rafmacalaba/datause-encoder-data huggingface dataset:
regulation (20). Panel C shows that almost 60 percent of endogenous changes (or 18 out of 31) are associated with bad times and the remaining cases with good times.

Figure 7 shows endogenous and exogenous changes in the ECRR measure for each country, as well as the composition of endogenous changes into those corresponding to measures aiming at offsetting output fluctuations in good and bad times. Macroprudential use of RR (i.e., endogenous changes) has been common in our three Latin American economies in the period surrounding the 2008 global crisis. Interestingly, yet not surprisingly, the use of RR for macroprudential (i.e., output stabilization) purposes has not been, by any means, a recent phenomenon as some of the latest papers in the literature seem to suggest. Changes in RR have been frequently used for countercyclical purposes in the past. For example, the central banks of Argentina and Brazil actively used them before and after the financial crisis of 1995. Argentina also reduced the RR on several occasions in 2001 in an attempt to stimulate economic activity after several quarters of negative output growth.

# **5 New measure of central bank interest rate shock**

The exogenous shock to the interest rate is calculated based on the strategy proposed by Romer and Romer (2004) and relying on data from the World Economic Outlook (WEO) historical forecast data. This dataset contains 2-years of historical data and 6-years of forecast data, for three variables: GDP growth, inflation, and the current account balance as percent of GDP. The 6-years of forecast data appear twice every year, once in the Spring and once in the Fall. For instance, in 1991 there is historical data for 1989 and 1990 as well as forecast data for the years 1991-1996 that were projected in the Spring and in the Fall.<sup>19</sup> The Spring forecast of inflation and GDP growth is used as the forecast for the second and third quarters while the Fall forecast is taken as the forecast for the fourth and first quarter. In the spirit of Romer and Romer (2004), the change in the policy rate for each country is regressed on two lags of inflation, GDP growth, the policy rate, and on (2-quarter) forecasted values for inflation, the growth rate as well as changes in lags and in forecasted values of these variables. The residuals from this regression represent the exogenous shocks to the policy rate

> 19The WEO historical data used in this paper corresponds to the April 17, 2018 update. This version does not have inflation forecast for Argentina in 2014.

24
decision-making instances for the application of duties and for the adoption of safeguard measures. The general modification of the tariff that a safeguard implies must have the approval of the instance studying tariff policy (Customs and Tariff Affairs Committee) and the President of the Republic. In contrast, a tariff modification resulting from a process of defense against unfair competition is the responsibility of the Minister of Trade.

The second factor relates to the so-called "special safeguard", which isn’t strictly a safeguard but a mechanism that makes it possible to increase tariffs above the Andean commitments, without infringing multilateral rules, and gives the government greater flexibility in the decision-making process.

# **III.  ANALYSIS OF DUMPING AND SAFEGUARD INVESTIGATIONS IN COLOMBIA.**<sup>**15**</sup>

The first dumping and safeguards investigations in Colombia date back to the end of the 1990’s and mid 1994, in coincidence with the implementation of each of the laws on these matters.

Between 1990 and 2004, 37 dumping investigations have taken place in Colombia (an average of 2.6 cases per year), while there were 34 safeguards-related investigations between 1994 and 2004 (3.4 cases per year). The greater dynamism of safeguard investigations is due to the number of cases in the Andean Community. During this period, 12 investigations were conducted in the Andean zone, while the investigations carried out within the framework of the WTO and for the so-called _special safeguard_ amount to 11 and 10 cases, respectively (Chart III.1.) If the Andean safeguards are excluded, the yearly average would drop to 2.1 investigations per year.<sup>16</sup>

> 15 This section draws on the information included in Annexes 3 and 4.

> 16 As seen in the second section of this paper, the special safeguard is not strictly a safeguard but rather an arrangement that allows for tariff measures to be levied up to the level consolidated in the WTO, and is higher than the tariffs applied pursuant to the Andean Common External Tariff (AEC.)

19
-20-

**Table** 7: **Income Shares by Deciles**

|Decile|Share in total declared|Share in capital|
|---|---|---|
||income|income|
|1|1.2|0.2|
|2|2.9|0.1|
|3|4.0|0.4|
|4|5.2|0.8|
|_5_|6.5|1.4|
|6|7.8|1.9|
|7|9.4|2.8|
|8|11.6|3.8|
|9|15.4|8.1|
|10|35.9|80.6|
|Total|100.0|100.0|
|Gini-coefficient|<br>0.49|0.84|

Source: TARKI, 1995.

