rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Table 2. Evolution of Bank-Level Indicators after the Shock**

Regressions at the bank*month level using bank balance sheet data. Dependent variables are listed in the columns. _ExposureEnergyb,m_ represents lending to the energy sector as a share of its tier 1 capital of bank _b_ in month _m_ . _Total Lendingb,m_ are total monthly loans of bank _b_ in logs. _CDS Spreadsb,m_ is the log of CDS spreads of five-year maturity bonds of bank _b_ in month _m. Stock Priceb,m_ is the log of the stock price of bank _b_ in month _m. Delinquencyb,m_ is the share of delinquent loans of bank _b_ in month _m_ . The regressor _ExposureEnergyb,Aug14_ represents the exposure to the energy sector of bank _b_ in August 2014. _Postm_ is an indicator for month _m_ after the energy price shock in August 2014. All regressions include bank and month fixed effects. The results show that there was relocation of lending toward the energy sector by banks that were more exposed to it. However, other margins were unchanged, suggesting that there was a reallocation across sectors. Robust standard errors are double clustered at the bank and month levels. Detailed variable definitions are provided in Table A1. Observations are at the bank-month level <u>for January 2013 to June 2016. *** p<0.01, ** p<0.05, * p<0.1.</u>

||ExposureEnergyb,m<br>(1)|Total Lendingb,m<br>(2)|CDS Spreadsb,m<br>(3)|Stock Priceb,m<br>(4)|Delinquencyb,m<br>(5)|
|---|---|---|---|---|---|
|ExposureEnergyb,Aug14*Postm|0.192***|-0.003|0.016*|-0.
0002)|0.0027***<br>(0.0002)|
|Observations|870,826|871,295|870,942|870,826|871,295|870,942|
|_R_<sup>2</sup>|0.6205|0.7624|0.4095|0.6179|0.7559|0.4070|
|Kleibergen-Paap rk Wald-F-stat||||129.528|128.008|128.850|
|importer-exporter-year efects|Y|Y|Y|Y|Y|Y|
|Industry-year efects|Y|Y|Y|Y|Y|Y|

_Notes_ : Table reports OLS and IV results of equation (23) in text, excluding outliers from the baseline estimation sample (robotization and dependent variables censored). Columns (1)-(3) report the OLS results, while columns (4)-(6) report the IV and corresponding first stage estimates. Robust standard errors clustered by developed country are presented in parentheses. ***1% level, **5% level, *10% level.
between markets and financial development. Using stock market turnover ratio as their primary measure of stock market liquidity, they find strong evidence that stock market liquidity facilitates longrun growth. Importantly, they also argue that the link between banks, stock markets, and growth is through productivity growth rather than physical capital accumulation.

The pure cross-country evidence in the above studies is problematic because the unobserved country-specific heterogeneity is part of the error term and may bias the estimates of

> 13 A more recent detailed database of measures of financial development can be found from the Financial Structures Database in Beck and Demirguc-Kunt (2009)

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Lewandowski et al. 2021; Lo Bello, Sanchez-Puerta, and Winkler 2019; Alatas, Granata, and Posadas 2020). To avoid this, the SDS draws on O*NET and other proven methodologies and takes a countryspecific approach. The SDS asks workers in Vietnam about their tasks and skills for a set of occupations that are either in-demand or that are of strategic importance for economic growth. In doing so, the SDS seeks to generate practical, detailed information at the occupation level that policy makers and practitioners can use to inform their efforts to build skills in Vietnam. This report explains the SDS’s methodology, provides summary results for the 30 occupations targeted by this round of the SDS, and presents an example of a detailed occupational profile for one of these 30 occupations, Nursing Associate Professionals. The SDS has also spurred further investigation of skills in Vietnam, particularly those needed for green jobs (Doan et al. 2023).

