rafmacalaba/data-use-mentions-extended huggingface dataset:
and income similar to natives. The empirical findings related to the effects on the social cohesion of the host communities, with the analysis of indicators such as refugees’ political values, trust and civic engagement, suggest that a larger concentration of refugees in co-ethnic enclaves can lead to lower levels of social cohesion in the long run.

Martén et al. (2019) make use of the random assignment policy of refugees across Swiss cantons to investigate the causal effects of ethnic clusters on economic and social integration. The authors rely on the registry data from the State Secretariat of Migration that covers the universe of asylum seekers, combined with rich administrative data from the registry for all migrants living in Switzerland. They examine refugees who obtained subsidiary protection over 2008–2013. The authors find that larger local ethnic networks of refugees predict higher employment probabilities compared to refugees assigned to smaller ethnic communities. These effects are largest within the first three years since arrival and then they fade afterwards. These findings are consistent with the theory that larger networks of co-ethnics facilitate information transmission and better matching.

On the other hand, Battisti et al. (2021) find mixed evidence. They examine the role of co-ethnic networks in Germany over 2013–2015. Their identification strategy relies, as in previous studies, on the quasi-random allocation of refugees (and ethnic Germans) across locations. The authors use rich German social security data matched to migration survey data, which allow controlling for a large set of fixed effects and individual characteristics. They find that the larger initial ethnic networks are associated with higher employment probabilities for low and medium-level educated refugees in the first three years since arrival. However, in the long-run the effect dissipates and these refugees no longer have an advantage over those with smaller co-ethnic networks. The underlying mechanism they suggest is the lower incentives to invest in human capital among those with a larger initial network.

Another aspect related to initial conditions, which has been understudied, is the role of local attitudes towards refugees. The two existing studies, both
for two different values of θ, 0.5 and 0.8, which span the main range of estimates found in U.S. validation studies. Under the assumption that the measurement error is mean zero, we can then estimate Ec ¡Yi,t<sup>∗</sup> −1¢ with the sample crosssectional mean Y c(t−1),t−1, and therefore estimate αc by: Under assumptions (i)-(iii), as the number of individuals in each cohort goes e to infinity, αc will provide a consistent estimate of the cohort effect αc.

Based on these estimates, one can then go back to equations (4) and (5) at the cohort mean level, in order to determine whether any specific cohort faces a poverty trap by determining the slope of the estimated cubic income mapping at the income level(s) at which the 45 degree line is crossed.

Note that at no stage did we impose any restrictions on the serial correlation properties of the measurement error. Since different individuals are observed in each time period, we can allow for arbitrary patterns of correlation over time in the measurement errors provided that we assume cross-sectional independence of the measurement errors across individuals.<sup>8</sup> Secondly, note that extension of this method to higher order polynomials in the lagged dependent variable will require imposing more structure on the distribution of the measurement error term in order to estimate αc. One can still obtain consistent estimates of the slope parameters β without these additional assumptions, but the corrections for measurement error needed to estimate αc will involve assumptions on higher-order moments of the εi,t’s. For example, with a quartic, the correction requires an estimate of the fourth moment of the measurement error, along with an estimate of the cross-sectional variance of true income.

In practice this methodology offers several advantages over using genuine panel data. In many situations genuine panel data are not available, or are available for
rural areas) may be explained by the high correlation between the WBL variables and other country-level historical variables reflecting social norms around inheritance and family structures, which suggests that legislative changes may not be as effective in increasing women’s land ownership in environments with adverse social norms.

We briefly discuss our historical and current controls at the country-level. Confirming the descriptive analysis, there is no significant relationship between per capita GDP and property ownership, which suggests that economic prosperity _per se_ is not a key driver of women’s property ownership. With respect to the variables reflecting historical cultural attitudes and gender norms only the absence of historical inheritance rights and the prevalence of extended families in the society (especially in rural areas) are significantly associated with women’s property ownership. The absence of inheritance rights historically is linked to the absence of private property rights _per se,_ which according to Engels (1902) implies a relatively more gender egalitarian society. A high prevalence of extended families in rural areas has a negative association with both land and housing ownership, reflecting perhaps a larger number of claimants to immoveable property reducing women’s chances of even joint ownership. A higher proportion of the population following matrilocality (patrilocality) in the society historically is associated with higher (lower) property ownership among women but the effects are not statistically significant.

Model (3) includes a rich set of individual, spouse, couple and household characteristics. Individual attributes such as age and education of women show the expected associations for property ownership – older women are more likely to own property as are highly educated women (mostly greater than 13 years of education). Wives’ property ownership in urban areas is positively impacted when both spouses are working, reflecting greater purchasing ability. Rather surprisingly, women’s own employment is either negatively associated (mostly in urban locations) or has no association (mostly in rural locations) on her ownership. Being assigned head of household is beneficial for wives in rural areas when compared to the husband being the head
its labor force so as to increase or maintain effort. The implications for firm growth are thus potentially quite significant.

