rafmacalaba/datause-encoder-data huggingface dataset:
**Table 2a. Impact of China's WTO accession estimated with dynamic GTAP** _(cumulative volume changes relative to baseline for the period 2001-2010, US$ millions)_

||Japan||Tawain(C|hina)|HongKong(|China)|Korea||Singapo|re|
|---|---|---|---|---|---|---|---|---|---|---|
||Output<br>Ex|ports<br>|Output<br>Ex|ports<br>|Output<br>Ex|ports<br>O|utput<br>Ex|ports<br>|Output<br>Ex|ports|
|Foodgrains|-27|-3|-91|-20|0|0|-8|1|<br>0|0|
|Feedgrains|5|0|-32|1|<br>0|0|12|0|0|0|
|Vegetablesandfruits|-17|-1|-11|-6|<br>0|0|0|2|1|1|
|Oilseeds|0|0|-3|0|0|0|1|0|0|0|
|Sugar|-5|3|-9|-3|0|0|1|12|-1.|-1|
|Plantfibers|0|0|2|-1|<br>0|0|0|0|0|0|
|Livestock|-88|-12|-107|8|<br>8|2|-59|-16|8|4|
|Beveragesandtobacco|1|7|3|-4|<br>64|64|1|5|-1266|-1283|
|Otherfood|-506|-228|-286|59|-27|-13|-898|-654|-130|-77|
|Woodproducts|372|442|129|364|420|336|1007|827|-13|33|
|Extractive|29|21|-8|21|17|0|14|-6|2|16|
|Textiles|2953|2838|5090|4180|339|826|3332|2951|-77|-77|
|Apparel|865|214|-343|-121|-2940|-2953|-377|-394|-89|-90|
|Lightmanufactures|-15|6|101|87|-3|4|23|57|-2|-2|
|Processingindustry|6294|5819|6543|5689|222|201|2242|1851|-208|-36|
|Autos|-6130|-2411|-2097|114|-399|0|-1739|-334|-223|-143|
|Electronics|-1650|313|794|1296|512|498|-1931|-1710|371|370|
|Othermanufactures|1482|3043|228|843|1991|1885|-1036|_-55_|688|625|
|Total(GDP)_(%)_|-0.08||0.83||0.10||0.02||-0.23||
|Welfare(EV)|189*||1535||86||242||-146||
|Rateofreturn(%)|0.08||0.33||0.18||0.10||0.03||
|Termsoftrade (%)|0.03||0.38||0.17||0.22||-0.04||
|Foreignownership(%)|-0.52||3.96||0.45||0.76||-0.45||

_Source: Authors' simulations with dynamic GTAP._ _* Japanese welfare increase despite a decline in real GDP relative to the baseline due to increases in incomefrom Japanese investments abroad._ Japan, Taiwan (China), Korea, and Hong Kong (China) are expected to raise their output of textiles in response to increased demand from China's expanding apparel industry (Table 2a). Their own garment industries will be squeezed, however, particularly in the markets where the quotas on Chinese textile and apparel exports are removed-North America and the European Union. The growth of their textile exports to the Philippines, Vietnam, India, and other South and Southeast Asian economies is also expected to drop, as these countries' garment industries contract in the face of competition with China in third markets.

China is expected -to increase demand for metals and petrochemicals from Korea; electronics and other manufactures from Singapore; light manufactures, petrochemicals, machinery, equipment, and electronics from Taiwan (China); and metals, petrochemicals and other manufactures from Japan. In electronics, China will source its additional inputs from the countries that get the largest tariff reductions-the US, India and other South 12
controlled 56% of the fixed assets of industry, but produced only 33% of the gross output. The performance of state enterprises has improved over time so that in the aggregate they produce substantial profits. However, both the aggregate statistics and micro evidence show that state enterprises are less productive and profitable than private ones. A large survey of private and state firms in comparable sectors carried out by the World Bank in 2002 and 2003 finds that private firms have TFP about twice the level of state-owned ones in the same sector (Dollar, Hallward-Driemeier, and Mengistae 2005). The median pre-tax rate of return on assets was in the 30% range, far higher than that of state firms. The aggregate statistics show high profitability of private companies as well (after-tax rate of return on physical assets of 15% in 2003).

