rafmacalaba/datause-encoder-data huggingface dataset:
# **<mark>Table A3B – Comparison of Means Between Banks with Low and High Exposure to Local Public Debt Before FD Law</mark>**

This table displays the summary statistics of banks given their lending to state governments in March of 2016, one month prior to the implementation of the FD Law. A bank is defined as with low (high) local public lending if its share of lending to local governments in March of 2016 is below (above) the median. _Lending to States_ is the share of bank loans channeled to local governments. _Assets_ is the volume (in logs) of bank assets. _Capital_ is the tier1 capital ratio (percent). _Liquidity_ is the ratio of liquid assets to total assets (percent). _Delinquency_ is the share of loans that are more than 90 days in arrears (percent). The first two columns in each panel show the mean and standard deviation of each variable. The last column— _Standardized Difference—_ shows the normalized differences of variables between banks with high and low local <u>public debt (based on Imbens and Wooldridge, 2009).</u>

||**Banks with L**<br>Mean|**ow Local Public Debt**<br>Standard Deviation|**Banks with Hi**<br>Mean|**gh Local Public Debt**<br>Standard Deviation|Standardized<br>Difference|
|---|---|---|---|---|---|
|Lendingto states(share)|0.0|0.0|0.2|0.2|-1.22|
|Assets (logs)|12|1.8|12.4|1.5|-0.22|
|Capital (%)|11|3.8|9.6|6.8|0.18|
|Liquidity (%)|6.9|2.3|6.4|3|0.04|
|Delinquency (%)|2.3|1.8|2.2|1.4|0|

67
**Table 12: Heterogeneous Treatment Effects - Subsample of rural schools**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||Grade 5|Grade 6|Grade 7|Grade 8|
|Stipend District|||||
|2004|0.205|3.339***|-0.253|0.0325|
||(0.107)|(0.176)|(0.134)|(0.126)|
|2005|0.906***|4.845***|2.398***|0.716***|
||(0.122)|(0.214)|(0.159)|(0.144)|
|2006|0.363**|5.705***|3.357***|2.493***|
||(0.131)|(0.224)|(0.172)|(0.164)|
|2007|-0.322*|4.028***|3.237***|2.805***|
||(0.126)|(0.212)|(0.185)|(0.172)|
|2008|0.406**|3.300***|3.071***|3.688***|
||(0.133)|(0.195)|(0.171)|(0.164)|
|Stipend District * 2004|0.00803|-0.553*|1.368***|0.776***|
||(0.187)|(0.249)|(0.185)|(0.166)|
|Stipend District * 2005|-0.126|0.455|1.110***|1.482***|
||(0.202)|(0.307)|(0.231)|(0.201)|
|Stipend District * 2006|0.528*|0.289|1.693***|1.321***|
||(0.207)|(0.341)|(0.273)|(0.248)|
|Stipend District * 2007|1.441***|1.747***|2.384***|2.276***|
||(0.212)|(0.331)|(0.282)|(0.257)|
|Stipend District * 2008|1.305***|1.839***|2.347***|1.908***|
||(0.223)|(0.297)|(0.267)|(0.261)|
|Constant|10.65***|14.70***|12.82***|10.50***|
||(0.0708)|(0.110)|(0.0866)|(0.0798)|
|Observations|54419|54982|54977|54970|
|Number of Schools|9590|9586|9586|9586|
|Adjusted R-squared|0.007|0.049|0.059|0.063|
|R-square within|0.00722|0.0494|0.0594|0.0632|

* p<0.05 ** p<0.01 *** p<0.001. Standard errors in parentheses are robust to heteroskedasticity and clustered at the level of the school. The dependent variable in each column is girls’ enrollment in a particular grade. The sample excludes mosque and primary schools as well as two districts whose treatment status was interchanged once the program began: Khanewal (changed from treatment to control) and Multan (changed from control to treatment. All columns report estimates from a fixed effects specification at the school level. The excluded group is no facilities. The excluded year is the baseline year 2003.

50
that have calorie intake assigned and account for 98.6 percent of total expenditures on food consumed at home.

