rafmacalaba/data-use-mentions-extended huggingface dataset:
|0.873|0.742|0.0115|0.223|0|
||||**Panel B: W**<br>|**omen**<br>||
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Information|0.013|0.014|-0.018|0.006|0.011|
|(M1)|(0.030)|(0.036)|(0.036)|(0.006)|(0.011)|
|Observations|654|649|649|649|666|
|R-squared|0.069|0.065|0.064|0.018|0.037|
|Control mean|0.811|0.683|0.311|0.00305|0.012|

Notes: The table shows the results of regressing the variables regarding intention to use parental leave on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women). Source: Postintervention survey.

# _4.2. Effects on recent parents_

In this subsection we report the results of the intervention for individuals in experiment 2, that is, formal workers who recently were parents and are benefiting from maternal and paternal leave. Recall that for this sample of participants there is no pure control group (i.e., individuals who did not receive a message). Thus, 17
endogeneity and missing variables that would better capture demand effects, a correct identification of causality between dependent and independent variables and supply versus demand effects is no simple matter. Thus, the supply side perspective often fails to sufficiently account for the growth implications of the recurrent macro and trade shocks and large demand fluctuations (both domestic and external) that have dominated LA’s growth history.<sup>6</sup> Similarly, at the firm level, the Hsieh and Klenow (2009) conclusion that differences between firms in revenue-based TFP measures reflect resource misallocations is critically dependent on the strong assumption that firms face iso-elastic demand functions. As shown by Restuccia and Rogerson (2017) and Haltiwanger, Kulick and Syverson (2018), and as illustrated in the model of Appendix I, it is only under this assumption that differences in revenue between firms using similar factors of production can be uniquely associated with resource misallocations. Moreover, the research inspired in the Hsieh and Klenow approach typically assumes that firms within the same industry do not compete based on different mark-ups associated with product quality differences. As a result, the higher revenues of a firm with the same factor usage as another are attributed solely to higher productive efficiency. But under less restrictive assumptions, firms with similar factors could have different revenues not only because of differences in efficiency but also because of differences in demand leading to differences in mark-ups (hence in prices) due to differences in quality and product appeal (see Appendix I for a simple overview; and Cusolito and Maloney, 2018, for a deeper discussion based on a comprehensive survey of the literature).<sup>7</sup> As a result, the supply side perspective tends to understate the growth relevance of “what” is produced, that is, of differences in the broad categories of goods being produced—tradable and nontradable, commodities, manufactures or services—as well as in the quality of individual goods produced within these categories.<sup>8</sup> Yet quality
# **7.3 Do perceptions of non-state actors and donors have consequences for deference to the police and to the courts?**

Findings suggest that perceptions of non-state actors and donors have a positive effect on citizens’ willingness to defer to the tax department. Do perceptions of non-state actors and donors also have an effect on citizens’ willingness to defer to the police and to the courts? The Afrobarometer asked respondents whether they agree or disagree with the following: “The police always have the right to make people obey the law” and “The courts have the right to make decisions that people always have to abide by”.

I estimate two additional models to test whether perceptions of donor and non-state actor service provision have consequences for citizens’ willingness to defer to the courts and to the police. I include the same control variables as model 1. I also include the same measures of procedural justice, government performance, and enforcement. I replace the indicator of respondents’ perceptions of corruption among tax officials with an indicator of perceptions of corruption among the police and an indicator of perceptions of corruption among judges and magistrates. I also replace the measure of beliefs about who is responsible for collecting taxes with a measure of beliefs about who is responsible for maintaining law and order in their community.

