rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 1:  Community Level Indicators in the regions**

||Rural<br>North|Rural<br>Centre|Rural<br>South|Total|
|---|---|---|---|---|
|Population Density|0.18|0.14|0.25|0.20|
|Numberof Agricultural HouseholdsinCommunity|302.3|295.3|267.2|283.3|
|Distance toAsphaltRoadKM|38.9|16.8|16.5|19.8|
|DailyMarketDistanceKM|12.5|9.5|5.3|8.0|
|WeeklyMarketDistanceKM|7.5|5.3|5.2|5.5|
|ADMARCMarketDistanceKM|12.9|12.9|9.0|11.1|
|Distance to NearestMarketKM|3.5|3.2|2.4|2.9|
|Post OfficeDistanceKM|12.5|10.1|9.3|10.1|
|Primary School DistanceKM|26.9|2.3|3.1|5.5|
|DistrictBomaDistanceKM|55.9|39.2|38.5|41.4|
|UrbanCenter DistanceKM|170.5|106.9|94.2|110.4|
|Bank DistanceKM|62.3|29.2|23.0|31.3|
|AverageDistances to VariousFacilitiesKM|46.3|27.2|23.4|28.3|

36
entirely), but will also weaken the bargaining position of the Southern subsidiary, making it unwilling to hire as much as would be jointly optimal. In Antras’ model, headquarters in the North cannot “command” the optimal level of hiring and so must live with this sub-optimal outcome. If the industry is capital intensive, Antras shows that the ensuing distortion is less costly than if it is labor intensive. Econometric evidence motivating the theory shows that intrafirm trade is more prevalent in capital-intensive industries. The relevance for our investigation of trade patterns is this: when Northern firms set up subsidiaries in Southern countries, those subsidiaries are likely to engage in direct trade relationships with the Northern parent company. This will show in our data as a higher incidence of direct exports to OECD. If this reasoning is correct, we should observe a correlation, at the product level, between capitalintensiveness and the probability of a direct export.

Antras uses a direct measure of capital intensiveness (the stock of capital per employee), but such measures are typically available only at the level of broad sectors in UNIDO and other databases. We take a different approach here and use indices of revealed factor intensity calculated at the SITC4 level in Cadot, Shihotori and Tumurchudur (2008). Briefly, the method used to calculate those indices goes as follows. Step one consisted in constructing a systematic database of country endowments (capital per head, human capital per head, and land per head) updating Easterly and Levine (2002) for capital and Barro and Lee (1993) for human capital. Step two consisted in calculating revealed comparative advantage indices for each product and country at the SITC4 level. Step three consisted in calculating revealed factor intensity indices for each SITC4 good as follows: Index goods by _k_ and countries by _i_ , let  _ik_   _X ik_ / _X i_  _X k_ / _X_  be country _i_ ’s revealed comparative advantage (RCA) index for good _k_ , and let<sup></sup> _i_<sup>be</sup> country i’s capital endowment (stock of capital per head). Good _k_ ’s revealed capital intensity is calculated as a weighted average of the capital endowment of exporters of good _k_ , each of them weighted by its RCA in good _k_ : 36
domain. The second most important factor for which this set of countries underperform is _Business Environment_ , which comprises starting a business, resolving insolvency and paying taxes.

**<u>Table 2. How developing East Asian countries perform in terms of institutional factors, 2017</u>**

|**country**|**Political environment**|**Regulatory environment**|**Business environment**|
|---|---|---|---|
|Cambodia|0|1|**1**|
|China|1|1|**1**|
|Indonesia|1|1|**1**|
|Malaysia|0|1|**0**|
|Philippines|1|1|**1**|
|Thailand|1|1|**0**|
|Vietnam|0|1|**1**|

Source: Own elaboration based on GII dataset and WDI dataset. Note: a value of 1 indicates a variable where the country is below the median country AND below the level predicted by its GDP per capita. The median value (for each variable) is calculated within the 127 countries covered under by the GII dataset.

