rafmacalaba/data-use-mentions-extended huggingface dataset:
data are available and relatively complete. The resulting sample includes eleven countries in each of the developed and developing sub-samples: Austria, Malta, Slovak Republic, Cyprus, Lithuania, Japan, United Kingdom, New Zealand, Korea, Italy, Ireland; and Indonesia, Turkey, Azerbaijan, Jordan, India, Philippines, Malaysia, Chile, Morocco, Egypt, Thailand. Table 1 reports the values of _FLi_ in our sample of sectors. It is clear that there is wide variation in the share of women in sectoral employment. While the mean is 27 percent, these values range from the high of 71 percent in Wearing Apparel and 62 percent in Knitted and Crocheted Fabrics to the low of 8 or 9 percent in Motor Vehicles, Bodies of Motor Vehicles, Building and Repairing of Ships, and Railway Locomotives.<sup>5</sup> The export shares _ωict_<sup>_X_are calculated based on the COMTRADE database, which contains bilateral</sup> trade data starting in 1962 in the SITC revision 1 and 2 classification. The trade data are then aggregated up to the ISIC Revision 3 classification using a concordance developed by the authors.

> 5One may be concerned that these values are very different across countries in general, and across developed and developing countries in particular. However, it turns out that the rankings of sectors are remarkably similar across countries. The values of _FLi_ computed on the OECD and non-OECD samples have a correlation of 0.89. Pooling all the countries together, the first principal component explains 77 percent of the cross-sectoral variation across countries, suggesting that rankings are very similar. We also experimented with taking alternative averages: medians instead of means across countries; and dropping outlier values of female shares in individual sectors. The results were very similar.

18
73.1|98.0|49.9|52.2|
||CEPR, low energy prices (lower bound) **|109.2|143.3|98.9|172.9|111.9|
||Comprehensive energy price reform (CEPR), baseline energy price forecast<br>|100.0|100.0|100.0|100.0|100.0|
|Pakistan|CEPR, US$12.50 carbon tax|75.3|75.0|95.9|86.9|74.4|
||CEPR, low energy prices (lower bound) **|108.7|178.4|95.9|131.7|120.4|
||Comprehensive energy price reform (CEPR), baseline energy price forecast|100.0|100.0|100.0|100.0|100.0|
|Si Lk|CEPR, US$12.50 carbon tax|65.4|61.9|137.4|86.3|72.4|
|r ana|CEPR, low energy prices (lower bound) **|106.6|181.1|137.3|171.9|117.6|

Source: Authors’ estimates based on CPAT. Notes: Unless otherwise stated, the Table shows comparisons relative to CEPR (=100) (first row for every economy). * Deviations in the consumption-based Gini coefficient are calculated assuming all revenues are recycled via mode (c) as described in Section 3.3. ** The ‘low energy prices’ rows assume prices in 2024 remain at their lowest level this decade, namely, their value in the second quarter of 2020.

# **4.6.2 Lower carbon tax rate assumption**

As shown in Table 1, the level of the carbon tax consistent with the stated
Instead of creating the fiscal space needed for the subsidy by raising its receipts, the government may alternatively reduce spending in other areas. For the scenario _sub+spnd_ , the government budget is balanced via scaled-down growth in all areas of government spending except for food subsidies, household transfers and investment in agriculture. Compared to _sub+tax_ , the main macro difference is that the _sub+spnd_ simulation raises private final demands while government demands decline. The GDP and absorption growth rates are virtually identical for the two simulations. The fact that GDP growth for the two scenarios is almost identical indicates that the gain from more rapid private investment growth and capital accumulation is counterbalanced by productivity losses stemming from less public investment, especially in public infrastructure. In the HD area, the growth acceleration compared to _sub+tax_ primarily benefits high-income households (Table A2.2), leaving the headcount poverty rate unchanged but leads to weaker performance for other HD indicators, indicating that higher aggregate private consumption does not make up for slower growth in targeted government services.<sup>22</sup> In the real world, the net difference between these two simulations in terms of impact on non-poverty HD indicators is likely to be context-specific, depending on the details of the changes in both government and private spending.<sup>23</sup> The second set of simulations considers an alternative policy response: instead of using subsidies to protect households from the negative repercussions of higher food prices, governments may instead design transfer programs targeted at low-income households, perhaps also conditional on other actions such as school attendance or participation in health programs. In the last two simulations ( _trn+tax_ and _trn+tax2_ ), the tax rate increases are the same as for the simulation _sub+tax_ but, instead of allocating them to an untargeted food subsidy, they are transferred to the bottom half of the income distribution in rural and urban areas. Relative to food subsidies, such a transfer program may be relatively demanding of administrative resources. To explore
and administrative support jobs is below $3 per hour, the contracts being outsourced to India represent relatively skilled work that involves programming and technical skills.

