rafmacalaba/data-use-mentions-extended huggingface dataset:
In contrast, the country’s large microfinance institutions (MFIs) may be a major source of support for microenterprise expansion and growth for a variety of reasons: (a) MFIs have a large network of outreach; (b) they do not require physical collateral to lend; and (c) they charge an effective interest rate close to 32 percent. We find that some 8 percent of microenterprises acquired loans from MFIs to start-up microenterprise activities in 2010. Lack of access to affordable finance for start-up capital is perhaps a barrier for microenterprise growth.<sup>17</sup> Our findings suggest that improved access to affordable loans through microfinance or other sources would help further microenterprise growth and promote poverty reduction in Bangladesh. As the returns to microenterprise investment are found high and meet the cost of borrowing from microfinance institutions, there are clearly large potentials of microfinance for supporting microenterprise growth in Bangladesh.

> 17 It is possible that MFIs support microenterprises by providing working capital. Such information is however, not available from the HIES survey.  Note also that MFIs support activities such as poultry raising, livestock, and farmrelated activities like gardening are not included in defining microenterprise activities in this paper. erprises is needed to identify the potential of this source of finance for microenterprise growth in Bangladesh.

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# **IV. Methodology and Data**

The previous sections explained the demographic component of women’s increased labor market participation. Now we explore individual and household characteristics and labor market indicators to estimate a model to identify the main determinants of the increase in women’s employment. We follow Klasen and Pieters’ (2015) approach –mentioned in Section II– estimating a probit model to decompose marginal contributions of different covariates to women’s propensity to be employed. Nevertheless, we pool the selected years, 2007 and 2017, in the same regression rather than estimating a model for each year separately. We also include a new variable for Mexico’s context—namely “childcare facilities”—in consideration that in 2007 the Mexican government initiated its National Program of Childcare Services for Working Mothers (CFWM) that operated throughout the decade. We also included lagged labor market indicators to control for possible endogeneity.

## **Data**

We use Mexico’s National Employment and Occupation Survey (ENOE), a quarterly householdlevel rotating panel for which each individual is interviewed for five consecutive periods.<sup>5</sup> The ENOE, Mexico’s official labor force survey since 2005, provides representative information on labor market characteristics at the national, state, urban, and rural levels. It includes information for individuals aged 15 and above; however, we restrict our sample to women aged 18-to-65. Our analysis includes observations from 2007―when CFWM started operating and one year before

> 5 For this paper, we do not use the ENOE panel data design since comparing the same population after 10 years is not feasible. As mentioned, each individual is surveyed for 15 months at the most, making it difficult to identify the before and after dynamics of the 2008 economic crisis.

19
change in the price of the subsidized product. The second type of elasticity is cross-price elasticity of demand. The question addressed here is to what extent households will substitute the consumption of the subsidized product for other substitute products subject to an increase in the price of the subsidized product. For example, we try to assess the increase in household consumption of pasta when the price of rice increases.

The estimation of **demand elasticities** is the subject of a rich literature and is product specific. There are at least three types of settings we can consider. Under the first setting, the analyst can draw on the specific literature and borrow demand elasticities estimated elsewhere. For example, in certain countries and for certain products there exist a large literature on the estimation of demand elasticities which can then be used “off the shelves” for modeling. The demand elasticity for electricity in the US is one example. In this case, our manual will simply allow practitioners to input these values into the model.

Under the second setting, the analyst has longitudinal panel data and price shocks of the subsidized product along the period. This is the ideal scenario where demand elasticities can be estimated and then used in the simulation model. In reality, this setting is rare either because panel data are rarely available or because no significant change in prices has occurred.

More often the analyst has only one cross-section household survey, which is the reason why we have chosen to focus on this data setting. With one cross-section survey it is still possible to estimate demand elasticities but this requires price variability in the data. For example, we have representative data by region or district where the subsidized product price changes. Or, alternatively, we have a product with multiple prices across the nation according to quantities consumed (such as utilities prices). In these cases, we can exploit this variability to estimate demand elasticities based on the assumption of homogeneous behavior across households. We can also improve on this assumption by controlling for household characteristics. As compared
**Figure 1. Pre-primary education gross enrollment rates (percentage)**

<!-- Start of picture text --> 70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>1970 1980 1990 2000 2010 2020<br>Egypt, Arab Rep. World<br>Percentage<br><!-- End of picture text --> _Source:_ Authors’ creation based on World Development Indicators (World Bank, 2022a)

# _2.4.2 Education challenges in Egypt_

Generally, investment in early childhood development in the MENA region has been comparatively low (El-Kogali & Krafft, 2015). The region also tends to have the lowest scores in the world on international assessments during the primary and secondary grades (El-Kogali & Krafft, 2020). The catch-up between the earlier grades of primary school and later grades suggests that gaps in early learning may play an important role in learning and human capital challenges in the region (El-Kogali & Krafft, 2020).