In this context, how to explain the outstanding performance of China and Vietnam in terms of innovation output performance despite their (low) scores of institutional framework? A potential answer might lie within the remaining enabling factors; results displayed in Table 1 suggest that these two countries outperform in other underlying factors which might compensate for the negative result for the _Institutions_ pillar. Precisely, China outperforms in all remaining enabling factors: _Human capital_ , _Infrastructure_ , _Market Sophistication_ and _Business sophistication_ . Vietnam outperforms, at least relative to the predicted value, in _Human capital_ , _Infrastructure_ , and _Market Sophistication_ . Figures 8.a to 8.d display these results.

**Figure 8. Actual, predicted and median values of innovation input index pillars across developing East Asian countries, 2017**

<!-- Start of picture text --> 8.a. Human Capital 8.b.Infrastructure<br>60 70<br>50 60<br>50<br>40<br>40<br>30<br>30<br>20 20<br>10 10<br>0<br>0<br>Observed Infrastructure index value<br>Observed Human Capital index value<br>Median value<br>Median value<br>Predicted value<br>Predicted value<br><!-- End of picture text --> 10
of providers that can be used. For example, the Business Link Pacific program provides subsidies for small and medium enterprises in several Pacific nations to use a provider (such as an accountant) from a network of approved providers.<sup>8</sup> Such programs are not only used in developing countries. For example, the £30 million Growth Vouchers<sup>9</sup> program in the United Kingdom provided small firms with vouchers of up to £2,000 to help them access specialist support on hiring, financial management and marketing. Coupling these subsidy schemes with a mechanism to allow feedback into provider reputations would be a useful area for future policy and research.

# **References**

Abadie, Alberto, Matthew Chingos and Martin West (2018) “Endogeneous stratification in randomized experiments”, _Review of Economics and Statistics_ 100(4): 567-580.

Allen, Treb (2014) “Information frictions in trade”, _Econometrica_ , 82(6): 20412083.

Anderson, Stephen and David McKenzie (2020) “Improving business practices and the boundary of the entrepreneur: A randomized experiment comparing training, consulting, insourcing and outsourcing”, _World Bank Policy Research Working Paper_ no. 9502.

Bloom, Nicholas, Benn Eifert, Aprajit Mahajan, David McKenzie, and John Roberts (2013). "Does Management Matter? Evidence from India" _Quarterly Journal of Economics, 128_ (1), 1-51.

Bruhn, Miriam and David McKenzie (2014) “Entry regulation and formalization of microenterprises in developing countries with Miriam Bruhn”, _World Bank Research Observer_ , 29(2): 186-201.

Dellarocas, Chryanthos (2003) “The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms”, _Management Science_ 49(10): 1407-24.

> 8 <u>https://about.businesslinkpacific.com/use-the-subsidy-to-access-quality-business-advice/</u> [accessed 27 February, 2021]

> 9 <u>https://www.gov.uk/government/news/small-business-big-support-confirmed-by-prime-minister</u> [accessed 27 February, 2021].

17
institutions. Since the measure we use comes from 1996, which is in the middle of the decade of growth that we are studying, we take the issue of the halo effect seriously. By using instrumental variables we hope to deal with this issue.

We use an instrument set that has been developed in cross-sectional growth empirics: (i) the share of the population that speaks English, (ii) the share that speaks a continental European language, (iii) distance from the equator, (iv) the logarithm of population, and (v) the logarithm of population interacted with each of the other variables. Other work has shown that the language and distance variables are correlated with per capita GDP in 1990 and with institutional quality. Recall, from Table 1, that population is highly correlated with aid receipts relative to GDP. Since we ultimately want to instrument for institutions interacted with aid, it makes sense to interact population with the other exogenous variables. It is difficult to instrument for three (and ultimately four) variables at once, and below we examine the question of how valid and relevant the instruments are.

In the Table 2 IV regressions, aid continues to have a negative coefficient in the specification without regional dummies, but now the coefficient is not significantly different from zero. We start with this table to make two points. First, in three out of six specifications aid has a negative coefficient. If one is looking for evidence of hypothesis #1, that aid has the same positive effect on growth everywhere, it is difficult to find any encouragement in the growth evidence from the 1990s. A second important point is that even for a variable that is considered very robust in the growth literature, such as institutional quality, it is not hard to find plausible specifications in which the variable is not statistically significant, as is the case in all three IV regressions.

