rafmacalaba/datause-encoder-data huggingface dataset:
constructed to isolate variation like that in (a). Because product-country-year fixed effects play a role in our estimation strategy, variation arising from (b) is swept out. However in later calculations where we quantify the impact of the customs reforms on trade we characterize the customs reforms in terms of reductions, over time, in (b).<sup>34</sup> Our working hypothesis is that reduced inspections affect trade through the channel of less time spent to clear customs (which implies a lower cost burden). A key parameter is the causal impact of time to clear customs on trade flows. Absent any endogeneity concerns, we would wish to estimate the following equation:<sup>35</sup> ln ����� ������� ����� ���� ����� ������ (1) where _i_ stands for an importing firm, _j_ for an HS 6-digit product, _c_ for an origin country, and _t_ for a year, ����� is import value, and ����� is an independent and identically distributed (i.i.d.) error. The main coefficient of interest is that on the variable �����, which is the median time spent at customs by imports of product _j_ from country _c_ by firm _i_ during year _t_ . Our choice of median instead of average time spent at customs for ����� follows Volpe Martincus _et al._ (2014). The use of median minimizes the influence of transactions with more extreme delays. Use of the average time in customs would also be problematic because the formula for the average includes the number of shipments in the denominator, and the number of shipments affects the left-hand-side variable, Mijct.

The various types of fixed effects included in Eq. (1) play a crucial role: (i) firm-HS 6-digit product-origin country fixed effects ���� allow the effect of clearance time on imports to be identified based on the within (time-series) variability in clearance times, (ii) firm-year fixed effects ��� account for importing firms’ evolving probabilities of being inspected and/or any firm time-varying shocks to profitability or performance which could affect their import decisions, (iii) HS 6-digit product-origin country-year fixed effects ���� account for the possibility that different product-origin country pairs face a different probability of being subject to inspections by customs and for any trade policy, transport, or demand shocks that hit the product-origin country pair. In particular these latter fixed effects control for tariff liberalization during the

> 34 These estimates are taken from Table 2.

> 35 This follows the baseline specification that Volpe Martincus _et al._ (2014) propose for Uruguayan exports.

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with 5 or more employees and cover manufacturing and services firms largely collected via face-to-face interviews with business owners or top managers. Our sample for the analysis is restricted to firms that have geo-located information. The final sample consists of about 88,000 firms (depending on the specification) across 174 surveys over 118 economies in the time period 2009-2019. Summary statistics are provided in table A1. The full list of countries and survey years are presented in table A2. In addition, for countries where there were several rounds of surveys, we can track whether the firm exited the market regardless of whether they were re-interviewed in successive waves. We can obtain information on firm exit across 55 countries.

The ES methodology includes a consistent definition of the universe of inference, a standard sampling methodology, a standardized survey instrument, and a uniform methodology of implementation. The selection of firms in each country is achieved by stratified random sampling with three levels of stratification: sector, size, and location within the country. Sampling weights are used to correct for unequal probability of selection, ineligibility and non-response. The data are largely collected using ComputerAssisted Personal Interviewing (CAPI) software. The CAPI software collects geo coordinates of the firm’s location that we use to match rainfall and temperature data with the firm-level data. To maintain anonymity of the respondents, the geo-codes are masked around a 2km radius.

The surveys are implemented uniformly across countries. Formal training sessions of supervisors and enumerators are undertaken to ensure the best practices are employed consistently. Quality control checks are implemented to guarantee the quality of the data throughout the data collection process. Consistency checks are employed for 10% and 50% batches of the data during the survey to facilitate callbacks to respondents to be undertaken when necessary to verify information. Information on the Enterprise Surveys global methodology and on the sample design and weights computation is available on the website http://www.enterprisesurveys.org. The data have been widely used by several studies analyzing the private sector in developing economies (Besley & Mueller, 2018; Chauvet & Ehrhar, 2018; Hjort & Poulsen, 2019).

6
Figure 10A. Treasury spread: 10-year Treasury bond yield minus 3-month Treasury bill rate (monthly average, in percent) <!-- Start of picture text --> 6<br>October 2017 = 1.27<br>4<br>2<br>0<br>-2<br>-4<br>1961 1969 1977 1985 1993 2001 2009 2017<br><!-- End of picture text --> Source: Federal Reserve Bank of New York. Note: This figure shows the US Treasury spread, which is calculated as the difference between 10-year Treasury bond yield and the three-month Treasury bill rate. The gray areas show recessions in the United States.

