rafmacalaba/data-use-mentions-extended huggingface dataset:
|Big firm (201+)|7.522***|0.595***|Big firm (201+)|6.076***|0.633***|Big firm (201+)|6.116***|0.728***|
||(1.874)|(0.136)||(2.287)|(0.179)||(2.287)|(0.182)|
|Country FE|Yes|Yes|<br>Country FE|||Country FE|||
|Country ctrl|||Country ctrl|Yes|Yes|Country ctrl|Yes|Yes|
|Year, Industry FE|Yes|Yes|Year, Industry FE|Yes|Yes|Year, Industry FE|Yes|Yes|
|Firm ctrl|Yes|Yes|Firm ctrl|Yes|Yes|Firm ctrl|Yes|Yes|
|Observations|49398|44358|Observations|55515|50720|Observations|55242|50670|
|Adjusted_R_<sup>2</sup>|0.081|0.381|Adjusted_R_<sup>2</sup>|0.053|0.346|Adjusted_R_<sup>2</sup>|0.058|0.348|

_Note:_ Standard errors are clustered at the country level. All business environment indicators are standardized with their sample mean and standard deviation. Other control variables include the two competition dummies (having one competitor, having two or more competitors) and their interactions with three firm size dummies (small, medium, and large, with the default being micro firms), biggest owner share, age 6–10, age 11<sup>+</sup> , exporter, foreign firm, foreign share, and city population category dummies.

Firm size: micro = no more than 20 employees; small
actual panels.<sup>5</sup> On average, the predicted poverty rates using LASSO-PMM were one percentage point away from the observed rates, when using data from the four validation countries.

To build the synthetic panels required to define the new vulnerability and middle-class lines, this paper follows Lucchetti et al. (2020) as a starting point. and introduces several improvements in its methodology. First, matching is performed only between subsets (donation classes) of individuals with the same time-invariant features. These constrained donation classes are built based on gender, birth year, and educational attainment. Second, sampling weights are used to harmonize the joint distributions of identified common variables across the two surveys (Renssen, 1998). This method (known as Renssen’s procedure) consists of a series of calibration steps of the survey weights (in both the donor and receiver surveys<sup>6</sup> ) implemented to achieve consistency between some aggregate estimates, e.g.: income, gender or age distributions. Third, when several donors are equidistant in terms of the predicted income, one of them is randomly chosen according to their sampling weights. The fourth improvement is a direct consequence of the two previous ones: this method allows to preserve the marginal distribution of income from the donor survey, which is essential not to alter welfare dynamics. In addition, although synthetic panels have their own set of limitations, they represent a significant improvement over using cross-sectional data to estimate the probability of falling into poverty. Without actual panels available, this would have been the case. Starting from two available rounds (i.e., = rounds 1 and 2) of cross-sectional microdata. Let i be the per capita household income for household i, (i.e., ), in survey round with sample size , jj yy and i be a vector of household head characteristics observed for household i in survey round j. These ii= 1, ... , NN jj NN
discusses fiscal risks in section VI. Section VII concludes, noting limitations and setting the stage for further analytical work.

# I. Literature Review

The impact of large debt-financed investments on debt sustainability depends critically on how these investments affect the economic growth. A large literature studies the contribution of infrastructure capital to productivity and output. Methodologies and estimates of this impact vary widely. Using panel data, research finds that output elasticities to aggregate measures of infrastructure investment range from 0.06 to 0.18 (Calderón, Moral-Benito and Servén, 2011) in developing countries. However, there is a recognition that different types of investment have different impact on growth (Calderon, 2009) and that the efficiency of investment spending may vary across countries. For example, the impact of the observed monetary measures of public investment tend to be overstated in many low-income countries with poor institutions (Keefer and Knack 2007) due to weakness in public investment management.

To identify the impact of BRI investment on growth in recipient countries this paper applies a theoretical approach and an empirical approach. Hence, it would address the two main issues which are identified in the literature. First, countries have different elasticities of investment on growth. Second, physical indicators for the stock infrastructure are better predictors of the impact of infrastructure investment on growth.

