rafmacalaba/data-use-mentions-extended huggingface dataset:
deflate nominal expenditures into "real" expenditures, we have to be very careful in defining how exactly "real" is calculated.<sup>2</sup> Table 1 illustrates the problem. Median nominal consumption expenditures increased by 1 10 percent from February 1996 to February 1999. How much did median "real" expenditures rise? If one made the mistake of defining "real" expenditures as purchasing power over non-food items only, then median "real" expenditures have actually _risen_ by 16.2 percent. If, in contrast, a price deflator was defined only as purchasing power over rice, then "real" expenditures have fallen by 26 percent. But this "rice only" deflator is just as unrealistic as a "non-food only" as all households actually consume a mix of goods.

If one uses the standard approach and deflates nominal expenditures by the consumer price index (CPI), this implies that "real" expenditures were only 1.3 percent _lower_ in February 1999 than in February 1996. But the share of food in the CPI basket (which is around 40 percent) is much lower than in actual consumption expenditures as recorded in the Susenas, and certainly understates the importance of food for the poor.<sup>3</sup> If one constructs a price index using the CPI price series but with weights for prices based on the actual consumption basket of the poorest 30 percent of households then inflation in that consumption basket was 136 percent and median "real" expenditures fell 10.9 percent. This sensitivity of the measurement of "real"

> **2** See Suryahadi and Sumarto (1999).

> 3Susenas is the "National Socio-Economic Survey" conducted by Statistics Indonesia (BPS). The "Core" of this survey, which contains summary characteristics of households and individuals, is conducted annually. The detailed "Consumption Module" of the survey, which forms the basis for the official poverty statistics, is conducted every three years. For more details
From the panel perspective, growth in Egypt was pro-poor. Whether in urban or rural areas, panel GICs demonstrate a clear and statistically significant negative slope- those who were among the poorest in 2005 have experienced fastest growth. Figure 3 suggests that the welfare of an average person who was poor in 2005 increased by 9.7 percent per year between 2005 and 2008, which was sufficient to move the household out of poverty. As documented in the Poverty Assessment Update (World Bank, 2007), an average poor person has a deficit of about 20 percent of consumption to reach the poverty line. Growth of about 10 percent per year accumulated over three years would fill in this poverty deficit.

A simple comparison of the magnitude of changes between cross-section (Figure 2) and panel data (Figure 3) is also quite informative. While in GIC growth rates by percentile lie within a narrow interval around the means and do not exceed 10 percent per year, the panel data show both larger changes (up to 20 percent per year) and much wider confidence intervals. With 95 percent certainty, however, it is the poorest 20 percent who have experienced positive growth over and above the growth rate of the mean.

Growth incidence curves for panel data are remarkably similar across urban and rural areas, unlike on Figure 2. In both locations we find a similar pattern and magnitude of gains for the poor. The difference for rural areas is particularly dramatic.

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15 between minimum needs and upper limits are larger), but no combination of foods can satisfy the nutrient requirements when the ranges narrow to allow for household sharing (Schneider et al. 2021).

We note that the least cost diets can only select from the menu of 51 items included in the price data set and where there is a price observation. To address potential concerns that missing data in the food prices (a common feature of consumer price index data sets) could bias the results, we present Figure 1 showing the pattern of price records. The darkest green cells indicate a price was observed for that item in all markets. The figure clearly shows that the most common items with missing prices are items where seasonal lack of availability makes sense, namely fruits and vegetables. It shows that maize is available in all markets in all months in at least one form, and that in all months there are some foods available in every food group.

Several items often missing have another clear substitute and no food group has all items missing at the same time. For example, there are two types of cooking oil whose price is monitored. From late-2015 onwards, some markets appear to have only a refilled bottle option, but cooking oil is nonetheless present. Other items may never be present by location, such as the fresh _chambo_ fish (tilapia), but other types of fish (cichlid, sardines, sometimes dried _chambo_ ) that are nutritional substitutes do have price observations in most markets and months.
# **1. Introduction**

The shock associated with the COVID-19 pandemic has been large and persistent. As documented in Apedo-Amah et al. (2020) and Cirera et al. (2021), the private sector has experienced a large, persistent negative impact on sales across all regions of the world. Most firms had still not recovered the levels of sales in 2019 more than a year after the beginning of the pandemic (Cirera et al., 2021) and some sectors are experiencing structural changes in the way products or services are produced and demanded. These changes and the associated reallocation of resources have already been identified in the data from the United States (Barrero et al., 2021).