Figure 10B. Probability of US recession predicted by treasury spread (12-month ahead, monthly average, in percent) <!-- Start of picture text --> 100<br>October 2018 = 9.04%<br>80<br>60<br>40<br>20<br>0<br>1961 1969 1977 1985 1993 2001 2009 2017<br><!-- End of picture text --> Source: Federal Reserve Bank of New York.

Note: This figure shows the probability of a US recession predicted by the Treasury spread. This prediction is based on a model estimated using data from January 1959 to December 2009. Recession probabilities are predicted using data through October 2017. The gray areas show recessions in the United States.

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contribution of women and the aggregate output in the economy. The variant set the fertility parameter to 2.35, a quantity necessary to ensure a net reproduction rate of 1.0 starting in the 2015-2020 period. The economy of Liberia was estimated to experience a 3.1 percent growth if fertility were equal to its instantreplacement rate (Figure 3). Per worker output was estimated to grow by 1.59 percent.

The growth due to lower fertility rates was driven through several channels. The average years of education was predicted to grow from 4.6 to 5.0 while we expected the entire supply of labor to increase by 28,600 workers. The variation in output due to lower fertility rates was further driven by an increased allocation of the workforce to the modern sector and more representation of women within it. The modern sector was estimated to expand its share from 29.5 percent to 32.5 percent which led to a modern sector output growth of 18.3 percent for women and 8.8 percent for men.

The dividend would be less, but still economically meaningful, if the fertility rate in Liberia were equal to the regional average (4.1 children per woman) in 2015-2020. A reduction of an average of 0.37 children per women in Liberia would lead to an economic expansion of 0.57 percent in GDP and women’s contribution would increase by 0.28 percent. Alternately, when using OECD fertility rates in the same years,<sup>24</sup> GDP would grow an additional 3.71 percent in comparison to our baseline, reaching USD 5 billion.

**Figure 3. Selected macro-economic changes due reduction of fertility rates to instantreplacement rates (in USD, growth in %)**

<!-- Start of picture text --> $4,500 23%<br>4,176<br> $4,000<br>18%<br> $3,500 18.3%<br> $3,000 13%<br> $2,500<br>8%<br>8.8%<br> $2,000<br> $1,500 3%<br>1,150<br>3.1% 1,042<br> $1,000 0.8% 634 712<br>516 -2%<br> $500 -2.1%<br>-3.3%<br> $- -7%<br>GDP Female Trad. Mod. Trad. Mod.<br>Female Male<br>Millions<br>Growth (in %)<br><!-- End of picture text --> Source: author’s calculations Closing the gender pay gap within sector would increase GDP by 4.8 percent (Figure 4). Liberia is not an exception to the global disparity that exists between male and female remuneration at work and especially in Sub-Saharan Africa (UN Women, 2016). Whereas some of such disparity can be explained by observable attributes of the female workforce such as attainment in education, the gender pay-gap is also reflective of wage discrimination between men and women. In Liberia, male workers earn 6.55 percent

> 24 The value adopted is 1.95 and sourced from the UN Population estimates given that the same source is used for Liberia and SSA estimates, yet this differs from recent estimates given by the OECD which put the average in 2019 to 1.6 children per woman (OECD, 2021).

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We find that with schooling, controlling for experience and type of employment, the difference in male-female earnings tips in favor of males in Bolivia, Georgia, and Ukraine (the total earnings difference ranges from 0.44 in Colombia to 0.99 in Georgia). Without selection correction, however, we find wages favoring males across all countries with differences ranging from 0.15 to 0.45. Differences in educational attainment between men and women explain about 13 percent of the earnings gap in Bolivia and a staggering 70 percent of the earnings differential in Vietnam. In the ECA countries (Armenia, Georgia and Ukraine) we find that schooling does not explain differentials in the wages of men and women. This follows from the high average educational attainment among men and women in these countries (see Table 3).

In Panel B, the addition of noncognitive skills scores increases the total difference in the earnings of men and women in Bolivia and Georgia. Differences in men and women’s noncognitive skills (emotional stability, specifically) accounts for an additional 3 percent of the wage gap in Bolivia, controlling for schooling and other observed characteristics. The role of noncognitive skills in Ukraine indicates that differences in personality traits (specifically, extraversion, emotional stability, and conscientiousness) are associated with gender differences in earnings. In Vietnam, noncognitive skills over-explain the wage gap, as seen in Panel A of Table 8.