Both private and state firms in China have high rates of reinvestment of retained earnings (enterprise savings). Our interpretation of this is that private firms see a high rate of return and good expansion opportunities. In the case of public firms, however, we hypothesize that the high rate of reinvestment instead reflects poor governance. State steel firms in China, for example, are making a lot of money and reinvesting it in steel. Despite their overall profitability, most state firms have not paid any dividends to the government owners in over a decade. With better governance, it seems likely many SOE investments would not be made.

There is also a very high level of direct government investment from the budget. The government finances 4-5% of GDP in infrastructure investments from its budget, and also makes a roughly equivalent capital transfer to state enterprises in power, water, and transport. It is harder to measure the returns from this budget-financed investment, but it is certainly plausible that the returns to this are similar to those in the commercial state enterprise sector. Between state enterprises and the budget, the public sector finances something on the order of 20% of GDP in investment each year. Various pieces of evidence suggest that a substantial amount of this is wasted. So, in looking ahead to plausible scenarios, one possibility is that further economic reform will lead to less waste and a reduction in the very high degree of public sector savings. The kind of reforms that would lead in this direction are commercialization and eventual privatization of enterprises in the infrastructure sectors, further privatization of state enterprises in competitive sectors, development of capital markets that intermediate more efficiently 22
average number of branches per community is 1.59 for urban areas compared to only 0.32 in rural areas. Signals are mixed about which group has better financial/economic literacy: judging by _Fin Class_ and _Fin Literacy_ , urban residents are more likely to pursue financial education and have greater financial literacy. However, _Econ Literacy_ and an understanding of expected value (as reflected in the _Risk for Return_ variable) are similar for rural and urban residents.

# **<u>Reasons for borrowing</u>**

CHFS data allow us to understand how various loans are used. For each respondent’s loans, the following potential uses are offered as responses, and multiple choices are allowed: agricultural production, own business, tuition for education, housing purchases, car purchases, financial product purchases, and other purposes. The last two options are available only for informal loans. Loans for “other purposes” would include medical expenses, marriages, funerals, and expenses associated with other important life events. We also know the specific amount of borrowing associated with each of these needs. The results are summarized in Table 5.

The amount of and reasons for bank loans differ greatly between urban and rural regions. While roughly 16% of residents borrow from banks in both rural and urban regions (Table 2), the average amount for urban residents (115,488 RMB, roughly 18,000 U.S. dollars) is _almost 9 times_ as large as that for rural regions (roughly 2,000 dollars). In urban areas, borrowing is most frequently for housing purchases: 12.4% of urban households borrow for this purpose, 1.9% of urban households borrow for business uses, 1.7% for car purchases, and 1.2% for tuition expenses (Table 5, Panel A). However, borrowing frequency patterns differ greatly from those for amounts borrowed. While housing purchases is by far the most frequently cited reason for borrowing, those loans represent only 47% of the total amount of borrowing from banks (Table 5, Panel A). In contrast, though only 1.9% of households take bank loans for business 14
Treated households also report experiencing increased COVID-19 exposure during the mid2020 period when the survey was collected, which coincided with a peak period of the pandemic (Panel D, Table 5). Treated participants displayed an average of 0.84 additional COVID-19 symptoms compared to a control group mean of 0.47 (q-value _<_ 0.01), and treated households had 0.46 more symptomatic adults and 0.17 more symptomatic children (q-value _<_ 0.01 and q-value=0.01, respectively). And perhaps related to this health shock, there is lower weekly adult income in the treatment group, which decreased by 20 USD PPP (SE 7.19, q-value = 0.02), during Jordan’s first COVID-19 lockdown. This reduction in income, however, did not persist after lockdowns were lifted (see Appendix). These households were also 16 percentage points more likely to report non-adherence to social distancing guidelines (q-value = 0.08). Increased household size, as noted above, may have contributed to increased COVID-19 exposure, especially if individuals from other households were likely to be moving in and out of the home. Another more speculative explanation is that the HSP may have allowed household members to travel outside the home more often, for instance, by improving the security of the home through better window, door and lock installations. As noted above, analysis also shows an increase in women’s physical mobility (Table B.1). Perhaps surprisingly, treated households were significantly less likely to have visited healthcare institutions at midline, with 0.56 fewer visits on average compared to 1.14 in control (q-values _<_ 0.05). A definitive explanation for the overall pattern of health and COVID-19 results remains elusive.

