rafmacalaba/datause-encoder-data huggingface dataset:
<mark>Jadhav and Choudhury, 2019).</mark> Higher economic growth also usually translates into higher tax and non-tax revenues for the government and improves the performance of the private sector due to higher demand and capacity utilization. A rise in public revenue and corporate performance can lead to higher infrastructure expenditure resulting in greater physical infrastructure development (Randolph et al.,1999; Arimah, 2005). Higher economic growth leads to improved living standards for the general population and, correspondingly, increases the demand for infrastructure facilities such as transport, telecom, and power (Vinogradova et al. 2015), thereby leading to an increase in the supply of infrastructure services. Similar results are found when GSDP is replaced with per capita GSDP. Therefore, States with higher GSDP (or per capita GSDP) tend to have higher levels of physical infrastructure development. This finding is also supported by literature that suggests that income disparities are one of the major factors for interstate differences in infrastructure levels in India (Nayyar, 2008; Majumder, 2012; Bandyopadhyay, 2012; Ghosh, 2017). Further, the coefficient of share of the manufacturing sector (SMNF) in GSDP has a positive impact on physical infrastructure. It indicates that states with higher SMNF and industrialization have a better physical infrastructure. Moreover, higher manufacturing GDP leads to higher infrastructure development, which facilitates higher manufacturing GDP through backward linkage<sup>13</sup> (Luger et al. 2013). States such as Punjab, Maharashtra, Gujrat, Karnataka, and Tamilnadu have higher manufacturing GDP, and these states have also performed better in physical infrastructure development.

**Table 5: Determinants of Physical Infrastructure**

|**_Variables_**|**_Model 1_**|**_Model 2_**|**_Model 3_**|**_Model 4_**|**_Model 5_**|**_Model 6_**|**_Model 7_**|**_Model 8_**|
|---|---|---|---|---|---|---|---|---|
|**_Constant_**|-2.16**<br>(-3.85)|-2.54**<br>(-5.91)|-1.35**<br>(-4.45)|-3.61**<br>(-3.61)|-1.05**<br>(-3.23)|-3.54**<br>(-5.99)|-3.22**<br>(-4.45)|-3.87**<br>(-5.35)|
|**_PINFRAt-1_**|0.72**<br>(25.34)|0.65**<br>(19.34)|0.77**<br>(21.54)|0.65**<br>(19.34)|0.77**<br>(21.54)|0.65**<br>(19.34)|0.77**<br>(21.54)|0.65**<br>(19.34)|
|**_GSDP_**|0.24**<br>(3.94)|-|0.11*<br>(2.64)|-|0.20*<br>(2.51)|0.17**<br>(6.22)|0.12**<br>(2.77)|-|
|**_PGSDP_**|-|0.55**<br>(5.10)|-|0.48**<br>(3.66)|-|-||0.31**<br>(7.61)|
|**_INV_**|0.004**|<br>0.002**|-|<br>-|0.002**|0.005**|0.006**|<br>0.007**|
||(4.93)|(4.43)|||(4.62)|(4.32)|(5.21)|(3.12)|
|**_CEXP_**|-|-|0.006*<br>(2.54)|0.007*<br>(2.06)|-|-|-|-|
|**_SMNF_**|0.008**|0.012*|0.014*|0.002**|0.012*|0.014*|0.002*|0.002|
||(3.04)|(2.10)|(2.45)|(3.82)|(2.10)|(2.45)|(2.88)|(0.89)|
|**_Debt_**|-0.004**|-0.006*|-0.002**|-0.005**|-0.014*|-0.008|-0.005*|-0.005*|
||(-3.64)|(2.86)|(-3.03)|(-3.20)|(-2.68)|(-1.92)|(-2.05)|(-2.71)|

13 Infrastructure development boosts industrial development by encouraging the demand for input and helping the ancillary industry to develop through its multiplies effect.

21
A Road to Trust determinants of change in access to markets<sup>10</sup> and in log monthly per capita consumption. All regressions include municipality dummies, and standard errors are robust to arbitrary covariance structure within villages. Results are available in Table 5. While living in a road village does not affect per capita consumption, it has a positive impact on access to markets. This is consistent with the argument that road construction positively affects trust through interactions.

