rafmacalaba/data-use-mentions-extended huggingface dataset:
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# **Notes**

**Private Credit to Gross Domestic Product (GDP)** measures the domestic private credit to the real sector by deposit money banks as percentage of local currency GDP. Data on domestic private credit to the real sector by deposit money banks is from the International Financial Statistics (IFS) line 22D published by the International Monetary Fund (IMF). Local currency GDP is also from IFS. Missing observations are imputed by using GDP growth rates from World Development Indicators (WDI), instead of substituting the levels. This approach ensures a smoother GDP series.

**Accounts per Thousand Adults from Commercial Banks** is the number of depositors with commercial banks per 1,000 adults. For each type of institution the calculation follows: (reported number of depositors)*1,000/adult population in the reporting country. Number of depositors from Commercial Banks is from Financial Access Survey reported by the IMF. Adult population data is from WDI.

**Lending-Deposits Spread** is lending rate minus deposit rate. Lending rate is the rate charged by banks on loans to the private sector and deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings deposits. Both lending and deposit rate are from IFS line 60P and 60L, respectively.

**Z- Score weighted average from Commercial Banks** is estimated as follows: (ROA + Equity / Assets)/(Standard Deviation of ROA). Return of Assets (ROA), Equity, and Assets are from Bankscope. The standard deviation of ROA is estimated as a 5-year moving average.

**Stock Market Capitalization plus Outstanding Domestic Private Debt Securities to GDP** measures the market capitalization plus the amount of outstanding domestic private debt securities as percentage of GDP. Market capitalization (also known as market value) is the share price times the number of shares outstanding. Listed domestic companies are the domestically incorporated companies listed on the country's stock exchanges at the end
wages. A 10 percentage point increase in the share of sales that are exported causes female employment levels to increase by 1.2 percent and female wage levels to decrease by 1.5 percent. The insignificant OLS yet significant IV results indicate that reverse causality is likely a concern, such that the average correlation is not significant.

Exploring further the positive relationship with female employment but negative relationship with female wages for Georgian exporters is limited due to data constraints. Nevertheless, the results are in line with the global literature. In a cross-country data set, World Bank (2019, 2020) shows that exporters tend to hire more women than non-exporters, but these women are more likely to be in labor-intensive production jobs or occupations that require lower skills and pay less. At the same time, these firms are significantly less likely to be majority female owned and are also significantly less likely to have a top female manager. Women’s placement stems, in part, from disadvantages in endowments—such as assets, access to credit, education, skills training, and social capital, among others. These arise because of gender-biased regulations or discriminatory social norms (World Bank 2019, 2020).

Segregation of female employment across sectors may be another explanation for higher female employment but lower female wages on average for Georgian exporters if women are more likely to work in sectors that pay lower wages. As identified in World Bank (2018), women tend to be employed in lower-productivity sectors such as agriculture in Georgia, which is confirmed with the data used for this paper’s analysis, as in Figure 1. Because the regressions control for industry fixed effects at the 1-digit industry level, they would capture this type of segregation of women across broad industries. Nevertheless, if this segregation also happens within more closely defined industries that also have pay gaps for women, such as garment production within the manufacturing industry, which also has higher female labor shares, this could be one possible explanation of the results. The inefficient allocation of female
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). In recognition of the lower status and heightened vulnerability of sonless women, the Chinese government in 2002, prohibited ill-treatment of women who give birth to girls or who are infertile (Li 2007).

There is also evidence to suggest that the adoption of ultrasound technology – a key innovation in helping those with son preference prenatally select the sex of their baby and avoid unwanted daughters – improves women’s welfare. Ebenstein et al. (2013) combine data from various Chinese surveys and the Local Chronicles, and exploit the timing of ultrasound diffusion across counties to find causal evidence that the adoption of ultrasound technology increases the probability of having a son at second parity, and this in turn leads to lower family size, an increase in labor market employment outside agriculture, an increase in intra-household bargaining power, and decrease in suicide rates for women in rural China. While we do not view such improvements in women’s welfare – that are conditional on having a male child – as related to improvements in women’s status overall, we do argue that ultrasound bans are likely to worsen women’s outcomes in the absence of interventions that change the intrinsic value of females.

Having a son improves a women’s circumstances during old-age too. For example, Cain (1986) draws on survey data from rural Bangladesh and India, and uses a correlation-based approach, to find that older women without sons in these contexts were at a higher risk of economic uncertainty and mortality. Similarly, Rahman et al. (1992) use data from the Matlab, Bangladesh and a discrete time hazard model (correlation-based approach) to find that women ages 45 or more who had an adult son living with them faced a lower mortality risk. Vlassoff (1990) finds similar results from descriptive survey data analysis for widowed women in a rural village in Maharashtra, India. In the absence of any other substitute, those women who would rely on sons for old-age security are likely to be worse off due to ultrasound bans.

