rafmacalaba/datause-encoder-data huggingface dataset:
follow-up survey, we ask enterprise owners to recall the work performed by each apprentice during their last working day, and to estimate how much they would have had to pay an occasional worker to accomplish the same tasks. We can then estimate the value of work performed by each apprentice by multiplying this estimated value of work by the number of days worked in the last month. This apprentice-level measure is then aggregated at the firm level across all apprentices who started since randomization. The third column of Table 5 shows that the program led to a strongly positive and significant increase of the value of work by apprentices in treatment firms. The estimated value of work by apprentices increases by 25 543 FCFA per month, a significant 62 percent increase.

Separately, we can estimate the payments made by firms to apprentices (wage bill). The survey describes precisely the types of compensation received by apprentice for meals, transportation, clothing and “motivation”, including both in cash and in-kind payments. We sum all these components at the apprentice level and aggregate again at the firm level. We also compute a net value of work at the firm level by taking the difference between the value of work and wage bill for apprentices. Columns 4 and 5 of Table 5 show that although the number of apprentices substantially and significantly increased, the total wage bill for apprentices did not increase significantly in treatment firms. A small increase in the wage bill is observed as employers provide some payments to program apprentices, but the increase is not significant. As such, the impact on the net value of work (value of work minus wage bill) remains large. This increase amounts to 21 380 FCFA per month, more than doubling the net value of work by apprentices in control firms.

The increase in the net value of work provided by apprentices in firms can be interpreted as marginal profits being positive. It is possible that the net value of work may provide an overestimation of marginal profits, since it accounts for the value of work performed by apprentices and their direct labor costs, but does not take into account indirect training costs incurred by firms. Still, these indirect training costs are unlikely to be large.<sup>34</sup> It thus likely that the marginal value of an additional apprentices is positive, even net of indirect training costs in firms. A positive marginal profit would be consistent with a form of compensation for the training provided by firms to apprentices.

> 34For instance, the number of hours that apprentices spend working under direct supervision of master trainers is rather limited, suggesting youth learn largely by working alongside master craftsmen, inducing limited investments in time spent by master craftsmen solely teaching apprentices.

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**Table 3: Download Statistics for policy reports**

|**Fiscal Year**|**Total**|**Total**|**Total with**||**Average**|**Standard**||
|---|---|---|---|---|---|---|---|
|**Published**|**Published**|**DL**|**no DL**|**% DL**|**DL**|**Deviation of DL**|**Max DL**|
|**2008**|303|215|88|71%|159|271|2403|
|**2009**|328|229|99|70%|138|267|2955|
|**2010**|337|244|93|72%|117|256|2591|
|**2011**|368|243|125|66%|87|246|1901|
|**2012**|275|163|112|59%|46|132|1302|
|**2008–2012**|1611|1094|517|68%|110|245|2955|

Source: Omniture and World Bank Documents & Reports.

**The most downloaded policy reports tend to have a long shelf-life.** Some reports with a high number of downloads experience very high single-day downloads.  For example, one report released in FY11, had 212 downloads on a single day, but then averaged only 3.42 downloads per day during its first year. Other reports had a high number of first year downloads that were more evenly spread throughout the time period. Another policy report had an average of 4.14 downloads per day during its first year of release, yet never had more than 18 downloads in a single day.   The most downloaded policy report, _Vietnam Development Report 2009: Capital Matters_ , received a total of 2,955 downloads, 1,976 of which were for the English language version of the report and 979 of which were for the Vietnamese language version.  Figure 6 below shows the daily download trends for the five policy reports (disaggregated by language) with the longest shelf-life; i.e. they had the highest average second-year downloads in the dataset and were all downloaded on average once a day.  It is the case that two of these reports, were downloaded more often during their second year of release than during their first. One report was even downloaded more often in its third year of release, 544 times, than in its first two years combined (500 times).

