rafmacalaba/datause-encoder-data huggingface dataset:
measures the quality of input process. Despite this, Gupta et al. (2014) normalize the index on a 0‐1 scale and use it as a measure of efficiency‐adjusted public capital effects on growth based on a sample of 52 countries. They find that upper middle‐income countries have on average 57% efficient public capital stock against 46% for lower middle‐income countries, and 38% for low‐income countries.<sup>21</sup> Table 2 summarizes the PIE‐X and PIMI and how they compare against the IEI. While caution should be exercised in comparing outcomes in absolute terms across different methodologies, one crucial takeaway is that all methodologies point to a gap between high‐income countries and low‐income countries. Thus, there appears to be substantial room for improvement in efficiency in low‐income countries which could lead to a better growth performance.

**<u>Table 2: Indicators of Efficient Public Capital Stock ‐ Different Methodologies</u>**

|**Infrastructure**<br>**Index**<br>**(this pa**|<br>**Efficiency**<br>**(IEI)**<br>**per)**|<br>**E**|<br>**Public Inv**<br>**fficiency Indi**|<br>**estment**<br>**cator (PIE‐X)**||<br>**Efficiency‐Adj**<br>**Capital, usi**<br>**Investment M**<br>**Index (**|**usted Public**<br>**ng Public**<br>**anagement**<br>**PIMI)**|
|---|---|---|---|---|---|---|---|
||**Average**||**Hybrid,**<br>**average**|**Physical,**<br>**average**|**Survey‐**<br>**based,**<br>**average**||**Average**|
|High Income<br>(21)|0.844|All countries<br>(132)|0.730|0.570|0.800|Upper Middle<br>Income<br>(14)|0.572|
|Upper Middle<br>Income<br>(25)|0.767|Advanced<br>(26)|0.870|0.700|0.900|Lower Middle<br>Income<br>(22)|0.456|
|Lower Middle<br>Income<br>(27)|0.738|Emerging<br>Markets<br>(62)|0.730|0.600|0.800|Low Income<br>(14)|0.382|
|Low Income<br>(10)|0.576|Low Income<br>Developing<br>Countries<br>(44)|0.600|0.450|0.750|Notes:<br>The capital accum<br>process is based<br>public investmen|ulation<br>on efficient<br>t. The portion|
|||||||of public investm|ent deemed|
|Notes:<br>Mean of simple an<br>average of output<br>delivery (for elect<br>water) and paved<br>percentage of tot|d weighted<br>not lost in<br>ricity and<br>roads as<br>al roads.|Notes:<br>Based on efficie<br>stock per capita<br>and/or quality a<br>mean of the phy<br>Source: IMF (20|ncy frontier an<br>as input and in<br>s output. The<br>sical and surve<br>15).|alysis with publi<br>frastructure qu<br>hybrid indicator<br>y‐based indicat|c capital<br>antity<br>is a simple<br>ors.|<br>as efficient is bas<br>invariant PIMI sc<br>1 and 4) that hav<br>normalized (4 to<br>Source: Gupta et|<br>ed on a time‐<br>ore (between<br>e been<br>1).<br>al. (2014).|

> 21 We group Gupta et al. (2014)’s individual country calculations according to the World Bank income classification scheme.

16
# **4. Descriptive Statistics**

The descriptive statistics shown in Table B5 suggest that 42% of the firms are microenterprises and that the service sector comprises 43% of the observations. The hospitality subsector, which was one of the hardest hit sectors in the pandemic, accounts for almost one-third of the firms in the service sector. Among the firms, 40% are young (less than five years), 13% are maturing (between five and fourteen years), and 47% are what we call established (with fifteen years or more in the market). Only about one-fifth of the firms in our sample are female-led.

The primary outcome of interest, the change in sales compared to the value before the pandemic, has been previously studied in (Apedo-Amah et al., 2020; Cirera et al., 2021). The three rounds of data show that, on average, and without controlling for any other factor, firms exhibit a decrease of 29% percentage points against their pre-pandemic level. This value, of course, includes periods of recovery. We also analyze recovery for the last two rounds, measured as ∆ _Change in salest,t−_ 1. Though recovery is, on average, around 11 percentage points, the median is still zero, indicating that half of the firms have not recovered and that the dispersion of recovery is very high with the standard deviation being around four times the mean, which underscores the need to analyze sources of heterogeneity in recovery after shocks.