Thus, the descriptive data suggest a special role for diaspora connections in sending work to India. The next sections more carefully quantify this role when taking into account potential confounding factors (e.g., the types of projects being outsourced), finding that this special role persists. But we also should not lose sight of the absolute quantity of the shares. Ethnic Indians in the United States account for about 5% of the U.S.’s outsourced work to India. The average across the top 20 countries is 7%, falling to 3% when excluding the UAE. While ethnic Indians are more likely to send work to India, the rise of India to be the top worker source on oDesk also appears to have much broader roots than diaspora connections.

The unpublished appendix provides additional descriptive statistics. The top company contacts that send work to India display significant heterogeneity in terms of their geographic location and the overall degree to which they rely on India for outsourcing work. These company lists also highlight that, while much of the diaspora’s effect comes through the small actions of many individuals, the actions of a few can have an enormous impact. In particular, there is one company contact in the UAE that accounted for 906 of the UAE’s 989 contracts to India. This outlier is an ethnic Indian entrepreneur who uses oDesk for placing and managing outsourcing work, much of which is sent to India. Studies of diaspora networks often speculate about the concentrated importance of single individuals (e.g., Kuznetsov 2009), and oDesk provides some of the first quantifiable evidence of this concentration. This individual accounts for 7.7 times more contracts being sent to India than the next highest company contact and 2.4 times the volume from the Netherlands, the sixth-ranked country in Table 1.

# **4. Persistence in Outsourcing Patterns
Appendix B) clearly show that reductions in Google search index for the term “restaurant” strongly traces out actual percentage changes in restaurant bookings for all seven countries. As shown in Table B.3 (in Appendix B) weekly Google search indices explain about 60 percent of the variations in actual restaurant reservations.

Finally, we use monthly retail data from the U.S. Census Bureau to assess the validity of our retail-related Google search terms (“shopping” and “mall”). We compare actual trends in monthly retail spending and Google search indices associated with retail services. Figure B.2 indicates that our search terms and associated indices indeed mimic the trends in actual monthly retail spending. Table B.4 (in Appendix B) shows that these indices are strong predictors of actual retail spending.

These pieces of evidence, along with previous validations in the literature, confirm that Google search data strongly capture actual consumer demand for the services that we investigate in this paper. This is particularly encouraging as traditional economic data are not available in a timely manner or with high frequency to inform immediate policy responses in times of emergency such as the COVID-19 pandemic.

