rafmacalaba/data-use-mentions-extended huggingface dataset:
**Figure 7** : Availability of personal protective equipment in facilities in Sierra Leone by facility type (hospital vs. non-hospital)

<!-- Start of picture text --> Hospitals Non-hospitals<br>Apron<br>Boots<br>Facemask<br>Gloves<br>PPE all available<br>0 20 40 60 80 100<br>Facilities with item available (%)<br><!-- End of picture text --> Importantly, the SDI (like other large-scale health facility surveys) does not collect information on the depth of stocks of equipment and supplies. A facility will be recorded as having disposable gloves even if only one pair is available. Given the breadth of the survey, counting stocks of supplies is infeasible; however, it also means that the estimates of availability described here do not necessarily reflect adequate supply. In addition, stocks to address surges in demand for equipment and supplies may not be stored in the facilities, so even if gaps in availability at the facility-level were measured comprehensively, this would not necessarily imply a shortage at the national level.

# Availability of basic medical equipment

Availability of key medical equipment is imperative to ensure that adequate triage, diagnosis, and treatment can be provided during a pandemic. Three pieces of basic equipment (thermometer, stethoscope, and sphygmomanometer) are crucial to, among other activities relevant to COVID-19, diagnose fever, examine for respiratory symptoms, and identify the risk factor hypertension, respectively. Any health facility, regardless of level, should have these three pieces of basic equipment available.

In general, availability of basic equipment is high but imperfect. Specifically, in Kenya and Tanzania, over 90% of facilities have a functioning thermometer, while only 80% in Sierra Leone do. Across all three countries, over 90% have a functioning stethoscope. Only 77% of facilities in Sierra Leone have a functioning sphygmomanometer, but over 90% of facilities in Kenya and Tanzania do. Taken together,
# Health Insurance and the Know-Do Gap

Do health insurance schemes affect the know-do gap? We are not aware of any studies to date in LMICs that causally link health insurance to supply responses among providers, at least for inpatient care, where the bulk of the money is spent. It is difficult to use administrative claims data to come to any conclusion regarding quality in these settings; see Morton et al. (2016) on how claims data are recorded. Nevertheless, we will offer an educated guess based on a collage of evidence from newspaper reports, audits, and related studies on how doctors respond to price changes in LMICs.

As one example, media reports and field investigations from the Indian states of Andhra Pradesh, Gujarat and Chattisgarh, shortly after the introduction of the national health insurance program RSBY, showed that many women were getting hysterectomies based on rudimentary diagnostics and for conditions such as heavy menstrual bleeding that could be medically managed. <mark>As reported by A</mark> verill and Dransfield (2013): A study by a non-profit organization, AP Mahila Samatha Society, in 2009 of over 1,000 women in Andhra Pradesh found an increase of 20 percent in hysterectomy cases since July 2008. They also reported that doctors had told 30 percent of the women that they would die if they did not have the operation. A few months ago, the Chhattisgarh State Health Department initiated action against 22 nursing homes, which were carrying out hysterectomies without legitimate medical reasons in order to claim money from the national health insurance scheme, Rashtriya Swasthya Bima Yojana (RSBY).

Subsequent research indeed confirms much higher rates of hysterectomies in the states of Gujarat, Bihar and Andhra Pradesh, but also cautions that causal claims on the impact of insurance are harder to establish (Desai, Sinha, and Mahal 2011; Desai et al. 2019).

Cataract surgery seems to be another area with sharp increases after the arrival of health insurance. For instance, Rana (2017) reports that in the Indian state of
We also use several auxiliary data sources. Data on trade openness (defined as the sum of the merchandise import and export shares in GDP) and real GDP per capita growth are from Eurostat and _World Development Indicators_ <u>(World Bank 2022a). Data on the capital stock of information</u> and communication technology (ICT)—a proxy for technological change in a country—are from the KLEMS data set <u>(Jorgenson 2012). Our measure of job protection corresponds to two</u> Employment Protection Legislation (EPL) indices calculated by the OECD <u>(2021). We use the</u> index measuring the protection of regular workers from dismissal (EPR, version 1) and the index measuring the difficulty of hiring temporary workers (EPT, version 1). Both range from 0 to 5, with higher values corresponding to a higher degree of job protection (more difficult dismissal of regular workers and more difficult hiring of temporary workers). Table 1 presents descriptive statistics of the variables included in our empirical analysis.