Table 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that 'Meat’ is the most expensive food group, followed by ‘Breads, Cakes and Pies’. On the other hand, ‘Kitchen Oil’ is displayed as the most caloric food group, as expected. It is followed by ‘Flour Derivatives’ (which includes pasta). The cheapest food group is ‘Beans and Legumes’, while the least caloric is ‘Fruits’. Therefore, the data – including our calorie mapping – show some expected patterns of caloric and price distribution across food groups.

**Table 2. Top 10 Calorie intake food groups average kcal and price per 100 g, in 2018 urban Southeast prices**

|**Food Group**|**Kcalper 100g**|**Average priceper 100g**|
|---|---|---|
|Cereals|485.6|2.7|
|Kitchen Oil|1173.8|7.6|
|Meat|254.5|13.1|
|Sweets|548.7|5.0|
|Breads, cakes and pies|497.2|10.8|
|Flour Derivatives|508.0|4.5|
|Dairies|151.0|5.5|
|Potatoes and tubers|450.6|3.3|
|Beans and Legumes|430.7|3.7|
|Fruits|89.7|3.8|

_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.

Next, we calculate the household-specific cost per calorie of each food item consumed by dividing the expenditures reported on each food item by the product of the associated quantity purchased in kilogram and the calorie intake per kilogram. We then calculate household-specific average costs per calorie by computing a weighted average of the costs per calorie of the items consumed by each household h , as follows: _Reference group and consumption aggregate._ We choose a reference population for the estimation of the poverty line on the basis of their position in the welfare distribution. As noted above, the average cost per calorie of this reference population is used to estimate the food poverty line. Although in Brazil welfare and distributional analysis is usually based on income, for the purpose of estimating the CBN poverty lines, we use a consumption aggregate, as this would be consistent with the poverty line we are estimating. For this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services. It considers monetary expenditures (made in cash or 8
Table 4: Share of Firms Adopting New Technology, by Country and Manufacturing Sector

||Food &<br>Agroindustry|Textiles|Garments &<br>Leather|Metal &<br>Machinery|Electronics|Chemicals and<br>Plastics|<br>Wood & Paper|All Manufacturing|
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|Cambodia|-|1.00|0.85|-|-|-|-|0.85|
|China|0.49|0.30|0.33|0.44|0.51|0.48|-|0.44|
|Indonesia|0.37|0.44|0.44|0.49|0.79|0.51|0.47|0.46|
|Malaysia|0.32|0.30|0.30|0.50|0.61|0.39|0.36|0.39|
|Philippines|0.67|0.61|0.46|-|0.76|-|-|0.60|
|SouthKorea|0.63|0.61|0.24|0.51|0.92|0.76|0.48|0.55|
|Thailand|0.65|0.57|0.63|0.79|0.81|0.53|0.50|0.66|
|Vietnam|0.58|0.58|0.65|0.62|0.75|0.64|0.55|0.61|
|All Countries|0.54|0.50|0.47|0.55|0.60|0.51|0.50|0.53|

Source: Author's calculations are based on the _Enterprise Suveys_ (World Bank) Note: Table reports the share of firms in the sample recently adopting new technology, by country and manufacturing sector. A firm reports having adopted new technology when it significantly changed the way its main product is produced. The last row reports the mean share of firms adopting new technology for all the countries. The latter is a weighted average of the mean shares in each country weighted by each country's sample size in the total sample. The last column reports the mean share of firms with adopting new technology for all manufacturing sectors. Again, this is the weighted average of the mean shares in each sector, weighted by sector's size in the total country sample.
<u>Table 10. Occupation-to-skill match among native Danes and immigrants, 1995-2002, percent</u>

||Overeducated|Adequately<br>educated|Undereducated|Total|
|---|---|---|---|---|
|NativeDanes|11.0|71.1|17.9|100.0|
|Immigrants,<br>all non-Western|16.3|66.1|15.6|100.0|
|Turkey|8.2|54.2|37.6|100.0|
|Pakistan|10.3|71.3|18.4|100.0|
|Vietnam|10.4|75.0|14.6|100.0|
|Iran|21.3|72.4|6.3|100.0|
|Iraq|23.9|59.9|16.2|100.0|
|Ex-Yugoslavia|18.4|64.3|17.3|100.0|
|Somalia|15.5|65.8|18.7|100.0|
|Stateless|16.7|72.1|11.2|100.0|
|Other non-West.|18.6|67.6|13.8|100.0|

Note: See note for Table 1.