# **3. Empirical Strategy**

The Google search data and COVID-19 data provide spatial and temporal (weekly) variations in the search intensity for services and COVID-19 cases and deaths, respectively. We exploit these dimensions of variation to identify the impacts of additional COVID-19 cases and deaths on demand for various services. We estimate the following fixed effects specification to quantify the impacts of COVID-19: where c stands for the demand for various services, as captured by Google search intensity index, for country _c_ and week _t_ , captures country fixed effects, C C c represents the number of DD confirmed COVID-19 cases for each country and week, cc stands for weekly time dummy, and c αα CC is an error term assumed to be uncorrelated with the number of COVID-19 cases/ deaths cc δδ εε conditional on government measures and country and week fixed effects. The
the 2005 RDHS, each of Rwanda’s twelve provinces was divided into an urban and a rural stratum, resulting in 23 strata (the province of Kigali City only consists of urban areas). In a first stage, primary sampling units were drawn from a listing of enumeration areas prepared for the 2002 Census. Primary sampling units were selected with probability proportional to size regarding the number of households in each enumeration area. This exercise was conducted separately in every stratum. In a second stage, 20 and 24 households within each urban and rural primary
|**90-10 ratio**|4.3|4.3|
|---|---|---|
|Rural|4.1|4.0|
|Urban|4.5|4.4|
|**Poverty- National poverty line**|||
|**Headcount ratio, FGT(0)**|39.0|19.2|
|Rural|42.8|21.9|
|Urban|29.7|12.6|

Note: The National poverty line is 446 rupees, expressed in 2004/05 All India rural rupees, spatially deflated. Source: Calculations based on IHDS, 2005 and 2012.

While the Gini index and the ratio of the 90<sup>th</sup> and 10<sup>th</sup> percentiles of the distribution remained almost unchanged between 2005 and 2012, the poverty headcount rate fell from 39 percent in 2005 to 19.2 percent in 2012, which made for an annual rate of decline of 2.8 percent, a trend that is broadly consistent with official NSS-based estimates. Rural areas saw a higher reduction in poverty, with the 2012 poverty rate almost half of what it was in 2005. High reduction in poverty with almost no change in inequality suggests that poverty in both rural and urban areas has declined mainly as a result of growth as opposed to redistribution, which is confirmed by standard growth-inequality decompositions (not shown here).<sup>11</sup>

# _3.2. Transitions in and out of poverty_

Panel data allow us to look into some of the dynamics of poverty transitions, as we are able to identify those households that stayed in poverty in both periods (12 percent), that fell out of poverty between 2005 and 2012 (27 percent), that fell into poverty between 2005 and 2012 (7 percent) and that stayed out of poverty in both periods (54 percent
the respondents – men and women – reported a lack of confidence in identifying genuine friendships; were hesitant about trying new things; and disliked juggling different things at the same time.

Table 5 presents the mean difference test for the personality groups as well as gender role attitudes index for the analytic sample. Differences in gender role attitudes between men and women are highly statistically significant, with men being more traditional than women (see Table 3). There are significant differences in personality groups between men and women as well. While men and women show similar levels of impulsiveness and optimism, men have higher measures of work centrality, compared to women. Men are also more tenacious, have a higher desire to achieve, and also have stronger power motivations. Women, on the other hand, demonstrate higher levels of polychronicity, have greater desire for control in their lives and businesses, and also show greater strengths in being organized, compared to their male counterparts.

10
# **Data**

# <u>Output and inputs</u>

Output is measured as agricultural GDP in 1990 US dollars.<sup>25</sup> Inputs to agricultural production include land, capital, labor, and fertilizers. Hectares of agricultural area are used for the measure of land. This includes arable land, land under permanent crops, and permanent pastures. Agricultural labor is defined as the economically active population in agriculture. Fertilizer consumption is measured in metric tons. It is often viewed as a proxy for the whole range of chemical inputs.<sup>26</sup> The construction of the agricultural capital data has already been described.

# <u>State variables</u><sup>27</sup>

State variables are meant to represent the economic environment related to the choice of the implemented techniques. Since the motivation for including these variables have already been discussed in full in the previous papers, we review them briefly here.

# <u>Technology</u>

Three variables are included to account for differences in technology. The most commonly used variable to represent the level of technology in a country is a measure of human capital, which can be proxied by schooling years of the labor force (aged 15+).<sup>28</sup> The peak yield variable represents a measure of the frontier of implemented technologies. It is measured by country-specific Paasche indices, constructed from peak commodity yields weighted by land areas. The development indicator variable measures the overall level of development in a country, which would incorporate the effects of various public goods such as infrastructure, public health, research, and institutions. The hypothesis is that higher levels of technology would have a positive effect on agricultural productivity.

