rafmacalaba/datause-encoder-data huggingface dataset:
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31
The third column of Table 4 extends our main analysis by controlling for the effect of fear in citizens’ perception of COVID-19 on economic outcomes. The coefficient on the fear variable is negative and significant, indicating that a higher perception of fear among the population negatively affects economic activity. A 1 percent increase in the “fear” of the pandemic (search frequencies) reduces the electricity consumption by about 0.02 percent. Compared to the baseline specification shown in column (1), the introduction of the fear variable reduces slightly the magnitude of the wait interaction effects without affecting their significance. All other coefficients are quantitatively similar to the coefficients in the baseline specification. Similar results are observed in regressions when we used the terms “infection” and “COVID.” One of the problems with using Google trends data is the issues of attribution (e.g., Mavragani and Ochoa 2019). What if the searches of the term “death” are not connected with perceptions but rather reflect a time trend when people use Google more frequently for all searchers? To rule-out such possibility, we use searches for “neutral” terms “piano” and “milk.” We believe these terms not to be correlated with perceptions of fear. Our falsification test confirms that these terms have no significant impact on the consumption of electricity.

# **6. Assessing the role of press coverage**

The results presented in the last specification of Table 4 show that individual perceptions of the pandemic may have a role in explaining how economic activity varies along the reopening process. Fetzer et al. (2020) show that economic anxiety driven by the COVID-19 pandemic can be detected in the increase in the volume of searches for “recession” and “stock market crash” as soon as the first cases of the disease were detected in each location. This points to the importance of diffusion of information as a factor driving individual behaviors. In fact, individual attitudes and perceptions have also been shown to depend on media coverage, for instance, on views on immigration (Benesch et al., 2019), domestic violence (Banerjee et al. 2019), fertility choices (La Ferrara et al., 2012) and voting (DellaVigna and Kaplan, 2007; Martin and Yurukoglu, 2017). The COVID-19 pandemic is no exception. Bursztyn et al. (2020) show that areas with greater exposure to TV shows that downplayed the threat of COVID-19 eventually saw a greater number of cases and deaths by the disease as viewers of such shows were less prone to adopt precautionary behaviors in the event of the pandemic. Hence there is a reason to believe that changes in media coverage may have an effect on the outcome of reopening.

19
- 18 -

Table 5. Three-Stage Least Squares Estimation of Bids, Entry and Technical Evaluation

|Estimation method|Three-stage least|squares||
|---|---|---|---|
||3rd stage|3rd stage|3rd stage<br>|
|Dependent variable|ln_b_<br>|ln_n_|_D _<sup>_TE_</sup>|
|ln_n_<br>|–0.561 <sup>**</sup><br>(0.230)||0.092<br>(0.110)|
|_D _<sup>_TE_</sup>|0.430 <sup>***</sup>|–0.214 <sup>**</sup>||
||(0.163)|(0.099)||
|_D _<sup>_DAM_</sup>|–0.022|–0.223|–0.037|
||(0.107)|(0.144)|(0.138)|
|_D _<sup>_CIVIL_</sup>|0.305 <sup>**</sup>|0.512 <sup>***</sup>|0.180 <sup>*</sup>|
||(0.139)|(0.105)|(0.098)|
|ln_GCAP_|0.008 <sup>**</sup>|–0.008 <sup>*</sup>|0.019 <sup>***</sup>|
||(0.004)|(0.005)|(0.004)|
|ln_LINE_|0.004|0.022 <sup>***</sup>|–0.003|
||(0.008)|(0.005)|(0.005)|
|ln_SUBS_|0.012 <sup>***</sup>|0.007|0.007|
||(0.004)|(0.005)|(0.005)|
|ln_COST_|1.024 <sup>***</sup>|0.105 <sup>***</sup>|–0.129 <sup>***</sup>|
||(0.053)|(0.037)|(0.036)|
|ln_WAID_||–1.772 <sup>***</sup><br>(0.541)||
|ln_CAID_||–0.013||
|||(0.044)||
|Constant|14.415 <sup>***</sup>|17.184 <sup>***</sup>|1.021 <sup>***</sup>|
||(0.336)|(4.781)|(0.208)|
|Obs.|131|||
|_R_-squared|0.971|0.487|0.172|
|Wald chi2|4535.22|125.74|28.30|
|Recipient country dummy|Yes|China|No|
|Bidder nationality dummy|Yes|No|No|
|Donor dummy|Yes|Yes|No|

Note that the standard errors are shown in parentheses. *, **, and *** indicate the 10%, 5% and 1% significance levels, respectively.