<u>Table 11. Occupation-to-skill match among native Danes and immigrants, 1995-2002, percent</u>

||Overeducated|Adequately<br>educated|Undereducated|Total|
|---|---|---|---|---|
|NativeDanes|11.0|71.1|17.9|100.0|
|Immigrants, total|16.3|66.1|15.6|100.0|
|– Immigrants with<br>Danisheducation|15.3|72.7|12.0|100.0|
|– Immigrants with<br>foreigneducation|17.3|60.3|22.4|100.0|

Note: See note for Table 1.

Table 12. Percentage overeducated immigrants by ethnic origin and source of education, 1995- <u>2002, percent</u>

||All|Danish<br>education|Foreign<br>education|
|---|---|---|---|
|Immigrants,<br>all non-Western|16.3|15.3|17.3|
|Turkey|8.2|6.4|9.6|
|Pakistan|10.3|8.6|11.6|
|Vietnam|10.4|10.9|8.4|
|Iran|21.3|20.0|27.9|
|Iraq|23.9|28.1|22.0|
|Ex-Yugoslavia|18.4|14.3|19.7|
|Somalia|15.5|11.2|17.6|
|Stateless|16.7|16.5|17.0|
|Other non-Western|18.6|18.2|18.9|

Note: See note for Table 1.

40
absorb roughly the whole income of the poorest 60 percent of the population. Currently, the cost of a basic asymmetric digital subscriber line (ADSL) package (3 megabits per second or Mbps) in Djibouti is about 90,000-100,000 DJF per year (Table 1). This is higher than the estimated average annual consumption per capita of the bottom 20 percent of the population, and roughly equal to the estimated yearly per capita consumption of the third decile of the population (Table 2). A 4 Mbps internet package would instead be equivalent to the yearly consumption for an individual belonging to the fifth decile of the population. The cost for the fastest ADSL service (6 Mbps) is roughly equal to the consumption level of an individual belonging to the average household in the ninth decile.<sup>8</sup> It is notable that these prices are substantially lower than the costs a couple years ago. According to ITU (2018), DT cut the price of an entry-level plan by 70 percent between 2016 and 2017.<sup>9</sup>

**Table 1. Djibouti Telecom costs, by service**

|**ADSL for private use***<br>|||||
|---|---|---|---|---|
|Available plans|3 Mbps|4 Mbps|5 Mbps|6 Mbps|
|Monthly plan|9,000 DJF|14,000 DJF|18,000 DJF|25,000 DJF|
|Yearly plan (15% discount)|91,800 DJF|142,800 DJF|183,600 DJF|255,000 DJF|

Source: Djibouti Telecom, <u>http://www.djiboutitelecom.dj/particulier-internet-adsl-particulier.html</u> [accessed November 22, 2018] Notes: * Internet for _particuliers_

**Table 2. Average per capita consumption (in DJF) by quintile**

|**Quintile**|**Average per capita**<br>**consumption(annual)**|
|---|---|
|1|56,182|
|2|108,506|
|3|156,757|
|4|224,548|
|5|496,690|
|National|208,224|

Source: Calculations based on EDAM4.

Note: Quintiles based on the distribution of consumption per capita.

> 8 To put this estimate in context, in 2018, the Broadband Commission for Sustainable Development agreed on a target for 2025 for entry-level broadband services at less than 2 percent of monthly gross national income (GNI) per capita. The current estimate for Djibouti for a fixed-broadband basket is 9.8 percent of GNI per capita (ITU 2018).

> 9 The price drop was accompanied by a reduction in the cap (from 50 GB to 30 GB per month) and the speed (from 3 to 1 Mbit/s).

8
- 25 -

infrastructure placement. In addition, there are few timeseries data measuring transport accessibility.

