rafmacalaba/datause-encoder-data huggingface dataset:
entrepreneurial activities by increasing both the ongoing costs of doing business and the uncertainty in the future (Djankov et al., 2004).<sup>8</sup> Based on firm-level studies, the effective corporate tax rate has a large adverse impact on entrepreneurial activities (Djankov et al. 2010), the perception of high taxes has a negative effect on the growth expectations of small firms in Lithuania (Aidis and Mickiewicz 2006)), and corruption has a significantly negative impact on firm productivity for Chinese firms (Cai et al. 2011).

Third, since the political connections of firms are a natural consequence of strong government power, to capture government power (and likely government expropriation), we also measure political connections and consider their determinants and likely effects. To overcome the obstacles introduced by institutional imperfections, entrepreneurs in developing countries actively cultivate political connections with the government and local officials, which helps them secure favorable regulatory conditions (Faccio 2005) and gain access to resources such as bank loans (Khwaja and Mian 2005; Li et al., 2008; Cull et al. 2014). However, if using political connections to do business is routinized by local entrepreneurs, it will create additional barriers for local institutions to evolve from relation-based governance to rule-based governance, as those connected entrepreneurs benefitting from the status quo would become vested interest groups of the current governance system and have the incentive to maintain or even further strengthen their positions (Acemoglu, Johnson, Robinson, 2005). Furthermore, with sufficient support from connected entrepreneurs, and with abundant rents to share under this system, the central government would have few incentives to reform the _de jure_ property rights protection system.

As a result, we expect the progress in property rights institutions and in restraining government expropriation to be slow, and that the prevalence of political connections may worsen the adverse effects of government expropriation and regulation discussed above. Thus, we have the following hypotheses:

**Hypothesis 1.** The overall BE for Chinese firms has improved over time. Firms gradually

> 8 It should be mentioned that taxation, when being perfectly enforced (without arbitrary government discretion) could be viewed not as government expropriation. Now many scholars argue the key, and potentially positive, roles that can be played by government taxation in boosting state capacity and resulting in long-term development. For this literature, see Besley and Persson (2009, 2010).

11
# **Figure II-2: Spectral Density Function of Filtered Series**

<!-- Start of picture text --> Saudi Arabia: Spactral Density Function of Filtered Series Egypt: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50 0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency Frequency<br>Evaluated at the natural frequencies Evaluated at the natural frequencies<br>USA: Spactral Density Function of Filtered Series<br>0.00 0.10 0.20 0.30 0.40 0.50<br>Frequency<br>Evaluated at the natural frequencies<br>6 6<br>6.00 6.00<br>4 4<br>4.00 4.00<br>2 2<br>2.00 2.00<br>0 0<br>0.00 0.00<br>log Periodogram-2 log Periodogram -2<br>-2.00 -2.00<br>-4 -4<br>-4.00 -4.00<br>-6 -6<br>-6.00 -6.00<br>6<br>6.00<br>4<br>4.00<br>2<br>2.00<br>0<br>0.00<br>log Periodogram -2<br>-2.00<br>-4<br>-4.00<br>-6<br>-6.00<br><!-- End of picture text --> In the literature on credit cycles, the primary variable under observation is the real private credit growth. To compute credit growth, we start with a series of stocks of credit. The WDI data set of the World Bank contains series for the domestic credit to private sectors as a percentage of GDP. We calculate the real private credit growth using different ratios. In Table II-1 we report the three separate measures, that we calculated using this data to obtain the actual credit growth. Given the availability of data for different ratios, we find the private domestic credit (%GDP) * Real GDP to be the most convenient measure of real credit.

