rafmacalaba/data-use-mentions-extended huggingface dataset:
dataset. The methodology attempts several cuts of the data, randomly selected, separately between 2005 and 2011, and also non‐random holdout of each of four agro‐climactic regions of Ethiopia. Overall the model performs consistently across different `cuts’ of the data when the sample is randomly divided into training and testing data. The outcome variable (root‐mean‐squared error of the model) is not very different across either the models or the choice of training and testing dataset. The analysis also tests the support of non‐linearity in the drought impact, and we find that a quadratic model (a squared term in the regression) best fits the data. However, there is data paucity at higher levels of the shock (low probability but high impact events), so we cannot be certain that the model would accurately predict drought impact for e.g. 70% crop failure and above.

Based on these results, the paper concludes that the analysis demonstrates some level of external and internal validity of the derived relationships. However, several caveats remain. An attempt was also made to test the model on 2012 data from a different source (Ethiopian Rural Socioeconomic Survey), which has fewer observations, and a slightly different questionnaire, leading to difficulties in comparability of the main welfare measure. Further, the econometric question of identification is never completely solved when using cross‐section data, so a follow‐up robustness check with panel data would be extremely useful.

With these caveats in mind, the paper concludes that the derived relationships could form the basis of a ‘vulnerability module’ within a CAT risk model, and outlines how the model developed could be then applied to a CAT risk modeling framework. The impact of drought at various levels of severity on poverty is shown, and a suggestion is made that after some further robustness checks a full CAT risk model could be built. This would entail development and application of a stochastic model of rainfall that could be applied into the drought (LEAP)
the GDP deflator to match the definition of prices in the model.

Some approximations are needed due to data limitations. The rate of capacity utilization is for manufacturing which, empirically, is the biggest chunk in our core goods sector. We use hours worked in manufacturing only, which covers all of our estimation period.<sup>24</sup> For low-skilled wage inflation, we use money wages of construction workers, 80% of whom have less than high school education (Cengiz and Tekg ̈u ̧c 2022). For high-skilled wage inflation, we take wages of information and communication sector, which attracts mainly university degree holders. For global fossil fuel prices, we use OECD producer prices from the IEA.

> 22Data sources are as follows: Components of GDP, employment, interest rates, capacity utilization rate and hours worked (OECD Main Economic Indicators), public-private investment, compensation of employees, unemployment, high-skilled and low-skilled wages (TurkStat), price indexes, EU output, inflation and prices (Eurostat), energy production and prices (IEA), emissions (CAIT) and real effective exchange rate (BIS).

> 23Public and private investment expenditure series are not available after 2016Q2. Low-skilled wage inflation is available over 2005Q2-2017Q4 and high-skilled wage inflation is available after 2009Q2.

> 24Total hours worked data from TurkStat is available after 2010, and there is a close match between quarterly growth rates of manufacturing hours worked and total hours worked over 2010-2022.

58
highest growth firms and products (Melitz, 2003). Pakistan’s performance in this respect is considerably deteriorating over time. The rates of survival after 2004 increased. At the same time, the number of new exporters each year steadily decreased. These two results together are a potentially worrisome signal of a decrease in firm dynamism, whereby the greater persistence in export relationships is due to less competition in the domestic market. This may contribute to explain why Pakistan’s export performance in the past decade was positive but more moderate than the one of other Asian economies.

**Figure 28 Survival rates after 1 year of export activity**

<!-- Start of picture text --> 1-Year Exporter Survival Rate<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>2004 2005 2006 2007 2008<br>PAK BGR COL ECU PER ZAF<br><!-- End of picture text --> Source: Authors’ calculations based on data from the Exporter Dynamics Database (World Bank).

To investigate further, whether the lower dynamism of firms’ exports has had a detrimental effect on export growth, the next sub-section will decompose the year-on-year contribution of the extensive and the intensive margin to aggregate export growth.

Exports growth is computed using the mid-point growth rate (Davis and Haltiwanger 1992 as applied to firm-level exports by Bricogne et al 2011). The main advantage of this approach is that it allows the computation of the contribution of the extensive and the intensive margin to export growth. Other methodologies only allow a static assessment of the extensive margin (Hummels and Klenow 2005).

Table 11 that provides the decomposition reads as follows. In each of the columns the average annual growth relative to the indicated period is reported. The aggregate figure for all trade flows is reported in the last row.
The large welfare gains we estimate in our data stand in sharp contrast to Gibson et al.’s (2010) estimates of the adverse effects of winning a New Zealand migration lottery program on the extended family members of Tongan emigrants who remained behind. That study is the closest to ours in terms of research design and rigor, but their setting is very different from ours in several important aspects. Most importantly, their lottery program was for permanent migration where the winner could migrate with their spouse and children to New Zealand. Hence, any effects measured in Tonga are for distant household members, who naturally would not share in as much of the gains from migration. In contrast, Bangladeshi migrants leave their families behind and remit a large share of their income back to their families in Bangladesh, which results in large gains for their families. Likewise, permanent migration programs like that between Tonga and New Zealand represent a relatively small share of the global flow of migrants.<sup>2</sup> The migration episodes we study in Bangladesh typify the most common form of global flows, with Bangladesh being one of the largest suppliers of such migrants.

