rafmacalaba/datause-encoder-data huggingface dataset:
|**Table 4: A comparison of distance-**|**shortening with**<br>|**other policies in**<br>|**the BICs and K**<br>|**orea**<br>|
|---|---|---|---|---|
||**Korea**|**China**|**Brazil**|**India**|
|**Macro and ****_Trade liberalization _reforms**|||||
|Timing|1970s|1980s|1990s|1990s|
|Stabilization|Yes|Yes|Yes|Yes|
|Degree of opennes_s_ (trade as % of GDP -1980s-2006)|67 to 84|27 to 71|18 to 26|14 to 46|
|Level of tariff and non-tariff barriers(Text tables)|Very low|Very low|Medium low|Medium high|
|**_Industrial Policy_**|||||
|Preference for export-led growth(Export/GDP (%);<br>|23 to 43|5 to 40|7 to 14|5 to 21|
|1970s and2005-06,WDI)|||||
|Preference for hi- tech exports (% in total exports,<br>OECD-A15_)_|35|35|8|5|
|<br>Preference for natural resource-based exports<br> <sup>_16_</sup>|0.2|2.3|20.6|9.6|
|_(% in total exports, OECD B) _|||||
|<br>**_Intellectual property indicators_**|||||
|Patent applicationsfiled under PCT (2005;OECD)|5152|3774|339|978|
|Triadicpatent families<sup>~~17~~</sup> (2005,OECD A)|2811|356|58|113|
|Intellectual property protection 2007 _(Scale 1 – 7,_<br>_KAM)_|5.4|3.4|3.3|4.0|
|Total royalty payments and receipts(US$ mil.)<br>2006 _(KAM, World Bank)_|6497|5478|1506|446|
|**_Distance shortening in early stages_**|Mostlystate-led|Mostlystate-led|Mostlystate-led|Mostlystate-led|
||**_Financing of R&_**|**_D_**|||
|Gross domestic R&D expenditure/GDP (2006,<br>OECD A)|3.23|1.43|1.02|0.71<sup>~~18~~</sup>|
|Business enterprise R&D expenditure /GDP|2.49|1.02|0.49||
|(2006, ,OECD A)|||||
|GERDpercapita (current US$ (PPP,OECD A)|743|66|92|13|
|**_Unive_**|**_rsity-industry li_**|**_nkages_**|||
|University-industry research collaboration in<br>2007 (Scale 1 – 7,KAM,World Bank)|5.4|4.1|3.4|3.5|
|University-industry linkages (indicator -Share|75 %|69%|39%|16%|
|financed byindustry, OECD Factbook,2008)<br>**_Inc_**|**_entives for rese_**|**_arch _**|||
|**Tax subsidies for USD 1 of R&D**(OECD Scoreboard,<br>2007)19|0.18|0.339|0.254|0.266|
|Subsidies for firm level research (Scale1–7; 2003,<br>WEF)|3.52|1.86|2.03|2.87|
|Government procurement of advanced|4.5|4.7|3.6|3.3|
|<br>technology products(Scale 1 – 7;2003WEF)|||||
|, <br>**_Role_**|**_of foreign inno_**|**_vators_**|||
|Prevalence of foreign technology licensing (Scale<br>1 – 7;2003,WEF)|5.32|3.79|4.65|3.27|
|Role of foreign innovators – (indicator: Patents|4.60|27.87|28.42||
|<br>with foreign co inventors (2002-04,OECD A))<br>**_Hum_**|**_an resources fo_**|**_r S&T_**|||
|Quality of S&T research[Scientific and technical|339.5|31.89|52.9|13.35|
|<br>journalarticles per millionpopulation,2005 (KAM) ]|||||
|Researchers/1000 total employment (2006,|8.65|1.60|1.48|0.3|
|<br>OECDA)||||(in 2000)|
|Science and engineering degrees as a % of all<br>newdegrees (2005,OECD A)|37.80|39.18|10.78||

15 OECD A – OECD S&T Outlook, 2008 16 OECD B S&T Scoreboard, 2007 17 Patents applied for at the US Patent Office, European Patent Office and Japanese Patent Office in 2005.

18 OECD Factbook 2008.

> 19 The corresponding tax subsidy for the U.S. is  0.66.

