rafmacalaba/data-use-mentions-extended huggingface dataset:
hold that export processing activities (most likely to include those by foreign firms) may provide women with higher earning opportunities relative to an alternative job in the informal sector (e.g., Glick and Roubaud, 2006; Madani, 1999). However, the previous studies usually did not differentiate the impacts of foreign ownership on wages by gender.<sup>29</sup> Columns 1-2 in Table 5 show the regression results run separately by gender comparing the foreign wage levels to those in the informal wage sector. Foreign firms appear to pay higher wages for both male and female workers, as the coefficients for the foreign dummy turn out to be positively significant at the one percent level. The wage differentials between the foreign sector and the informal wage sector are found to be larger for females at around 58 percent relative to 43 percent for males. In fact, women seem to experience a generally higher “formal wage premium” as the coefficients for foreign, state and private dummies all turn out to be larger for women than for men. This suggests that the lower wages in the informal sector for women are likely to be the source of larger foreign-wage premiums for women relative to men.

In order to investigate further the source of wage differentials, columns 3-4 and columns 5-6 in Table 5 report the results of gender-specific regressions run by subsets of skilled workers (with at least an upper-secondary education diploma) and unskilled workers (without upper-secondary education) respectively. The gender difference in the foreign wage premium is particularly large for the subset of unskilled workers (57 percent for women vs. 31 percent for men), suggesting that foreign employment provides higher earnings opportunities for unskilled women relative to unskilled men. The results of the Wald Tests in Table 5 indicate that the equality of the

> 29 One exception is a study by Braunstein and Brenner (2007) which compares the foreign wage premiums by gender in urban China in 1995 and 2002. Using province-level macro data and household survey data for 1995 and 2002, Braunstein
24 positive impact of sales on income and net worth<sup>identified in earlier</sup> discussions easily outweighs the tendency to shift the<sup>portfoLio away from</sup> fixed capital, and the familiar accelerator relationship<sup>obtains. Similarly,</sup> sales growth tends co increase net peso trade credit,<sup>once net worth effects</sup> are accounted for.

# Firm Size and Portfolio Composition

It was noted earlier that scale economies and other factors may cause large firms to desire different portfolios than small firms operating<sup>in the</sup> same economic environment. We observe in passing that Table<sup>4 confirms such</sup> effects aire indeed significant, but only for cash and securities.

# IV. Summary and Conclusions

Generally, Uruguayan data fit our model of corporate financial behavior well. The portfolio composition equations suggest<sup>significant</sup> substitution effects in expected directions when real yields 6r expected revenue growth rates _cnang_._ Also, there appears to be a straightforward linkage between net income and net worth expansion. Finally, the adverse effects of contractions in output demand or financial<sup>cost increases on firms'</sup> profitability are severe in the short run. Hence, for example,<sup>major ctanges</sup> in che exchange regime can mean boom or bust for the industrial sector.

Examining the short-run effects of net worth expansion on balance sheet composition, we find that liquid asset stocks correlate positively<sup>with</sup> net worth growth, and peso debt does not correlate at all. So the results imply that when maxi-devaluations or other shocks reduce corporate income, operating expenses are met by drawing down net liquid assets. And of course once these stocks are exhausted, **slack** markets for inventories **and** fixed
ing variables, no education and 7 to 8 years of education, are negative. One interpretation of the education rank variables is that the government was actively trying to place family planning programs in areas where the population is less educated but not overwhelmingly lacking in education. Presumably those with more education are likely to live in areas where there are other means of obtaining family planning services or have lower desired fertility. The F-test for all instruments being jointly equal to zero is 6.14. Despite the low number of observations, the F-test indicates that the instruments perform well.

# **4.1 Effect on Fertility**

Table 4 presents the results for the effect of access to family planning in 1990 on the number of children ever born by 2004.<sup>19</sup> Models I and II assume that program placement is exogenous and estimate the effect of family planning using OLS. Models III and IV treat program placement as endogenous and use the predicted probability of access to a family planning program from Table 3.<sup>20</sup> Models I and III estimate the average effect of access to family planning services on children ever born across all women in the sample. Because the effect of access is likely to vary by age, Models II and IV include interactions between family planning access and the five year age group dummies.

The average effect of access to family planning on children ever born is negative and strongly statistically significant for both OLS and IV estimations. The OLS estimate indicates that providing family planning reduces the number of children ever born by 0.7 children. Taking account of program placement leads to an even larger estimated impact of access to family planning with fertility falling by 0.9 children. Given the sample’s average, the effect is equivalent to an approximately 20 percent reduction in the number of children born per woman.

