rafmacalaba/data-use-mentions-extended huggingface dataset:
CMS anesthetist fee payment department), and used the national average anesthetist conversion factor of $17.71 per anesthetist unit (see appendices 2 and 3 for an estimate of anesthesiology fees).<sup>24</sup> We then calculated the inpatient prices for our selected procedures in the US by adding hospital payments, physician fees and anesthetist charges for the year 2004 (Column 1, Table 4).

Since outpatient treatments under Medicare are also subject to a patient co-payment, we used the minimum adjusted co-payment for reported by CMS for the relevant CPT codes. Examples of minimum co-payments include $157 for a glaucoma procedure and $370 for a prostatectomy procedure such as TURP. We then calculated Outpatient prices by adding hospital fees, physician fees, anesthesia charges, and co-payments for the year 2004 (Column 3, Table 4).

# _Magnitude of Demand_

We also obtained patient volume data from the H-CUP Nationwide Inpatient Sample database. The latest year for which H-CUP data were available was 2002. However this dataset covered only inpatient treatments in US community hospitals for 36 US states (see Column 2, Table 4), and thereby excluded the large volumes of the selected surgeries that are performed in an outpatient setting (Column 4, Table 4). Since, we were unable to find a centralized source of data on the number of outpatient surgeries by procedure, we had to collect outpatient volume data from a variety of sources. In each case, we contacted the relevant professional organization of surgeons, e.g., the American Podiatric Medical Association provided data on the annual number of Bunionectomies (toedeformity repairs). In some cases, we had to rely on outpatient volume estimates from health care research firms, e.g., OptiStock, a vision care research firm, provided estimates on the annual number of cataract procedures. See appendix 3 for the detailed list of sources from which outpatient volume data was estimated.

# _Foreign prices_

Vanbreda International, a Belgium-based employee benefits consulting and administration firm, whose
**Table 2.** Aggregate data of hazards and climate change databases.

|**Source**|**Hazards**|**Index**|**Type**|**Level**|
|---|---|---|---|---|
|ThinkHazard!|Floods (river, urban, and coastal),<br>landslides, cyclones, wildfires,<br>extreme temperatures.|TH index<br>Average of TH index|Modeled|ADM 2<br>ADM 1|
|EM-DAT|Floods, landslides, droughts,<br>cyclones, wildfires.|Number of occurrences in<br>the survey year and lag<br>year|Historical|ADM 2<br>ADM 1|
|SPEI|Drought|SPEI-6<br>SPEI-24|Modeled|ADM 3,<br>ADM 2,<br>ADM 1|
|CCKP|Climate change|Temperature trend<br>difference (trend 2000-<br>2020 vs trend 1940-2020)|Historical|ADM 1|
||Precipitation|Number of extreme<br>aggregated accumulated<br>precipitation in mm events<br>in 1901-2021|Historical|ADM 1|

Note: we aggregate the three floods TH indexes into one. We construct a weighted indicator considering that some areas can experience the three types of floods. In contrast, other sites can experience only two or one of them (e.g., districts far away from the sea without shore or sea exit are less likely to experience coastal floods. Moreover, the coastal flood TH index has no data in these cases). Table B1 in the Appendix shows how this weighted index is constructed. The SPEI-6 and SPEI-24 index time scales correspond at the beginning of the household survey period. The SPEI has a spatial resolution of 0.5°; thus, to aggregate to ADM 3, ADM 2
While these datasets offer valuable insights into population movements and return conditions, it is important to keep in mind that DTM data aims to provide general trends on population figures, rather than precise figures. This methodology introduces some limitations to the dataset, and therefore to this study: DTM data is collected through interviews with key informants such as mukhtars, local authorities or local security actors, and not via household surveys or registration, and the number of individuals is calculated by multiplying the number of displaced / returnee households by six (being the average size of a family in Iraq.) Iraq’s difficult environment when it comes to limited access and security issues also affects the data collection process. Yet, while recognizing these limitations, it is important to note that no alternative datasets exist with the same geographic scope and breadth of time as DTM; and as such, the authors chose to rely on these datasets as one tool of analysis. For more information on DTM methodology and data collection procedures see: http://iraqdtm.iom.int/MasterList#Methodology

12
are aggregated over the four-year period. Investment in countries frequently regarded as tax havens is excluded from the calculation. Statistics in panels (c) and (d) are based on foreign affiliates of Korean MNCs that satisfy all of the following criteria: (i) total investment by the Korean headquarters firm is at least 2 million US dollars, (ii) classified as a firm in the non-financial sector, and (iii) reliability of the survey response is verified by the Korea Export-Import Bank, the administrator of the survey. About 60-70% of all foreign affiliates subject to data collection are included in the sample each year because of the third criterion, which requires caution in interpretation. All data come from the Korea Export-Import Bank Foreign Direct Investment Database.

