rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 14: Percent change in wages related to a 10 percentage point increase in female labor share, trading vs. non‐trading firms**

<!-- Start of picture text --> 5<br>4<br>3<br>2<br>1<br>0<br>‐1<br>‐2<br>‐3<br><!-- End of picture text --> Note: The graph shows the results of regression analysis described in Appendix 10 and 11 using the firm’s average wage rate (in natural logarithms) as dependent variable and the female labor share as independent variable. The model interacts exporter, importer, GVC participant and FDI dummies with the female labor share variable to assess the mediating role of trading/FDI status. Exporters are firms with an export share (direct or indirect) of at least 10% of total sales. Importers are firms with an imported input share of at least 10% of total inputs. GVC participants are firms that are classified as both exporter and importers. FDI refers to firms with a foreign ownership share of at least 10%. All regressions control for age, firm size, capital intensity, skill intensity and country‐sector, subnational region and year fixed effects and are at least significant at the 5% level.

# **5. Conclusions**

Using a cross‐section of more than 29,000 manufacturing firms in 64 developing and emerging countries from the World Bank’s Enterprise Surveys, this paper assessed whether trading firms have a female labor share premium relative to non‐trading firms. It focused on four types of trading firms: exporters, importers, GVC participants and FDI firms. The study found a female labor share premium for all four types of trading firms, controlling for firm output, capital intensity, total factor productivity and fixed effects. Exporters and GVC participants show the highest female labor share premium compared to non‐exporting
_|
|Other finance||4|14|29|47_(4.27%)_|
|Demand|1|10|11|6|28_(2.54%)_|
|Business climate||9|10|8|27_(2.45%)_|
|Business advice|||2|14|16_(1.45%)_|
|Total of instruments|12|220|372|497|1,101|
|_Number of countries_<br>_with support measures_|7<br>_(22.58%)_|26<br>_(55.32%)_|41<br>_(68.33%)_|50<br>_(62.5%)_|124<br>_(57.88%)_|
|_Total number of_<br>_countries_|_31_|_47_|_60_|_80_|_218_|

Source: World Bank Group, Map of SME-Support Measure in Response to COVID-19. <u>https://www.worldbank.org/en/data/interactive/2020/04/14/map-of-sme-support-measures-in-response-to-covid19</u> Note: In the last column, figures in brackets represent the percentage of each type of support in the total of support measures. Figures in brackets in the second last row represent the percentage of low, lower-middle, upper-middle and high-income countries that adopted support measures. The different instruments in each type of support used by countries are presented in the annex.

> 25 From the Financial Services to MSMEs notes in the document “Government policy responses to support the private sector” (Aijazuddin& Savonitto, 2020).  Original Source: IFC MSME Finance Gap Report, 2017).

17
and then as a Probit model<sup>20</sup> to show the robustness of results.

where _Vijz_ is a dummy taking the value 1 if school _i_ in municipality _j_ and province _z_ has at least one Venezuelan student enrolled in the school and zero otherwise. _Sijz_ is a vector of school characteristics involving average class size, excluding Venezuelans, teacher-student ratio, proportion of teachers with undergraduate (college) degree, proportion of teachers with MA degree, gender ratio, proportion of white students, average age, type of school (dummy taking the value of 1 if public school and 0 otherwise), total number of Brazilians in the school, proportions of Brazilian who attend classes, lower than his age equivalents, school amenities and access to public services. School amenities include a dummy which takes a value of 1 if the school has simultaneous access to internet, science labs and computer labs and 0 otherwise, while the access to public services is measured as a dummy taking the value of 1 if the school has simultaneous access to electricity, water, sanitary and garbage collection. _Xjz_ includes municipality level characteristics including log of natural number of the total number of Venezuelans living in the municipality,<sup>21</sup> while _yz_ includes indicator variable representing the province or state level fixed e↵ects.

