rafmacalaba/datause-encoder-data huggingface dataset:
Unfortunately, in the ICA surveys there are no data on liquid assets, and therefore it is not possible to construct a good proxy for financial savings.<sup>5</sup> However, we consider several proxies that are related to cash flows, since cash flows are related to savings as represented in the Flow of Funds Chart in Figure 2. Specifically, the ICA data allows for a calculation of operating income to sales. This is the closest measure for cash flows available in ICA data because there is no data to calculate net income or retained earnings and the total assets data are unreliable. Specifically, we construct Gross Operating income, which is defined as sales revenues minus the costs.<sup>6</sup> To make the comparison of firms of different sizes meaningful, GOI is scaled by firm sales, which in effect becomes a proxy for gross operating margin (GOM).

While there is no data on total retained earnings, there are questions in the survey about what proportion of investment and working capital is financed with retained earnings. These questions are used to gauge to what extent the firm has to rely on its own earnings to finance their investment and working capital.

To summarize, for ICA data we use five measures related to savings:

- Indicator for whether the firm makes any investment in a given year (called “any investment”);

- Investment to capital ratio, IK;

- Operating income to sales (a proxy for cash flows);

- Proportion of retained earnings used to finance working capital;

- Proportion of retained earnings used to finance investment.

> 5 The ICA data contain very few balance sheet indicators. Specifically, it contains data on total assets, total gross and net value of fixed assets (i.e. property, plant, equipment, land and buildings), and data on inventories and accounts receivables. Theoretically, it is possible to use  these items to estimate the value of liquid assets as total assets minus the sum of fixed assets, accounts receivable and inventory. However, using the current ICA data this method does not produce reliable results as the resulting measure of cash stocks is often negative or unreasonably large. Therefore the balance sheet method for calculating liquid assets is deemed unreliable for ICA data for Egypt. 6 The costs measures available in the data include purchases of raw materials and intermediate goods, cost of labor, including wages, salaries or bonuses, rent on land and buildings, rent on machinery and equipment and vehicles, and the overhead costs, including energy, transport, administrative expenses and others.

8
**Figure 4** :  Costa Rica’s Exports by Destination, 2000 versus 2008 (US$)

<!-- Start of picture text --> A 77% increase<br>in total exports<br>A 130%<br>increase in<br>exports to CA<br><!-- End of picture text --> According to calculations by SIECA, for the region overall,<sup>11</sup> since the signing of the Central America Free Trade Agreement, the share of intraregional exports in total export has fluctuated between about 26 and 30 percent in US$ FOB terms, reaching the highest share in 2008 and then decreasing slightly in 2009 (Figure 5). In fact, over the past five years, the region was the second largest trading partner for the individual Central American economies.

Source: LCSSD Economics Unit calculations based on WITS data, 2010

**Figure 5** :  Central America’s Intraregional and Total Export Flows, 2005-2009 (US$’000)

<!-- Start of picture text --> Total Exports<br>Intraregional Exports 29.5%<br>27.2% 26.5%<br>26.6%<br>27.2%<br><!-- End of picture text --> Source: SIECA According to the model results produced for the current paper, the size of the domestic exporting markets and the distance between trading partners are the main factors affecting bilateral trade in Central America. Given a 1-percent increase in GDP of the exporting country, it is expected that exports from that country to the rest of the region increase by nearly 1.7 percent. Conversely, a 1-percent average increase in distance between each country and its export partners is associated with reductions in expected exports by 1.6 percent.

In contrast to the EU15, in Central America, the “gravitational pull” (proxied by the Importing Countries’ GDP) is relatively low or even insignificant when the model is disaggregated. A 1- percent increase in the GDP of the importing country (i.e. the export partner) increases expected

> 11 Calculations exclude Panama

10
we lengthen the reference lockdown by between 2 and 8 weeks. If anything, for the lowerincome countries, the mortality ratio rises under longer lockdowns, as longer lockdowns have a disproportionately larger effect on child mortality relative to adult lives saved.

On the other hand, delaying the lockdown onset lowers the mortality ratio substantially, putting it firmly below 1. Evidently, delaying the lockdown is beneficial. Looking closer, it appears that the fall in the mortality ratio is driven entirely by the number of adult lives saved. The second and third columns of the table report the number of children’s deaths and the number of adult lives saved in each alternative scenario, normalized by the number of deaths in the baseline reference lockdown. Delaying the lockdown by 2 to 8 weeks hardly changes the number of child deaths. This is not surprising, as the child mortality comes from the economic downturn engineered by the lockdown, and so it doesn’t matter very much whether the economic downturn occurs now or 8 weeks from now. However, the third column shows that delaying the lockdown leads to many more adult lives saved, compared to the baseline lockdown onset, due to a flattening of the epidemic peak which renders health system constraints less binding. Overall, the results do not change qualitatively. There is a substantial number of children’s lives lost per COVID-19 fatality averted in low-income countries in all hypothetical scenarios.

