rafmacalaba/data-use-mentions-extended huggingface dataset:
to be higher, and by moving to a system where money follows the patient, thereby providing incentives for quality improvements in both public and private health facilities. Both the fact that health insurance is layered on existing networks of subsidized public care and that it is used as a mechanism for improving quality, either by reducing the cost to the patient of care in the private sector or providing incentives for the public sector is again common across LMICs as no country has chosen to defund its publicly provided system as health insurance has grown. Of course, the relative importance of these two mechanisms depends on the context. In Latin America, for instance, the share of the private sector is smaller and insurance schemes tried to address differences in quality between clinics run by the social security system (accessible only to those employed in the public sector) and those run by the public health system, rather than between private and public care.

# Provider Reimbursements

Interestingly, the convergence that we see across countries in how health insurance is financed breaks down when we look at how providers are reimbursed. The London School of Hygiene and Tropical Medicine’s RESYST study documents 19 different purchasing mechanisms in 10 LMICs (Hanson et al. 2019). These include, inter alia, fee-for-service (the hospital is reimbursed for each service given as part of the stay), capitation (physicians agree to a fixed amount per patient under their care for a given duration), diagnostic-related groups (bundle all goods and services for one hospitalization episode into a single price depending on patient characteristics), line-item budgeting (health ministry pays for each item according to a budget) and global budgeting (a hospital signs a contract for a sum over a period to cater to the population in its catchment), used either by themselves or in combinations. Prices may be set through individual negotiations, group negotiations or administratively. Administrative prices themselves vary in their degree of sophistication, ranging from average accounting costs of procedures to more sophisticated, risk-adjusted marginal cost pricing.<sup
<mark>cost means of increasing awareness of the number of people engaging in open defecation and correcting misperceptions about the frequency of a behavior.</mark>

# **<mark>Social and economic context</mark>**

<mark>The adverse health effects of open defecation are well known: stunting and malnutrition, diarrhea and enteric parasite infection in young children, childhood death, and lower human capital (Ayalew, et al., 2018; Hammer & Spears, 2016; Mara, 2017; Patil, et al., 2014). Reducing open defecation requires access to improved sanitation facilities. Recent years have witnessed an increase in latrine construction and access among rural Indian households (Swachh Bharat Mission, 2019; National Annual Rural Sanitation Survey, 2017‐18).</mark><sup>1</sup> <mark>However, the transition from defecating in the open to using latrines appears to have been slower, at least in four north Indian states (Gupta, 2019).</mark><sup>2</sup> <mark>Among other factors, behavior change in the north Indian context may be influenced by social norms and cultural beliefs, especially those related to ritual purity and impurity (Coffey et al., 2015; Gupta, Coffey and Spears, 2016). These include the belief that open defecation is acceptable and harmless or that pits may require more frequent cleaning if all household members use the latrine, the cultural anxiety that building a latrine close to the house can make it “impure”, the social norm that pit cleaning is not only an unpleasant but also a demeaning (especially for higher caste members) and socially threatening job or that open defecation is a “masculine” activity (Coffey, et al., 2016). Similarly, when a sample of latrine owners in Uttar Pradesh were asked why they defecated in the open, 27% indicated that they were simply used to it, 23% did
in the IRRs of individual projects, ranging from ‒23 percent to 25 percent, with seven PPPs with negative IRRs. This suggests that public infrastructure projects are intrinsically risky, and that private participation entails significant risk shifting from the budget to concessionaires and their financiers.

The rest of the paper proceeds as follows. In section 2 we develop a conceptual framework to think about fiscal risks. In section 3 we present the history of Chilean PPPs, with an emphasis on the management of fiscal risks. In section 4 we present the estimation of the ex-post IRR of PPPs. Section 5 concludes.

# **2 PPPs and fiscal risks: A conceptual framework**

In this section we develop a simple framework that identifies the sources of fiscal risks in PPPs.

# <u>2.1 Risk and infrastructure</u>

Though it might sound like a platitude, the origin of the fiscal risks brought about by PPPs lies in the fact that infrastructure projects are risky (see our analysis of Chilean PPP project returns in section 3). It is useful, therefore, to start by showing that the risk added to the economy by a public infrastructure project does not depend on the procurement mechanism. To a first approximation, a new infrastructure project creates a fixed amount of risk, distributed between the public budget and the parties that finance the PPP, i.e., debt and equity holders.<sup>7</sup>

> 6 The concessionaire or a related party can perform the additional works only if they offer a price that is lower than the lowest price obtained in the competitive auction. In addition, the concessionaire is entitled to an administration fee for supervising the construction of the additional works.

> 7 That is, if we exclude the incentive effects of PPPs.

