rafmacalaba/data-use-mentions-extended huggingface dataset:
bias mattered only for our results in Jordan.<sup>24</sup> 23 Some regressors were ultimately excluded as they were not significant and had no effect on the other coefficient estimates.

24 In the equation to predict whether all key variables were available and estimate controls for non-response, we found that for Jordan the probability of completed responses was higher if the respondent to the COVID follow-up survey was the same person as in the 2019 WBES; for medium sized firms (but not large ones); and for managers with more experience in the sector. For Georgia, full response probabilities were not related to any of these variables. Unfortunately, we were unable to control for non-response there, because none of the Z variables were significant determinants of joint non-response.

18
|**Tool name:**|**Integrated Macroeconomic Model for Poverty Analysis**|
|---|---|
|**What is it?**|IMMPA is a dynamic CGE model.|
|**What can it be used for?**|IMMPA can be used to analyze the impact of macroeconomic policy and external shocks on income<br>distribution, employment, and poverty.|
|**What does it tell you?**|One of the main features of IMMPA is that it integrates the real and financial sides of the economy; in<br>this regard, IMMPA is useful for analyzing both the impact of structural reforms (such as changes in<br>tariffs or the composition of public expenditure) and the effects of short-term stabilization policies<br>(such as a cut in domestic credit or a rise in deposit interest rates). The detailed treatment of the labor<br>market is key for the assessment of the poverty reduction impact of macroeconomic policies. Also, it is<br>useful for drawing the distinction between rural and urban sectors by completing separate projections<br>on output and employment fluctuations for both areas and therefore to study poverty in different<br>geographical areas.|
|**Complementary tools:**|IMMPA would complement and be complemented by the use of household surveys to map impacts<br>into distributional changes. Stakeholder analysis can be useful for identifying different groups that are<br>of interest.|
|**Key Elements:**|The main features that distinguish IMMPA from other CGE models are as follows. First, IMMPA<br>offers a very detailed specification of the labor market, which is the main transmission channel of<br>macroeconomic shocks and adjustment policies to economic activity, employment, and relative prices.<br>The labor-market specification permits a disaggregation at the urban and rural levels and, within each<br
households without electricity access were provided with SHS. This figure will go up to about 15% if the grid-electricity generation is used as the energy baseline to estimate the carbon benefit from SHS.

Second, under the assumption that the existing micro-credit scheme plus a cash subsidy is made available to all non-electrified households in rural Bangladesh, the affordability assessment indicates that scaling-up SHS adoption is indeed possible in many parts of rural areas. Among the 41 districts in the survey, about 17 districts have an affordability rate over 25% as well as high concentration of non-electrified households in comparison to the national average. This means there exists a potential market for SHS and it is also possible to reduce the costs of SHS dissemination due to economies of scale to promote a profitable participation of the private sector in the SHS market.

Third, contrary to the commonly held views that subsidies for promoting SHS dissemination in rural areas benefit mainly better-off households, our policy simulation shows that SHS subsidies in rural Bangladesh are progressive when programs are appropriately targeted even using the private sector delivery model.

> 11 The 2005 poverty map is constructed using the 2005 Household Living Standard survey by the World Bank.

13
participation and voice in the village development process (Wong 2022; previous studies include Mansuri and Rao 2013; Wong and Guggenheim 2018).

_Resilience_ A range of development interventions seek to bolster resilience, and a large amount of evidence has shown their positive effects. For example, a meta-analysis of 129 impact evaluations assessing the effects of group-based livelihoods programs – such as communitybased microfinance schemes, self-help groups, or savings and producer groups, often based on gender, economic, social, cultural, or ethnic affinity – found that they provided much-needed safety nets and led to reduced vulnerability in contexts of extreme deprivation and adverse events like conflict or climate shocks (Barooah et al. 2020). Moreover, the meta-analysis found that group-based livelihoods programs systematically improved social cohesion in the form of greater access to entitlements and intra- and inter-group relationships.

Other evidence also suggests that resilience has strong links to social cohesion and inclusion and that the three may be mutually reinforcing. Building and maintaining social relations and network structures are critical for resilience, as are the content of those relations— including the roles of trust, reciprocity, and mutual support (Adger 2000; Bodin, Crona, and Ernstson 2006; Peiling and High 2005; Traerup 2012). For example, a study of natural disaster response across four countries found that communities with robust social networks were better able to coordinate recovery and minimize long-term consequences (Aldrich 2012). In contrast, a study of Chicago’s 1995 heat wave found that various forms of social breakdown, including the physical and social isolation of senior citizens, contributed to high fatality rates (Klinenberg 36
Our main objective in this paper is not to study the precise channel through which trade policy affects market power. Rather, our goal is to provide clear-cut evidence about the sign of these theoretically ambiguous effects. Still, our results are informative about the channel. If NTMs affect mostly variable costs, we would expect their effect on PTM to be similar to that of tariffs. The fact that we find a negative effect of tariffs on PTM and a positive one of NTMs is a first indication that the latter are operating mostly through a fixed cost channel. We also discuss in Section 6 a number of additional estimations where we regress various indicators of trade margins (number of exporters, average price and size, concentration indices) on NTMs and tariffs to shed some additional light on those channels of transmission. We show in particular that while tariffs are associated with changes in firms’ performance at the intensive margin of trade, NTMs are mostly related to indicators at the extensive margin.<sup>14</sup>