In addition to the firm outcomes affected by the shock, we also consider variables related to the adjustment response by firms (e.g., use of digital technologies) and access to public support. About 50% of the firms started or increased their use of digital technologies, and 61% report having online sales. Only 22% of firms report having received support from the government, which varied widely across the three rounds.<sup>13</sup>

# **5. Results**

# **5.1 Informal firms tend to be smaller, in sectors with low entry costs (retail and services) and also younger**

Informality is highly correlated with small businesses (see, for example, La Porta and Shleifer, 2014; Ulyssea, 2018, 2020). The BPS data for South Asia shows that the share of microenterprises (fewer than nine employees) in the formal sector, after controlling for firm characteristics such as sector, country, time, and age, is 38%. This contrasts sharply

> 13During the first round, on average, only 5% of the firms received support, while for the second round, support increased to around 20% of the firms.

11
TABLE I Balance Tests on School- and Student-Level Variables at Baseline

||(1)<br>Panel A:|(2)<br> Balance test on school-lev|(3)<br>el variables|
|---|---|---|---|
||Treatment|Control|Diference (F.E)|
|Number of classes per school|8.89|8.32|0.57|
||[4.88]|[4.49]|(0.35)|
|Number of students per school|190.85|184.49|6.36|
||[133.80]|[135.32]|(10.41)|
|Class size|20.60|20.99|-0.39|
||[6.76]|[7.16]|(0.64)|
|Number of teachers per school|9.35|9.07|0.27|
||[5.20]|[4.59]|(0.36)|
|Observations|120|240||
||Panel B:|Balance test on student-lev|el variables|
||Treatment|Control|Diference (F.E)|
|Raw math score|0.41|0.40|-0.00|
||[0.23]|[0.23]|(0.01)|
|Raw science score|0.51|0.52|-0.00|
||[0.21]|[0.21]|(0.01)|
|Raw Indonesian score|0.58|0.59|-0.01|
||[0.21]|[0.20]|(0.01)|
|Raw English score|0.40|0.39|0.01|
||[0.18]|[0.17]|(0.01)|
|Student assets index|0.55|0.53|0.00|
||[0.24]|[0.24]|(0.01)|
|Observations|20,970|41,192||

_∗ ∗∗ ∗∗∗ Notes: p <_ 0 _._ 10, _p <_ 0 _._ 05, _p <_ 0 _._ 01. Table compares average baseline values between treatment and control groups based on a regression model that includes district-triplet fixed effects, which are the strata used for randomization. Within-group standard deviations are reported in brackets in columns (1) and (2). School-level clustered standard errors of the estimated difference between treatment and control is reported in parentheses in column (3). For the student asset index we calculate the fraction of the following seven items that are available in the household of the student: television, fridge, mobile phone, bicycle, motor bike, car, computer.
Russia and China during the 1990s. Stiglitz (1999) emphasizes that, over the decade beginning in 1989, China‘s gross domestic product (GDP) doubled while Russia‘s GDP almost halved. Different explanations have been offered for these two performances. For example, Blanchard and Shleifer (2001) explain China‘s better performance on the basis of the existence of strong central control of the economy by the Communist Party, which kept centrifugal forces under check; in contrast, they claim that a weak federal government in Russia was unable to control destructive behaviors of the regions. Similarly, Fisher and Sahay (2000) argue that Russia lagged in the implementation of structural reforms and failed to solve its fiscal problems, which led to large fiscal deficits and ultimately to the financial collapse in August of 1998. Shleifer and Treisman (1999) blame Russia‘s poorer performance on the common pool incentive problem created in Russia by the revenue sharing of all main taxes between the federal government and the regions. In contrast, Montinola, Qian, and Weingast (1995) and Qian and Weingast (1996) argue that China‘s revenue assignments were ―market preserving‖ (unlike Russia‘s) in that the Chinese assignments stimulated local governments to become entrepreneurial and to seek the growth of their local economies. Whatever the explanation for the poorer economic performance of Russia, the fact is that large proportions of the Russian population suffered income declines throughout much of the 1990s. For example, Graham and Pettinato (2002) use data from the Russia Longitudinal Monitoring Survey, and find that 77 percent of the population sampled had income declines from 1995 to 1998. Interestingly, at the time of the third Longitudinal Survey in 1999 the Russian economy experienced positive rates of growth. Alm, MartinezVazquez, and Torgler (2006) observe significant regional differences in individuals‘ attitudes toward paying taxes. There are strong ethnic, economic, and fiscal disparities across the Russian regions. Each region generally shows a diverse picture, with significant changes between 1995 and 1999 in tax morale. The newest data they analyzed suggest a tendency for wealthier regions to have a higher tax morale than poorer ones. Furthermore, the data suggests a tendency toward lower tax morale prevails in regions possessing strong separatist sentiments, a prevalent belief that the region has been exploited by the central government, and a general political dislike for Moscow. The 42
Croke et al. (2016) would only involve the addition of the one multiple dose study the authors identify but classify as separate from the MDA studies – Stephenson et al. (1993).<sup>23</sup> 2. The 2012 Cochrane Review changed this approach, however, introducing a distinction that effectively distinguished between test-and-treat and MDA studies (Taylor-Robinson et al. 2012). Applying this classification used in the 2012 Cochrane Review to the studies in TMSDG also leads to the inclusion of Stephenson et al. (1993), which was classified as an MDA study by TaylorRobinson et al. 2012 (or, as a “target population treated” study, using the language in that review) rather than a test-and-treat study.