Egypt has had substantial success expanding access to primary, secondary, and higher education, but education quality remains an issue (Elbadawy, 2015). Emphasis on rote memorization, large class sizes, and a focus on credentials over skills have been long-standing challenges in Egypt’s education system (El-Kogali & Krafft, 2020; Elbadawy, 2015; Moustafa, Elghamrawy, King, & Hao, 2022; World Bank, 2008). Returns to education in MENA and Egypt are low and stagnant as a result of both education and labor market challenges (Krafft, Branson, & Flak, 2019; Said, Galal, & Sami, 2022; Salehi-Isfahani, Tunali, & Assaad, 2009).

# _2.4.3 Education 2.0 reforms_

Starting in 2018, MoETE began a series of system-wide educational reforms, referred to as education 2.0 (Moustafa, Elghamrawy, King, & Hao, 2022
measured by distance from S1) are statistically different across MAPS and AFSIS specifications.<sup>16</sup> Seasonal rainfall is only a moderately statistically significant input into production when controlling for plot-level soil suitability, but it is a strong and significant predictor in the AFSIS model. The latter finding comes contrary to expectations, as the plot-level soil data does not directly incorporate climatological variables while the AFSIS data does. A final notable difference in output elasticities across the overall specifications is that of inorganic fertilizer. Under the AFSIS model the application of inorganic fertilizer does not yield any statistically significant gains in production, contrary to expectations and findings in the MAPS-based model. This raises the first caution against the use of geospatial data as a substitute for plot-level soil data.

> 16 A test of the difference in coefficients across the overall MAPS and AFSIS specifications, columns 1 and 2 in Annex Table A2, indicates that the coefficients (-0.082 and -0.050, respectively) are significantly different from each other at the 1 percent level. The test of difference in coefficients is implemented by the execution of a seemingly unrelated estimation of the two OLS models, followed by a Wald test of equality of the specific coefficients.

17
intermediate input effects, and technological spillovers. Nadvi (1997, 1999) and Meyer-Stamer (1998) recognize that clustering offers unique opportunities for firms to take advantage of a wide array of domestic links between users and producers and between the economy’s knowledge sector and its business sector. Such linkages have the potential for stimulating learning and innovation.

Clusters, however, are not necessarily innovation systems (McCormick and Oyelaran-Oyeyinka 2007), and innovative clusters are not necessarily high-technology clusters. Mytelka (2004) also emphasizes the role of clusters in promoting the kind of interactivity that stimulates innovation but cautions that the geographic proximity of actors does not automatically lead to learning and innovation. However, there is a growing recognition that cluster initiatives could be an effective means for producing an environment conducive to innovation (Andersson et al. 2004). All these arguments can find their roots in different cluster examples.

Although clusters come in several different forms and various scholars have tried different typologies, all clusters share one commonality: each comprises a multitude of firms of different sizes belonging to one branch of industry. Markusen (1996) has classified clusters into four categories: Marshallian, hub and spoke, satellite platform, and state anchored (see table 1.2). Others have described them by development stage, such as agglomeration, emerging, potential, and mature.

# **SEZs and Clusters: “Top-Down” versus“Bottom-Up”?**

While SEZs are normally constructed through a “top-down” approach by government policies, most clusters are formed in an organic way through a “bottom-up” process. Some clusters, however, have emerged from or within industrial parks or exportprocessing zones over time but rarely in developing countries. A study of 11 African clusters across several countries reveals that most of them formed spontaneously, with the exception of the Mauritian textile cluster, which evolved from an export-processing zone (Zeng 2008).

Table 1.2 Markusen’s Typology of Industry Clusters

|Cluster type<br>growth|Characteristics of<
is replaced by another to maintain the continuity of data collection. The survey methodology encompasses various social areas, including basic demographic characteristics of households and their members, monetary indicators reflecting living standards and social stratification, non-monetary indicators related to living standards, economic activity, employment, unemployment, social services or programs, and household participation in them.

**Since the EU-SILC focuses mainly on income, the study also relies on the 2021 Household Budget Survey, which provides critical consumption data to estimate consumable income in the commitment to equity (CEQ) model.** The household budget survey in Bulgaria aims to gather data on income, expenditure, consumption, and living standards, focusing on understanding changes over the years. The unit of observation is an ordinary household, whether consisting of a single person or multiple individuals sharing meals and an expected budget. The sample size has varied over the years, with 3,000 households observed up to 2009 and a subsequent adjustment to 3,060 households divided into three subsamples since 2010. The selected households participated in the survey for one year, with a rotation sample method implemented.