The theoretical approach, based on Devadas and Pennings (2018), uses a constant elasticity production function to derive the marginal productivity of BRI investments. This model assumes that the impact of additional investment on growth declines as the public capital stock rises. It assumes a constant investment spending efficiency over the medium-term. This model has the advantages of being based on data that are available for most countries and providing country-specific growth estimates based on the level of capital stock and amount of investment. However, it is calibrated using a measure of capital stock derived from real public fixed capital formation, which has been shown not to be a reliable indicator of the increased capital stock.

In the empirical approach, the contribution of physical infrastructure
database 2015 <!-- Start of picture text --> 40<br>20<br>0<br>2003 2006 2009 2012<br>Year<br>China FDI (% of total FDI)<br><!-- End of picture text --> Figure 18: China’s FDI represents large share of total FDI (2003-2012) Source: Authors’ own calculation based on World Development Indictors World Bank 2015 21
%, or _β_ “ 0 _._ 10.

Table 1 illustrates the development accounting results.<sup>10</sup> I display _Share_<sup>_US_</sup> _c_ for countries at the 15<sup>_th_</sup> , 30<sup>_th_</sup> , 45<sup>_th_</sup> , 60<sup>_th_</sup> , 75<sup>_th_</sup> and 90<sup>_th_</sup> percentiles of the income distribution as well as

> 7The cross-country variance of log GDP can be additively decomposed between the variances of log human capital, physical capital and technology, as well as all the covariances. Some authors, including Klenow and Rodr ́ıguez-Clare (1997b), consider measures of success for human capital which include some of the covariance terms. Given that these covariances are positive, this would result in a larger success term for human capital.

> 8To measure _y_ , I use GDP per worker in PPP terms for 2010, which I take from version 9.0 of the Penn World Tables. The baseline sample is made of 130 countries for which the data to conduct exercises on educational attainment, health, migration outcomes and imperfect substitution are available. I consider smaller samples when I look at test scores and returns to experience.

> 9Several authors, including Hall and Jones (1999) and Caselli (2005), consider versions of (5) where years of schooling enter non-linearly in the exponent, to match the fact that returns to schooling are often found to be different across levels of educational attainment. These modifications have limited impact on the development accounting results.

> 10I use average years of schooling for the population above 25 from Barro and Lee (2013).

12
# Digitalization, Remote Work and Firm Resilience: Evidence from the COVID-19 Shock<sup>∗</sup>

Cristina Constantinescu<sup>†</sup> Arti Grover<sup>‡</sup> and Gaurav Nayyar<sup>§</sup>

## **JEL classification** : D22, F14, L20, L25, O10.

**Keywords** : Digital, Remote work, Firms, Resilience, COVID-19, Trade

∗The authors are immensely grateful to the many World Bank Group colleagues and counterpart institutions that have supported the Business Pulse Survey (BPS) data collection, without whom this work would not have been possible. Likewise, we thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the Enterprise Survey data available. We thank Gene Grossman, Mona Haddad, Denis Medvedev, Antonio Nucifora, Marcelo Olarreaga and Daria Taglioni for their guidance and strategic support of the BPS initiative, and the Umbrella Trade Trust Fund for financial support. We thank Yewon Choi, Shwetha Eapen, and Mauricio Latorre for their research assistance. The authors are also grateful to the following colleagues for their feedback at the concept stage of this project: Asya Akhlaque, Ana Fernandes, Caroline Freund, Mary Hallward-Driemeier, Leonardo Iacovone, Michele Ruta, Daria Taglioni, Gonzalo Varela and Erik von Uexkull. We thank Edgar Avalos and Jesica Torres for their contributions in the initial stages of the project and Arthur Johannes Giesberts for his generosity in sharing his tool for converting text variables into 4-digit industry codes. We thank Alvaro Espitia and co-authors of Espitia et al. (2021) for kindly sharing the remote work flexibility indicator and the output share of durable products by industry. The views expressed in this paper are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> †The World Bank; email: ineagu@worldbank.org