> 20This estimated Probit model will be used in calculating the inverse Mills ratio for estimating the integration model. This correction term controls for the fact that selection bias of schools which receive Venezuelans.

> 21It should be mentioned that the population data on Venezuelans are not updated regularly.

12
data and, vice-versa, used the 2007 model to estimate poverty for both the 2001 and 2007 years. The imputation-based poverty estimates are found to be close to the official poverty estimates, regardless of which model is used. Next, we used the 2001 and 2007 consumption models to estimate poverty with the LFSs for the period 2001-2010; also here we obtained almost overlapping quarterly poverty trends, even when we disaggregated by urban and rural areas.

This exercise provided several new insights for the study of poverty in Morocco. The imputation methodology adopted has implicitly validated the quality and comparability of the 2001 and 2007 HESs and the quality and consistency of the LFSs used. The decline in poverty depicted by the estimated quarterly poverty rates has shown that Morocco has been able to withstand domestic and global shocks, a finding that would not have been possible with the 2001 and 2007 HESs alone. Disaggregated poverty estimates at the regional level show that the poverty trends in Morocco have not been homogeneous across regions, a finding that was not visible when only two points in time were considered. As a byproduct of this work, the High Commission for the Plan of Morocco can now estimate poverty rates every quarter and within weeks of the administration of the LFSs.

24
**Table A.2a.  OLS regressions of agricultural productivity on female plot owner without control variables (2008)**

|||**Log [Se**<br>|**lf-reported Pr**<br>|**oductivity (X**<br>|**OF/ha)]**<br>||
|---|---|---|---|---|---|---|
||(1)<br>All crops|(2)<br>Export<br>crops|(3)<br>Food crops|(4)<br>All crops|(5)<br>Export<br>crops|(6)<br>Food crops|
|Female plot Owner|-0.375***|-0.151|-0.429***|-0.309***|0.025|-0.406***|
||(0.037)|(0.102)|(0.038)|(0.037)|(0.093)|(0.038)|
|Constant|11.306***|11.277***|11.329***|11.458***|11.486***|11.470***|
||(0.018)|(0.032)|(0.021)|(0.103)|(0.176)|(0.109)|
|Region fixed effect|No|No|No|Yes|Yes|Yes|
|Adjusted R-squared|0.011|0.001|0.018|0.067|0.079|0.093|
|**% gap**|
In these cases, deleting one or more spiderlegs fixed the problem. Five spiderlegs affecting 4 cities were deleted.

# **A.4 Route calculation**

To prepare for building the network, the roads were intersected with all land borders, so that the resulting border posts could be used as barriers—non-traversable points on the network. Coastlines were not treated as borders in this operation, because the only reason a road would cross a coastline is because of misalignment—the resulting route is most likely legitimate.

A network dataset was built using the roads dataset. The “Closest Facility” solver was used with the following settings. All light centroids were used as the “Incidents”, centroids of primate cities were used as “Facilities”, and the intersections of the roads and the land borders were used as “Barriers”. Each city was assigned a network location on the closest road within 5 kilometers of its centroid.

49
In order to investigate whether the CPS program improved preschools in domains likely to enhance child performance, we investigate the relationship between preschool quality and performance in the last panel of Table 4 (“Correlation qualityperformance”). Here we analyze the correlation between each preschool quality index and midline cognitive skills, conditional on the baseline variables used for the reduced form estimations.<sup>42</sup> We find that the process quality indices (“Pedagogy” and “Interactions”) are indeed significantly correlated with the midline cognitive development score while “Equipment”, the structural quality index that is the most affected by the construction program (+1.3 SD), shows no significant correlation with midline performance.

It is noteworthy that the indexes for “teacher characteristics and “Classroom setting” are also significantly correlated with the cognitive development index.<sup>43</sup> Overall, therefore, this analysis suggests that while the CPS program did improve some dimensions of quality, it may not have improved the domains that are the most associated with child cognitive development.