**Figure 6: Download history of selected reports**

<!-- Start of picture text --> 120<br>100 Malaysia economic monitor : repositioning for growth<br>Investing in a more sustainable Indonesia : country environmental analysis 2009<br>80<br>Sudan - The road toward sustainable and broad-based growth<br>60 Malaysia - Productivity and investment climate assessment update<br>Vietnam development report 2010 : modern institutions<br>40<br>20<br>0<br>1 51 101 151 201 251 301 351 401 451 501 551 601 651 701 751 801 851 901 951<br>Days Since Release (Selected Reports)<br>Source: World Bank ISPStats and World Bank Documents & Reports.<br>Daily downloads<br><!-- End of picture text -->

**Citation counts in this study are based on data from Google Scholar.** Google Scholar has great breadth in coverage because it includes not only journal articles, conference proceedings, and other academic

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# **Appendix II:  Variables Used in Meta-Analysis Regressions**

The regressors included in the meta-analysis regressions are defined as follows: 1) Dummy FE regression: dummy variable indicating that the method of estimation of the coefficient used as the dependent variable in the meta-analysis regression is firm fixed effects.

2) Dummy size =20-49, Dummy size = 50-249, Dummy size =250-499: dummy variables indicating that the coefficient used as the dependent variable in the meta-analysis regression is obtained for a subsample of firms in one of the three size classes: 20-49 workers, 50-249 workers, or 250-499 workers. 3) No. of observations: total number of observations used in the regression that produced the coefficient used as the dependent variable in the meta-analysis regression.

4) Dummy establishment level data: dummy variable indicating that the coefficient used as the dependent variable in the meta-analysis regression was obtained based on a sample whose unit of observation is an establishment (rather than a firm).

5) Average year: midpoint of a country’s sample period.

6) GDP: average of GDP at Purchasing Power Parity in constant 2000 international USD during each country’s sample period [ _Source: World Development Indicators database_ ].

7) GDP per capita: verage of GDP per capita at Purchasing Power Parity in constant 2000 international USD during each country’s sample period [ _Source: World Development Indicators database_ ].

8) Openness: average trade (exports plus imports of goods and services) share in GDP during each country’s sample period. [ _Source: World Development Indicators database_ ].

9) Ease of doing business: aggregate index measuring the quality of business regulations. Higher values represent more business-friendly regulations. The index is obtained as the simple average of country rankings in each of the 10 topics in the Doing Business database (starting a business, dealing with licenses, employing workers, registering property, getting credit, protecting investors, paying taxes, trading across borders, enforcing contracts, closing a business). These country rankings are based on a total of 135 countries and the simple average is normalized by the largest value, so the aggregate index varies between 0 and 1. [ _Source: Doing Business 2005 database_ ].

10) Government effectiveness: normally distributed index with mean 0 and standard-deviation of 1 (across a total of 207 countries) whose higher values imply a better institutional framework. The index measures the quality of public services, the quality of the civil service and its degree of independence from political pressures, the quality of policy formulation and implementation, and the credibility of the 66
Table 2 illustrates the substantial variation in our transport costs measure over time and across a selection of 4-digit industries. Since some countries may not export a product to Chile due to prohibitive transport costs, our measure is a lower bound for transport costs accounting only for those of exports that actually occur (Hummels, 2001). However, as this feature of our measure is common to all products, it does not impair our analysis which focuses on differences in the relative, rather than the absolute, magnitude of transport costs across industries and time.

Transport costs proxy adequately for the exposure to import competition of plants in Chilean industries during the sample period for four reasons. First, export choices are to some extent driven by freight costs. For example, within disaggregate product categories, exporters with the lowest freight rates are shown to have the largest import shares based on data for the U.S., New Zealand, Argentina, Brazil, Chile, Paraguay, and Uruguay (Hummels, 2001). Second, transport costs can play an important role in “altering patterns of trade across goods and partners” due to their size and variability across trade partners (Hummels et al., 2008). Third, transport costs represent currently a greater share of trade costs than tariffs for most countries including Chile (Anderson and Wincoop, 2004).<sup>6</sup> Fourth, our transport costs measure excludes insurance costs and, therefore, does not suffer from the related concerns of endogeneity.

