rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.068|
|Control mean low BPs|0.075|0.622|11.324|10.015|5.617|0.070|
|Control mean high BPs|0.242|0.699|11.344|9.705|6.167|0.013|
|N|683|665|542|516|782|866|

Notes: This table shows OLS estimates for the effect of receiving the information sheet on business practices and performance. The outcome variables in columns 1 through 4 are from the follow-up survey, conducted in 2019. The number of observations varies due to nonresponse. The outcome in column 1 is equal to one if the firm reported having received business development services from a provider other than SEBRAE and zero otherwise. The outcome in column 2 is the percentage of 16 business practices the firm reported using. IHS stands for inverse hyperbolic sine. Number of employees in column 5 is from 2019 RAIS. It is missing for firms that did not report to RAIS in 2019. The outcome in column 6 is a dummy variable equal to one if the firm was officially closed by end of 2019 according to the Federal Tax Authority (RFB) and zero otherwise. Panel A pools all treatment groups. Panel B shows separate treatment effects for each of the four different versions of the information sheet. Panel C shows heterogenous treatment effects for firms using below and above the median percentage of baseline business practices. BP stands for business practices. Low BP refers to below the median (50%), while high BP refers to above the median. All regressions control for strata fixed effects. The regressions in Panel C additionally control for a dummy variable equal to one if the firm had low BPs at baseline. Columns 2 also controls for percentage of business practices used in 2018, and column 5 for number of employees in 2015. Baseline outcome variables are not available for columns 1, 3, and 4. All firms were
D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. Quarterly Journal of Economics, 121(2), 635-672.

- Beshears, J., Choi, J. J., Harris, C., Laibson, D., Madrian, B. C., & Sakong, J. (2015). Self control and commitment: can decreasing the liquidity of a savings account increase deposits? (No. w21474). National Bureau of Economic Research.

- Bisin, A., & Hyndman, K. (2020). Present-bias, procrastination and deadlines in a field experiment. Games and economic behavior, 119, 339-357.

Brenner, L., Meyll, T., Stolper, O., & Walter, A. (2020). “Consumer fraud victimization and financial well-being”. Journal of Economic Psychology, 76, 102243.

- Busse, M. R., Knittel, C. R., & Zettelmeyer, F. (2013). Are consumers myopic? Evidence from new and used car purchases. American Economic Review, 103(1), 220-56.

- Button, M., Lewis, C., & Tapley, J. (2009). Fraud typologies and the victims of fraud: Literature review, National Fraud Authority report.

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example, in science there a strong positive association between high performing students in large cities (>500,000) and performance in 2003; for students below the median the positive association is strongest for medium-size communities (15,000-500,000). However, small communities (<3,000) are consistently associated with lower performance in both mathematics and science. In 2003, students in schools with low proportion of disadvantaged students performed better; however, this association seems to have disappeared or reversed in 2007. There is some evidence that ability grouping by schools is negatively associated with performance, especially I science. Students in schools with a severe absenteeism problem performed worse, while high performing students in schools with high index of good attendance performed better.

Teacher age is negatively associated with student performance, while the opposite is the case for teacher years of experience. Having a teacher with university education (ISCED 5A) results in a significant increase in student scores (but mostly in science), and this effect increased in 2007 compared to 2003. Finally, the practice of pay incentives to teachers has a moderate by consistently positive association with performance, and more so at higher points in the distribution.

Decompositions results (given in tables 5a and 5b, with detailed results in tables A5a and A5b in the appendix) are derived from the 2-step procedure outlined in the methodology section. In the first step, the changes in score and its two constituent components are derived using reweighting, without imposing specific functional forms or parametric distributions when deriving estimates of the total gap. In deriving the re-weighting functions, the probability that a person belongs in the “year 2003” group conditional on (propensity score) is estimated from a logit regression. In the second step, estimates of the effects of each individual variable on the test score gap are generated. The estimation of unconditional quantile regression involves deriving

> 6 The Ministry of Education (2004) of Ghana in its summary of its key findings on TIMSS 2003 found a positive correlation between
drop in exports during the global recession in 2009 (Biesebroek and Volpe Martincus, 2016). However the COVID pandemic is different as it hit the export sector directly by disrupting global supply chains. How has the pandemic affected the functioning of TPOs? Were they able to adapt? These are the questions addressed in the World Bank survey of TPOs.