9
’ prices are the result of an equilibrium between demand and supply functions that are differentiable in land characteristics relevant to the seller and buyer respectively (Palmquist 1989). If land markets are homogenous (Huang _et al._ 2006), knowledge of the price and characteristics of a transacted parcel then allows to recover the marginal contribution of each characteristic (Palmquist 2005). Applying these regression coefficients to parcels not transacted then provides the basis for predicting the prices of all parcels.

Formally, the hedonic model to be estimates takes the form i i i i , (1) ii where i is the sale price of a parcel log PP in region = ααZZ + ββXX in constant 2024 USD per ha, + aa + ee i is a vector of parcelspecific characteristics including parcel size, land use at the time of transfer (crops, forest, built up, grass PP ii jj XX

> 17 Law No. 340-IX from December 5, 2019, "On Amendments to Certain Legislative Acts of Ukraine Regarding the Counteraction of Raiding" makes price reporting mandatory. Subsequent adjustments to the registry’s software made this mandatory in the registration process with land price data now transmitted automatically to the State Land Cadastre (SLC) through an automated information exchange protocol.

> 18 The tax -free allowance is 60 m2 for apartments, 120 m2 for residential buildings, and 180 m2 for other buildings in addition to a luxury tax of UAH 25,000/a for apartments and houses above 200 and 500 m<sup>2,</sup> respectively. Local governments are allowed to impose land taxes of up to 1 percent of NMV per year on land and 1.5 minimum wages. Families with three or more children, disabled individuals, retirees, and war veterans are exempt from land tax irrespective of income or wealth.

12
Dauphin, A. 2013. “The Role of Polygyny in the Intra-Household Efficiency of Agricultural Production in West Africa.” CIRPEE working paper 13-23, Gatineau (Québec).

Dauphin, A., and B. Fortin. 2001. “A Test of Collective Rationality for Multi-Person Households.” _Economics Letters_ 71 (2): 211–16.

De Laiglesia, J. R., and C. Morrison. 2008. “Household Structures and Savings: Evidence from Household Surveys.” Working paper. OECD Development Centre Working Papers 267, Paris.

DeVreyer, P., S. Lambert, A. Safir, and M. Sylla. 2008. “Pauvreté et structure familiale: pourquoi une nouvelle enquête?” _Statéco_ 102 : 5-20.

Fenske, J. 2015. “African Polygamy: Past and Present.” _Journal of Development Economics_ 117: 58–73.

González, L., and B. Özcan. 2013. “The Risk of Divorce and Household Saving Behavior.” _Journal of Human Resources_ 48 (2): 404–34.

Grossbard, A. 1976. “An Economic Analysis of Polygyny: The Case of Maiduguri.” _Current Anthropology_ 17 (4): 701–07.

———. 1980. “The Economics of Polygamy.” _Research in Population Economics_ 2: 321–50.

Grossbard, S. 2014. _The Marriage Motive: A Price Theory of Marriage: How Marriage Markets Affect Employment, Consumption, and Savings_ . Springer-Verlag, New York.

Grossbard-Shechtman, A. 1986. “Economic Behavior, Marriage and Fertility: Two Lessons from Polygyny.” _Journal of Economic Behavior & Organization_ 7 (4): 415– 24.

Jacoby, H. G. 1995. “The Economics of Polygyny in Sub-Saharan Africa: Female Productivity and the Demand for Wives in Cote d’Ivoire.” _Journal of Political Economy_ 103 (5): 938–71.

