rafmacalaba/datause-encoder-data huggingface dataset:
Kingdom, and the United States less than half of the firms in our sample conducted an equity offering or sold a bond over the period 2003-11. There is also remarkable skewness across virtually all countries. Only in Australia; China; Japan; Hong Kong SAR, China; the United Kingdom; and the United States is the amount raised by the top-5 equity issuers less than 50% of the total amount raised in equity markets. Even in other G7 economies like Germany, France, and Italy, the top-5 equity issuers captured 61%, 65%, and 71% of the total amount raised in our sample period, respectively. The concentration of funds raised by a few large firms in corporate bond markets is also pronounced across most countries.

# **4. Which Firms Use Capital Markets?**

To assess which firms access capital markets, we compare the characteristics of non-issuing firms, issuing firms, and the different types of issuing firms. We compare (1) firm size (measured by assets and sales in 2011 U.S. dollars and the number of employees), (2) firm growth (measured by the growth rate of assets, sales, and employees), (3) firm leverage, (4) the liability structure of firm debt (measured by the ratio of long-term debt to total firm liabilities), and (5) firm profitability (measured by retained earnings over assets and return on assets, or ROA). Besides differentiating by whether firms issue equity and/or debt, we also examine whether firms issue equity domestically, equity in foreign markets, bonds domestically, and/or bonds in foreign markets. In comparing firm traits across non-issuing and issuing firms, we use the median across countries of the median firm in any given country, after obtaining the average over time for each firm. Because for the rest of the paper we use the matched SDC-Orbis data set on capital raisings and balance sheet information, the sample is restricted to 20032011.

Issuing firms are different from non-issuing firms along many dimensions. Issuers are typically much larger than publicly listed firms that do not issue equities or bonds (Table 1). The median issuer 12
model. We consider values of _<u>k</u>_ that correspond to 100 equally spaced points in this range.

**The size of the indivisible investment.** As our focus is on small firms, we calibrate the size of the indivisible investment to match the 99th percentile of the ratio of investment over sales in small firms, using the World Bank Enterprise Survey, which consists of firm level data for a sample of developing economies (and is described in detail in the next section). By Table 2, the 99th percentile of investment over sales in small firms is 1.373 for machinery and 2.25 for land and buildings. The average inflation rate in our sample of developing countries is 7.4%. We choose the size of the indivisible investment, _C_ , so that, in the steady state cycle of a country with average inflation ( _π_ = 7 _._ 4%), the maximum ratio of investment over sales matches the data (we consider two specifications: one in which the maximum level of investment over sales is targeted at 1.373, corresponding to machinery investment, and one in which it is targeted at 2.25, corresponding to investment in land and buildings).

We run the simulation for the following levels of inflation: -3%, corresponding, in this case, to the Friedman rule; 7.4%, corresponding to the average inflation rate in our sample; 15.7% and 24%, corresponding to one and two standard deviations above the mean, respectively (the standard deviation of inflation is 8.3%); and 30% and 40%, which are on the high end of our sample.

The results are presented in table 1. In the Friedman rule economy ( _π_ = _−_ 3% annually), the average capital stock of the firm in its steady state cycle is actually somewhat higher than the steady state capital level of the continuous-investment economy ( _k_<sup>_ss_</sup> ). This results from the indivisibility of capital and the concavity of the production function.<sup>5</sup> 5Note that, due to the concavity of the production function, it is more costly for the firm to have one unit less of capital than one unit more - thus, optimally, the average capital level at the steady state cycle is higher than in the continuous-investment economy.

12
Definitions of LG and SG are quite vague. In particular, it is not always clear if some intermediate tier should be ascribed to LG or SG. IMF’s GFS does not give a precise definition either, which results into countries deciding for themselves and reporting corresponding data. As a result, France with 3 tiers has only LG, and Spain - which in many ways has similar administrative structure - has 1 tier of SG, and 2 tiers of LG. Giving more precise definitions for LG and SG, which could be applied to all countries, proves to be pretty difficult. We cannot use responsibilities by outlays (such as LG are responsible for municipal services, SG are not) or some political variables, because the cross-country variation of these variables is too high, and - precisely for this reason - we use many of them in our set of variables to obtain decentralization index (measure). The way distinguish between LG and SG now is the following: we approach each country separately, take whatever data is reported for it, and penalize those countries, which include larger governmental units into LG structure.