Several key findings emerge from the survey. First, there is significant heterogeneity in TPOs’ size in terms of budget: the budget of the median TPO in high-income countries is 20 times larger than that of the median TPO in low-income countries. TPOs’ budget responses during the pandemic also varied across income levels: TPOs in high-income countries increased their budget whereas TPOs in low-income countries saw a decline in their budget even after lockdown measures eased. Second, TPOs in high-income countries have a stronger hierarchical structure with the number of board members per employee being eight times smaller than in TPOs in low-income countries. This could partly reflect the finding that TPOs in high-income countries tend to have broader mandates whereas TPOs in low-income countries mainly focus on export promotion. These two observations put together may be explained by the fact that a broader mandate may require a more hierarchical structure. Third, TPOs target generally medium-size established exporters, rather than large or small non-exporters or occasional exporters. The focus on established exporters strengthened in high-income countries during the pandemic, whereas non-exporters became relatively more important in low-income countries. Marketing activities remained the main expenditure item of TPOs during the pandemic. However, its relative importance declined in low-income countries as the pandemic hit. This is partly explained by the fact that TPOs in low-income countries were not as quick to adapt to virtual tools such as business-to-business (B2B) matching events. Indeed, at the time of the survey, among low-income countries there was no TPO that had put a COVID recovery plan in place. Among high-income countries 59 percent had a recovery plan in place. Interestingly, one of the most popular virtual tools used during the pandemic in both high- and low-income
# **3.1. Sample and Regression Specification**

Our sample consists of an unbalanced panel dataset that comprises 82 countries. For each of them, the dataset includes at most 8 observations, consisting of nonoverlapping 5-year averages spanning the 1960-2000 period. The sample includes 22 developed countries and 60 developing ones. Among the latter, 18 are from Sub-Saharan Africa, 12 from Asia, 9 from the Middle East and North Africa, and 21 from Latin America and the Caribbean. Appendix 1 provides the full list of countries in the sample. The basic regression equation to be estimated is the following where the subscripts _i_ and _t_ represent country and time period, respectively; _y_ is the log of GDP per capita, _CV_ is a set of control variables, and _OP_ represents trade openness; μ _t_ and η _i_ denote unobserved time- and country-specific effects, respectively; and ε is the regression residual.

As is standard in the literature, the dependent variable is the average rate of real per capita GDP growth (i.e., the log difference of GDP per capita normalized by the length of the period). The regression equation is dynamic in the sense that it includes the initial level of per capita GDP as an explanatory variable. Our measure of trade openness is the (structure-adjusted) ratio of real exports and imports to real GDP. We select the set of control variables considering both their importance as growth determinants _per se_ and their potential for affecting the growth response of trade opening. The control set includes variables that vary both across countries and over time, as well as variables that vary only across countries (that is, assumed constant over time). Among the former, we have the average rate of secondary school enrollment to account for human capital investment, the average ratio of private credit to GDP as a measure of financial depth, the average inflation rate to account for macroeconomic price stability, and the average number of main telephone lines per capita as proxy for public infrastructure. Among the variables
the log of initial GDP, and a constant. The standard errors are clustered at the country level to account for repeated spells.<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _Source_ : Authors’ analysis based on data from the Penn World Tables version 7.0, Penn World Tables version 9.0, and the World Development Indicators. Additional data come from the Polity IV project, Desmet et al. (2012), and the Federal Reserve Economic Data (FRED) database.

