rafmacalaba/datause-encoder-data huggingface dataset:
Table 5 explores this correlation in more detail. It uses the detailed 10-digit import data and examines whether growing imports from China in a product or industry are associated with expanded exports to the United States, controlling for the significant variables from Table 4. For all goods (Table 5A), exports to the US are not significantly correlated with growing imports from China. For strategic industries (Table 5B), there is evidence that increased imports from China are correlated with increased exports to the United States. When we control for intra-industry trade, growth in 2-digit and 6-digit imports matter for strategic industries. The former is consistent with value chain trade, while the latter could reflect transshipment or limited additional value added before goods being shipped to the United States. The coefficient of 0.04 on 6-digit imports implies that a 10 percentage points increase in imports from China is associated with 0.4 percentage points higher exports to the United States. The effect of growth in imports from China at the 2-digit level is about twice as important, as reflected by the higher coefficient (albeit lower significance). In terms of economic importance, using standardized coefficients, intraindustry trade is roughly six times more important than what could be transshipment (as measured by growth in 6-digit imports). For other goods (Table 5C), growth in imports from China is not important.

# **_5. Conclusion_**

This paper uses detailed trade data from 2017 to 2022 to study how US trade policy is impacting global supply chains. Despite the tariffs imposed by the US administration in 2018-19, the aggregate data show that overall US openness to the world, as measured by its aggregate imports, has not declined. Even so, we find that US tariffs on imports from China have induced significant direct decoupling, with China’s share of US imports falling by more than 5 percentage points relative to its level in 2017.

This decoupling has not led to a major diversification across import sources. Rather specific countries have benefitted—especially large developing countries, both those on the border with the US and those far from the US. Countries with revealed comparative advantage in a product and those more deeply engaged in supply chains with China have also benefitted the most, especially in strategic sectors. The results are consistent with a “China + 1” strategy, where within product categories one alternative supplier has been the main beneficiary, while China has remained an important trade partner even in the products where decoupling has occurred.

This evidence highlights the potential tension between, on the on hand, efficiency and, on the other hand, decoupling and diversification. China remains an attractive source of manufacturing imports for firms, 16
Table 1: Estimated impacts of the forest fire prevention program on forest fire occurrences per calendar month, for the period 2014-2019

|||(1)<br>Fire occ|(2)<br>urrences|||(1)<br>Fire occu|(2)<br>rrences|
|---|---|---|---|---|---|---|---|
|Month|Year|C30|C50|Month|Year|C30|C50|
|Oct|2014|–0.033|–0.027|Jan|2015|–0.013|–0.016|
|||[0.116]|[0.178]|||[0.994]|[0.878]|
||2015|–0.024<br>|–0.013<br>||2016|–0.056<br>|–0.047<br>|
|||[0.184]|[0.218]|||[0.957]|[0.882]|
||2016|–0.024|–0.035||2017|0.036|0.035|
|||[0.776]|[0.727]|||[0.641]|[0.612]|
||2017|–0.014|–0.039||2018|0.005|–0.000|
|||[0.102]|[0.461]|||[0.731]|[0.837]|
||2018|0.000|–0.000||2019|–0.001|–0.010|
|||[1.000]|[0.856]|||[0.621]|[0.813]|
||2019|0.001<br>22|–0.002<br>|Feb|2015|0.020|0.031|
|||[0.3]|[0.859]|||[0.728]|[0.568]|
|Nov|2014|–0.073|–0.040||2016|0.031|0.027|
|||[0.284]|[0.706]|||[0.568]|[0.690]|
||2015|-0.093***|-0.098***||2017|0.062|0.062|
|||[0.002]|[0.004]|||[0.162]|[0.169]|
||2016|-0.126***|-0.115***||2018|–0.029|–0.044|
|||[0.000]|[0.006]|||[0.239]|[0.201]|
||2017|-0.026***|-0.103**||2019|0.031|0.022|
|||[0.003]|[0.020]|||[0.121]|[0.293]|
||2018|–0.054<br>[0.459]|–0.075<br>[0.636]|Mar|2015|–0.014<br>|–0.016<br>|
||2019|0.023|0.015|||[0.304]<br>|[0.247]<br>|
|||[0.212]|[0.298]||2016|–0.017<br>|–0.012<br>|
|||||||[0.442]|[0.881]|
|Dec|2014|–0.017|0.028||2017|-0.022***|–0.002|
|||[0.901]|[0.401]|||[0.000]|[0.854]|
||2015|0.090|0.067||2018|–0.005|–0.006|
|||[0.165]|[0.199]|||[0.920]|[0.724]|
||2016|0.044|0.017||2019|0.021***|0.020|
|||[0.856]|[0.947]|||[0.000]|[0.702]|
||2017|–0.093|–0.085|||||
|||[0.241]|[0.277]|Apr|2015|0.005<br>|0.005<br>|
||2018|0.162|0.127|||[0.540]<br>|[0.834]<br>|
|||[0202]|[0139]||2016|0.002<br>|0.002<br>|
||2019|.<br>–0143|.<br>–0121|||[0.707]|[0.238]|
|||.<br>|.<br>||2017|–0.001|–0.001|
|||[0.384]|[0.370]|||||
|||||||[0.864]|[0.838]|
||||||2018|–0.001<br>|–0.001<br>|
|||||||[0.586]|[0.254]|
||||||2019|0.015|0.015|
|||||||[0.159]|[0.497]|

