rafmacalaba/datause-encoder-data huggingface dataset:
32 economies, then this may lead estimates of the gap to be biased upwards. However, Web Appx. Fig. 7(b) shows that the number of hours worked does not significantly increases with experience, whether in developed or developing countries. Thus, any training that could differentially affect the returns in developed vs. developing countries would be at work.

To see whether the differences in the experience premium might be accounted for by differences in hours spent training, we draw on recent data from _OECD.Stat_ (OECD, 2021). The data contain information on 33 developed economies and 4 developing economies.<sup>51</sup> Let us assume that all this time was compensated, and that none of it involved training that was performed simultaneously with production. Assuming the data are reasonably representative of developed and developing economies, we can use them to explore the impact of any adjustment of hours to account for time spent in formal or informal training on our measured gaps in the returns. To do this, first we obtain an estimate of the share of time that workers spend in training. Then, given that the Ben-Porath framework has human capital accumulation concentrated in the early years, we explore different scenarios for the years in which this training is concentrated.

First, in developed economies 59% of workers report spending time formally and/or informally training, compared to 31% in developing economies. On the other hand, when they do train, workers train slightly more in developing economies than in developed economies, 154 hours compared to 120 hours. This implies that a typical worker obtains 71 hours of training per year in developed economies, compared to 48 in developing economies. According to I2D2 data, workers in developed economies work 43 hours per week on average compared to 50 in developing economies. Assuming three weeks of time off, we get that workers in developed economies on average train for 3 _._ 4% of their working time, compared to 2 _._ 0% in developing economies.<sup>52</sup> Now, suppose that this training is entirely concentrated among the leastexperienced workers. This is an extreme assumption perhaps, but it does serve to verify whether our results might be sensitive to the potential issue described above.

Suppose these workers are in the lowest two experience bins, i.e. 0-4 and 5-9. In

> 51Colombia, Costa Rica, Mexico and Turkey

> 52Recall that we estimate returns separately for workers with and without college. College-educated workers do receive slightly more training than their less-educated counterparts. However, if we focus on the college-educated we get training ratios of 3 _._ 5% and 2 _._ 2% for developed and developing countries, respectively. Since the differences are very small, we abstract from them for clarity.
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**Table 3 Sensitivity to choice of openness measure**

|Depend.<br>variable|Const.|GDP/<br>capita|Urbanization|Education|Tax<br>rate|Political<br>rights|No of<br>obs.|R<sup>2</sup>|
|---|---|---|---|---|---|---|---|---|
|MNTs|26.7|-0.00|-0.04|0.31|46.4|8.0**|48|0.24|
||(1.2)|(-0.5)|-(0.13)|(0.22)|(0.6)|(2.7)|||
|TARIFF|64.3|-0.00|-0.02|0.53|-94.4*|-2.6|48|0.19|
||(4.2)|(-1.1)|(-0.1)|(0.5)|(-1.8)|(-1.20|||
|BMP|15.9|0.01|_-0/05_|9.2|-|47.7**|67|0.23|
||(0.14)|(0.4)|(-0.0)|(1.4)|1082**<br>(-2.8)|(2.7)|||

Notes:

** significant at the _5%_ level

- significant at the 10% level

-numbers in parentheses are t-statistics

-the education variable is defined as the secondary school enrollment rate

Estimates of the parameters of equation (16) are presented in Table 4. As predicted by our model, there is a strong positive correlation between the DOLLAR index and the index of wage distortion. However, none of the other parameters is statistically significant. The index of political liberties becomes significant only when the DOLLAR index is dropped from the equation-which is not surprising, since we have already shown that they are highly correlated. Our results now show that in addition to having more trade distortions, authoritarian countries also tend to have more labor market distortions. However, those results, especially the fact that the wage distortion appears to be only affected by openness and political liberties, may simply reflect the weakness of our wage distortion index.

Table 5 shows the estimates for equation 17. They support our previous conclusion concerning the link between openness and labor policy. The parameter on the DOLLAR index is negative and significant, indicating that more closed economies generally have relatively smaller formal labor markets. The parameter on political liberties is also negative, but significant only in equations where the DOLLAR index is dropped, which is probably a reflection of the high
Figure 3 Dominican Republic and Haiti: Consumer Price Inflation Deviations from US Inflation, 1964-2007

**___** Dominican Republic ___ Haiti

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0<br>-0.2<br>-0.4<br>19641965196619671968196919701971197219731974197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007<br><!-- End of picture text --> Source: World Bank.

