries managed by courts are less efficient, costlier for businesses, and due to their discretionary nature increase uncertainty for investors. However, we are not aware of any papers that measure the effects of reforming business registration from a judicial to an administrative procedure on firm creation. This paper aims to fill this gap by studying a reform in Serbia that transferred business registration from regional courts to a new centralized administrative agency in 2005. Although this reform only eliminated one procedure in the registration process, going from 11 to 10, it implied a large drop in the number of days required to register a business, going from 51 to 15 (World Bank, 2006). The World Bank’s 2006 Doing Business called this reform one of the “boldest reforms” to the business environment that took place between 2004 and 2005 (p. 2).

Moving business registration away from courts may have large effects on firm creation since courts can entail significant bottlenecks and inefficiencies (see Chemin, 2009, and Kondylis and Stein, 2018). They may also pose opportunities for corruption. In fact, prior to recent reform efforts, the great majority of citizens and lawyers in Serbia perceived the presence of corruption in the judicial system (World Bank, 2014). Moreover, business registration at court was highly discretionary (Jersild and Skopljak, 2007). We thus expect the 2005 reform in Serbia to have relatively large effects on firm registration.

To measure the effects of the reform, we obtained data on the number of new firms from the Serbian Financial Accounts Registry for all years from 2003 to 2011. This data set covers formal companies (not sole proprietors) that submitted their financial statements to the registry.

We employ a difference-in-difference strategy that compares the number of new firms before and after the 2005 reform across districts based on the level of distrust in regional courts (as measured in the 2006 Life in Transition Survey). That is, we test whether the regions where a large fraction of individuals report distrust in courts saw
|0.007706|
|<br>|||||[0.013]|[0.005]|[0.005]|[0.013]|
|Indicator for the Governorate<br>Being Controlled by Houthi Forces|‐|‐|‐|0.104777***|‐|‐|‐|0.104493***|
|||||[0.035]||||[0.034]|
|Governorate Fixed Effects|N|Y|Y|N|N|Y|Y|N|
|Month‐Time Fixed Effects|N|N|Y|Y|N|N|Y|Y|
|Observations|614|614|614|614|614|614|614|614|

Notes: This table estimates the correlation between food coping strategies and violence in the month before the survey between February 2016 and August 2018, using monthly data reported for each governorate. Data on food coping strategies were obtained from the WFP mVAM mobile phone survey; and violence data was obtained from ACLED. Columns (1) and (5) estimate a sparse specification without any time or governorate‐ specific factors; columns (2) and (6) estimate a specification including governorate‐specific factors; columns (3) and (7) estimate a specification including both time (month‐year) and governorate‐specific factors; and columns (4) and (8) estimate a specification that includes a Houthi indicator and only time‐specific factors. Standard errors clustered by governorate are reported in parentheses; * denotes statistical significance at the 10 percent level, ** denotes statistical significance at the 5 percent level, and *** denotes statistical significance at the 1 percent level.

13