<u>Figure 1</u> displays average tenure over the sample period by subgroup. There is no clear trend; macroeconomic fluctuations seem to be driving most of the movement in these aggregate measures. The average worker in Europe spent about 10 years in his or her current job. There is some heterogeneity across regions of Europe, with workers in Southern Europe having, on average, job tenure that is two years longer than their peers in Northern Europe. This difference increased over time, as tenure in Northern Europe decreased while remaining stable in Southern Europe. Tenure in Central and Western Europe mainly remained stable, at 10 years, during the sample period.

<u>Figure 2</u> shows the average tenure by cohort and age for all countries in the sample. As expected, it increases with age, stabilizing toward the end of an individual’s working life. The age profiles of each cohort
of firm-level data from 19 Sub-Saharan African countries suggest that a 10 percentage points increase in the share of output sold to the government is associated with a 4 percentage points increase in firm productivity. Public procurement also increases a firm’s capital intensity, ability to pay higher wages, and introduce new products (Hoekman and Sanfilippo, 2018). In Brazil, winning a government contract had a positive impact on firm-level employment growth, which was persistent well beyond the expiration date of the contract. Firm growth mainly occurred because winners were more likely to win more contracts in the future, entered more valuable auctions, expanded in more markets and increased the number of products sold (Ferraz et al., 2015).

# **5. Knowledge gaps in current firm support programs**

# **5.1. Design**

There are four types of knowledge gaps in the available evidence on the design of firm support programs. _Breadth of instruments covered._ Although there are many instruments available to help address both supply and demand-side constraints, most often interventions have experimented with one or two instruments. For example, interventions enabling access to finance have most often focused on grants or loans. There is little evidence on the impact of lines of credit, partial credit guarantees, early-stage financing instruments, fiscal incentives or vouchers, inducement awards etc. This is because either the intervention itself is not frequently used or more critically because the interventions are constrained by weak empirical foundations, poorly articulated logical frameworks, and largely absent monitoring and evaluation systems (including impact evaluations and cost-benefit analyses). Likewise, interventions to upgrade firm capabilities frequently use different types of training programs, however, there is no systematic evidence on the impact of export promotion programs, technology upgrading and so on.

_Evidence on interventions addressing multiple constraints._ Firms face several constraints simultaneously on both supply and demand side, however, most interventions address a single constraint, such as access to finance, or firm capability needs. This could partially explain why many firm
wealth. For these reasons the most comprehensive population-scale study<sup>17</sup> outside the U.S. and Europe, a population-scale study in China, finds a strong _positive_ relationship between pollution and income. More broadly, for countries where anthropogenic sources of pollution dominate, there is a long recognized potential trade-off between urban-based development and environmental quality.

A primary reason that the few existing studies in LMICs have been mostly limited to urban areas is that data on air pollution and income or wealth are disproportionately available for cities. To overcome this challenge across a broad geographic scale we leverage recently released spatially resolved estimates of wealth for 103 LMICs.<sup>25</sup> These relative wealth indices (RWI) represent the most comprehensive micro-level wealth estimates to-date. They were derived from machine learning models trained on survey based measures of wealth. We combine these relative wealth estimates with global monthly spatially resolved estimates of ambient PM2 _._ 5 for 2015-2020 that are derived from a combination of satellite observations and modeled output.<sup>29</sup> Together, the data allow us to estimate the correlation between relative wealth and average ambient PM2 _._ 5 levels across grid cells in more than 100 LMICs at increasingly fine scales: across the full sample, separately within each country, and within cities.