In addition to the decentralization variables themselves, dataset used here contains a detailed information about administrative structure of every country. In particular, we report which tiers of GG are ascribed to a local government, and number of governmental units at each tier. Further, we calculate the average size (in terms of population and area) of a local government in each country. The descriptive statistics of administrative structure variables is reported in Table 4. In particular, of the sample of 182 countries only 20 have state governments (SG), while the rest of the countries have only local and central governments. 26% of the countries have only 1 tier of local government, 46% have 2 tiers, while 23% and 6% have 3 and 4 tiers correspondigly. The average population of a local government unit (note that it accounts for a number of tiers) ranges from 1.4 thousands people in Equatorial Guinea to 1.8 million people in Indonesia, with an country-average population of 101 thousand people. The average area of a local government unit ranges from 4.4 th. sq.m in Malta to 70 th. sq.km in Lybia (cross-country average is 2.2 th. sq.km).

# 3.1 Decentralization variables

7
# **Ahmedabad: More but Different Government for “Slum Free” and Livable Cities**

Patricia Clarke Annez, Alain Bertaud, Marie-Agnes Bertaud, Bijal Bhatt, Chirayu Bhatt, Bimal Patel, Vidyadhar Phatak<sup>1</sup> Keywords: Basic services, Slums, Urban Infrastructure, Housing and Land Policy in India JEL Classification: R21 Housing Demand, R31 Housing Supply and Markets, R52 Land Use Regulations, R52 Land Use Regulations, R53 Public Investment and Capital Stock Sector Board: Urban

> 1 Annez, Bertaud , Bertaud and Phatak (consultants to the World Bank), Bijal Bhatt (Self-Employed Women’s Association Mahila Housing Trust), Chirayu Bhatt and Bimal Patel (Environmental Planning Collaborative Ahmedabad) . Ms. Purti Sharma of the India Development Foundation  provided generous assistance  and invaluable insights in preparing computations using the NSS data. Ms. Mridusmita Bordoloi downloaded and analyzed the NSS Household Expenditure Data. Ms. Vasudha Thawakar of the World Bank organized and facilitated the consultations in Ahmedabad and provided invaluable advice as the reports were written and reviewed. The authors are grateful to the many persons who gave us comments and very helpful insights for this work, including Ms. Ela Bhatt, Mr. Sundar Burra, Mr. I.P. Gautam,  Mr. Shubhagato DasGupta, Ms. Renana Jhabvala, Mr. Rajendra Joshi, Ms. Darshini Mahadevia, Mr. Arun Maira, Mr. Augustin Maria, Mr. Dinesh Mehta, Ms. Meera Mehta, Mr. G. Mohapatra, Ms. Reema Nanavaty, Mr. Prem Pangotra, Ms. Sheela Patel, Ms. Giovanna Prennushi, Mr. Harris Selod, Mr. Mike Slingsby, Mr Howard Spodek,  Mr. S. Sundar, Ms. Tara Vishwanath., and Ms. Berenice Van Bronkhorst. In addition, officials of the Planning Commission and the Ministry of Housing and Urban Poverty Alleviation have been most generous of their time in providing feedback as this work progressed. All errors and omissions remain those of the authors. Views expressed are personal.
contemporaneous correlation between our proxies for R&D activities and economic growth (Table 3) is capturing the positive effect of economic growth on R&D activities -- tne reverse of the expected causation. Higher initial R&D activities do not necessarily lead to subsequent economic growth (Table 5).

Is this result contradictory !o numerous micro-studies, which document high returns to R&D expenditures at the firm or industry level? (Griliches [1984], Jaffe [1986], Mansfield [1977]) At face value, our results suggest that high private returns to R&D expenditures in micro-studies do not reflect high social returns. They may result from enhanced monopoly power of a firm at the cost of its competitors. Alternatively, it may be that evidence of high returns fromr, micro studies of firms and industries in highly developed countries, mostly the United States, does reflect social gains, but such gains to R&D activities become important only after a country or a firm advances to a technologically leading group."<sup>8</sup>

# IV. Determinants of R&D Expenditures

This section analyzes what variables are important in explaining international differences in R&D expenditures as a percentage of GDP. The issue itself is interesting. In addition, this analysis provides an indirect test of the quality of our data. If large measurement errors or differences in national specifications are a major reason for our previous finding, it is less likely that we will find meaningful determinants of our R&D proxies.