Despite these negative effects, there were also several indications of improved household outcomes due to the HSP. Consistent with the subsidy program relaxing the household’s budget constraint, midline survey data indicate that there were reductions in credit usage among treated participants (Panel E, Table 5): treated participants reported taking fewer loans before and during Jordan’s COVID-19 lockdown by 20 and 34 percentage points, respectively. We largely view this outcome in a positive light considering the high debt burden in the sample: control households hold average total rental debt of 806 USD PPP at endline.

Finally, there are mixed midline estimates on child outcomes. Children in treated households 25
months of the pandemic in Rwanda in Figure 1a, leveraging fortnightly cabinet announcements and daily COVID-19 updates. On March 14, 2020, immediately following the detection of the first COVID-19 case in Rwanda, MINISANTE recommended the use of masks in health care settings. A compulsory mask mandate for all public settings was introduced on April 19, just before lockdown restrictions were partially lifted on May 4. This mandate was strictly enforced, with deterrence measures including fines in Kigali City Province, and complementary measures to promote compliance, including infomercials by the national broadcaster and education campaigns by the Rwanda National Police. These measures were effective—by early June, according to the Innovations for Poverty Action’s RECOVR survey, 95% of households always wore a mask when they went out in public.

To increase the availability of certifiably high-quality masks, the Rwanda FDA issued licenses to textile manufacturers to produce surgical and barrier masks. We present the timeline of key policy actions targeting the manufacturing of certified masks in Rwanda in Figure 1a. On March 21, 2020, in response to reported shortages of imported masks, the Ministry of Trade and Industry (MINICOM) imposed price restrictions and the FDA issued a public announcement citing concerns about the sale of substandard masks. To combat this shortage and to mitigate concerns about the sale of unregulated low-quality masks, the FDA licensed 21 garment manufacturers that had submitted applications to produce masks on April 17, two days before the national mask mandate.<sup>1</sup> Licensing was coupled with additional incentives to engage in mask production—MINICOM announced the intent to facilitate access to machines and raw materials, and a VAT exemption for domestically manufactured masks was

> 1 By May 26, an additional 65 smaller firms from the textile industry had been licensed. These firms comprise only 3% of the domestic mask production in our data. In Appendix D, we find licensed firms shift production away from textiles and into masks, but do not increase profits; this is consistent with firm entry into mask production until firms earn no profits from mask production, suggesting mask demand was sufficient only to accommodate the initial manufacturers.

8
**7 Tables and Figures**

Table 1: **Mean School Characteristics by Treatment Group**

||(1)<br>Full<br>Sample|(2)<br>_Clubs_|(3)<br>_Virtue_or<br>_Mindful_|(4)<br>_Virtue_|(5)<br>_Mindful_|
|---|---|---|---|---|---|
|_School characteristics_||||||
|School is located in a highly violent community|0.52|0.43|0.57|0.43|0.71|
|School is in an urban area|0.71|0.57_†_|0.79+|0.57|1.00|
|Total school enrollment (1st to 6th courses)|306.62|269.86|325.00|302.86|347.14|
|Total school enrollment (7th to 9th courses)|104.29|81.43|115.71|145.00|86.43|
|School has its own building|0.81|0.71|0.86|0.86|0.86|
|School is connected to a water supply|0.86|0.86|0.86|0.86|0.86|
|School has computers|0.76|0.86|0.71|0.71|0.71|
|School has a health program for students|0.43|0.29|0.50|0.43|0.57|
|School has a foodprogram for students|0.81|0.71|0.86|0.71|1.00|
|Observations|21|7|14|7|7|