Another closely related study, Clemens and Tiongson (2017), estimates the impacts of a G2G program for temporary migration from the Philippines to South Korea. They employ a regression discontinuity (RD) design around the potential migrants’ performance on a mandated Korean language test and find large consumption increases with migration. Unlike this paper, however, they do not measure household income increases that can rationalize the large cross-country gap in wages observed in macroeconomic data (Clemens et al. 2018). Our paper also differs in the sample and representativeness. Applying to the Korean employment scheme required a high school degree, work experience, and Korean language ability. In contrast, applicants to the Bangladesh-Malaysia lottery program were lower skilled and more representative of the high volume of international migrants from developing countries. Their RD generates internally valid estimates for the set of applicants who invested in learning Korean
emphasizing group discussions. Rather than being a remedial tool like other computer-assisted learning (CAL) programs, the PSI-PMI model is directly integrated into the teaching and learning curriculum, replacing the way science and mathematics subjects are traditionally taught in the country. The Gambia was the first country to adopt this program in

> 2 One World in Data: https://ourworldindata.org/search?q=gross+enrollemt+ration+primary+education+Africa. One World in Data: https://ourworldindata.org/search?q=gross+enrollment+ratio+secondary+education+africa. One World in Data: https://ourworldindata.org/tertiary-education. All accessed August 13, 2019.

2
Past Research**

Our paper contributes to the literature on forced displacement, and economic and social integration of refugees and migrants in the host communities and aims to extend the literature in two ways. First, to the best of our knowledge, it is the first study to look into the recently displaced Venezuelans’ access to social protection programs and education in a Latin American country, which provides universal access to education, health care and social protection irrespective of legal status. Second, it provides the first evidence outside Europe on the association between education of children and concentration of forcibly displacement individuals.

Most past research on the recent episode of Venezuelan forced displacement has focused on the labor market. Descriptive statistics in Cavalcanti et al. (2020) shows that Venezuelans consisted of 12% of the total migrants and refugees working in the formal labor sector in 2019 in Brazil. Graham et al. (2020) find that Venezuelan migrants in Colombia, the country hosting the largest number of Venezuelan refugees and migrants, work in lower-paid and more informal work and that before the COVID-19 pandemic, employed Colombians were earning 43 percent more on average than employed Venezuelans, despite the fact that Venezuelans are highly educated. Caruso et al. (2019) and Penaloza (2019) focus on the impact of the influx of Venezuelans on the labor market in Colombia and find that it reduces wages, especially for male workers in the informal sector. Olivieri et al. (2020) focus on Ecuador and report that although Venezuelans are more educated and more likely to be employed, they tend to work in low wage and informal work and experience significant occupational downgrading. Bahar et al. (2021) provides evidence of asymmetrical e↵ects of a large scale amnesty program that granted work permits to undocumented Venezuelans in Colombia that

> 12Race data is available only for a sample of the population showing that 25% of the Venezuelan population is white, while the rest being indigenous, brown and black. The dataset does not include information on the specifics of the
# **6. Conclusions and implications**

The paper explores the determinants of labor force participation (i.e., employed, unemployed, not in the labor force) and employment type choice (wage vs. self), with a focus on the role played by gender role attitudes and personality traits using multi-topic household survey data collected in two regions – Ashanti and Northern – of Ghana. The study uses both descriptive and econometric analytical approaches. The econometric approach employs multinomial logit and linear probability models to examine the drivers of labor market participation and employment choice, respectively.

The descriptive results indicate that employment is higher among educated individuals, and those that live in urban areas and non-poor households. Self-employment is more prevalent than wage employment in the study area, and more common among women than men. The gender disaggregated descriptive comparison shows that men are more traditional, compared to their female counterparts. While some personality traits are more prevalent among men (work centrality, tenacity, achievement and power motivation), others are more dominant among women (i.e., polychronicity, locus of control and organization skills).

The empirical results in general show that the main drivers of labor market participation and employment choice include the age and education level of an individual, geographic location of the household, gender of the head, and poverty status of the household. In terms of the choice between wage and self-employment, the results show that individuals who have at least secondary education who live in male-headed households, and reside in urban areas are more likely to be employed in a wage job. On the contrary, Muslim individuals who live in large-sized households are more likely to be self-employed. In the gender disaggregated analysis, the results show that the education level of an individual is vital in shaping their decisions to be employed in wage jobs, while Muslim men and women are more likely to be self-employed. In addition, women who live in urban areas are more likely to land a wage job than those that live in
their prospects of receiving a loan, how much more discouraged were they likely to be in the midst of a banking crisis?

