rafmacalaba/data-use-mentions-extended huggingface dataset:
results in Subsection 3.1 and 3.2. We conclude in Section 4.

# **2 Data**

We base our analysis on information from three main data sources. First, the Business Environment and Enterprise Performance Surveys (BEEPSs) provide information about firms’ characteristics. Second, the World Development Indicators (WDI) and Global Development Finance (GDF) contain the macroeconomic variables to correct for inflation and exchange rate fluctuations, and capture the macroeconomic conditions faced by firms. Third, the National Accounts Estimates of Main Aggregates and Trade Policy Information System (TPIS) data allow us to estimate demand shocks.

The World Bank jointly with the European Bank for Reconstruction and Development administered the BEEP Surveys for four years (2002, 2005, 2009 and 2013) in 27 Eastern European and Central Asia countries.<sup>3</sup> The firms interviewed were selected to form a representative sample of the

> 1In a study of Spanish manufacturing firms, Campa and Shaver (2002) suggest that firms may decide to export to foreign markets to smooth their cash flow and thus exploit the imperfect correlation between the destination country and the Spanish business cycles.

> 2Pavcnik (2002) concludes that Chilean manufacturing plants in import-competing sectors underwent large productivity improvements during the massive trade liberalization of the late 1970s. Similarly, examining a period of significant changes in Colombian trade policy across industries between 1977 and 1991, Fernandes (2007) finds that tariff liberalization boosts plant productivity and the effect appears to be particularly strong if the firm is larger and operating in a less competitive industry.

> 3Specifically the surveys are fielded in Albania, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croa-

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Bibliography:

- Aguiar, Angel, Maksym Chepeliev, Erwin L. Corong, Robert McDougall, and Dominique van der Mensbrugghe. 2019. “The GTAP Data Base: Version 10.” Journal of Global Economic Analysis 4 (1): 1–27. https://doi.org/10.21642/jgea.040101af.

- Brahmbhatt, Milan, and Arindam Datta. 2008.  On SARS type economic effects during infectious disease outbreaks. World Bank Policy Research Paper No. 4466, World Bank, Washington, D.C. http://documents.worldbank.org/curated/en/101511468028867410/On-SARS-type-economiceffects-during-infectious-disease-outbreaks.

- Bloom, E., V. de Wit, and M. J. Carangal-San Jose. 2005. “Potential Economic Impact of an Avian Flu Pandemic on Asia”, ERD Policy Brief No.42.

- Burns, Andrew; Mensbrugghe, Dominique van der; Timmer, Hans. 2006. Evaluating the economic consequences of avian influenza (English). Washington, DC: World Bank.

- Evans, David, Marcio Cruz, Francisco Ferreira, Hans Lofgren, Maryla Maliszewska, and Mead Over. 2014. “Estimating the Economic Impact of the Ebola Epidemic: Evidence from Computable General Equilibrium Models.” https://doi.org/91219.

- Lee, J.-W. and W. J. McKibbin. 2004.  “Globalization and Disease: The Case of SARS,” Brookings Discussion Papers in International Economics, No. 156, February.

- McKibbin, Warwick, and Roshen Fernando. 2020. “The Global Macroeconomic Impacts of COVID-19.” Brookings Institute, no. March: 1–43. https://www.brookings.edu/wpcontent/uploads/2020/03/20200302_COVID19.pdf.

- McKibbin, Warwick J. and Alexandra A. Sidorenko. 2006. “Global Macroeconomic Consequences of Pandemic Influenza,” Lowy Institute Analyses, Lowy Institute for International Policy, February, Sydney.

- OECD.
We focus on the effect of government support on the following main aspects of firms’ operations: workforce, hours of operations, and expectations. In particular, we use the following four outcome variables available in COV-ES: (i) percentage change of permanent full-time workers since December 2019, and three binary outcome variables, (ii) whether the firm decreased its total number of temporary workers since the onset of the pandemic, (iii) whether the firm decreased its total hours of operation per week compared with the same month one year ago, and (iv) whether the respondent anticipates falling in arrears on outstanding liabilities within the next six months of the date of the COV-ES interview.