Our main findings are virtually unaffected by the choice of data sets and preference for a particular national income series. Table 5 takes our preferred specification, including the mean-centered interaction term and region fixed effects, but replaces the dependent variable with the duration estimated by running our break search algorithm on these different data sets. Columns (1) to (3) focus on GDP per capita, while columns (4) to (6) report results for GDP. As usual, we consider two type I error rates in panels (a) and (b). All results are qualitatively (and often even numerically) similar. Note that the underlying list of slumps 30
constrain women’s (and not men’s) labor. Computing the female shares at the sector- rather than at the plant-level allows to reduce the endogeneity concerns of this exposure measure.

The second dimension is the exposure to different degrees of preschool expansion: as Figure 2 demonstrates, there is a substantial variation in access to preschools across time and districts. For any given initial share of female workers, the plants located in districts with a greater increase in preschool expansion are more likely to increase their performance due to the related labor supply shock.

More formally, we estimate a triple difference regression of the type: where Yidst is the outcome variable of manufacturing plant _i_ in district _d_ and sector _s_ in year _t._ Our key outcome variables include TFPR and TFPQ, which measure plant-level productivity in revenue and quantity terms, respectively, and markup. TFPQ captures the pure technical efficiency of plants as it carves markup out of the TFPR (see Appendix I). αi , γdt , and δst are plant fixed effect, district-time and sector-time fixed effects, respectively. Xidst is a vector of timevarying plant level controls (raw materials and share of foreign ownership). The variable Preschooldt captures preschool access in district _d_ at time _t,_ measured as the number of preschools per 1,000 children of eligible age, as described above. We expect preschool availability to affect plants differently, depending on how strongly they rely on female labor. Thus, we interact preschool density with FemSharest0 - the share of female workers in sector _s_ at t0 (2002 or in the first year the plant entered the manufacturing survey, whatever comes later).<sup>4</sup> The inclusion of plant, district-time, and sector-time fixed effects addresses two primary threats to the causal identification of the effects of preschool availability on plant productivity. First, preschool expansion may be correlated with other governmental programs which may affect firms’ performance. For example, if the Ministry of Education targets a subset of districts for all its
|

a. Child development workers are found in ECD program areas only (see text). n.a. not applicable to this provider type. _Source_ : OPS (2005).

The program has also significantly increased several primary health care and other services designed to promote ECD. Table 3 shows the percentage of municipalities that had functioning elements of various ECD-related services in the first round and the latest available round depending on whether they are in program or non-program regions. This table omits many of the programs listed in Appendix A, because these services were present in all municipalities in Round 1 and Round 3, but received important upgrades of equipment, training and supplies in program areas. Between the baseline and latest round, there is a clear positive trend in the spread of functioning feeding programs, parent education programs, and home-based day care in program areas that is either not observed or not as strong in non-program areas.

**Table 3. Percentage of Municipalities with ECD-related Programs by Program Regions and** **<u>Survey Round, Philippines ECD Study</u>**

|Type of Program<br>Program Areas|Non-Program Areas|
|---|---|
|_Feeding Programs_||
|Round 1<br>32.3|28.1|
|Round 3<br>69.4|38.6|
|_Parent Education/Effectiveness Seminars_||
|Round 1<br>77.4|68.4|
|Round 3<br>100.0|63.2|
|_Home-Based Day Care_||
|Round 1<br>16.1|8.8|
|Round 3<br>44.4|0|
|_Number of Municipalities_||
|Round 1<br>31|57|
|Round 3<br>36|57|

_Source_ : Calculated based on data shown in OPS (2005).

7
sample, 4,818 were tracked and interviewed, a response rate of 96.1%.<sup>18</sup> Table 1 summarizes the composition of the follow-up sample, which we discuss in more detail in the next section.