**The SDS makes several contributions** . First, the SDS provides a comprehensive but also in-depth portrait of skill, task, and educational requirements for a set of detailed occupations in Vietnam. The SDS is among the first surveys to collect this information at the detailed occupational level in a developing country setting. This is an important step beyond relying on data collected in other (developed) countries. The collection of information about detailed skills means that these skills can be flexibly grouped into different categories (for example, socioemotional skills, digital skills, routine skills, interpersonal skills) as needed. The use of a consistent scale anchored to the time spent using or performing a skill or task yields a measure of skill and task importance that is easily interpreted. The SDS approach also has some advantages over collection of skill and task data via online job vacancy data, which has become common in recent years. In developing country contexts, online vacancy data tends to be biased towards higher-skilled digital-intensive occupations and jobs at firms that use the internet (CSC 2019; World Bank 2021)
## 3. Section 4 highlights results of some sensitivity analysis followed by discussion on policy

insight in Section 5. Finally, conclusions are presented in Section 6.

# **2. The Model and Data**

We used a multi-regional, multi-sector, recursive dynamic CGE model<sup>7</sup> for the purpose of this study. The basic data for the calibration of this model is derived from the GTAP 7.0 database with base year 2004. Although the GTAP database provides information for 113 countries and 57 production sectors/commodities, we disaggregated some sectors<sup>8</sup> further and aggregated others and ultimately have 28 sectors and 25 countries/regions. The main reason of using the GTAP database is that there exists no other comprehensive global database required by this study. Moreover, most CGE models simulating biofuels are based on the GTAP database, and use of this database could help compare our results with those models where appropriate. The list of countries/regions and sectors/commodities considered in our model is provided in Table 1.

> 7 CGE models are based on disaggregated representations of various agents (households, firms, governments) exhibiting optimizing behavior and interacting via different markets (sectors, countries, regions). Underlying a CGE model are equations describing these variables and their interaction as well as the data needed to calibrate the model. Simple CGE models are comparative static, which means that they allow for comparison of the equilibrium state of the economy after a policy intervention has taken place with the situation in absence of the intervention. Dynamic models allow for tracing variables over the whole adjustment path. The advantage of CGE models over partial equilibrium analysis is that they account for interactive effects within and between sectors, agents and markets.

> 8 We disaggregated coarse grain to corn and other coarse grain as corn is the major biofuel feedstock. We also disaggregated petroleum products to gasoline, diesel and other petroleum products. We will briefly discuss the method of disaggregation later.

6
.37|183.41|173.10|

Source: Author’s own elaboration from enterprise surveys

# **6. ICT use, innovation and productivity**

## **6.1 Are ICT adopters more productive?**

A critical challenge when examining the relationship between ICT adoption, innovation and productivity, is reverse causality; while ICT use can increase productivity, it is also possible that only more productive firms adopt ICT. In what follows, we start exploring this question by looking at basic distribution plots between labor and multifactor productivity and ICT adoption. Specifically, we focus in two categories of ICT adoption: computer users and internet users. We use the relative distribution of productivity between adopters and non-adopters as a first approximation for stochastically dominance, which provides a better measure of productivity differences between these groups than evaluating it by simple averages.

_Computer users are more productive than non-users._ Figure 1 shows the relative distribution of labor productivity for all firms in all countries pooled together, measured by sales per worker, and of total factor productivity (TFP) estimated at the sector level.<sup>14</sup> As observed, differences between computer users and non-computer users are wider in labor productivity compared to TFP. Computer users show higher productivity level throughout the whole relative distribution of labor productivity. Looking at their averages, computer users are four times more productive than non-users. Regarding TFP, productivity differences between computer users and non-users are larger for low productivity firms than for most productive firms. Evaluating at their mean, computer users are 26.2% more productive than non-users in terms of TFP.