**Table II-1: Measures of Credit**

|Variable|Calculation|
|---|---|
|Real_Private_Credit_ USD|private domestic credit (% GPD) * Real GDP|
|Real_Private_Credit_ USD_CPI|private domestic credit (% GPD) * Nominal GDP / CPI|
|Real_Private_Credit_LCU_DEF|private domestic credit (% GPD) * GDP (LCU) / GDP deflator|

7
<!-- Start of picture text --> Global Annual Average Oil prices per barrel<br>1986- 2008<br>120<br>100<br>3/<br>+80<br>bs)<br>a<br>5 60<br>a<br>a 40<br>2<br>20<br>0<br>© g\ a © © oS ob &® of & © ol DAM OW dm OO cl<br>FILLDEB? LPIOd” O_O?PGDD?PIAH PIAD MMOPSSOVOSLTOA SLOMSe<br><!-- End of picture text -->
# Figure 8. Policies to strengthen drivers of potential growth

## A. Investment growth

<!-- Start of picture text --> Percent 2011-21<br>10 Baseline (2022-30)<br>Investment surge (2022-30)<br>8<br>6<br>4<br>2<br>0<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br><!-- End of picture text -->

## C. Life expectancy

<!-- Start of picture text --> Number of years 2011-21<br>85 Baseline (2022-30)<br>Health improvements (2022-30)<br>80<br>75<br>70<br>65<br>60<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br><!-- End of picture text -->

## B. Secondary schooling completion

<!-- Start of picture text --> Percent 2011-21<br>100 Baseline (2022-30)<br>90 Education improvements (2022-30)<br>80<br>70<br>60<br>50<br>40<br>30<br>AEs EMDEs EMDEs Exp. Non-exp.<br>excl. CHN<br>D. Female labor force participation<br>Percent 2011-21 Percent<br>70 Baseline (2022-30) 40<br>Improvements in labor markets (2022-30)<br>30<br>60<br>20<br>50<br>10<br>40 0<br>AEs EAP ECA LAC SSA<br>EMDEs MNA (RHS) SAR (RHS)<br>EMDEs<br>excl. CHN<br><!-- End of picture text -->

## D. Female labor force participation

Sources: Penn World Tables, World Bank.

Note: AEs = advanced economies; CHN = China; EMDEs = emerging market and developing economies; Exp. = commodity exporters; Non-exp. = Commodity importers; EAP = East Asia and Pacific; ECA = Europe and Central Asia; LAC = Latin America and the Caribbean; MNA = Middle East and North Africa; SAR = South Asia; and SSA = Sub-Saharan Africa.

A. Baseline investment growth assumes investment forecasts of Consensus Economics, and investment surge assumes best ten-year improvement record for each country.

B. Baseline secondary school completion rate assumes the trend improvements in education and education improvements assumes best ten-year improvement record for each country.

C. Baseline life expectancy assumes the trend improvements in education and health improvements assumes best ten-year improvement record for each country.

D. Baseline female labor force participation (LFPR) assumes the predicted value of female LFPR based on the trend improvements in determinants of the LFPRs and improvements in labor market assumes best ten-year improvement record in female LFPR for each country.
# **3.2**

_The external dependence and asset tangibility measures_ FVs), we use the Rajan and Zingales (1998) measure of external dependence (ED). They calculated a sector’s need for external finance as the fraction of capital expenditures that were not financed with cash flows from operations for a sample of publicly traded US firms in the 1980s. For each firm, the ratio was averaged over the 1980s and the final ED measure corresponds to the sector median. The key assumption underlying the validity of their ED proxy is that the ranking it generates across sectors is stable across countries because a sector’s need for external finance is intrinsically linked to sector-specific, but country-invariant, technological characteristics. By using U.S. data on publicly traded firms, Rajan and Zingales (1998) increase the likelihood that they correctly identify a sector’s technological demand for external financing. Large firms typically face fewer financing obstacles than small firms and if there is any country in which firms’ actual use of external finance reflects their desired level, the United States is perhaps the closest one can find given the sophistication of its financial system.

Firms which rely on external to conduct their day-to-day trading operations or invest in new growth opportunities at home can be expected to be those which need external financing to expand abroad. The establishment of a foreign affiliate requires substantial purchases of new foreign fixed assets, e.g. land, building, machinery. It is also plausible that any product which entails high R&D, marketing or distribution costs at home will similarly involve large customization, marketing and distribution fixed costs when produced and sold in foreign markets (Manova, 2013). The ED measure developed by Rajan and Zingales (1998) is based on a sample of large U.S. companies, which are likely to have activities abroad. This sample composition helps to make it a good proxy for the typical external financing needs of MNEs in a given sector.