# **3 Data and descriptive statistics**

Testing our predictions requires gathering three main types of data: (i) firm-level data on export flows, (ii) macroeconomic data and (iii) trade policy variables. Note that when testing the impact of trade policy on market power, the use of firm-level data is key because it enables us to control for firm characteristics which affect their reaction to exchange rate variations (e.g. marginal costs, size or other firm characteristics such as financial constraints) and can be correlated with trade protection.

## **3.1 Firm-level trade data**

Our data were obtained from the customs administrations of twelve developing countries. Data for Kenya, Rwanda, Tanzania and Uganda were obtained by the International Growth Center and data for Bangladesh, Chile, Jordan, Kuwait, Lebanon, Mexico, Morocco, and the Republic of Yemen were obtained by the Trade and Integration Unit of the World Bank Research Department, as part of the Exporters Dynamics Database
the local level are utilized to investigate the movement in earnings and quantities supplied. As a last step, those changes are mapped to the household survey and fed into the household welfare function using a farm household model so as to measure the changes in real income.

# **3.1 International prices and domestic prices**

The successful Doha implementation would have an effect, albeit small, on the prices of various products important both in the consumption baskets and the income sources of Mexican households. However, it is widely recognized that the international prices of products and their retail prices are only loosely linked, as internal factors such as transportation costs and local supply of substitute products act as filters between the two (Frankel et al, 2004 and Winters et al, 2004). The isolation of local markets is particularly true in rural areas where marketing infrastructure is poorly developed or altogether missing Given the fact that domestic price transmission is imperfect, to measure poverty effects of trade reforms, it is necessary first to estimate the magnitude of changes in local retail prices consequent to changes in world prices. In other words, movement in average prices consequential to trade policies (those estimated by the CGE model) need to be translated in changes in retail prices (those faced by the households). The model utilized here to measure the extent to which local prices vary relative to the international prices follows the approach of Nicita (2004) and is based on the tariff and exchange rate passthrough literature (Goldberg and Knetter, 1997 and Campa and Goldberg, 2002).

9
EDI.

# **5.3 Education enrollment**

For each child enrolled in primary school, we collected enrollment histories with regard to enrollment in education, including preschool education, starting from age two. These are used to construct data on enrollment by age. Two variables are constructed. The enrollment variable indicates whether or not the child was enrolled at any time, and the number of months of enrollment indicates the number of months the child was enrolled over a 12-month period.

Figure 3 reports the gender gaps and associated 95% confidence interval at each age for children in our sample both in terms of whether or not children are enrolled and in terms of duration of enrollment. At age 2, the earliest age for which we have enrollment histories, there is no observed gender gap. At age 3, girls are 4 percentage points more likely to be enrolled in preschool than boys, a point estimate that increases to 6.7 percentage points at age 4. At age 5 they are 5.7 11
Table 2 presents robust pooled-OLS and within-group (WG) estimates of the four models considered (M1, M2, M3 and M4). Time dummies are included in all models. As noted above, we use the headcount poverty rate (with poverty line of US$ 2) as our baseline measure of poverty.<sup>24</sup> For ease of comparison with the existing literature, we also report estimates of (12) omitting the poverty rate and including only inequality. For each model, we can then assess the contribution of

> 22 We use the residuals of a regression of the openness index on country size and two dummies indicating whether the country is landlocked and whether it is an oil exporter (Loayza et al., 2005).

> 23 The infrastructure index is an updated version of that used by Calderón et al. (2015).

> 24 Following López and Servén (2009), we drop Nigeria and Eswatini (formerly Swaziland) from the sample because of the poor quality of their PWT7.1 GDP data. Moreover, for models M2, M3 and M4, we found several big outliers for the investment price and inflation times series. For instance, there are 3 observations above 4 and even 5 standard deviations for the domestic investment price, and 6 big outliers for inflation. These anomalous observations affect mainly the specification tests of system GMM (the Hansen and m2 tests), bringing them closer to rejection in some cases, but have only minor incidence on the estimation results.

14
sup> They derive two main propositions: first, there is a support effect, by which multinational banks extend capital to affiliates that have been affected by a financial shock; and second, there is a substitution effect, by which multinational banks shift capital among affiliates in response to real sector shocks that affect the profitability of lending in a country.