> ‡The World Bank; Corresponding author
The paper contributes to a literature on the value and incentives of rural leaders in China, while demonstrating the importance of panel data and controlling for unobserved ability in estimating the returns to cadre status. While a preponderance of empirical studies to date have concluded that officials in rural China benefit from their political status and connections, the vast majority of these studies are based on single cross-sections of data and do not allow the researcher to control for unobserved dimensions of ability.<sup>2</sup> Higher incomes of cadres or Party members may not be driven by political connections, but simply the fact that high ability individuals are recruited into public service.<sup>3</sup> Li et al (2007), for example, demonstrate the importance that unobserved heterogeneity may play in biasing estimates of the returns to Communist Party membership in urban China. Using a set of data on 870 pairs of identical twins, the authors show that the apparent returns to Communist Party membership disappear after controlling for the effects of unobserved ability and family background.

Using the wide geographic coverage and the lengthy span of the survey, from 1986 to 2003, we examine both the geographic variation in returns to being in a rural cadre household and the evolution of returns during the period of transition from plan to market in rural China. By controlling for household fixed effects and exploiting the fact that we have information before and after households have a member who is a cadre, we control for unobserved ability, leadership and family background and obtain consistent estimates of the effect of cadre status on household income. Even if unobserved

> 2A partial list of studies suggesting significant returns to local officials in rural China include Nee (1996), Cook (1998), Walder (2002), Morduch and Sicular (2000), Parish, Zhe and Li (1995) and Parish and Michelson (1996). 3Morduch and Sicular (2000) provide one exception among these studies in that they use a longitudinal data set (1990 to 1993),
share of the underreporters third-party reports (5), and as a share of total third-party reports (6). Rows 7-11 convert unreported sales into tax liabilities. They show an estimate of the unreported tax liability (7), the underreporters' reported tax liability (8), and the total reported tax liability (9), and the unreported tax as share of the underreporters' reported tax (10), and as a share of the total reported tax (11). The estimation of the unreported (gross) tax liability assumes that the prot rate on unreported sales is the same as the prot rate on reported sales (capped at 100%), and applies the tax schedule as displayed in Table I. Rows 12-15 analyze internal constency in ling. Rows 12 and 13 compare self-reports for the income tax to a rm's own third-party reports (third-party reports submitted by the rm itsself about transactions with other rms), and rows 14 and 15 compare income tax reports to sales tax reports. All calculations are based on 2010 data. Results are similar for other years, and when focusing only on rms that le according to the regular scal period.

35
|16,458,902|16,394,979|16,458,819|16,394,924|
|_D. Switched to private school_|||||
|Warning index x 2020|-0.002|-0.002|-0.002|-0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Warning index x 2021|-0.001|-0.001|-0.002|-0.002|
||(0.000)***|(0.000)***|(0.000)***|(0.000)***|
||[0.001]|[0.001]|[0.001]|[0.001]|
|Pr(2020=2021)|0.000|0.000|0.026|0.014|
|Mean outcome|0.032|0.032|0.032|0.032|
|Adjusted R<sup>2</sup>|0.003|0.053|0.066|0.100|
|Observations|16,458,902|16,394,979|16,458,819|16,394,924|
|Controls|No|Yes|No|Yes|
|Year effects|Yes|Yes|Yes|Yes|
|Fixed effects|Municipality|Municipality|School|School|

_Source_ : Authors’ calculations from Guatemalan educational administrative records.

_Notes_ : The table reports difference in difference estimates of the effects of differential exposure to COVID-19 (see Section 4 for
In low-income countries, increased public education spending over the last 20 years has largely funded efforts to expand access to primary and secondary education rather than to increase the amount spent per student (Figure 8). In countries with complete data, we found that 66 percent of the total increase in spending at the primary level and 80 percent at the secondary level went towards expanding access (Table 4). This partly reflects the relatively low rates of access in these countries in the early 2000s. For example, between 1998 and 2001, the average net enrollment rate in primary school in low-income countries was 56 percent while the average rate in secondary schools was only 16 percent.

