rafmacalaba/datause-encoder-data huggingface dataset:
# Policies to Support Businesses through the COVID-19 Shock: A Firm Level Perspective<sup>_∗_</sup>

Xavier Cirera (World Bank) Marcio Cruz (World Bank) Elwyn Davies (World Bank) Arti Grover (World Bank) Leonardo Iacovone (World Bank and Hertie School)<sup>_†_</sup> Jose Ernesto Lopez Cordova (World Bank) Denis Medvedev (World Bank) Franklin Okechukwu Maduko (Central European University and World Bank) Gaurav Nayyar (World Bank) Santiago Reyes Ortega (World Bank) Jesica Torres (World Bank) Originally published in the Policy Research Working Paper Series on _January, 2021._ This version is updated on _February, 2021._ To obtain the originally published version, please email prwp@worldbank.org.

JEL classification: D22, L20, L25, O10 Keywords: COVID-19, Firms, Policies, Uncertainty.

> _∗_ The authors thank Caroline Freund for her guidance and support of the Business Pulse Survey initiative. We gratefully acknowledge feedback and advice on the survey questionnaires from Nicholas Bloom, Francisco Campos, Ana Paula Cusolito, Mark Dutz, Caroline Freund, Jorge Rodriguez Meza, David McKenzie, Silvia Muzi, and Bilal Zia. Similarly, we are grateful to all the colleagues and counterparts institutions that have supported the data collection (a complete list of all colleagues, collaborators and institutions that have been crucial in providing support during data collection is provided in the online appendix). We would like to especially thank Edgar Avalos for his outstanding research support, and Tanay Balantrapu, Joao Belivaqua Basto, and Kyungmin Lee for their help through the data collection. 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. Financial support from USAID, the World Bank, and the IFC is gratefully acknowledged. The views expressed in this article are solely those of the authors and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

> _†_ Corresponding author, email:liacovone@worldbank.org
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33
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|||Model 6|Model 7|Model 8|Model 9|
|---|---|---|---|---|---|
|Inte|rcept|**3**_._**20**|**3**_._**64**|**3**_._**64**|**3**_._**51**|
|||[2_._77; 3_._63]|[3_._30; 3_._97]|[3_._35; 3_._93]|[3_._15; 3_._87]|
|EA:|Mean War Experience|_−_0_._03|_−_0_._03|_−_0_._02|_−_0_._03|
|||[_−_0_._21; 0_._14]|[_−_0_._21; 0_._14]|[_−_0_._20; 0_._15]|[_−_0_._21; 0_._14]|
|EA:|Distance to Chiefdom HQ|0_._00|0_._00|0_._00|0_._00|
|||[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|
|EA:|Distance to District HQ|**0**_._**00**|**0**_._**00**|**0**_._**00**|**0**_._**00**|
|||[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|[_−_0_._01; 0_._00]|
|EA:|Distance to Freetown|0_._00<br>[0_._00; 0_._00]|0_._00<br>[0_._00; 0_._00]|0_._00<br>[0_._00; 0_._00]|0_._00<br>[0_._00; 0_._00]|
|EA:|2008 % of Gov. Health Care Use|0_._05|0_._05|0_._04|0_._05|
|||[_−_0_._06; 0_._16]|[_−_0_._06; 0_._16]|[_−_0_._06; 0_._15]|[_−_0_._06; 0_._16]|
|EA:|2008 % of Gov. School Use|_−_0_._10|_−_0_._10|_−_0_._11|_−_0_._10|
|||[_−_0_._26; 0_._06]|[_−_0_._26; 0_._06]|[_−_0_._27; 0_._05]|[_−_0_._26; 0_._06]|
|EA:|2008 ELF score|_−_0_._18|_−_0_._18|_−_**0**_._**20**|_−_0_._17|
|||[_−_0_._37; 0_._01]|[_−_0_._38; 0_._01]|[_−_0_._40; _−_0_._01]|[_−_0_._36; 0_._02]|
|EA:|2008 Mean Poverty Score|_−_0_._16|_−_0_._16|_−_0_._16|_−_0_._15|
|EA:|2008 Dist. to Passable Road|[_−_0_._34; 0_._02]<br>0_._00|[_−_0_._34; 0_._02]<br>0_._00|[_−_0_._33; 0_._02]<br>0_._00|[_−_0_._33; 0_._02]<br>0_._00|
|||[0_._00; 0_._01]|[0_._00; 0_._01]|[0_._00; 0_._01]|[0_._00; 0_._01]|
|EA:|2008 Mean Agricultural Work|_−_0_._04|_−_0_._09|_−_0_._06|_−_0_._09|
|||[_−_0_._24; 0_._16]|[_−_0_._28; 0_._11]|[_−_0_._25; 0_._12]|[_−_0_._30; 0_._11]|
|EA:|2007 Malaria Treatment|0_._04|0_._03|0_._02|0_._04|
|||[_−_0_._14; 0_._22]|[_−_0_._15; 0_._22]|[_−_0_._16; 0_._20]|[_−_0_._15; 0_._22]|
|EA:|2007 PC Visit|0_._01|0_._01|0_._01|0_._01|
|||[_−_0_._07; 0_._09]|[_−_0_._07; 0_._09]|[_−_0_._07; 0_._09]|[_−_0_._07; 0_._09]|
|EA:|2007 LC Visit|0_._03|0_._03|0_._03|0_._03|
|||[_−_0_._05; 0_._11]|[_−_0_._05; 0_._11]|[_−_0_._05; 0_._11]|[_−_0_._05; 0_._11]|
|EA:|2007 Well in Community|0_._01|0_._01|0_._01|0_._01|
|||[_−_0_._07; 0_._08]|[_−_0_._07; 0_._09]|[_−_0_._07; 0_._09]|[_−_0_._07; 0_._09]|
|LC:|% Female|1_._05<br>[_−_0_._55; 2_._65]||||
|LC:|% CDF Participation||_−_**2**_._**19**|||
||||[_−_3_._85; _−_0_._53]|||
|LC:|% SLPP|||_−_**0**_._**02**||
|||||[_−_0_._04; _−_0_._01]||
|LC:|Competition||||_−_0_._11|
||||||[_−_0_._31; 0_._10]|
|BIC||861.29|857.05|860.75|866.00|
|Num|. obs.|607|607|607|607|
|Num|. groups: Local Council|19|19|19|19|
|Vari|ance: Local Council (Intercept)|0.12|0.09|0.06|0.12|