There are at least two particular remarks to interpret the estimation results correctly. First, our quality measurement is far from perfect. It merely captures a part of quality attributes related to infrastructure projects. The technical evaluation embedded in the ordinary infrastructure procurement process helps to ensure the contract implementation as agreed, but only given the main scope of the contract. Whether the project design is _technically_ best is a different question that the above discussion cannot answer. This issue may have to be addressed by the cost benefit or least cost analysis and careful review of alternative designs at the much earlier stage, i.e., at the timing of project appraisal. In addition, whether the project was ex post implemented as contracted is also left unsolved. This also requires a different measure to assure the _quality_ of projects, such as project monitoring and auditing.
**17 |** P a g e

focused on export oriented activities. According to a recent study (Silajdzic 2007), an average share of exports in total sales of firms with foreign participation was 64 percent as compared with an average of 33 percent for locally‐owned firms. This is in line with the findings for other transition economies also pointing to higher propensity to exports of foreign owned firms (see Kaminski and Smarzynska 2001).

Due to the war and institutional and policy dysfunctional nature of the BiH economic regime in its aftermath, FDI inflows were extremely low until around 2001‐02 and subsequently increased although not as strongly as to Serbia and Montenegro. As of 2003, BiH had the smallest stock of FDI per capital among SEE‐economies. It stood at 53 percent of the FDI stock per capita in Albania; 28 percent of the level in Macedonia; and it was the same as that of Serbia and Montenegro. By 2007, it stood at 223 percent of the FDI per capita in Albania; 102 percent of the level in Macedonia; and it was at 80 percent of that in combined Serbia and Montenegro.

BiH has been very successful in attracting FDI in 2003‐07. In terms of GDP, BiH with 7.4 percent scored well above Albania and Macedonia (Table 10). It has also registered on average stronger growth than Romania (6.7 percent per year) but well below the levels of FDI inflows into Bulgaria and Montenegro. The latter following independence attracted huge FDI inflows exceeding 20 percent in both 2006 and 2007. In terms of FDI stock per capita, BiH has caught up with Macedonia. But except for Croatia with FDI stock per capita significantly higher than in all other countries including the 2007 new EU members, other SEE countries are well below the levels of FDI stock in Bulgaria and Romania. The value of total FDI stock in BiH amounted by the end of 2007 to around US$6 billion or US$1,576 per capita. Despite an impressive surge in 2007, this is still less than around one third of FDI stock per capita in Bulgaria; one fifth in Romania; and around seventh of the level in neighboring Croatia (Table 10).

**Table 10: FDI inflows to SEE‐economies and Bulgaria and Romania in terms of percent of GDP and values of FDI stock per capita (in US dollars)**

|||||||Average<br>|FDI, st<br>|ock per capi<br>|ta in US$|
|---|---|---|---|---|---|---|---|---|---|
||2003|2004|2005|2006|2007|2003‐07|2003|2006|2007|
|Albania|3.1|4.6|3.1|3.6|4.4|3.8|352|433|708|
|BiH|4.6|7.1|5.6|5.9|13.9|7.4|193|1,044|1,576|
|Croatia|6.9|3.0|4.6|8.0|9.6|6.4|2,057|6,219|10,143|
|Macedonia, FYR|2.1|2.9|1.7|5.5|4.2|3.3|687|1,382|1,542|
|Montenegro|..|..|..|22.9|25.2|24.1|352(a)|1,433(a)|1,960 (a)|
|Serbia|6.7|3.9|6.1|14.7|7.8|7.9|...|...|...|
|Bulgaria|10.5|10.8|15.9|23.7|22.7|16.7|652|3,090|4,933|
|Romania|3.1|8.6|6.6|9.3|5.7|6.7|441|5,903|7,912|

