rafmacalaba/datause-encoder-data huggingface dataset:
- 10 -

Table x State Specific Poverty Lines for Rural Areas (Rupees per person per month at current prices)

|States||Poor|||Ultra P|oor|||
|---|---|---|---|---|---|---|---|---|
||73-74|77-78|83|86-87|73-74|77-78|83|86-87|
|ANDHRA PRADESH|43.2|53.0|76.0|86.3|34.5|42.4|60.8|69|
|ASSAM|50.9|61.7|99.5|118.7|40.7|49.3|79.6|94.9|
|BIHAR|60.8|61.5|98.3|107.1|48.6|49.2|78.6|85.7|
|GUJRAT|48.7|56.4|85.0|104.9|39|45.1|68|83.9|
|Haryana|50.5|61.4|92.0|111.4|40.4|49.1|73.6|89.1|
|Jammu-Kashmir|48.4|63.0|93.0|113.8|38.7|50.4|74.4|91|
|KARNATAKA|47.9|53.7|85.4|97.0|38.3|42.9|68.3|77.6|
|KERALA|52.0|59.7|100.1|125.3|41.6|47.8|80|100.2|
|MADHYA PRADESH|51.2|57.2|83.9|97.0|41|45.7|67.1|77.6|
|MAHARASHTRA|51.5|59.7|89.9|108.3|41.2|47.7|71.9|86.6|
|ORISSA|55.7|60.7|96.0|106.6|44.6|48.6|76.8|85.2|
|PLinjab|50.5|61.4|92.0|111.4|40.4|49.1|73.6|89.1|
|RAJISTHAN|51.9|59.7|83.8|103.2|41.5|47.8|67|82.5|
|TAMIL NADU<br>|46.3|58.5|94.8|109.9|37|46.8|75.8|87.9|
|UTTAR PRADESH|50.7|56.2|84.6|99.2|40.5|44.9|67.7|79.3|
|WEST BENGAL|56.6|65.8|104.9|124.4|45.2|52.6|83.9|99.5|
|ALL INDIA|50.0|57.1|90.3|102.1|40|45.6|72.2|81.7]|

the incidence of rural poverty (Minhas 1989). Using these price relatives and the CPIAL available at the state l1evel (see Table 1) we worked out the statespecific poverty lines at the current prices for the years 1973-74, 1977-78, 1983, and 1986-87 (see Table 2).7/ The poverty line varies substantially across the states as well as over time, due entirely to differences in prices. For instance, West Bengal Z/ We performed calculations for both 1972-73 and 1973-74 years but in the present paper the results are presented only for 1973-74 because this was a normal agricultural year.
- 9 -

Figure 2. Energy demand shift conditional on output demand shocks <!-- Start of picture text --> Price<br>S<br>D<br>D’<br>0 Demand<br>Supply<br><!-- End of picture text --> Source: Author’s illustration.

If a new equilibrium price caused by some of these exogenous shocks is not allowed to take place for some reason, both direct and indirect costs would be imposed on the economy. First, if the price of retail energy is kept lower than the sustainable supply cost, governments need to subsidize the sector and fill in the gap between the retail and production prices either through direct subsidy to utilities or by hiding such costs somewhere off the budget. This is a direct cost of underpricing, which is one of the important factors of quasi-fiscal deficits in the public energy provision.<sup>10</sup> In either case, the lack of financial viability would threaten the sustainability of infrastructure development sooner or later. In some countries of the Europe and Central Asia (ECA) region, the quasi-fiscal deficit in the electricity sector is estimated to reach more than 10 percent of GDP (Table 1). In our sample, it was about 0.9 percent for Croatia, while it was estimated to exceed 4 percent of GDP for Albania, Bulgaria and Serbia and Montenegro in 2003 (World Bank, 2006b).

> 10 The other two factors of quasi-fiscal deficits are associated with excessive technical losses and commercial losses.
- 37 -

with respect to small changes in the wage rate and in the price of materials are both statistically significant.

**TaIb 37(**

Pakistani chemical and ghary ceutical LIZAustries Elasticities of R&D exgenditures. phvsigal investment. outgut.labour and materials with resPect to small chance_ in the investment tax credit. midpoint of the **samAlO** eD2p - - 0.108 IpeMP 0.386 eymp = - 0.026 eLMp - - 0.0796 - O.0(015 Table (7) above reports the effect on the endogenous variables of the model of a small change in the tax credit for pi,ysical investment. An increase in the tax credit for physical investment reduces the rintal price of the services from physical capital, which in turn results in more investment in physical capital, which is no doubt the effect intended by policy makers. The increase in investment causes adjustment costs in the form of a temporary reduction in output, which in turn lowers the demand for the variable inputs labour and materials.