To further understand if increased interactions explain the impact of road construction on generalized trust, we now assess whose trust levels increases after road construction. To do so, we run household-level regressions of the determinants of _changes_ in generalized trust (as in Table 4) and introduce interactions terms. Specifically we are interested in assessing whether farmers gain more than non farmers, more educated individuals gain more than less educated ones and wealthier individuals gain more than poorer ones. We use data from 2003 to reduce endogeneity concerns. All regressions include municipality dummies and standard errors are robust to arbitrary covariance structure within villages. Results are available in Table 6.

First, road construction has a positive impact on trust levels of farming households. No such result is obtained for households not engaged in farming. This is consistent with the argument that roads increase trust through increased interactions as farmers are most likely to benefit from increased access to markets. Second, when we look at the role of education in explaining the response to road construction, our results indicate that more

> 10Our measure of access to markets is the response to the question “With respect to access to markets, would you say that you are better off now than three years ago? “ (worse off, same, better off)

15
preferential access to Central American countries through a series of trade programs. These programs included the Caribbean Basin Initiative (CBI), which was established in 1983 and aimed to promote economic development in the Caribbean region through trade preferences. The CBI provided duty-free treatment for certain products from eligible countries, including many Central American countries. In addition, the Generalized System of Preferences (GSP) provided preferential access for a wide range of products from developing countries, including many products from Central American countries. These trade programs were important for Central American countries, as they provided a competitive advantage for their exports to the United States.

These regional dynamics show that while the CAFTA-DR agreement represented a significant step forward in the region’s trade integration efforts, as it provided for greater market access, the reduction of tariffs and non-tariff barriers, and the protection of intellectual property rights, it was part of a long-term trade liberalization dynamic in the region. Many initiatives have aimed to boost trade and investment within the region and have also facilitated the participation of Central American countries in the global economy. We aim to study the impact of CAFTA-DR in this context and to solve the empirical identification challenges arriving from those dynamics.

# **3 Data**

We use the Eora Multi-Region Input-Output (MRIO) database, which provides a panel data set of trade flows, including reliable data on intra-national flows (Lenzen et al., 2012, 2013). This dataset covers bilateral trade flows across sectors for 189 countries over the period 1990–2015, and has several advantages for our purposes.<sup>7</sup> First, the dataset includes intra-national trade flows, which are needed for theory-consistent gravity estimations. As demonstrated by Baier et al. (2019), the estimates of RTAs are affected by the inclusion of domestic trade flows. Second, it has a wide coverage both in terms of countries and time. The large country coverage is essential for analyses focusing on developing countries, which are often left out due to data constraints. The time span of the dataset is also relatively long, and it covers a period of intense globalization efforts with a number of new RTAs. Third, consistent with our focus, the dataset has already been used to analyze 7The services data tend to be of lower quality than goods data due to the nature of services and how these data are recorded, but we include it for completeness of the analysis and the interest in the service sector.