18The other explanation may be the poor quality of the R&D proxies. International standardization in measuring R&D activities is still in the experimental stage. The UNESCO data, even with adjustments, may not provide an adequate basis for study of the effects of R&D on growth compared, for example, to other data, such as those on education enrollments, that routinely used in studies of growth.

13
|**Table 27. Estimation Results of Model of the Det**<br>**the ‘Poor’, Rural Areas**<br>|**erminants of L**<br>Robust OLS,<br>stepwise|**ending to**<br>Robust OLS,<br>provinces|
|---|---|---|
|Supply: Fund Capital|||
|Initial VF capital||0.000<br>_(0.000)_|
|VF allows for saving|-0.140<br>_(0.101)_|-0.161<br>_(0.156)_|
|VF requires members to save||0.040<br>_(0.139)_|
|Supply: Characteristics of VF Committee|||
|Proportion who have some higher education||-0.264<br>_(0.199)_|
|Proportion who have accounting experience||<br>0.136<br>_(0.421)_|
|Need training in loan appraisal (yes=1)||0.170<br>_(0.120)_|
|Accounts are computerized (yes=1)|-0.201**<br>_(0.100)_|-0.270**<br>_(0.127)_|
|VF officers are chosen, not elected (yes=1)||0.112<br>_(0.158)_|
|VF would cut loans equally if funds were short (yes=1)||-0.055<br>_(0.111)_|
|Demand: Village Size and Organization|||
|Number of households in village|-0.004***<br>_(0.001)_|-0.003**<br>_(0.001)_|
|Village has a market (yes=1)||0.011<br>_(0.147)_|
|Demand: Household Characteristics|||
|Proportion of households who are self-employed farmers|0.620***<br>_(0.209)_|0.613*<br>_(0.313)_|
|Proportion of income generated by wages||0.164<br>|
|||_(0.392)_|
|Proportion of population with some secondary education|0.593*<br>_(0.332)_|<br>0.532<br>_(0.443)_|
|Household assets (excluding land)||-0.000<br>_(0.000)_|
|Proportion who have a loan from a formal bank||<br>0.016<br>_(0.252)_|
|Proportion of borrowers who are women||<br>-0.440<br>_(0.398)_|
|Expenditure per capita|0.097**|<br>0.187***|
||_(0.039)_|_(0.061)_|
|Loan terms and conditions|||
|VF borrows to on-lend to members|0.187*|0.157|
||_(0.097)_|_(0.117)_|
|Memo items|||
|Number of observations<br>|808|661|
|R<sup>2</sup>|0.088|0.169|

Source: Village Fund Survey 2010, and SES 2009. Robust standard errors in parentheses. Stepwise regression uses p=0.2 cutoff for dropping variables. Right-hand column includes provincial dummy variables (not shown).

* p<0.05, ** p<0.01, *** p<0.001.

38
**Table A7 – Impact of treatments on unwillingness to get vaccinated**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Expert advice treatment|-0.0124|||-0.0114|
||(0.0305)|||(0.0293)|
|Social norms treatment||-0.0559||-0.0514|
|||(0.0312)||(0.0305)|
|Relative safety treatment|||-0.107***|-0.106***|
||||(0.0298)|(0.0300)|
|Male|-0.0603|0.0129|-0.0168|-0.0271|
||(0.0330)|(0.0332)|(0.0316)|(0.0244)|
|Over 40 years|0.0558|0.0697*|0.0349|0.0281|
||(0.0352)|(0.0344)|(0.0328)|(0.0253)|
|Highlands region|-0.105|-0.0689|-0.0632|-0.0533|
||(0.0541)|(0.0537)|(0.0525)|(0.0399)|
|Islands region|-0.0239|0.0443|0.0662|0.0467|
||(0.0554)|(0.0565)|(0.0544)|(0.0411)|
|Momase region|0.0254|0.0278|0.0614|0.0336|
||(0.0503)|(0.0505)|(0.0491)|(0.0372)|
|Secondary school or less|0.0141|0.0421|0.00693|-0.00419|
||(0.0300)|(0.0300)|(0.0288)|(0.0224)|
|Constant|0.317***|0.221***|0.249***|0.279***|
||(0.0574)|(0.0570)|(0.0556)|(0.0439)|
|Observations|917|878|896|1521|

Note: This chart is based on answers to question 34 in Appendix B.***, **, and * indicate significance at the 1, 5, and 10 percent critical level. Standard errors in parentheses.