Recent empirical evidence is consistent with both the ―support‖ and ―substitution‖ propositions. Using data from 45 of the largest multinational banks from 1991 to 2004, de Haas and van Lelyveld (2010) find a negative relationship between GDP growth in the home country

> 10 For ease of exposition, we use affiliates to refer to both branches and subsidiaries in foreign countries.

8
research budget restrictions, Casey et al. (2019) only make use of surveys conducted with community leaders and a limited number of measures based on direct observation of public infrastructure. In this study, we do the same but also collect data from randomly selected households, including children’s test scores, and from the users and directors of the public infrastructure. One benefit of these additional data sources is that it provides a richer set of measures per outcome family. Another benefit is that these data allow us to explore a larger set of outcome families, such as additional dimensions of service provision, education, health, and women’s empowerment; outcomes that are central to many CDD interventions.

Second, the size of the experiment we study here is much larger. Casey et al. (2019) employ data from 236 villages (113 treatment, 113 control). In contrast, our measurement strategy builds on data from 735 villages (367 treatment, 368 control), significantly increasing statistical precision and decreasing the possibility for Type-II errors.

Finally, we contribute through replication. Similar to Sierra Leone, the Democratic Republic of Congo scores badly when it comes to development outcomes. However, Congo is different in important ways, including the proliferation of armed groups and continuing conflict and violence, which may affect the effectiveness of a CDD program. As such, this study contributes to generalizable knowledge by understanding the impact of a similar program in a different context, specifically a fragile and conflict setting.

In sum, we find very similar results as those reported in Casey et al. (2019). We find no evidence for software results, despite the change in context, the additional precision, and the wider set of software-related outcomes. In addition, we find positive effects on some hardware-related outcomes. Specifically, the positive effect on the stock and quality of local public infrastructure (0.23SD, p<0.01) as reported in Casey et al. (2019), is very similar to the
age. A second correction is applied only to women with children, as suggested in Munich et al. (2005) and Pastore and Verashchagina (2011). A year per child is subtracted from the mother’s age to calculate work experience. In a few cases, the implied total number of years of potential work experience turned out to be negative; we set these equal to zero.

The other variables used in the estimates are self-explanatory.

Any data source has both advantages and disadvantages. The advantages of the SWT survey are many and they include the following: the availability of a relatively large number of observations for young people rarely available in labor surveys; the availability of information on household characteristics which can be used as instruments in two-step procedures able to control for endogeneity and sample selection bias; very detailed information on demographic and individual characteristics, including aspects of young people’s labor market attitudes, such as aspirations and life goals, job satisfaction, job search methods and the like. Nevertheless, some other important variables, typically used as controls in Mincerian earnings equations, are missing in the SWT survey, thus constraining our ability to explore wage differences more fully. This is the case, for instance with respect to the following variables: the ownership of firms; the sector of employment; the location of respondents, and, except for the distinction between rural and urban residential areas, information on whether they moved to their current place of residence or not.

# **3. Results**

## **3.1. Descriptive Statistics**

### **3.1.1. Demographic factors**

Figure 2 reports the age distribution of first marriage by gender for the entire sample and for employed and non-employed women respectively. The average age at first marriage is different for men and women: 20.3 years for women and 22.6 years for men. It is also different from the official statistics reported in the introduction simply because the data set we use
interest expenditure. In addition, business climates and policy frameworks improved in most of the 2001 LICs.

Rapid economic growth in LICs has contributed to poverty reduction. The share of extreme poor in the population of 2001 LICs has fallen by 16 percentage points, on average, and this has contributed 20 percentage points—about one-third—to the decline in the global poverty headcount between 2001 and 2015. This contribution, however, mostly reflects sharp declines in the poverty headcount of the 2001 LICs that reached middle-income levels, while masking broadly unchanged poverty headcounts among the countries that have remained, or became, LICs (Figure 2). Today’s LICs account for less than one-tenth of the global population (one-quarter their share in 2001). However, they are home to around 40 percent of the world’s extreme poor. This share is expected to remain elevated amid continued fragility in many of these economies, while the poverty headcount is expected to continue to decline elsewhere.