That the IV estimates are larger than the OLS estimates is in line with the results of earlier studies using longitudinal data (Rosenzweig and Wolpin
and extremely valuable glimpse of trends in welfare, consumption, and poverty in a country going through a period of upheaval. Indeed, the South Sudanese economy has since displayed all the characteristics of a war economy, including severe output contraction, rapid currency devaluation, and soaring inflation (FAO and WFP, 2017; International Monetary Fund, 2016). Unsurprisingly, driven by these powerful shocks the incidence of poverty has risen to extremely high levels. In 2016, the HFS estimated that more than 4 in 5 people across seven of the ten former states were living under the international poverty line of US$ 1.90 PPP 2011 (82 percent). Such high levels of deprivation are not merely a direct result of the crisis but also reflect a history of instability, characterized by a poorly functioning state and a lack of institutional services provision (de Vries and Schomerus, 2017; de Waal, 2014; World Bank, 2017). In 2017 South Sudan ranked 187 of 189 countries in the Human Development Index, with a life expectancy of merely 57 years.<sup>5</sup> The HFS was designed with the expectation of potential instability and thus capitalized on recent technological and methodological innovations to obtain reliable national poverty statistics in difficult contexts.<sup>6</sup> Closely monitoring fieldwork is key to implementing such a large project in a risky context. The HFS leveraged the expansion of cellular networks across South Sudan to build a near real‐time monitoring system, whereby the data could be uploaded daily to a dedicated server and checked for consistency. Computer Assisted Personal Interviewing (CAPI) also allowed built‐in consistency checks, eliminating the need for expensive and potentially dangerous re‐visits. Adherence to the sample design can be closely monitored with GPS software, tracking enumerators inside and outside areas with mobile phone coverage. The HFS also leveraged innovations in questionnaire design which permitted reducing the number of consumption items asked to the respondents while still obtaining unbiased poverty estimates through

> 2 See, UNOCHA:
conditions than those in the public sector, with respective elasticities of -0.26 and -0.15 respectively. Finally, earnings of employees with higher education are significantly less sensitive to local unemployment rates than are earnings of less educated individuals. Estimated elasticities are -0.08 with and -0.20 without higher education. These estimates are derived from models (not shown in the tables) with interaction terms.

Numerical values of the elasticity of wages with respect to unemployment are higher than found in the OECD (2003a) study for several reasons. One potential cause of difference is the methodology applied.<sup>15</sup> Still, some of the difference is attributable to the fact that labor market flexibility has somewhat increased since 2000, the year of the OECD (2003a) data. These issues are discussed in more detail below.

**Regional effects.** Earnings functions may also shed light on the degree of geographical segmentation of labor markets due to barriers to mobility.  The inclusion of regional variables representing the capital city, the capital district, port city, small city, and rural area in the earnings function generally have highly significant effects.  These estimates are shown in Appendix Tables 24 and 25.  In the year 2002, an employee working in a rural area earned 10 percent less than an otherwise similar employee in a small city. Employees working in Riga earned 37 percent more than those working in smaller towns (Appendix Table 24, column (2)). Working in Jurmala, Riga district, Ventspils or Liepaja also increases earnings significantly, although less than working in Riga. Employees in Daugavpils, however, earned 11 percent less than in small cities (other things equal). The wage differential associated with working in Riga is very big by international standards. In Estonia, Lithuania and Poland the wage premium associated with working in the capital city is just above 20 percent (see Table A3.1 in OECD (2003a); and Newell (2001))
of heterogeneity in multipliers. I find some suggestive evidence that multipliers are larger in recessions, in countries that are relatively less exposed to international trade, and in countries with flexible exchange rate regimes.

This paper builds on my previous work in Kraay (2012), which exploited lags between the approval of and subsequent disbursements on individual World Bank projects to isolate a predetermined component of World Bank-financed public spending that could be used as an instrument to estimate government spending multipliers. Out of necessity, that paper focused on a small set of 29 mostly very poor countries where World Bank-financed spending is large relative to the size of the recipient economy, and over the period 1985-2009. In contrast, the DRS data used in this paper covers lending by virtually all multilateral and bilateral official creditors to all developing countries, and extends back to 1970. The combined disbursements on loans from all these creditors account for a much larger fraction of public spending than World Bank financing alone. This substantially strengthens identification compared to the previous paper, and moreover permits extending the analysis to a much larger set of 102 developing countries where lending from official creditors is an important source of financing for public spending.<sup>1</sup> This greater sample size in turn expands the relevance of the findings to a broader set of countries, and in addition makes it feasible to assess a variety of possible sources of heterogeneity in estimated multipliers, as is done in this paper.