The role of cognitive skills in explaining the gender wage gap, controlling for experience, education, and personality, is examined using reading proficiency scores (Panel C, Table 8) and computer use on the job (Panel D, Table 8). For reading proficiency scores we find that the wage gap, although still favoring males, is slightly smaller in Bolivia. The differences in cognitive skill endowments are significant in Bolivia, Vietnam, and Kenya but do not significantly explain the gender wage gaps in these countries over and above schooling and noncognitive skills. In contrast to these findings, Nordman et al. (2014) found that adding cognitive skills to the model reduces the unexplained portion of the wage gap by 2 percent in Bangladesh. The measure of cognitive skills used in their study is self-reported, and not a direct assessment of reading proficiency skills like the one available in the STEP data.

For our second measure of cognitive skills—complexity of computer use at work—we find that the estimated wage gap shrinks significantly in Bolivia and Georgia but increases in Armenia and Kenya when computer skills are added to the model. The addition of computer skills, along with schooling, noncognitive skills, and reading proficiency scores, explains an additional 3 percent of the unexplained wage gap in Bolivia but shows no effect in other countries.

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standardized indexes for each of these outcomes. All scores are standardized using the means and standard deviations of the control group.

_Parental Engagement._ We also collected information on parental participation in school activities, such as meeting the principal of the primary school or the teacher of the target child, and being part of the school community. These outcomes are measured as dummy indicators that take the value 1 if the parent or other household members reported meeting the principal or teacher or being part of the school committee. Moreover, we also collected information on the intensive margin of parental involvement. Specifically, we also asked parents to report the number of meetings they or any other member of the family attended with the principal or teacher.

_Sibling Enrollment_ . To study spillovers of the program on siblings, we use data on the preschool and primary school enrollment collected through the household roster. We include in the estimation sample all children aged 3 to 12 years that were either the child, stepchild, or adopted child of the household head. The preschool and primary school enrollment outcomes are measured as dummy indicators that take the value 1 if the household head reported that each non-target child aged 3 to 12 living in their same household was enrolled in a preschool center or in primary school at the time of the survey.

# **Other Measures**

_Quality of Program Key Components._ We used an adapted Measure of Early Learning Environments (MELE) instrument. It was adapted by specialists to the Mozambique context and uses two approaches to measure the quality of early learning environments: classroom observation tool<sup>12</sup> and oral interviews to caregivers, teachers (facilitators), and school administrators (CCC member).<sup>13</sup> Using these two approaches, we collected infor-

> 12The MELE Classroom Observation Tool collects information about the activities and interactions that occur during a typical class session, including learning activities; classroom interactions and approaches to learning; classroom space and materials; and facilities and safety. Most items are based on an actual classroom observation and are scored on a 1-to-4-points scale, with higher scores reflecting higher levels of quality.

> 13We adapted the MELE interviews to collect information on pre- and in-service training of teachers; teacher qualifications; supports and training offered to teachers; feedback of the program among teachers,

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Figure 17: Self-employment rates vary widely across OECD countries <!-- Start of picture text --> Self-employed as percentage of total employed, 2012<br>Greece<br>Korea, Rep.<br>Chile<br>Italy<br>Poland<br>Portugal<br>Czech Republic<br>Spain<br>Ireland<br>Slovenia<br>Slovak Republic<br>Switzerland<br>Netherlands<br>United Kingdom<br>Belgium<br>Finland<br>Austria<br>Iceland<br>Japan<br>Germany<br>France<br>Australia<br>Sweden<br>Denmark<br>Canada<br>Luxembourg<br>Estonia<br>Norway<br>United States<br>0 5 10 15 20 25 30 35 40<br><!-- End of picture text --> Source: World Bank, World Development Indicators database, 2014 edition.

The existence of a possible positive association between strict labor market regulations and selfemployment rates seems to be confirmed by Figure 18, which presents the correlation between labor market flexibility and the share of self-employment in EU and OECD countries. The figure uses two different measures of labor market flexibility. The labor market flexibility index, built by the World Economic Forum, scores countries from 1 to 7, with higher scores indicating higher levels of flexibility. The redundancy cost indicator, developed by _Doing Business_ , is the sum of the average notice period and severance payments, expressed in weeks of salary, required when dismissing employees with 1, 5 and 10 years of tenure, with higher values indicating more rigid regulations. In line with the results of other studies, self-employment is higher in countries with a lower level of labor market flexibility and a higher cost of redundancy.