14
In this context, decentralization can play an important role in curbing corruption, if the political and institutional environment permits. Decentralization destroys the barrier between citizens and centralized governments by encouraging citizen participation in government decision making, provided that the society possesses basic political and civil freedoms to express opinions. In smaller communities, individuals can find more opportunities to monitor the functioning of local governments and to take action more effectively and promptly, if necessary. As governments come closer to the people they are intended to serve, the civil service re-orients itself towards a service-oriented system to satisfy the basic needs of the society. Armed with strong judicial and political control mechanisms, decentralization may change the social and bureaucratic culture and incentives creating a less isolated thus more accountable civil service. Whether these relationships hold in practice remains an unsettled empirical question.

# **The Model and Variables**

# **_Dependent Variable: Corruption_**

Corruption is a broad concept covering a wide range of phenomena. It ranges from petty corruption in the form of tips and speed money to grand corruption. The available data on corruption reflect this heterogeneity. Some institutions collect data on irregular payments in the public service, whereas others try to capture corruption in the higher levels of government. Risk rating services, such as Political Risk Services and the Economic Intelligence Unit, typically rely on panel of experts who rate countries using a defined set of criteria. Other organizations, such as the World Economic Forum and the Institute for Management Development, rely on surveys of citizens and entrepreneurs.

A common concern with corruption indicators is that they can be inconsistent or unreliable, or affected by the biases of the observer. A country rated high by one agency or panel of experts may be rated lower by another. One solution is to aggregate indicators from several resources into an average or composite index – a poll of polls. This is the approach that Transparency International uses for its Corruption Perceptions Index 12
and welding). Because robot applications data are at the country-level, we first construct country-level robot stocks for each application, exactly as we describe in section 3 for (overall) robot data at the country-industry level. We approximate robot applications at the country-industry-level, by multiplying the country-industry-level overall robot stock, with the (country-level) share of robots of a given application. We do so to avoid country-level regressions and enable a consistent estimation specification across regressions.

> 18 The 10% highest quality products – “the frontier” are defined as having zero quality gap.  In unreported results, our findings are robust to excluding these frontier export products.  The results are also robust to restricting to only products that at least 10 countries export in each year (see Table A7 in the Appendix).

> 19 In unreported results, we find the results are robust using the number of intermediate inputs, as an alternative measure of product complexity.

> 20 We also examine robustness to using new exports defined after 2005 or 2010 and find broadly similar results.  Most varieties are already exported before 2000.

19
We analyze multi-topic household survey data from a total of 22 survey rounds from six countries: Bangladesh (3), Ethiopia (1), Malawi (5), Nigeria (3), Tanzania (6), and Viet Nam (4), with the number of survey rounds for each country noted in parentheses. In the four Sub-Saharan African countries (Ethiopia, Malawi, Nigeria, and Tanzania), the data originate from the nationally-representative, multi-topic household surveys that have been implemented by the respective national statistical offices with support from the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. Being similar to the LSMS-type surveys supported by the World Bank, the surveys from Viet Nam are implemented biennially by the country’s General Statistical Office (GSO) with technical support from the World Bank. These surveys are generally regarded as being of high quality and are regularly employed by the national governments, international organizations, and academic researchers to provide estimates on household welfare.<sup>9</sup> The data sets include

- i. the Bangladesh Integrated Household Survey (BIHS) 2011/12, 2015, and 2018/19

- ii. the Ethiopia Socioeconomic Survey (ESS), 2018/19 round

- iii. the Malawi Integrated Household Survey (IHS), 2010/11, 2016/17, 2019/20 rounds

- iv. the Malawi Integrated Household Panel Survey (IHPS), 2010 and 2013 rounds

- v. the Nigeria General Household Survey (GHS)–Panel, 2010/11, 2012/13 and 2018/19

- rounds<sup>10</sup>

> 9 For example, Baulch (2011) considers the VHLSSs as having high quality data and heavily uses these surveys for poverty analysis. Other researchers analyze the LSMS-ISA surveys for various topics such as agricultural input uses (Sheahan and Barrett, 2017) or temperature shocks and household consumption (Letta, Montalbano, and Tol, 2018).

> 10 We did not include
yield measures use the GPS-based plot areas. As we do not find statistically significant differences between crop cut yields as a function of the size of the crop cut, in the rest of the analysis, CC yields are tied to the 8m x 8m sub-plot.

We see that the average difference between SR yields and each of CC yield is statistically significant at the 1% level, and on average, SR yields are 21% higher than their CC counterparts. The distributional differences between SR and CC yields are, however, not statistically significant. On the other hand, although there are statistically significant distributional differences between SR and CC sorghum production estimates, but we find no statistical difference in the means. The fact that we find significant differences between means but not distributions for yield measures and vice versa for production measures can in part be explained by the standard errors, which are relatively larger at the mean in the case of the production measures, making it difficult to detect differences for the point estimates. These differences are picked-up when we conduct the tests of distributional differences.