Does this build a strong case for taxing capital income, on distributional grounds, since capital income is extremely concentrated on the richest households? No, because the tax base is too small so that using capital income taxation as a tool to make income distribution more egalitarian would not be effective. Consider the highest income decile which earns 35.6% of total income (HUF 610.4 billion). Capital income makes up HUF 31.6 billion, i.e., 5% of total declared income of this decile. Taxing this amount would have a negligible impact on income distribution, regardless of whether taxation is are flat-rated or progressive. The tax base is too small. In addition, progressivity will increase the incentive to avoid or evade taxes, further undermining the already small potential for distributive impact.

As an illustration, we present in table 8 a simulation using the 1994 data. The table shows how average tax rates on total income (that is, labor income plus capital income) of representative households respond to different tax schemes. Total income tax payments for each decile are given assuming that capital income is not taxed (column 4); that capital income is taxed at a flat-rate of 20% (column 5) and that capital income is taxed under the PIT schedule (comprehensive income 27 tax, in column 6). These results show that, compared with not taxing capital income at all, a flat27 By construction, every decile in table 8 contains the same number of taxpayers (namely 431,598) that is, the total number of tax filers divided by 10. The average income of every decile is easily calculated by dividing the total declared income of the decile (obtained by distributing total income reported in table 6 to the 10 deciles by using the pattern of table 7) by 431,598.
contribute to looser global financial conditions to stimulate the economy. They may also lead to “search for yield” effects which tend to drive up asset prices and may induce more risk taking. If crypto-assets are perceived as risk assets, crypto volumes would tend to rise when real yields are low. It is important to keep in mind that asset purchases by the Federal Reserve, including Treasury securities, took place during the sample period (e.g., to combat the impact of the pandemic) which has influenced price formation and the signals that can be extracted from it. C is a dummy which assumes a value of one after March 2020 and zero otherwise. This variable captures the broad and global impact of the pandemic on crypto-assets jj PPCCLLPPC PPCCPPC including public health, behavioral, and economic impacts as well as the unprecedented monetary, fiscal, and financial sector policy response. Finally, represents country-fixed effects to account for time-invariant country characteristics (e.g., level of economic development, institutional framework) that may drive differences of crypto jj γγ volume across countries. is the error term. j Second, we run cross-sectional OLS regressions on 2020 data to explore the role of financial εε development, remittances, economic freedoms, and ICT factors to explain variation of crypto volume across countries. Our main specification is: C <u>C C</u> L ( ) 0 1 C C C 2 C C + G jjC EEjjC CCjj 3 3 C 4C C C , (2) jj jj jj CC = ββ + ββ LLCCaaGGP PPC + ββ CCPPLLCCLLCCPPC PPCCaaC LLCC ββ RRC PPC CCLLC ttjj + ββ EEC LLC PPC C <u>CCCPPC</u> ttjj + ββC IIPPCCaaC LLCCjj + εεjj Where the dependent variable Ln ( ~~)~~ represents the log of the yearly value of cryptoG jjC EEjjC CCjj assets transactions as a fraction of a country’s nominal annual GDP for country CC jj _j_ in 2020, C C C captures the general level of economic development, C jjC includes credit to private sector (% of GDP) and account ownership LLCCaaGGP PPC (% of adult population) for country jj _j_ ; is personal remittances received (as % of CCPPLLCCLLCCPPC PPCCaaC LLCC GDP) for country _j_ ; C include economic freedom indexes such as Monetary jj RRC PPC CCLLC tt Freedom Index and Financial Freedom Index for country jj _j_ ; C C C is mobile cellular EEC LLC PPC CCPPC tt subscriptions (per 100 people) and fixed broadband subscriptions (per 100 people) for countryjj _j_ ; IIPPCCaaC LLCC is the error term. jj εε 27
<!-- Start of picture text --> Fraction value sent by Year and Crypto-asset Type for all transactions Fraction value sent by Year and Crypto-asset Type for all transactions less than $10k<br>100% 100%<br>16.1%<br>24.3% 25.1%<br>30.6%<br>80% 80%<br>= r=<br>®<br>260% 18.9% 2 60% ne ae<br>3 40.1% 3<br>©o<br>>><br>5 12.9% 6<br>oO 40% 5 40% 15.6%<br>o 0<br>ira 118% ire 14.7%<br>20% 9% 20%<br>dhs, 33.2%<br>23.8% 24.5%<br>0% 0%<br>2020 2021 (YTD) 2020 2021 (YTD)<br>Year Year<br>Crypto-asset Type @BIC @DefFi and Others @ETH @Stablecoins Crypto-asset Type @BTC @ Defi and Others @ETH @Stablecoins<br><!-- End of picture text -->
# **_B. Investigate the socio-economic channels and contextual factors driving climate-related (im)mobility_**