# **5.2 Preschool enrollment results**

We now turn to child enrollment into preschool, a key stated objective for of the program.<sup>44</sup> Table 6 summarizes the impact on enrollment into each type of preschool, and into primary school, by treatment status. The measure of enrollment in this table is the child’s status on the day of the midline and endline survey visits. At midline, the interventions increased enrollment into any type of (pre)-school by 10.6 pp, compared to a control group enrollment rate of 43.5%.<sup>45</sup> The overall increase in preschool enrollment (observed on the day of the visit) is entirely driven by an increase in CPS enrollment of 41.0 pp at midline and 31.9 pp at endline (Column 2 of Table 6—the decline at endline is caused by the
210|0.200|0.189|0.196|0.198|0.190|0.187|0.185|

_Notes:_ Robust, clustered standard errors in parenthesis. * p<0.10; ** p<0.05; *** p<0.01. In all specifications we control for the characteristics of the household head, i.e. headship, age, education level; the household composition, and wealth proxied by a housing index and land holdings. _Source:_ VUP household survey, 2009, 2011, 2014.

27
These predictions are confirmed in the Appendix, where we use the different proxies for "good institutions" of Table lb. Table 2 gives an example of four measures of institutions which show a highly significant pattern of voice and accountability, civil liberties, government effectiveness and graft, with signs indicating more social cohesion leading to better institutions. Table 3 gives an overview of the more and the less significant relations. All of our measures of institutional quality are positively associated with growth (as was shown by Kaufmnann, Kassy and Zoido-Loboton (KKZ) 1999 for their measures of institutions). Here, we have used three stage least squares to take into account the possible endogeneity of institutions - our two indicators of social cohesion make natural instruments that allow us to identify a causal link from good institutions to growth. Interestingly the two measures of the International Country Risk Guide seem to be less reliable, within this framework, than the other measures. Also, the CPIA index of the World Bank is less reliable, which should not surprise as the standard deviation is small (see Table lb).

Thus our findings support the two-stage hypothesis we outlined at the beginning -- that more social cohesion leads to better institutions and that better institutions lead to higher growth. This is true regardless of how we measure institutions.

In Table 4 a typical example is given of the sensitivity analysis for trust, group membership and the Gini coefficients for the institutional variable "voice and accountability". {Table 4 about here). This example is part of the pattern: all sensitivity analyses show the following:

- The Gini coefficient and the middle class share are separate dimensions of social cohesion. Including the Gini coefficient hardly reduces the significance of the effect of the middle class share on institutions, while the effect of the Gini coefficient itself is highly significant.

22
which is measured as the sales over the number of permanent<br>employees threeyears ago.|
|Corruptionc|City-industry-level average of the firm’s answer on whether corruption constitutes an obstacle, ranking<br>from 0 to 1. The subscript c indicates that it is based on city-level average rather than a firm’s answer.|
|Formal Financec|City-industry share of firms having a line of credit or loan from a financial institution. Based on WBES<br>calculation.|
|Trade Creditc|City-industry average of the proportion of total annual sales of goods and services that are paid for after<br>delivery. Based on WBES calculation.|
||Cit-industr share of firms who sa that the directl comete with informal firms It is a measure of the|
|Inf. Competititonc|yy y y y p .<br>importance of the informal sector and its competition with the formal sector. Based on WBES<br>calculation.|
|Outagec|City-industry share of firms that experienced a power outage in the survey year. Based on WBES.|
|Webc|City-industry share of firms that answer that they use websites to conduct business.|
|Cap. Agglomerationc|The share of firms whose number of employees exceeding 50, as a proxy of capacity agglomeration.<br>Computed based on sample firms.|
||The social Institutions and Gender Index (SIGI) index: Discriminatory family code Value, come from the|
|Discriminatory family|<br>OECD Development Centre. Equals to 1 if the value of the index above sample median, otherwise to<br>0.|
|Restricted<br>physical<br>integrity|The social Institutions and Gender Index (SIGI) index: Restricted physical integrity Value, come from the<br>OECD Development Centre. Equals to 1 if the value of the index above sample median, otherwise to<br>0.|
|Good Rule of Law|World Bank’s Governance Indicators:
Exporting firms are also in general more capital-intensive; in 2013, the capital-labor ratio was 322 for exporters and 22 for non-exporters (Saad 2020).

