rafmacalaba/datause-encoder-data huggingface dataset:
12

# **<u>References</u>**

- Akin, J. S., W. H. Dow, et al. (2004). "Did the distribution of health insurance in China continue to grow less equitable in the nineties? Results from a longitudinal survey." Social Science <u>& Medicine</u> **58** (2): 293-304.

- Baldwin, M., L. Zeager, et al. (1994). "Gender differences in wage losses from impairments." <u>Journal of Human Resources</u> **29** : 865-887.

- Baldwin, M. L., L. A. Zeager, et al. (1994). "Gender Differences in Wage Losses from Impairments: Estimates from the Survey of Income and Program Participation." Journal <u>of Human Resources</u> **29** (3): 865-87.

- Behrman, J. R. and A. B. Deolalikar (1988). Health and Nutrition. Handbook of development <u>economics. Volume 1. Handbooks in Economics, vol. 9. H. Chenery and T. N. e.</u> Srinivasan. Amsterdam; New York and Oxford, Elsevier Science **:** 631-711.

- Bound, J. (1991). "Self-reported versus objective measures of health in retirement models." <u>Journal of Human Resources</u> **26** : 106-138.

- Bound, J., M. Schoenbaum, et al. (1999). "The dynamic effects of health on the labor force transitions of older workers." Labor Economics **6** (179-202).

- Contoyannis, P. and N. Rice (2001). "The impact of health on wages: Evidence from the British Household Panel Survey." Empirical Economics **26** (5): 599-622.

- Currie, J. and B. C. Madrian (1999). Health, Health Insurance and the Labor Market. Handbook <u>of labor economics. Volume 3C. O. Ashenfelter and D. e. Card.</u>

- Dercon, S. (2002). "Income Risk, Coping Strategies, and Safety Nets." World Bank Research <u>Observer</u> **17** (2): 141-66.

- Dercon, S. and P. Krishnan (2000). "In Sickness and in Health: Risk Sharing within Households in Rural Ethiopia." Journal of Political Economy **108** (4): 688-727.

- Disney, R., C. Emmerson, et al. (2003). Ill health and retirement in Britain: a panel data based analysis, IFS Working Paper W03/02.

- Gertler, P. and J. Gruber (2002). "Insuring Consumption Against Illness." American Economic <u>Review</u> **92** (1): 51-76.

- Grossman, M. (1972). "On the Concept of Health Capital and the Demand for Health." Journal <u>of Political Economy</u> **80** (2): 223-55.

- Haveman, R. and B. Wolfe (1990). "The Economic Well-Being of the Disabled: 1962-84." <u>Journal of Human Resources</u> **25** (1): 32-54.

- Himmelstein, D. U., E. Warren, et al. (2005). "MarketWatch: Illness And Injury As Contributors To Bankruptcy." Health Aff (Millwood).

- Kochar, A. (1995). "Explaining Household Vulnerability to Idiosyncratic Income Shocks." <u>American Economic Review</u> **85** (2): 159-64.

- Levy, H. (2002). The Economic Consequences of Being Uninsured, ERIU Working Paper 12.

- Liu, Y. (2002). "Reforming China's urban health insurance system." Health Policy **60** (2): 133150.

- Liu, Y. L. (2004). "Development of the rural health insurance system in China." Health Policy <u>and Planning</u> **19** (3): 159-165.
and hence the quick ratio is alternatively (receivables + cash and cash equivalents)) / current liabilities. A firm’s debt is considered “at risk” if the QR is less than or equal to 1, namely liquid current assets cannot cover current liabilities. In normal times, firms may be able to operate with a QR less than 1 in the expectation that they can raise funds quickly through lines of credit with banks or by issuing in capital markets to cover short-term obligations. However, the pandemic has impaired funding conditions considerably, particularly in EMDEs. We define D as the total debt associated with companies that are “at risk” in country _C_ in quarter _t_ according to indicator _Y_ (i.e. ICR or QR). Next, we define D 1,C as the debt at risk in YY,C country _C_ in quarter DD _t_ according to one or more indicators. DD Where _i_ denotes individual firms and 1(.) is an indicator variable which is equal to one if the logical statement inside the bracket is true and zero otherwise. To quantify the impact of COVID-19 related earnings and liquidity shocks on non-financial corporate vulnerabilities, we calculate D , D Q ,C and D 1,C for each shock scenario. IICCII,C iiQ 4. DDData DD DD