Thus, applying either of these procedures from Cochrane Reviews prior to TMSDG results in the addition of Stephenson et al. (1993) to the TMSDG sample. With Stephenson et al. (1993) classified in this way but otherwise using the data set in the TMSDG study, the null hypothesis of a common zero effect is strongly rejected with a p-value of 0.009 (see Table 2, row 2).

# **5.2 Testing the hypothesis of a common zero effect**

We now turn to the full sample of 22 treatment effect estimates. Table 3 shows that the null hypothesis of a common zero effect is rejected at p<0.001 in the full sample, in areas with prevalence above the thresholds at which WHO recommends MDA (20% prevalence), and in areas where it recommends multiple annual dose MDA (50% prevalence). This result is not reliant on the addition of any one study to the TMSDG sample: any one of six updates leads to rejection of the hypothesis of a common zero effect with p<0.05 (see Table 2).

> 23 Note that we believe that Stephenson et al. (1993) should be included in the set of MDA studies, since individuals were treated without first being screened, as discussed in Appendix A.3.

23
killed; 100 or more people reported affected; declaration of a state of emergency; or call for international assistance. These disasters include hydro-meteorological disasters such as floods, wave surges, storms, droughts, landslides and avalanches; geophysical disasters such as earthquakes, tsunamis and volcanic eruptions; and biological disasters such as epidemics and insect infestations. To provide a comprehensive picture, he compared the frequencies and impacts of disasters across countries by employing four measures, including the number of events divided by land area, the number of events divided by population, the number of affected persons divided by total population, and damage divided by GDP. He found that developing countries, particularly small island states in the Eastern Caribbean Currency Union (ECCU), face higher relative costs than advanced countries when measured in terms of the number of person affected and the value of the damage. The author also assessed the short-term impacts of 12 major disasters occurred in the ECCU and observed its negative effects on economic output as well as external and fiscal balances. The analysis showed that natural disasters led to a median reduction of 2.2% in the same-year real GDP growth. Moreover, a median increase in the current account deficit amounted to 10.8% of GDP in the disaster year. The median public debt was also observed to increase by a cumulative 6.5% over three years following disaster events.

Closely related to this approach, Heger, Julca, and Paddison (2008) investigated the macroeconomic impact of natural disasters with the specific focus on the Caribbean region. Their analysis was based on the annual dataset that included sixteen Caribbean states over the 1970-2006 period, drawn from the EM-DAT database. The authors first selected proxies for natural disasters through a simple OLS estimation. They identified the frequency of disasters, the estimated costs of disasters, and the number of total affected as the major explanatory variables for different macroeconomic outcomes. With those variables in the corresponding OLS regression analysis, the results illustrated that natural disasters negatively impact growth, fiscal balance, and external balance. These results coincide with those of Rasmussen’s presented above. Another significant finding 6
Policy Research Working Paper 9275

# **Abstract**

Studies of female business leaders and economic performance are rarely conducted with worldwide observational data, and with considerations on the underlying cultural, institutional, and business environment. This paper uses worldwide, firm-level data from more than 100 countries to study how female-headed firms differ from male-headed firms in productivity level and growth, and whether the female leader performance disparity hinges on the underlying environment. Female-headed firms account for about 11 percent of firms and are more prevalent in countries with better rule of law, gender equality, and stronger individualistic culture. On average, female-headed firms have 9 to 16 percent lower productivity and 1.6 percentage points lower labor productivity growth, compared with male-headed firms. The disadvantage is mainly in manufacturing firms, largely nonexistent in service firms, and present in relatively small firms. Although the female leader performance disadvantage is surprisingly not related to gender equality, it is smaller where there is less emphasis on personal networks (better rule of law, lower trade credit linkages, lower usage of bank credit, and more equalizing internet), less competition, and the culture is more collective. The study does not find that the female leader disadvantage is amplified in corrupt environments. Africa differs significantly in that it features lower female disadvantage, stronger female advantage in services relative to manufacturing, and stronger sensitivity of female business leaders to electricity provision and bank credit access.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at lxu1@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
although some changes were made to the order of information and the words used to describe it in the Asian instrument, given that English was not the first language of most of the Asian experts.<sup>9</sup>