A critical question for policy is how these changes will affect firm growth and aggregate productivity. Are surviving firms coming out stronger from the pandemic? The answer to this question depends on many different channels that affect productivity, which in many cases are in opposing directions. For example, Harris and Moffat (2021) implemented a survey of UK firms and found that, on the one hand, 18% of firms have stopped and 45% reduced doing R&D but, on the other hand, 40% of firms have increased ICT investments. Andrews et al. (2021) find a positive reallocation effect for three OECD countries, mainly driven by technology readiness to face the shock and the additional investments in technology. This paper explores firms’ digitalization trends during the COVID-19 pandemic using a novel firm-level data set of 57 countries across three periods of time since the start of the pandemic. The paper identifies the digitalization effects on firms’ resilience, differences in adoption, and changes in market share distribution.

There are at least two forces associated with the pandemic, in opposite directions, driving the decision of businesses to invest in and expand the use of digital technologies. On the one hand, the reduction of economic activity and the increased uncertainty may reduce the overall willingness to invest. On the other hand
accounting for the more recent technological progress.

Using Indonesian labor force survey data (described in Section 4.3), we compute the distribution of workers in occupations at high risk of automation by their educational attainments in 2007, the first year such information is available.<sup>5</sup> Using the GM definition, secondary education is the typical educational attainment of workers in occupations at risk of automation in Indonesia (column 1 of Table 1). The results in column (2) of

> 5As in FO, we deem an occupation as highly likely to be automated if the probability of computerization is larger than 0.7.

6
Cambodian provinces that constituted the original experiment population. These children were in grades 3 and 4 in 2008. Data collection for the baseline took place from December 2008 to January 2009, and data collection for the new round of follow-up surveys took place from December 2016 to May 2017. The subsample targeted those children that had not attrited in a previous round of tracking in 2011. The team was able to survey 3,294 respondents, or 86.1% of the target subsample. However, overall attrition rates were higher than in Pakistan and differential across experimental groups (e.g., 24.2% among grade-4 students in the control group and 32.7% among grade-4 students in the treatment group). Another shortcoming of the data collection in Cambodia is that we did not systematically collect the current location of respondents (this information is missing for 18% of the surveyed sample). The survey content was very similar to that of Pakistan. In particular, the team implemented both a cognitive and socioemotional skills assessment and collected data on labor market outcomes and family formation.

The cognitive assessment was a computer-adaptive math test in which respondents answered ten questions from a larger pool of 23 items. These items are aggregated in a cognitive skills index with a two-parameter Item Response Theory model.

To measure socioemotional skills, the team used the same Grit and “Big Five” self-reported scales as in Pakistan. We also measured growth mindset—the belief that one can get smarter through hard work and practice—using a 4-item scale. We also measured respondents’ locus of control—the degree to which people believe that they have power over the outcome of events in their lives, as opposed to external forces beyond their control—using a four-item scale. Finally, we administered the Strengths and Difficulties Questionnaire (SDQ), which is a brief behavioral screening questionnaire. All items are answered using a 5-point answer format that ranges from 1 (“Disagree strongly”) to 5 (“Agree strongly”). Cronbach
are arguably the most _unaffected_ , including manufacturing, construction, and retail.<sup>20</sup> _σi_ represents the individual-specific fixed effects, which capture time-invariant unobserved characteristics (e.g. innate ability). _θm_ represents the month-specific fixed effects, which absorb any unobserved monthly variations affecting the country-wide fishery industry. _εim_ represents idiosyncratic standard errors clustered at the district-level.

For estimations using the VIIRS Boat Detection data, we run DiD regressions of the primary form: where the subscripts refer to a 10-mile-square grid cell _c_ located within the marine zone of province _p_ , and stores the monthly-aggregate boat-detection value in month _m_ in year _y_ . Thus, the outcome variable _ycpmy_ provides a measure for fishing intensity at each 10-milesquare fishing grounds, spanning the entire Vietnam EEZ.<sup>21</sup> We focus on two measures of fishing intensity: (1) boat detection likelihood (extensive margin); and (2) aggregate boat counts (intensive margin). The standard DiD indicator terms are and _γc_ represents the grid-specific fixed effects, which capture the time-invariant unobserved characteristics within each 10-mile-square fishing ground. _λmy_ represents the month-by-year fixed effects, which absorb the month-specific and year-specific single fixed effect terms, and essentially control for any monthly unobserved variations affecting country-wide fishing activities. _πpm_ represents the province-by-month fixed effects, which captures the existence

> 20 Our estimates remain highly robust when we adopt workers in each of the 3 industries as the “control group”.

> 21 Note that in this paper we do not consider the effect of boat detection outside Vietnam’s maritime EEZ. Even though illegal fishing outside of Vietnamese boundary is a possibility, we consider such action rare and of second-order concern.