**Our sample consists of 2019Q4 balance sheet and income statement data of 16,934 listed nonfinancial companies in 8 industries in 73 countries spanning all six World Bank regions (see Annex).** We use pre-pandemic, 2019Q4 data as the basis of our stress tests since this is the latest, most complete information available. Bloomberg is our primary data source.

**The companies in our full sample represent US$ 22.1 trillion in total assets and US$ 6.05 trillion debt** . **For the median country, total assets and total debt of these companies stand at 25 percent to GDP and 7 percent to GDP, respectively.** Exhibit 2 shows our sample coverage by region: EAP and SA regions stood out as having the highest debt and total assets, both in terms of absolute level and in terms of importance relative to country GDP. Although our sample contains a large number of firms in ECA and SSA, the shares of their debt and total assets relative to GDP are low.

**Notwithstanding some regional and sectoral heterogeneity, we find that prior to the pandemic, the ICR is well above 1, the QR is at or around 1, and current liabilities slightly**

7
# **3.1 Data sources and main variables**

Data in this paper were collected from three sources. Information on portfolio holdings by Colombian Pension Funds was provided by the Superfinanciera de Colombia (SFC), a supervisory agency within Colombia’s Finance and Public Credit Ministry which oversees all financial, insurance and pension services in the country. The data include detailed security allocations for all private pension funds on a monthly basis between January 2004 and December 2014. Figure 1 presents the time series of pension funds monthly cumulative net flows into stocks (solid line) normalized by the size of the domestic stock market. PFAs have consistently increased their participation in the stock market, with the exception of a few short-lived periods of net outflows. By December 2010, these Colombian Pension Funds had 32% of their $59.6 billion invested in domestic stocks, which amounted to 13% of total market capitalization.

We augment our dataset by including monthly trading activity from the Colombian Securities Exchange. The data include monthly trading volume in each stock by all PFAs. The information, however, is not disaggregated by PFA and only includes industry totals. Figure 1 displays PFAs monthly trading volume as a percentage of the total amount traded in the stock exchange (dash lines). On average, pension fund trades represent over 12% of the value traded in each month. Overall, not only do these funds make up for a significant portion of the domestic equity market, but they also represent a large fraction of the trading volume.

We use Compustat Global for daily stock prices and firms’quarterly balance sheet information.<sup>11</sup> To avoid stocks with small trading activity, we restrict our sample to stocks that trade at least 25% of the business days when the stock was active. We also limit our sample to stocks in which at least one PFA had any holdings during the period. Our final sample includes 57 different stocks, which represent 93% of total stock market capitalization and 95% of the trading volume.

> 11Compustat Global has missing information for several small Colombian firms. We collect balance sheets for these companies directly from the SFC.

10
Table 5 Continued: First stage regression from Table 4

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Dependent variable: Share of l|abor with c|omputer|||||
|IV1|0.113***|0.120***|0.103***|0.093***|0.085***|0.090***|
||(0.022)|(0.023)|(0.022)|(0.022)|(0.022)|(0.024)|
|IV5|0.126***|0.137***|0.136***|0.115***|0.120***|0.127***|
||(0.031)|(0.036)|(0.034)|(0.034)|(0.034)|(0.037)|
|First stage F-value|20.3|17.2|15.8|11.7|11.7|9.7|
|Underid p-value|0.0000|0.0000|0.0000|0.0000|0.0000|0.0001|
|Sargan p-value|0.88|0.93|0.62|0.63|0.69|0.69|
|Controls|||||||
|Age|No|Yes|Yes|Yes|Yes|Yes|
|Firm size|No|No|Yes|Yes|Yes|Yes|
|Share of white-collar workers|No|No|No|Yes|Yes|Yes|
|Capital/worker|No|No|No|Yes|Yes|Yes|
|Exports/sales|No|No|No|No|Yes|Yes|
|FDI share|No|No|No|No|Yes|Yes|
|Time efects|No|No|No|No|No|Yes|
|Observations|1402|1402|1402|1402|1402|1402|