> 8The household size may appear to be large but it is typical of rural households in the region: based on 153 observations in the Mali LSMS-ISA 2017 data, the mean number of individuals in households with at least one sorghum plot in the district of Dio ̈ıla is 17, with a 95% confidence interval of [14.9, 19.6].

> 9These results are robust to the treatment of outliers in the SR variable via winsorizing. See Table A1 in Appendix A.

> 10Total CC-based sorghum production is computed by multiplying the 8x8m CC yield with the GPS-based plot area. The harvest measure for one of the four 4m x 4m quadrants is randomly chosen in each of the 8m x 8m subplots to compute the yield and harvest for the CC 4m x 4m method.

6
FHS term would be negative: more egalitarian attitudes and perceived norms would be associated with a lower female household share of labor. Table 1 shows the result of these regressions, presenting only the coefficient for the FHS term. Full results are available in the Appendix.

Table 1: Female Household Share, Attitudes, and Perceived Norms

||Attitudes|Pooled PN|PN Men|PN Women|PN Neighbors|
|---|---|---|---|---|---|
|Female Household Share|_−_0_._009|_−_0_._128*|_−_0_._212**|_−_0_._052|_−_0_._114|
||(0_._075)|(0_._050)|(0_._066)|(0_._073)|(0_._082)|
|Num.Obs.|888|888|888|888|888|
|Covariates Included|Yes|Yes|Yes|Yes|Yes|

+ p _<_ 0.1, * p _<_ 0.05, ** p _<_ 0.01, *** p _<_ 0.001 _Note:_ The table shows the coefficient estimated for the variable indicating the share of household tasks undertaken primarily by women (female household share) from weighted OLS regressions using models taking as the left-hand side variable, respectively, the personal attitudes score (1), the pooled perceived norms score (2), the perceived norms of men score (3), the perceived norms of women score (4), and the perceived norms of neighbors (5). Covariates are included in all models; full results are in the Appendix.

Contrary to expectations, I find that female household share is not significantly correlated with personal attitudes. That is, respondents who report living in homes where women are responsible for a higher share of household tasks do not
**Figure 5 - Potential electricity price hikes ($ cents/kWh)**

CIS countries are expected to experience the highest energy price increases <!-- Start of picture text --> Kazakhstan<br>Ukraine<br>FYR of Macedonia<br>Serbia<br>Bosnia and Herzegovina<br>Kyrgyzstan<br>Tajikistan<br>Azerbaijan<br>Belarus<br>Russian Federation<br>Estonia<br>Bulgaria<br>Armenia<br>Republic of Moldova<br>Georgia<br>Turkey<br>Poland<br>Slovak Republic<br>Romania<br>Albania<br>Croatia Electricity Price subsidies<br>Latvia Carbon subsidies<br>Lithuania<br>Hungary<br>-10 -5 0 5 10 15<br>cents/kWh<br><!-- End of picture text --> _Source_ : World Bank staff calculation based on IEA (2011) and ERRA tariff database. _Note_ : Electricity price subsidies are determined by the gap between prevailing electricity price and the average long-term cost recovery price at 12.5 cents/kWh. Carbon subsidies are determined by the CO2 intensity of power generation and an energy price at $15 tons per CO2 equivalence.

In terms of employment, the new member states of the European Union; i.e. Czech Republic, Slovak Republic, Slovenia, Bulgaria, Hungary, Estonia as well as Russia and FYR Macedonia were the most vulnerable, with around one third of their employment in energy-intensive industries. Poland, Serbia, Romania, Croatia, Latvia, Lithuania and Turkey also had a significant portion of their jobs mapped to these industries. At the other end, the South Caucasus countries (Georgia, Azerbaijan and Armenia) were the three countries in the ECA region with the lowest dependency on energy-intensive employment (Figure 6) as of 2007.

10
2019), which excludes Ethiopia, since otherwise the values for cost-reflective tariffs in this particular country are unrealistically high.

6 For most countries, the surveys are nationally representative. In Vietnam, the survey includes only five regions: Hanoi City, Da Nang, Dak Nong, Thu Dau Mot City and Bình Dương Province, and Ho Chi Min City.

7 Country estimates might differ slightly from those of the Joint Monitoring Programme (JMP) of the World Health Organization and United Nations Children’s Fund (UNICEF), due to some minor differences in variable definitions and sources. One common difference between our computation and JMP’s is that we define a household as connected to piped water if it uses piped water as its main source of drinking water either in the dry or wet season. The JMP also includes public standpipes as part of its piped water definition in some countries such as Ethiopia and Mali, whereas we include only household connections. Thus, our figures may be easily compared against the JMP’s for household connections only. For example, figures for Ethiopia and Nigeria are slightly higher in table 1 (19.8 percent and 13.6 percent, respectively, in column 1) than those presented by the JMP (15.2 percent and 12.2 percent, respectively). 8 As one might expect, there are also wide variations across the administrative geographic areas covered by the household surveys in each country, variations that are not shown in this paper.