_Notes:_ This table shows descriptive statistics of the available variables at baseline for the sample of participant schools. All information was obtained at the school level. Data were obtained from the Educational Censuses for El Salvador and Guatemala (2018) and self-collected by the authors for Honduras. Column (1) presents the mean characteristics for the full sample of schools and column (2) for the schools assigned to the _Clubs_ group. The average characteristics for schools assigned to either of the psychology-based interventions are presented in column (3). Characteristics for schools in each of the interventions ( _Virtue_ or _Mindful_ ) are presented in columns (4) and (5), respectively. Total school enrollment refers to the enrollment in the school, not to the ASP or study. “Observations” indicates the number of schools within each group. The symbols representing unadjusted _p_ -values in the balance tests for all available variables are as follows: Clubs vs (Mindful + Virtue): *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Clubs vs Mindful: _† † †_ p _<_ 0.01, _††_ p _<_ 0.05, _†_ p _<_ 0.1. Clubs vs Virtue: _§§§_ p _<_ 0.01, _§§_ p _<_ 0.05, _§_ p _<_ 0.1. Mindful vs Virtue: +++ p _<_ 0.01, ++ p _<_ 0.05, + p _<_ 0.1. For more information on balance tests, exact _p_ -values, see Table A4 in the Appendix.

46
Table 5: Bilateral Tariffs and Value-Added Content

||O|LS|To|bit|
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Log DVA-Ratio: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>_/IM i_</sup><br>_xjt_<sup>)</sup>|-0.48***<br>(0.18)|-0.55***<br>(0.21)|-1.32***<br>(0.43)|-1.40***<br>(0.46)|
|Log FVA-Ratio: ln(_FV A_<sup>_i_</sup><br>_xt_<sup>_/IM i_</sup><br>_xjt_<sup>)</sup>|-0.31**<br>(0.15)||-0.51<br>(0.36)||
|Log Inv. IP-Ratio: ln(_FG_<sup>_i_</sup><br>_xt_<sup>_/IM i_</sup><br>_xjt_<sup>)</sup>|0.88***<br>(0.30)||1.95***<br>(0.70)||
|Log IP-Ratio + Log FVA Ratio (_γ_<sup>_IP_ </sup>+_γ_<sup>_FV A_</sup>)||0.63***<br>(0.22)||1.53***<br>(0.50)|
|Reciprocal Trade Agreement: _RTAijt_|-4.59***<br>(0.89)|-4.50***<br>(0.90)|-7.19***<br>(1.34)|-7.13***<br>(1.33)|
|Observations|8,707|8,707|7,643|6,229|
|R-Squared|0.520|0.536|||
|Column Fixed Efects|||||
|Importer-Year|Y|N|Y|N|
|Industry-Year|Y|N|Y|N|
|Importer-Industry|Y|N|Y|N|
|Importer-Industry-Year|N|Y|N|Y|
|Exporter-Industry-Year|Y|Y|Y|Y|