To alleviate further concerns about selection bias and endogeneity, we use two methods, matching, and panel estimation. For matching estimation, we use a rich set of information about each firm from the baseline ES to match firms that received government support in COV-ES with the ones that did not. In this process, we take into account details of support measures of each country. For example, when matching firms in the Georgia data, we forced the matching algorithm to perform exact matching within the sectors of hotels and restaurants because the Government of Georgia provided a specific set of support to firms in these sectors. For panel estimation, we use both rounds of COV-ES in fixed effects regression analysis which addresses selection bias stemming from the time-invariant characteristics of firms. In these specifications, we also include a long list of controls from COV-ES to account for time-varying aspects. Panel regressions also enable us to use COV-ES questions that break down the government support that firms received into separate categories, such as cash transfers, fiscal relief, and wage subsidies (with many firms receiving combinations of support types).

We re-estimate models using a restricted sample as an additional check on selection bias. In particular, we try to exclude from the analysis the firms that were not eligible to receive the support and are likely not appropriate comparators to those that did. In forming
# Table (F6) Impacts of the Program on Migration

|**Outcomes**|**Treatment**|**(se)**|**FDR**<br>**q-values**|**Control**<br>**mean**|**(sd)**|**N**|
|---|---|---|---|---|---|---|
|Probability of confict resolving in two years (index)|-0.00|(0.16)|[1.00]|0.02|(1.01)|1,130|
|Probability of returning to Syria w/ confict unresolved (index)|0.10|(0.16)|[1.00]|-0.02|(0.96)|1,300|
|Probability of returning to Syria w/ confict resolved (index)|0.08|(0.18)|[1.00]|0.01|(1.00)|1,294|
|Confict more likely to increase in next three months {0,1}|0.07|(0.09)|[1.00]|0.54|(0.50)|1,073|

**_Notes_** :

- The table shows the regression results on pre-specified main outcomes using the 2022-2023 phone survey data. Each row is its own dependent variable.

- Probability of conflict resolving in 2 years is a standardized likert scale (1-4) from very unlikely to very likely. Probability of returning to Syria with conflict unresolved/resolved are standardized likert scales (1-4) from very unlikely to very likely. Conflict more likely to increase in the next 3 months is an indicator.

- The regressions also have assessment month-by-year fixed effects,
|(0.006)|
|Year 2022#share>=500ha & <2500ha|0.013*|0.014**|0.013*|0.013*|0.013*|0.016**|
||(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|(0.007)|
|Constant|0.133***|0.132***|0.133***|0.133***|0.133***|0.118***|
||(0.031)|(0.031)|(0.031)|(0.031)|(0.031)|(0.031)|
|No. of obs|36,684|36,684|36,684|36,684|36,684|36,684|
|R-squared|0.363|0.363|0.363|0.363|0.363|0.364|

_Note_ **_:_** Results are from 2019-2022 panel regressions where the unit of observation is the village council and the dependent variable is the NDVI for fields planted with winter wheat in each of the relevant years. Crimea and areas that have been occupied since 2014 are excluded. GDD is cumulative growing degree days above 0°C between October 1and May 31 approximated hourly by sinusoidal distribution of minimum and maximum temperature (d’Agostino and Schlenker, 2016); and fall, winter and spring months are Oct-Nov, Dec-Feb, and Mar-May, respectively. ‘Any’ (Col. 5) is the maximum over the 8 two-week periods considered whereas ‘conflict intensity’ (col 6) accounts for the temporal dimension by taking a value of 1 for any of the 8 two-week periods when any conflict activity
discussed below, FDI data compilation efforts have sometimes been linked to specific modeling projects, like FTAP (Hanslow et al., 2000), the Michigan model (Brown and Stern, 2001) and the Worldscan model (Lejour, Rojas-Romagosa and Verweij, 2008). As an alternative, the French research institute CEPII has endeavored to follow the entropy and reverse gravity methods pioneered for merchandise trade flows to map out a picture of the geographic pattern of FDI refined enough for CGE modeling (Boumellassa, Gouel, and Laborde, 2007). However, such estimates reflect potential FDI (based on what we observe, we guess at what we do not know) and so do not necessarily reflect actual stocks and flows. On top of these concerns with FDI stock and flow data, to actually model establishment trade in services (mode 3), ideally we need – foreign affiliate trade statistics (FATS) data on the value of sales of services by foreign affiliates – as well as value added and employment. As things stand, data on stocks of FDI are often used as a proxy for this more comprehensive, but largely unavailable set of FATS data requirements.