> 25 Various sources of National Accounts data were used to compile longer time series, including the United Nations, World Bank, OECD, IMF, and if necessary, country-specific sources.

> 26 Data on agricultural area, agricultural
# **Section 1: Management in Croatia and Why it Matters?**

The persistence in productivity differences between firms, within the same industry, is striking and puzzling. For example, labor productivity for plants in the United States at the 90<sup>th</sup> percentile, within narrowly defined four-digit manufacturing industries, is four times as high as plants at the 10<sup>th</sup> percentile. Likewise, the difference in Total Factor Productivity (TFP) is twice as high, when controlling for other factors (Syverson, 2011). These differences persist over time and are robust to controlling for plantspecific prices in homogeneous goods industries. Such TFP heterogeneity is not an artifact of the United States establishment data but is evident in several countries. As an example, the average 90<sup>th</sup> to 10<sup>th</sup> percentile TFP ratios is over 5:1 in China and India (Hsieh and Klenow, 2009). Most often within industry productivity differentials are attributed to “hard” technological innovations, as embodied in patents or in adoption of advanced equipment. Recently, Bloom et al. (2016a) provide an alternative explanation, that is, productivity differences reflect variations in management practices, which is also the approach adopted in this paper to explain the heterogeneity in performance of firms in Croatia.<sup>1</sup> Persistent differences in productivity can be explained by several other factors, such as higher quality labor and capital, differential investment in information technology and research and development (R&D), learning by doing, firm structure, productivity spillovers, regulatory behavior, and differences in competitive regime. Bloom et al. (2016a) propose that some forms of management practices are like “technology”, where management is perceived as an intangible capital in which output is monotonically increasing. Differences in managerial quality are critical in explaining the cross-country differences in income levels, productivity, innovation and firm dynamism
# **Figure A1 – Willingness to get vaccinated disaggregated by different demographic characteristics of respondents (Phone survey)**

<!-- Start of picture text --> a) Sex of respondents b) Location of respondents<br>.3 .3<br>.25 .25<br>.2<br>.2<br>.15<br>.15<br>Female Male Highlands Islands Momase Southern<br>c) Age of respondents d) Education level of respondents<br>.26 .3<br>.24<br>.25<br>.22<br>.2<br>.2<br>.18 .15<br>18-39 years 40+ years Post-secondary school Secondary school or less<br>Note: This chart is based on answers to question 3 in Appendix B.<br>Figure A2 – Unwillingness to get vaccinated disaggregated by different demographic characteristics of<br>respondents (Phone survey)<br>a) Sex of respondents b) Location of respondents<br>.65<br>.6<br>.6<br>.55<br>.55<br>.5<br>.5<br>.45<br>.45 .4<br>Female Male Highlands Islands Momase Southern<br>c) Age of respondents d) Education level of respondents<br>Share of respondents willing to get vaccinated Share of respondents willing to get vaccinated<br>Share of respondents willing to get vaccinated Share of respondents willing to get vaccinated<br>Share of respondents unwilling to get vaccinated Share of respondents unwilling to get vaccinated<br><!-- End of picture text -->

**Figure A2 – Unwillingness to get vaccinated disaggregated by different demographic characteristics of respondents (Phone survey)**

30
Modeling the impact of COVID-19 in FY20 As the LFS 2020 covers the pandemic shock, we estimate from the data the size of job losses and who was more likely to stop working in Q4 of FY20. Figure 3 shows relative changes in labor force states between Q3 and Q4 of FY20. These figures are the ones used in the simulation of job losses. We model the likelihood of working using LFS data and generate a propensity score that we apply in the HIECS data. Working household members are selected to experience an employment shock in the last quarter of FY20 based on their ranked propensity score: those with the lowest score sequentially exit the pool of workers until the predicted number of workers who lost their jobs is matched in each relevant labor market state.