Following the trade liberalization, Egypt’s unemployment rate initially declined before skyrocketing after the financial crisis in 2009 and Arab Spring in 2011. The unemployment rate resumed falling until the COVID-19 pandemic hit in 2020. As demonstrated in Robertson et al. (2021), despite the boom in exports following the trade reforms, Bartik (1991) analysis did not detect any significant labor market effects at sub-national levels. To better understand the dynamics at play, we turn to firm-level data to explore how export flows relate to labor demand at the firm level.

# **3. Conceptual Framework**

## **_3.1 Model Overview and Main Results_**

To illustrate the relationship between firm-level exporting and local-labor-market employment, consider a simplified general equilibrium Melitz (2003) model in which there are two sectors. The first is a “reserve” sector, like the informal or service sector, where firms are homogeneous and produce using only labor with decreasing returns-to-scale technology. The second sector is a heterogeneous sector that can export. For now, we assume that the local labor market has full employment in the sense that, lacking unemployment or other social insurance, workers must engage with the labor market to secure an income.

For simplicity, we assume two periods of time during which firms in the heterogeneous sector have the option to shut down (or not produce), produce for the domestic market, or produce for the foreign market (export). We simulate this model to illustrate how an increase in export opportunities (represented by an increase in the export market price) affects local employment. In particular, we show that an increase in the export price of 5 percent would increase employment 7
the period 2011 to 2020. It is based on financial statements and contains balance sheet information such as firm tax identification number, year of incorporation, operating revenue, average number of employees, number of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies received from the government,<sup>6</sup> 4-digit NACE industry code, and the county location of the firm. The data has about 1.2 million unique firms, and over 7 million firm-year observations, across the period 2011-2020. The Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest. To identify whether the firm is an SOE, the analysis relies on the new WBG BOS database.

# 3.2 World Bank Global BOS Database

**The World Bank BOS database maps the footprint of the state within the corporate sector and across economic activities based on a uniform definition** . The BOS dataset tracks all corporations where national or subnational governments have an ownership stake of at least 10%, either directly or indirectly (Dall'Olio et al. (2022a)). In this dataset, corporations are business entities that are (a) capable of generating a profit or other financial gain for their owners, (b) recognized by law as legal entities separate from their owners and with limited liability, and (c) set up for purposes of engaging in market production. The database was built using data from ORBIS and complemented with data from government sources, such as business registries, central depositories, central oversight bodies, and the Ministry of Finance. It tracks several variables on SOEs such as company names, unique company ID, 4-digit NACE code, financial variables such as revenue, employment,  profit and loss for  2019, percent of state ownership stake, and different layers of the ownership chain. The latter two variables allow us to partition
four for both stunting and underweight.

We have considered two extreme cases, one where the cost _per capita_ is fixed, and the other where it varies with the individual level of malnutrition. In both cases, we found that geographic targeting alone leaves large room for improvement. Clearly, geographic targeting is not a panacea for maximizing the impact of limited resources. However, this does not undermine the usefulness of geographic targeting because geographic targeting can be easily combined with other forms of targeting in most cases in order to further improve the efficiency. In fact, we found that commune-level targeting alone improves efficiency by more than two times compared with the stratum-level targeting. The improvement is much more dramatic when the goal is a modest reduction. Given this, the commune-level estimates we produced can indeed substantially improve the efficiency of targeting.