Table 7(b) Impact of a 10% Increase in Investment _ax Credit on Pakistani Chemical **and** Pharmaceutical **Industries** - in 1980.

Increase in Investment = Rupees (million) 20.530 Reduction in Corporate Tax Revenues - Rupees (million) 7.986 Increase in Inventment / Reduction in Government Revenues - 2.6 Source : Model-based calculations Table 7(b) reports on the impact of a small change in investment tax credit on government revenues and investment. It shows that for a 10% increase in investment tax credit, investment in physical capital would have increased by
- Corbo, V. and K. Schmidt-Hebbel (1991), "Public Policies and Saving in Developing Countries", _Journal of Development Economics,_ 36, 89-115.

- Deaton, A.S. (1977), "Involuntary Saving through Unanticipated Inflation", _American Economic Review,_ 67, 899-910.

- Deaton, A.S. (1991), "Savings and Liquidity Constraints", _Econometrica,_ 59, 1221-1248.

- Deaton, A.S. (1992), "Household Saving in LDCs: Credit Markets, Insurance and Welfare", _Scandinavian Journal of Economics,_ 94 (2), 253-273.

- de Melo, J. and J. Tybout (1986), "The Effects of Financial Liberalization on Savings and Investment in Uruguay", _Economic Development and Cultural 'Change,_ 34, 561-588.

- Demetriades, P. and K. Luintel (1997), "The Direct Cost of Financial Repression: Evidence from India", _Review of Economics and Statistics,_ 79(2), 311-20.

- Dombusch, R. and A. Reynoso (1989), "Financial Factors in Economic Development", _American Economic Review,_ Papers and Proceedings, 79, 204-209.

- Engle, R.F. and B.S. Yoo (1987), "Forecasting and Testing in Cointegrated Systems, _Journal of Econometrics,_ 35, 143-59.

- Fry, M.J. (1995), _Money, Interest, and Banking in Economic Development,_ (2nd Edn.) (Baltimore: Johns Hopkins).

- Hall, R.E. (1988), "Intertemporal Substitution in Consumption", _Journal of Political Economy,_ 96, 339-357.

- Haque, N.U. and P. Montiel (1989), "Consumption in Developing Countries: Tests for Liquidity Constraints and Finite Horizons", _Review of Economics and Statistics,_ 34, 408-415.

- Honohan, P. and I. Atiyas (1993), "Intersectoral Financial Flows in Developing Countries" _Economic Journal,_ 103 (418), pp. 666-679.

- Im, K.S., M. H. Pesaran and Y. Shin (1995), "Testing for Unit Roots in Hetei-ogeneous Panels", University of Cambridge, mirneo.

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- Jappelli, T. and M. Pagano (1994), "Saving, Growth and Liquidity Constraints", Quarterly Journal of Economics, 109, 83-109.

- Koskela, E., H.A. Loikkanen and M. Viren (1992), "House Prices, Household Savings and Financial Market Liberalization in Finland", _European Economic Review_ 36, 549-558.

24
37 Table 5.3 Major Dimensions of Host Country FDI Policies

|Country|Steadiness|Transparency|Hospitality|
|---|---|---|---|
|Argentina|_-/-_|_+/+_|-/+|
|Brazil|+/+|+/+|+/+|
|Colombia|_-/-_|_--_|-/+|
|Mexico||_-/-_|_+/+_|
|India|+/+|_+/+_|_-/-_|
|Indonesia|+/+|+/+|+/+|
|Korea, Rep. of|_-/-_|+/+|-/+|
|Malaysia|_-/-_|_-/-_|_+/+_|
|Thailand|_-+_|_--_|_+/+_|
|Kenya||_-/-_|_+/+_|
|Nigeria|_-/-_|-/+|-/-|

wy.3te Sign before the slash refers to overall record of the past two-and-a-half decades. Sign after the slash refers to the period since the early 1980s.

Source: These are summary judgments based on the evidence in Becsky, Lee and Ordu (1990), as supplemented by codings by Thomas L. Brewer and Alovsius Ordu.