> 16 A participation rate for non-beneficiaries cannot be calculated as their participation was not recorded nominally. 17 Specifically, all the households with a proxy means test score below 1.05 times the beneficiary selection threshold were selected, while half the households with a proxy means test score above 1.05 times the beneficiary selection threshold were selected. Stratification was based on the PMT score “predicted” from the baseline survey so that the sampling process would remain similar in the treatment and control group.

> 18 The follow-up survey used the SurveySolutions electronic data collection platform. Questionnaires with pre-filled data were downloaded from an online server onto supervisors’ laptops, and later transferred to enumerators’ tablets. Consistency checks were integrated in the Surveysolutions CAPI application installed on enumerators’ tablets. Upon completion of the questionnaires and synchronization back to supervisors’ laptops, supervisors were responsible for validating questionnaires before uploading them to the server. A dedicated team of quality controllers verified the data once uploaded to the server. Automated consistency checks were also performed.  As for the baseline survey, household and child survey teams followed each other in the field. Household survey teams were responsible to identify children eligible for the child survey. The list of children obtained by the household survey team provided the sample for the child survey. To ensure a smooth transition

12
be representative of the spatial distribution of manufacturing firms in Uganda, and consistent with the official administrative boundaries of Uganda. Because the air pollution assessment was designed to accompany a firm survey designed to be representative of the firm distribution across the country, exclusion of the Northern region was informed by low population density and low firm density of the targeted firm categories. The study set out to collect 6-month pollution data with primary focus around the major emission sources in urban centers<sup>12</sup> and targeted firms within the sampled locations.

As mentioned above, the decisions for deployment of monitors started from the pre-selected subcounties and was supplemented by collation of grey literature to help establish parish level boundaries for the monitoring exercise (i.e. re-aligning with the pre-selected boundaries from the firm-level survey) and map out tentative locations for stationary monitors including boundaries for mobile monitoring-partly informed by the authors’ knowledge of the local environment.

The field validation exercise to all the selected sub-counties followed, with the objective to confirm static deployment locations and identify boundaries/routes for supplementary mobile monitoring for areas where static would not have adequate spatial coverage. Spatial boundaries and buffer zones of key pollution generating activities with the potential to affect local air quality were also identified during the field validation exercise. As anticipated, major pollution generating activities including target firm categories were largely situated in urban areas. For stationary deployment, a maximum of 1-2km impact zones (region within the physical environment and activities that can affect local air quality) were considered as most major emission sources within the study areas are closer to the ground (<3m above the ground) having largely localized effects. This was partly informed by study objectives and the fact that there is no official air quality monitoring in Uganda (i.e. there are no Air Quality Management Areas).<sup>13</sup> Lists of all the sampled sub-counties with respective parishes were uploaded onto a portable mapping tool (ArcGIS collector) for precision during the field visits and corresponding GPS coordinates
servicing of debt payments to the creditor (e.g. an annual principal and interest installment); and

- an immediate payment of all guaranteed debt outstanding.

# **iii) Data availability**

NTSA uses a variety of information sources to inform risk analysis. Annual reports and financial statements (income statement, cash flow statement, and statement of financial position) are an essential resource. Statements are prepared according to International Financial Reporting Standards, audited, and are usually available within six months following the end of a fiscal year (ending March 31). In addition to backward‐looking financial statements, NTSA receives corporate plans. Corporate plans describe planned performance, and initiatives and capital expenditure programs for a five‐year period. Corporate plans also contain projected financial statements.

As no SOC benefitting from guarantees has defaulted in the past, no historic default data are available. However, the Credit Risk Directorate is in the process of compiling a database of credit events consistent

> 9 This is particularly relevant when estimating probabilities of distress and when defining rules for accessing future contingency funds for payouts related to guarantees.