Standard errors in parentheses

* _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01

All specifications include firm’s fixed effects. Source: Authors’ calculations using data from ENTIC, INEGI IV1: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of urban households with computers (2000 and 2010 Population and Housing Censuses) IV2: IT intensity based on investment from (O’Mahony and Van Ark, 2003) and Share of households with internet (MODUTIH) IV3: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of households with internet (MODUTIH) IV4: IT intensity based on investment from (O’Mahony and Van Ark, 2003) and Share of urban households with computers (2000 and 2010 Population and Housing Censuses) IV5: IT intensity dummy based on name from (Bloom et al., 2012b) and Share of urban households with Internet (2000 and 2010 Population and Housing Censuses) 20
**Appendix Table 1: Descriptive** **<u>statistics at household level</u>**

||**Total**|**Q1**|**Q2**||**Q3**||**Q4**||
|---|---|---|---|---|---|---|---|---|
|**_Household characteristics_**|||||||||
|Female headed hhd=1|0.28|0.43|0.28|***|0.23|**|0.19|**|
|Number of children (0-14)|2.09|1.88|2.01|*|2.11||2.43|***|
|No. female adults|1.31|1.12|1.25|***|1.32|*|1.53|***|
|No. male adults|1.10|0.77|1.04|***|1.17|***|1.43|***|
|Age head|46.32|48.56|46.76|**|45.13|**|46.16||
|No. years of schooling head|3.21|2.01|2.61|***|3.45|***|4.64|***|
|**_Asset endowments_**|||||||||
|Total land owned|0.65|0.24|0.44|***|0.60|***|1.31|***|
|% of high quality soil|0.36|0.35|0.34||0.37||0.37||
|% of medium quality soil|0.15|0.14|0.16||0.15||0.14||
|% of wetland|0.07|0.07|0.07||0.08|*|0.08||
|% of irrigated land|0.05|0.04|0.04||0.06|**|0.06||
|Total borrowed, USD|23|8|11|**|24|***|48|***|
|Sale value of hhd assets, USD|56|5|15|***|38|***|159|***|
|Sale value of livestock, USD|163|32|94|***|184|***|363|***|
|**_Social capital_**|||||||||
|No. of adult children in village|0.36|0.46|0.42||0.34|*|0.27||
|Hd born in the village|0.71|0.70|0.70||0.75|**|0.68|***|
|Hd displaced|0.47|0.47|0.42|**|0.51|***|0.49||
|Member of farm cooperative|0.22|0.16|0.21|**|0.27|***|0.26||
|Listen to the news at least once a week|0.69|0.39|0.68|***|0.82|***|0.89|***|
|Head/Spouse holds political office|0.10|0.03|0.08|***|0.12|***|0.19|***|
|Relative holds political office|0.21|0.16|0.20|**|0.23||0.26|*|
|Distance to the market, min|371|433|407||343|***|303|*|
|Distance to the main road, min|276|331|283|*|271||231|*|
|**_Input use and agricultural productivity_**|||||||||
|Used chemical fertilizer|0.17|0.10|0.13|*|0.20|***|0.24|**|
|Used manure|0.73|0.66|0.71|**|0.76|**|0.80|*|
|Used pesticides|0.13|0.08|0.10|*|0.14|**|0.18|*|
|Used improved seed|0.26|0.17|0.21|**|0.31|***|0.35|*|
|Used extension advice|0.19|0.14|0.14||0.24|***|0.25||
|Male family labor days per ha|136|132|157|*|146||101|***|
|Female family labor days per ha|326|462|339|***|299||199|***|
|<br>Used hired labor|0.43|0.22|0.31|***|0.48|***|0.74|***|
|Hired labor days per ha|146|150|124||122||154|*|
|Total labor days per ha|488|656|537||465||418||
|Yieldper ha in USD|444|474|417|**|443||439||
|**Total Observations**|3600|823|824||823||824||