# **3.  Degree of Symmetry of Shocks**

As noted earlier, when a country joins a CCA or a currency union in the last stage of an EMU, the exchange rate becomes unavailable to act as a buffer when shocks hit the economy. A key issue is therefore the extent to which the exchange rate is, to begin with, a “shock absorber.” In general, the larger the degree of asymmetry of shocks across a potential currency union the greater the need to absorb shocks at the country level. Put differently, if the potential partners to a CCA have a common business cycle (in the sense that they tend to be affected by shocks in similar ways) rather than divergent business cycles, the costs of fixing the exchange rate across the union will be smaller. By contrast, if members of a currency union are often at different stages of the business cycle, then a common monetary 13
# **A.3 Sampling Frame**

The sampling frames were based on the most comprehensive and latest establishment census available from national statistical agencies or administrative business register. Table A.14 provides the main data sources used in the sample frame for each country.

Table A.14: Sampling frame by country

|Country|Source|Sampling frame|Year|
|---|---|---|---|
|Bangladesh|Bangladesh Bureau of Statistics.|Est. census, 2013|2019|
|Brazil|Ministry of Labor|Employer census, RAIS, 2018|2019|
|Burkina Faso|Business Registry|Business Registry|2021|
|Cambodia|Tax Registry|Tax Registry|2022|
|Chile|Business Registry|Census on Establishments|2022|
|Croatia|Financial Agency (FINA)|FINA Data|2023|
|Ethiopia|Ministry of Trade and Industry (MoTI)|Business Registry|2022|
|Georgia|National Statistics Ofce of Georgia|Est. census, 2021|2022|
|Ghana|Ghana Statistical Service|Est. census, 2013–18|2021|
|India|Central Statistics Ofce of India|Est. census, 2013–17|2020/23*|
|Kenya|Kenya National Bureau of Statistics|Est. census, 2017|2020|
|Korea, Rep.|Statistics Korea|Est. census, 2018|2021|
|Poland|Statistics Poland|Est. census, 2020|2021|
|Senegal|National Agency for Statistics (ANSD)|Est. census, 2016|2019|
|Vietnam|General Statistics Ofce of Vietnam|Est. census, 2018|2019|

* Note : The states of Tamil Nadu and Uttar Pradesh were surveyed in Wave 1 in 2020. The states of Gujarat and Maharashtra were surveyed in 2023.

The universe of study includes establishment with 5 or more employees in agriculture, manufacturing and services. The sector classification is based on the International Standard Industrial Classification of All Economic Activities (ISIC), Rev. 4. More specifically, our sample includes firms from the following ISIC rev 4 sectors: Agriculture (ISIC 01, from Group A); All manufacturing sectors (Group C); Construction (Group F), Wholesale and retail trade (Group G), Transportation and storage (Group G), Accommodation and food service activities (Group I), Information and communication (Group J), Financial and insurance activities (Group K), Financial services (ISIC, 64), Travel agency (ISIC 79, from group N), Health services (ISIC 86, from group Q), and Repair services (ISIC 95, from Group S).

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We examine how far one can go towards reconciling the difference between profits and revenue-expenses through asking more detailed survey questions and through better matching of revenues with the expenses incurred to meet these revenues. We use reported markups of sales over input costs to adjust for the mis-match in the timing of the purchase of inputs and sales resulting from those inputs. We find these corrections do bring the levels of the two measures much closer to one another, and lead to a marked improvement in the correlations—to 0.61-0.73.

We next turn to the issue of recall error. We ask firms for sales data with different amounts of recall, and find firms understate revenues by about 10 to 15 percent with recall over four months compared to one month. However, there is little recall error in asking for annual sales compared to asking monthly sales at quarterly intervals. To correct for recall error we randomly allocated some firms ledger books, to keep diary records of firm revenue and expenses. Firms complied well with this over a one month period, but compliance fell over longer periods. The use of diaries led to significantly higher expenses and to higher revenues of similar magnitudes, suggesting that recall leads firms to underreport both revenue and expenses. However, the use of books did not have any sizeable or significant effect on reported profits, suggesting that they are less affected by recall errors. The use of books does not improve the correlation between reported revenue minus expenses and reported profits.

Finally we examine whether firms deliberately underreport revenues. As in the corruption literature we ask firms about firms like theirs, with the expectation that firms will answer in large part based on their own behavior. The majority of firms think that revenues are underreported, with a median level of underreporting of 30 percent. We had research assistants observe firms 15 to 16 times during a month and record transactions, and use this to estimate actual revenues for these firms. The reported revenues of firms are 31 percent lower than we estimate, confirming the level of underreporting suggested in the self-reports.