31
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30
effects hold across most continents, reassuring that a few countries and regions do not drive the average impacts. In particular, these findings confirm that our search terms and languages are relevant to all regions and hence can capture demand for the important sectors and services we are studying.

# **7. Concluding Remarks**

To uncover the impacts of the COVID-19 pandemic on various services, we assemble Google search data that capture the temporal and spatial evolution of consumer demand for a carefully selected list of services. These novel data provide a unique opportunity to estimate the impacts of the pandemic in contexts where other types of conventional data sources are not available. These data are used to predict human preferences, and responses to fast-shifting events and pandemics (Choi and Varian, 2009; Goel et al., 2010; Pavlicek and Kristoufek, 2015). We use fixed effects specifications to quantify the impact of the COVID-19 spread across countries. We exploit temporal variations in the spread of COVID-19, both confirmed cases and deaths, across 182 countries that are currently affected by the pandemic.

We find that demand for some services has significantly increased because of the pandemic, while some services experienced significant loss. Most importantly, we quantify the impact of the additional spread of the burden on winning and losing sectors economies. More specifically, we find that the spread of the pandemic has significantly reduced demand for services that require face-to-face interactions while boosting demand for services involving less in-person interactions. The size of the impacts is quite large. For instance, countries with at least 10 confirmed cases are likely to experience a 63-79 percent reduction in demand for hotel and restaurant services while enjoying a comparable increase in demand for ICT services. Most of these patterns are observed in all four continents (Africa, Americas, Asia, and Europe) in our sample. Our estimates suggest that the impacts are driven both by supply-side shocks due to social distancing and lockdown measures and demand-driven contractions (expansions), with the latter dominating in most of the services we study.

These findings can inform policy efforts and responses to the pandemic. More importantly, such evidence on the relative impacts of the pandemic on various sectors and services of economies can improve governments' ability to identify and target the most impacted sections of the economy. This is particularly imperative for governments with limited fiscal space, which hence are usually 26
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# **4.2 Robustness check**

A critical assumption in the identification is the exclusion restriction, which means that the instruments are orthogonal to the residuals in the first and second stages. While we cannot test this assumption directly, there are some standard robustness checks that provide additional support to the results. First, table 9 shows the F statistic for joint significance of the instruments Z in the first stage. It is noticeable that we reject the null hypothesis that all excluded instruments are not significant at 1% significance. Stock-Yogo shows the critical value for the null hypothesis that the bias of 2SLS is less than a given fraction (e.g. 5%) of the bias of OLS. Based on the F-statistics of the first stage we can reject the null hypothesis at 5% of maximal IV relative bias.

Also, due to the fact that we used two variables and the interaction among them as instruments, we can test if at least one of the instruments is exogenous (see Hansen J test on table 9). We do not reject the null hypothesis that all instruments are valid and conclude that the overidentifying restrictions are valid. In addition, we ran the specification using two instruments (number of NOs by state and the lag of sectors (CNAE 5 digits) assisted in each state weighted by Peiex existence) without the interaction term and the null hypothesis is rejected.

|Test|(1)|(2)|(3)|
|---|---|---|---|
|F test of excluded instruments:||||
|F-stat|61.37|72.72|73.03|
|Prob_>_F|0.00|0.00|0.00|
|Stock-Yogo weak ID test critical values:||||
|5% maximal IV relative bias|13.91|13.91|13.91|
|10% maximal IV relative bias|9.08|9.08|9.08|
|20% maximal IV relative bias|6.46|6.46|6.46|
|30% maximal IV relative bias|5.39|5.39|5.39|
|10% maximal IV size|22.3|22.3|22.3|
|15% maximal IV size|12.83|12.83|12.83|
|20% maximal IV size|9.54|9.54|9.54|
|25% maximal IV size|7.8|7.8|7.8|
|Hansen J statistic :||||
|Overidentifcation test of all instruments|0.22|2.69|2.33|
|Chi-sq(2) P-val|0.89|0.26|0.31|
|Underidentifcation test||||
|(Kleibergen-Paap rk LM statistic):|39.6|47.64|47.51|
|Chi-sq(3) P-val|0.00|0.00|0.00|

Table 9: Overidentification test: Fixed effects instrumental variable Note: Columns (1), (2) and (3) refer respectively to the three specifications used for the results presented on table (7).