# Illustrative Recent Policy Changes

Investors, of course, find some policy changes to their liking, particularly changes that are comprehensive liberalizations of policy and are likely to remain in effect for an extended period of time; sue.. policy changes can have a significant effect on FDI flows. Comprehensive packages of liberalization of policy are often followed by significant increases in FDI, and comprehensive packages of increased restrictiveness are similarly often followed by dramatic declines in FDI. (Becsky, Lee, and Ordu 1990, contains detailed analyses of several instances; a few are highlighted here for brief illustrative purposes.) The correspondence between comprehensive FDI policy change and changes in FDI flows has been obvious in Korea. A generally liberal policy, including incentives for FDI, was in place from 1960 to 1973. After an initial period when FDI remained at modest levels, it increased substantially by the early 1970s. (An increase in restrictiveness in 1965 was largely directed at Japanese investors and not effectively implemented in any case.) In 1°73 more extensive regulations were imposed, including major restrictions on ownership; foreign ownership was generally limited to 50 percent, minimum investment levels were established, and investments that competed with domestic Korean firms were rarely approved. As a result annual FDI flows declined during the mid and late 1970s.
|**1980**|90|70|95|75|71|61|65|52|
|---|---|---|---|---|---|---|---|---|
|**1990**|94|69|97|73|80|58|74|51|
|**2000**|94|55|97|64|76|48|69|44|

Source: Author‟s Calculations. %WT and % WM refer to the percentage of world trade and migration in each sample respectively.

The remaining covariates, geodesic distance, contiguity, common language, shared colonial relationship, belonging to a regional trade agreement, common legislation, common currency and GSP are all taken from Head, Mayer and Ries (2010).<sup>19</sup> _6. Cross-section Results_

**<u>Table 2 Direct Link Cross-Section Regressions, 1960-2000</u>**

|**Dependent Var.: Log Exports**|||<br>**Migrant Links**|||
|---|---|---|---|---|---|
||**2000**|**1990**|**1980**|**1970**|**1960**|
|**Log immigrants**|0.051***|0.044***|0.046**|0.071***|0.054***|
||(0.012)|(0.013)|(0.019)|(0.018)|(0.018)|
|**Log emigrants**|0.074***|0.078***|0.054***|0.060***|0.068***|
||(0.012)|(0.013)|(0.018)|(0.018)|(0.019)|
|**Log distance**|-1.017***|-0.939***|-1.119***|-0.886***|-0.755***|
||(0.055)|(0.047)|(0.075)|(0.070)|(0.077)|
|**Contiguity**|0.163|-0.066|-0.345|-0.257|0.103|
||(0.139)|(0.14)|(0.215)|(0.221)|(0.22)|
|**Common Language**|0.188*|-0.051|0.001|0.090|0.062|
||(0.100)|(0.096)|(0.16)|(0.154)|(0.161)|
|**Colony**|0.302**|0.481***|0.895***|1.036***|0.810***|
||(0.146)|(0.147)|(0.207)|(0.199)|(0.226)|
|**RTA**|0.277***|0.086|-1.007***|-0.276|-0.741***|
||(0.092)|(0.095)|(0.162)|(0.198)|(0.179)|
|**Common Legislation**|0.313***|0.439***|0.134|0.159|0.213**|

> 19 See: http://www.cepii.fr/anglaisgraph/bdd/gravity.htm

15
**Table A8** Impact of Treatments on Male Time Use <u>(Male</u> Data)

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||**Bos**|**Goal**|**Endline**||
||**y**<br>**Treatment**|**Treatment**|**Control Mean**|**Observations**|
|**In the past day, hours spent...**|||||
|Sleeping|||||
|treatment x post|-0.324<br>|0.160<br>|8.407|2,314|
||(0.267)|(0.255)|||
|treatment x post x Goal|-0.003<br>||||
||(0.359)||||
|In school|||||
|treatment x post|-0.198|-0.410|3.017|2,314|
||(0.336)|(0.252)|||
|treatment x post x Goal|0.290<br>||||
||(0.394)||||
|Studying|||||
|treatment x post|0.095|-0.065|0.936|2,314|
||(0.170)|(0.160)|||
|treatment x post x Goal|0.199<br>||||
||(0.192)||||
|On sports/ leisure|||||
|treatment x post|0.602**<br>|0.333<br>|2.262|2,314|
||(0.236)|(0.234)|||
|treatment x post x Goal|-0.403<br>||||
||(0.386)||||
|Doing domestic tasks|||||
|treatment x post|-0.233|0.093|0.994|2,314|
||(0.226)|(0.200)|||
|treatment x post x Goal|0.514**<br>||||
||(0.243)||||
|Working on family farm|||||
|treatment x post|0.109|0.330*|0.273|2,314|
||(0.136)|(0.173)|||
|treatment x post x Goal|-0.204<br>||||
||(0.217)||||
|On income generating activities|||||
|treatment x post|0.096|-0.632|4.337|2,314|
||(0.429)|(0.433)|||
|treatment x post x Goal|0.020<br>||||
||(0.573)||||
|Praying|||||
|treatment x post|-0.078|-0.810*|4.273|2,314|
||(0.477)|(0.424)|||
|treatment x post x Goal|0.252||||
||(0.580)||||
|With girlfriend|||||
|treatment x post|0.017|0.135|0.343|2,314|
||(0.083)|(0.102)|||
|treatment x post x Goal|-0.127||||
||(0.143)||||