4
Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank (EBRD <u>2023). The survey covers the transition economies of Europe and Central Asia and several</u> comparator countries in Western Europe, the Middle East, and North Africa. It included 34 countries in 2016 and 39 countries in 2023. The survey is based on a nationally representative sample of around 1,500 households per country in the 2016 round and around 1,000 households 4
percent in another 39 countries (Drechsler and Jütting 2005; Pettigrew and Mathauer 2016).<sup>5</sup> Second, the RSBY was not new insurance. Instead, it was layered on top of existing access to free or highly subsidized care through public clinics themselves financed through general taxation. This new layer was designed to improve health care by reducing the cost of using private facilities, where quality was believed

> 5 The ubiquity of public subsidies for financing raises the important question of whether coverage differs by household characteristics. Household wealth and education are two widely-used markers of socioeconomic status in low- and middleincome countries, the former measured by an index of asset ownership and the latter by completion of different levels of schooling. We estimated correlations of insurance coverage and wealth, as well as insurance coverage and education for 61 countries using the latest available round of DHS data and plot these coefficients in Appendix Figure A1 with data and details available in the line index. In most countries, health insurance coverage is regressive, with higher coverage for those with greater education and more wealth.  This pattern reflects both household demand for insurance and policy choices. In some countries, particularly those in Latin America, health insurance has always been available for government employees and was then extended to those in the formal sector with mandatory contributions, which explains the regressive pattern. In contrast, India’s health insurance scheme is targeted to those below the poverty line, and the correlation of coverage with education and wealth is close to zero in the DHS data.

6
,800, thus forcing a flat relationship for higher actual GDP per capita.

12 There are also a number of other measures that can be used to estimate the output loss. These differ in terms of the assumptions made about trend growth and the timing of the pre-crisis and post-crisis period. Generally, we got similar results for the fiscal outlay, policy, and institutional variables. Still, it is worthy to note there are many other factors determining the potential GDP level of a certain country at a certain time. Furthermore, since it is not possible to isolate the effect of the banking crisis on GDP from other shocks, none of these output loss measures correctly captures the effect of the banking crisis on GDP and the full economic costs of a banking crisis. This caveat is the more important as there can be large differences between the measures. In his discussion of the analysis of Hoggarth, Reis and Saporta (2002), for example, Honohan (2002) shows that their cumulative loss in output has a correlation of only 33% with the IMF-loss of growth measure. Hoggarth et al. also show that using a lost cumulative output—instead of the lost cumulative growth measure, output losses during crises in developed countries are as high, or higher, on average than those in emerging economies, a conclusion which contrasts with other research.

18
firms in the COV-ES database that received at least some government support. While there is no general rule about the minimum number of observations necessary, we restrict our focus to the countries with at least around 150 firms that have received government support by the second round of COV-ES.

To apply our first criterion, we used the Map of SME-Support Measures in Response to COVID-19 created and made publicly available by the World Bank, retrieved in early March of 2021, and covering information as of late October 2020.<sup>4</sup> Our first criterion ruled out the overwhelming majority of countries covered in the Map. Additional countries were ruled out after supplementing information given in the map through country-specific searches of information about policies that we conducted. The vast majority of governments tried to increase the reach of their fiscal capacity by concentrating resources on the firms that were hit the most, often using revenue as a measure of this hit and using drops in employment and other characteristics. Our second criterion, a minimum of 150 firms receiving support by Round-2 of COV-ES, also eliminated several countries, mostly in Africa, where government policy coverage by October/November was limited. To the best of our knowledge, these two conditions combined ruled out all the countries covered in COV-ES except for El Salvador and Georgia.

Governments in El Salvador and Georgia announced a set of support measures to businesses as well as to individuals and families to help cope with the pandemic and with the strict lockdowns that were introduced to limit the spread of the virus. Support measures for private businesses in El Salvador mainly targeted small, micro, and medium-sized companies (MSMEs) as well as informal businesses, and included access to new credit; wage subsidies; fiscal incentives for investment in new productive projects; deferrals of utility payments; and other supports for simplification of procedures for MSMEs. The Georgian government also implemented support policies, with some targeting of the tourism sector (loosely defined as hotels and restaurants) and small firms. The support measures in Georgia included fiscal
# **1. Introduction**

The take-up and correct usage of anti-mosquito bed nets to prevent malaria are key development issues in Africa and of substantial concern to donors. In order to address behavioral and economic constraints to take-up and correct usage, donor- and government-sponsored bed net distribution programs rely on both pricing strategies (subsidized or free bed nets) and media campaigns. For example, radio broadcasts of health messages is a prominent part of the international Roll Back Malaria program. These programs raise three central questions that are the focus of the analysis here. First, the literature shows that households with greater media access are more likely to receive cash transfers in more established democracies. Does media access also improve household ability to demand non-cash benefits from government in less established democracies? Second, does radio access increase households‘ adoption of healthy practices? And, third, does it support or undermine the implementation of pricing strategies meant to stimulate the take-up of mosquito bed nets? We examine these questions using original data we collected to exploit a ―natural experiment‖ in radio access in Benin, a country with a large donor-funded government program for the distribution of free bed nets. Analysis of these data demonstrates significant, and surprising, effects of media access.