A complementary and parallel solution to the _Adaptation_ module suggested above is the development of an _Intention-to-migrate (ITM)_ module on potential migrants to investigate the issues of immobility and climate-induced reductions in migratory flows. Such an _ITM_ module would draw from the available evidence on climate risk perception (Helbling et al., 2021; Koubi, Stoll and Spilker, 2016; Zander, Richerzhagen and Garnett, 2019) and, in order to investigate the potential of climatic hazards to trap people in immobility, its framing should also be inspired by the theoretical insights of the sound literature on asset-based and geographic or shock-driven poverty traps (Barrett and Carter, 2013; Carter and Barrett, 2006; Carter et al., 2008; Jalan and Ravallion, 2002; Letta et al., 2018). With _ITM_ information at hand, it would also be possible to fully unleash the potential of one of the main advantages household surveys hold over other data sources on migration, the possibility of assessing the role of transmission mechanisms and contextual factors that affect the magnitude, or even the direction, of the climatemigration link. The LSMS-ISA collection, given its multi-topic nature, can be particularly informative on these issues, because the analyst can fully exploit the wealth of information available about household demographic, economic, and geographic characteristics to investigate a broader range of issues involving the causal links between poverty, agriculture, climate and the (in)ability to migrate.

# **_C. Other possible data improvements in longitudinal surveys_**

**Migration data.** The two _Adaptation_ and _ITM_ modules proposed above imply a change of perspective to investigate immobility and transmission mechanisms. But we also call for a series of other data improvements in longitudinal household surveys such as those of the LSMS-ISA. These surveys, in fact, exhibit a clear potential given their intertemporal nature (Carletto and Gourlay, 2019), which also allows them to track over time, and include in the subsequent waves, households that relocate within the country, as well as individual members who split off from previously selected households. A possible additional tool to enhance this traceability is the design and implementation of an “associate module” meant to keep track of “ _people who are not members of the surveyed households but who are somehow associated with them, either because they contribute to the household standard of living and/or because they moved out of the household, and/or simply because they are close relative”_ (Beauchemin, 2020). A complementary solution could be to proxy this information by collecting histories and retrospective data in panel waves covering years between surveys. In any case, the data gaps on migration require one to track people over time and across space, either directly or indirectly. Finally, a third route could consist of establishing a

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therefore enables us to examine the long-term persistence of these large changes in management practices.

# **_III.B. Results on Management Practices_**

In Figure 1 we plot the management scores over time after re-visiting the plants in January 2017 evaluated on the same 38-management practice scoring grid as in the prior experiment. We find substantial persistence of the management intervention, which we summarize below with four main results.

<u>Treatment Experimental Plants: First, the management scores in the treatment plants fell</u> from 0.60 at the end of the last wave to 0.46 eight years later. This drop of 0.14 points in the management score reverses 40% of the original 0.35 increase (noting these firms started pretreatment with an average management score of 0.25) over an eight-year period. This fall in the management practice score is equivalent to about an annual depreciation rate of 6% in the original increase in management practices.

<u>Control Experimental Plants: Second, the control plants also saw a drop in their</u> management scores, falling by 0.08 points from 0.40 at the end of the last wave to 0.32. This is smaller in absolute terms compared to the fall in scores in the treatment plants, but the increase in management practices in the control plants was only 0.12 points (from an original score of 0.28), so that the drop in practice scores is 66% of the intervention gain, implying about a 13% depreciation rate of the original management increase.

Together this indicates that, even eight years after the initial intervention the treatment firms still had higher management practices. Table 2 reports the results from running the Ancova specification for plants (i) at time (t): Managementi,t = a + b1*Treatmenti*Year=2011 + b2*Treatmenti*Year=2017 + c*Managementi,2008 +ei,t Indeed, we see that the long-run treatment effect in 2017 of 19.7 percentage points is similar in magnitude to the short-run effect in 2011 (20.6 percentage points), and we cannot reject equality of these treatment effects over time (p=0.802). These effects are individually statistically significant both using conventional (large-sample normality-based) inference as well as permutation procedures with exact finite sample size (the corresponding p-values are also reported in Table 2). Thus, the intervention generated persistent impacts on the treatment plants. Moreover, the greater percentage depreciation of the improvements in the control plants (66%) versus the treatment plants (40%) suggests that small improvements in management may be less 9
probability _ψ_ , while a new entrepreneurial productivity is drawn with the complementary probability 1 _− ψ_ .