The results may reflect problems with the instruments. Table 3 shows the firststage regressions. For initial income and institutions, the _R_<sup>2</sup> s are 0.60, while for aid (0.37) and aid interacted with institutions (0.26) they are fairly high as well. However, with several endogenous variables the first-stage regressions by themselves do not indicate whether the instruments are good. Some additional insight comes from Table 4, which shows correlation among the right-hand-side variables and the predicted values from the first-stage regressions. For each variable the predicted value is highly correlated with the actual value. But a potential problem is that predicted 1990 per capita GDP and 15
# Table 6

# ALTERNATIVE SPECIFICATION ALLOWING FOR ADJUSTMENT LAGS

# Dependent variable: Log P

|||||ImDlied<br>|long run c<br> <br>|oefficients|
|---|---|---|---|---|---|---|
||SITC 6|SITC 7|SITC 8|SITC 6|<br>SITC 7|SITC 8|
|Intercept|1.141<br>(.370)|-.147<br>(.153)|-.002<br>(.184)|-|-|-|
|Lag P|.711<br>(.056)|.876<br>(.027)|.848<br>(.033)|-|-|-|
|Investigation<br>(Duty)|-.040<br>(.024)|.004<br>(.007)|.014<br>(.006)|-.140|.031|.095|
|Investigation<br>(No duty)|.008<br>(.018)|.003<br>(.008)|.009<br>(.014)|.027|<br>.027|.061|
|Duty|.034<br>(.016)|.009<br>(.005)|-.011<br>(.006)|.118|.075|-.072|
|Log w*|.044<br>(.071)|.155<br>(.032)|.152<br>(.036)|.152|<br>1.252|0.998|
|F-Test (a)|9.5|0.9|0.0|-|-|-|
|R<sup>2</sup>|.85|.94|.93||||
|_N_|184|460|299|-|-|-|

Standard errors in ().

Dummy variables included for each four-digit SITC code to control for industry fixed effects. All equations also include quarterly time dummies.

- (a) F value for the test that the long run coefficient on Log w* - 1.
- 29 -

bulbs. But this may change in the future. Given the expected demand effect, cost effectiveness of new technologies can improve dramatically.

The choice between such technologies is theoretically dependent on the market energy and carbon prices. But these prices remain highly uncertain and volatile. There is no agreement on how much the carbon price should be.<sup>27</sup> All these uncertainties make the traditional public procurement systems less useful for promoting energy efficiency projects.

Because of flexible weighting, multidimensional auctions are considered to be an effective system for the simultaneous evaluation of various factors, such as contractors’ abilities and technical reliability of their proposals. This is essential for the ESCO procurement, because individual proposals are different and unique. However, it is also noteworthy that the evaluation method should be standardized to a certain extent for transparency purposes (Singh and others 2010).

One important key feature of ESCO projects is that they usually rely on performance-based contracting. In an ESCO project, any individual inputs, such as equipment and generators, are not a meaningful measurement to verify the completion of the contract. What really matters is the ultimate outcome, which is energy savings in this case. In other words, facility owners purchase energy efficiency services, not particular goods. ESCOs deliver energysaving solutions and are responsible for achieving the agreed target. As the result, facility owners can transfer many technical and commercial risks to the private sector through performance-based contracting.

> 27 There is no theoretical convergence of the social cost of carbon (SCC). The last IPCC report estimates the SCC at $3 to $95 per tCO2 (IPCC 2007). Tol (2005), surveying more than 100 estimates, shows that the estimates have a long tail of the distribution from nearly zero to over $100 per tCO2; the mean is $25. The UK government recommends an SCC of $28 per tCO2 in public decisions, with a range of $14 to $57 per tCO2 (Watkiss 2005). The United States proposes $21 per tCO2, with a range of $5 to $65 (U.S. Department of Energy 2010). The French government recommends a much higher price of $135 per tCO2 by 2030 (Quinet 2008).
by the informal sector (both self-employment and wage contracts) increased from about 5% to 19%. The rate of absorption by the public sector increased from under 1% to almost 8%. The rate of absorption by the formal private sector increased from under 2% to about 10%.