With regard to the first question, whether radio access affects household ability to demand free bed nets, strong evidence from other parts of the world indicates that governments are more likely to target programs offering private benefits to citizens with better access to mass media, including natural disaster relief in India (Besley and Burgess 2002), and welfare payments during the US Great Depression (Strömberg 2004). The results here, in contrast, show that in an environment with additional barriers to government responsiveness, besides citizens‘ lack of information, greater access to mass media does not enable citizens to extract more benefits from government programs. As a consequence, and in contrast to prior research, households living in villages with greater radio access report no more, and in some specifications significantly fewer,
To test such hypothesis, we first check whether or not the application of PSI on imported inputs induces plants to switch towards domestically produced inputs. To that end we exploit highly disaggregated plant-level data on the use of intermediate inputs at 9 digit-level. Importantly, the data allows to distinguish between domestically sourced and imported inputs. More specifically, we estimate the following linear model: In Equation 2, _Yjmt_ is an outcome variable for input _m_ used by plant _j_ at time _t_ . The variable _Input PSImt_ is a dummy taking value 1 if PSI is applied on an intermediate input _m_ at time _t_ . The vector **X** _mt_ includes import tariffs and dummy variables for whether NTMs other than those under analysis are applied to input _m_ .<sup>24</sup> We also include 5-digit industry-year fixed effects ( _uit_ ) that control for differences in competition and other industry-level confounders, and region-year fixed effects ( _urt_ ). Plant-input fixed effects are denoted by _ujm_ . Standard errors are clustered at the plant-level.

Table 6 presents the estimates for Equation (2).<sup>25</sup> The dependent variable in column (1) is a dummy variable taking value 1 if a plant switches from an imported to a domestic variety of a given 9-digit input. The dummy takes value 1 if the imported quantity of an input decreases and the domestic quantity of the same input increases.

The coefficients in column (1) suggest that while PSI does not induce plants to switch to domestic inputs, import tariffs do. The results in column (1) are also supported by those in column (2), where the dependent variable is the share of imported quantity of an input over the total use of the same input (i.e., imported plus domestically sourced).

This switch to domestic inputs induced by tariffs may help explain the negative impact of tariffs on TFPQ, as domestically produced inputs may be of lower-quality than imported inputs. This is supported by the result in column (
This section answers the questions above using longitudinal matched employer-employee data from Brazil, Costa Rica, and Ecuador. These data sets are based on social security administrative records. Thus data coverage is restricted to formal workers. As discussed in the previous section, formal workers accounted for around 55 percent of total employment in Latin America in 2015.

Supplementary online appendix S1.3 describes these administrative data sets. Administrative data cover all workers registered (by their firms or voluntarily) in social security, which provides them access to the benefits and labor protections afforded by the legal employment system. Hence, the definition of formality does not match one to one the one used in the

> 21However, due to limitations in their data, these papers were unable to account fully for selection of workers into firms based on unobservables. Using data for manufacturing in Germany, Card, Heining, and Kline (2013) overcome this limitation and, applying the Abowd, Kramarz, and Margolis (1999) framework, find that the variance of firm fixed effects accounts for around 20 percent of the total variance of wages. The main component is worker effects, which accounts for 45 to 50 percent. The covariance of worker/firm fixed effects accounts for around 15 percent, with the remaining variance being accounted by time trends, age, skills, and the residual.

> 22See Alvarez et al. (2018) for evidence focusing on male workers from Brazil’s manufacturing sector.

23
important to analyze the effects of these trends on poverty.

**Table 3:  Decomposition of Changes in Poverty, 1995-2002 (percentage points)**

|**Poverty change **|**1995**|**1996**|**1997**|**1998**|**1999**|**2000**|**2001**|**2002**|
|---|---|---|---|---|---|---|---|---|
|Total|-0.4%|-1.5%|-0.3%|-1.6%|1.3%|0.5%|0.8%|1.0%|
|Due to growth|0.4%|-2.1%|-1.1%|-2.0%|0.5%|0.4%|0.6%|0.6%|
|Due to inequality|-0.9%|0.3%|0.8%|0.5%|0.8%|0.0%|0.2%|0.5%|

_Source:_ Staff calculations based on GUS’s data.