5
*<br>**and competition**|•<br>Wholesale spot prices have fallen by 88% in real terms over the past decade,<br>from 77 US$/MWh in 2005 (yearly average, inflation adjusted to 2015) to 9<br>US$/MWh in 2017.|
||•<br>The average price of liberalized contracts has decreased by 25% in real terms<br>over the past decade, from 63 US$/MWh in 2005 (inflation adjusted to 2015) to<br>57 US$/MWh in 2017.|
||•<br>Market concentration has fallen steadily over the past decade in Peru, from 2,783<br>in 2005 to 1,509 in 2017(Herfindahl-Hirschman Index).|

23
and not statistically significant positive effects. There were no differential impacts by deforestation risk ( **Appendix Table A9** ).

Finally, because the distribution of payments is often not directly to _ejido_ or _comunidad_ members but rather through community investments, we tested whether households that directly received payments experienced differential increases in wealth (see **Appendix Table A10** ). Generally, we did not find positive and significant impacts for those households. This is most likely because the decision of whether or not to distribute payments in this way is highly likely to be endogenous to community wealth levels – in other words, poorer communities may be more likely to distribute cash payments directly, which results in biased estimates of the effect of such payments on wealth.

# **5.4 SOCIAL CAPITAL**

**Table 9** presents impacts on social outcomes at the community-level (Panel A) and household-level (panel B).<sup>18</sup> The first row shows that PES payments increased the community-level social capital index for those cohorts with longer exposure to PES (a 15.3% increase compared to the mean value for the controls); as well as for the 2013-14 cohorts (6.7%). The effect on the social capital index is 8.6% when combining both cohorts. Results are consistent across the different specifications. Although in percentage terms these changes are relatively small, they are large relative to the variation in the data. The effect sizes are 0.65, 0.35 and 0.40 standard deviation for the earlier, later cohorts and both combined, respectively.

Panel A shows program impacts on the sub-indices that make up the community-level index. We find suggestive evidence that the intervention increased both the trust and the infrastructure sub-indices (each by 22%) for the 2011-12 cohorts; and the infrastructure sub-index for the 2013-14 cohorts (24.1%). For both cohorts combined, the intervention increased communal infrastructure by 19.8%
null and statistically insignificant.

Second, to address concerns that variables omitted from the specification do not potentially drive zone classification as well as the economic output of a district, we estimate equation (1) with a restricted sample of districts that border each other but have different containment policies (600 districts). Geographical proximity implies that districts have similar economic, health and cultural characteristics, so the comparison in the restricted sample is more precise (Jain 2017).

Our third analysis omits the 17 districts with the largest metropolitan cities from the sample, since these districts generate a disproportionate share of the nighttime light and also experienced high COVID-19 infections. Results from the remaining sample should more reliably indicate the effect of the zone classification on nighttime light intensity in the average Indian district.

We also check the sensitivity of our results to removing 15 districts where the zone classifications changed after two weeks.