**Additionally, the study relies on external EC estimates of the consumption basket and administrative data on beneficiaries of social programs** . The consumption basket serves as a reference basket to evaluate the restrictiveness of the income thresholds. As there is no official measurement of the

12
SCDs go further and provide a detailed account of exclusion in all three. To illustrate, the Panama SCD makes powerful use of data – nightlight maps – to show that areas that lack electricity coverage are also those with high populations of indigenous peoples (Figure 3.3). The Mali SCD, similarly details the exclusion women face in social spaces, including in their homes. They have limited control over household income and have restricted access to other means of production such as equipment, raw material, and technology; high levels of domestic and other forms of violence, including genital mutilation – all contributing to their exclusion from multiple domains.

**Figure 3.3. Night Lights in Panama: 1997 and 2010**

Source: World Bank 2015g, 52.

The analysis in some SCDs cuts across domains to show how exclusion in one area (e.g. in transport or education) can _aggravate_ exclusion in another (e.g. in the labor market). The Botswana SCD is an example of how these links can be made. The SCD notes the failure of the education system, in terms of unequal and generally weak outcomes, and the role it has played in restricting the long-term prospects of youth in the labor market. Limited access to public transportation is identified as a barrier to inclusion, as poor Batswana are forced to spend higher shares of their income on transport due to greater distances between housing and employment opportunities. The SCD sees exclusion in access to water as another factor contributing to persistent inequalities – especially in farming. The Chad SCD sees domestic violence as a manifestation of women’s lack of autonomy within households.<sup>9</sup> It notes that “while poor and non-poor women suffer similar rates of domestic violence, the impact on poor women is likely to be more severe given their lower access to healthcare, justice and social 21
Figure 15. Debit Card Ownership Equity Adjust Coverage, (Household-level, LITS) <!-- Start of picture text --> 80<br>70.1<br>70 64.8 63.8<br>60.7<br>55.7 57.5<br>60 53.0 51.6 50.5<br>47.0<br>50<br>40<br>30<br>20 11.4 12.1<br>7.3 6.5 8.9<br>10<br>0<br>Latvia Czech Republic Turkey France Croatia<br>Coverage Dissimilarity EACR<br>Equity Adjust Coverage, (Individual level, FINDEX)<br>2011 2014<br>90 90<br>77.0<br>80 80<br>7060 50.3 55.4 56.6 58.0 52.0 60.7 52.1 7060 69.6 67.1 58.4 65.7 56.5<br>50 39.9 45.2 42.9 50 39.9 43.3<br>40 40 33.3 33.9<br>3020 20.8 18.5 24.3 10.4 14.1 3020 16.5 9.6 21.6 12.9 14.1<br>10 10<br>0 0<br>Belarus United Arab Turkey Rep. of Korea Mongolia Belarus United Arab Turkey Rep. of Korea Mongolia<br>Emirates Emirates<br>Coverage Dissimilarity EACR<br><!-- End of picture text --> _Notes:._ FINDEX is an individual level data base and LITS is household level. In FINDEX, 145 countries are sorted on Coverage, and only nearest neighbors in coverage rates are shown. In LITS, 35 countries are sorted on Coverage, and only nearest neighbors are shown
of disease control, education, poverty alleviation, and other sectors with ―soft‖ or amorphous outcomes, long ambiguous causal chains and impatient donors. Ravallion (2009) presents some alternative claims about political economy concerns and market failures in the supply of evaluations.

12 Most studies summarized in this review (except potentially those examining the impacts of electrification) are underpowered to detect gender differences; we have included these studies otherwise our empirical base would be virtually a null set. Future research should consider working with the large-scale household surveys such as DHS and LSMS with several thousand observations, some of which measure gender differentiated outcomes, in pursuing the kinds of impacts – health, time spent, forest products extracted – examined in this report.

13 Experiments use the power of randomization to ensure that potential confounders are balanced across intervention and control units and therefore any differences in the outcomes between the two can be attributed to the intervention. The quasiexperimental designs that use econometrics to compare intervention to ―controls‖ include: (a) matching—finding a control group that ―matches‖ important observable characteristics of the program group; (b) instrumental variables—using variables that are uncorrelated with the outcome but correlated with program participation to identify a control group; (c) difference-indifference—using repeated measures of the same individuals (units) to account for time-invariant differences between control and program groups; and (d) Heckman two-stage models—using covariates to predict who participates and using the predicted probabilities to correct bias when comparing controls to program groups (Ravallion 2007).