**Source:** Own computation from WB survey 2011. **Note:** The wealth quartiles are calculated using a principal component analysis based on housing conditions, assets, livestock and land ownership. T-test is performed using the immediate lower quartile as a reference: * significant at 10%; ** significant at 5%; *** significant at 1%

25
**Figure 2: Regulatory capital holdings**

# **a. Mean regulatory capital holdings (% of RWA)**

# **b. Mean Tier 1 capital ratio (% of RWA)**

Source: Waves 4 and 5, BRSS.

Note: Off-shore financial centers are excluded from the calculations. DEV are developing countries. Below each regional label, within parentheses, we report the number of countries for which we have a response for the specific item/question for both rounds. For example, a country that has information from round 5 but not round 4 is excluded from the calculation. * next to the number of countries represents a statistically significant difference in the mean value for each regional group in the two reference years at the 10% level using a two-tailed test. In some countries, deductions to Tier 1 capital are applied when this is included in the calculation of total regulatory capital. In addition, the value of Tier 2 capital can be negative. Therefore, the value of Tier 1 capital could be higher than the value of regulatory capital. Moreover, it is worth noting that the definitions of regulatory capital and Tier 1 capital vary over time and depend on the regulatory capital framework that a country has adopted (e.g., Basel I versus Basel III).

Drawing on bank-level information on over 20,000 banks in 158 countries allows us to investigate these trends further, as well as differences across large versus small banks. Large banks are defined as the top 20 percent in terms of total banking assets and small as the bottom 80 percent. Figure 3, Panel A shows that post-GFC increases in the total regulatory capital ratio in high-income OECD countries occurred for both large and small banks. As discussed above, increases in the regulatory capital in developing country banks were modest, given these countries started from much higher levels. We also observe that the regulatory capital held by smaller banks was higher both in high-income OECD, as well as developing countries, though the overall patterns are similar to those of the large banks. Figures 3, Panel B and Panel C also show that improvements in simple leverage ratios (calculated as capital divided by total banking assets) were more limited, and capital increases particularly in high-income OECD countries were largely accompanied by declines in risk-weighted assets (as a share of total assets). Thus, while Figure 2 and Figure 3 Panel A and B suggest that capital buffers increased for banks in high-income OECD countries in the wake of the crisis, Figure 3, Panel C indicates that this conclusion partly hinges on the decline in risk7
Table E9: School Infrastructure <u>(Year</u> 2)

||Spending|Number|purchased|Facility|present (Y|/N)|Other|
|---|---|---|---|---|---|---|---|
||(1)<br>Amount (PKR)|(2)<br>Desks|(3)<br>Chairs|(4)<br>Computers|(5)<br>Library|(6)<br>Sports|(7)<br># Rooms Upgraded|
|High|606.00|0.56|1.16|0.06|-0.00|0.05*|0.24|
||(6537.56)|(1.39)|(0.83)|(0.05)|(0.03)|(0.03)|(0.37)|
|Low Treated|353.44|-0.92|0.84|0.14**|0.00|0.02|0.31|
||(7911.96)|(1.44)|(0.54)|(0.06)|(0.03)|(0.03)|(0.36)|
|Low Untreated|1497.67|-1.46|0.28|-0.02|0.02|0.02|0.08|
||(7029.37)|(1.28)|(0.38)|(0.04)|(0.03)|(0.03)|(0.33)|
|Baseline|0.04|0.08**|0.01|0.31***|0.02|0.07*|0.74***|
||(0.03)|(0.04)|(0.02)|(0.05)|(0.03)|(0.04)|(0.05)|
|R-Squared|0.05|0.08|0.04|0.16|0.04|0.11|0.51|
|Observations|770|746|780|784|784|784|784|
|# Schools (Rounds)|770 (1)|746 (1)|780 (1)|784 (1)|784 (1)|784 (1)|784 (1)|
|Mean Depvar|57258.48|14.59|10.92|0.39|0.35|0.19|6.36|
|Test pval (H=0)|0.93|0.68|0.16|0.26|1.00|0.06|0.52|
|Test pval (_L_<sup>_t_</sup>= 0)|0.96|0.53|0.12|0.03|0.95|0.46|0.39|
|Test pval (_L_<sup>_t_</sup>=H)|0.97|0.32|0.74|0.21|0.95|0.44|0.86|