The relatively high rates of transition from unemployment during the period 1997-2000 were also sustained in the next four years. The probability of someone reporting unemployment in the 2000 sample taking up some form of employment by the time of the 2004 survey was 41 percent. This is significantly higher than the corresponding transition rate for the period 1997-2000, but by an amount that could be explained entirely by the fact that the second interval is one year longer.<sup>14</sup>

# **Informal sector/self-employment transitions**

Like rates of transitions out of unemployment, probabilities of transitions between the formal and informal sectors of employment would seem to be negligible over any given year. But, again, the probability of transition rises significantly when we expand the time horizon by three more years (table 3, panel b). This is largely on account of movement of workers to the private formal sector. The probability that someone reporting selfemployment or informal employment in the 1994 survey would have joined the public sector by the 2000 survey was only 5 %. The probability that the same individual would have taken up a job in a private firm was about 15 %. These probabilities do not change much as we expand the horizon further to 10 years (table 3, panel c).

> 14 Other related transition rates of interest for the years 2000-2004 are the following: About 24 % of the unemployed of 2000 sample landed formal sector jobs by 2004; another 17 % became selfemployed, or entered into informal job contracts; some 13 % joined private firms; about 11 % joined the public sector. To illustrate the origins of the unemployed, some 17 % of those who were out of the labor force in the 2000 sample joined the unemployment pool by 2004 (table 3, panel e). Given that the out-of-the-labor force group is by far the largest category of survey respondents in any year, this suggests that fresh entry into the labor force must be the largest source of entry into unemployment in the economies from which the sample was drawn. The second largest source were private sector job losses were the second important source of gross addition to employment for the same period. The probability of a formal private sector job loss was 14 %. The probability of an informal sector job loss was even higher at 17%. This contrasts with 7-8 % rate of public sector job losses. Given that there were more private sector employees than there were public sector workers in the 2000 sample, a larger proportion of the unemployed of 2004 must have come from the private sector than did from the public sector.

9
22 several countries that have implemented UHC-inspired reforms have prioritized MDG interventions. The MDG indicators are: ANC, immunization, SBA, and treatment for ARI and diarrhea. Given the weights we have chosen, the five MDG indicators combined have weights totaling 25%; the non-MDG indicators get the remaining 25% of the service coverage weight of 50%. We thus weight MDG and non-MDG indicators equally in our service coverage index, _SC_ .

On financial protection, we chose a catastrophic payment threshold of 25% of total consumption and a poverty line of $2.00-a-day for the impoverishment indicator. We weight the two domains of financial protection equally, i.e. set  =0.5.

# **V. The UNICO countries and their UHC-inspired reforms**

The countries to which we apply our UHC index are the 24 countries that the World Bank’s ‘UNICO’ project identified as having implemented major UHC-inspired reforms (Cotlear et al. 2015).<sup>19</sup> Table 1 shows the reform timelines in each of the 24 countries during the course of the last four and a half decades. The timelines draw on the UNICO case studies, but, as most of the case studies focus on a specific reform, we have also drawn on other sources to show the full timeline of reforms.

Like the UNICO study, we have focused on reforms that have their aim of reducing inequalities in coverage. Sometimes this involves completely desegregating the health system and creating a unified health system; Brazil and Costa Rica did this

> 19 The individual cases studies are available at the <u>UNICO project’s website.</u>

22
- <u>Table A1.12: European Economic Communities;  Products for which the volume based special safeguard was operational</u>

|||<br>Marketing Year||
|---|---|---|---|
|1995/1996|96/97|1997/1998|1998/1999 through 2005/2006|
|Not invoked|Tomatoes<br>Cucumbers<br>Oranges<br>Clementines<br>Mandarines, including tangerines,<br>satsumas, wilkings and similar<br>citrus hybrids<br>Lemons<br>Apples<br>Pears|Tomatoes<br>Cucumbers<br>Oranges<br>Mandarines, including tangerines,<br>satsumas, wilkings and similar<br>citrus hybrids<br>Lemons<br>Table grapes<br>Cherries|Tomatoes<br>Cucumbers<br>Artichokes<br>Courgettes<br>Oranges<br>Clementines<br>Mandarins, including tangerines,<br>satsumas, wilkings and similar<br>citrus hybrids<br>Lemons<br>Table grapes<br>Apples<br>Cherries<br>Pears<br>Apricots<br>Peaches, including nectarines<br>Plums|