Finally, to ensure that the results are directly attributable to zone classification and not to either unobserved common time-varying characteristics or spurious correlations in the data, we randomly rematch the zone classification to a different district. We then estimate equation (1) with these rematched placebo districts (Card and Giuliano 2013; Jain and Langer 2019). If 1O 2are null and statistically insignificant, we have greater confidence that the main results are due to the zone classifications. ββ ddββ

# **4.2   Channels**

We analyze the impact of zone classifications on intermediate variables – short distance mobility, household income and consumption – that illustrate potential channels through which pandemic containment policies could influence nighttime light intensity. First, we use district level short-distance mobility data from Face- book as a way to measure where and how severely the zonal containment policies impacted movement of individuals. We estimate equation (1) with a disaggregated _Postt_ variable using standardized mobility in district _i_ for month _t_ as the outcome variable _yit_ . Since individual movement across the country was most severely restricted in April, we expect an increase in mobility in subsequent months. In May, we expect
the survey data. Second, _market income_ , is computed as _disposable income_ plus direct taxes and contributions minus direct transfers. It includes direct personal income taxes, social security contributions (health and injury), and direct- and near-cash transfers.<sup>14</sup> _Market income_ , which is the primary income concept for the CEQ, is used to rank households into income deciles or quintiles as it represents income before state actions through taxes and transfers. Third, _consumable income_ is computed as _disposable income_ plus indirect transfers/subsidies minus indirect taxes. Finally, _final income_ is _consumable income_ plus imputed values of education and health care services. The monetarized value of education and health care services is conditional on the composition of households, declared use of public services and calculated values of these benefits in specific circumstances.

where h is income before taxes and transfers, are the taxes paid by the households for range of taxes analyzed, are the transfers received by households for range of transfers analyzed. and are ii II TT ii the amounts of tax and transfer paid and received by households, respectively, and H is the number of jj iih jjh BB jj SS SS individuals in the household. ii jj

# **Data sources**

The primary data providing individual and household-level information necessary to allocate the fiscal policy elements is the 2019/20 Cambodia Socio-Economic Survey (CSES).<sup>15</sup> This survey is collected by the National Institute of Statistics every two years. CSES 2019/20 includes modules covering education, health, labor market activity, income sources, and household consumption expenditure. CSES also provides a roster of individuals in each household that provides individual and demographic characteristics. The 2019/20 CSES uses the 2019 General Population Census of Cambodia as the sampling frame and is representative at the national level; by Phnom Penh, other urban, and rural areas; and by four geographic zones. The survey was administered to 10,075 households (44,549 individuals).
____, C. Helmenstein and A. Prskawetz. 1997. “A brain drain with a brain gain.” _Economics Letters_ 55: 227-34.

_____, C. Helmenstein and A. Prskawetz. 1998. “Human capital depletion, human capital formation, and migration: a blessing or a “curse”?” _Economics Letters_ 60: 363-67.

_____, and Y. Wang. 2002. “Inducing Human Capital formation: Migration as a Substitute for Subsidies.” _Journal of Public Economics_ 86 (1): 29-46.

Strauss, J. and D. Thomas. 1998. “Health, Nutrition and Economic Development.” _Journal of Economic Literature_ 36 (2): 766-817.

Vidal, J.-P. 1998. “The effect of emigration on human capital formation.” _Journal of Population Economics_ 11 (4): 589-600.

Warner, A. 2002. “International Wage Determination and Globalization” (March).

Winters, L. A. 2005. “Skilled Labor Mobility in Europe 1945-2000. Mimeo. Development Economics Research Group. World Bank.

World Bank. 2000.

35
One potential explanation for this is that information and quality concerns may not be the binding constraint for many firms in the sample, and instead issues like a lack of funding to pay for these services may be more binding. However, we are unable to reject a lack of heterogeneity in treatment effects when we interact the information treatment with whether or not they said knowledge was an issue at baseline (p=0.853), that it was difficult to assess the quality of providers (p=0.833), and that a lack of funding was the constraint (p=0.920). We also find no evidence of heterogeneity by the predicted likelihood of using these services at endline, using the endogenous stratification approach of Abadie et al. (2018). Our statistical power is low for examining treatment effect heterogeneity, and it may still be the case that there is a subset of firms for which purely providing the business services marketplace would be enough to get them to use this market, but this is not the case for the majority of firms.