# **5 Conclusion**

At the start of the COVID-19 pandemic, countries around the world imposed lockdown measures similar in severity. Our analysis, however, suggests that optimal policies substantially differ, depending on the vulnerability of child survival to income shocks, countries’ demographic characteristics, and patterns of social contacts. The reason is that economic contractions in low- and middle-income countries increase child mortality, and policy responses to the pandemic contributed to declines in national income in much of the world. This paper highlights and then quantifies this relatively neglected consequence of lockdowns purely in terms of inter-generational mortality tradeoffs, thereby informing country-specific assessments of the costs and benefits of lockdowns as policies to fight a pandemic.

Since our main objective is the formulation of a new tradeoff, we abstracted from other 45
gas). The dummy variables provide tests against excluded dummies for inferior toilet facilities (none), superior water sources (pipes and public taps), and inferior cooking fuels (principally charcoal, wood, straw and dung).<sup>12</sup>

# **Results**

# _<u>Results for DHS Variables</u>_

We present our full-sample results for the DHS variables in the first column of Table 2.<sup>13</sup> These results are of independent interest, since they provide new insight into the determinants of infant mortality in Bangladesh.<sup>14</sup> All of the significant parameters have the expected signs: The probability of death is significantly lower for infants whose mothers are older, more highly educated, and live in female-headed households with access to piped or tap water and flush toilets or pit latrines. We find no significance in the full sample for child gender, household wealth, tubewell water sources or clean cooking fuels.<sup>15</sup>

# _<u>Results for Salinity</u>_

Columns (2) - (4) of Table 2 summarize an extensive exploration of lagged salinity impacts that is illustrated for the 40 km distance bound in Table 3. Using the selected DHS variables, we successively introduce lagged values of our salinity indicator, starting with the first month before each child’s birth. Our standard and robust probit and logit estimators all yield the same strong result: Only salinity in the

> 12 Appendix A provides more detailed information on variable selection for toilet facilities, drinking water sources and cooking fuels.

> 13 It is important to note that these are regression results for data on individuals, not statistics for sub-groups in the population.  Employment of the latter would require sample weighting.  As the illustration in Table 4 shows, our microeconometric results can be used to construct estimates for arbitrarily-segmented sub-groups.

14 The term “infant mortality” is commonly understood to mean mortality of infants less than twelve months old. For expositional simplicity in this paper, we use the term for mortality of infants less than two months old.

15 Previous micro-level research by the authors on fuel use and indoor air pollution in Bangladesh (Dasgupta, et al. 2006) finds a significant relationship between cooling fuels and indoor levels of harmful particulate air pollution. However, the results also show that structural conditions (e.g., building materials, kitchen locations) play a more significant role. In addition, the previous work does not test the impact of indoor particulate pollution on infant survival probability. Our weak results in the current research suggest that robust inferences about the impact of cooking fuel choice on infant health require much more detailed structural and environmental data than are available for this exercise.

10
employees in using a new machine. Table 1 summarizes the approved activities over a period of two years for one of the programs we wanted to evaluate.

Among firms which had previously received grants, one of the most successful cases was a windgenerated electricity producer. It used a matching grant of $8,000 to develop the feasibility studies that allowed it to enter into a joint-venture with a European firm and obtain an investment of US$1 million. It took four years after funding for the project to become commercially operational, and within the first year of operation the firm was already supplying 14 percent of all the electricity consumed in the two districts it services. This project serves as a possible example of multiple levels of impact, with the firm receiving greater profits, consumers receiving better quality electricity service (brown-outs and blackouts were reduced), and the environmental benefits through clean energy provision. Even in this case it is not possible to ascertain absolutely whether or not this would have happened without the matching grant, but it is certainly the type of case where multiple market failures and externalities seem apparent, and public benefit from the grant seems likely. Another two examples of former grant recipients concern investment in worker and firm training. A civil engineering firm used the grant to help fund quality certification training for its workers, while a communications firm used the grant to develop an employee training scheme. Both firms said that they had been unsure about the effectiveness of such training, but after seeing the results from the grant funding, they had subsequently invested more of their own funding in continuing this activity.