Note: (a) no information available separately for Montenegro and Bosnia. The total is for both countries. Source: own calculations from data in the UNCTAD FDI database.
**Table 4: Test for respondent fatigue No. of Entries per crop (E) No. of Crops per household (C)**

|Fortnight 2 (F2)|0.754***|0.905***|
|---|---|---|
||(27.76)|(15.74)|
|Fortnight 3 (F3)|0.614***|0.694***|
||(21.53)|(11.67)|
|Fortnight 4 (F4)|0.596***|0.748***|
||(20.18)|(12.11)|
|Fortnight 5 (F5)|0.547***|0.782***|
||(17.91)|(12.20)|
|Fortnight 6 (F6)|0.527***|0.794***|
||(16.60)|(11.88)|
|Fortnight 7 (F7)|0.509***|0.787***|
||(15.37)|(11.28)|
|Fortnight 8 (F8)|0.488***|0.711***|
||(14.05)|(9.736)|
|Fortnight 9 (F9)|0.417***|0.694***|
||(11.47)|(9.071)|
|Fortnight 10 (F10)|0.408***|0.696***|
||(10.61)|(8.566)|
|Fortnight 11 (F11)|0.393***|0.648***|
||(9.526)|(7.441)|
|Constant|-0.238|4.612***|
||(-0.871)|(8.207)|
|Observations|176,535|38,898|
|R-squared|0.164|0.143|
|Dummies included|Time (fortnight)|Time (fortnight)|
||District|District|
||Crop||
|**_Tests for (F-stat):_**|||
|F2.. F11= 0|12.93***|2.13*|
|F3.. F11= 0|8.47***|1.01|
|F2- F3= 0|27.26***|12.87***|
|F3- F4= 0|0.43|0.80|
|F4- F5= 0|2.97*|0.31|
|F5- F6= 0|0.50|0.04|
|F6- F7= 0|0.37|0.01|
|F7- F8= 0|0.52|1.38|
|F8- F9= 0|5.12**|0.06|
|F9- F10= 0|0.07|0.00|
|F10- F11= 0|0.19|0.93|

t-statistics in parentheses. *** p<0.01, ** p<0.05, * p<0.1 19
Where �� is the relative weighting attributed to the individual �.

To construct the measures of intensity of each task each one of the elements of the same group of tasks is added up and each one of the five measures of intensity is standardized.

# 4. Empirical results

# 4.1 Relative importance of each task in the job

In this section we shall analyze job tendencies according to the relative importance of each type of task, starting from the implementation of the methodology just described. Specifically, the focus of attention is the evolution of the type of employment during approximately the last 20 years, which can be defined as the sum of persons employed in some occupation, which, in turn, is constituted by a set of tasks.

In order to put the labor market into context briefly, Figure 1 presents the employment rate and unemployment rate for the period between the years 1995 and 2015.

Figure 1. Employment rate and unemployment. Years 1995 – 2015 a) Employment rate b) Unemployment rate <!-- Start of picture text --> 70.0% 25%<br>60.0%<br>20%<br>50.0%<br>40.0% 15%<br>30.0%<br>10%<br>20.0%<br>5%<br>10.0%<br>0.0% 0%<br>19951997 1999 2001 2003 20052007 2009 2011 20132015 19951997199920012003200520072009201120132015<br>Argentina Uruguay Argentina Uruguay<br><!-- End of picture text --> Source: Compiled based on World Bank-SEDLAC and Uruguayan National Institute of Statistics

- 11 -
Although the risk of unintended health problems may vary by age and a number of other contributory factors, the RR has been used as an average measure of the increased risk for the exposed population in this study.