45
<u>Table A15. Relationship between schooling and skills formation in Cambodia</u>

||(1)<br>Cognitive skills|(2)<br>Cognitive skills|(3)<br>Socioemotional skills|(4)<br>Socioemotional skills|
|---|---|---|---|---|
|Years of schooling|0.191***|0.183***|0.060***|0.051***|
||(0.012)|(0.014)|(0.011)|(0.013)|
|Respondent Age|-0.097***|-0.102***|0.002|0.007|
||(0.009)|(0.010)|(0.008)|(0.010)|
|Respondent is female|-0.280***|-0.242***|-0.106***|-0.091**|
||(0.030)|(0.033)|(0.034)|(0.039)|
|Household head can read (2011)|0.016|0.015|0.336***|0.338***|
||(0.108)|(0.127)|(0.105)|(0.122)|
|Household head can write (2011)|0.128|0.095|-0.269**|-0.285**|
||(0.109)|(0.129)|(0.107)|(0.122)|
|HH SES in 2008|0.038|0.022|0.006|-0.027|
||(0.024)|(0.027)|(0.021)|(0.026)|
|Constant|0.208|0.349|-1.739***|-0.297|
||(0.199)|(0.222)|(0.183)|(0.219)|
|Observations|3,285|3,285|3,264|3,264|
|Adjusted R-squared|0.175|0.222|0.014|0.038|
|<br>Sample|All|All|All|All|
|<br>Village FE|No|Yes|No|Yes|
|<br>Province FE|Yes|No|Yes|No|
|Number of clusters|423|423|423|423|

* p<.10  ** p<.05  *** p<.01

_Notes_ . The dependent variable in columns (1) and (2) is the Mathematics score, computed using Item Response Theory with a two-parameter logistic (2PL) model. The items included in the model are those from the tablet-based computer-adaptive test. The dependent variable in columns (3) and (4) is a socioemotional skills index computed using principal-component factor analysis on 15 Big Five items and 8 Grit items. The sample for this long-term follow-up is 3,294 respondents. Among them, some did not complete the computer-adaptive test, and some answered “Don’t know” to the socioemotional skills questions. Therefore, the sample is 3,285 respondents for the cognitive index and 3,264 respondents for the socioemotional skills index. Regressions in even columns control for village fixed effects, while regressions in odd columns control for province fixed effects. Standard errors are in parentheses (clustered at the village level).

99
**Figure 1: Total cases of COVID-19 per million population**

<!-- Start of picture text --> 3000<br>2500<br>2000<br>1500<br>1000<br>500<br>0<br>Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun,<br>2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021<br>Ethiopia Malawi Nigeria Uganda<br>Cases of COVID-19<br><!-- End of picture text --> _Source_ : Our World in Data (2021) To mitigate the spread of COVID-19, countries around the world introduced lockdown measures and Ethiopia, Malawi, Nigeria, and Uganda were no exception. Appendix B describes the government responses in each country. Figure 2 shows Oxford’s COVID-19 Government Response Stringency Index,<sup>4</sup> a composite measure based on nine response indicators, which include school closures, workplace closures and travel bans, rescaled to a value from 0 to 100 (100 = strictest)<sup>5</sup> (Thomas, et al., 2020).

**Figure 2: Government response stringency index**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb, Mar, Apr, May, Jun,<br>2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021<br>Ethiopia Malawi Nigeria Uganda<br>index<br>Government response stringency<br><!-- End of picture text --> _Source_ : Oxford COVID-19 Government Response Tracker (2021)

> 4 The COVID-19 Government Response Stringency Index is calculated by the Oxford COVID-19 Government Response Tracker (OxCGRT), which collects publicly available information on 20 indicators of policy responses that governments have taken to respond to the pandemic, such as school closures and travel restrictions. It includes information on 180 countries. See more here: <u>https://www.bsg.ox.ac.uk/research/researchprojects/covid-19-government-response-tracker</u> 5 The nine indicators included are the following: (1) School closings, (2) Workplace closings, (3) Cancelling public events, (4) Restrictions on gatherings, (5) Closing of public transport, (6) Stay at home requirements, (7) Restrictions on internal movement, (8) International travel controls, and (9) Presence of public information campaigns. More details about the methodology can be found here: <u>https://www.bsg.ox.ac.uk/sites/default/files/2020-12/BSG-WP-2020-032-v10.pdf</u>

4
- Sovacool, B.K., Dhakal, S., Gippner, O. and Bambawale, M.J. (2013). Peeling the Energy Pickle: Expert Perceptions on Overcoming Nepal’s Electricity Crisis, _South Asia: Journal of South Asian Studies_ , Vol. 36 (4), pp. 496-519.