_Notes._ This table presents estimates of _β_ 1, _β_ 2, and _γ_ 1 from equation 2. See notes for Table 3 on table structure and control variables. The outcomes are the hours the male reports spending in each category on the day prior to survey. Standard errors, clustered at the club level, are presented in parentheses below coefficient estimates in columns 1–2. .01 ***, .05 **, .10 *. _Source._ Male baseline and endline data, balanced panel.

67
Despite attempts in the past to encourage Australian to take up annuity-type products people still have a strong bequest motive and are very averse to products with little or no residual value and no possibility of withdrawal of capital. Allocated pensions accord with retirees stated preference for products which offer taxation advantages, the possibility of a residual sum, a control of the portfolio supporting the pension and flexibility regarding the timing and size of a pension. Over the past decade sales of allocate pension products have grown dramatically whereas the market for annuities has stagnated as shown in Table 18. The data indicates that in the last two years sales of lifetime annuities have almost completely dried up.

**Table 18:  Sales of Annuities and Allocated Pensions**

|||**Annu**|**ities**||**Allocate Pe**|**nsions**|**Total**|
|---|---|---|---|---|---|---|---|
||**Term**|**Term with**<br>**Residual**<br>**Capital**<br>**Value**|**Lifetime**|**Total**<br>**Annuities**|**Allocated**<br>**Pensions and**<br>**Annuities**|**Term**<br>**Allocated**<br>**Pensions**||
|2006|519|1,008|29|1,557|13,570|527|15,654|
|2005|547|877|27|1,451|10,352|457|12,260|
|2004|2,757|1,073|279|4,106|7,888|47|12,041|
|2003|1,357|1,352|200|2,909|5,935|-|8,844|
|2002|1,100|1,897|155|3,152|6,595|-|9,747|
|2001|794|1,631|167|2,592|7,613|-|10,205|
|2000|672|2,324|183|3,179|6,607|-|9,786|
|1999|666|1,835|246|2,747|5,717|-|8,464|
|1998|603|1,766|194|2,663|4,153|-|6,816|
|1997|563|1,625|201|2,386|3,522|-|5,908|
|1996|646|1,224|190|2,060|1,838|-|3,898|
|1995|390|1,171|184|1,745|881|-|2,626|

**Source:  Plan for Life (Victoria) Pty Ltd**

Only $29 million in lifetime annuities were sold in 2006 of which $9 million was from superannuation moneys and $20 million was from private savings. With few providers, declining sales, declining competitiveness (as measured by the MWR) and the removal of the of the asset test exemption for long-term annuities, sales are likely to fall further and may even result in the extinction of lifetime annuities from the Australian market.

# **_7.2 Equity Release Products_**

Almost one-quarter of Australians have indicated that they will rely on home ownership to support them in their retirement<sup>39</sup> . However, far fewer have indicated an intention to sell their home as part of their financial plans for retirement.

Equity release products allow a person to access the equity which they have in their home without having to sell and move from the home. They provide the opportunity for people to add to their retirement funds. The most common equity release product is a reverse mortgage. In a reverse mortgage, the consumer’s house is used as security for a loan, which is provided to the consumer in the form of a lump sum, a regular stream of payments or both. The consumer retains title to the property but grants the provider a mortgage to secure repayment of principal and interest under the loan. The outstanding balance of the loan grows over time, as the interest is capitalized (rather than repaid). The

> 39 Cameron (2001)