9
_The effect of the presence of schools_ The last relationship reported here is that between educational enrollment and the presence of schools within the community. The sample is restricted here to rural settings as identifying "communities" in urban settings for this purpose is very difficult. In addition, the number of countries for which this relationship can be estimated is much lower as community questionnaires were not carried out in the majority of the DHS (the results are available for 21 surveys in 19 countries).

The estimating equation given in (1) is augmented with a dummy variable equal to one if there is a primary school in the community, a dummy variable equal to one if there is a primary and a secondary school in the community, and interaction terms between these and the dummy for male gender.<sup>2 4</sup> These school presence variables are constructed from the response by the community survey respondent to the questions "is there a primary school in this community" and "is there a secondary school in this community". Although the children under analysis (ages 6 to 14) are not likely to be attending secondary school, the access to secondary places may have an impact on primary schooling and the dummy variables for secondary school are therefore included in the multivariate analysis (see Lavy, 1997).

In addition to these school facility variables, the equation includes a set of community infrastructure variables in order to ensure that a relationship with school presence is not simply reflecting the fact that communities with more infrastructure in general, including schools, may tend to have higher enrollment. While the exact list of variables varies from survey to survey, the typical list includes: a dummy variable equal to one if the nearest urban center is less than 10 kilometers away; a set of dummy variables each equal to one if there is a post office, a local

> **24** In addition a dumrny (and interaction) equal to one if there is a secondary school, but no primary school, in the community is included for the countries where this occurs. This is a very rare occurrence even in countries where the relationship can be estimated and the results are not reported here.

36
||§.3.1<br>Total Toxic Pollution Inte|nsities|by||
|---|---|---|---|---|
||Medium<br>.<br>.<br>.<br>.<br>2.<br>2<br>ee<br>e|e<br>wt<br>we|te|we<br>le<br>te<br>4|
||5.3.2<br>Metals<br>Intensities<br>...|....|..|..<br>49|
||5.3.3<br>Air Pollution<br>Indicators|....|..|.<br>.<br>52|
||5.3.4<br>Water Pollution Indicators|...|..|...<br>60|
|6.<br>Criti|cal Assessment and Plans for Further|Work .|.|...<br>65|
|6.1.|Sources of Bias .... n|||2)|
|6.2.|International Applicability ....|....|..|..<br>66|
|6.3.|Plans<br>for Further Work<br>......|...<br>.|+.|..<br>.<br>67|
|Annex|ListofTRIChemicalse|e||-<br>;|
and use of a caliper of 0.03 to match observations. The effect on sales increases from 6.6 pp with least squares to 7.6 pp. with PSM. Under this alternative estimate for _γ_ the indirect effect of technology sophistication would increase to 1.3 and the total effect to 6.7.<sup>17</sup> In the NNM specification, we pair observations exactly on country, major sector (agriculture, manufacturing, services), and exposure to the COVID-19 shock (the dummy indicator), and use Mahalanobis distance to find the nearest neighbors on the remaining covariates ( _22_ ).<sup>18</sup> The estimated average effect of additional digital adoption increases to 8.8 pp for a total indirect effect of technology sophistication of 1.5 and a total effect of 6.9 (Table 3).

Thus, the indirect effect of technology readiness via the increase in the use of digital technologies is robust across specifications, and ranges between 1.1 and 1.5.

||OLS|PSM|NNM|
|---|---|---|---|
|Marginal effect of additional digital tech on sales (pp)�_γ_|6.6***|7.6***|8.8***|
||(2.2)|(2.8)|(2.8)|
|Indirect effect of tech sophistication on sales (pp) <sup>�</sup>_β ×_�_γ_|1.1|1.3|1.5|

Table 3: Least squares and matching estimates for the effect of additional digital adoption during the pandemic on the percentage change in sales. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Standard errors in parenthesis. Full set of results available in the appendix B. Percentage change in sales the 30 days prior to the interview relative to the same period of 2019.