6
23

**Table 3a:  Probit Estimation of Firm Perceptions, with Country Groups**

||**Electricity**|**Labor**<br>**Regulation**|**Corruption**|**Cost**<br>**Finance**|**Access to**<br>**Finance**|**Acc Land**|**Tax admin**|**Skilled**<br>**Labor**|**Custrade**|
|---|---|---|---|---|---|---|---|---|---|
|Intercept|-1.61***|-1.36***|-1.03***|-0.87***|-0.03|-0.65***|-1.43***|-1.2***|-0.90***|
||(0.10)|(0.10)|(0.09)|(0.12)|(0.09)|(0.09)|(0.10)|(0.09)|(0.20)|
|Size|0.01|0.11***|0.05***|-0.02|-0.14***|-0.08***|0.02|0.13***|0.03|
||(0.02)|(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|(0.02)|(0.01)|(0.03)|
|Exportd|0.02|0.06|0.04|-0.02|-0.02|-0.07|0.11*|0.005|-0.07|
||(0.06)|(0.06)|(0.05)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.09)|
|Fgnown|<br>-0.02|<br>-0.02|<br>0.07|<br>-0.17**|<br>-0.14***|<br>-0.09|<br>-0.03|-0.04|<br>0.05|
||(0.06)|(0.06)|(0.05)|(0.07)|(0.05)|(0.06)|(0.06)|(0.05)|(0.09)|
|days lost|0.004***||---||---|---|---||---|
||(0.0003)||---||---|---|---||---|
|Maj. Union|---|0.016**|---|---|---|---|---||---|
||---|(0.06)|---|---|---|---|---||---|
|Bribe pmt|---|---|0.25***|---|---|---|---||---|
||---|---|(0.04)|---|---|---|---||---|
|Overdraft|---|---|---|0.23***|---|---|---||---|
||---|---|---|(0.06)|---|---|---||---|
|Audit|---|---|---|0.07|-0.14***|---|---||---|
||---|---|---|(0.06)|(0.05)|---|---||---|
|Ownland|---|---|---|---|---|-0.38***|---||---|
||---|---|---|---|---|(0.05)|---||---|
|Daytax|---|---|---|---|---|---|0.01***||---|
||---|---|---|---|---|---|(0.002)||---|
|Training||||||||0.11**<br>(0.05)||
|Exp clear|---|--|---|---|---|---|---||0.01***|
||---|--|---|---|---|---|---||(0.005)|
|low income|1.27***|-0.49***|0.39***|0.98***|0.64***|0.51***|0.94***|--<br>-0.18***|<br>0.48***|
||(0.07)|(0.06)|(0.05)|(0.07)|(0.06)|(0.06)|(0.07)|(0.05)|(0.11)|
|low middle||||||||||
|income|1.07***|-0.04|0.72***|1.35***|0.71***|0.50***|1.08***|-0.09|0.49***|
||(0.07)|(0.06)|(0.06)|(0.07)|(0.06)|(0.07)|(0.07)|(0.06)|(0.11)|
|N|4061|4599|4583|2926|4377|4441|3989|4898|1016|
|Log<br>likelihood|-2354.2|-1852.4|-2867.6|-1812.2|-2784.1|-2321.4|-2356.7|-2704.8|-605.7|

Note: Sector dummies are included but not reported in Tables 3a and 3b.
<u>(2022)</u> argues that “ _a smarter, better-educated citizenry would not necessarily diminish polarization, lead to better policy decisions, or improve democracy._ ” Overall, there is little evidence to suggest that public apathy towards climate change is driven primarily by a lack of knowledge (Hornsey et al. 2016; <u>Kahan et al. 2012; Kellstedt et al. 2008; Malka et al. 2009).</u>

# **3. Data and definitions of variables**

Our data come from the 2023 round of the Life in Transition Survey (LiTS) conducted by the European Bank for Reconstruction and Development (EBRD) and the World Bank and covering the “transition” economies of Europe and Central Asia and several comparator countries in Western Europe, the Middle East, and North Africa (EBRD 2023). The 2023 round of the LiTS was conducted in 37 countries in Europe, Central Asia, and Northern Africa and includes nationally representative samples of around 1,000 households per country.<sup>6</sup>

# _3.1 Perceptions of climate change_

To understand the beliefs and preferences regarding climate change, we examine responses to the question, _“How convinced are you personally that climate change is real?”_ for which we construct a binary indicator that takes the value of 1 if the respondent answers _“quite convinced”_ or _“entirely convinced,”_ and 0 otherwise. We coded the question _“How convinced are you personally that climate change is manmade?”_ in the same way. To capture respondents’ awareness of the potential impacts of climate change, we examine responses to two questions: _“Do you think climate change seriously affects or will seriously affect you during your lifetime?”_ and _“Do you think climate change seriously affects or will seriously affect the children of today during their lifetime?”_ Two dummy variables take the value of 1 if the respondent answers _“Yes”_ and 0 if “No” to each question.

To understand whether respondents’ policy preferences translate into actions, we examine the question, _“To what extent do you agree or disagree with each of the following statements: I would be willing to pay more in taxes if the extra money were used to...”_ with options including _“...reduce/prevent pollution such as improving the quality of air or water, or dealing with_

> 6 The 37  countries surveyed in the 2023 round of LiTS are Albania, Algeria, Armenia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Estonia, Georgia, Germany, Greece, Hungary, Jordan, Kazakhstan, Kosovo, the Kyrgyz Republic, Latvia, Lebanon, Lithuania, Moldova, Mongolia, Montenegro, Morocco, North Macedonia, Poland, Romania, the Russian Federation, Serbia, the Slovak Republic, Slovenia, Tajikistan, Tunisia, Türkiye,  Uzbekistan, and West Bank and Gaza.