Jacoby, H. G, G. Li, and S. Rozelle. 2002
# **Online Appendix A Data, Variable Definitions and Data Cleaning**

# **A1. Additional information on key variable sources**

**Stock market data:** To replicate and extend Fisman’s (2001) analysis testing for the value of political connections through the relationship between share prices and news of Suharto’s health, we use stock market data from Bloomberg. Unfortunately, Bloomberg does not retain information on the stock prices of de-listed companies, forcing us to focus on surviving firms only.

**Political connections:** Mobarak and Purbasari (2006a) identify politically connected firms as follows: First, they identify firms whose market value on the Jakarta Stock Exchange exhibited abnormally negative movements in response to news episodes about Suharto’s deteriorating health that occurred between 1994 and 1997 before the onset of the East Asian financial crisis (and thus not contaminated by confounding financial turmoil), following Fisman’s (2001) event study methodology. Whereas Fisman examined the stock price responses of 79 firms belonging to the 25 largest conglomerates for whom a Castle Suharto Dependency Index score was available, Mobarak and Purbasari examine the share price responses of all 285 firms traded on the Jakarta Stock Exchange at the time. Out of these, 29 firms were significantly adversely affected and thereby identified as politically connected. The authors used newspapers, and other media to confirm that these firms were indeed connected.  Second, they trace the shareholders and members of the boards of management and commissioners of each of the adversely affected firms. They subsequently list all conglomerates owned by each of the members as well as all firms that are part of these conglomerates. They then merge these data with the annual manufacturing survey in 1997. In our data we have 246 politically connected firms. A Suharto family member owned or served on the board of 86 (35 percent) of these firms. To identify such Suharto family firms, Mobarak and Purbasari used information obtained from the Castle Group on board membership. To avoid potential endogeneity, they
kilometer, there is limited room for area expansion among smallholders (Dorward 2006; RickerGilbert et al. 2014). Second, Malawi is also an important case study to estimate inorganic fertilizer profitability because it has been implementing a large-scale Farm Input Subsidy Program (FISP) since the 2005/2006 agricultural season. In terms of scope and coverage, FISP is perhaps the most well-known farm input subsidy program in Africa. Through FISP, the Government of Malawi currently provides approximately 50 percent of the country’s agricultural households with coupons that allow for inorganic fertilizer and improved maize seed purchases at up to a 95% discount for fertilizer and 100% discount for seed. Focusing on Malawi provides an opportunity to analyze how fertilizer profitability can inform the geographical targeting of large-scale farm input subsidy programs, and how the subsidy program affects the profitability of fertilizer use.

The present study adds to the existing literature by extending the work of Xu et al. (2009) and Sheahan et al. (2012). Xu et al. (2009) and Sheahan et al. (2012) use nationally representative panel data from Zambia and Kenya, respectively, to analyze the profitability of fertilizer use in maize production. We build upon and extend their work in three main ways. First, by virtue of the availability of a variable that identifies plots over time in the two-wave nationally representative panel data used in the analyses, this study is able to control for plot-level unobserved heterogeneity using plot-level fixed-effects (FE) estimation.<sup>2</sup> Controlling for such plot-level unobserved heterogeneity helps improve upon the validity of the NUE estimates in the study. To our knowledge, this is the first study on SSA to use plot-level FE with nationally representative, nonexperimental data when estimating fertilizer profitability.

Second, this study accounts for different spatial and temporal maize prices in Malawi, including i) farm-gate harvest-season maize price, ii) lean season retail-market maize price and iii)

> 2 A plot is defined as a continuous piece of land that is
of the households in Punjab report that their children are within walking distance (15 minutes) of a primary school, only 55 percent of the households have access to a middle or high school within the same travel time (Pakistan Social & Living Standards Measurement Survey - PSLM, 2004). Differences in access are even larger in rural areas, making it even far less likely that children in rural villages enroll in schools at these levels (Andrabi et al., 2006; Sathar et al., 2003). In 2003, before the program started, while enrollment in primary

> 3 Estimates for Pakistan and region were obtained from published documents ―Pakistan at a Glance‖ 2004 available online; Punjab estimates were derived from the MICS 2003-04 Report, table titled Major Summary Results.

> 4 Around 90 percent of private schools offer only primary classes (Andrabi et al, 2006).