The remaining parameters to be calibrated are _α_ + _θ_ , _η_ , _ψ_ , _β_ and the collateral constraint _λ_ of the initial stationary equilibrium. To do so, we target the following moments in the US data: employment share of the top decile of establishments by size (number of employees), the top 5 percent earnings share, the annual exit rate of establishments, the real interest rate, and the ratio of external finance to total non-financial assets of the non-financial business sector.

||US Data|Model|Parameter|
|---|---|---|---|
|Top 10% Employment Share|0.69|0.69|_η_ = 5_._25|
|Top 5% Earnings Share|0.30|0.30|_α_+_θ_ = 0_._79|
|Establishment Exit Rate (Annual)|0.10|0.10|_ψ_ = 0_._97|
|Real Interest Rate (Annual)|0.04|0.04|_β_ = 0_._98|
|Credit Market Instruments to Non-Financial Assets|0.70|0.70|_λ_= 7_._5|

**Table 1:** Calibration

Table 1 shows the moments in the US data and their counterparts in the calibrated model. The decile of the largest establishments (in terms of employment) accounts for 69 percent of total employment in 2000. The earnings share of the top 5 percentiles is 30 percent in 1998. The annual establishment exit rate is 10 percent in the Business Dynamics Statistics from the US Census. We assume that the annual interest rate is 4 percent. Lastly, we target the ratio of credit market instruments to total non-financial fixed assets in the non-financial business sector of 0.7, a level attained one year before the 2008 financial crisis.

Although all parameters are jointly pinned down in the model equilibrium, we can determine which empirical moments are mostly dependent on each parameter. For instance, the tail parameter of the Pareto distribution of entrepreneurial productivity, holding other parameter values constant, controls the fraction of employment accounted for by the decile of largest establishments. Similarly, _α_ + _θ_ can be mapped into the earnings share of the top 5 percent of the population, who are mostly entrepreneurs in the model as well as in the data. There is also a direct link from the parameter governing the persistence of the entrepreneurial productivity process _ψ_ to the probability that an entrepreneur exits from production and hence the annual establishment exit rate in the data. The discount factor, unsurprisingly, is closely tied to the target interest rate. The collateral constraint parameter _λ_ is primarily responsible for the ratio of external finance to capital in the aggregate, 17
studies sing test aerosols of appropriate diameter (but not infectious agents) have shown u that facemasks may have protective properties comparable to high end respirators29, 30.

# **PREDICTING THE COMMUNITY TRANSMISSION OF PANDEMIC INFLUENZA: AN OVERVIEW OF METHODOLOGIES**

Epidemiological modeling of future pandemic influenza transmission is complex because the biology of the virus, responses of a population, etc., are difficult to predict beforehand. Non‐epidemiological studies from the impact literature use historical data from 20<sup>th</sup> century pandemics to estimate a flat rate of infections across the entire population during a wave, known as the Gross Attack Rate (GAR). These assume the same risk of infection for every individual, and hence GAR is applied as a fraction of the population. The section below discusses methods for the _epidemiological_ modeling of infections in a wave of pandemic flu transmission. Results of the studies are compared in the next section.

**_Compartment or SIR/SEIR models of pandemic flu transmission_** : The simplest non‐stochastic (deterministic) models of influenza transmission ‐ pandemic or seasonal – are based on SIR (Susceptible, Infected, and Recovered) models of 1920s vintage. By adding the ‘exposed’ category, SEIR models are the root of _deterministic modeling_ of epidemics. Fig. 1 on this page is an unorthodox representation<sup>59</sup> , modified to show some (but not all) impact points of public health interventions. Individuals transition based on predetermined probabilities from being susceptible (S), to exposed (E, i.e., infection is latent), to infectious (I) and then to removed (R, recovered or dead). Other preset parameters are the length of time individuals stay in state E without becoming infectious (latency period), and the time they stay infectious before being removed. The model is specified in differential equations that define the stock of people in the various states after a discrete time step (usually a day). At time zero, a number of seed infections are introduced and the model is run.

8
- Borusyak, K., Jaravel, X., & Spiess, J. (2021b). Revisiting event study designs: Robust and efficient estimation.

- Byrne, K., Karpe, S., Kondylis, F., Lang, M., & Loeser, J. (2021). Sectoral heterogeneity in the covid-19 recovery: Evidence from rwanda. _Shaping Africa’s PostCovid Recovery_ .

- Callaway, B., Goodman-Bacon, A., & Sant’Anna, P. H. C. (2021). Difference-inwith a continuous treatment.

- Chernozhukov, V., Kasahara, H., & Schrimpf, P. (2021). Causal impact of masks, policies, behavior on early covid-19 pandemic in the us. _Journal of econometrics_ , 220(1), 23–62.

- Conley, T. G. (1999). Gmm estimation with cross sectional dependence. _Journal of econometrics_ , 92(1), 1–45.