_Note:_ This table presents the average decrease in the number of days a forest grid cell is on fire, as a result of the FIP program. The effects of the program are estimated separately for each month on an annual-panel of forest level fire occurrence in the given month. We dropped two project forests in the estimation of December effects and one project forest in the estimation of April effects because pre-treatment fire occurrence in these forests fell outside the convex hull. Variables in the SCM process include past fire occurrences from 2006 onwards, the size of the forests, and the amount of annual precipitation before the FIP program. The _p_ -values of the impact estimates (as derived from the inference tests) are presented in square brackets; those that are not “significant” at the 10% level have been grayed out. * p _<_ 0.10, ** p _<_ 0.05, *** p _<_ 0.01. 20
countries with both PPPs available. The mean poverty line using the 1993 PPPs is $91.25 (as compared to $87.59 using the 2005 PPPs), and the median is $66.70 (as compared to $60.81). It is notable from Figure 4 that the 2005 ICP has tended to entail an even larger downward revision (in proportionate terms) in the $ value of the lowest stratum of poverty lines. This is clear from the density functions of the national poverty lines evaluated at the alternative PPPs given in Figure 5. (The Figure gives the national lines at 1993 PPP in both 1993 prices and 2005 prices, adjusting for US inflation.) The mean poverty line of the poorest 15 countries ranked by consumption per capita at 1993 PPP is $44.19, as compared to $37.98 using the 2005 PPPs. This is to be expected given that the revisions introduced by the 2005 ICP have tended to increase the PPP’s of developing countries, thus lowering the $ values of their poverty lines.

Based on Figure 4, what is the mean poverty line in 2005 PPP amongst countries with poverty lines around the $1.08 figure at 1993 PPP? In other words, what is the estimated value 05 93 _t_ of _E_ [ _Z i Z i_ = $32.74] where<sup>_Z_</sup> _i_ denotes the poverty line in PPP $’s in country _i_ using ICP round _t_ ? A simple way this can be answered is by estimating the parameter _a_ in the following linear regression based on the data in Figure 4:

> 93 ˆ ˆ where it is assumed that _E_ [ ε _i_<sup>_Z_</sup> _i_ ] = 0 .  We find that<sup>_a_</sup> _i_ = 36.06 (t=8.85) and<sup>_b_</sup> _i_ = .0857 (t=10.69).  This implies a poverty line of $1.19 a day at 2005 PPP. The estimate of _a_ is not significantly different from our new $1.25 line (t=0.48).33  But nor is it significantly different from the old line; we cannot reject the null that _a_ =32.74 (t=0.81). However, it is significantly lower than $1.45 a day, as obtained by updating the $1.08 line for inflation in the US; the t-ratio on the null that a=$44.26 is 2.01, which is significant at the 5% level.  A possible concern with this calculation is sensitivity of the linear regression to the outliers evident in Figure 4. Instead, 05 93 we tried a nonparametric regression of<sup>_Z_</sup> _i_ on<sup>_Z_</sup> _i_ , and linearly interpolated between the

predicted values for the two closest observations for _Z_ 93 , either side of $32.74; this method gave 05 93 _E_<sup>ˆ</sup> [ _Z i Z i_ = $32.74] = $34.50 or $1.13 a day—similar to (6). Of course, this would not be a defensible poverty line for 2005 since it assumes that the data that gave the old 1993 line were

> 33 In calculating the standard error we ignore the statistical imprecision of the $1.25 line, but that is unlikely to change the conclusion.

17
# ALLQCATION AND NANAGENENT OF PUBLIC 6PENDINO

67. This point would apply even If there were no<sup>backlog of unmet</sup> recurrent expenditure requirements, since new development expenditures generate additional recurrent expenditure commitments that need to<sup>be</sup> allowed for within recurrent expenditure guidelines. At project level it Is vital to anticipate the recurrent Implications of each project. The PIP proJect profile format (see Box 6) should take this Into account.<sup>As a</sup> minimum, for each project an estimate should be made of<sup>the eventual</sup> annual recurrent cost to Government arising from its completion. As the planning system strengthens It should be possible to become more precise and to track the year-by-year impact of each project on the recurrent budget.

68. - Several pragmatic ways to estimate total recurrent expenditure requirements, either for particular programmes/sectors or for Government as a whole, might be considered:

- (a) An extrapolation of requirements starting from a "normal" period within the country.

- (b) Cross-country comparisons.

- (c) Comparison of Estimates requests with actual provision.

- (d) Building up of an estimate from unit cost norms,<sup>as already</sup> discussed above.

69. These approaches are not mutually exclusive and each might have more relevance to some sectors than to others. With (a) the dlfficulty Is likely to be finding an appropriate reference point, since It Is so long since services were adequately funded and there have been such changes In price levels and In Institutional arrangements. With (c) there may be a problem that compilers of Estimates requests have become cynical about the exercise, knowing from experience that even funds provided in the approved Estimates are by no means assured (see Box 3). Availability of data may be a problem In attemptIng to employ cross-country comparisons. Also, some actlvities (primary education, say) may be lnstitut onally<sup>very</sup> similar In different countries, enabling compalIsons to be easily drawn, but for others (agricultural extenslon, perhaps) it may be harder to find good International analogies.

Page 30
**Table 3: The impacts of COVID-19 confirmed cases on demand for services**