We also ran a Poisson panel fixed effects estimator. In this case, Peiex’s treatment effect is identified under DID assumptions. An advantage of this method is that it takes into consideration the non-linearity 19
# **Table 6 - Impact on States’ Output Associated with Banks’ Exposure to the Energy Sector**

This table reports the results of a regression testing whether activity is impacted by the degree of exposure that states had to banks that were lending to the energy sector. Observations are at the state-quarter level. _GDPs,q_ is the log index—relative to January 2004—of state _s_ ’s GDP in quarter _q_ . We further split this indicator by type of sector (primary, secondary, and tertiary). _Primary_ sector includes mining and agriculture, _Secondary_ includes manufacturing and construction, and _Tertiary_ includes services. _AvgExposureEnergy_ s,Aug14 is the average exposure to the energy sector in August 2014, weighted by loan value in the state, of the banks operating in state _s_ . It proxies for the impact at the state level of the decline in energy prices through banks that operate in it. Columns indicating _NonEnergy_ refer to states that are non-producers of energy. Standard errors are clustered at the state level. Detailed variable definitions are provided in Table A1. <u>The sample period is from the first quarter of 2013 to the fourth quarter of 2016. *** p<0.01, ** p<0.05, * p<0.1.</u>

||(1)|GDPs,q<br>(2)|GD<br>(3)|P Primarys,q<br>(4)|GDP<br>(5)|Secondarys,q<br>(6)|GD<br>(7)|P Tertiarys,q<br>(8)|
|---|---|---|---|---|---|---|---|---|
|AvgExposureEnergys,Aug14*Postq|-0.67*|-1.14***|-3.67*|-2.94|-1.92**|-2.44***|-0.17|-0.30|
||(0.42)|(0.36)|(2.19)|(2.32)|(0.85)|(0.85)|(0.28)|(0.27)|
|Observations|512|480|512|480|512|480|512|480|
|R-squared|0.69|0.77|0.31|0.32|0.51|0.53|0.82|0.86|
|State FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Quarter FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|States|All|Non-Energy|All|Non-Energy|All|Non-Energy|All|Non-Energy|
|SD(AvgExposureEnergys,Aug14)|1.6|1.6|1.6|1.6|1.6|1.6|1.6|1.6|

34
<!-- Start of picture text --> Figure 1a U.S. Imports from India ($ million) Figure 1.b U.S. Exports to India ($ million)<br>60000 60000<br>50000 50000<br>40000 40000<br>30000 30000<br>20000 20000<br>10000 10000<br>0 0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011<br>Agriculture Processed Agriculture Mining Manufacturing Services Agriculture Processed Agriculture Mining Manufacturing Services<br><!-- End of picture text -->

|||||U.S.<br>|Imports from<br>|India($mill<br>|ion)<br>||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||2000|2001|2002|2003|2004|2005|2006|2007|2008|2009|2010|2011|
|Agriculture|548|445|413|406|491|516|517|562|699|554|657|972|
|Processed Agriculture|596|573|688|760|804|817|853|860|1046|852|1252|2277|
|Mining|27|38|25|77|57|74|80|44|85|31|47|50|
|Manufacturing|9941|9109|11199|12351|14880|18243|21214|23307|24762|20303|28428|33747|
|Services|1901|1821|1817|1985|2844|4985|7460|9883|12498|12486|14188|16921|
|Total|13013|11986|14142|15580|19076|24635|30124|34656|39091|34226|44572|53967|
|||||U.S<br>|. Exports to I<br>|ndia($millio<br>|n)<br>||||||
||2000|2001|2002|2003|2004|2005|2006|2007|2008|2009|2010|2011|
|Agriculture|119|284|180|256|189|225|287|386|406|473|490|585|
|Processed Agriculture|97|72|96|61|65|70|74|79|73|197|306|144|
|Mining|43|26|34|30|145|191|177|142|406|414|569|1141|
|Manufacturing|3221|3203|3668|4435|5460|6865|8765|15934|17437|12904|16335|18752|
|Services|2580|3016|3270|3776|4464|5137|6587|8851|10267|9945|10382|11108|
|Total|6060|6601|7248|8558|10323|12488|15891|25392|28587|23933|28083|31730|

_Sources:_ The UN Comtrade System for the goods data; Bureau of Economic Analysis for the services data, www.bea.gov.