Source: Tabulated from national notifications of SSG actions, available on the WTO documents website 61
# **1. Introduction**

More than 1.6 billion students have had their schools close temporarily because of the COVID-19 pandemic, over 91% of total enrolled learners (UNESCO, 2020). This has led school systems around the world to attempt to rapidly transition to remote learning. However, it is unclear how prepared students are to respond to this challenge, and whether a lack of availability of electronic devices and internet connections prevents many students from engaging in remote learning. Even when students do have access, there is little available evidence on how much of their time they will devote to learning. Newspaper reports from U.S. schools report high absence rates for lowincome students, with fewer than half of students regularly participating (Goldstein et al, 2020). The concern is that these problems of low access to remote learning technologies and limited attendance may be even greater in developing countries, resulting in limited schooling taking place during school shutdowns.

We examine remote learning of high school students in Ecuador, a country which has suffered one of the worst outbreaks of Coronavirus in the world (Cabrera and Kurmanaev, 2020). Schools in Ecuador were closed on March 13, 2020, and a national lockdown and quarantine period imposed. We began a rapid response phone survey on March 31, 2020 of over 1,500 students who are part of an ongoing evaluation in high schools in the Zone 2 administrative region of the country. Over the month of April we were able to interview 64 percent of our baseline sample, asking them questions about their access and use of remote learning technologies, detailed time-use questions, and questions on their mental health and the main stressors they face because of COVID-19. We then examine heterogeneity in these responses by gender, school grade, wealth quartile, mother’s education, ethnicity, province, and internet access.

We find that most students are engaging in remote learning. Access to remote learning technologies are reasonably high, with 74 percent having internet access at home, and 59 percent also having a computer or tablet. Three-quarters of students have done some form of online or teleeducative learning in past week, and 86 percent have done some form of schoolwork. The median student spends 4 hours on schoolwork per day. Gender differences in remote learning are small, with females doing slightly more schoolwork. Female students also do more household tasks than males, while male students are more likely to be working than females. Final year (Grade 12) students spend more time on schooling than younger grades. Students from more disadvantaged 2
compliance at the level of individual shipments. We show that the agency inspects infrequent shipments more often than other shipments, even after controlling for available indicators of underlying risk. We argue that this behavior likely represents a response to ambiguity about expected compliance rates among infrequently observed shipments.

We formalize these insights using a tractable form of a Choquet Expected Utility framework that represents ambiguity with a single parameter. Under our assumed objective function, search plays two roles: detecting non-compliant shipments and discouraging noncompliant behavior on unsearched shipments. The presence of ambiguity unsettles the inspecting agency’s belief about the efficacy of the discouraging role of search. The agency that is pessimistic about compliance reacts by increasing search on those shipments in which there is more ambiguity. Both our empirical evidence and discussions with operational experts support this view: inspecting agencies mitigate ambiguity with additional search.

We develop a quantitative model to illustrate, numerically, the role of ambiguity in a realistic parameterization of the distribution of search activity and firm-level compliance. Counterfactual changes in ambiguity - which are plausibly attributable to IT - change the distribution of search activity and the level of search activity needed to maintain compliance. Our results are indicative of the kinds of benefits that accrue to countries that successfully implement risk management in their border inspection agencies.

# **Appendix A: Summary statistics**

Summary statistics for the 2013-16 data are presented in Table 4. The mean annual number of import shipments per combination of product, origin country, and importing firm is 5 _._ 55 and the mean number of inspections per triplet is 0 _._ 40. The mean observation was inspected 7 _._ 8 times per 100 shipments. 54 percent of _ofk_ triplets had not been observed in the previous year. The mean observation saw 4 _._ 23 lagged shipments in the previous year.

Among the risk control variables, we see that the mean lagged inspection rate is 3 percent and the average logged unit value is 7 _._ 77 (Serbian dinars per kilogram). The mean _ad valorem_ tariff is 7 _/_ 100ths of one percent. The very large maximum value for this variable reflects the 32