Notes: * p<0.10, ** p<0.05, *** p<0.01 a) This table examines outcomes relating to school infrastructure using data from round 5 only. Column 1 is the annual fixed expenditure on infrastructure– e.g. furniture, fixtures, or facilities. Columns 2-3 refer to the number of desks and chairs purchased. Columns 4-6 are dummy variables for the presence of particular school facilities. Finally, column 7 measures the number of rooms upgraded from temporary to permanent or semi-permanent classrooms. Closed schools take on a value of 0 in all columns.

b) Regressions are weighted to adjust for sampling and include strata fixed effects, with standard errors clustered at the village level. The number of observations and unique schools are the same since we only use one round of data. The mean of the dependent variable is its baseline value.

c) The bottom panel shows p-values from tests that either ask whether we can reject a zero average impact for high (H=0) and low treated ( _L_<sup>_t_</sup> =0) schools, or whether we can reject equality of coefficients between high and low treated ( _L_<sup>_t_</sup> =H) schools.

46
10

- **X,M - redl exports and real imports**

- **D -** gross outstanding debt, net of reserves

In equation 4, the first term (RIR) measures the contribution of higher than expected interest payments and the remaining terms measure the e.;fect of changes in the terms of trade (TOT). The choice of periods implies that the proxy for the external environment, SHi, is expressed as a nercentage of the average value of GDP during 1976-81.

# 4. **Statistical Results**

Our main interest is in the effects of adjustment programs on growth. We use two indicators of growth: GDP growth and the share of investment in GDP. It could be argued that the investment share in GDP is an intermediate target. While this would be true in the long run, our limited time-frame for the post-loan period removes this concern. An improvement in growth alone could indicate an increase in capacity utilization. Hence the investment share is used as an additional indicator of sustainable long-run growth. We also use two indicators whose developments are followed closely by the IMF: inflation and the current account. (Another closely watched indicator, the government deficit, .s not included here because it is unreliable on a comparative basis.) The results after one round of exclusion of influential observations, and correcting for heteroskedacity, appear in tahle 3. 5/ The effects of participation in IMF-WB programs are measured Dy the coefficient on the **dummy** CON. 7/ The first remarkable result is that participation in IMF-WB programs does not appear to affect output growth in
productivity of indigenous firms operating in the same industries in Venezuela, Djankov and Hoekman (2000), Aghion et al. (2004) and Haskel et al. (2007) reach the opposite conclusion.<sup>2</sup> More recent studies have argued that while foreign investors have an incentive to prevent knowledge leakage to their competitors, they may encourage transfer of information to their local suppliers. And indeed work by Javorcik (2004) and Blalock and Gertler (2007) shows a positive association between FDI and productivity in upstream industries (for a literature review see Görg and Greenaway 2004).