9
Combined, Table 1 demonstrates that violence significantly affects displacement decisions. But despite the stronger increase in fatalities following displacement in origin districts, there are a number of ways in which the violence in origin districts of displaced households is similar to violence in the districts of residence of non-displaced households. First, Table 2 re-estimates the baseline specifications presented in Table 1, but uses the number of violent incidents as the dependent variable. The results demonstrate that there is little evidence for a similar pattern in the number of violent incidents. In the most complete specification in column (3), the increase in violent incidents in origin districts is indistinguishable from the increase for non-displaced households. Furthermore, we can rule out a relative increase in the number of violent incidents in origin districts of displaced households of larger than 0.44 monthly violent incidents.

Second, apart from the increase in violence over time, the average level of predisplacement violence in origin districts for displaced households is very similar to the average violence in the months before the most recent survey of non-displaced households. This is true of both fatalities and the number of violent incidents. Specifically, the coefficients on the displacement indicator in column (1) of Tables 1 and 2 are small in magnitude, vary in signs between specifications, and are indistinguishable from zero.<sup>16</sup> The same is true of migration districts in column (4) of Tables 1 and 2, as well.

Combined, these results demonstrate that living in close proximity to violence is a factor in displacement. But the results also raise questions about why the change in violence in origin districts of displaced households relative to the change in the district of residence of non-displaced households was not stronger in magnitude and more robust across measures of violence, particularly given the significant share of respondents reporting that violence was the primary reason for their displacement (e.g., TFPM 2016; OCHA 2018).

# **Section 5. Heterogeneity in How Violence Forces Displacement**

In this section
# **1. Introduction**

During the COVID-19 pandemic, firms have been hit through multiple channels, including disruptions in global supply chains, depressed demand, liquidity problems, and uncertainty. Globally engaged firms, in particular, became the center of attention with the immediate decline in global trade after the outbreak of the pandemic (Brenton et al., 2022). But global trade bounced back sharply (Meijerink et al., 2020) unlike the more protracted ‘U-shaped’ recovery pattern in firm sales and other outcomes documented in Cirera et al. (2021a).<sup>1</sup> The difference in the pattern of recovery for trade relative to sales and other economic outcomes at the firm-level raises two questions. _First_ , do globally engaged firms experience a different path of recovery relative to other firms? _Second_ , what explains such differences in performance, if any? Using three rounds of Business Pulse Surveys (BPS) data from 45 countries covering both globally engaged firms and domestically-oriented firms and customs data from 20 countries covering only the universe of globally engaged firms, this paper presents novel stylized facts on the impact of the COVID-19 pandemic and subsequent recovery for globally engaged firms as well as on their adjustment mechanisms.

Globally engaged firms, broadly defined as those that participate in international trade (directly or indirectly), are special for several reasons. They are known to be larger, more productive, and pay higher wages than domestically-oriented firms. Since firms with stronger capabilities, such as management practices or technology, tend to cope better than others during periods of turmoil (Grover and Karplus, 2021), it is crucial to understand if such capabilities pay off in the face of massive global demand and supply shocks. These dimensions are important from a policy perspective more so because global firms contribute substantially to aggregate fluctuations and the propagation of foreign shocks to the domestic economy (Cravino and Levchenko, 2017; Di Giovanni et al., 2014, 2018).

In a world of global value chains (
# **_Registration of refugees and asylum-seekers_**

Registration is the most common method of collecting data on refugees and asylum-seekers, accounting for 77 percent of UNHCR’s data on refugees at the end of 2014.<sup>62</sup> Asylum-seeker and refugee statistics are generally based on government and/or UNHCR registration records (UNRWA maintains registration records for Palestinian refugees).<sup>63</sup> Individual registration of refugees and asylum-seekers is the first step towards establishing a person’s identity, legal status and protection needs. Registration involves the collection of information including, but not limited to, name, sex, date of birth, country of origin, marital status, and place of displacement, and the issuance of documentation.<sup>64</sup> Refugees living outside of camps, sometimes irregularly, are underrepresented in registration exercises. While the majority of refugees in camps have been registered on an individual basis, refugees outside of camp environments are difficult to identify and capture in registration activities. In fact, refugee registration is used almost exclusively in camps established by or in collaboration with UNHCR (UNHCR 2015). Moreover, some refugees may not register because they are unaware that they should, and others may be reluctant to do so because they are skeptical of the integrity of the registration process (e.g. fair access to entitlements or opportunities for durable solutions) or lack confidence in protection measures. Individuals in irregular migration flows may also choose not to apply for asylum due to fear of declaring themselves to the authorities.

A significant challenge with refugee registers is keeping them up to date. Individual registration can provide a robust snapshot of the stock of refugees and asylum-seekers, but registers need to be updated regularly to reflect flows, i.e. increases in refugee and asylum-seeker numbers (births, new arrivals) and decreases (deaths, departures, durable solutions). In situations of sudden mass influxes, existing registration capacity may not be adequate and the scope of