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**Loan loss provisions, reserves, and expected losses.**<sup>**5**</sup>

**Appendix 1:**

Although regulatory capital is intended to provide an adequate buffer against adverse occurrences to banks' balance sheets it is not the only relevant buffer bankers can resort to. The prevailing conceptual framework, summarized in Figure 1, recognizes the existence of two categories of shock absorbers: loan loss reserves and capital. Regulatory capital should cope with the occurrence of "unexpected losses", that is losses that are large but infrequent and that therefore can be located far in the tail of the frequency distribution of loan losses. Loan loss reserves should, instead, cope with "expected losses", that is losses which occur on average and can be measured by the mean value of the frequency distribution of loan losses. According to this distinction, the occurrence of losses equal to _OB_ in Figure 1 should be buffered for the amount _OA_ by loan loss reserves and for the amount _AB_ by depleting regulatory capital. What Figure 1 makes clear is that the very effectiveness of regulatory capital as a buffer of unexpected shocks rests on the existence of the subsidiary buffer represented by the reserves created through loan loss provisions.

**Figure 1: PDF of loan losses, provisions and economic capital**

<!-- Start of picture text --> Genera Credit risk capital<br>Provisions<br>o 99th percentile<br>Expected Unexpected<br>losses losses<br>0 A Loss B<br><!-- End of picture text --> 5 This section draws on Cavallo and Majnoni (2001).

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**Figure 8 Deviations of real consumption in 2025 in the recession scenario with the collapse of financial flows as compared to the baseline and the current account/GDP in the baseline (percent).**

<!-- Start of picture text --> Recession in high income countries and the collapse of financial flows<br>15.0<br>10.0<br>5.0<br>0.0<br>-5.0<br>-10.0<br>-15.0<br>-20.0<br>-25.0<br>% deviations of total consumption from the baseline 2025 CA/GDP in the baseline 2025<br><!-- End of picture text --> Source: Authors’ simulations and calculations.

# Oil supply shock

In this stress test, we look at the implications of a supply shock in the non-African OPEC countries (that is, all except Nigeria) in 2013. The shock could arise due to a military conflict in the Middle East or some other temporary shock that disrupts the supply of crude and refined oil. In this scenario, oil production in OPEC drops 50% in the first year but recovers over the next couple of years to its pre-shock level. The rise in the world price of oil will encourage other oil producers to step up their production so that the decline in overall world output is mitigated. Even so, these other producers have higher marginal cost or lower productivity so that the world price of oil jumps by 22% initially. The increase in the world oil price benefits several net oil exporters. The changes in real domestic consumption can be traced back to the structure of oil production and consumption. Table 2 displays the benchmark share of imports in consumption and the share of exports in production. Countries such as Mozambique or Zambia that do not have any domestic production, and rely completely on imports of oil, experience a drop in real consumption due to higher prices of oil. On the other hand, oil producers and exporters such as Nigeria or rest of SSA benefit from higher oil prices. The impact on GDP is less pronounced than the impact on real consumption, because the latter is directly affected by terms-of-trade shocks. Due largely to more African countries producing oil, overall GDP of SSA in this scenario by 2025 is slightly higher than in the baseline (1%).

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# **Table 8: The Percentage of Parts and Components Products with Comparative Advantage in Latin American Countries**

||Exports - Pro<br>( % of Produ<br>|duction Ope<br>cts with RC<br>|rations<br> <br>A > 1)<br> <br>|Imports - A<br>( % of Pro<br>|ssembly Op<br>ducts with R<br>|erations<br>CA > 1)<br>|
|---|---|---|---|---|---|---|
|Country|1985|1995|2005|1985|1995|2005|
|Argentina|1.3|9.3|10.7|32.0|36.0|38.7|
|Bolivia|0.0|0.0|2.7|44.0|22.7|24.0|
|Brazil|6.7|14.7|18.7|28.0|34.7|49.3|
|Chile|1.3|0.0|0.0|40.0|33.3|26.7|
|Colombia|4.0|4.0|4.0|30.7|34.7|32.0|
|Ecuador|0.0|1.3|0.0|45.3|34.7|25.3|
|Mexico|17.3|22.7|29.3|62.7|41.3|38.7|
|Paraguay|0.0|0.0|1.3|29.3|17.3|13.3|
|Peru|2.7|1.3|1.3|48.0|34.7|33.3|
|Uruguay|4.0|4.0|5.3|14.7|26.7|22.7|
|Venezuela|0.0|0.0|0.0|57.3|37.3|36.0|
|United States|61.3|64.0|54.7|37.3|38.7|33.3|
|Canada|17.3|20.0|24.0|76.0|52.0|53.3|
|Average ANDEAN|1.3|1.3|1.6|45.1|32.8|30.1|
|Average MERCOSUR|3.0|7.0|9.0|26.0|28.7|31.0|
|Average above Latin America (LAIA)|3.4|5.2|6.7|39.3|32.1|30.9|
|Average LAIA excluding Mexico|2.0|3.5|4.4|36.9|31.2|30.1|
|Average NAFTA|32.0|35.6|36.0|58.7|44.0|41.8|
|Average above all countries (LAC)|8.9|10.9|11.7|41.9|34.2|32.8|
|Memo Items: Comparators|||||||
|China|12.0|20.0|30.7|40.0|44.0|34.7|
|Korea Rep.|10.7|16.0|26.7|28.0|37.3|21.3|
|Malaysia|8.0|13.3|20.0|52.0|36.0|37.3|
|East Asia(12)average|16.5|17.7|24.0|37.1|35.2|28.1|