**Table 2: Distribution of experts and WTP statements by country in Europe**

|**Country group**<br>**(numbers of**<br>**experts)**|**Country (number**<br>**of experts)**|**Stated Mean WTP for**<br>**Plan A, Round 2;**<br>**US$/household/year**|**Mean Group GDP**<br>**(PPP-adjusted),**<br>**US$/capita/year,**<br>**unweighted**|
|---|---|---|---|
||Denmark(2)|49.0||
|Nordic countries|Finland (3)|34.5|70,000|
|(11)|Norway (3)|114.2|(43,900)|
||Sweden(3)|40.4||
||Austria(2)|78.2||
|Northern and|Belgium(1)|61.2|48,900|
|Central Europe|Germany (3)|102.0|(39,800)|
|(18)|Ireland(2)|32.6||
||Netherlands (3)|40.8||
||Switzerland(2)|30.6||
||UnitedKingdom(5)|31.8||
||France (3)|47.6||
|Southern Europe|Greece (2)|10.9|36,200|
|(12)|Italy (4)|22.1|(31,600)|
||Portugal(1)|27.2||
||Spain(2)|31.3||
||Croatia(2)|5.0||
|Eastern Europe|Czech Republic (2)|27.2|15,700|
|(8)|Hungary (1)|5.4|(21,300)|
||Poland(2)|27.2||
||Romania(1)|34.0||

The more extensive protection plan, Plan A, implies no further forest loss from now until 2050. The less extensive plan, Plan B, implies that 12% of the current Amazon rainforest would be lost by that time. Both plans are compared to a costless (in terms of incremental _protection_ costs) “business as usual” (BAU) alternative, under which 30% of the current Amazon rainforest is assumed to be lost by 2050.<sup>10</sup> Respondents would incur annual payments on a per household

> 9 These changes were made as a result of cognitive interviews of a small number of Asian environmental economists conducted before the Asian exercise. Some smaller differences also exist between the European survey and the others. E.g., European experts were not shown pictures of possible threatened species.

> 10 This BAU alternative corresponds to the deforestation scenario set out by Soares-Filho et al. (2006), still considered the most authoritative. Note that the “good” to be valued was different in the Horton, et al. (2003) study,

7
conflict intensity in country _i_ , _h − j_ years earlier. _fji_ is a country specific functional form that maps conflict intensity to a loss for children of age _j_ that are exposed to the conflict in a given year.

To take this measure to the data using either the second or the third proxy for conflict intensity, we assume that all intense conflicts have an effect similar to what was observed during the Biafra civil war and less intense conflicts do not have any persistent effect. When considering the case with battle deaths per capita, we assume the following:

- _fji_ is linear, which means that if per capita conflict intensity doubles within the same country, the loss of each cohort of exposed children will also double;

- A given conflict intensity will create the same loss on children of age _j_ in all countries _fji_ = _fj ∀ i_

These assumptions allow us to rewrite equation (3) as Where _Lossj,biafra_ is the cm loss per year of exposure for children exposed to the Biafra civil war at age _j_ compared to non exposed children. We compute this loss using estimates in Akresh et al. (2012). We first compute a per month cm loss by dividing the total cm loss for each age group by the average number of months of exposure. This per month cm loss is then multiplied by 12 to have the equivalent height loss per year. We finally attribute the age group loss to each age cohort within a given group.

To obtain a direct measure of the productivity loss from the aggregate measure of cm loss, we use micro evidence that estimate the equivalent gain in labor productivity for an extra cm of height, holding education constant. Well (2007) provides different estimates for this parameter based on different studies. According to this paper, an extra cm increases labor productivity by 3 to 7 percent<sup>32</sup> . Multiplying this coefficient with our measure of cm loss yields therefore a measure of productivity loss in percent.

These estimates are however obtained holding constant factor prices and technology in a partial equilibrium framework. General equilibrium interactions can offset or even reverse partial equilibrium conclusions. As argued in Acemoglu (2010), one potential reason for such effect is the imperfect substitution between factors and diminishing returns leading to a change in factor productivities and prices. It is therefore not clear whether these findings would aggregate up after a massive loss of productivity in the population.

The first two graphs in Figure 9 depict our productivity loss measure for 2 specific cases of countries using 7 percent per cm as the marginal effect of height on productivity.

> 32Based on estimates of the impact of fetal growth on height, schooling and wages derived from cohorts of twins in Behrman and Rosenzweig (2004) and Black et al. (2007), Well (2007) finds a coefficient of 0.034 for the return to an extra cm of height on log wages. Average wage represents labor productivity according to classic production theory. Well (2007) also found a coefficient of 0.07 when using estimates based on UK long run historical data.

35