_Internet users are also more productive than non-users._ Observing the distribution of internet users versus non-users (Figure 2) shows similar results as for computer use; internet users tend to be more productive than non-internet users regarding labor productivity. For TFP, there are no productivity differences for very

> 14 Figures using value added per employee as labor productivity and TFP measures using sector as a
|19.7|21.1|19.7|0.07|0.00|
|Harvesting (1ha)|38.7|41.0|38.3|0.06|-0.01|
|Grain transport (t/30 km)|4.0|5.9|4.5|0.48|0.13|
|_Panel C: Area cultivated (mn. ha)_||||||
|Total|26.69|25.43|24.88|-0.05|-0.07|
|Winter|9.45|8.38|6.76|-0.11|-0.28|
|Summer|17.23|17.06|18.12|-0.01|0.05|

Source: Own computation based on UCAB data (panels A & B) and satellite crop monitoring (www.cropmaps-ua.com).

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countries managed to escape the fiscal pro-cyclicality trap, our study analyzes multiple fiscal policy variables side-by-side. Specifically, the main fiscal policy variables considered in our empirical analysis are government total expenditure, government revenue, and overall fiscal balance. We also consider revenue as a share of GDP as an alternative to tax rates, as an increase in both indicators during economic expansions and a decline during recessions points towards countercyclical fiscal policy measures. In addition, we zoom in on particular subcomponents of government spending (subsidies, grants, and other social benefits) and of government revenue (tax vs. non-tax revenue).

The data used in this study are publicly available from the following sources (we subjected the data to consistency checks). Annual data for general government revenue (percent of GDP) and general government total expenditure (percent of GDP) come from the International Monetary Fund’s World Economic Outlook database (IMF 2023). Annual data for tax revenue (percent of GDP) come from the IMF’s World Revenue Longitudinal Dataset (IMF 2022). Annual data for subsidies including grants and other social benefits (percent of GDP),<sup>2</sup> real GDP (constant local currency unit, LCU), and real GDP per capita

> 1 Building their own dataset on tax rates for 62 countries for the period 1960-2013, Vegh & Vuletin (2015) find tax policy to be acyclical in industrial countries but mostly procyclical in developing countries. However, their dataset covers very few low-income countries, which are of particular interest in our study.

> 2 More precisely, the subsidy variable includes all unrequited, nonrepayable transfers on current account to private and public enterprises; grants to foreign governments, international organizations, and other government units; and social security, social

~ 4 ~
We develop a model to derive productivity losses from the misallocation of labor between the informal and formal sectors. The model is adapted from the framework developed by Brandt et al. (2013), which extends the Hsieh and Klenow (2009) firm-by-sector level model, to examine factor misallocation and its effect on total factor productivity at a more aggregate level, between sectors, provinces, and China’s nonfarm economy. Under Hsieh and Klenow’s model, maximum allocation efficiency is achieved when the marginal revenue products of enterprises within a sector become equal.<sup>12</sup> Consider a static allocation problem in an economy consisting of two sectors ( ), formal ( ) and informal ( ), and regions ( ), . Employment in a year is given. We model ss the allocation of labor across sectors within a region. We however do not model allocation of ff ii nn rr rr= 1,2 ... nn labor across regions. Supporting this decision, based on GLSS 2016/17, we find that internal migration (across regions) by workers is limited in Ghana. Only 8.2 percent of workers have moved from their birth region to another region due to self-reported employment-related reasons.<sup>13</sup> More importantly, the distribution of workers between the informal and formal sectors in regions is unchanged by the extent of migration.