My strategy of exploiting delays between loan approval decisions and the ultimate spending that they finance is also related to Leduc and Wilson (2012), who study the dynamic effects of federal highway spending in the United States. The nature of the projects in question, and the institutional environment in which they are financed, also give rise to long delays between the authorization of federally-financed highway spending, and the actual state-level spending itself. Their use of this lag structure is different from mine, however, in that they estimate responses to "surprises" in spending, measured as deviations in spending
regression analysis, the dependent variable is defined as the difference between its post-treatment and pre-treatment values to get rid of fixed effects.<sup>8</sup> To obtain a fuller picture of SMEs’ financing options, we also look at several additional aspects of firms’ access to finance. _Line of credit or loan_ is a dummy variable with the value equal to 1 if the firm has some line of credit or loan from a bank, 0 otherwise. _Working capital financed by banks_ captures the proportion of a firm’s working capital financed by banks. _Fixed assets financed by banks_ shows the share of fixed assets funded by banks. _Required collateral_ is the share of loans of all loans for which collateral was required. _The collateral-to-loan ratio_ indicates how much collateral a firm must provide the bank for a loan of a given size.

_Basel Dummy Gest_ is our main explanatory variable of interest—the variable is equal to zero for all firms from (control) countries that did not implement Basel III. For firms from the treated group of countries, this variable takes a value larger than zero, depending on the gestation lag at the time of the first available Enterprise Survey after Basel III implementation—its maximum value is 1. The maximum difference between Basel III implementation and the post-treatment Enterprise Survey is five years, and the value of 1 is assigned in this case. The value of 0.8 is assigned in the case of four years, 0.6 for three years, and so forth. Defining the variable _Basel Dummy Gest_ in this way, we assume that it takes time until financial regulations fully affect SME financing. Alternatively, we employ a dummy variable for Basel III, with the value of 1 in the case of implementation and 0 otherwise. We discuss in the following section the source of our data on Basel III implementation and how we determine whether the country implemented Basel III.

Table 1 presents the summary statistics for firm characteristics, bank system indicators, and country and institutional variables. We
..<sup>64</sup> We calculate<sup>_<u>N</u>_</sup> _N_<sup>_<u>F G</u>_</sup> from the entire sample since the pre-ultrasound sample of births is likely to be underreported due to recall bias. The number of births in column (1), _N_ , is the 1995 figure from UN Statistics Division. The number of mothers, _m_ , is obtained as General Fertility _<u>N</u>_ Rate<sup>usingthegeneralfertilityrateestimated</sup> from NFHS-2 by Retherford and Mishra (2001) as 131.53 per 1000 women. ∆ _NFG_ is estimated to

> 64This number is derived from a regression specification similar to the one estimated by Bhalotra and Cochrane (2010).

74
by differences in patent quality around the globe, we focus here on the relevance of IP monetization options in addressing this divide. Section 3 briefly outlines the key concepts of patent monetization and presents multiple case studies in order to identify some of the instruments available to commercialize IP assets. It explores the role of patent securitization, online patent exchanges, technology transfer offices and joint public-private funding, and finally the impact of public litigation support for small innovating firms. A final section concludes.

# **2. The Intellectual Property Commercialization Divide**

## **The Global Distribution of Licensing Revenue**

In order to get a more detailed picture of the global licensing landscape, a set of 22 countries was selected for analysis. The selection process sought to ensure that a variety of income levels and geographic locations were represented in the study.<sup>2</sup> This was done to provide a more differentiated understanding of the global licensing landscape. Accordingly, we included a range of high-income, middle-income and low-income countries in our study. The selection also sought to emphasize large emerging economies that represent significant international markets. Those

> 2 We recognize that the countries compared in this study are highly heterogeneous. This was done explicitly in order to capture the differences in IP monetization between a variety of nations from different income groups. Population, region, extent of economic liberalization, political and legal structure (among others) may influence the variation depicted in licensing balance of payments and patent ownership. However, absent more disaggregated data, we believe this approach still provides valuable insights into national differences across the international IP system.

3
|Monetary|Sanitation|Deprived|Shelter D|eprived|Water D|eprived|Informatio|n Deprived|
|Poverty|non-depr|deprived|non-depr|deprived|non-depr|deprived|non-depr|deprived|
|non-poor|44%|11%|42%|12%|37%|18%|44%|11%|
|Poor|32%|13%|23%|23%|25%|20%|29%|16%|

Source: Authors’ estimates based on GHS Wave 1-3.

One hypothesis could be that the imperfect overlap between monetary and deprivation-based poverty is purely driven by those children with consumption levels close to the poverty line, but it does not apply to children at the top of this income distribution. Table 4 however proves this proposition wrong. Unlike one may expect, there are even among the children living in households with the top 1% income, up until wave 3 about half the children facing at least one deprivation. While this share remains constant until the top 10 percent consumption level for wave 3, the share of monetary non-poor children facing at least one deprivation is comparatively rising though when expanding the sample to children with top 5 percent and top 10 percent consumption levels in the earlier waves. This is consistent with findings presented in World Bank Group ‘s (2017) estimates of the top income quintile, not having complete access to sanitation and water, based on earlier other data sources. Among severe sanitation, shelter and water deprivation, Table

> 26 Health Deprivation did not exist in wave 3.