10
***</sup>|0.004<sup>***</sup>|0.002<sup>*</sup>|
|_(initial level, logs)_|_(0.000)_|_(0.000)_|_(0.000)_|_(-0.001)_|_(0.000)_|_(0.000)_|_(0.000)_|_(-0.035)_|
|ICRG Political risk index|0.006<sup>***</sup>|0.005<sup>***</sup>|0.006<sup>***</sup>|0.006<sup>***</sup>|0.006<sup>***</sup>|0.006<sup>***</sup>|0.006<sup>***</sup>|0.006<sup>***</sup>|
|_(principal components)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|_(0.000)_|
|<br>Credit to private sector|<br>..|<br>..|<br>-0.013<sup>***</sup>|<br>-0.011<sup>***</sup>|<br>-0.012<sup>***</sup>|<br>-0.011<sup
# **Exports and Labor Demand:**

# **Evidence from Egyptian Firm-Level Data**

Claudia N. Berg (The World Bank)<sup>1</sup> Raymond Robertson (Texas A&M University) Gladys Lopez-Acevedo (The World Bank) JEL: F1, J2, and C1 Keywords: Exports, Trade, Employment, Labor Market, Econometrics, Egypt

> 1 The authors gratefully acknowledge the financial and analytical support from the Office of the Chief Economist for the Middle East and North Africa (MNACE) under the regional Labor and Gender Research Programs.
# **Annex C: Vietnam firm‐level data**

Table C1: Selected questions from Vietnamese firm‐level survey

## **Section E: Technological and innovation capacity**

|Refers to set o<br>organization of<br>Question 8.3:|f questions that is concerned with the innovative capacities and the<br>technological progress in the enterprise of the respondent.<br>Does your enterprise undertake research and development (R&D) activities<br>in order to develop new technologies? Answers: 1. Yes | 2. No, if no skip to<br>question 8.4|
|---|---|
||The R&D activities are target at an innovation that is... (Circle the most<br>suitable answer) Answers: 1. New to the enterprise | 2. New to the market |<br>3. New to the world|
|Question 8.3:|How many national patents do you hold? Answers: 1. New in 2013 ... | 2.<br>Stock / total (the end of 2013) ...|
|Question 8.4:|How many international patents do you hold? Answers: 1. New in 2013 ... |<br>2. Stock / total (the end of 2013) ...|
|Question 8.5:|Are you currently involved in any research collaborations? Answers: 1. Yes,<br>since ... (year) | 2. No, skip to section 8.7|

Source: Survey Questionnaire Technology Use in Production, General Statistical Office Vietnam 37
tests indicate that schools assigned to the different ASPs are statistically similar in their average characteristics, except for school location: all schools in the _Mindful_ ASP are in urban areas. Moreover, most of the individual and household characteristics are balanced across treatment arms, except students’ age, students’ course, student’s travel time from home to school, and whether students are enrolled in the evening shift.<sup>46</sup> Moreover, some outcomes and mechanisms were imbalanced at baseline, including behavior at school, perseverance, risk-aversion behavior, fluid intelligence, arousal, and valence. We account for these differences by including the characteristics as controls and by using a differencein-differences approach as the main estimation model, as we describe in the next section.

A potential concern is that our results might be driven by differences in matching rates with administrative data on the main outcomes or in survey response rates for the data on the mechanisms. To test whether there is differential attrition among treatment arms, we create an indicator that takes the value of 1 for students for whom we were not able to collect administrative data on the main outcomes (behavior, school dropout, or math grades) or whom we were unable to contact for collecting mechanism data (social-emotional skills, and emotion at endline. Our estimations indicate an aver- fluid intelligence, regulation) attrition rate of 18% and an rate with administrative data age survey average matching of 99%. These rates are only slightly higher than in previous studies conducted in similar contexts (Dinarte-Diaz and Egana-delSol, 2023). As presented in Appendix Table A6, the estimations indicate that the matching rates with administrative data and attrition rates in the survey are balanced across all treatment arms.

46Because of limited infrastructure, and to meet most of the demand for education, schools in El Salvador operate in two shifts, one in the morning and another in the afternoon.

25