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assume that firms are profit-maximizing. Another strand of literature diverges from this common framework and analyzes, similar to our work, welfare-maximizing public firms, so-called state-owned enterprises (SOE). This focus is mainly motivated by the question of whether to privatize SOEs (or not). The majority of the extant empirical literature on the performance of SOEs finds that they are less efficient than private firms, at least with respect to short-term performance indicators like net income or value added, return on equity, sales or assets, or cost savings.<sup>3</sup> Belloc (2014) summarizes typical rationales for SOE inefficiencies into four channels: (1) Poor monitoring of managers and excessive bureaucracy; (2)

> 2For a comprehensive discussion on optimal tax rules under imperfect competition, see Requate (2006).

> 3See Vining and Boardman (1992) for a survey of 90 comparative studies; see Megginson and Netter (2001) for a literature review on performance changes of privatized companies.

5
2 association, we find significant evidence of media effects on parental investment in their children‘s education, and no evidence of greater government responsiveness.

The analysis rests on data from a March 2009 survey in Benin of more than 4,000 households and 210 villages, spread across 32 of the 77 communes in Benin, all located in the northern part of the country. In addition, a literacy test was given to 2,100 children in second grade (on average, eight to nine years old) in village schools. The data collection effort yielded unique information on learning among children, school inputs and PTA activities in village public schools, and on household education investments.

Appropriately for the questions we examine here, these villages exhibit substantial variation in access to a type of media that emphasizes public interest programming, community radio. Differences in village access to community radio are important because villages are the relevant jurisdictions within which households are likely to engage in the local action required to improve village public schools (for example, through Parent Teacher Associations). Twenty-one community (private, non-commercial) radio stations serve the communes in the sample. They receive support from donors and government precisely to broadcast public interest or educational programming and to promote local collective action in poor rural areas.

Variation in radio access is exogenous, driven by the nature of media markets in northern Benin. Community broadcasters have limited signal strength, so small geographical differences between villages are sufficient to yield large differences in access. Moreover, while these stations were established to cater primarily to the commune in which they are based, their signals spill over to villages in adjoining communes, leading to substantial variation in access to neighboring commune-based radio across villages within the same commune. Finally, villages within each commune were selected in order to be equidistant from radio broadcast towers so that exogenous
# **4. Empirical Findings**

## **_Sample Data_**

In Table 3 we report descriptive statistics for the overall sample (more detail is contained in Pattanayak 2005a). Table 3 shows that 89 percent of the sample is Sinhalese and 62 percent is Buddhist. The average family size is 4.8 members, with 1.3 children under 18 years of age. The typical household head is 52 years old and has 10 years of education. Over 90 percent of households have adults who have completed primary education and almost all households send their school-aged girls to school.

To compare poor and non-poor households, we first need to define these distinctions. The Sri Lanka Department of Census and Statistics identifies poor households using data on household expenditures from the Household Income and Expenditure Survey (HIES, Sri Lanka Department of Census and Statistics 2002). In order to be able to compare our results to the Census data, we also identified poor households through monthly consumption expenditures.<sup>7</sup> Using the data, we classified our sample based on monthly per capita consumption deciles. Poor households comprise the bottom two deciles, or quintile, of the distribution (or households who spend less than or equal to Rs. 3,356 per capita per month are poor). By this definition, 365 of our sample households are classified as poor, of which 124 live in the Greater Negombo and 241 live in the Kalutara-Galle strip. This definition of the poor not only allows us to explore poverty relative to the overall socio-economic distribution, but also enables us to directly compare our results with other regions and countries if necessary. Table 3 also contains descriptive statistics for the poor and non-poor sub-samples.

Major sources of water for our sample households were private connections to the piped water network, private wells, and water from neighbors. Households indicated that their dwellings are close to the piped water network. A typical household can access at least three sources, with approximately 54 percent of households claiming
**1%.

# **_4.2 Policy impact_**

A second set of potential questions revolves around the quantification of the effects of different trade policy strategies. Natural outcome variables varying at the country, sector and time level include trade flows, extent of/reliance on GVC participation and positioning, and (changes in) offshoring and reshoring. Observed trade policy strategies may also be associated with firm-specific performance, the timing and scope of fiscal policies or features of applied lock-down measures.

We take a first look at some of these effects by assessing the potential impact that decreases in the global supply of food and medical products could have on international prices because of an increase in export restrictions imposed on these products.<sup>13</sup> To calculate the impact of export restrictions imposed on product _k_ on its international price, we use a standard partial equilibrium approach and divide the changes in global supply of such product by its import elasticity: l l =<sup>∆GGl</sup> � (2) kk<sup>lll rrsssllsskk</sup> CChl IIl rrll kk Changes in global supply are captured by the share of world exports that are covered by exports bans EEl rrl rrl ss that have been imposed by exporters between January and October 2020.<sup>14</sup> Product elasticities are estimated for a panel covering 152 importing countries by Fontagné, Guimbar and Orefice (2019) and

> 13 For other applications see Espitia, Rocha and Ruta (2020a,b).