The results of a decomposition of changes in poverty for Poland into their growth and inequality components are presented in Table 3 and Figure 5<sup>7</sup> It is evident that economic growth was a main driving force in poverty reduction. Two periods are readily distinguishable. During Poland’s high growth years, 1994-98, the economic growth effect (5 percent) far outweighed the negative effect of the increase in inequality (-1 percent), resulting in a cumulative decline in poverty of about 4 percentage points. During the subsequent period, 1998-2002, and
scraping approach, descriptive statistics, and the methodology for estimating the BHP exemplarily for five countries: Albania, Costa Rica, Morocco, Pakistan, and South Africa.<sup>2</sup> Overall, we collect residential property price data from over 200,000 online listings and present an approach to clean, analyze, and visualize these data. In this paper, we only demonstrate the applicability of the collected house price data for the price estimation of a defined housing unit. We leave for future research on how a standard house (BHP) price may contribute to understanding affordability in EMDEs.

This paper proceeds as follows: After this introduction, Section 2 discusses previous efforts in collecting and estimating house prices, which are mainly focused on developed economies. This section outlines how – if collected at all – private property prices are mostly available in an indexed format which only allows for monitoring of changes over time. Section 3 describes the data collection process for the five countries and discusses the prospects and pitfalls of a web scraping approach to obtain data for EMEDs. Section 4 defines a standard housing unit, which is the underlying concept for estimating the BHP. This section also rolls out how we apply a Random Forest (RF) model to estimate the BHP. Section 5 presents BHP results for the five markets, including estimations for the largest cities in these countries. Section 6 concludes and outlines how the methodology we propose can be expanded to a more extensive set of EMDEs.

> 2  Our ongoing research extends the collection and estimation of private property prices to over 60 EMDEs and will be presented in future papers.

3
OECD, 2007a,b).

10 _RY_ denotes recipient-year, _agg_ denotes aggregate aid, and _comm_ denotes commitments. No superscript is used for disbursements. 11The data in DAC5 are a mix of disbursements and commitments. To account for this, I scale the DAC5 data to ensure that the sum of the sectoral aid variables matches the aggregate disbursements from DAC2a for every donor-year.

> 12Since the construction of the data for this paper, two new disaggregated aid datasets have become available. Ravishankar, Gubbins, Cooley, Leach-Kemon, Michaud, Jamison, and Murray (2009) construct data on health aid by estimating disbursements on the basis of the less incomplete CRS commitments and by adding data from separate reports for a number of NGOs and multilateral and private donors. These data are used by Lu et al. (2010) to estimate the fungibility of health aid. One disadvantage is that a large portion of the data cannot be allocated by recipient country. Lu et al. (2010, p. 1379) state that only 21% of all health aid in 1995 can be traced to recipient countries, and 30% of this aid can be traced to recipient countries in 2006. In addition, it is not immediately clear how one would further divide health aid into on- and off-budget components in these data. A second recent dataset, AidData (http: //www.aiddata.org), attempts to construct a more complete disaggregation of aggregate aid into all of its constituent parts according to a number of dimensions but focuses almost exclusively on commitments.

13Current US$ GDP from World Bank (2006c) is used to express sectoral aid disbursements as a percentage of the GDP.

> 14These data are not publicly available, although they have been used in a variety of publications (e.g., Gupta, Clements, and Tiongson, 1998; Baqir, 2002). I am grateful to Gerd Schwartz for sharing these data and to Ali Abbas for assistance in obtaining them.

> 15For Fiji, the observation
exporting rms can easily borrow to overcome temporary uctuations in real costs associated with exchange rate movements. The most appropriate nancial instruments to hedge exchange rate risk are nancial derivatives, such as futures and forwards of exchange rates that allow rms to x the exchange rate at a future time, or exchange rate swaps that allow rms to trade exchange rate risks. However, these derivatives are relatively sophisticated and are not widely available, especially exchange rate futures that require some form of standardization and are traded in open exchanges. In most cases where there is a market for exchange rate risk this takes place over the counter. As mentioned in section 3, data on the depth of foreign exchange rate derivative markets comes from the Bank of International Settlements. The survey conducted by the BIS covers a set of 52 countries where these markets have some meaningful degree of activity. In the rest of the world, forex derivatives are largely nonexistent. However, even if there are no derivative markets that allow a rm to hedge exchange rate risk ex-ante, a well functioning nancial market may provide liquidity to exporters facing temporary diculties arising from exchange rate uctuations. Thus, even in absence of direct hedging options the role of natural hedging may be stronger in nancially underdeveloped countries.

To test the hypotheses that the degree of trade openness, and the development of forex derivatives and nancial markets reduce the importance of natural hedge, I estimate the parameters of equation (7) separately for dierent groups of countries formed according to the median level of each these characteristics, allowing therefore the natural hedge parameter _β_ to vary with these characteristics. The results of the estimation are reported in the two panels of Table 9. The rst two columns separately estimate the coecient among closed and open countries (i.e. those with total trade to GDP below and above the sample median, respectively). The results in both panels show a larger coecient for natural hedge among open countries (Column (2)) than among closed ones (Column (1