**Figure 8: Primary and Secondary Spending Growth in Low-income Countries was Driven by Increases in Access**

_Decomposition of public education spending changes (%), 1998_ **–** _2017_ <!-- Start of picture text --> 800 800<br>600 600<br>400 400<br>200 200<br>0 0<br>-200 -200<br>Primary Education Secondary Education<br>Estimated change in total spending due to changes in per student spending<br>Estimated change in total spending due to changes in enrollment<br>Total spending change over period<br>Percentage change<br>Uganda Burkina Faso Benin Tanzania Malawi Rwanda Madagascar Niger Chad Burundi Mali Togo Comoros Nepal Senegal Gambia Togo Burundi Benin Mali Malawi Burkina Faso Nepal Chad Senegal Rwanda<br><!-- End of picture text --> _Source:_ World Bank calculations based on UIS and IMF data.

_Note:_ Only countries for which there are data that span across at least four out of five periods (1998 **–** 2001, 2002 **– – – –** 05, 2006 09, 2010 13, and 2014 17) between 1998 and 2017 are included in the figure. For each country, spending is from the earliest four-year period for which
capital stock we used the consumption of fixed capital price index.

11 Note that subscripts for country and industry are supressed for now as they are estimated for each of them. Moreover, Amadeus does in fact report the firm’s total wage bill but has worse coverage than the number of employees as we use in this paper. In Amadeus there is no variable available that distinguishes employees by skill category.

12 Moreover, upon request we are also able to show that in fact normalization at country‐sector‐level as could be done with the relative TFPQ measure cannot capture the average effect from a regression if explanatory variables are also country‐sector‐year specific under the assumption of log‐normality and constant effect for all firms.

10
Figure 1: Change in Sub-Saharan Africa’s Main Roads between 1971-2012 <!-- Start of picture text --> Africa's main road 1971<br>Africa's main road construction<br>between 1971-2012<br>Africa<br><!-- End of picture text -->

# **3.4 Electricity**

Information on the expansion of the electricity grids is obtained from World Bank/Energy Sector Management Assistance Program (ESMAP) data sources. Arderne et al. (2020) present the first composite map of the global power grid using publicly available open data. The map was generated using data obtained through Gridfinder and OpenStreetMap data. Gridfinder is an open-source tool used for predicting the location of electricity network lines using satellite imagery of nighttime light use. Combined with OpenSreetMap data, multiple filtering algorithms are applied to nighttime light imagery to identify locations most likely to produce light from electricity. These light sources (target-locations) are then connected to known electricity networks through a least-cost routing algorithm following roads and known distribution lines (adopted from OpenStreetMap). This results in connected networks at two voltage levels: high voltage and medium voltage. The data we use are for the year 2010. Time variation in connectivity to electricity within a country therefore stems from the use of multiple rounds of surveys for 11
Several policy implications can be drawn from the findings of the study. First, the results reveal that smaller firms, and those in developing economies are more susceptible to climate shocks. Building resilience among smaller firms and those in developing economies is essential. Second, water and power infrastructure are central to the narrative. As climate change makes precipitation patterns more variable and unpredictable, investments in public water and power infrastructure systems are an important way in which governments can help firms adapt. Second, institutions matter, and governance may play a critical role in how firms fare after extreme weather events. Third, encouraging innovation is one way to build resilience in firms. Finally, extreme weather events may not necessarily lead firms to adapt, and thus other policy interventions would be needed to increase green management practices and green investments.

In summary, the study makes the following contributions to the literature. First, the study provides a global analysis of extreme weather events and firm performance. Second, it tests several channels through which the effects of negative precipitation shocks can be identified in the literature while proposing a new channel related to governance. Third, the study explores the types of firms that are more resilient to shocks. And fourth, the study uses a unique dataset to dispel the notion that firms may become environmentally aware after climactic shocks.