For the pooled sample we regress average PM2 _._ 5 on relative wealth at the grid-cell-level and include separate intercepts for each country (Equation 1). The intercepts (fixed effects) account for baseline differences in wealth and pollution across countries and for the fact that wealth measures are normalized at the country level. The estimated coefficient on relative wealth in this specification provides a measure of average within-country correlation between wealth and pollution. Next, we estimate separate regressions for each country again regressing cell-level average PM2 _._ 5 on cell-level relative wealth (Methods). This exercise provides us with countrylevel estimates for the within-country correlation between wealth and pollution and allows us to assess which factors contribute
are fully exploiting the potential of the VAT-generated paper trail which has proved to be essential in other contexts (Pomeranz, 2015; Carrillo, Pomeranz and Singhal, 2017). Recent evidence indicates that, despite the adoption of EBMs, there are widespread discrepancies in data reported by buyers and sellers for the same transactions, both in Rwanda (Mascagni, Mukama and Santoro, 2019) and Uganda (Almunia et al., 2022). Such studies suggest that tax administrations are not fully equipped to unlock the potential of VAT cross-checks, mostly due to structural challenges with software and skills (see Section 6). More broadly, such challenge suggests that much more can be done in investing in automated data-based – monitoring systems, which have shown to be impactful in more developed contexts such as India (Mittal and Mahajan, 2017), Pakistan (Shah, 2020) and T ̈urkiye (Dogan, 2011).

# **3 Technology and Real Estate Taxes**

Although real estate taxes make up only about 3 percent of taxes in African countries (Figure 3b), they are often the primary source of revenue available to local governments. Increases in property taxes have also been shown to be more closely linked to increases in spending on public services, compared to other revenue sources such as transfers from the central government (Gadenne, 2017; Hoffman and Gibson, 2005). As many countries adopt fiscal decentralization programs, it will be important to have the appropriate information technology systems in place to support the implementation of real estate taxes.

At first glance, given the visibility and immobility of the tax base, real estate taxes would seemingly not suffer from the core information problem in taxation: the ability of authorities to identify the tax base and determine tax liabilities. Yet, in practice, many tax authorities in Africa struggle to keep accurate and comprehensive cadasters and other databases for real estate taxation purposes. Cadasters, when they exist, are often outdated, and property valuations often do not reflect existing market prices. Further
<!-- Start of picture text --> Mali 2006, rural areas<br>Gender gap in school enrollment, by age and mother’s marital status<br>,<br>ce) a<br>2<br>‘2<br>=<o ©<br>D<br>=®S7) a. o*<br>3 o<br>2<br>®> ase<br>2 BS scene<br>go «3<br>£ ° Married, once<br>a) — _o*”o? ===== Married,Married, prev.prev. widowdiv.<br>4 6 8 10 12 14<br>Age<br><!-- End of picture text --> <!-- Start of picture text --> Mali 2006, urban areas<br>Gender gap in school enrollment, by age and mother’s marital status<br>3 w<br>, Married, once<br>Ay === Married, prev. widow<br>2i© +E m= Married, prev. div.<br>D<br>£oOos6 %<br>8<br>a2a<br>®<br>eon ° -<br>2 . Se, o*<br>2 "“=a_ go?”<br>=HD oO<br>4 6 8 10 12 14<br>Age<br><!-- End of picture text -->
Next, we review the country context in which the collected coefficients were estimated. We focus on macro-financial factors potentially relevant for the interest rate pass-through: GDP per capita ( _gdppc_ ), trade openness measured as exports over GDP ( _openness_ ), net foreign assets ( _nfa_ ), financial depth measured as credit to GDP ratio ( _credit_gdp_ ), market capitalization ( _capitalization_ ), turnover ratio for stocks ( _stock_turnover_ ), and claims on central government divided by GDP ( _claims_gov_gdp_ ). Each macro-financial variable is measured at the median year of the sample used in the original study. For example, if a research study uses a sample from 2006 to 2010, then the median year for this research is 2008. Table 3 displays descriptive statistics for all macro-financial variables.