5
, a sixth of the initial sample is replaced with a new subset, ensuring the complete renewal of the sample every six years.</mark><sup>38</sup> This approach mitigates the issue of attrition commonly observed in full panel datasets over time. However, the six-year timeframe may limit the scope of dynamic analyses regarding income or multidimensional deprivation, particularly for capturing long-term trends. Each subgroup within the panel is designed to be representative of the entire population for a specific year, facilitating cross-sectional estimates and comparisons across different time points. The data is distributed in two variants: cross-sectional, which includes all six subsamples from different rotational groups for a given year, and longitudinal, which comprises observations from rotational groups previously observed. The longitudinal component enables analyses of poverty persistence over time, though limitations arise when dealing with newly introduced rotational groups, as they may not provide historical data for

> 35 The EUSILC does not have NUTS3 identifiers.

> 36 For example, some dimensions of multiple deprivations cannot be measured with the HBS, as there is no deprivation module as in SILC.

> 37 The 2021 FRA Roma surveys include the following eight EU Member States (Croatia, Czechia, Greece, Hungary, Italy, Portugal, Romania, and Spain) and addition to non-EU member states (North Macedonia and Serbia). The countries covered in these surveys, together with Bulgaria and the Slovak Republic, cover 87 % of the estimated Roma population in the EU or 53 % of the estimated Roma population in Europe. The microdata is not yet released. However, while these surveys can provide estimates for ethnic populations, these estimates are generally not comparable to official household surveys due to methodological differences related to sampling methodology and questionnaire design. Additionally, these surveys do not contain a proper income or consumption module to allow measurement of monetary poverty. Therefore, they cannot produce official national estimates for monitoring outcomes.

> 38 <mark>Following IESS regulation, starting from 2021 onwards, Eurostat recommends using at least a
Table 2 displays gender-disaggregated labor market indicators for Latvia and the EU-15. Comparison shows that both participation and employment rates in Latvia are higher than the EU-15 average for women but lower for men. Unemployment rates are higher for both genders in Latvia, but especially for men. All indicators of Latvian labor market, which compare unfavorably to the EU-15, became closer to the EU level in 2002, while Latvia’s advantage in women’s activity and employment has increased. Further progress (especially strong for men) has been made in 2003, when unemployment rates went down, while employment-population ratios increased compared to year ago.

**Table 2. Key Labor Market Indicators: Latvia vs. the EU-15 (age 15-64)**

||**Latvi**<br>|**a**<br>||**EU-15**<br>|<br>||
|---|---|---|---|---|---|---|
||**2001**|**2002**|**2003**|**2001**|**2002**|**2003**|
|Activity rate, men|72.8|73.9|74.0|78.4|78.4|78.6|
|Activity rate, women|62.1|64.1|64.8|60.3|61.0|61.6|
|Employment rate, men|61.8|64.3|66.1|73.3|72.9|72.7|
|Employment rate, women|56.1|57.0|57.9|55.1|55.6|56.1|
|Unemployment rate,men|14.7|13.1|10.3|6.5|7
<sup>6</sup>

> 5 Strong response to a reduction in fixed costs associated with meeting origin requirements are also obtained by Cherkashin et al. (2010) who study the effects of granting preferences with and without RoO for exports of woven apparel from Bangladesh. Their study is for a cross-section of 200 Bangladeshi firms (data collected over the period 1999-2004) exporting woven textiles to the EU and US markets under much the same assumptions as ours: all production is for exports, and exports are destined to one or both the EU and US markets.  They estimate that a $1 reduction in fixed costs would generate an increase in exports in the range 10$-40$ and conclude that easy-to-obtain preferences and/or reduction in fixed costs can have a catalytic effect and that preferences need not divert trade from other markets as predicted in a setting with no fixed costs. Though the methodology is different and they do not study the costs of RoO in a dynamic context panel like us, the magnitude of their estimates are in line with ours.

6 Under the EU’s “Single List”—also called ‘PANEURO’-- in operation since 2000, the EU GSP system also accepted bilateral cumulation between the EU and a beneficiary country (cumulation provisions allow contracting parties to use intermediate goods from each other without losing origin status). Similar rules were applied for Everything But Arms and 5
7 percent of nominal GDP and is characterized by the peaks of the mid 1960s,

> 6Work by Rosenberg (1969, 1974), Pakes and Schankerman (1984) and Grilinches, Hall and Pakes (1988) also stresses the importance of past technological improvements as factors of current R&D.