> 14 The trade flows used to compute export shares are based on UN Comtrade statistics for 2019 or most recent year available. These calculations do not account for the total duration of the export bans imposed by countries.

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**Table 1. Commonalities of Commodity Prices**

|**Panel A: Uni**<br><br>|**formSpacings **<br>|**Analysis**<br>||
|---|---|---|---|
|<sup>ˆ</sup><br>_Num_<br>_correl_|_ber of small_<br>_ationpairings_|_Small svr_|_Large svr_|
|0.308|85|0.145|4.504*|
|**Panel B**<br>|**:PANIC Analy**<br>|**sis**<br>||
|Factor|Idiosyncratic|Info|rmation Criteria|
|||IC1|IC2<br>IC3|
|-0.003, 0.835, -1.660, -1.693, -2.698<br>[0.335,0.162,0.118,0.107,0.059]|1.310|5|5<br>1|

_Notes_ : Panel A presents evidence on the degree of cross sectional correlation. <sup>ˆ</sup> is the proportion of correlations that are small. Ng (2006) Spacings Variance Ratio test statistic ( _svr_ ) provides evidence of whether correlation is significantly different from zero, distributed as standard normal, therefore the critical value is 1.65 (significant at 5% with asterisk, *). First order serial correlation is removed following Ng (2006), suing an AR(1) approach. There are _n_ = _N_ ·( _N_ -1)/2 = 276 correlations, for _N_ = 24. Panel B examines the stationarity properties of our panel time series by examining the unit root of factor nonstationarity and idiosyncratic nonstationarity. We use annual data on commodity prices from 1900 to 2008 ( _N_ =
resilience levels. The position within this range depends on energy efficiency, and the timing of the transition towards carbon‐free power generation (which creates stranded assets, such as coal power plants that need to be decommissioned before the end of their lifetime). An additional $106 billion and $282 billion a year would be needed for maintenance. How does building in more resilience to power systems increase these costs?

- _Scenario 1._ If only the assets exposed to hazards are made more resilient to those hazards, the incremental cost would be between $9 billion and $27 billion a year (depending on the initial spending scenario), which represents a 3 percent cost increase on average across the spending range, and a 6 percent cost increase in the most expensive case. These investments would reduce the risk of damage by a factor of two to three for new infrastructure assets.

- _Scenario 2._ If instead all new power assets were made more resilient to wind, floods, and earthquakes, because the spatial distribution of natural hazards is unknown, then an additional $96 billion to $296 billion a year would be needed. This is a 30 percent increase in capital cost on average over the spending range, and a 10‐fold increase compared with that in the scenario for which hazard data are available.

# Transport

In the transport sector, baseline investment needs—with current resilience standards—could range from $157 billion to $1.1 trillion a year between 2015 and 2030 in low‐ and middle‐income countries. The exact value would depend on the choice of mode (such as personal cars versus public transit in cities) and on the policies put in place to encourage those needing transport to switch to rail and public 5
# _Demand model sensitivity analyses_

We conducted two additional sensitivity analyses of the projections of physician demand resulting from the optimal demand model chosen to alternative input parameters.

First, we assess the stability of the predictions to alternative estimated future values of GDP per capita. We use alternative estimated real GDP per capita (US$2010) from the Economic Research Service (ERS) International Macroeconomic Data Set published by the United States ‐ Department of Agriculture (USDA), available at <u>http://www.ers.usda.gov/data products/international‐macroeconomic‐data‐set.aspx (Accessed on September 27, 2015). There</u> was a relatively small (9 percent) difference in the total estimated shortages in 2030 based on the two methods (15.6 million with the main method we used, and 17.0 million using USDA numbers) Second, we examine the possible upper and lower bounds of the predictions older than 65 resulting from high and low future population estimates. Because population is the largest driver of demand in our model, using the high and low variant estimates, we can obtain predicted total health worker deficits that may result from population growth among people older than 65 that is higher and lower than expected, compared to the median estimate that is presented in the main results. These alternative low and high estimates are shown in Figure A3 below, indicating a rather tight band for the resulting predicted values.

# **Figure A3. Projected total health worker deficit using low and high projections for the size of the population older than 65**

<!-- Start of picture text --> Deficit by population scenarios<br>2013 2030<br>year<br>Low scenario Median scenario<br>High scenario<br>22000000<br>Healthworkers<br>11000000<br><!-- End of picture text --> Note: where the predicted demand was less than the predicted supply, the estimated