6
other household characteristics including demographics, employment, assets, and incomes. This survey’s sampling frame comes from the 2004 Population and Housing Census and is divided into 89 strata (or sub-districts). The survey is typically administered over a 12 month period and follows a two-stage sampling design where census enumeration areas serve as primary sampling units (PSUs). For the 2010 survey round, 1,736 PSUs were selected in the first stage out of a total of 13,027 PSUs for the whole country using a systematic probability proportionate to size (PPS) sampling method. Within each selected PSU or cluster, 8 households were randomly selected at the second stage. The 2008 and 2010 rounds of the HEIS collected consumption data respectively for 10,961 and 11,223 households. The questionnaire design of these two survey rounds remains essentially the same.

# **III.2.3. Estimation Results**

We start first with checking on Assumptions 1 and 2 before discussing estimation results. Since the 2008 and 2010 rounds of the HEIS share the same sampling frame based on the 2004 Population and Housing Census, and their questionnaire design remains almost identical, Assumption 1 for a similar survey design is satisfied. Assumption 2 is usually assumed and can only be checked if data for both survey rounds are available. In this case, since we are validating estimates with the actual consumption data, we can check this assumption using these data in both survey rounds.

We propose an explicit test for this assumption. Specifically, we can use a decomposition that is similar in spirit to the Oaxaca-Blinder framework (Oaxaca, 1973; Blinder, 1973), where the change in poverty between the survey rounds can be broken down into two components, one due to the changes in the estimated coefficients (the first term in square brackets in equation (8) below) and the other the changes in the _x_ characteristics (the second term in square brackets in 21
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complements: households with more irrigated plots, making more use of modern agricultural inputs, and with off-farm enterprises are less likely to send migrants due to dry spells. This is in line with hypothesis H3 about substitutability between _in situ_ adaptation and migration as alternative risk mitigation strategies for farming households.

In contrast, the role of consumption is not as strong as the one played by previous variables. The relationship with treatment effects distribution is almost flat, with a pertinent exception: households with the lowest absolute level of consumption per capita are more likely to send climate migrants (this effect is close to 10%). Remember that, compared to the asset index, the consumption data capture short- and medium-term deprivation, whereas the asset index is a better indicator of long-term wealth (Aiken et al., 2023). While one might be tempted to interpret this particular result as a hint that the poorest households, unable to smooth consumption, tend to engage in risk-coping migration, this is not the case: upon further investigation, we found that these households simply experienced substantially milder weather conditions compared to the rest of households. The GS-SPEI value for this lowest consumption decile is 0.11, against a sample mean of 0.23 (cf. Table A1). Therefore, this positive effect should be interpreted as the casual result of minor shock exposure rather than as a signal of an underlying heterogeneity related to the conditioning variable. Concerning human capital, we do not observe relevant heterogeneity with respect to the share of members with higher education. Finally, the last panel of Figure 3 shows variation depending on the intensity of the treatment, the reversed GS-SPEI. As this is a continuous treatment, it is interesting to inspect _ex post_ the distribution of treatment effects with respect to the treatment 'dose', i.e., the intensity and sign of the experienced weather conditions. The relationship appears strongly nonlinear, and drier conditions (upper deciles of the reversed GS-SPEI) are associated with larger negative effects.

Now note the difference between these CATEs and the estimated placebo CATEs which are reported in the Appendix, Figure A2. The figure illustrates the distribution of the placebo treatment effects for the same set of variables. All the implied relationships are flat and around zero, pointing to absence of any type of heterogeneity in the placebo effects. This reinforces our point that what we find in Figure 3 is systematic heterogeneity and not noise.

Given the predominant role played by the three main heterogeneity drivers—lagged asset levels, 25
8 In calculations of poverty Incidence for each country we have used an officially calculated _subsistence (or social) minimum_ (see Appendix 1 for the description). _Official_ simply means that the minima were calculated by official institutions (generally attached to ministries), ar.d were used as poverty lines In broad social debates. In neither of the three countries, however, did the calculated minimum perform any real function, that Is was not used as an eligibility criterion for social aid.