We assume a linear production technology at the sector level with labor as the only input. Outputs produced in both sectors are gross substitutes with some degree of product differentiation. Average productivity equals marginal productivity in a linear production function. As a result, the condition for allocative efficiency in our model can be expressed in terms of average labor productivities. The production function in sector and region exhibits constant returns to scale (CRS): ss rr r r r , (3) YY = AA LL where r and r are output and employment in sector and region , respectively, and r is r labor productivity in sector YY
We estimate the margins for the variable of interest (e.g., size, sector, or country), for which we compare results across waves, keeping the distribution of size, sector, and country constant across waves to ensure that the results are not driven by differences in composition. To control for variations in the sample size by countries, estimations are weighted using the inverse of the number of observations in each country, that is, each country has the same weight in our summary statistics.<sup>7</sup> Our baseline estimation method uses pooled cross-section analysis including the full sample of firms in both waves. All results we presented are estimated conditional averages from regressions, unless specified. For robustness, we also replicate the analysis using a panel data approach, restricting the sample to firms responding to both waves of the survey. Overall, results are consistent across these different approaches.<sup>8</sup> To further understand the potential bias arising due to firm attrition, we implemented three alternative empirical strategies as a robustness check.<sup>9</sup> Using the subset of panel firms, we implemented the following robustness tests for the outcomes pertaining to the probability of being open, change in sales, probability of falling into arrears, and probability of accessing public support. _First_ , we re-weight the sample using the inverse of the probability of responding to the follow-up survey based on firm characteristics and performance during wave 1 to adjust for non-response (Wooldridge, 2002). _Second_ , we use the estimated probability of responding to the follow-up survey as a control in the regressions. _Third_ , we use a bounds approach where the outcomes are determined by a set of (extreme) assumptions on firm responses (Kling et al., 2007; Blattman et al., 2020). More specifically, we conjecture two scenarios: (i) estimate an imputed value of the outcome variable based on firm characteristics and past performance; and (ii) assume that all
Policy Research Working Paper 9964

# **Abstract**

- This paper investigates the causal impact of a Covid-19 lock down policy on the Comoros’s household welfare, poverty, and labor market outcomes. The identification strategy uses the national government lockdown policy implemented to curtail the unexpected outbreak of Covid-19. The lockdown policy coincided with the 2020 Harmonized Survey on Living Conditions of Households data collection, lending itself to a quasi-natural experiment in which households that were interviewed before the lockdown policy fall into the control group, while those that were interviewed after the lockdown fall into the treated group. The paper explores the impact of the Covid-19 using descriptive regression

analysis and estimates the causal impact using matching techniques. The analysis finds a reduction in household expenditure, increased poverty, and a reduction in the likelihood of employment. Investigation of differential impacts along the expenditure distribution finds larger impacts at the top of the distribution, suggesting that Covid-19 may have reduced inequality, although the poor were also negatively affected. The evidence also suggests that the ability to use assets as a coping mechanism was limited. In a context of limited safety nets and government interventions, stringent lockdown policies appear to increase the vulnerability of the poor.

This paper is a product of the Poverty and Equity 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 onsababera@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
Looking to the future, according to United Nations projections, by 2040, the world will have at least 9 billion inhabitants, one-third of whom will be living in India and China. The U.S. Grain Council (USGC 2011) estimates that real per capita income of the citizens of the world will increase considerably over the next 30 years, from US$9,727 to US$25,000. Tilman et al. (2011) estimate increases in the 2005 levels of per capita GDP for Asian nations—including the most populous countries in East and South Asia—between 2.5 and 4 times by 2050. The Asian Development Bank (ADB 2011) estimates that the middle class in China, which currently represents 12 percent of its population, could become as high as 75 percent, and that it could reach 70 percent in India and 80 percent in Indonesia by the year 2050. Parallel to this expansion, the upper classes are also expected to increase their global presence, reaching 190 million in China, 210 million in India, 40 million in Indonesia, and 35 million in the Republic of Korea. The population of Asia is expected to age considerably, reaching an estimated “over 65” cohort of 580 million, which in the case of China will represent 27 percent of all its inhabitants, a considerable change from its current level of 10 percent. The USGC study also indicates that the role of women is likely to change in important ways, with an increase in trends such as marrying at a later age or deciding not to marry at all; these trends are beginning to emerge in the wealthier Asian countries and in the Chinese middle class. Increasingly, Asian women are joining the work force, getting more education, and consequently enjoying higher incomes. Other processes, such as urbanization of the developing world (with urban concentration in China estimated to increase from 34 percent of the population in 2010 to 70 percent in 2040) and epidemiological transitions toward a greater prevalence of diseases that used
Washington, DC: World Bank Group.