Finally, note that our transport costs measure is obtained at the 4-digit ISIC revision 2 level. A more aggregate measure may not adequately capture the degree of import competition faced by plants. For example, 3-digit industry 311, food manufacturing, includes 4-digit industries ranging from fruit and vegetable canning to bakery. If we considered a transport costs measure at the 3-digit level, an increase in imported bakery products would erroneously suggest

> 6 The most usual measure of trade barriers - tariffs - is not informative in the Chilean context due to the uniform tariff structure across industries in place since the 1980s’ trade liberalization (Chumacero et al., 2004). Chile’s entry into preferential trade agreements with various countries and regions since the 1990s introduced a complex set of product- and country-specific exceptions to that uniform tariff structure that could provide useful variation for our analysis. However, such exceptions are subject to political economy pressures and likely to be endogenous to product quality in an industry.

9
from the EU results in a 0.1% and 1.6% increase in welfare in the short-run and long-run respectively. However, when both tariff and non-tariff barriers are taken into account, including the harmonization of standards, and a more efficient trading environment, the welfare impact increases to 3.7% in the short-run and 4.7% in the long-run. Lastly, an earlier study on Egypt, using a CGE model, suggests that larger welfare gains from an FTA would result only from the elimination of regulatory barriers and red tape measures, and a shallow agreement would merely be trade diversionary and lead to a small welfare decline (Konan and Maskus 1997).

Hence, even though tariff liberalization efforts, unilaterally or preferentially, are important in improving the price incentive structure for trade, increasingly, the importance of other trade-related complementary policies, such as, transport and telecommunication services, customs procedures, ports efficiency, standards and technical regulations, flexibility of factor markets, etc are being recognized as an important component in enhancing a countries trade performance. In other words, tariff reforms alone, though important, are insufficient in optimizing the potential contribution of trade to the development (welfare, growth, jobs etc) agenda. Hence, while considering the potential impact of tariff liberalization envisaged under the various free trade agreements, this study will take into account the contribution of improvements to trade facilitation on MENA trade and growth performance.

# **Quantifying the Impact of Trade Facilitation: Brief Literature Review**

Recent quantitative studies on trade facilitation continue to show its importance. Using a probit model, Hummels (2001) estimates that each day saved in shipping time, in part due to faster customs clearance, is equivalent to about 0.5 percentage point reduction in ad valorem tariffs.<sup>3</sup> UNCTAD (2001), observes that a 1 percent reduction in the cost of maritime and air transport services in developing countries could increase global GDP by some US$ 7 billion. Fink, Mattoo and Neagu (2002), using both OLS regression and probit models, observe that a 10 percent decrease in the bilateral price of phone calls is associated with an 8 percent increase in bilateral trade. Using a more comprehensive

> 3Admittedly, the benefit of each day saved to a trader will differ by product.

6
Chongqing and Sichuan). These nine provinces cover nearly half of China’s total population and span China’s eastern, central and western regions. Table 2 shows the sample size for each year.

A detailed description of the CHIP dataset can be found in Li, Sato and Sicular (2013). Here we highlight key features relevant to our analysis. The CHIP rural sample is a subset of the National Bureau of Statistics (NBS) annual rural household survey sample, which covers 68,000 households in all 31 provinces. Like the larger NBS rural sample from which it is drawn, the CHIP sample is representative at the provincial level. CHIP’s provincial sample sizes are not proportional to the provincial populations. For this reason, and also because of the deliberate selection of provinces covered by CHIP so as to represent China’s three major regions (eastern, central, western), for most analyses we use two‐level weights reflecting the provincial and regional populations. Weights are constructed using population statistics from China’s annual 1% population sample surveys (NBS, various years).

The nine provinces in the 2007‐09 CHIP sample exclude the Northeast and China’s autonomous regions in the Northwest and Southwest. These autonomous regions contain relatively high concentrations of the poor, which may explain in part why the CHIP dataset has lower poverty rates than the full NBS sample. Based on the 2009 official poverty line and the full NBS national rural household survey data for 2009, China’s poverty rate was 4.7%; using the same poverty threshold and (weighted) CHIP rural data, the poverty rate is 3.2%.