5
where  2  . stands for the standard bivariate normal cumulative distribution function (cdf) ) (and  2  . stands for the standard bivariate normal probability density function (pdf)).

However, since  is usually unknown in most contexts, assume also that it is bounded by the interval [0, 1] (Assumption 2’’).<sup>8</sup> Since for any _x, y_ , and _ρ_ ,  <u>2 </u> _x_ <u>,</u> _<u>y</u>_ <u>, </u>   2  _x_ , _y_ ,   0  (Sungur, 1990), equation (4) indicates that a lower (higher) value of  means a higher (lower) probability of being poor in the first period but non-poor in the second period. Thus the lower bound and upper bound estimates of mobility can be established by identifying the appropriate range of values for the correlation term  . Absent any other information, Dang et al. (2014) suggest one can start by assuming that  is either 0 or 1. However, by examining empirical estimates from actual panel data for other countries, they propose a narrower range for Indonesia during 1997-2000 of [0.3, 0.7]. This in turn yields a lower bound and upper bound interval of [8.1, 11.8] for the proportion of households that were poor in 1997 but non-poor in 2000, which encompasses the “true” proportion rate of 10.1 percent based on actual panel data (Dang et al, 2014).

# **III. Point Estimates on Poverty Mobility in a Generalized Framework**

Despite its relevance for identifying bound estimates on poverty dynamics at the household level, the method introduced in Dang et al. (2014) suffers from several drawbacks. First, unless one is content to work with the extreme case of  in the [0, 1] range, a group of countries with actual panel data that is comparable to the country under investigation must be found so that a more reasonable empirical range of values for  can be identified. This task

> 8 Dang et al. (2014) provide several reasons why this assumption can be expected to hold and show this is the case using household survey data from several countries. Also see Dang et al. for more discussion on the implications for these assumptions and proofs for the bound estimates.

8
Table 1 Summary Statistics for All Years, 1998-2007

||Levels<br>Number of|||Growth Rates<br>Number of|||
|---|---|---|---|---|---|---|
||<br>observations|Mean|Std. Dev.|<br>observations|Mean|Std. Dev.|
|logY|1,545,626|10.015|1.343|1,086,616|0.135|0.563|
|logL|1,545,626|4.808|1.152|1,086,616|0.013|0.503|
|logK|1,545,626|8.468|1.719|1,086,616|0.107|0.753|
|lnTFP|1,545,626|1.828|0.367|1,086,616|0.056|0.308|
|Foreign share (contributed by|||||||
|HK-Taiwan-Macau<br>investors)|1,545,626|0.089|0.267|1,086,616|0.012|0.377|
|Foreign share (contributed by<br>other investors)|1,545,626|0.079|0.249|1,086,616|0.0003|0.146|
|Stateshare|1,545,626|0.089|0.272|1,086,616|-0.007|0.147|
|Horizontal|1,545,626|0.254|0.142|1,086,616|0.004|0.046|
|Backward|1,545,626|0.077|0.046|1,086,616|0.002|0.015|
|Forward|1,545,626|0.103|0.173|1,086,616|0.004|0.066|
|Tariff|1,545,626|12.691|6.600|1,086,616|-0.869|2.295|
|Tariff_backward|1,545,626|8.191|3.769|1,086,616|-0.319|1.611|
|Tariff_forward|1,545,626|9.185|4.064|1,086,616|-0.359|2.066|

Notes: We define firm-level foreign share according to its different sources. Foreign share contributed by HK-Taiwan-Macau is defined as the share of firms‘ total equity owned by investors from HK-Taiwan-Macau. Foreign share contributed by other countries is defined as the share of firms‘ total equity owned by investors outside HK-Taiwan-Macau, principally from OECD countries. State share is defined as the proportion of the firm‘s state assets to its total equity. Horizontal captures the intra-industry FDI spillover while backward and forward represent inter-industry FDI spillovers. We define horizontal, backward, and forward in equation (2), (3), and (4) respectively. The unit for the tariff variable is percentage.