3
in production costs due to the above-mentioned RoO compliance. We call this the “RoO effect.” We examine the tariff and RoO effects separately. Separate examination such as this is important once one realizes that a simple reduction in MFN rates yields the tariff effect but not the RoO effect. In addition, other channels may exist. To compensate for the costs of using FTAs, potential FTA exporters may bargain over export prices with importers.

Several studies have empirically quantified the overall effects of FTAs on trade prices without differentiating between tariff and RoO effects. Most of these studies employ product-level import data to differentiate trade values according to tariff scheme. Cadot et al. (2005) found a rise in export prices in the context of Mexican textile and apparel exporters through use of the North American FTA (NAFTA) of around 80 percent of the tariff margin (the difference between the FTA and MFN rates).

<u>Ozden and Sharma (2006)</u> examined the impact of the United States’ Caribbean Basin Initiative on the prices received by eligible apparel exporters and found that export prices rose by around 65 percent of the tariff margin. African apparel exporters captured 16–53 percent of the tariff margin under the African Growth and Opportunity Act (Olarreaga and Ozden 2005). <u>Cirera (2014) found the rise in</u> export prices to the European Union through the use of the generalized scheme of preferences and its related schemes to be 17–80 percent of the tariff margin. Overall, previous studies using product-level data have found higher export prices for exporters trading under FTA schemes than under MFN schemes.

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our results mirror how much, for example, the UK average science score changed between 2009 and 2015 (5 points), and how much the Brazilian average science score decreased over the same period (4 points).

Unlike PISA, the data in Prova Brasil includes school identifiers that allow for a one-to-one match with the schools surveyed in the 2013 WMS wave.<sup>14</sup> In total, we have 262 matched schools in the public sector. We use this matched sample in Figure 3 where we show a school-level binned scatter plot of WMS management score against the Prova Brasil-based management score. There is a positive and significant correlation of 0.19, suggesting reasonable internal validation of the Prova Brasil index. As with the PISA index, we repeat the exercise of correlating the new index with student performance in secondary schools and find the same pattern (see Figure 4).

In Table 1 we formalize these relationships by reporting the average correlations between student learning and our management indices. For the student-level PISA dataset, we run OLS regressions via the OECD’s repest Stata command, which uses the five available test score plausible values for each student and subject. We report the standard errors in parentheses and _p_ -values in square brackets. The standard errors are clustered at the school level and use the appropriate survey weights.<sup>15</sup> In the PISA specifications we include country fixed effects, and successively introduce school controls (dummies for school location, student-teacher ratio, log of the number of students, share of government funding relative to total funding the school receives, and ratio of computers connected to the web used as a proxy for school resources) and then student controls (gender, grade, socio-economic status and immigration status). All panels use the same sample but have different subject outcome variables. The R-squared for each set of regressions is reported within each panel, while the common sample characteristics and controls included are reported at the bottom of the table. Column (1) shows the raw relationship between the PISA-based school management index and student performance, only controlling for country fixed effects. The raw relationship ranges from just over 4 to almost 5 points on the PISA scale.<sup>16</sup> Recall that 41 points on the PISA scale for math is equivalent to about one year of learning, and thus the raw correlation is equivalent to about one month of learning for math (similar for the other subjects). Column (2) includes school

> 14Prova Brasil was also administered in 2013.

> 15See Jerrim et al. [2017] for a thorough review of how to best use PISA scores and survey weights.

> 16PISA is standardized across years and countries such that the mean is 500 and the standard deviation is 100.

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**Figure 3: Civil service wage bill in fiscal perspective FY 2000/01 to FY 2008/09 (actuals) and projections**