# **7 Conclusion**

Estimates of the prevalence of child malnutrition were previously available only at the level of CDHS strata. Stratum-level estimates often mask great disparities in the prevalence of malnutrition within the stratum. Unless there are strata with an extremely high prevalence of malnutrition, targeting based on stratum level estimates is unlikely to capture many of the malnourished children and likely to misallocate resources.

To overcome the problem of limited data, we have developed a methodology to estimate the prevalence of child malnutrition at the level of small geographic areas. We disaggregated the estimates of the prevalence of child malnutrition in Cambodia from currently available 17 CDHS strata into 1,594 communes. We have extended the ELL small-area estimation technique to jointly estimate multiple indicators and allow for a richer structure of error terms. This is a crucial step to address issues unique to nutrition indicators. While we applied this methodology to the Cambodian data, it can be easily applied to other countries 36
# **Figure 2: Diet quality in restricted nutrients, by socioeconomic neighborhood**

<!-- Start of picture text --> absolute per price per 100g<br>Socioeconomic groups Socioeconomic groups Socioeconomic groups<br>low mid-low<br>mid high<br>1 .15<br>.15<br>.8<br>.1<br>.1<br>PDRNI (mean) .6<br>.4 PDRNI/price (mean) PDRNI/100g (mean)<br>.05 .05<br>.2<br>0 0 0<br><!-- End of picture text --> _Source:_ Estimates based on data of ENCONUT 2013.

_Note:_ The diet quality index, expressed in the Proportional Difference of Relative Nutrient Intake (PDRNI) index on the restricted nutrients, measures the distance to the ideal intake. The further away the value is from zero (ideal value), the less nutritious is the average menu offered in restaurants in the nutrients considered. The restricted nutrients are carbohydrates, fat and sodium. Data are weighted.

As with the socioeconomic groups, a similar pattern can be found in the relationship between the absolute PDRNI of restricted nutrients and the average per capita household expenditures in the 40 neighborhoods in the Lima Metropolitan Area (figure 3). Neighborhoods with low levels of average per capita household expenditures exhibit higher PDRNI values, indicating a lower diet quality of menus sold at restaurant in these neighborhoods. The higher the average household expenditure, the lower the PDRNI of restricted nutrients and the better the diet quality. It seems that poor neighborhoods show the highest variation in the diet quality of FAFH and, on average, the unhealthiest food environment; the situation is the reverse among the rich counterparts, among whom is the most nutritious environment.

12
In column 3 of Table A1, we consider an alternative definition of sustained poverty reduction that smooths out short term increases in the poverty rate. This alternative definition is equal to one in instances of sustained poverty reduction as defined by our baseline measure, but is also equal to one if the poverty rate has fallen by at least 5 percent between the first and last years of the fiveyear window. The market size variables are similar to those in column 1, but the threshold market size falls to 126 million people, as expected because the alternative measure is less demanding in classifying instances of sustained poverty reduction. This result suggests poverty reduction can be achieved with a smaller market size, though that it may not be sustained in all periods, as our baseline outcome variable requires.

Column 4 in Table A1 examines the implications of using higher frequency household surveys. This specification includes only surveys in which 0, 1 or 2 years of poverty headcounts are observed, reducing the sample to 200 observations. The signs and statistical significance of the estimated coefficients are robust to using this smaller sample, but the magnitudes change. The main takeaway from these results is that the inclusion of surveys with many missing observations for poverty does not push the estimates in a qualitatively different direction.

Columns 5 and 6 in Table A1 report results from specifications that have a weaker link to the theoretical framework. In column 5, the domestic middle class variable is lagged by 5 years to address potential concerns about reverse causation. Note that the middle class in our baseline specification is measured at the onset of each poverty-reduction-window, so by construction, it is not contemporaneous to poverty change. By using its lag (i.e., measuring it at the onset of the previous 5-year window), we lose 5 years for each country, so that we end up with 285 observations. The results from this specification are similar to those in column 4, in the sense that the coefficient signs remain robust, but the magnitudes change. The standard errors of the