Note: The regression specification is based on Equation (19). The dependent variable in all columns is the applied bilateral tariff of country _i_ in industry _x_ against exporter _j_ at time _t_ : _t_<sup>_i_</sup> _xjt_<sup>.LogDVA-Ratio</sup> (ln( _DV A_<sup>_j_</sup> _ijt_<sup>_/IM_</sup> _xjt_<sup>_i_))istheratioofdomesticvalueaddedfromtheimportingcountry(</sup><sup>_i_)embodiedinfinal</sup> production in industry _x_ in the exporting country ( _j_ ) to bilateral final goods imports for _i_ from _j_ in industry _x_ . Log FVA-Ratio (ln( _FV A_<sup>_j_</sup> _xit_<sup>_/IM i_</sup> _xjt_<sup>))istheratioofforeignvalueaddedinfinalproductionincountry</sup> _i_ and industry _x_ to bilateral final goods imports. Log IP-Ratio (ln( _p_<sup>_i_</sup> _xt_<sup>_q_</sup> _xt_<sup>_i/IM i_</sup> _xjt_<sup>))isfinalproductionin</sup> country _i_ and industry _x_ to bilateral final goods imports. With importer-industry-year fixed effects, only the sum of the coefficients on the log FVA-Ratio and log IP-Ratio is identified. Reciprocal Trade Agreement ( _RTAijt_ ) is an indicator that takes the value one if _i_ and _j_ have a RTA in force in year _t_ . Standard errors (in parentheses) are clustered by importer-exporter pair. Significance levels: * _p < ._ 1 , ** _p < ._ 05, *** _p < ._ 01.

41
CAPE VERDE’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE The extent of underpricing is exceptionally high for power, but apparently not for water. Despite Cape Verde’s relatively high power tariffs, the extent of underpricing in the power sector amounts to almost 1 percent of GDP, representing an economic burden almost twice as large as that found in the middle-income peer group (figure 12). The difficulties of cost allocation make it difficult to reliably establish the extent to which water is underpriced, although with tariffs in excess of $4/m<sup>3</sup> , this does not appear to be a major concern (in contrast to other middle-income countries).

# **Figure 12. Underpricing of power and water does not seem to be an issue in Cape Verde**

Financial burden of underpricing in 2006, as percentage of GDP <!-- Start of picture text -->
1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Power Water<br>Cape Verde middle-income countries<br>% of GDP<br><!-- End of picture text -->

_Source_ : Derived from Briceño-Garmendia, Smits, and Foster (2008). _Note:_ GDP = gross domestic product.

Underpricing of power and water represents an implicit subsidy to these sectors that is highly regressive in its distributional incidence. This to a large extent mirrors the distributional incidence of access to power and water services in the country. A recent study found that—in common with most other countries in Africa—subsidized utility tariffs in Cape Verde are highly regressive, with an omega value well below 1 (figure 13). This means that the share of power subsidies that reach the poor is only half as much as what should reach them, given their share of the population, while the share of water subsidies that reach the poor is only a quarter of what it should be.

Although cost-recovery tariffs are high, they appear to be within the reach of household budgets, at least at very modest levels of subsistence consumption. Given the exceptionally high costs of producing power and water services in Cape Verde, and the already high level of tariffs, it is legitimate to ask whether full cost-recovery tariffs would be affordable to consumers. How expensive would utility bills become if cost-reflective tariffs were applied? With a cost-recovery power tariff of $0.41/kWh and a monthly subsistence consumption of 25 kWh, the associated power bill would come to $10.25 per month. With a cost-recovery tariff of $4.43/m<sup>3</sup> and a monthly subsistence consumption of 4 m<sup>3</sup> , the associated water bill would come to $17.72 per month. Based on the distribution of household budgets in Cape Verde, monthly utility bills at these levels look to be affordable for just about the entire population (figure 14).

23
the total effect of childcare attendance on outcomes rather than the partial effect of the childcare attendance with other variables held constant (Duflo et al., 2008).

We limit the sample to women whose children were born in December and January of two consecutive years and therefore differ in age by only 1 month. In the terminology of regression discontinuity design, the bandwidth is 1 month. In the pooled sample of VHLSS, there are 3,869 children age 1-5 born in December and January. There are six twins, and we drop these twins from the sample. The final number of observations for analysis is 3,863.