# [Table 3 about here]

The third group of control variables covers monetary policy characteristics: a measure of central bank independence ( _cbi_G_ ), a dummy for inflation targeting framework ( _inf_target_ ), and a dummy for floating exchange rate arrangement ( _floating_exrate_ ). For the measure of central bank independence, we use the same approach as for macro-financial variables. That is, we choose the median year of the study’s sample and match it with the corresponding assessment of central bank independence from the Garriga (2016) data set.<sup>4</sup> Similarly, the median year of the sample determines the setting of the dummies for inflation targeting and floating exchange rate regime. Table A4 in the Appendix presents the descriptive statistics of the central bank independence variable.

The two dummy variables for the type of monetary policy framework and exchange rate arrangement are based on the Annual Reports on Exchange Arrangements and Exchange Restrictions from the IMF.<sup>5</sup> We differentiate inflation targeting from other monetary policy regimes. The corresponding dummy ( _inf_target_ ) takes a value of one if the country targets inflation, and zero otherwise. The dummy _floating_exrate_ then takes a value of one for a _de facto_ floating exchange rate arrangement as classified by the
Figure 2: How Does the City-Level Information Treatment Effect Vary with Relative Premiums?

<!-- Start of picture text --> Health Insurance Participation Pension Program Participation<br>0 .02 .04 .06 .08 .1 .1 .15 .2 .25 .3<br>Health Ins. Premiun Pension Ins. Premiun<br>Pension Program Participation (excl. Luoyang )<br>.1 .15 .2 .25<br>Pension Ins. Premiun<br>.15 .1<br>.1 .05<br>.05<br>0<br>0<br>-.05<br>-.05<br>Change in health ins. participation -.1 Change in pension ins. participation -.1<br>.1<br>.05<br>0<br>-.05<br>Change in pension ins. participation-.1<br><!-- End of picture text -->

**Source** : 2015 and 2016 waves of the Rural-Urban Migration in China (RUMiC) Survey, and RUMiC Information Intervention Survey, 2016.

**Notes** :An estimated city-level treatment effect is plotted against the relative premium for the urban employee health insurance and pension programs in the city. The relative premium is calculated as the premium required for self-employed workers to participate divided by the city-wide average monthly earnings of migrants. The citylevel treatment effects are coefficients from the health insurance and pension participation models estimated with city-specific information treatment indicators (city indicators interacted with the information treatment) in models that control for the city fixed effects.

38
The dataset of RZ includes 42 countries. When we merge it with the countries for which we estimate the competitiveness measure, the number of countries included in our dataset drops quite a bit; we are left with 17 countries. For the growth regressions, as in RZ, we need to drop the benchmark country (the United States) and hence are left with 16 countries. In all regressions, we use country dummies to control for country differences and use industry dummies to correct for industry-specific effects.

Table 2 presents the summary statistics of the country-specific variables (Appendix Table I presents the same statistics for growth, total capitalization, concentration, and competition— but by individual country). The average sectoral real growth rate is 3.5%. The average sector requires some 31% of external financing for its investments. Total capitalization to GDP is on average some 38% but with large variations across countries, from a low of 75% to a high of 219%. The top-three banking concentration measure is on average 55% but also with significant variation, from a low of 21% (Japan) to a high of 87% (Jordan). The average competitiveness measure is 0.72, with a low of 0.46 (Egypt) and a high of 1.0 (Netherlands), indicating that some form of monopolistic competition is most typical of the 16 banking systems.

**Table 2:  Summary statistics.**

|Variable|Mean|Standard<br>deviation|Minimum|Maximum|Number of<br>observations|
|---|---|---|---|---|---|
|Growth|0.0349|0.0920|-0.4474|1.0000|1251|
|Share in value added|0.0156|0.0208|0.0000|0.2244|1297|
|Financial dependence|0.3136|0.3971|-0
enrollment books, often using different colors for high and low castes, to identify caste-targeted entitlements such as stipends and uniforms provided by state governments. In villages, people are frequently called by their caste names. Following the common usage in this area and also the way that caste is recorded in school enrollment books, in revealing caste identity we used the traditional name for each caste (Thakur, Chamar, etc.).<sup>5</sup> We next describe the incentive schemes. Participants were given a packet of 15 mazes to solve in each of two 15-minute rounds.<sup>6</sup> Some participants had piece rate incentives in both 5 In the 1998-99 Indian National Family Health Survey, households were asked to name their caste. Most low-caste respondents gave their actual caste name ( _e.g._ Chamar), but a few used the more generic and politically correct names, Dalit, _harijan_ , or Scheduled Caste (Marriott 2003).