**Figure 4: Civil service wage bill as share of GDP FY 2000/01 to FY 2008/09 (actuals) and projections**

<!-- Start of picture text --> Wagebill % recurrent exp<br>6%<br>Wagebill % total revenue<br>Wagebill % total exp<br>50% 5%<br>40% 4%<br>30% 3%<br>20% 2%<br>10% 1%<br>0% 0%<br>2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15<br><!-- End of picture text --> _Source: GOL—Fiscal Policy Department, MOF_ Accelerated growth has occurred very recently from FY2007/08 onward. Compared to the fiscal picture in other countries, the Lao civil service wage bill at less than 30 percent of total spending remains within reasonable bounds—several other East Asian countries have wage bills between 30 and 40 percent of total expenditure. With a civil service size similar to Lao PDR as a proportion of the population, for example, wage bill shares of total expenditure (most recent data available) are 33 percent in Cambodia, 31 percent in the Philippines, 34 percent in Singapore, and 40 percent in Thailand. Yet—projections notwithstanding—the cause for concern would remain if the wage bill’s share of recurrent expenditure were to continue to rise, since this would represent a crowding out of spending on complementary goods. Moreover, as a share of total revenue, the wage bill has climbed from 16.7 percent in FY2000/01 to 34.0 percent in FY2008/09, gradually bringing the wage bill’s continued affordability into question.

The size of the wage bill as a share of GDP exhibits the same pattern of steady increase and an acceleration in growth from 2007/08 onward, rising from 2.7 percent of GDP in 2000 to 5.5 percent in 2008—although the most recent wage bill projections by the Ministry of Finance (MOF) indicate that the government intends to check continued increases (Figure 4). A number of explanations account for this wage bill growth. First, the GOL has been slowly increasing civil service compensation to make up for a steep decline in real salaries following the 1997-98 Asian financial crisis. Average wages have grown more than threefold in nominal terms and have almost doubled in real terms since 2001 (Figure 5). Second, the growing wage bill is, to a major extent, simply a consequence of the increasing size of the civil service and its evolving structure over the last decade, which has brought more civil servants into Grades 3 and 4 than in the past.

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business owners with different characteristics evolved throughout the pandemic, so we use the household head definition. This allows us to add 14 countries to the sample, which had been excluded from the short-term analysis due to a lack of data on variables necessary for those exercises. The 27 countries included in the larger sample are home to more than 78 percent of all Sub-Saharan Africans.<sup>14</sup>

# **2. Methodology**

**The analysis focuses first on the initial impacts of the crisis and then on the medium-term effects** . First, using samples derived from both the household business and household head definitions, we explore how they fared during the early period of the pandemic by looking at changes in income, the use of coping strategies, and the extent to which use of coping strategies are associated with income declines.<sup>15</sup> Second, using a sample generated by the household head definition,<sup>16</sup> we look at the medium-term impacts of the pandemic by exploring the prevalence of household enterprises, income decline and coping strategies changes during the first year of the pandemic, namely April 2020 through April 2021. In both exercises, we pay particular attention to differential effects by household welfare, and when using the household head definition, by gender and age of the head. Since we are working with cross-sectional, recall data, our results are limited to correlations of events occurring within a defined time period.

**Table 1: Data from the first wave of the survey in each country, and month of the first pandemic-induced lockdown (2020)**

|Country|Month of<br>first<br>lockdown|Month of<br>first survey<br>wave|Share of<br>respondents who<br>are heads(%)|Included in<br>HH business<br>sample|Included in<br>household<br>head def|
|---|---|---|---|---|---|
|Burkina Faso|March|June|87.3|---|X|
|Chad|April|June|n/a|X|---|
|Ethiopia|April|April|84.4|X|X|
|Madagascar|March|June|67.6|X|X|
|Malawi|April|June|97.2|X|X|
|Nigeria|March|April|82.3|X|X|
|São Tomé and Príncipe|N/A|July|100|---|X|
|Senegal|March|May|100|---|X|
|Sierra Leone|April|July|60.6|X|X|
|Somalia|March|July|n/a|X|---|
|South Sudan|March|June|54.4|X|X|
|Sudan|March|June|59|X|X|
|Zimbabwe|March|June|100|X|X|

Source: HFPS data, Our World In Data. Month of first lockdown based on first appearance of stay-at-home restrictions. n/a indicates that the data are not available and --- indicates that the country is not included in the sample of countries generated by the corresponding definition.

> 14 The countries with multiple waves are Burkina Faso, the Central African Republic, the Democratic Republic of Congo, the Republic of Congo, Ethiopia, Gabon, Ghana, Guinea, The Gambia, Kenya, Madagascar, Mali, Mozambique, Mauritius, Malawi, Niger, Nigeria, Sudan, Senegal, Sierra Leone, Somalia, South Sudan, São Tomé and Principe, Chad, Uganda, Zambia,Zimbabwe. 15 The WHO declared COVID-19 to be a global pandemic on March 11, 2020.

> 16 We only use the household head definition of a household enterprises since this exercise focuses on how different groups were affected, and adapted, during the first year of the pandemic.

7