Instead, we think two other explanations are likely. The first is that there are other information and knowledge frictions that simply providing access to the marketplace does not correct. Surveys of management practices find that many badly managed firms do not know that they are badly managed (Bloom et al. 2013, Iacovone et al., forthcoming). A consequence is that firms may not know that they need to improve. Then even if they know they need to improve, the information they may be unsure about is whether the business service providers can provide the right expertise and advice for their specific needs. Even knowing that providers have offered quality services to other firms may not be enough to resolve this uncertainty, and as a result, firms may only be able to learn the value of using professional business services through learning-by-doing. Evidence for this is seen in the fact that many of the firms in the experiment of Anderson and McKenzie (2020)
the fifth focuses on implications for Latin America (Jank 2004).

All of those 2004 studies were completed well before the July Framework Agreement was reached in the early hours of 1 August 2004, and before the public release in December 2004 of the new Version 6 database of the Global Trade Analysis Project (GTAP) at Purdue University. That Version 6 database is a major improvement over the previous version for several reasons. One is that it includes global trade and protection data as of 2001 (previously 1997). Another is that the new protection data include, for the first time, bound as well as applied tariffs, non-reciprocal as well as reciprocal tariff preferences, the _ad valorem_ equivalents of specific tariffs (which are 5
student assessment data for 408, 417, and 391 schools for baseline, midline, and endline rounds of data collection, respectively. It is unclear the source of the variation in number of schools; however, it is likely that, at least for the endline, the outbreak of Ebola might have been a contributing factor.

Most data collection activities were carried out using the existing government systems (no external survey firm was used) and took place simultaneously in each region of the country. Student and teacher tests, as well as principal, teacher and student questionnaires were administered by decentralized government staff selected on a competitive basis and trained as enumerators and supervisors. Quality of the data collected through the above mechanisms was validated through spot check visits from field coordination teams.

The data collection effort was set up as a panel of schools, not of teachers or students. Some teachers —those still teaching in the surveyed grade— appeared in multiple rounds of the data collection; other teachers were new and others left the grade of the school. As such, 7
The analysis shows that earnings for all groups in our sample increase up to 2003, reflecting the recovery from the sharp fall observed in the 1995 financial crisis (Figure 2).<sup>9</sup> After that, they stagnate for employees who have completed primary and junior high, and fall for those who have completed high school or university education. In fact, by 2015, earnings for the latter group were 7 percent below the 1996 level, and, for the former, the same as in 1996. The contrast with employees who have completed junior high is revealing: as seen in table 5, the supply of persons of this educational group grows more quickly than those with university education. Yet, earnings of employees with completed junior high in 2015 are 26 percent higher relative to 1996. These asymmetries clearly indicate that there are other forces aside from changes in supply determining the behavior of earnings. The earnings path for each educational group (together with changes in their relative shares) results in a decrease in average employee earnings (labeled total in Figure 2), which, by 2015, are the same as in 2000.

> 9 There are no employment surveys for 1992–94, and the available ones for earlier years cannot be compared with ENE-ENOE (Encuesta Nacional de Empleo-Encuesta Nacional de Ocupación y Empleo). To corroborate our statements, in appendix S2 we use data from household surveys to show (a) a sharp fall in earnings in 1995 following that year’s financial crisis; (b) that by 2012 earnings had yet to reach the levels observed in 1994; and (c) a similar trend in earnings after 1996 in the two surveys (contrast Figure 2 with figure S2.3 in appendix S2). Appendix S2 also describes the procedure used to correct for missing observations on earnings.

19