A comparison of the reasons that firms did not apply for loans provides additional clues about why the financial constraints reported by small firms changed little during the early phase of the crisis. Among large firms that did not apply for loans in 2005, 32.3% reported that they had no need of external finance (Table 6). In the 2008-9 survey, that figure rose to 83.3%. In other words, nearly the only reason that large firms did not apply for loans in the early phase of the crisis was because they were financially self-sufficient. Among small firms, the percentage of non-applicants that reported no need for external finance rose, but only from 38.4% in 2005 to 56.9% in 2008-9. This too is consistent with the idea that lingering impediments to obtaining credit for small firms made them more reluctant to turn to banks for loans during the crisis.

Further evidence that large firms were more likely to change their status regarding financial constraints than small ones comes for a transition matrix similar to the one in Brown et al. (2011). Firms in the 2005 BEEPS were sorted into three categories in the left-hand column of Table 7: (1) had a loan; (2) did not apply for a loan because the firm reported no need for external finance; or (3) did not apply for a loan because firm owner was discouraged from doing so. Potential reasons for being discouraged were: application procedures for loans or lines of credit are complex; interest rates are not favorable; collateral requirements are too high; size and maturity of loan are insufficient; and firm owner did not think it would be approved. Because a sizable share of firm owners that did not apply for loans chose not to answer these questions, our sample is too small to present results for specific reasons. We therefore lump all non-loan applicants that did
we do, then our measure is actually the relevant one.<sup>36</sup> Furthermore, we argue that our use of instrumental variables reduces any measurement error problem generated by using industry data for this variable.

Using industry level data for x does imply that the interpretation of β is now as the average EU-wide extra weight taken over the different industries rather than products. We are comfortable with this interpretation, since producers tend to organize at the industry level to lobby for protection. This is particularly true in the EU, where there is more variation in protection across industries than within them. Therefore, to the extent that the extra weight re fl ects a political economy motive, the

> 34 We exclude products with a zero MFN tariff before the Uruguay Round for two reasons. First, when the MFN tariff is zero there is often more noise in the data about whether a preference exists or not, since it is in effect irrelevant. Second, all the tariffs in the sample that were initially zero remained unchanged and are likely to share an unobserved common characteristic. Thus, including those observations would bias the estimates if the proportion of zero tariffs is different for PTA goods relative to the rest of the goods.

> 35 In terms of production this entails simply adding it up over the EU members. The interpretation of the political economy weight estimated in this case is (ωi − 1) =<sup>�</sup> c<sup>(ω</sup> i<sup>c−1)ξc</sup> i<sup>whereωc</sup> i<sup>istheindividualmemberweightforagiven</sup> producer and ξ<sup>c</sup> i<sup>istheproductionshareintheEUasshowninsectionA.1oftheappendix.</sup>

> 36 In calculating the variable (XI /MI )/εI the remaining variables that we employ are at the same level of aggregation as the production data. Moreover, the import demand elasticity we calculate is for the
income classes, and severity levels of the shock is very similar to the distribution of observations in the full sample (see Table B2), which suggests that the country-level data sets included in the analysis are not strongly biased due to missing values.

The data sets we analyze combined cover around 45,000 interviews from 49 low-, middle-, and highincome countries in the six regions where the World Bank Group (WBG) is present (see Table B1 in the appendix).<sup>6</sup> We follow Apedo-Amah et al. (2020) and exclude businesses in the sample that were permanently closed at the time of the interview and businesses operating in the education and health sectors, which results in a data set with almost 39,000 firms where the gender indicator is available.<sup>7</sup> These data include micro, small, medium, and large businesses across five broad sectors–i.e. hospitality, manufacturing, retail and wholesale, other services, and others.<sup>8</sup> Businesses in the sample are largely formal, though informal firms are included in Cambodia, Senegal, South Africa, Sudan, and Tunisia. We focus on the short-term impact of the pandemic on women- vs men-led businesses and constrain our sample to interviews conducted between April and September 2020.