6 The mazes are Xerox copies from <u>http://games.yahoo.com/games/maze.html, level 3. Gneezy, Niederle,</u> and Rustichini (2003) showed that individuals do not solve mazes just for fun, they respond to incentives.

11
# **1. Introduction**

The increase in global financial integration over the last twenty years has been remarkable, and U.S. institutional investors that purchase foreign assets have been significant participants in this growth.<sup>1</sup> Given standard economic theory, one would expect to see greater international diversification accompanying the expansion of global investment opportunities. Indeed, a number of studies show the gains that may be achieved from such diversification.<sup>2</sup> To date, however, evidence on how investors actually allocate their portfolios around the world and what determines it is still limited.

In this paper, we aim to fill the gap in the literature by constructing a unique micro dataset of asset-level portfolios for a group of important institutional investors. We then document the main stylized facts regarding their global asset allocations and study the factors behind their portfolio decisions, in particular how the organizational structure of financial intermediaries affects international investments. To conduct the research, we analyze U.S. equity mutual funds with an international investment scope. We find that, although mutual funds that invest globally have significantly expanded the size of their assets, they have been noticeably restrictive in the range of stocks they hold. In fact, they tend to invest in about the same number of stocks (100 stocks) than mutual funds with a more limited (regional or country) mandate do. Moreover, they forgo potential gains from international diversification. This investment practice is not explained by lack of information or instruments, transaction costs, or a better ability of global mutual funds to minimize tail risk. Instead, it seems to be largely driven by industry practices related to how mutual fund families structure their operations. These organizational factors seem important to understand how managers and families (companies) allocate their portfolios internationally.

> 1 See, for example, Obstfeld and Taylor (2004), Stulz (2005), and Kose, Prasad, Rogoff, and Wei (2009). 2 On the supply
These arguments can be translated into the context of Sub-Saharan Africa. Here, cabinet appointments and changes are used by presidents to consolidate their hold on power, in attempts to contain the threat of losing to their political opponents in democratic elections or of being toppled by insiders through coups d’états (Francois et al. 2015, Arriola 2009). This co-opting of elites helps to explain why cabinets can get larger at strategic moments, notably ahead of elections.<sup>5</sup> Moreover, a “winner-takes-all” political system can be unstable, and governments may face frequent threats of coups. Where presidents use ministerial appointments to buy loyalty from members of the elite who might otherwise have mounted leadership challenges, a large cabinet can increase political stability (Hyden 2006: 104). Arriola (2009) tests this hypothesis using data on cabinets for 40 African countries over the period 1970-2000 and finds that cabinet appointments can be used to extend a leader’s tenure and lower the probability of being deposed through a coup, unless leaders overextend their coalitions (Arriola 2009: 1350). Torgler and Frey (2012) analyze more than 100 countries over 20 years and find that large cabinets reduce the likelihood of an assassination attempt on high government officials or politicians. This helps to explain why power sharing arrangements have been used across Africa during attempts to resolve conflict. Somalia, for example, had especially large cabinets following the establishment of the Transitional Federal Government in 2004.

Overall, this discussion yields expectations about the relationship between cabinet size and governance outcomes. Elite clientelism implies that an increase in the number of cabinet ministers is likely to increase corruption and lower the quality of policy formulation and implementation. Yet, in analyzing this relationship, we need to account for other factors that might also be associated with both cabinet size and governance outcomes, in particular pressures to accommodate political allies and to buy off potential rivals.

# **3. A new data set**

To take a fresh empirical look at the relationship