- Alrababa’h, Ala’, Andrea Dillon Scott Williamson, Jens Hainmueller, Dominik Hangartner and Jeremy Weinstein. (2020). “Attitudes Toward Migrants in a Highly Impacted Economy: Evidence From the Syrian Refugee Crisis in Jordan.” _Comparative Political Studies_ 54(1): 33-76.

- Alsharabati, Carole and Jihad Nammour (2015). “Survey on Perceptions of Syrian Refugees in Lebanon.” Institute des Science Politiques - USJ. Université Saint-Joseph. Beirut: Université Saint-Joseph.

- Barriga, Alicia, Neil Ferguson, Nathan Fiala and Martin Leroch (2020). “Ethnic Cooperation and Conflict in Kenya”. Ruhr Economic Papers No. 872.

- Becker, Gary (1968). “Crime and Punishment: An Economic Approach.” In _The Economic Dimensions of Crime_ (pp. 13-68). Palgrave Macmillan UK.

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arriving to Peru have taken up jobs in the informal sector, directly competing with relatively low skilled native workers. To causally identify the relationship, we use a shift-share instrumental variable strategy that exploits local exposure to exogenous national-level export shocks (Jaeger et al., 2018). As informal employment and discrimination could both be related to other area characteristics, we also control for the local industrial structure, household expenditure, population size, distance from the capital and center of economic activity (Lima), and, importantly, the number of Venezuelans based in each location prior to the current immigration wave, which we show to be a significant pull factor for where Venezuelans settle.

Our results show that weaker informal labor markets lead to significant increase in the discrimination reported by Venezuelans in Peru. Overall, a 10% decrease in the informal employment rate increases discrimination by 2.3-3%. This effect is twice as large for men as for women. The data we use also collects information on where discrimination occurs. We find that weaker informal labor markets lead to more discrimination for men in public places, as well as on public transit and, for women, on public transit exclusively. We do not find evidence of an impact on workplace discrimination for either gender.

One interesting pattern is that We find that more educated Venezuelans are more likely to report being discriminated against. A potential explanation for this, consistent with the previous results, is that higher skilled Venezuelans are disappointed with their situation in Peru, especially when they settle in areas with strong labor markets, and this lack of opportunity is either caused by or perceived as discrimination (Guerrero-Ble et al., 2020).

In the second part of our analysis, we examine the impact of immigration in terms of changes in the number of Venezuelans as a share of the local population in a province on a wide-variety of outcomes. We rely on administrative data to measure the number of Venezuelans newly registered in each district in Peru on a monthly basis between January 2015 and December 2020
in the sense that it in some respects extends well beyond currently accepted standards for measuring carbon footprints of air travel, by making them more precise. The WBG could, by its approach to measuring its own footprint, lead the way in establishing more accurate such measures.

Aside from how much to offset – an issue with philosophical and political as well as economic and environmental dimensions – a second consideration is how to offset. A relevant current issue in this regard is that the European Union, from 2012 on, has embedded all flight activity within and to and from the EU countries in the EU-ETS, which requires all airlines to hold quotas to cover the respective assessed emissions. Appropriately embedding the WBG’s flight activity to and from the EU area in the EU-ETS would be one way for the WBG to mitigate its air travel emissions, as distinct from only purchasing offsets for this activity. The ultimate status of this EU initiative is currently uncertain, however, and we are currently awaiting initiatives

> 29 For an overview of the WBG’s carbon footprint and how this is currently measured, see: <u>http://crinfo.worldbank.org/wbcrinfo/node/23#MeasuringGHG. For a presentation of the WBG’s more general</u> offsetting policy in the context of corporate responsibility, see http://crinfo.worldbank.org/wbcrinfo/node/7. 30 For extensive information on these issues, see the WBG’s corporate responsibility website: <u>http://crinfo.worldbank.org/wbcrinfo/.</u>

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