33
References <mark>Anderson, M. L. (2008). Multiple inference and gender differences in the effects of early intervention: A reevaluation of the Abecedarian, Perry Preschool, and Early Training Projects.</mark> _<mark>Journal of the American statistical Association</mark>_ <mark>,</mark> _<mark>103</mark>_ <mark>(484), 1481-1495.</mark> <mark>Araujo, M. C., Bosch, M., & Schady, N. (2016).</mark> _<mark>Can Cash Transfers Help Households Escape an InterGenerational Poverty Trap?</mark>_ <mark>(No. w22670). National Bureau of Economic Research.</mark> <mark>Baez, J. E., & Camacho, A. (2011).</mark> _<mark>Assessing the long-term effects of conditional cash transfers on human capital: evidence from Colombia</mark>_ <mark>. The World Bank.</mark> <mark>Baird,Sarah Jane; Mcintosh,Craig; Ozler,Berk. 2019. “When the Money Runs Out: Do Cash Transfers Have Sustained Effects on Human Capital Accumulation?” Journal of Development Economics, Vol. 140, Sep. 2019, pp. 169-185</mark> <mark>Barham, T., Macours, K., & Maluccio, J. A. (2018).</mark> _<mark>Are Conditional Cash Transfers Fulfilling Their Promise? Schooling, Learning, and Earnings After 10 Years</mark>_ <mark>. CEPR Discussion Paper 11937</mark> <mark>Behrman, J. R., Parker, S. W., & Todd, P. E. (2009). Medium-Term Impacts of the Oportunidades Conditional Cash-Transfer Program on Rural Youth in Mexico. The MIT Press; Chapter 7 in Poverty, Inequality, and Policy in Latin America. 2009.</mark> <mark>Behrman, J. R., Parker, S. W., & Todd, P. E. (2011). Do conditional cash transfers for schooling generate lasting benefits? A five-year followup of PROGRESA/Oportunidades.</mark> _<mark>Journal of Human Resources</mark>_ <mark>,</mark> _<mark>46</mark>_ <mark>(1), 93-122.</mark> <mark>Bernard, Tanguy, Cheryl Doss, Melissa Hidrobo, Jessica B. Hoel, Caitlin Kieran. (2020). Ask me why: Using vignettes to understand patterns of intrahousehold decision making in rural Senegal.</mark> _<mark>World Development</mark>_ <mark>, forthcoming.</mark> <mark>Buller, A. M. B., Ranganathan, M., Bleile, A., Hidrobo, M., & Heise, L. (2018). A mixed-method review of cash transfers and intimate partner violence in low and middle-income countries.</mark> _<mark>World Bank Research Observer.</mark>_ <mark>Fiszbein, A., & Schady, N. R. (2009).</mark> _<mark>Conditional cash transfers: reducing present and future poverty</mark>_ <mark>. The World Bank.</mark> <mark>Ham, A., & Michelson, H. C. (2018). Does the form of delivering incentives in conditional cash transfers matter over a decade later?.</mark> _<mark>Journal of Development Economics</mark>_ <mark>,</mark> _<mark>134</mark>_ <mark>, 96-108.</mark> <mark>Jerusalem, M., & Schwarzer, R. (1995). Generalized self-efficacy scale.</mark> _<mark>Measures in health psychology: A user’s portfolio. Causal and control beliefs</mark>_ <mark>, 35-37.</mark> <mark>Kling, J. R., & Liebman, J. B. (2004).</mark> _<mark>Experimental analysis of neighborhood effects on youth</mark>_ <mark>. John F. Kennedy School of Government, Harvard University.</mark> 22
_COTE D’IVOIRE: Competitiveness, Cocoa, and Real Exchange Rates_

# **3. Trade Partners and “MREER Coverage”**

## **Measuring the Completeness of MREER Indices**

For a country with many trade partners, it can be awkward and difficult to compute an MREER index that includes all partners. However many partners it includes, it will remain a representative index. And, some trade will not enter into the official statistics. MREER indices are approximations, so it is interesting to evaluate how complete the MREER index is. At the same time, we can address the evolution of trade patterns.