For other approved matching grants the additionality needed to justify the government support is not as clear, particularly taking into consideration the information available to firms on the benefits of these investments and their lack of constraints in accessing them. In fact, according to a survey commissioned by the government in one of the countries where we started an evaluation, approximately 25 percent of the firms receiving the matching grant stated that they would have pursued the activity fully anyway and an additional 49 percent confirmed that they would be pursuing the activity in the absence of the matching grant, albeit using a less expensive consultant (31 percent of total), at a later stage (10 percent), or at a smaller scale (8 percent). Examples of less successful uses of the matching grant, or at least of apparent limited public benefits, include providing business services to firms who have the skills, knowledge and capacity to conduct the activities and are expected to invest in those areas. These comprise, for instance, an IT company receiving a matching grant to develop a website or a travel agency wanting continuous support to participate in various international tourism fairs. Applications like these are attractive both to the business (little risk and they continue doing the 10
Moreover, by 2007, the ratio of government domestic to foreign debt had risen even more. Argentina, Brazil, and Uruguay prepaid their IMF debts and Mexico swapped its World Bank and Inter-American Development Bank debt for domestic debt.

# **4. Why has Government Domestic Debt Grown?**

The large growth of government domestic debt since the 1990s mainly reflects three supply factors and one demand factor:

# **Supply:**

- 1) Financial crises led to large increases in government domestic debt.

- 2) Central bank debt has grown, often as a result of recent attempts to sterilize capital inflows and thereby limit both inflation and exchange rate appreciation.

- 3) Governments have found domestic debt issuance attractive, not only because of post-crisis concerns about the volatility of foreign debt but because of the decline in the relative cost of domestic debt owing to the rising cost/declining supply of foreign borrowing following the financial crises of the 1990s, decreased inflation, and the development of domestic government debt markets.

# **Demand:**

- 4) Demand for government domestic debt has grown from new intermediaries, such as fully-funded pension funds and investment funds.  In addition, non-resident demand for government domestic debt has grown recently, with the decline in interest rates in industrial country markets and emerging markets’ greater stability, international reserves, and prospects for exchange rates appreciation.

The rest of this section discusses the role of these four factors.<sup>16</sup>

- 4.1 A. Financial Crises and Increases in Government Domestic Debt

Countries that experienced major financial crises represent the majority of countries with large increases in government domestic debt increases. Much of their debt increases appear to reflect their governments’ recapitalizations of their banking systems after the crises. However, the crisis-related growth in their domestic debt appears to be well beyond the recapitalizations. Precisely determining the part of the increased domestic debt that was due to the crisis and precisely why the debt increases differ across crisis countries is practically impossible. The separation and analysis of the debt increases is complicated by theoretical and empirical issues related to the timing of the

> 16 The section does not discuss in detail how these factors affect the ratio of government domestic to foreign debt. Nonetheless, it is interesting to note that the ratio of government domestic to foreign debt has increased (Table 1). This rising ratio reflects a mix of rising domestic debt (Figure 3); the slowing of many countries’ foreign borrowing between the late 1990s and 2002 for both demand and supply reasons, as discussed in World Bank’s _Global Development Finance_ volumes between 2000 and 2003; and the real exchange rate appreciations that have occurred in some countries once the crises of the mid-1990s passed.

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Figure 4. Levels of Gender Inclusion in Business Environment Reform

**→ Gender Focused** Policy priorities targeted at enhancing WEE

**→ Gender**

**Mainstreaming** Analysis gender sensitive Reform design and implementation are inclusive

**→ Gender Aware** Data disaggregated and efforts to minimize harm. No explicit priority on WEE and greater inclusion in policymaking

**→ Do No Harm** Focused on minimizing risk/unintended consequences. No positive equity goals

**Gender Blind** No inclusion of gender lens in analysis, design or implementation

Source: ILO (2021). Note: WEE stands for Women Economic Empowerment.

At the internal level, Table 2 presents the share of firms reporting each variable as the biggest obstacle. A large heterogeneity is observed across different regions. For instance, while firms report that access to finance is a major obstacle in Sub-Saharan Africa, those in the MENA region complain mainly about business licensing, permits, corruption, and political instability. In Latin America, business and licensing, crimes and disorder, customs and trade regulations, and practices of the informal sector seem to be major obstacles. Finally, in Europe and Central Asia, problems related to inadequately educated workforce, labor regulations, tax rate, and administration are onerous. Obviously, such problems that affect the business environment might also hinder women’s participation. This is why we take different obstacles into consideration to see how they can amplify the impact of GVC on women’s participation.

8
- Li, H.B., L.S. Meng, Q. Wang, and L.A. Zhou. 2008. “Political Connections, Financing and Firm Performance: Evidence from Chinese Private Firms.” _Journal of Development Economics_ 87(2), 283-299.