# **_3. Estimates of Population Exposed to DDT_**

A number of countries currently use DDT for malaria control.<sup>12</sup> In addition, the re-introduction of DDT for lowering the spread of malaria is under consideration in some other countries, especially in Sub-Saharan Africa. By and large the population of the geographic location where DDT is currently being used or where it may be re-introduced faces the risk of exposure to DDT and is the focus of this analysis<sup>13</sup> . In order to determine the population exposed to DDT from its continued use or potential future use, detailed geographic data of DDT use at a sub-country level are required. However, while production and usage data on DDT is generally available at country level, sub-country level DDT usage data are scarce. In its absence, this study focused on a proxy, the malaria endemicity across countries to capture the current and potential exposure of population to DDT within each country given that the use of DDT is restricted to malaria eradication.<sup>14</sup> The malaria endemicity in this study is based on the Global malaria atlas compiled by the Malaria Atlas Project (MAP) of the Oxford University. The data are based on 24,492 parasite rate surveys ( _Plasmodiumfalciparum._ 24,178; _Plasmodium vivax._ 8,866) from an aggregated sample of 4,373,066 slides prepared from blood samples taken in 85 countries. The MAP study employs a new cartographic technique for deriving global clinical burden estimates of 12 The following 13 countries reported using DDT for malaria control and prevention: Eritrea, Ethiopia, Gambia, India, Madagascar, Malawi, Morocco, Mozambique, Namibia, South Africa, Swaziland, Zambia, and Zimbabwe (WHO 2001a).

13 LAC countries are excluded from the analysis, because all LAC countries phased out DDT use. 14 As per WHO guidelines, use of DDT is only restricted to vector control. All other uses of DDT have been banned worldwide under the Stockholm Convention on Persistent Organic Pollutants. So, it is reasonable to assume that any future use of DDT will primarily be restricted to malaria endemic areas.

8
# **Figure B18: Maize**

<!-- Start of picture text --> Log scale<br>7<br>Log price Permanent component<br>6<br>5<br>4<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> <!-- Start of picture text --> Log scale<br>0.75<br>Medium-term cycle Transitory component<br>0.50<br>0.25<br>0.00<br>-0.25<br>-0.50<br>1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020<br>Source : Authors’ calculations<br><!-- End of picture text --> _— 59 —_
|Mozambique|2010|Agriculture|100.0|26.1|10.1|8.9|0.9|89.9|
|---|---|---|---|---|---|---|---|---|
|||Manufactures|15.5|15.9|7.4|7.2|1.0|8.1|
|Nepal|2011|Agriculture|41.5|21.1|14.6|8.6|0.6|26.9|
|||Manufactures|23.6|21.8|12.0|9.1|0.8|11.6|
|Nigeria|2010|Agriculture<br>Manufactures|150.0<br>48.9|32.8<br>22.3|14.0<br>10.6|7.7<br>8.2|0.5<br>0.8|136.0<br>38.3|
|Pakistan|2009|Agriculture|95.6|43.3|19.1|16.6|0.9|76.5|
|||Manufactures|54.8|50.6|14.4|11.9|0.8|40.4|
|Paraguay|2011|Agriculture|33.1|13.1|7.7|7.9|1.0|25.5|
|||Manufactures|33.5|11.5|8.5|7.4|0.9|25.0|
|Peru|2011|Agriculture|30.8|16.8|2.6|3.1|1.2|28.2|
|||Manufactures|30.0|16.2|3.2|4.2|1.3|26.9|
|Philippines|2010|Agriculture|35.3|27.2|8.6|10.7|1.2|26.7|
|||Manufactures|23.3|19.1|5.0|6.6|1.3|18.3|
|Saudi Arabia|2009|Agriculture<br>Manufactures|12.3<br>10.5|11.8<br>12.4|3.0<br>4.1|2.6<br>2.0|0.9<br>0.5|9.3<br>6.4|
|Senegal|2011|Agriculture|29.8|15.6|15.7|7.0|0.4|14.2|
|||Manufactures|30.0|13.7|13.1|6.9|0.5|16.9|
|Singapore|2010|Agriculture|9.5|0.0|0.0|0.0|..|9.5|
|||Manufactures|6.4|0.0|0.0|0.0|..|6.4|
|South Africa|2011|Agriculture|41.5|10.7|6.4|10.0|1.6|35.1|
|||Manufactures|15.7|15.4|7.0|10.8|1.5|8.7|
|Sri Lanka|2011|Agriculture|49.8|29.9|21.8|14.9|0.7|28.0|
|||Manufactures|19.8|18.9|8.2|11.4|1.4|11.6|
|Taiwan, China|2011|Agriculture|14.0|19.4|8.7|13.3|1.5|5.4|
|||Manufactures|4.8|6.9|3.9|6.2|1.6|0.9|
|Thailand|2009|Agriculture|35.9|33.9|23.0|18.9|0.8|12.9|
|||Manufactures|24.1|20.3|9.7|11.1|1.1|14.5|
|Tunisia|2008|Agriculture|116.0|33.4|38.6|19.9|0.5|77.4|
|||Manufactures|40.1|29.5|21.0|16.6|0.8|19.1|
|Uganda|2011|Agriculture|77.4|21.7|18.1|14.1|0.8|59.4|
|||Manufactures|50.9|16.2|11.6|11.2|1.0|39.4|
|Uruguay|2011|Agriculture|34.0|12.1|6.2|7.0|1.1|27.9|
|||Manufactures|31.2|13.1|9.7|7.9|0.8|21.5|
|Venezuela|2011|Agriculture|55.2|13.7|13.9|10.5|0.8|41.4|
|||Manufactures|33.6|12.7|11.7|8.0|0.7|21.9|
|Vietnam|2010|Agriculture|19.0|30.0|13.5|14.9|1.1|5.5|
|||Manufactures|10.3|13.3|6.6|10.4|1.6|3.8|
|Zambia|2011|Agriculture|123.3|18.7|9.4|11.1|1.2|113.9|
|||<br>Manufactures|43.5|15.1|10.6|10.2|1.0|32.9|
|Zimbabwe|2007|Agriculture|144.5|38.5|23.3|28.5|1.2|121.2|
|||<br>Manufactures|10.4|40.8|14.2|14.3|1.0|-3.8|
|Average: All above||Agriculture|61.1|21.3|14.4|21.5|1.3|46.7|
|developingcountries||Manufactures|31.7|17.2|9.0|8.9|1.1|22.7|
|**Memo:**|||||||||
|Average: Developed||Agriculture|17.3|9.3|6.2|12.2|2.3|11.1|
|countries||Manufactures|6.6|5.5|3.2|4.5|1.9|3.4|