50. The paper particularly contributed to two issues. First, the paper applied spatial data and techniques to generate detailed georeferenced connectivity data in the region, using micro shipping data over 10 years. As shown, this is a useful approach to measure historical changes in transport accessibility at the highly disaggregated level. The same approach can be used everywhere as long as shipping data are available. Second, using such granular connectivity data, a dynamic panel regression model was applied to estimate the unbiased impact of transport connectivity on firm agglomeration. This is an alternative approach to the IV technique which is used in recent empirical studies investigating various impacts of transport infrastructure (e.g., Datta, 2012, Donaldson, 2018).

51. For the Caucasus and Central Asian countries, regional integration and transport connectivity are of vital importance. The region experienced significant economic transition during the 1990s. Since the early 2010s, a number of large transport investments have been made to reestablish regional connectivity. The paper cast light on such a dynamic period of time in the region and examined how seemingly improved transport connectivity affected the spatial distribution of firms, i.e., agglomeration economies.

52. It is found that the system GMM regression could control for the endogeneity problem to generate consistent estimation results. The systematically generated lagged differences and levels of the endogenous variables were found to be valid instruments. As expected, it is shown that agglomeration economies are significant in the region. Firms tend to be located where other firms are also located. The agglomeration process was also found to be highly persistent. Thus, the existing firm clusters are likely to continue growing, attracting more new enterprises. The evidence is consistent with the economic geography literature. However, it was also found that populated areas in the region suffer from congestion diseconomies. Holding everything else constant, firms are less likely to choose populated areas. This indicates that large cities are faced with a certain growth constraint: While firms tend to flock together, city growth is still not selfsustained to not only consume firms’ outputs but also supply quality labor.
Second- rapid monetisation of government debt would not bring much anymore because most problem debtor countries are by now extremely demonetised. Table 8 demonstrates this point. The money base is now about 2 X of GOP in Brazil (down from 3.5 X in 1981), and 4.5 X of GOP in Mexico (down from 15.9 X in 1981). A policy that doubled the money base in a week (e.g. through open market purchases) would reduce publicly-held debt by a mere 11 % in both Brazil and Mexico. Note that the respective figures are even lower for Belgium and not significantly higher for Ireland and Italy. Consequ,ntly, an inflationary erosion of domestic public debt does not work and would not justify the subsequent costs of inflation.

# 2. Default

Outright default, be it on foreign or on domestic debt, cannot prevent further growth of public debt unless tax revenues exceed non-interest spending. This condition seems only satisfied in Chile and Mexico. In addition. default is likely to impose heavy financial costs on the government. Brazil's temporary interest moratorium (from February 1987 to February 1988) has cost the country from between 710 million US dollars (according to the government; see NZZ of 16.2.88) and 1.5 billion US dollars (according to some Brazilian economists; see International Currency Review, Vol. 19.4). These figures would include higher spreads on short-term trade loans (140 million); the transfer of official reserves to the BIS to avoid seizure (20 million); delayed restructuring of the debt so that Brazil has had to continue paying for higher interest margins over a longer period of time (550 million); support for foreign affiliates of Brazilian banks which had been excluded from interbank business (750 million); and substantial private capital flight as a result of reduced confidence surrounding the moratorium. Domestic default may generate similar problems. According to a fiscal theory of private portfolio allocation and capital flight (Ize, 1987), the private sector keeps at home the part of its financial wealth on which it expects the government to honor its obligations and sequesters the rest abroad. Loss of reputation due to domestic default would thus impede domestic government finance and stimulate capital flight. The situation may develop in a different way, however, when an old regime collapses and the new regime can credibly commit not to default again. This may apply to the Philippines, but

-15-
# **1 Introduction**

The COVID-19 pandemic has disrupted the daily operations of firms on a staggering scale. Sharp, short-term impacts of the pandemic on firms have been reported across geographies and sectors, with some of the largest effects concentrated in small and medium-sized firms (Apedo-Amah et al., 2020),the back-bone of many developing economies. Using a novel data set collected by the World Bank Group and several partner institutions in 51 countries covering more than 100,000 businesses, their work provides a comprehensive assessment of the short-term impact of the COVID-19 pandemic on businesses across the world-wide with a focus on developing countries. Their findings provide a better understanding of the magnitude and distribution of the shock, the main channels affecting businesses, and how firms are adjusting. While their findings suggest that the distribution of impacts across firms within nations, sectors, and size categories varies widely, and that organization-level factors may be driving these outcomes, they do not offer insights on the mechanisms through which firms become more resilient. This paper explores one such channel, that is, whether structured management practices can help firms cope with the COVID-19 crisis.