Coefficients with _p <_ 0 _._ 05 in **bold** Table 6: DV: 2011 EA-level perceptions of recent improvements in government health services. Multi-level linear models with random intercepts at the council level level.
compounds ( _VOC_ ) to _NOx_ (Sillman, 1999). The relationship between _O_ 3 , _VOC_ , and _NOx_ is nonlinear, and given that there is evidence that _NO_ 2 (and perhaps, _VOC_ , as well), was impacted by the DM, ex-ante it is not possible to know whether or not the DM would affect _O_ 3 , and if it does, in what direction. Hence, we do not use _O_ 3 . Table S1.4 in supplementary appendix S1 presents the results when we estimate equation (1) using _SO_ 2 at _ITO_ as the dependent variable. We examine impacts for the first extension of the Yellow Line and for the introduction of the Blue Line; the two discontinuities for which we found significant declines in transportation source pollutants in table 4.<sup>37</sup> We had expected to see no impact but table S1.4 shows large positive effects for both extensions. This may be explained by the presence of three coal based power plants within the National Capital Region of Delhi (marked as triangles in figure 2) during our study period. According to Gurjar et al., 2004, and Guttikunda and Calorie, 2012, 68 and 55 percent, respectively, of _SO_ 2 in Delhi is generated by these power plants. Figure S1.3 in the supplementary appendix S1 shows wide variation in the monthly power production of these plants. More importantly, it shows that monthly output was either high or rising when the two extensions were made. Since _SO_ 2 emissions are strongly correlated with power produced, there will be corresponding variations in _SO_ 2 . Thus, it is possible that it is the coal plants that are behind the significant impacts seen in table S1.4. In any case, that the two metro expansions were accompanied by an increase in _SO_ 2 increases the credibility that these expansions led to reductions in some transportations source pollutants (as seen in table 4).

**Missing Data Patterns:** Since our analysis so far has been based on examining particular segments of good quality pollutant data, it is important that

> 37. In this exercise using _SO_ 2 , for both discontinuities, we use a nine week window and once again adopt the criteria of at most 20 percent missing observations for each included week.