|||**Pan**<br>|**el A: The im**<br>|**plication of**<br>|**the pandem**<br>|**ic in the pre**<br>|**sence (absen**<br>|**ce) of social**<br>|**distancing m**<br>|**easures**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Hotel an<br>|d restaurant<br>|Tran<br>|sport<br>|Tou<br>|rism|Re<br>|tail<br>|I<br>|CT and deli<br>|very<br>|
||hotel|restaurant|flight|airport|tourist|museum|mall|shopping|Zoom|Skype|delivery|
|100 cases(0)*social distance(1)|-0.35<sup>***</sup>|-0.38<sup>**</sup>|-0.03|0.00|-0.16<sup>**</sup>|-0.15<sup>**</sup>|-0.08|-0.05|0.59<sup>***</sup>|0.31<sup>**</sup>|-0.16<sup>***</sup>|
||(0.12)|(0.15)|(0.07)|(0.08)|(0.08)|(0.06)|(0.09)|(0.06)|(0.20)|(0.13)|(0.06)|
|100 cases(1)*social distance(0)|-0.42<sup>***</sup>|-0.60<sup>***</sup>|0.46<sup>**</sup>|0.26|-0.07|-0.19|-0.30<sup>***</sup>|-0.06|0.81<sup>***</sup>|0.54<sup>***</sup>|-0.10<sup>*</sup>|
||(0.10)|(0.15)|(0.20)|(0.20)|(0.07)|(0.15)|(0.10)|(0.06)|(0.15)|(0.13)|(0.06)|
|100 cases(1)*social distance(1)|-0.82<sup>***</sup>|-0.82<sup>***</sup>|-0.36<sup>***</sup>|-0.52<sup>***</sup>|-0.28<sup>***</sup>|-0.37<sup>***</sup>|-0.62<sup>***</sup>|-0.19<sup>*</sup>|0.81<sup>***</sup>|0.54<sup>***</sup>|-0.06|
||(0.22)|(0.21)|(0.09)|(0.08)|(0.05)|(0.04)|(0.16)|(0.10)|(0.23)|(0.06)|(0.10)|
|No observations|8160|8160|8160|8160|8160|8160|8160|8160|7344|7344|7344|
|.|**Pan**<br><sup>***</sup>|**el A: The im**<br><sup>***</sup>|**plication of**<br>|**the pandemic**<br>|**in the pres**<br><sup>**</sup>|**ence (absenc**<br><sup>*</sup>|**e) of lockdo**<br><sup>***</sup>|**wn measures**<br>|<sup>**</sup>|<sup>***</sup>|<sup>*</sup>|
|100 cases(0)*lockdown(1)|-0.33|-0.62|-0.16|-0.12|-0.27|-0.24|-0.30|0.00|0.51|0.38|0.11|
||(0.08)|(0.18)|(0.12)|(0.09)|(0.12)|(0.12)|(0.10)|(0.12)|(0.24)|(0.13)|(0.05)|
|100 cases(1)*lockdown(0)|-0.67<sup>***</sup>|-0.67<sup>***</sup>|-0.26<sup>***</sup>|-0.45<sup>***</sup>|-0.19<sup>***</sup>|-0.33<sup>***</sup>|-0.58<sup>***</sup>|-0.18<sup>*</sup>|0.66<sup>***</sup>|0.42<sup>***</sup>|-0.08|
||(0.23)|(0.23)|(0.06)|(0.07)|(0.04)|(0.05)|(0.17)|(0.11)|(0.19)|(0.05)|(0.11)|
|100 cases(1)*lockdown(1)|-0.67<sup>***</sup>|-0.90<sup>***</sup>|-0.18<sup>***</sup>|-0.26<sup>**</sup>|-0.32<sup>**</sup>|-0.24<sup>***</sup>|-0.57<sup>***</sup>|-0.06|0.62<sup>*</sup>|0.65<sup>***</sup>|0.28<sup>***</sup>|
||(0.11)|(0.15)|(0.05)|(0.10)|(0.13)|(0.08)|(0.12)|(0.08)|(0.35)|(0.19)|(0.07)|
|No. observations|8208|8208|8208|8208|7872|7872|7776|7776|7968|7968||

Note: the data used covers January-April of 2018-2020. For every country where google trends data is available, the search index reflects searches in English and the most dominant local language. Each regression includes weekly dummies to capture aggregate temporal trends. Standard errors, clustered at country level, are given in parentheses.<sup>*</sup> _p_ < 0.10,<sup>**</sup> _p_ < 0.05,<sup>***</sup> _p_ < 0.01.

24
school. The new regulation represents a change from the previous rules whereby children were usually given priority by residence, although it formally constitutes a recommendation to the provinces rather than a binding rule.<sup>6</sup> In practice, however, parents still are more likely to send their children to the neighbourhood school, and school authorities do manage to apply some level of discretion in the selection of students on non-reasonable basis (Fiszbein 1999, Veleda 2003, Veleda 2005). Together, they lead to a particular composition of students according to their socioeconomic status and school segmentation within the state system.

Private schools, on the other hand, have several (legal) ways of selecting students. Family background, psychometric tests, recommendations, or interviews are among the common criteria. Naturally, the ability to pay the fee as a requirement is in itself enough to determined a relatively homogeneous composition of students according to their family background.