Regression results suggest that perceptions of government performance, administrative competence and honesty, and procedural justice are positively associated with the willingness to defer to the police and to the courts.<sup>14</sup> As illustrated in Figure 6 and Figure 8, the effect sizes of procedural justice, government performance and administrative competence and honesty on the willingness to defer to the courts and to the police are smaller than the effect sizes of these measures on the willingness to defer to the tax department. The results point to a positive association between some measures of donor and non-state service provision and deference to police and to courts. Citizens who believe that donors and non-state actors exert too much influence, rather than too little influence
as roads to enhance job creation and employment in developing countries, and documents how the benefits of this complementarity vary across individuals and societies. It does so using both individual and firm-level data from various sources and employing an identification strategy that involves a new instrumental variable based on the arbitrary ethnic composition and political history of African countries. Our analysis adds to the recent literature on the impacts of internet access on economic development. Hjort and Tian (2021) provide an overview of the nascent yet already sizeable empirical body of research on the economic impact of internet connectivity in developing countries. Theoretically, internet access can drive economic development through its impacts on both the supply-side and the demand-side of an economy. In many contexts, internet access appears to improve the productivity of workers and firms (Draca et al., 2006; Khanna and Sharma, 2018; Goldfarb and Tucker, 2019; Houngbonon et al., 2022). Research findings show that labor market outcomes for female workers especially improve when firms use more ICT (Dutz et al., 2017), but the findings are mixed on whether internet connectivity plays a role in building skills. Demand-side forces focus on the expansion of market access for firms, workers, and/or consumers, and the reduction of information frictions (Allen and Arkolakis, 2014; Fan et al., 2018; Couture et al., 2021). Internet access has also been shown to improve health-related human capital by reducing HIV/AIDS prevalence (Abbasi and Pongou, 2022). In contributing to this literature, our work breaks new ground by analyzing not just the effect of the internet in isolation, but also the additional impacts that arise from the complementarity between internet and road infrastructure investments on employment opportunities and outcomes.

Our paper also adds to the literature on the impacts of transport infrastructure expansion on economic growth and development. Several papers have shown that the net welfare gains of highways are large (Aschauer, 1989; Shatz et al., 2011; Duranton and Turner, 2012;
to capture early grade learning in relation to what students are expected to acquire in the first few years of primary school in Indonesia. The general cognitive items on the test are based on the Raven’s Colored Progressive Matrices.

Two versions of the test were administered: one for younger children ages 6 and 7, which had a total of 52 items and another one for older children ages 8 and 9, which had a total of 64 items. For both tests, the language section tested children’s ability to recognize letters and words, match words to objects, and comprehend short reading passages. The mathematics section tested children’s abilities to add, subtract, and order one to two digit numbers. The items based on Raven’s Progressive Matrices were intended to measure children’s general cognitive skills.

We calibrated the test score of children into a common scale using the 6 year-olds as the reference group. We used the mean and standard deviation of the 6 year-old group to normalize the test scores. The advantage of normalizing using the 6 year-olds as a reference group is the ease of interpretation, as we can understand the magnitude of increase in test scores associated with increase in age. The typical child in the sample scored 0.63 SD on language, 0.54 SD on mathematics and 0.28 SD on cognitive skills (see Table 1).

# <u>Early childhood histories</u>

ECED enrollment histories were collected by asking children’s primary caregivers to retrospectively report the types of ECED service a child had ever been enrolled in (including “no ECED” as a type) and the number of months enrolled in each type of ECED during each academic year since 2008-2009. Based on this information, we generate a variable for ECED sequence, which denotes the sequence of ECED programs attended by each child in the sample. In addition, based on a child’s age at the time of the survey in 2013, we are able to extrapolate the age of
makers have more options to draw from among the general interventions. The second is that insofar as governments and other actors are experimenting with innovative girl‐targeted interventions, there may be value in evaluating these to build the evidence base.

For general interventions, do impacts on girls tend to be larger?

Previous research shows that the demand for girls’ schooling tends to be more responsive than that for boys’ to gender neutral education policies (Glick 2008; J‐PAL 2017). However, we do not detect much difference between girls and boys in responding to interventions to increase access at any point in the distribution – the least effective programs, the median programs, and the most effective programs all have similar impacts in access for boys and girls (Figure 9). If anything, general interventions seem to be slightly more effective for girls than for boys, confirming previous work.<sup>9</sup> Once in school, the impacts of general interventions on learning outcomes are also quite similar for girls and boys (Figure 10). Median interventions increased learning for girls and boys by 0.1 standard deviation for both, with slightly larger impacts for girls (Figure 10). At the top of the distribution (90<sup>th</sup> percentile), the effect for boys is 0.51 standard deviation, comparable to that

> 8 The Burkina Faso program, evaluated in (Kazianga et al. 2013), includes girls explicitly in the name of the program: the Burkinabe Response to Improve Girls’ Chances to Succeed.

> 9 We also examine whether impacts on girls are larger in places with low levels of initial performance, using the harmonized learning indicators from the World Bank’s Human Capital Index (World Bank 2018a) as well as various access indicators from the World Development Indicators. We find no relationship.

10
not only are productivity and consumption higher in urban areas, but amenities and infrastructure services are often superior: among low-income countries with urban population shares of less than 25 percent, access to water and sanitation in towns and cities is around 25 percentage points higher than in rural areas (World Bank, 2008). These differences create strong incentives for rapid rural-urban migration. Confronted with land scarcity and high land costs in large cities, this migration has led to construction in at-risk areas (Burby et al., 2006, 2001; Lall and Deichmann, 2012). In the most marginal and risky locations, informal settlements and slums are often present, putting poor and vulnerable populations in a situation of extreme risk (Ranger et al., 2011).