The social minima are constant In real terms: we arp thus using an absolute standard which, particularly In conditions of stagnant Incomes, seems reasonable. It Is evident from Table 3 that the level of what is officially considered to be subsistence minimum (poverty line) In Poland, Hungary and Yugoslavia Is quite similar.

_Table 3. Comparison between Official Poverty Lines (four-member urban household; per capita; 1985)_

||za/|aPP<br>bJ<br>_X._of|averaze waze /|
|---|---|---|---|
|Poland|52.5|85.4|38.6|
|Hungary|52.7|125.6|45.0|
|Yugoslavia|51.3|110.5|34.1|

a/ Converted at the official exchange rate. Monthly amount. b/ In dollars of International consumption purchasing power parity. From United Nations Statistical Commission and Economic Commission for Europe (1988, p.24 and Annex Table 3).

c/ Average wage In socialized sector.

In nominal dollar terms poverty lines are almost identical. In terms of purchasing power, Hungarian subsistence minimum is the highest reflecting relatively low forint prices of consumer goods. In terms of average wage, poverty lines seem high compared to their West European equivalents. ..tkinson (1991) reports that the value of per capita poverty lir.e for a married couple with two children ranges from only 12.5 percent of average production worker wage in West Germany to 33.7 percent In Sweden. There seem to be two possible explanations for relatively high (in terms of average wage) poverty lines In Eastern Europe. First, a less developed country would tend to have a higher poverty line In terms of average wage, simply because poverty line will normally Increase less than In proportion to average Income (or wage).
25

# NOTES TO TABLE

1/ SOURCE: Petroleum product prices are quoted in local currency and are converted to US$ at prevailing market exchange rates as noted. In coutries where no distinction is made between grades of gasoline or grades of diesel oil, prices for these products are listed as premium gasoline and motor diesel. For further product specification, such as gasoil in Africa, see notes for each country.

2/ SOTJRCE: All prices from de Lucia and Associates, converted at Tk3O.3 = $1 (July '86) and Tk27.5 = $1 (1985). Fuel oil price is ex-depot.

3/ SOURCE: All petroleum prices from de Lucia and Associates. Fuel oil price = Rs 1650/mt, assumed 6.7 bbl/mt. Prices converted to $US at Rs 16.82 = $1 (July '86) and Rs 16 = $1 (July '85). Natural gas prices, from EGYDI, World Bank, are for the first 7 mcf/month consumed at domestic rates.

4/ SOURCE: All prices from de Lucia and Associates, converted to $US at Rs 13 = $1 (1986) and Rs 12.35 = $1 (1985).

_/ SOURCE: World Bank Staff Appraisal Report #5942-IND, 11 April 1986 and World Bank Economic Memorandum #6201-IND, 20 May 1986. Petroleum product prices remained unchanged between 1 April 1985 and March 1986. These prices are reported as November 1985 prices and are converted to $US at 1123 Rp = $1. July 1986 prices announced by te Department of Mine and Energy, Indonesia on 9 Ju;y 1986, were converted at 1131 Rp = $1.

6/ SOURCE: All prices from official Government price change announcements obtained from East Asia adn Pacific Country Programs Department, Philippines Division, World Bank and EGYD1, World Bank. Fuel oil price is wholesale. January 1986 prices are pre-election price reductions. Average product prices before the price change of 25 January were 60 Centavos/liter higher. January 1986 prices converted to $US at 19.1P = $1; June 1986 prices at 2.05 P = $1.

7/ SOURCE: Eastern and Southern Africa, Country Programs Department, South Centrall Division, World Bank. All prices are Reference Prices (without distribution margin) in Kinshasa and are, therefore, retail prices in Kinshasa. Diesel prices are for "gasoil" in Zaire. January 1986 prices coverted to $US at 55.1 = $1; August 1986 prices at 58.75 Z = $1.

8/ SOURCE: 1985 prices from Kenya Solar Water Heating Project Green Cover, March 1986, World Bank. July 1986 prices were announced in the June 1986 Budget, Government of Kenva. Diesel fuel prices for "gasoil" in Kenya. Industrial diesel prices are for 2500 sec fuel oil andi tuel oil prices are for 1000 sec fuel oil. November 1985 prices converted to $US at 16.5 Ksh = $ 1; July 1986 prices at 16 Ksh = $1.