21
The table below illustrates the breakdown of manufacturing components of industrial production: Table **Al. Break-down of Manufacturing Components of Industrial Production Index**

||Industry**Group**|**Weight**|
|---|---|---|
|**I**|Food manufacturing|6.34|
|2|Beverage industries|0.63|
|3|Tobacco industries|0.86|
|4|T1extiles|2.18|
|5|Wearing apparel except footwear|1.75|
|6|Footvear except vulcanized or moulded rubber or plastic footwear|0.08|
|7|Wood, wood & cork products except furniture|5.42|
|8|Paper & paper products|1.19|
|9|Industrial chemicals|6.63|
|10|Other chemical products|1.67|
|1 1|Crude oil refineries|0.96|
|12|Misc. products of petroleum & coal|0.29|
|13|Rubber products|3.72|
|1 4|Plastic products, n.e.c.|2.59|
|_1_5|Glass & glass products|0.46|
|1 6|Non-metallic mineral products|2.97|
|17|Iron & steel basic industries|2.23|
|1 8|Non-ferrous basic industries|0.58|
|19|Fabricated metal products except machinery & equipment|3.19|
|20|Machinery except electrical|2.94|
|21|Electrical machinery, apparatus, appliances & supplies|20.23|
|22|Transport equipment|2.57|
|23|Professional & scientific & measuring & controlling equipment, n.e.c.|0.95|
|||70.43|

_Source: Department ofStatistics_ 32
# **C DESCRIPTIVE STATISTICS**

Table C.1. Descriptive Statistics – Electoral Outcomes

||Year<br>(1)|Observations<br>(2)|Average<br>(3)|St. Deviation<br>(4)|
|---|---|---|---|---|
|_Panel A. PEP Take-up_|||||
|PEP holders*|2018|1,098|0.003|0.043|
|Municipalitywith PEP Holders|2018|1,098|0.740|0.439|
|_Panel B. Mayoral Elections (2000, 2003, 2007, 20_|_11, 2015 and_|_2019)_|||
|Registered voters|2000-2019|6,212|27,701.96|166,629.00|
|Total votes|2000-2019|6,212|15,677.75|82,668.48|
|Votes of left|2000-2019|6,212|1,724.70|26,780.75|
|Votes of center|2000-2019|6,212|8,813.67|39,424.01|
|Votes of right|2000-2019|6,212|2,212.02|10,870.86|
|Election turnout (Votes / Registered Voters)|2000-2019|6,212|0.65|0.11|
|Left (% of Votes)|2000-2019|6,212|0.06|0.15|
|Center (% of Votes)|2000-2019|6,212|0.66|0.26|
|Right (% of Votes)|2000-2019|6,212|0.16|0.21|
|Electoral competition|2000-2019|5,992|0.63|0.19|
|_Panel C. Presidential Elections (2002, 2006,2010,_|_2014, 2018,_|_and 2022)_|||
|Registered voters|2002-2022|6,564|23,532.14|80,693.46|
|Total votes|2002-2022|6,564|11,145.94|41,003.64|
|Votes of left|2002-2022|6,564|2,375.6|11,788.09|
|Votes of center|2002-2022|6,564|3,059.43|13,397.85|
|Votes of right|2002-2022|6,564|5,327.39|20,775.21|
|Election turnout (Votes/Registered voters)|2002-2022|6,564|0.46|0.49|
|Left (% of Votes)|2002-2022|6,564|0.17|0.18|
|Center (% of Votes)|2002-2022|6,564|0.29|0.22|
|Right (% of Votes)|2002-2022|6,564|0.50|0.22|
|Electoral competition|2002-2022|6,557|0.54|0.19|

_Notes:_ PEP holders is the share of migrants who reported having PEP divided by the total population in 2018 in each municipality. Electoral competition among candidates and political ideologies at municipal level is estimated following Chac ́on et al. (2006): 1 - (% winning candidate - % second-place candidate).