# **6 Concluding remarks**

This paper presents new evidence on the importance of technology readiness on firm resilience during the COVID-19 pandemic. We show that the pre-pandemic level of technology sophistication had both direct and indirect effects on firm sales in the early stage of shock. The indirect channel operates through firms speeding up the introduction of or increasing the use of digital technologies in response to the crisis. The results also suggest heterogeneity across firms: those in

> 17Figure B1 of the appendix shows the distribution of propensity score for treated (increase the use of any digital) and untreated groups. Table B7 and Table B8 of Appendix B compare the differences across variables between treated and untreated groups for the original (unbalanced) and the balanced sample matched through PSM.

> 18We follow the Mahalanobis nearest-neighbor algorithm implemented in STATA where the distance between the covariates of individuals _i_ and _j_ is _{_ ( **x** _i −_ **x** _j_ )<sup>_′_</sup> **S**<sup>_−_1</sup> ( **x** _i −_ **x** _j_ ) _}_<sup>12</sup> , with the variance-covariance matrix of the covariates in the sample as the weighting matrix **S** .

19
# **Figure 6: Better or worse than expected?**

This figure shows the expected outcomes (center of distributions, blue dashed lines) and the observed deforestation levels (colored bars), which are red if deforestation is worse and green if better than expected. The text boxes demarcate the years when a policy was introduced to combat deforestation. For monthly results, see Figure 13, Appendix.

<!-- Start of picture text --> 35,000<br> Amazon Region Protected Areas Program (2002)<br>30,000 Plan for Prevention and Control of Deforestation in the Legal Amazon (2004)<br>25,000<br> Amazon Soy moratorium (2006)<br>20,000 Blacklisting of municipalities (2008)<br> Cattle moratorium (2009)<br>15,000<br> Updated forest code (2012)<br>10,000<br>5,000<br>0<br>1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021<br>Benchmark deforestation Observed deforestation<br>benchmark outcome slightly below benchmark: weak KPI slightly above benchmark: weak KPI<br>50% confidence interval below benchmark: strong KPI above benchmark: strong KPI<br>95% confidence interval significantly below benchmark: very strong KPI significantly above benchmark: very strong KPI<br>2)<br>Annual Amazonian deforestation (km<br><!-- End of picture text --> Source: Instituto Nacional de Pesquisas Espaciais (INPE) PRODES, calculation by authors.

action under the federal government. This finding is in line with (de Smit, 2022) who also finds a mostly positive but not significant effect using synthetic control methods.

# **4.4 Incentives under constant benchmarks**

The main argument behind the above analysis is that estimating benchmark outcomes requires robust and transparent empirical methods instead of simple extrapolation of historical trends. In this section we explore a hypothetical method for setting constant benchmarks and discuss the implications of those.

for results-based financing that penalizes emissions from deforestation and forest degradation, rewards forest carbon stock conservation and enhancement, and sustainable forest management (UNFCCC, 2011, paragraph 70). Payments are contingent upon the observed difference between FRLs/FRELs and the observed deforestation (Granziera et al., 2021). This is consistent with our suggestion not to use observed outcomes as KPIs, but instead compare the outcomes with a benchmark. In fact, the guidelines in UNFCCC (2014) assess how historical data were taken into account when establishing the FRLs/FRELs.

However, some reference levels discussed in Granziera et al. (2021) are simple historical averages over 4-year to 20-year periods, reassessed periodically. Various reference levels were used for results-based financing (see Figure 7), which correspond to historical averages over an expanding window since 1996. These remain constant for 5-year periods, after which the FRELs are updated. Within the 5-year periods, the results were evaluated against the prevalent benchmark. This approach to estimating a baseline scenario has been criticized in the past as too simplistic (Fletcher 16
We measure commodity shocks using a commodity terms of trade index following Gruss and Kebhaj (2019). Commodity-price shocks are calculated by taking the first differences of the log of the commodity terms of trade index, using historical fixed weights. The commodity terms of trade index is based on international prices of up to 45 individual commodities, constituting broad categories of energy, metals, food and beverages, and agricultural raw materials. External debt-financing shocks are calculated as the log difference of the US T-bill (10-year, long term) multiplied by the lagged external debt. The debt shock variable is included in a few specifications as it entails a drop in the number of observations due to availability of debt data.