A large literature finds that structured management practices are strongly related to firm performance, explaining one-fifth of the variation in total factor productivity across firms within countries (Bloom et al., 2016). This positive association extends to environmental stewardship (Bloom et al., 2010), fair labor practices (Distelhorst et al., 2017), and health or educational outcomes in hospitals and schools, respectively (Bloom et al., 2013, 2015). Given the important and multifaceted role of structured management practices in long-run firm outcomes, it is of interest to understand if these disciplines also help firms adjust in the face of shocks such as the COVID-19 pandemic. On the one hand, highly structured practices could lead to less nimble decision-making, if they require analysis of (not yet available) data or reaching consensus; or, these practices could lead to more sound decisions in crisis, for example, if employees are attuned to changing circumstances and motivated to act in the best interest of the 1
**Table 5: Drought Impacts on Child Anthropometrics**

**OLS with Province  and Year of Survey Fixed Effects and Full Set of Controls**

|**VARIABLES**|(1)<br>**Stunted**|(2)<br>**Underweight**|(3)<br>**Wasted**|(4)<br>**Stunted &**<br>**Underwt**|(5)<br>**Any**|
|---|---|---|---|---|---|
|3 Decad Dry Spell: Birth Season|0.04***|0.01*|-0.00|0.01***|0.04***|
||[4.53]|[1.95]|[-0.20]|[2.64]|[4.43]|
|3 Decad Dry Spell: Season after Birth|0.04***|0.00|-0.00|0.01|0.04***|
||[4.33]|[0.29]|[-0.23]|[0.86]|[4.00]|
|Mother ht between 152 and 160 cm|0.12***|0.05***|0.01***|0.05***|0.12***|
||[16.21]|[10.26]|[2.63]|[10.98]|[15.99]|
|Mother ht below 152 cm|0.25***|0.11***|0.01***|0.11***|0.25***|
||[25.99]|[14.26]|[2.84]|[15.32]|[25.46]|
|Mother under 19 yrs|0.05***|0.01|-0.00|0.02*|0.05***|
||[4.28]|[1.57]|[-0.84]|[1.77]|[3.76]|
|Age:7-24 months|0.21***|0.07***|-0.01|0.07***|0.18***|
||[21.23]|[9.54]|[-1.39]|[10.44]|[17.40]|
|Age:25-59 months|0.25***|0.06***|-0.05***|0.08***|0.19***|
||[26.35]|[8.65]|[-8.91]|[11.91]|[19.52]|
|Lean Season|0.03***|0.01**|-0.00|0.01**|0.02***|
||[3.94]|[2.24]|[-0.87]|[2.55]|[3.60]|
|No Livestock|0.02**|0.00|0.00|0.00|0.02**|
||[2.53]|[0.53]|[0.83]|[0.69]|[2.35]|
|Does not treat water|0.02**|0.01*|0.00|0.01*|0.02***|
||[2.51]|[1.93]|[1.05]|[1.87]|[2.78]|
|Mother Underweight|0.07***|0.11***|0.03***|0.09***|0.09***|
||[5.80]|[9.76]|[4.39]|[8.85]|[7.39]|
|No Car, Motor, Fridge|0.04***|0.03***|0.01**|0.02***|0.05***|
||[3.56]|[3.96]|[2.43]|[3.17]|[4.47]|
|Mother able to read only part of sentence|0.02|0.01|0.00|0.01|0.02|
||[1.46]|[0.82]|[0.19]|[0.87]|[1.56]|
|Mother cannot read at all/Unknown|0.02***|0.02***|0.01**|0.01***|0.03***|
||[2.90]|[2.74]|[1.99]|[2.64]|[3.46]|
|Constant|-0.03|-0.07**|0.05***|-0.08***|0.04|
||[-0.74]|[-2.16]|[2.93]|[-2.66]|[1.00]|
|Observations|40,817|40,802|40,714|40,817|40,817|
|R-squared|0.10|0.05|0.03|0.05|0.09|

Notes: *** p<0.01, ** p<0.05, * p<0.1. Robust standard errors are clustered at the DHS cluster level. All regresssions employ province and survey year fixed effects. Refer to the Table 3 notes for all other details.