51
comparable to those from actual trips in cities, including relatively small cities where one might worry more about poor data quality.

The speed indices in Akbar et al. (2021) are based only on trips within the built-up urban cores, whereas we sample establishments from the broader metropolitan area surrounding it. However, given the monocentric nature of most metropolitan areas in the developing economies, it is safe to assume that the effect of mobility within the urban core on the agglomeration economies of establishments within and outside of it are correlated. Finally, for ease of interpretation, in the regression analysis we use the additive inverses of the mobility indexes.

# _Other agglomeration diseconomies_

We control for several agglomeration diseconomies, including whether a firm experiences crime, theft, and disorder as an obstacle to doing business (measured on a 0-4 scale), whether a firm perceives competition from informal firms as an obstacle to doing business (measured on a 0-4 scale), and whether a firm experienced electricity outages during the previous year (measured with a dummy variable). We also control in our regressions for the average level of air pollution (PM 2.5), obtained from the Urban Centre Database, which is a part of the Global Human Settlement Layer Database.

# _Controlling for physical geography_

Several other metropolitan area-level variables may confound the relationship between urban density and labor productivity, particularly non-human endowments, such as average temperature and precipitation. We obtain information on these variables from the Urban Centre Database, available as part of the Global Human Settlement Layer Database, and control for them in our regression analysis.

# _Controlling for firm sorting and initial sorting of workers_

To control for firm sorting, we include the size of the firm by accounting for (1) the number of full-time employees working in the establishment and (2) a dummy variable indicating whether the establishment is part of a large conglomerate. As noted by Van Oort et al. (2012) the effects of agglomeration that are often found in area-based studies can be compositional in nature
It should be remarked at the outset that whereas the great majority of the respondents in the household survey were male, both sexes were strongly represented in the focus groups.

Beginning with the individual households, the respondents in those nine villages that had received an all-weather road at any time after January 2007 were asked to rank the three domains according to the size of the corresponding benefits they had enjoyed from the new road thus far. The commercial and health domains were ranked roughly equally (with 31 and 29 first, and 23 and 28 second ranks, respectively), whereas education lagged far behind. It is possible that the latter reflects the fact that 28 of the 30 villages have their own primary school, but only two have a secondary school, – – whereby these poor families or perhaps their male respondents may place little value on secondary education. At all events, the null hypothesis that the rankings are independent of the domains is resoundingly rejected ( _χ_<sup>2</sup> (4) = 13 _._ 28 _, p_ = 0 _._ 000). The respondents were also asked to rank the benefits stemming from the commercial domain against those from education and health combined: 24 ranked the former first, 18 put them on a par, 28 ranked the latter first, and one expressed no opinion. The null that these rankings are independent of the (two) domains cannot be rejected at conventional levels ( _χ_<sup>2</sup> (3) = 4 _._ 34 _, χ_<sup>2</sup> 0 _._ 10<sup>=6</sup><sup>_._25).Thesefindingssuggestthatinthe</sup> 33
discussion of disaster risk measures in the household, and community resources to deal with disasters), we first show basic descriptive charts by country. Subsequently, we display the results of simple linear regressions, mostly examining the association between the main outcome variables and basic socio-demographic characteristics of the household (location, composition, and economic situation) and the household head (sex and educational attainment).<sup>8</sup> We present results separately by country (instead of pooling the data) because, as discussed in the next section, countries’ socioeconomic contexts differ and the disaster risks to which countries are exposed are not identical. Although straightforward, the approach provides useful insight into ability to cope with major disasters, with important policy implications discussed in the closing section of the paper.

# **3. Context**

# **3.1 Socioeconomic context**

In this subsection we describe the socioeconomic context of the countries included in the analysis. We do this by combining descriptive statistics based on socioeconomic variables derived from the phone survey with readily available information from the World Bank’s World Development Indicators<sup>9</sup> and the United Nations Development Programme’s (UNDP) Human Development Index.<sup>10</sup> We first discuss the location and population of the five countries, going from west to east on the map. Belize is located in Central America. Bordered by Mexico, Guatemala, and the Caribbean Sea, Belize had a total population of about 405,000 in 2022. Haiti makes up the western part of the island of Hispaniola in the central north of the Caribbean Sea. With an estimated 11,585,000 inhabitants in 2022, it has the biggest population of the five countries included in this study. Dominica and Saint Lucia are small island states, located on the eastern edge of the Caribbean Sea. They respectively had a population of about 73,000 and 180,000 in 2022. Suriname is located to the