Notes: Percentages based on the 75 products of parts and components from textiles & clothing and machinery & equipment at SITC 4/5-digit level in Revision 2.

Due to the missing data, the 1985 data for Mexico is used 1986.

Source: Computations based on UN COMTRADE Statistics.
This desire is also accompanied by a willingness to pay. More specifically, 67.9% and 69.5% of respondents respectively indicate that they are willing to receive 0.5% to 1% less monthly income for full coverage for occupational injuries. Most respondents prefer to have retirement savings in its current form, that is, to make regular contributions to EPF while also receiving regular contributions from their employer, in return for retirement savings in the form of lumpsum payment at the age of 55. Nonetheless, more than 35% of respondents indicate that they would be willing to receive 5% or 10% less income in return for regular contributions to EPF from their employer and a monthly pension among retirement. There is also a high willingness to pay 0.2% to 0.5% of monthly income in return for unemployment benefits of RM800 or RM1,200 monthly until they find a job.

b. T-Tests for Verifying Randomization of Treatment Arms Tables 1 a, b and c below show the results from an ANOVA used to perform a joint test of significance with the null-hypothesis that the means of key demographic and other characteristics of the respondents are not statistically distinct from each-other. We see that the choice of job descriptions assigned is on balance, not correlated with individual characteristics. Table 1a: F test of joint significance of treatment arm means (H0: X B1 = X B2 = X B3 = X B4): Unemployment Insurance

||Unemploymen<br>RM 800/ month<br>|t Allowance of<br>up to 6 months<br>|Unemploymen<br>RM 1,200/ m<br>mon<br>|t Allowance of<br>onth up to 6<br>ths<br>|||
|---|---|---|---|---|---|---|
||0.5% lower<br>earnings|0.2% lower<br>earnings|0.5% lower<br>earnings|0.2% lower<br>earnings|||
|**Variable**|**Job B1**|**Job B2**|**Job B3**|**Job B4**|**F-stat**|**P-value**|
|Male|0.59|0.53|0.54|0.53|0.755|0.519|
|Has Children|0.34|0.42|0.40|0.37|1.491|0.215|
|Single/ Unmarried|0.51|0.47|0.46|0.50|0.483|0.694|
|Age|35.19|35.44|36.74|34.96|1.427|0.233|
|Chinese|0.14|0.19|0.20|0.14|1.758|0.153|
|Indian|0.05|0.04|0.05|0.10|**2.929****|0.033|
|Bumiputera|0.79|0.76|0.72|0.73|1.406|0.240|
|EDU: up to|0.18|0.18|0.18|0.18|0.009|0.999|
|secondary|||||||
|EDU: Post-|0.22|0.27|0.25|0.26|0.485|0.693|
|secondary|||||||
|EDU: Bachelor’s|0.47|0.42|0.42|0.39|1.146|0.330|
|EDU: Professional|0.03|0.04|0.06|0.05|0.697|0.554|
|EDU: Masters+|0.09|0.09|0.10|0.12|0.428|0.733|
|Low Income|0.34|0.33|0.30|0.35|0.568|0.636|
|Med Income|0.36|0.29|0.34|0.35|1.055|0.367|
|High Income|0.30|0.38|0.36|0.30|**2.183***|0.088|
|In Standard|3.75|3.71|3.91|3.87|0.149|0.930|
|Employment|||||||
|Time Preference<br>Index|0.53|0.48|0.53|0.47|1.106|0.346|

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