- Lin, Yi-min, and Tian Zhu. 2001. “Ownership Restructuring in Chinese State Industry: An Analysis of Evidence on Initial Organizational Changes”, _China Quarterly_ , 166, 305-341.

- Luez, C., F. Oberholzer-Gee. 2006. “Political Relationships, Global Financing, and Corporate Transparency: Evidence from Indonesia.” _Journal of Financial Economics_ 81(2), 411-439.

- McGregor, Richard. 2010. _The Party: The Secret World of China's Communist Rulers_ .   New York: Harper Collins Press.

- Poncet, Sandra, Walter Steingress, and Hylke Vandenbussche, 2010. “Financial Constraints in China: Firm-level Evidence.” _China Economic Review_ 21(3): 411-422.

- Stein, J. 2003. “Agency, Information and Corporate Investment.” forthcoming in the _Handbook of the Economics of Finance_ , edited by George Constantinides, Milton Harris, and Rene Stulz.

- Wang, Xiaozu, Lixin Colin Xu, and Tian Zhu. 2004. “State-Owned Enterprises Going Public – The Case of China.” _Economics of Transition_ 12(3): 467-487.

- Weisbach, Michael S. 1995. “CEO Turnover and the Firm’s Investment Decisions.” _Journal of Financial Economics_ 37, 159-188.

- Wu, W.F., C.F. Wu, and O.M. Rui. “Ownership and the Value of Political Connections: Evidence from China.” _European Financial Management_ 18(4), 695-729.

- Wu, W.F., C.F. Wu, C.Y. Zhou, J. Wu. “Political Connections, Tax Benefits and Firm Performance: Evidence from China,” _Journal of Accounting and Public Policy_ 31(3), 277-300.

- Xu, Lixin Colin. 2011. "The Effects of Business Environments on Development: A Survey of New FirmLevel Evidence,” _World Bank Research Observer_ 26 (2), 310-340.

- Xu, Lixin Colin, Tian Zhu, Yi-Min Lin. 2005. “Politician Control, Agency Problems, and Ownership Reform: Evidence from China” _Economics of Transition_ 13(1), 1-24.

- Zhu, Tian. 1999. “China’s Corporatization Drive: An Evaluation and Policy Implications,” _Contemporary Economic Policy,_ 17(4), 530-539.

41
firms comply with 91 percent of compliance questions, 92 percent of nondisclosure questions, but 88 percent of disclosure questions.

**Table 6: Number of firms and mean compliance rates over time**

|**Year**|**Firms**|**All**|**Disclosure**|**Non‐disclosure**|
|---|---|---|---|---|
|2010|65|0.888|0.876|0.890|
|2011|116|0.911|0.902|0.913|
|2012|136|0.908|0.902|0.909|
|2013|139|0.917|0.895|0.921|
|2014|177|0.901|0.843|0.911|
|2015|219|0.887|0.834|0.895|
|2016|253|0.911|0.871|0.918|
|2017|286|0.922|0.881|0.929|
|2018|106|0.942|0.876|0.947|
|All years|457|0.910|0.875|0.916|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

Table 7 explores the non‐compliance rate before and after program announcement, for all compliance questions, all compliance questions that are related to public disclosure, and all compliance questions within each compliance cluster. For now, we limit the analysis of the public disclosure program to focus on the date of program announcement.<sup>8</sup> The non‐compliance rate for all questions and non‐disclosure questions are different: 10% versus 13% before the program announcement, and 8% and 12% after the program announcement.

It is also clear that compliance is much higher in some compliance clusters than others. For example, nearly all firms comply with discrimination and forced labor, with non‐compliance rates in these clusters of less than 1%. On the other hand, non‐compliance within occupational health and safety is near 20%. For working time and contracts and human resources, firms show noncompliance with about 13% of compliance questions within these clusters.

Table 7 also calculates differences in the mean noncompliance rate before and after program announcement, without controlling for any other potentially relevant variables. We find a positive result for all questions, that is, noncompliance has fallen, as well as most compliance clusters. Exceptions are child labor, discrimination and forced labor. In addition, we see initial evidence of a reduction in noncompliance before and after program announcement within compliance questions that relate to public disclosure.<sup>9</sup>

> 8 Testing for changes in compliance around the launch of the public disclosure portal is left for follow‐up work, given that only one year of data is currently available post‐portal launch.

> 9 Alternatively, we could regress the non‐compliance outcome of each compliance question for all firms and all years on a dummy variable equal to one if the compliance visit was post‐program announcement.

15