- Srivastava, L. and Misra, N. (2007). Promoting Regional Energy Co-Operation in South Asia, _Energy Policy_ , Vol. 35 (6), pp. 3360-3368.

- Thakur, T. (2004). Regional Power Market for Energy Development in South Asia: Some Issues, _South Asia Economic Journal_ , Vol. 5 (2), pp. 301-310.

- Thakur, T., Deshmukh, S., Kaushik, S., and Kulshrestha, M. (2005). Impact Assessment of the Electricity Act 2003 on the Indian Power Sector, _Energy Policy_ , Vol. 33 (9), pp. 1187– 1198.

- Timilsina, G., M. Toman, A Ahmad, M. Iqbal, and R. Shrestha (2015). How Much Could South Asia Benefit from Regional Electricity Cooperation and Trade? Insights from a Power Sector Planning Model.  World Bank Policy Research Working Paper, forthcoming.

- Tongia, R. (2007). The Political Economy of Indian Power Sector Reforms, In: Victor, D. and Heller, T. (Eds.), The Political Economy of Power Sector Reform: The Experience of Five Major Developing Countries. Cambridge University Press.

- USAID (2010). Pakistan Power Sector: A Profile and Opportunities, United States Agency for International Development.

- Victor, D.G. (2005). The Effects of Power Sector Reform on Energy Services for the Poor, Department of Economic and Social Affairs, Division for Sustainable Development, The United Nations, New York.

- Victor, D. and Heller, T. (2007). The Political Economy of Power Sector Reform: The Experience of Five Major Developing Countries, Cambridge University Press.

- Wijayatunga, P. and Fernando, P.N. (2013). An Overview of Energy Cooperation in South Asia, No. 19, Asian Development Bank, May.

- Williams J.H and Ghanadan, R. (2006). Electricity Reform in Developing and Transition Countries: A Reappraisal _, Energy_ , Vol. 31 (6-7), pp. 815-844.

Wolak, F. (2008). Reforming the Indian Electricity Supply Industry, in Jagdish N. Bhagwati, and Charles W. Calomiris, eds., _Sustaining India’s Growth Miracle_ , Columbia University Press.

27
One possible explanation for this result is that improved water and sanitation infrastructure has a larger impact on the severity than on the incidence of diarrhea. Another explanation could be that water and sanitation infrastructure does not only decrease the number of children that die from diarrhea but also from other water related diseases (e.g. various worm infections). In addition, according to the Mills-Reincke Multiplier, a lower incidence of diarrhea also prevents child mortality from other diseases by strengthening the immune system of children.

In a next step we estimate two further specifications with different definitions of improved and unimproved water and sanitation access (see Table 1 and Table 5). First, improved water and sanitation are defined in line with the WHO/UNICEF definitions, where improved latrines (e.g. ventilated pit latrines) are counted as improved, but simple latrines as unimproved sanitation and where improved wells and springs (i.e. covered or protected) are counted as an improved but uncovered wells and springs are counted as an unimproved water source. We still keep open defecation and surface water separate, considering it as the lowest water/sanitation quality, whereas in the WHO/UNICEF definition no difference is made between open defecation and simple latrines and between surface water and traditional water sources (Table 1).

Second we distinguish whether the infrastructure is privately or publicly used (see again Table 1). It could well be that the water quality at the point of use (and hence its health impact) does not depend on the “extracting” technology but rather depends on whether the source is privately used or shared with other households. A private water source is usually closer to the household with less contamination during transport and storage. Similarly, the health impact of sanitation infrastructure might also increase if sanitation is only used by a single household, since free-rider problems of cleaning and hence hygienic maintenance might be less problematic than for public latrines and/or flush toilets.

Using the WHO/UNICEF classifications, our results do not change much, except that traditional latrines do not seem to have any impact on child mortality and that the impact of improved sanitation, including both flush toilets and modern latrines, is less than the health impact of flush toilets only (Table 5). Hence, even “unimproved” sanitation and water 24/36