In addition to job losses, impacts occurred in terms of lower earnings. LFS asked workers if they experienced earnings losses due to COVID-19. Table 3 presents the share of workers in the LFS data who did not lose employment but reported a negative income shock due to COVID-19. Mean changes in earnings between Q3 and Q4 are used in each relevant labor force state, respectively, to adjust onequarter of the workers’ labor income.

Figure 3: Change in the share of workers in each labor force state between Q3 and Q4 of 2020 (percentage points) <!-- Start of picture text --> Agriculture 0.6<br>Industry - Informal -1.8<br>Industry - Formal -0.3<br>Services - Informal -3.1<br>Services - Formal -0.7<br>Not working 5.3<br>Informal work -2.9<br>Formal work 0.2<br>Not working 2.7<br>Men<br>Women<br><!-- End of picture text --> Source: Authors’ calculations using LFS 2020.

9
supplied by the secondary

> 26In Tables 2 and 3, the two electricity infrastructure proxies, electricity access and generation capacity, enter the regression separately, and electricity access is the only electricity proxy that is instrumented. Therefore, the access variable captures the supply-side channel, which benefits services, whereas the capacity variable captures the demand channel, which benefits manufacturing (as Table 2 suggests). In the simulation exercise, we combine them to get the _Gt_ measure. Estimation results using (and instrumenting) electricity access times generation capacity as a single regressor are qualitatively similar to the ones obtained for electricity access, and in line with the supply-side implications of the production-function estimates in Table 5. The estimated IV coefficients are all strongly significant and imply that agriculture is the sector that experiences the largest gain, followed by services and manufacturing.

44
Policy Research Working Paper 9508

# **Abstract**

This paper studies the trade effects of Covid-19 using monthly disaggregated trade data for 28 countries and multiple trading partners from the beginning of the pandemic to June 2020. Regression results based on a sector-level gravity model show that the negative trade effects induced by Covid-19 shocks varied widely across sectors. Sectors more amenable to remote work contracted less throughout the pandemic. Importantly, participation in global value chains increased traders’ vulnerability to shocks suffered by trading partners, but it also reduced their vulnerability to domestic shocks.

This paper is a product of the Macroeconomics, Trade and Investment Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at aespitiarueda@worldbank.org, amattoo@worldbank.org, nrocha@worldbank.org, mruta@worldbank.org, dwinkler2@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
common mean across all 14 categories and years has been used to calculate the correlations. Not surprisingly, the correlations are remarkably high. Despite large differences in the level of prices across countries and products, they move together. The lowest correlations are those of Mexico's prices and even in that case, with one exception, they exceed .6. For Bangladesh, all correlations except that with Mexico, are larger than .83.

Finally, we need to confront the issue of whether or not quotas are binding. It will be too much to expect that quotas are binding in all years for all categories for all countries in our sample. All we can offer here is broad evidence in favor of the assumption. Several points may be noted. First, as a minimal defense, we note that a large body of the recent literature on the evaluation of the future impact of the MFA phase out under the Uruguay Round, based on Computable general Equilibrium models, uniformly assumes binding quotas (e.g., Whalley 1996). Second and more directly, Table 8 shows detailed data on quota utilization rates for 1993, the latest year for which we could obtain reliable data. We report quota utilization rates for the various products for all countries in our sample except Hong Kong."<sup>7</sup> These rates are remarkably high for virtually all countries in the majority of categories suggesting binding or near-binding quotas. Third, quota rents in most countries have been found to be positive even when the utilization rate is below 100%. Partly due to group quotas (see the next paragraph) and partly due to the manner in which quotas are administered, quotas seem to bind even when the utilization rate is below 100%. Finally, Dean (1991) has tested econometrically for whether the quotas are binding and concluded in "<sup>7</sup> The reporting year for quota utilization is from April 1, 1992 to March 31, 1993. Data for Hong Kong were