An illustrative example of poor people settling in risky areas is Mumbai, prone to high flood risk from the Mithi River. Within the Mithi River basin lie a dense population estimated at 1.5 million and a high concentration of assets. Flood hazard, combined with high socio-economic exposure pose a major risk, evidenced by unprecedented floods in July 2005, which resulted in more than 500 fatalities and $2bn in asset losses (Ranger et al., 2011). Patankar (2015) reports on a surveys of poor households living in Mumbai’s flood areas, and shows that poor people are well aware of this risk, and are making a deliberate decision to live there to benefit from higher-wage jobs, better schools and medical care, and existing social networks.

Risk-taking can also increase welfare through environmental amenities (e.g., from sea views) and generate revenues from tourism. One measure of the importance of tourism to an economy is the percentage of international tourism expenditures as a percentage of total imports. As of 2012, in eleven countries (Bahamas, Cabo Verde, Dominica, Grenada, Macao, Maldives, Montenegro, Samoa, Sao Tome & Principle, St. Lucia, St. Vincent and the Grenadines, and Vanuatu) tourism accounted for more
# **Abstract**

This paper focuses on how urban policies and the clustering of creative industries has influenced urban outcomes. The set of creative industries include those with output protectable under some form of intellectual property law. More specifically, this subsector encompasses software, multimedia, video games, industrial design, fashion, publishing, and research and development. The cities that form the basis for the empirical investigations are those where policy induced transitions have been most evident, including Boston; San Francisco; San Diego; Seattle; Austin; Washington DC; Dublin (Ireland); Hong Kong (China); and Bangalore (India).

The key research questions motivating the paper are: What types of cities are creative? What locational factors are essential? What are the common urban policy initiatives used by creative cities? The paper first explores the importance of the external environment for innovation and places it in the larger context of national innovation systems. Based on a study of development in Boston and San Diego, the paper isolates the factors and policies that have contributed to the local clustering of particular creative industries. In both cities, universities have played a major role in catalyzing the local economy by generating cutting-edge research findings, pro-actively collaborating with industries, and by supplying the needed human capital. In addition, these two cities benefited from the existence of anchor firms and active industry associations that promoted fruitful university-industry linkages.

Many cities in East Asia are aspiring to become the creative hubs of the region. However, their investments tend to be heavily biased towards infrastructure provision. Although this is necessary, the heavy emphasis on hardware has can lead to underinvestment in developing the talents and skills needed for the emergence of creative industries in these cities.
our sample in the 1990s. The reemergence of an active sovereign bond market subsequently encouraged more and more emerging markets to start issuing foreign-currency bonds in London and New York. By the early 2000s, bonds had regained their once dominant position in international sovereign lending. As a result, our sample grows rapidly and reaches a second peak in 2016, with more than 300 foreigncurrency sovereign bonds of 61 countries being actively traded. Further details on the sample and coverage are shown in Appendix B1.

Figure 4: Sovereign bonds in default, 1815-2016 <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>1815 1835 1855 1875 1895 1915 1935 1955 1975 1995 2015<br>Bonds in default (full or partial), share weighted by principal amounts<br>Not in default, share weighted by principal amounts<br>Share in default, in %<br>(weighted by principal amounts)<br><!-- End of picture text --> Figure 4 uses the full sample of bonds in the sample and combines it with the granular default data from Section 3. To compute shares in default, we use the bond’s principal for weighting purposes (issuance amount in USD). In line with Reinhart and Rogoff (2009), we find several main sovereign default waves in the historical data, with more than 40% of external bonds in default in the 1830s, the 1870s, and following the Great Depression. The default wave of the 1980s is not captured in the graph since it involved syndicated bank loans but not bonds. In the modern sample (post-1994), bond default ratios are low in historical comparison, with small peaks during the defaults of Russia in 1998 and Argentina in 2001. From 2006 to 2016, default rates remained close to zero, comparable to the pre-WW1 spell 190013.

# 4.2. Measuring bond returns

The ex-post nominal return
directly, but has only an indirect effect on the growth rate of micro and large firms. Micro firms are much more credit constrained and must rely less on external funds to finance investment. Improving access to finance might boost the entry rate and the growth of small firms, perhaps at the expense of larger incumbents.