As mentioned previously, the paper will identify peer effects using the variation of class composition within schools, across classes, and hence is robust to endogenous sorting described above.

# **2.1 Data: ‘Operativo Nacional de Evaluaci ́on’**

The _Operativo Nacional de Evaluaci ́on_ (ONE) is a standardized test set up by the Argentine Ministry of Education in 1993. The test covers Mathematics and Spanish at different levels of the educational system, two periods during primary school and two periods in secondary school. Tests are multiple-choice, and build on basic knowledge and capacities previously agreed among all provincial offices. In the year 2000, all schools in the country were covered by the test. I restrict my analysis to the students of the sixth grade (approximately 10 years of age) and their test score for Spanish.

All students present at the day of the survey in the chosen class were tested. Unfortunately, it is not possible to know exactly the proportion of absentees on that day in each school, so there is a possible bias if, for instance, teachers suggested students of lower ability not to come on the day of the test. However, given that the results of the tests are not linked with remuneration or otherwise, and that the results are only released at the provincial level (and not to the school), there are no clear incentives for

> 6Provincial governments maintain autonomy in regards to these matters. Therefore, the Federal Law can only be interpreted as a ‘recommendation’ from the central government. In some provinces school choice prevailed before 1994, while other provinces changed the admission system in response to the law. Still others, notably the City of Buenos Aires, continue to selects pupils according to neighbourhood of residence.

6
In this note, we use household level data on mortality, national accounts data on past shocks, and the most recent IMF projections of future growth to estimate the impact of the current global slowdown on infant mortality. Our estimates suggest that there will be on the order of 30,000 to 50,000 excess deaths in Africa in 2009—deaths that would not have taken place had the subprime crisis which began in the United States not spread to African countries. This is a large number. Nevertheless, it is substantially smaller than alternative estimates, generally based on aggregate (rather than household) data, and with cruder if any controls for household characteristics. The increase in infant mortality that can be attributed to the global slowdown is also a small fraction of overall infant deaths in Africa: In Africa, approximately 30 million children are born, and more than 3 million of them die before they reach their first year of life. The bigger tragedy is perhaps not that 30,000 to 50,000 additional children are likely to die in Africa in 2009, but rather that so many children die in any given year.

Nevertheless, the likely increase in infant mortality presents some important policy challenges, as does the fact that the bulk of the additional children who will die is likely to be found among poorer households (in rural areas, and those with lower education levels) and is concentrated among girls. This note cannot discuss the benefits and limitations of individual policies in great detail. However, we briefly mention a handful of policy responses that are perhaps important for policy-makers to consider: � _Policies that limit the extent to which the aggregate GDP shortfalls translate into income shortfalls for households_ : Such policies could include a well-designed public works scheme, perhaps with guaranteed employment at low wages (the latter, to ensure self targeting), or cash transfers to households in poor areas, areas that appear to have been particularly hard-hit by the contraction, or to households with characteristics that are correlated with low incomes (for example, low education levels).

� _Policies that limit the extent to which the contraction in aggregate GDP leads to a decline in the quantity or the quality of critical health services_ —such as prenatal and post-natal check-ups for women, attended birth deliveries, and growth monitoring for children. Paxson and Schady (2005) show that, in Peru, public health expenditures fell by more than 50 percent during the crisis of the late 1980s, and that the utilization of critical health services by households also declined. Ferreira and Schady (2009) show that, by contrast, in Indonesia there was a much smaller contraction in public expenditures in 1997-98, and that this contraction was made up by increases donor assistance; they argue 10
Table 2: Discriminant Analysis

|Variable set used<br>Opportunity<br>Necessity<br>in classifcation<br>correctlyclassifed(%)<br>correctlyclassifed(%)|Classifed as<br>opportunity (%)|Classifed as<br>necessity (%)|
|---|---|---|
|**Panel A: Full sample**|||
|Measures of performance only (1)<br>52.37<br>62.85|40.37|59.63|
|Business characteristics only (2)<br>59.18<br>56.19|46.48|53.52|
|Cognitive skills only (3)<br>59.06<br>56.45|47.17|52.83|
|Non-cognitive skills only (4)<br>57.13<br>53.53|48.78|51.22|
|All variables combined<br>60<br>62.75|42.06|57.94|
|**Panel B: Opportunity group and frstquartile of self reported daily profts of Nec**|**essity group**||
|All variables combined<br>60.00<br>74.11|43.13|56.87|
|**Panel C: Opportunity group and fourthquartile of self reported daily profts of N**|**ecessity group**||
|All variables combined<br>60.00<br>49.46|56.92|43.80|