- de Chaisemartin, C. & D’Haultfœuille, X. (2020). Two-way fixed effects estimators with heterogeneous treatment effects. _American Economic Review_ , 110(9), 2964– 96.

- Donaldson, D. (2018). Railroads of the raj: Estimating the impact of transportation infrastructure. _American Economic Review_ , 108(4-5), 899–934.

- Duflo, E. (2001). Schooling and labor market consequences of school construction in indonesia: Evidence from an unusual policy experiment. _The American Economic Review_ , 91(4), 795–813.

- Goldsmith-Pinkham, P., Sorkin, I., & Swift, H. (2020). Bartik instruments: What, when, why, and how. _American Economic Review_ , 110(8), 2586–2624.

30
**Table** IV-l ,

||||UsePrivete<br>||Characteriltics<br>of<br>Contract<br>ith Pit|Project|Evaluation|
|---|---|---|---|---|---|---|---|
|Country.<br>Title<br>of Project|Funder,<br>Status<br>of Activity|Project<br>Summary|Distribution<br>S7te.|Employe<br>Benefit|w rvae<br>Providers|Training|Coqamnt|
|FlOIUinWwrjDzU|Sm!SAr<br>VIES(<br>|cont'd)<br> <br> d<sup>i</sup>|Ys|Yes|No. PVO|Yes, of|Assessat|
|Thailanid<br>Factory-Based<br>Family Plaing<br>Services<br>iimuri<br>mv*uno|USAID Enterprise<br>Progra<br>Propoal<br>t=in<br>MFMini<br>RA|Projet<br>WIU extenda<sup>mprove</sup><br>Necha's<br>existing<br>factory-based<br>family planning<br>servicce.<br>KIS<br>for operation<br>and evaluation<br>to<br>cut costs.<br>IUP survey to deter-<br>m<br>d_mnd.<br>Foods will also<br>reorientation<br>nMd Ltrelamling<br>of existin<br>pror<br>**d expur-**<br>dion to maw sites.<br>Mn|||<br>Implementing<br>projet|<br>volunteer<br>motivatora|of progre|
|Indonaesi|||||N/A|N/A|Project Is|
|feasibility<br>of<br>Commercial<br>Clinics Providing<br>Steriliztation<br>in<br>Jakarta|USAID Enterprise<br>Progra mPrposal|Moe project will<sup>**assess**attitudes</sup><br>of likely candidate<sup>couples and</sup><br>tbeir wi1ligmes<br>to pay; the<br>lIiklhood<br>of MS_raeferal; and_<br>the coat of outfitting<br>and<br>|N/A|MIA|<br>|<br>|<br>am aseesaomet<br>of udat might<br>be possible<br>|
|The WK<br>Entrepreneurial<br>Dmvlojast|USAID Entrprise<br>Progra Proposal|MWO<br>clinics<br>(of 11)~i,ich<br>are<br>underperforming. Enterprise<sup>wll</sup><br>wourk witb TIM to improve<sup>their</sup><br>pricing and msanement<sup>and</sup><br>raise overall utilization.|Tes|_NO_|_NO_|No|N|
|_Thailand:_<br>Family Planning<br>Nawkat Survy<br>and<br>arketatio<br>Capability<br>Evaluation|USAID Enterprise<br>Program Proposal|PopAultion & commity<br>Devlopmt<br>Association, (PDA) wll<br>support a<br>PDA--Coopers and<sup>Lybrand study of</sup><br>the c'uudoe market<sup>in Thailand and</sup><br>the developmmut of a<sup>basinea</sup><br>plan<br>for IDA. The latter<br>will Include<br>pricing poliles,.<br>distribution<br>strategies.<br>promotional efforts.<br>and gmneial strategise<br>a finan-<br>cia<br>eassesments.|N/i|*/A|N/A|N/A|Kmuasman and<br>buoineaa planning|
|Brazil:<br>Aaaeasing Coats<br>VAd Benefits of<br>incorporating<br>Family Planning<br>Into H16)|USAID Population<br>Council Opera-<br>tional Resarch<br>Project|Cost-burnlt itanlysis<br>of adding<br>family planning to NM<sup>benefits.</sup><br>Analysis estimated that<sup>beneitse</sup><br>would he esaer and<sup>mmoreffective</sup><br>contraceptive<br>_me.,_fewer births,<br>(by 63), 57.52<sup>reduction in abor-</sup><br>tion complications.<br>Costs would<br>he increased In<sup>DID and inpatient</sup><br>services for family<sup>plaming.</sup>|yes|**yes**|N/A|N/A|yes|