The rest of the paper is structured as follows. Section 2 describes the data and the empirical approach. Section 3 provides the results with robustness checks. Section 4 concludes.

# **2. Empirical Approach**

## **2.1 Data**

### **_2.1.1 Enterprise Surveys_**

The main data source is cross-sectional firm-level surveys across the world from the World Bank’s Enterprise Surveys (ES). The ES are nationally representative surveys of private formal (registered) firms 5
)</u>

- Jalan, Jyotsna and Martin Ravallion. 1999. “China’s Lagging Poor Areas,” _American Economic Review_ 89(2): 301-305.

Jalan, Jyotsna and Martin Ravallion. 2002. "Geographic Poverty Traps? A Micro Model of Consumption Growth in Rural China," _Journal of Applied Econometrics_ 17(4): 329-46.

- Jensen, Robert T. 2003. “Do Private Transfers ‘Displace’ the Benefits of Public Transfers? Evidence from South Africa”, _Journal of Public Economics_ , (2003): 89-112.

- Jiang, Chen and Xiaojun Zhao. 2009. “A Study on the Opportunity Cost of China’s Elderly Care,” _Management World [Guanli Shijie]_ , 10:80-87.

- Kanbur, Ravi and Xiaobo Zhang. 1999. “Which Regional Inequality? The Evolution of Rural-Urban and Inland-Coastal Inequality in China from 1983 to 1995,” Journal of Comparative Economics, 27(4): 686-701.

- Kanbur, Ravi and Xiaobo Zhang. 2005. “Fifty Years of Regional Inequality in China: A Journey through Central Planning, Reform, and Openness,” _Review of Development Economics_ , 9(1): 87-106.

- Knodel, J. and N. Chayovan. 1997. “Family Support and Living Arrangements of Thai Elderly,” _AsiaPacific Population Journal_ 12(4): 51-68.

- Lee, Y.-J. and Z. Xiao. 1998. “Children’s Support for Elderly Parents in Urban and Rural China: Results from a National Survey.” _Journal of Cross-Cultural Gerontology_ 13:39–62.

- Liang, Zai and Zhongdong Ma. 2004. "China’s Floating Population: New Evidence from the 2000 Census," _Population and Development Review_ 30(3): 467-488.

- Li, Shuzhuo, Song Lu and Marcus W. Feldman. 2009. “Intergenerational Support and Subjective Health of Older People in Rural China: A Gender-Based Longitudinal Study,” _Australasian Journal on Ageing_ ,
almost 70 percent of Haiti population is less than 30 years old. The following section focuses on the labor income determinants in urban areas in 2007 and 2012; data limitations prevent the extension of the analysis to 2001.<sup>11</sup>

- A. Labor Income Increased between 2007 and 2012, but with Important Sectoral Disparities.

Both monthly and hourly labor income increased by about 11 percent annually between 2007 and 2012 in urban areas. Monthly income in the Metropolitan area grew at an annualized rate of 11 percent while other urban areas saw an increase of 7 percent. A more uniform increase of 11 to 13 percent occurred across other urban areas in the case of hourly incomes. In those areas, this increase was most notable in the private informal sector, where 50 percent of the workforce is employed. In Metropolitan areas, a wage expansion of close to 60 percent hints at the role played by labor dynamics linked to the reconstruction.

Hourly wages in the public and NGO sectors are higher than those in the private formal sector. The wage distribution in 2012 shows that average wages in the public and NGO sectors are significantly higher than

> 10 The _Haiti Poverty Assessment_ (World Bank, 2014a) found that 52 percent of Haitians lived in a rural setting in 2012, while UN data used by Glaeser (2013) refer to a 53 percent of the population as urban in 2011, with urban areas overtaking rural ones around 2008/9.

> 11 In addition to the Haiti Poverty Assessment (World Bank, 2014a), analysis focused on rural areas can be found in Coello, Oseni, Savrimootoo and Weiss, 2014.

15