# **Figure 2. LIC poverty**

## **A. LIC poverty rates and headcounts**

<!-- Start of picture text --> Millions Percent<br>Poverty headcounts<br>800 80<br>Poverty rate (RHS)<br>600 60<br>400 40<br>200 20<br>0 0<br>2001 Latest 2001 Latest<br>LICs turned MICs Continued LICs<br><!-- End of picture text -->

## **B. World poverty and LIC share of world population**

<!-- Start of picture text --> Percent of world population<br>45<br>Other countries<br>2001 LICs turned MICs<br>30<br>Today's LICs<br>LIC population<br>15<br>0<br>2001 Latest<br><!-- End of picture text --> Source: United Nations, World
To account for potential biases resulting from the random assignment of the month for individuals with missing start or end of their job we ran two robustness checks for which we show the results in the appendix. We repeated the analysis limiting the sample to those who reported the month omitting all observations with imputed month. We found that the results hold for this sample as well (Figures 16-22 in the appendix). Additionally, we limited the sample to the years 2019 and 2020 given that individuals are generally more likely to report the month for jobs taken in recent years than earlier ones. While the results are attenuated compared to the main analysis, the main conclusion holds: Syrians are significantly disadvantaged compared to Jordanians during the pandemic and this disadvantage is mainly driven by Syrians residing in camps.

# **Conclusion**

Syrian refugees, like other refugees, face substantial barriers to entry to the formal labor market and are therefore confined to informal employment. Even when they obtain work permits, this often merely translates into formalization of their right to work and not necessarily their employment. Concentration in informal employment means that Syrian refugees are particularly vulnerable to shocks. Moreover, Syrian refugees in camps may be experiencing an additional layer of vulnerability stemming from restrictions on movement and their inability to compete in the labor market like refugees in host communities.

In this paper we tried to explore the interplay of these factors for Syrian refugees during the COVID-19 pandemic. We compared the employment outcomes of Jordanians and Syrians over a period of four years before and during the COVID-19 pandemic. This was possible by constructing a synthetic semi-annual panel using the Survey of Young People in Jordan collected in 2020 and 2021. Running a fixed effects model, we found that Syrians and Jordanians experienced similar trends in job separation and job finding rates prior to the pandemic. With the onset of the pandemic, 28
Figure 3 presents the maps of the spatial accessibility of job opportunities in Amman, Beirut, and Cairo. In our sample, in Amman, households have on average access to 27% of total jobs within 60 minutes using public transportation and walking. In Beirut, the same figure is at 21% and in Cairo (a larger city), it is at 16%. These accessibility levels are lower than many cities in developing countries where such analysis has been done. Peralta-Quiros et al. (2019) perform a benchmarking of 11 cities in Africa, according to this benchmarking, all three cities can be classified as worst performers in connecting people with employment opportunities.<sup>22</sup>

# _b. Measuring availability_

Availability focuses on the availability of public transport close to the residential locations. In practice, we look at the proximity to transit stops factored by the frequency of service. This indicator is used to assess the density of service within immediate reach of the household, but without considering the destinations of the transit.

The same street grid data and transit network data that was used for measuring accessibility is also used to measure availability. For each household, we compute the number of public transport ‘runs’, that is the number of vehicle departures at all stops available within a 10 minute walking time over the course of an hour. The number of runs is normalized to create an index between 0 and 100%.<sup>23</sup> A value of 100% means that public transport is highly available within 10-minute walking distance from an hour while a value of 0 means that no public transport is available within a 10-minute walking distance of a household.

Figure 4 presents the maps of the availability of public transport in Amman, Beirut, and Cairo. In Amman, for our sample of households, the average value of the index is 41%. In Beirut, the same figure is at 29%, and Cairo it is at 52%.

> 22 Benchmarking data
e included this variable in the analysis to assess the macroenvironment in which central banks operate. Alternatively, we use the number of currency crises and the current account balance as a percentage of GDP as another means of describing the macroenvironment in which the central bank operates.

**_Reserve Adequacy Variables._** Finally, a central bank's risk tolerance and investment policy (number of eligible assets and currencies, portfolio duration, allocation to nontraditional asset classes) are linked to reserve adequacy.  All else being equal, a central bank with higher reserve adequacy typically has a higher risk tolerance and can deploy a broader range of asset classes, currencies, and longer duration. Therefore, we use reserve adequacy measures to control for differences in reserve adequacy across central banks. We include two reserve adequacy metrics as control variables: reserves to GDP and to M2. Separately, we also control for GDP per capita and other macroeconomic variables, such as short-term external debt stocks as a percentage of total reserves.

# **4.2 RAMP Survey Variables**

RAMP's global surveys on reserve management practices are the source for information on central banks' governance arrangements, investment policies, and measures of risk taking. RAMP conducted two surveys in 2017 and 2019 with responses from 99 and 105 central banks, respectively.<sup>13</sup> The RAMP survey data are unique. For the first time, data on governance

> 10. See https://www.transparency.org/en/cpi and

> <u>https://images.transparencycdn.org/images/2019_CPI_SourceDescription_EN-converted-merged.pdf.</u> 11. See https://www.msci.com/documents/10199/e092c439-34e1-4055-8491-86fb0799c38f. 12. See https://www.eiu.com/n/solutions/country-risk-model/.

> 13. Anasashvili et al. (2020); see

<u>https://openknowledge.worldbank.org/bitstream/handle/10986