The importance of labor market institutions relative to intra-firm organizational mechanisms, in terms of their effects on wage, is hard to assess based on the available research. What we do know is that the effects are economically large. In the case of Ghana, for instance, Söderbom and Teal (2004) estimate that if a firm with 350 employees faced the same wage as a firm with 20 employees, total costs in the large firm would fall by 20-25 percent.

# _iv) Infrastructure_

Based on several studies of growth determinants in Africa, Collier and Gunning (1999) argue that poor infrastructure is a serious constraint to growth in the continent. Compared to other regions public expenditure as a share of GDP has been higher in Africa, while service provision has been worse. The poor infrastructure in Africa is likely to be a particularly severe constraint to manufacturing growth. Many firms spend own resources directly on buying infrastructure services or providing it on their own. One reason for the prevalence of small manufacturing firms in Africa (see below) is that the transport infrastructure is poorly developed, which creates pockets of demand that tend to generate small-scale localized producers. To be able to take advantage of international trading opportunities and to engage actively in the emerging system of outsourcing requires reliable and cheap infrastructure. Poor infrastructure thus presents a significant problem for exporters.

The firm surveys have generated a lot of information about how firms perceive infrastructure problems and recently also about time and monetary costs of different bottlenecks. Bigsten et al. (1999b) show that attempts to use data on perceptions to explain productivity is problematic. It is shown that the firms that rank infrastructure problems as very severe are at the same time the most productive ones. This is due to the fact that these are the firms that sell on larger markets and they are thus more dependent on the infrastructure. The use of objective measures – e.g. days in
*

Due to the armed conflict that Colombia suffered for more than five decades, the country has 8,785,305 victims registered in the Victims Registry.<sup>21</sup> In 2011 Law 1448 or the "Victims Law" was enacted as a regulatory framework for truth, justice, assistance, reparations, and a guarantee of non-repetition to this population. The law includes, among others, measures for immediate humanitarian aid for victims; assistance through inclusion in programs that lead to the restoration of the rights of this population and the generation of decent living conditions and return to social life; reparations, which include restitution, indemnification, rehabilitation, satisfaction and guarantees of non-repetition.

Within the framework of the Victims Law, entities at the national level attached to the National System of Comprehensive Assistance and Reparations for Victims (SNARIV) have increased the resources and efforts devoted to the integral assistance and reparations of the victims. Following this, the Ministries and Agencies of the State have implemented various programs on issues of education and training for work, health and psychosocial care, humanitarian care, returns and relocations, housing, job creation, among others, especially focused on victims. In this incidence analysis, the amount reported by the beneficiaries was considered as the victimization transfer. However, neither the program nor the transfer associated with that program can be accurately identified. According to DNP (2018), the budget allocated for the victims’ policy was $ 13.8 trillion in 2017, about 1.5 percent of GDP.<sup>22</sup>

# **_<u>Familias en su tierra (FEST)</u>_**

This study includes transfers through this program, which is administered by the Department for Social Prosperity (DPS) for the accompaniment of the Internal Displaced Population (IDP), who decide to return

> 21 Cut off to March 23, 2019.

> 22 Budget Report on Public Policy Directed to the Victims of Forced Displacement Population 2017-2018.
|9|Wholesale and retail trade; repair of motor vehicles,<br>motorcycles|1428|1128|79.0|
|---|---|---|---|---|
|10|Mining and quarrying|708|564|79.7|
|11|Other community, social and personal service activities|1476|1188|80.5|
|12|Fishing|1644|1392|84.7|
|13|Education|2256|1932|85.6|
|14|Construction|1344|1212|90.2|
|15|Public administration and defense|2256|2052|91.0|
|16|Real estate, renting and business activities|2052|1896|92.4|
|17|Health and social work|2388|2232|93.5|
|18|Transport, storage and communications|1680|1968|117.1|

Source: ILOSTAT database, International Labor Organization Table 8 shows that in majority of the sectors with higher share of female workers in Sri Lanka, women earn anywhere between 28-36 percent less than their male counterparts for doing the same job. In agriculture, hunting and forestry sector gender wage gap is around 28 percent, while employed women earn just 64 percent of their men counterparts in the manufacturing sector. However, the picture is not entirely bleak as greater gender parity in wages exists for women professionals in some service sectors such as education, health, and social work.