The main reason that we focus on a 1-month bandwidth is that parents can choose seasons or even the month of birth for their children. A wider bandwidth is more likely to be associated with bias. However, we also try to extend the bandwidth to 2 and 3 months for sensitivity analysis. For example, a 2-month bandwidth means that we compare women with children born in November and December with women whose children were born in January and February of the following year. A wider bandwidth can improve efficiency since it allows for a larger number of observations. However, it can also increase estimation bias, since children born in October to December may differ significantly from those born in January to March in different aspect such as health and non-cognitive skills which can affect parental employment. Thus, we use the results from a 1-month bandwidth for interpretation.

In the second stage, we regress the employment variable of women on the childcare attendance of children as follows where Y�,� is the employment variable of interest. Equation (3) is estimated using instrumental variable regression, where the instrumental variable for childcare attendance is a dummy variable indicating whether the child is born in December.

<mark>Our dependent variables include both continuous and dummy variables. For continuous variables, such as log of wages and log of the number of working hours, we use 2SLS. When the dependent variable is a binary, 2</mark> SLS regression can be applied for the linear probability model with a dummy endogenous variable (e.g., Angrist 2001). A major limitation of 2SLS is that explanatory variable estimates can be smaller than -1 or larger than 1, which is unrealistic. This problem is more likely to arise when the value of dependent <mark>variables is close to 0 or 1 (e.g., Long, 1997). To address this, we use a bivariate probit model</mark> 12
relationship between average stated WTP by experts and per-capita income appears strong and reasonable. We come back to these relationships later.

**Table 3: Main results from the Delphi surveys for Plan A. Figures in US$ per household per year in the country of the respective expert, calculated as group averages. R1 = Round 1, R2 = Round 2.**

|WTP<br>measure|North<br>America<br>(82)|Oceania (16)|Europe (49)|Asia (70)|Low/Lower-<br>middle-<br>income Asia<br>(46)|Upper-<br>middle<br>income Asia<br>(24)|
|---|---|---|---|---|---|---|
|Mean,R1|89.2|45.6|46.6|27.8|22.5|38.0|
|Mean,R2|71.1|39.3|42.8|16.1|11.0|25.8|
|Median,R1|51.1|25.6|25.6|25.2|22.3|30.8|
|Median,R2|41.1|22.9|22.6|13.8|11.9|17.5|

Table 4 is analogous to Table 3, but with lower figures as the “good” valued is less comprehensive: a 12% further deforestation is accepted under Plan B, while there is no further deforestation under Plan A. The differences are consistent, on the order 25-35% (and similar to the relative differences in deforestation rates that are being valued).

**Table 4: Main results from the Delphi surveys for Plan B. Figures in US$ per household per year in the country of the respective expert, calculated as group averages. R1 = Round 1, R2 = Round 2.**

|WTP<br>measure|North<br>America<br>(82)|Oceania (16)|Europe (48)|Asia (70)|Low/Lower-<br>middle-<br>income Asia<br>(46)|Upper-<br>middle-<br>income Asia<br>(24)|
|---|---|---|---|---|---|---|
|Mean,R1|56.9|28.8|39.9|19.9|17.1|25.0|
|Mean,R2|47.5|25.0|32.4|11.3|8.1|17.6|
|Median,R1|33.1|15.9|19.5|16.4|15.6|18.0|
|Median,R2|28.4|13.9|16.5|9.2|8.3|10.9|

Having estimates of mean and median WTP for a particular area allows us to trace out the probability density function of the WTP distribution if we are prepared to assume a twoparameter parametric distribution.<sup>17</sup> Effectively one has two equations, one for the mean and one for the median, that are expressed in terms of the assumed distribution’s two underlying parameters so that it is possible to solve for those parameters. These parameter estimates can

> 17 In retrospect it would have been desirable to ask our experts to estimate the fraction of their country’s households that would hold a zero WTP for each of our Amazon protection programs.

11