Compared with work on manufacturing industries, studies on the implications of FDI inflows in service sectors are relatively scarce. Eschenbach and Hoekman (2006) document a positive relationship between progress in services liberalization, including openness to FDI, and economic growth in transition countries for the period of 1990-2004. Mattoo et al. (2006) present econometric evidence from a sample of 60 countries over 1990-1999 indicating that openness in the financial and telecommunications sectors influences long-run growth performance. Arnold et al. (2006) analyze firm-level panel data from the Czech Republic and find a significant positive effect of FDI in the services sectors on downstream manufacturing firms’ total factor productivity (TFP). Using industry-level panel data, Fernandes (2007) shows progress in service sector liberalization leads to an increase in labor productivity of downstream manufacturing in transition countries. The results of these studies suggest that the quality and availability of services inputs used by manufacturing industries may be positively affected by entry of foreign services providers. There is, however, no work documenting the possible implications of foreign entry into services in general (and retail sector in particular) for the performance of manufacturing firms in the supplying industries.

The second literature relevant to this study consists of case studies on the evolution of the retail sector in developing and transition countries. A series of studies describe the rise of modern retail formats, contrast them with traditional retailers and examine the implications of this phenomenon for agricultural producers. Dries, Reardon and Swinnen (2004) draw a detailed picture of the evolution of supermarkets in Central and Eastern European (CEE) countries and discuss their implications for the agricultural sector. Swinnen et al. (2006) document how FDI in the retail sector in some CEE countries facilitates productivity growth of local dairy farmers. Reardon and Berdegue (2002) and Reardon, Timmer, and Berdegue (2003), Minten,

> 2 For a review of the literature, see Görg and Strobl (2001).

4
# Appendix

## Appendix A1: A Novel Database of Laws and Decrees in the GCC

Our first contribution is a novel database of past and current economic reforms from all the GCC countries. We obtain these data from each country’s national legislative archives by web-scraping from the following sources:

- **<u>Bahrain:</u>** The Legislation & Legal Opinion Commission (link)

- **<u>Kuwait:</u>** The General Secretariat of the National Assembly (link)

- **<u>Oman:</u>** Oman Law (link)

- **<u>Qatar:</u>** Al-Meezan (Qatar Legal Portal) (link)

- **<u>Saudi Arabia:</u>** National Center for Archives and Record (link)

- **<u>UAE:</u>** The Ministry of Justice (link)

After extracting laws and decrees from the online archives, we tagged them according to a classification system corresponding to the WEF’s Global Competitive Index (GCI) pillars and an additional diversification category corresponding to UNCTAD’s Export Concentration Ratio. We conducted the tagging by creating keywords for each category and searching for those terms in each title. For example, the “innovation” category’s key terms include research, intellectual property, artificial intelligence, technology, inventions, etc. If at least one word in a category’s keywords is found, we apply that category’s tag. We allow for a law to fall under multiple categories. We do not include laws for which we found no category matches in our analysis. The WEF’s GCI pillars and their definitions are in Table A1.1.

### **Table A1.1 Definitions of policy pillars**

|**Pillar**|**Definition**|
|---|---|
||The legal and administrative framework within which<br>individuals, firms, and governments interact to create wealth.|
|_Institutions_|This measure is weighed down by excessive bureaucracy, lack<br>of transparency, corruption, improper management of public<br>finances, etc. Private institutions are also included.|
||Transportation and communications infrastructure, including|
|_Infrastructure_|roads, ports, railroads, air transport, fixed telephone lines, and<br>mobile penetration. The availability and reliability of electricity<br>supplies are also included|
|_Macroeconomic Environment_|Macroeconomic environment’s stability, including fiscal<br>deficits, public debt, inflation rates, and national savings.|
||A general assessment of the population’s health (including|
|_Health and Primary Education_|investment in the provision of health services) and the quality<br>and quantity of basic education|
||Measures secondary and tertiary enrollment rates as well as|
|_Higher Education and Training_|the perceived quality of education by business leaders. Staff,<br>vocational and on-the-job training are also included|
||Measures the ease of trade and doing business, healthy market|
|_Goods Market Efficiency_|competition,<br>absence<br>of<br>restrictions<br>on<br>FDI,<br>buyer<br>sophistication, customer orientation, etc.|
||Measures the flexibility of wages, productivity, female|
|_Labor Market Efficiency_|participation in the labor force, hiring and firing practices, and<br>labor-employer relations. Workplaces must also promote<br>meritocracy|

20