The nine provinces covered in the CHIP sample also have lower concentrations of dibao participants than is the case nationwide according to the official data. In 2009 the nine provinces covered by the CHIP rural sample contained 47% of China’s rural population but only 38% of China’s rural dibao recipients.<sup>2</sup> Nevertheless, the mean values of key variables such as income are not dissimilar to those in the full NBS sample (Table 2; Li, Sato and Sicular 2013). Thus, with

> 2 Population data from NBS (various years).  Provincial and national rural dibao data are for the month of December, 2009, and are published on the Ministry of Civil Affairs website.  Note that in December 2008 the nine provinces contained 36% of rural dibao recipients in China.  See <u>http://files.mca.gov.cn/cws/201001/20100128094132409.htm</u> and <u>http://cws.mca.gov.cn/accessory/200905/1243323064255.htm, accessed December 31, 2012.</u>

9
identical in both rounds of the VLSS. Moreover, these categories are not mutually exclusive. A child may respond that she participates in all of them as well as none of them.

Three constructed variables in the top panel of table 2 measure child participation in a combination of these activities. Market work indicates participation in wage work or work inside the household in agriculture or a home enterprise. Any work indicates participation in market work or household production. Much of this study's analysis focuses on the participation measure labeled "active." A child is active if she participates in market work of spends 7 or more hours in the last 7 days in household production.

This participation measure "active" corresponds to the definition of child labor employed by the International Labor Organization in many of its SIMPOC country studies. This study uses the word "active" instead of child labor in order to avoid argument over how to define child labor. Instead "child labor" is understood to refer broadly to the set of activities (other than schooling) described in table 2. Many studies of the activities of children tend to focus on market work alone (for examples, see the studies collected in Grootaert and Patrinos 1999). In the present context, some of the aspects of the household production question fall within the ILO's official definition of "economically active", so the household production data should not neglected even if domestic duties were not of substantive interest. Moreover, the consideration of time in household production overcomes three main conceptual problems that arise in studies that from failing to consider the activities performed by children in the production of nontradable goods (home production). First, when a child works outside of its household as a paid domestic servant or a slave that child is classified as a child laborer under the most stringent of definitions. Reclassifying the child's production activities as something other than work if the child's employer changes (even if it changes to a parent) seems arbitrary. Second, treating the 16
The second method through which we try to isolate the mobility restriction effects of the closures on the labor market exploits the questions in the PLFS about the reduced labor supply decisions of workers. In particular, the questionnaire asks workers why they were absent from work in the previous week (if applicable). For each locality in each quarter we compute the share of respondents who reported closure as the reason for their absence. We use this share as a proxy for the extent to which closures restrict workers’ mobility in each locality and quarter. In column (1) of Table 9 we present the results of adding this variable to the employment regression. As expected this share has a negative and significant effect on employment probability. In line with the results in Table 6, this addition reduces little the absolute size of the checkpoint coefficient (from -0.0041 to -0.0039), confirming once again that the direct effect of closures on workers’ mobility explains only a tiny part of the negative effects of closures on employment. That is the case also for the checkpoints’ impact on wages, which is unaffected by the addition of the new variable (column 2). The latter has a positive and significant association with wages, suggesting that employers pay a small premium to workers from locations which are disproportionately affected by the restrictions. The coefficient of checkpoints on the number of days worked is also unaffected by the inclusion of the new variable, which has a negative impact on the days worked in line with the results in Table 6 (column 3).

We obtain the same results also when we use a different variable to construct the proxy for the closures’ restrictiveness on workers’ mobility, i.e. the share of workers who reported closures as the reason for working less than 35 hours in the previous week (column 4). This variable is particularly relevant to measure the effects of the barriers via their restriction to mobility on the supply of labor. As both questions which the new variables are based on concern directly the effects of closure on the labor supply, we generate two individual level dummies which take the value of 1 if the worker’s response is closure to the question about absence from work and to the question about working less than 35 hours respectively. The former dummy has the expected negative association with the number of days worked but again the size of the checkpoint coefficient is unchanged (column 5). The checkpoint coefficient is robust to the addition of these different variables also when the dependent variable is the number of hours worked per week (columns 6-8). The coefficients of the new variables are negative and significant, implying that individuals in localities which are most affected by the closures’ mobility restrictions and individuals who are mostly affected by restrictions work fewer hours 27
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