Furthermore, Figures C.1 and C.2 illustrate the geographic distribution of outcomes in the mayoral and presidential elections. The figure illustrates several trends of political behavior in Colombia. First, there is a large geographical variation among political outcomes across municipalities. Second, the majority of political participation in Colombia is concentrated in the center and northern areas of the country. Finally, there is more 63
provides payment when the average yield in an area, as determined by a random sample, falls below a threshold. Moreover, CADENA also offers traditional and remote sensing index insurance for livestock (Arias et al. 2014). This analysis will focus on the drought index insurance because it has historically been the largest component of the CADENA program, and going forward we will simply refer to it as index insurance. Through this index insurance component, CADENA currently insures farmers growing staple crops on less than 20 hectares of rainfed land (SAGARPA, 2014). The insurance provides coverage during three pre-determined phases that run from sowing to harvesting. If precipitation as measured by the corresponding weather station falls below the threshold in any of the three phases, the insurer makes indemnity payments to the state, which in turn transfers these to eligible farmers in the insured area. Because of restrictions regarding the maximum distance between the weather station and the insured area, a municipality may be insured by multiple policies each linked to a different station.

The data for this evaluation come primarily from four sources. Policy data from SAGARPA include information on the insured crop, rainfall triggers and corresponding stations, area insured, and record of all payouts for each insured municipality for the period 2005 to 2013. Weather data from the National Water Commission (CONAGUA) allow us to calculate the precipitation at each of the weather stations linked to an insurance policy, which in turn is compared to the policy thresholds and used to determine if that policy should have paid out. To determine the effect of insurance payments on yields and area sowed, we use agricultural production data from SAGARPA detailing the annual hectares sowed, hectares harvested, and total production in metric tons at the municipality-crop level. Lastly, to study the economic impacts of the insurance, we use national household income and expenditure surveys (ENIGH), which are carried out every other year with the latest one occurring in 2014. These household expenditure and income surveys are repeat crosssections of households with a rotating sample of municipalities.<sup>4</sup> Households are surveyed between the months of August and November and the income, expenditure, and remittance variables refer to the previous three months. Lastly, the analysis sample when using ENIGH data is limited to households living in rural localities. The Mexican Statistical Institute (INEGI) defines rural localities as those with less than 2,500 inhabitants.

> 4The rotating sample means that in any given year some insured municipalities will not be included in the analysis sample because they do not have income or expenditure data, which substantially decreases the sample of municipalities for analyzing economic outcomes.

3
governments in Brazil have no fear of federal intervention, in contrast to those in Argentina, at least until the 1994 constitution. Macroeconomic conditions in Argentina since the stabilization have been more favorable for fiscal balance-growing income and tax revenue, lower initial debt stocks, and lower real interest rates. The following section shows the results of these differences.

Table 3. Summary of Explanatory Variables for Argentina and Brazil in the 1990s

|_Variable_|_Argentina, 1991-98_|_Brazil, 1994-98_|
|---|---|---|
|_Fiscal authority_|||
|1. Subnational governments raise much of their<br>own revenue|No|Yes for large states;<br>No for small states|
|2. Transfers are specified by legal formula, not ad<br>hoc|Yes; set by fonnula and limited in<br>aggregate, but special transfers<br>were important for some small<br>provinces|No; grants from the budget<br>and tax sharing set by<br>formula, but large debt<br>relief sometimes|
|3. Central govemrnent can effectively delegate<br>functions to subnational governments to go along<br>with the delegation of revenue sources|Yes|No|
|4. Subnational governments have authority to cut<br>their costs|Yes; although central government<br>intervened recently on teacher<br>wages|No; legally difficult to cut<br>labor costs|
|_Borrowing constraints_|||
|_5._Central government strictly controls subnational<br>borrowing ex ante|No; federal government has some<br>authority but mainly requires<br>reporting|Yes, in principle; very<br>sophisticated rules but also<br>significant loopholes|
|6. Central government credibly commits not to<br>have bailouts, prohibiting explicit bailouts and<br>forcing subnational governments to service their<br>debt|Yes|No; cap on debt service<br>allows interest<br>capitalization|
|7. Regulators force creditors to accept the losses<br>implied by any failure to service debt|Yes, but rare because debt service<br>is deducted from transfers|No|
|B. The central bank (and bank regulators) is more<br>autonomous and has a strong anti-inflation<br>mandate|Strong autonomy and commitment<br>to fixed exchange rate|Limited autonomy;<br>discretionary monetary<br>policy|
|_Intergovernmental political relations_|||
|9. Presidents are constitutionally strong at the<br>national level|Yes|Yes|
|10. Govemors have little constitutional autonomy|Yes; federal intervention common|No; no federal intervention|
|11. Electoral rules orient congress toward national,<br>not local, interests|Yes|No|
|12. Party discipline is strong|Yes; closed lists|No; open lists|