# 2.2.5 Nepal

Among all the South Asian countries, Nepal surprisingly has the highest female labor force participation. ILO reports that overall female labor force participation in Nepal stood at 80.1 percent in 2008. Though these numbers are encouraging, a detailed analysis at a sectoral level reveals a gloomy picture. As shown in Table 9, of the total women workforce employed in the registered sector in Nepal in 2008, almost 83 percent of women were
|on|3749|5479|9228|on|0.289|0.423|
|**Survey**|off|1405|2321|3726|off|0.108|<br>0.179|
||Total|5154|7800|12954|Svy Unde|r report|-31.4%|

2. Day Before Yesterday (Any Outage) 3. Past 10 Days (Any Outage)

# III – Accounting for fixed effects and time trends

A complimentary approach to document systematic differences between survey-based data and SSBs uses a regression framework to establish average cluster-level differences between survey-based reported outages and actual outages, while accounting for temporal fixed effects: where Ygt is the outcome variable of interest (such as average outage duration, or number of outages) aggregated in PSU _g_ for time _t_ . The variable Survg is a dummy variable indicating whether the observation was obtained using data collected from the survey (==1) or from the automated system (==0). The term τt is a vector of calendar date dummies and εgt is a random error term.

The first set of results provided in Table 5 demonstrate statistically significant differences between the data collection methods. The significantly negative differences between survey responses and SSB results confirm the expectations given the summary statistics in Tables 3 and 4. Survey-based estimates of outages and outage duration were systematically lower than automated measures. In terms of magnitudes, the largest errors were present in comparison 8, where, over a 10-day reference period of having “any outage”, survey estimates are off by about 4 days out of ten, on average.

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and Portugal, much of Spain, and some parts of the United Kingdom. Among our sample of countries, all regions are included, with the exception of Prague (Czech Republic), Bratislava (Slovakia) and the following Hungarian regions: Eszak-Alf ̈old,<sup> ́</sup> D ́el-Dun ́ant ́ul, ́Eszak-Magyarorsz ́ag, K ̈ozp-Dun ́ant ́ul, Nyugat-Dun ́ant ́ul.

We use data from the 2008 round of the European Union’s Survey on Income and Living Conditions (EU-SILC). As income variable, we use the household equivalent disposable income using the modified OECD scale. We then deflate incomes using the harmonized consumer price indexes provided by Eurostat (2011), in order to have real and comparable incomes. As poverty line _z_ , we use 11 USD _per day_ which is the threshold set by the World Bank for these set of countries. Since income data are expressed in euros, we use the exchange rate euro-dollar (1 _._ 43 euro per 1 dollar in 2008). Following Medeiros (2006) and Eisenhauer (2011), we define the richness line as a function of the poverty line. In particular, considering that the absolute poverty line _z_ may be interpreted as 60% of the median income _ym_ of an hypothetical income distribution, we set the richness line at 200% of _ym_ (see Peichl, Schaefer, and Scheicher, 2010).

Table 2 summarizes the values of the different indexes ( _P_<sup>0</sup> , _P_<sup>1</sup> , _IL_<sup>_prop_</sup> , _IL_<sup>_flatA_</sup> , _IL_<sup>_progA_</sup> , _IL_<sup>_progB_</sup> , _IL_<sup>_progC_</sup> ) for the sample of European countries. Recall that _P_<sup>0</sup> stands for the percentage of poor individuals in
of refugees in high-income countries, refer to Brell et al. (2020) and for an excellent review of the existing studies on the impact on the labor market of the host countries see Verme and Schuettler (2021). Among the general facts that have been established in the literature is that refugees have worse integration outcomes such as employment probabilities and wages in high-income host countries than economic migrants. In the medium and long run (10 to 20 years), even if employment probabilities of these two groups of migrants converge, wage gaps are still persistent (Brell et al. 2020; Fasani et al. 2020b). Existing studies have also shown heterogeneity of the effects by gender, age at arrival and level of educational attainment and skills. However, these explanatory factors do not explain a significant share of the variation in the observed migrant-refugee gap in labor-market outcomes (Brell et al. 2020).

The literature has mainly examined separately a limited number of initial conditions: primarily the role of labor market conditions (i.e., unemployment), and the role of networks. The country-specific studies discussed in what follows rely on exogenous government placement policies allocating refugees across locations, which allows exploiting the exogenous variation in local labor market conditions.

Access to the labor market of the host country after arrival is important, and limitations<sup>16</sup> or exposure to high levels of local unemployment may have long-term consequences. The evidence in the case of refugees has consistently found that less favorable initial conditions in the labor market may have long-term effects on refugees’ employment probabilities and earnings.

Åslund and Rooth (2007) show the effects of the initial conditions in terms of unemployment rates, encountered by refugees at arrival on long-term earnings and employment in Sweden. To document this, the authors use the FLYDATA, which offers detailed information on all refugees who received a residence permit between 1987 and 1991. Data on labor market outcomes come from the Sweden Longitudinal Individual Study. The authors are thus able to follow
impacted differently depending on how such inputs are taxed at the border. In addition there could be non-tariff barriers (NTBs) related to policies that effectively raise the price wedge between a domestically sold good and its price at the border.