Even though the sampling frames for the BPS or the WBES follow-up are not nationally representative for men-led and women-led businesses, the harmonized data offer a comprehensive window into the differentiated impact of the pandemic on women entrepreneurs.<sup>9</sup> Consistent with many other studies, the fraction of women-led businesses in our data is the highest in East Asia and Pacific and Latin America and the Caribbean (approximates 40%) and the lowest in the Middle East and North Africa and South Asia (only around 10%; see Table 1). In terms of firm traits, we find that the fraction of women-led businesses in our data is the
%)|||1.673***|1.692***|1.642***|
||||(0.415)|(0.419)|(0.405)|
|WBL Index|||17.580**|17.641**|29.339**|
||||(8.651)|(8.608)|(11.496)|
|Women can work in the same|||-2.120|-1.756|-1.808|
|industries as men Y:1 N:0||||||

74
aiming to implement its 12-year compulsory education program. Of all these initiatives, the most beneficial program in terms of improving school participation was the _SD Inpres Program_ . The _SD Inpres Program_ started with the construction of thousands of new elementary schools. Through this program, elementary schools had to be built in every village to help children to go to school. In the period 1973-83, using _Inpres_ funding, 136,140 new schools were built, 570,000 schools were renovated, and 697,180 teachers were assigned (Tilaar, 2015). Based on the results of various Population Census, this effort increased school participation for children aged 7-12 during the period 1971-81 by 20 percentage points, or up to around 81 percent. Gelander (2012) found that _<mark>SD Inpres program</mark>_ <mark>exposure significantly increased schooling both for men and for women, but women benefitted more from the program than men.</mark> At the national level, the GPI in school participation conveys that there is no gender disparity in favor of males, but sub-nationally there is considerable variation in gender disparity, with girls at a disadvantage in many districts and boys at a disadvantage in others. This condition holds across all education levels. For example, Kotamobagu has a GPI for the NER at primary level of 1.15, which indicates that enrollment favors females. Meanwhile, in Central Buton, West Sulawesi, the GPI for the same category is 0.8, which indicates the gender parity favors males. At junior secondary level, Wonosobo, Central Java, has a significant gender gap, with the NER for females at 85 percent, while the NER for males is 64 percent. However, we find the reverse pattern in Wajo, South Sulawesi, with the NER at junior secondary for females at only 46 percent, while the NER for males is 72 percent.<sup>18</sup> These magnitudes of variation are also observed at senior secondary and higher education levels in
reported in panel B are approximately twice as high (in absolute value) as the ones shown in panel A.

In Table 1 panel B we find that the estimated coefficients of the incidence variable reported in columns (1) and (2) are fairly similar. According to these results, civil conflict in country _i_ at time _t_ correlates negatively with GDP per capita growth which ranges from 2 to 3 percentage points. In column (3) we use night light as an economic indicator. The estimated effect of conflict on night light is statistically significant and is equal to -0.075. What does this number mean in terms of GDP per capita growth? One way to think about this is the estimates from Henderson et al. (2012) who argue that the relationship between GDP and light can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0.25 percent. If we apply this model to our result from Table 1 panel B, the conflict reduces GDP growth by almost 1 _._ 9 percentage points. This is strikingly similar to the 2 percent found in column (1).

Table 1: Conflict Incidence and Economic Performance: Country-level data

||**Pan**|**el A**||
|---|---|---|---|
||(1)<br>GDP growth|(2)<br>GDP growth|(3)<br>Night light growth|
|Confict Incidence|-0.011**|-0.016***|-0.032*|
||(0.004)|(0.004)|(0.016)|
|Observations|8,004|8,076|3,924|
|R-squared|0.097|0.124|0.267|
|Country FE|YES|
Education, Science and Technology, 2018) and a spreadsheet-based tool was developed to guide districts in completing allocations according to the rules.

In this section, we employ administrative data to create lists of schools that would have been eligible to receive new teachers in each of the years 2017-2019 according to the new rules.

# **Maximizing the efficiency of allocation rules**

## **T1: One teacher per grade**

The first stipulation of the revised rules – to provide adequate teachers to schools to ensure that they have at least one teacher per grade offered – is intended to eliminate multi-grade teaching. This rule is most likely to be relevant in smaller schools, often in remote areas, which are the most likely to lack one teacher per grade owing to small enrollments.<sup>7</sup> Such schools are large in number and concentrated in particular districts. In 2017, one district, Mzimba South, required 423 teachers to meet this rule, and the top five districts combined required 1,039 new teachers – more than 40 percent of all the new teachers needed to fully attain T1 in the whole country. These districts, unless experiencing severe overall shortages of staff, are poorly allocating their existing teacher resources and may not be expected to appropriately allocate additional teachers. In addition, meeting this need entirely would be likely to severely reduce the potential for PTR reduction at more understaffed schools, and would entail allocating a significant number of teachers to schools with PTR below 60 which are otherwise adequately staffed. This may not be an effective use of limited resources, given overall constraints on teacher availability.

We therefore apply the following modifications to T1:

- Schools with a PTR of 60 or below are excluded from receiving new teachers.

- Schools whose PTR would drop to 60 or below on receipt of a new teacher, are excluded from receiving new teachers.

- Districts where more than 50 percent of schools meet T1 are excluded from receiving teachers based on T1, unless their overall district PTR is