5
**Figure 4:  Value of Domestic Equity and Debt Issuance**

<!-- Start of picture text --> 7,000<br>6,000<br>5,000<br>4,000<br>3,000<br>2,000<br>1,000<br>0<br>1999 2000 2001 2002 Aug-03<br>equity debt<br>US$ millions<br><!-- End of picture text -->

**Source:  CVM**

In Brazil, the various types of institutional investors are all similarly active, while the retail investor base is rather inactive, so that no single investor type dominates the demand side, and hence sets the standards for issuance. There exist issues across the spectrum of credit ratings, and maturities range from the short term to over ten years. Roughly 95% of all bond payments are indexed (usually to inflation), indicating the continuing concern over the value of the currency. In addition, a popular vehicle for financing has been through the commercial paper market, which is out of the scope of this paper.

# **B.  Costs of Issuance for Brazilian Firms**

In contrast to the other two countries in this paper, the investment banking costs of a new debt issue have been well recorded in Brazil. This is due to the existence of a selfregulated bond market association, called ANBID, which keeps data on structuring, placement, and underwriting fees charged in each domestic bond deal. As a result, the historical data on issuance was used to create average fees based on issue size, which are represented in the table below. These fees averaged around one percent for the larger issue sizes (over US$200 million), but increase to almost 3% of issue size for those under US$20 million.

15
Figure A16: Decomposition of Theil indexes, sorted by GDP per capita <!-- Start of picture text --> (A) DOU (B) DB<br>1 1<br>.8 .8<br>.6 .6<br>.4<br>.4<br>.2<br>.2<br>0<br>0<br>WDI three letter country codes<br>Between Within National<br>Source: International Urban Poverty Database.<br>Note: Countries are sorted in ascending order of log of GDP per capita, measured in PPP (constant 2017<br>international $). WorldPop 250m is used for the DOU and DB methods. See Figure A17 for the same chart with<br>countries reordered by the highest to the lowest Theil indexes.<br>Figure A17: Decomposition of Theil coefficients and GDP, sorted by Theil index<br>(A) DOU (B) DB<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>.2 .2<br>0 0<br>National Between Within<br>NERMWITCDETHGNBBFAUGA GINTZALSOSENGHA CIVMRTAGOGAB<br>NERMWITCDETHGNBBFAUGA GINTZALSOSENGHA CIVMRTAGOGAB<br>MWIAGO LSOUGAGHA BFA TZANERGABSEN ETH CIVTCDGNBMRT GIN MWIAGO LSOUGAGHA BFA TZANERGABSEN ETH CIVTCDGNBMRT GIN<br>Theil index<br>Theil index<br>Theil coefficient Theil coefficient<br><!-- End of picture text --> Source: International Urban Poverty Database. Note: For the DOU and DB methods, WorldPop 250m is used.

38
The pattern in Figure 1 suggests countries seeking to grow should target entry and expansion of exports in growing markets. This goal can be challenging, of course, as growth forecasts are notoriously difficult. An observed boom in coal exports induced by the loss of natural gas supply elsewhere does not mean that coal exports offer a long-term value proposition. Nonetheless, recent industrial policy initiatives have reflected this approach, targeting for instance electric vehicles. China and Korea, which today have captured this growth through exports, also anticipated it in their industrial strategies over a decade ago. The fastest growing four-digit products between 2018 and 2021, the most recent three-year period with global import data in COMTRADE, are roasted iron pyrites (SITC 2814); hemp, raw or processed but not spun (SITC 2652); and castor oil seeds (SITC 2235), all with global demand growing above 40 percent per annum. A challenge in targeting these industries is that they are commodities. In the electric vehicle example, countries targeted an industry in which it was feasible to develop a differentiated product that is protected to some extent from competition, given heterogeneity in consumer preferences.

# _International Competition_

The presence of tough competition suggests targeting growth or entry in an export sector is higher risk. Simple measures of the toughness of competition are the number of exporters (or the inverse of the Herfindahl index of national export shares). Atkin, Costinot, and Fukui (2021) observe that complex products are exported by a larger number of countries, raising a question about whether industrial policy targeting complex products rather than simple products can be successful. This result is a consequence of the fact that product complexity is defined based on whether the product is exported by countries that export many other products. Industrial policy targeting products exported by countries with high GDP per capita face the same issue, since countries with high GDP per capita export a greater variety of products (Hummels and Klenow 2005).

Of course, just because there are few competitors in a market does not mean