27
from asset 01. The results are presented in Figures 1 and 2. We can summarize the results in three main points:

- A crisis in emerging country 1 affects more the international asset than to the pure domestic asset in country 2.

-A crisis in emerging country 1 that is caused by a shock to the international asset has a greater repercussions in country 2 than a crisis that is caused by a shock to the pure domestic asset.

-Finally, most of the price change in response to a shock comes from the expectation component rather than the portfolio balance component. Therefore, asymmetric information plays an important role in determining the magnitude of contagion.

# 3.7.2 Shock to international Asset 01 from Emerging Market 1

In this section, we specifically trace back the effect of a one standard deviation negative information/liquidity shock to the international asset in country 1 to all other assets. We give the intuition of the sign of the price response, and the intuition for the results summarized earlier in the context of our baseline case.

Negative Information Shock Let’s assume that we start at equilibrium for all portfolios. Then informed investors receive some additional negative information about asset 01. As a consequence, they decrease their expectation of asset 01 liquidation value by dθ01. Their optimal response is to sell some of asset 01 regardless of whether the investor is from developed market 0 or from emerging market 1. The sale decreases the price of asset 01. Uninformed investors infer from the price decrease that informed investors have received a negative signal for asset 01 and they also sell some asset 01, which reinforces the initial price decrease.

Informed investors in emerging 1 have only access to asset 01 and asset 1, so when they liquidate asset 01 their only option is to buy more of asset 1. Thus, the demand for asset 1 should increase and the price of asset 1 should increase.

Informed investors in the developed country 0 have access to three assets: 0, 01, and 02. So when they sell some of asset 01, this lowers their exposure to f1 below optimal level, they adjust their portfolio by buying some of asset 0, but this increase their exposure to f2 above optimal level, which they adjust by selling some of asset 02.

Informed sales of asset 02 are partially absorbed by investors from 2. They do so by selling off some of the pure domestic asset 2, whose price decreases. As 15
# **Figure 7. Growth of Global Liquidity**

<!-- Start of picture text --> Significant increase in the monetary base in the U.S.... ...contributed to global liquidity expansion<br>US$ tril. US$ tril.<br>2.0 12<br>10<br>1.5 Dollar Euro Yen Sterling<br>8<br>US Monetary Base<br>1.0 6<br>4<br>0.5<br>2<br>0.0 0<br>Jan-07 Jun-07 Nov-07 Apr-08 Sep-08 Feb-09 Jan-00 Jul-01 Jan-03 Jul-04 Jan-06 Jul-07 Jan-09<br>Source: US Federal Reserve Source: IFS, US Federal Reserve, Bloomberg, Bank of Japan<br><!-- End of picture text -->

**Global liquidity corrected for exchange rate changes**

<!-- Start of picture text --> 10 16%<br>US$ tril. percent<br>14%<br>8<br>In percent of world GDP (RHS)<br>12%<br>6<br>10%<br>4<br>8%<br>2<br>6%<br>0 4%<br>Jan-00 Jul-01 Jan-03 Jul-04 Jan-06 Jul-07 Jan-09<br>Source: Source: IFS, US Federal Reserve, Bloomberg, Bank of Japan<br><!-- End of picture text --> .

Two phases need to be distinguished. In the period from 2002 to mid-2007, major central banks followed monetary policies that were initially expansionary but gradually tightened, while other central banks, especially in Asia and in the oil exporting countries, accumulated large quantities of reserves as a result of substantial balance of payments surpluses. During this period, China’s central bank, for instance, increased its foreign exchange reserves from $200 billion to $1.5 trillion. These reserves were mainly held in dollars. After the onset of the crisis, the motor for liquidity expansion was the rapid growth of 16