# II. Tests of the System and Outcomes in the 1980s and 1990s

Federalism and other systems of intergovernmental relations unavoidably contain many tensions and contradictions, and the actual operation of the system becomes clearer when crises bring conflicts into the open, forcing politicians and other decision makers to reveal their priorities and to choose which compromises will set the precedents and establish reputations.

13
had no effect on opening a bank account or use of existing bank services, although the evaluation was somewhat flawed.<sup>6</sup> One potentially effective and natural way of improving financial literacy in developing countries might be through the existing educational system. A study now underway in Brazil, Bruhn and Zia (forthcoming), evaluates a three-semester long financial education program for high school students in an experiment involving almost 900 schools across several states. So far, results from the first semester show significant effect on knowledge, financial autonomy, intention to save, savings and spending behavior. Students are also more likely to talk to parents about financial matters and help with organizing household budget. The study also examines potential knowledge spillovers for parents, but has found no statistically significant impact thus far, suggesting the need for a parallel intervention for parents.

# **_Experimenting with financial education in low-income countries_**

Many of the new programs in developing countries are using innovative and interactive delivery formats. For instance, Carpena, Cole, Shapiro, and Zia (forthcoming) are conducting an RCT on the impact of financial literacy training delivered through well-produced videos on the use of financial services in Gujarat, India. Similarly, Karlan and Valdivia (forthcoming) are studying households in Peru to test the effectiveness of financial literacy education delivered through video and radio. Berg and Zia (forthcoming) are testing the impact of financial literacy messages delivered through mass media and soap operas in the Gauteng region in South Africa.

A number of other development agencies, nonprofit organizations, and private sector entities have also sponsored innovative financial education programs in African countries, but it is unclear whether they are being subject to evaluation. For instance, USAID (2011) briefly describes the recent launch of financial literacy program target towards youth in Somalia. The program takes the form of a soap opera broadcast through mobile phones.

> 6 A control group was formed from the entire Postbank client base in the same regions, though there appear to be systematic differences between the two groups, as the trainees were more likely to be female. In addition, about 40 percent of observations from the treatment group had to be discarded from the analysis, due to insufficient identification data and other reasons. The program was evaluated using pre-test and post-test surveys of both the control and treatment groups, as well as in-depth interviews.

29
<!-- Start of picture text --> 0 5 10 15 20<br>Per capita KgOE/month<br><!-- End of picture text --> <!-- Start of picture text --> 20<br><!-- End of picture text --> <!-- Start of picture text --> 25<br><!-- End of picture text --> <!-- Start of picture text --> 4<br><!-- End of picture text --> <!-- Start of picture text --> LPG<br><!-- End of picture text --> <!-- Start of picture text --> Biomass<br>LPG<br><!-- End of picture text --> Source: IHDS, 2005. **Figure 3a: Total rural energy consumption showing source share, by income decile** Source: IHDS, 2005. **Figure 3b: End-use rural energy consumption showing source share, by income decile** The pattern of total and end-use energy consumption differs dramatically in urban areas (Figure 2b). While end-use energy trends upward as income levels rise, total household energy use declines until income decile 7, after which the trend reverses upward. The explanation for this apparent anomaly is that many of India’s poorer urban households consume more energy than their wealthier counterparts (income deciles 8–10); however, this energy is consumed in a less efficient way. Biomass consumption steadily declines as urban households move from lower to higher income levels, and this decreasing trend in biomass use lowers total household energy consumption for income deciles 1–7; however, as incomes rise, households replace biomass intake with LPG, which is about 4 times more efficient. As a result, they consume more end-use energy than their poorer counterparts, whose energy basket is dominated by biomass. Thus, when it comes to enduse household energy consumption in urban India, LPG makes the most difference, although for the highest income group, electricity consumption also peaks (Figures 4a and 4b).

18