**Our MREER measure covers most of Cote d’Ivoire’s official trade.** Figure 3 shows the percentage of official imports, exports, and total trade of Cote d’Ivoire is conducted with the twelve partners in the representative set.  While the percentage has declined over the period, it has usually included 70% or more of all trade and always more than 60%.

**Figure 3: “Coverage” of MREER Indices – Percent of Cote d’Ivoire’s Imports, Exports and Trade with the Twelve Countries Used in the Measure of the Multilateral Real Exchange Rate**

### **Cote d'Ivoire: "Coverage" of the Twelve-Country Measures of the Multinational Real Exchange Rate**

<!-- Start of picture text --> 90%<br>EXPORT COVERAGE<br>IMPORT COVERAGE<br>85%<br>TRADE COVERAGE<br>80%<br>75%<br>70%<br>65%<br>60%<br>55%<br>Years 1960-2003<br>Percent Trade Covered by 12-Country Index<br>1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002<br><!-- End of picture text --> Refer to the “pie charts” in the Annex ( Figure 14, Figure 15, Figure 16, and Figure 17 ), which portray the distribution of trade among the twelve partners of the MREER index (plus “other” not in the index) in 1960, versus 2000 (the last “normal” year before conflict broke out). Next, we look at the distribution of trade by partners over time.

5
<!-- Start of picture text --> 350<br><aBaBa 225300<br>28<br>58 275<br>feoBE22G 260225<br>a<br>$5 200<br>3a<br>8 175<br>au<br>Be 10<br>Le 125<br>BY?<br>Bp<br>75<br>Ze 100<br>oso 2 50<br>GG 25<br>0<br>ora@e2ooooooooosceeenececeeeoeeeryerrtrrrreMPAAaqnaooooeraonnddscaeaescesescsesceseecegcoeeoe0eceococeseaerr KFmtrKF obKF orwewaoaodcreroadndeowrweoreaeoagorensortwnonrKF KF KH KEN NNNNNNNNNNNNNNNNN©<br>Year<br>——ECA. _ ————. MENA<br><!-- End of picture text -->
# Figure 2. Inequality <u>declines after taxes and social spending</u>

A. Poland. Gini Coefficient B. Emerging markets: Gini Coefficient (pensions as deferred income) <!-- Start of picture text --> 0.55 0.60<br>0.53<br>Old-age pensions as<br>Brazil<br>deferred income<br>0.55 Chile (2009)<br>0.50<br>(2009)<br>Old-age pensions as<br>Mexico<br>transfers 0.50 (2010)<br>0.45<br>0.41 Uruguay<br>0.45 (2009)<br>0.40 Georgia<br>(2013)<br>0.35 0.40<br>0.34 Armenia<br>0.35<br>(2011)<br>0.35<br>Russia<br>0.30 0.28 Poland (2010)<br>0.30 (2014)<br>0.25 0.25<br>Market Income Disposable Consumable Final Income Market Disposable Consumable Final Income<br>Income Income Income Income Income<br><!-- End of picture text --> Source: Own estimates using PHBS 2014.

Source: Armenia: Younger et al (2016); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2010) Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Russia: Lopez-Calvo et al (2016); Uruguay: Bucheli et al (2014); Poland: <u>own estimates using PHBS 2014.</u> <u>Figure 3. Decline in Inequality from Market to Disposable Income</u> <!-- Start of picture text --> 0.00<br>-0.05<br>-0.10<br>-0.15<br>-0.20<br>-0.25<br>-0.30<br>Pensions as deferred income Pensions as transfers<br>Belgium (2014)* Germany (2014)* Hungary (2014)* Austria (2014)* Czech Republic (2014)* Croatia (2014)* Romania (2014)* Poland (2014) Estonia (2014)* Bulgaria (2014)* Russia (2010) Armenia (2011) Uruguay (2009) Brazil (2009) Chile (2009) Mexico (2010)<br><!-- End of picture text --> Source: Armenia: Younger et al (2016); Brazil: Higgins & Pereira (2014); Chile: Ruiz-Tagle & Contreras (2010) Georgia: Cancho & Bondarenko (2016); Mexico: Scott (2014); Russia: LopezCalvo et al (2016); Uruguay: Bucheli et al (2014); EU countries (*): Euromod (2014); Poland: own <u>estimates using PHBS 2014.</u> 16