According to Dollar, Hallward-Driemeier, and Mengistae (2005), improving the business environment is an important complement to trade policies aimed at increasing international trade integration. Factors such as fast customs clearance times, good infrastructure, and availability of financial services have a significant impact on the probability of a firm’s exporting and receiving foreign investment. Freund and Rocha (2010) provide more evidence of the link between the business environment and international trade. Using data from Africa, they find that even though poor trade infrastructure is one of the main obstacles to trade, most of the burden is due to heavy “red tape,” bureaucratic customs practices that increase the time and cost of trade.

Gelb and others (2007) use subjective data on the business environment from 26 African countries to show that perceived constraints are not always independent of scale. Complaints about access to finance and land are more common among small firms, while complaints about infrastructure and corruption are more evenly distributed. They also find that a country’s level of development strongly determines which constraints are present (country fixed effects are more important than within4
0.540***|0.210|0.211||0.248***|
||(0.105)|(0.105)||(0.0397)|(0.148)|(0.148)||(0.0593)|
|Observations|576|572|579|579|306|306|313|313|
|R-squared|0.048|0.092|0.153|0.375|0.048|0.050|0.188|0.239|
|Number of|||||||||
|countries|38|38|38|38|38|38|38|38|

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 All estimations include period fixed effects and country fixed effects (demeaning approach). Source: Author’s calculations based on data from the IMF. See text for details.

15
reported by de Rezende et al (2012). All studies were from OECD countries. More recently, a systematic review conducted by Serra-Burriel et al (2020) of multi-drug resistant HAIs reported 20 studies (19 OECD countries and China) that estimated the additional costs of AMR. Across the 20 studies identified by Serra-Burriel et al, the average increase in cost was 33%. This increase is a combination of increase in intensity of treatment (e.g., different drugs and more laboratory tests) and increase in hospital length of stay. The former accounted for 6% of the increased cost and the latter 27% of the increased cost. However, none of these studies were from Africa.

Epeh and Fenny (2022) report a review of HAI medical costs in Africa, finding 13 publications from 10 African countries. There was significant heterogeneity across the studies in the additional cost and additional length of stay due to HAI. One study from DRC that was reviewed by Epeh and Fenny (2022) – estimating a cost per HAI of US$10.2 (Bukasa et al, 2018) – was given a low-quality rating based on the statistical methods used.

Due to lack of published data available on the costs of treating HAIs, it is necessary to construct the cost based on available cost datasets on cost per hospital bed day from studies conducted by IHME (2014, 2015). This is shown in Table 7, and is done by adjusting unit costs in local currency units in the year of cost data (2010) to the year of this present study (2022) using the GDP deflator, and then applying the PPP exchange rate conversion for 2022 to convert to US dollars in the year 2022.

**Table 7. Cost per bed day in US$ in 2022**

|**Country**|**Health center**|**District hospital**|**Higher hospital**|
|---|---|--
# **A Descriptive Statistics**

Tables 3 and 4 report the mean values of the variables used in the model by year, for urban and rural areas. The descriptive statistics generally reflect India’s rapid economic development. Average log per capita consumption, in real terms increased from 4.5 to 4.74 between 2004-05 and 2011-12. When expressed in levels, per capita consumption grew approximately 24% growth over the seven-year period, or 3.1% a year. Household size steadily fell during this time. In the decade between the first and last survey, the share of the population living in households with 4 or fewer persons rose from 39 to 47 percent in urban areas, and from 29 to 35 percent in rural areas. A byproduct of smaller households is a drop in the share of household members that are children ages 0-14, which fell by 5 percentage points in both urban and rural areas. Regular wage work became slightly more prevalent in urban areas, and the share of rural workers working in agriculture declined 3 percentage points, as industrial work became more common in rural areas.

Average spending on transport declined in 2014-15 after a consistent rise between 2004-05 and 2011-12. In contrast, expenditure on household miscellaneous services and recreational spending showed a continual strong increase from 2004-05 to 201415. The drop in transport expenditures may in part result from differences in the components of transport expenditure in 2014-15.<sup>24</sup> To the extent this is causing the drop in reported transport expenditure, the model may underestimate poverty reduction. We therefore also consider a model that only includes whether households reported any consumption on these three items in the past month, rather than including the amounts, as a robustness check to see how much including expenditure amounts matters.

The final rows of Tables 3 and 4 report the population-weighted mean rainfall, across districts, in terms of deviation from historical district means. Monsoons in India typically occur between June and October, making the third quarter rainfall important for agricultural production