> 7In adjusted GDP, contrary to GDP reported in NIPAs, R&D is treated as investment rather than expense. An extensive discussion about the data on R&D investment is presented in section 6.

9
|

_Notes_ : IHPS data are used and the sample includes owned parcels only. Indicator variables for women’ ownership or men’s ownership and their interactions with non-matrilineal community are controlled for. Household fixed effects and parcel size are controlled for throughout. Parcel characteristics include soil type (sandy, between sandy and clay, or clay), land slope (flat, slight slope, moderate slope, or hilly), and whether land is in swamp/wetland. Robust standard errors are clustered by district. Figures in square brackets are p-values from wild cluster bootstrap (Roodman, 2018) with Rademacher weights and 1000 replications. *** significant at 1%; significant at 5%; * significant at 10%.

20
Accordingly, the coefficients of the explanatory variables of equation (6) can be interpreted as the marginal effects on the probability of depositing money in a dollar-denominated account.

# **3.1** Data and Estimation Results

The data for this study are drawn from several sources: the 1999 International Financial Statistics (IFS) published annually by the International Monetary Fund (IMF), Levine, Loayza and Beck (1998), Caprio and Klingebiel's (1999) data set, and several central banks. The countries included in the study are Argentina, Belize, Bolivia, Costa Rica, Dominica, Ecuador, El Salvador, Honduras, Mexico, Nicaragua, Paraguay, Peru and Uruguay. Due to data limitations we concentrate on the 1990-1998 period.<sup>5</sup> The resulting sample has 117 observations (13 countries, 9 years).

The variables used in the estimation of equation (6) were calculated as follows. The ratio of FCDs to domestic currency deposits measured at the end of the period was obtained from the IFS, from some central banks, and from the IMF Western Hemisphere Department.<sup>6</sup> The nominal GDP per capita was computed by dividing the nominal value of GDP by total population and then multiplying it by the end-of-period exchange rate.<sup>7</sup> The interest rates were proxied by the deposit rates reported by the IFS. Finally, the proxy used for expected appreciation was calculated by subtracting the U.S. gross rate of inflation from the domestic gross rate of inflation. The summary statistics for the sample are presented in Tables 1 and 2.

Although each country shows important peculiarities, some stylized facts can be derived. The first important thing to notice is that the dollarization ratio has been, in general, steadily increasing over time. While in 1990 the average ratio for these countries was 0.23, in 1998 rose to 0.37. A second interesting fact is that this increase comes in
of government expenditure, it does provide a snapshot of the main concerns among the residents of Europe.

We stack the surveys for all years and countries and estimate the following specification of equation (1): In addition to the variables of equation (1), we control for cohort of birth and survey year. We consider twelve 5‐year‐of‐birth groups, starting with those born between 1925 and 1929, and moving on to those born between 1980 and 1984. There is a large literature regarding the identification of age, cohort and time effects (see Schulhofer‐Wohl, 2013; and McKenzie, 2006). A well‐known challenge in this literature is that such effects cannot be identified without making specific assumptions, since they are perfectly collinear. We follow Deaton and Paxson (1994) and implement the normalization of time effects by assuming that trends are only captured by cohort effects, so that the time effects are orthogonal to a time trend.

Table 6 displays the estimates of equation (2) without controlling for cohort effects, and Table 7 displays the estimates including cohort dummy variables. To facilitate the description of the results, Figure 5 shows the estimated coefficients of a specification that introduces age and cohorts in a linear form. The results are consistent with those reported in Table 6 and Table 7. The left panel shows that, consistent with the findings that emerged from using the cross‐sectional data of LITS II, older people are more likely to consider pensions and health care as policy priorities, while being less likely to consider education as a top priority. The results also suggest that fighting crime is another important concern for the older residents of Europe. Finally, inflation, taxation, housing and unemployment seem less of a concern among older individuals.

The right panel of Figure 5 shows that, once we control for year of birth, the estimates become less precise, possibly due to the strong correlation between age and cohort. However, the age patterns regarding preferences for education and pensions