Note: The classification of sector is defined as agricultural products HS 1-24 and manufacturing products HS 25-97.

Sources: UNCTAD TRAINS (applied tariffs) and WTO IDB (bound tariffs) databases.

groups with the exception of applied tariffs in agriculture, where there is little difference between the average for the low and middle-income developing countries. Similarly, the average differences in the margins between applied and bound tariffs tend to be highest in the low-income countries and lowest in the highest-income ones.

10
cess to higher education on emigration for the purpose of education. The dependent variable (on the y-axes) is the share of the migrants who chose home-state destinations among all migrants who moved for education related reasons. This variable is constructed from the bilateral data from the 2001 Census as discussed earlier. The explanatory variable on the x-axis comes from the employment module of the National Sample Survey (1999-2000). It measures the share of college attending students among all 18-22 year old state-natives in each state. Log average household income per capita is also calculated from the NSS data, and controlled for in the regression. The positive slope in the graph is consistent with the argument that state government policies granting preferential access to higher education to in-state students tend to induce potential migrants moving for education to choose home-state institutions.

# **6 Conclusion**

That international borders limit migration is obvious. More surprising is the role of provincial or state borders in inhibiting mobility within a country. We are able to demonstrate the existence of these “invisible walls” by putting together, with the help of the Indian census authorities, detailed district-to-district migration data from the 2001 Census. Even after controlling for key bilateral barriers to mobility, such as physical distance and linguistic differences, and for origin and destination specific factors through district fixed effects, we find that average migration between neighboring districts in the same state is at least 50 percent larger than between neighboring districts on different sides of a state border. This gap varies by education level, age and the reason for migration, but is always large and significant. The evidence from the recent creation of three new states in 2000 provides additional evidence that these state borders are not natural barriers.

There are no barriers at state borders or explicit legal restrictions on people’s mobility between states in India, and we control for distance and difference in language. Then the question is what other reasons can explain the presence of these 28