Measures of performance (1) include Composite Business Practice score; Business characteristics (2) include: age of entrepreneur, age of business, proportion with one worker or more and monthly salary expenses; Cognitive skills (3) include raven (standarized) test score, digit span (standarized) test score and years of schooling; Non-cognitive skills (4) include (standarized) measures of agreeableness, conscientousness, extraversion, intellect/imagination, neuroticism, impulsiveness, locus of control, risk attitude, self-efficacy, optimism, self-confidence, self-satisfaction, attitude towards trust and attitude towards growth.

Table 3: OLS and 2SLS Estimations

||(1)<br>(2)<br>OLS model<br>2SLS model|(3)<br>2SLS model<br>Sample: _year ≤_2010|
|---|---|---|
|**Panel A: Week**|**ly profts(**_log_(_x_+ 1)**transformation)**||
|Opportunity|0.269<sup>_∗∗∗_</sup><br>2.609<sup>_∗_</sup><br>[0.038]<br>[1.332]|2.452<br>[1.493]|
|**Panel B: Comp**|**osite Business Practice score**||
|Opportunity|0.270<sup>_∗∗∗_</sup><br>2.067<sup>_∗_</sup>|3.538<sup>_∗∗_</sup>|
||[0.036]<br>[1.118]|[1.783]|
|**Panel C: First**|**stage**||
|Growth|-<br>1.237<sup>_∗∗∗_</sup><br>[0.467]|1.236<sup>_∗∗_</sup><br>[0.542]|
|F-statistic|-<br>7.014|5.204|
|_Prob > F_|-<br>0.008|0.023|
|Observations_†_|4549<br>4549|2728|

Note: All specifications include as controls: age of entrepreneur, Raven test score, Span test score, years of schooling, locus of control, risk attitude, self satisfaction, optimism, and fixed-effects for years when the entrepreneurs opened their own businesses. Robust standard errors in brackets;<sup>_∗_</sup> _p <_ 0 _._ 1,<sup>_∗∗_</sup> _p <_ 0 _._ 05,<sup>_∗∗∗_</sup> _p <_ 0 _._ 01 _†_ Observations in top 1% of (transformed) weekly profits were not included.

13
<!-- Start of picture text --> a<br>*<br>,<br>EAusoai Sad a<br>\. Gee pee = x<br>t ror , Fe 4<br>¢ 5 ne<br>"at i x “yo0o, hag<br>a Sf coud, br,<br>“y 6 n ? :<br>. “J“# .7alon ia - dz 1<br>Current Irrigation (% Area) BD Large scale IRR <= 6%<br><1% @u-5% @-su BD Lage sale IRR <= 12%<br>.<br>i Dams BB hare scale IRR <+ 20% scale IRR <+ 20% IRR <+ 20% <+ 20% 20%<br>TB Large scale IRR> IRR>> 20%<br>BE smal wate ink <= 10%<br>small seale IRR <= 30%<br>BBE Small scale IRR <= 50%<br>BD small cale RR= RR== 50%<br><!-- End of picture text --> <!-- Start of picture text --> BD Large scale IRR <= 6%<br>BD Lage sale IRR <= 12%<br>.<br>BB hare scale IRR <+ 20% scale IRR <+ 20% IRR <+ 20% <+ 20% 20%<br>TB Large scale IRR> IRR>> 20%<br>BE smal wate ink <= 10%<br>small seale IRR <= 30%<br>BBE Small scale IRR <= 50%<br>BD small cale RR= RR== 50%<br><!-- End of picture text -->