We account for political institutions using data from the Worldwide Governance Indicators - WGI (Kauffman et al., 2010) following other studies in the literature (Antras and Chor, 2013; Grembi et al., 2016). In our estimations we include Voice and Accountability, Political Instability, Rule of Law, and Control of Corruption. Voice and Accountability is a measure of perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media. Political Instability measures perceptions of the likelihood of political instability and/or politically motivated violence. Rule of Law captures perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence. Control of Corruption captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.

We control for internal armed conflict using the UCDP/PRIO Armed Conflict Database updated by Pettersson and Öberg (2020). We follow a definition, similar to Abu Bader and Ianchovichina (2019) where armed internal conflict is a contested incompatibility where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths per year. For the 18
|**Table 7: Impacts of Business Traini**|**ng on Bus**|**iness Start-up a**<br>**Impact**|**nd Survival**<br>**on Survival**|**Impact on St**|**art-up**|
|---|---|---|---|---|---|
|**Study**|**Gender**|**Point estimat**|**e**<br>**95% CI**|**Point estimate**|**95% CI**|
|Bruhn and Zia (2012)|Mixed|0.013|(-0.09, +0.10)|0|n.r.|
||Female|-0.125|n.r., not sig.|0|n.r.|
||Male|0.072|(-0.07, 0.21)|0|n.r.|
|Calderon et al. (2012)|Female|-0.034|(-0.13, +0.06)|n.r.|n.r.|
|<br>De Mel et al. (2012)||||||
|Current Enterprises|Female|-0.026|(-0.102, +0.051)|n.r.|n.r.|
|Potential Enterprises|Female|n.r.|n.r.|**+0.09 (4 months)**|**(0, 0.18)**|
|||||-0.02 (25 months)|(-0.11, 0.07)|
|Giné and Mansuri (2011)|Mixed|0.034|(-0.021, 0.089)|-0.006|(-0.02, +0.01)|
||Male|**0.061**|**(-0.012, 0.133)**|-0.011|(-0.04, +0.01)|
||Female|0.001|n.r., not sig.|0.002|n.r., not sig.|
|Glaub et al. (2012)|Mixed|0.05|n.r.|n.r.|n.r.|
|Karlan and Valdivia (2011)|Female|n.r.|n.r.|-0.019|(-0.05, +0.01)|
|Klinger and Schündeln (2011)||||||
|selected in first phase|Mixed|n.r.|n.r.|0.044|(-0.12, 0.21)|
|selected in first phase|Female|n.r.|n.r.|-0.019|(-0.31, +0.27)|
|getting trained in second phase|Mixed|n.r.|n.r.|**0.465**|**(0.10, 0.82)**|
||Female|n.r.|n.r.|**0.572**|**(0.04, 1.10)**|
|Mano et al. (2012)|Male|**0.095**|**(0.022, 0.167)**|n.r.|n.r.|
|Premand et al. (2012)|Mixed|n.r.|n.r.|**0.04**|**(0.02, 0.06)**|
||Male|n.r.|n.r.|**0.06**|**(0.04, 0.08)**|
||Female|n.r.|n.r.|**0.03**|**(0.01, 0.05)**|
|Valdivia (2012)||||||
|General training|Female|**-0.045**|**(-0.094, +0.004)**|0.014|(-0.03, +0.06)|
|Training+ technical assistance <br>_Notes:_<br>95% CI denotes 95 percent confide<br>n.r. denotes not reported. Not sig.<br>Berge et al. (2012) and Drexler et a|Female<br>nce inter<br>denotes<br>l. (2012) d|0.021<br>val. Impacts sig<br>point estimate<br>o not report im|(-0.014,+0.056)<br>nificant at the 10% l<br>is not significantly d<br>pacts on either surv|-0.006<br>evel or more reporte<br>ifferent from zero.<br>ivorship or start-up|(-0.05,+0.04)<br>d in**bold.**|

95% CI denotes 95 percent confidence interval. Impacts significant at the 10% level or more reported in **bold.** n.r. denotes not reported. Not sig. denotes point estimate is not significantly different from zero. Berge et al. (2012) and Drexler et al. (2012) do not report impacts on either survivorship or start-up <mark>Note Valdivia (2012) survival is ba</mark> sed on whether they stopped any business in the past two years, while start-up is based on whether they started a new business in the last year.

33