When assessing levels of industry protection in Egypt, one faces the additional major complications that the government has maintained subsidies on the provision of some goods and high levels of subsidies on energy sources that impact across many industries. So one should not only account for the effect of the trade regime at the border but also internal advantages to certain industries that result from these two sources of subsidies. This study takes care to separate two effects: that of the tariff schedule and output subsidies and that of energy subsidies. As a practical matter, we make use of information from the 2006/2007 input-output matrix that indentifies five fuel-energy sectors (natural gas, industrial and diesel fuels, LPG and gasoline) and estimates of price subsidies associated with these energy sources.

After several years of economic reforms and trade liberalization, this is a good moment to assess the level of protection of various Egyptian industries, and to discuss the implications of the current profile of protection and subsidies for the at-least implicit (and perhaps unintended) discrimination between sectors, privileging some and 1
Giavazzi et al.’s (2010) study explores the behavior of household savings using household-level micro data from the Egyptian Income, Expenditure and Consumption Surveys. Different from aggregate time series, the survey data clearly observed an immense reduction in household savings, from 14% in 2004/05 to 10% in 2008/09. The authors find evidence that indicates the reduction can be attributed to improvements in access to finance and credit. Moreover, they find a negative correlation between savings and measures of financial development. The study also suggests that the pension reform considered in Egypt—in which contributions to pension funds are compulsory--is less likely to result in the saving shortfall observed in other countries which left individuals the option of savings for retirement.

Love (2010) examines corporate savings in Egypt, which represents 50% of total savings. Her study focuses on two measures of corporate savings – financial savings and physical savings.<sup>7</sup> It employs two data sets - Investment Climate Assessment and data for publicly listed firms on EGX to analyze the determinants of corporate savings behavior. The study finds that larger firms invest more (physical savings), which is imperative to maintaining competitiveness and growth. Moreover, the study also concludes that small firms invest less because of their lack of access to credit. Therefore, improving access to credit for small and medium enterprises is likely to have a positive impact on investment. Furthermore, despite the financial deepening, the use of credit products observed a reduction during the last decade. The main policy recommendation from the study reaffirms the importance of improving access to financial services in Egypt. In addition, policies aimed at reducing macroeconomic volatility are likely to result in increased investment.

Touny (2008) shows that Egypt’s domestic savings ratio has an average of only 14.6 percent of GDP through the period 1980 - 2005, which is relatively low compared to other countries at a similar level of per capita income. He examines the long-run determinants of domestic
(2018) for a review). Attempts to measure vulnerability to poverty in African contexts have highlighted the existence of sizeable welfare fluctuations and fractions of households exposed to poverty risk typically larger than realized poverty rates as measured in traditional cross-sectional poverty surveys (Dercon and Krishnan, 2000; Christiaensen and Subbarao, 2005; Demissie and Kasie, 2017; Hill and Porter, 2017; Skoufias et al., 2021).

In this paper, we develop a new framework inspired from catastrophe risk modeling methods (henceforth referred to as cat risk models or cat models) with the broad objective of improving our understanding of the nature, magnitude and distribution of impacts of weather shocks on household welfare. The primary aim of the proposed methodology is to produce reliable food insecurity forecasts that can support the design and targeting of annual interventions. We focus more specifically on food consumption for at least two reasons. First, ensuring food security in Sub-Saharan Africa remains a prime challenge and food consumption is still an essential welfare dimension considered for the design of anti-poverty programs in the region. Second, poverty surveys traditionally exploited to examine the impact of climate shocks on household welfare often have a comprehensive consumption module but are only carried out every 5 to 10 years. On the other hand, food security surveys primarily focus on food consumption and coping strategies but are generally conducted at least once a year. They therefore provide consumption realizations from a larger set of rainy season conditions scenarios. From an econometric perspective, this simply means that we can rely on relatively more identifying variation to estimate the relation between the rainy season quality (RSQ) and food consumption. We focus first on the estimation of a reduced form that quantifies the impact of the RSQ on food consumption. We investigate the heterogeneity of the RSQ impacts considering both geographic and household characteristics and our findings do reveal marked differences across locations (e.g. regions, livelihood zones...) and according to key household features, such as income sources or livestock ownership. We determine an optimized heterogeneity
>**ime to trad**|**North**<br>**America**<br>**e(hours)**|**South**<br>**Asia**|**Sub‐**<br>**Saharan**<br>**Africa**|
|---|---|---|---|---|---|---|---|
|**Border Delay**|126.96|75.50|122.40|156.87|67.51|166.56|175.47|
|**Reduced Border Delay**|95.61|57.82|109.77|111.32|55.25|102.19|159.43|

18 Data on border compliance are from the World Bank’s Doing Business 2018 report.

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