45
Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

**Table 8b: Manufacturing growth with nonfinancial corporation credit**

|**Dep. Var:**|OLS|OLS|OLS|FE|GMM|2SLS|2SLS|
|---|---|---|---|---|---|---|---|
|**growth**||||||||
|**_**<br>**manuf.**|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|nfccredit|0.0104|0.1897**|0.7959***|0.3123*|0.2009*|0.9735**|1.1723**|
||(0.831)|(0.030)|(0.001)|(0.090)|(0.086)|(0.045)|(0.016)|
|nfccredit<sup>2</sup>||-0.0005*|-0.005***|-0.001***|-0.0007*|-0.0037|-0.007**|
|||(0.056)|(0.003)|(0.005)|(0.055)|(0.353)|(0.030)|
|fdi|-0.1164|0.0579|0.4811|0.7786***|0.2418|0.2268|0.2133|
||(0.657)|(0.849)|(0.246)|(0.008)|(0.473)|(0.533)|(0.542)|
|enrolment|0.1251|0.0586|0.1202|0.0345|0.0987|0.0437|0.0146|
||(0.226)|(0.605)|(0.273)|(0.830)|(0.451)|(0.710)|(0.900)|
|initialgdppc|-13.93***|-13.25***|-16.90***|-39.42***|-15.78***|-15.22***|-19.11***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.002)|(0.000)|(0.000)|
|financialcrisis||-19.37***||-15.93***|-19.92***|3.81||
|||(0.000)||(0.000)|(0.000)|(0.724)||
|financialcrisisX||||||||
|nfccredit||||||-0.0601||
|||||||(0.675)||
|bsupervision|||-4.5266**<br>(0.021)|||||
|Constant|43.83***|41.06***|33.67***|108.70***|42.98***|14.60|28.26**|
||(0.000)|(0.000)|(0.001)|(0.000)|(0.006)|(0.635)|(0.025)|
|# of Obs.|146|144|104|144|144|93|100|
|R-squared|0.310|0.395|0.434|||||
|Prob > F|0.000|0.000|0.000|0.000|0.000|0.000|0.000|
|# of countries||||36|36|||

*: in 2SLS, legal origin is used as instrument.

Robust p-values are presented in parentheses and significance at 1% (***), 5% (**), and 10%(*) levels are indicated.

Taking a deeper look at the relationship between the manufacturing growth and the characteristic of the borrower, the paper examines the relationship between manufacturing value 24
6. Trade Linkages between China and other B&R Economies

# **6. Trade Linkages between China and other B&R Economies**

# **_6.1. Gross Trade between China and other B&R Economies_**

B&R Economies are already important trading partners of China. In terms of the destination of total gross exports, B&R economies were the destination of 22 % of China’s gross exports, while non-B&R accounted for 66 % (Figure 36). Repeating the exercise in Figure 44 from the point of view of China’s gross imports, B&R economies are much more important to China as sources of imports, accounting for 30% of its total imports in 2010 ( Figure 37).

## FIGURE 36: EXPORTS OF CHINA BY DESTINATION, 1995-2010

<!-- Start of picture text --> 1995 2000<br>7%<br>22% 17% 7%<br>71% 75%<br>2005 2010<br>8% 12%<br>17%<br>22%<br>66%<br>74%<br>non-B&R B&R<br>ROW<br>Graphs by year<br><!-- End of picture text -->

**Source:** Author’s calculations using the Tiva database.

FIGURE 37: IMPORTS OF CHINA BY ORIGIN, 1995-2010 <!-- Start of picture text --> 1995 2000<br>5% 8%<br>30% 29%<br>65% 63%<br>2005 2010<br>7% 9%<br>31% 30%<br>62% 61%<br>non-B&R B&R<br>ROW<br>Graphs by year<br><!-- End of picture text --> 31
**Table 1. Doing Business Report 2010**

<!-- Start of picture text --> Figure 4. Brazil Spread Breakdown (2009)<br><!-- End of picture text --> <!-- Start of picture text --> 9.14<br>Ease of Doing Business Rank 124<br>7.21<br>Getting Credit Rank 87<br>Protecting Investors Rank 73 1.94<br>Enforcing Contracts Rank 98<br>NIM I&R Pure spread<br>(Macro)<br><!-- End of picture text --> _Source:_ Authors’ calculations based on BankScope data _Source:_ Authors’ calculations based on BankScope data Our analysis suggests a positive correlation between NIM and I&R factors in Brazil (Figure 3b). This positive correlation is an additional indication that there is a strong case for institutional and regulatory reforms in Brazil. Our results presented in Figure 3aand Figure 5a show that, despite the falling significance of I&R factors, they clearly remain the dominant components of the NIM.

**Figure 5a. Summary breakdown of NIM Spread**

# **Figure 5b. NIM and I&R Vs Actual SELIC rate**

|**year**|**Obs. per**<br>**Year**|**R2**|**NIM**|**I&R**|**Pure**<br>**spread**<br>**(macro)**|40<br>45||
|---|---|---|---|---|---|---|---|
|1995|99|0.831|15.8|16.91|-1.11|35||
|1996|111|0.374|13.12|9.5|3.62|30<br>ts||
|1997|119|0.588|11.06|12.14|-1.08|oin||
|1998|123|0.448|11.68|7.56|4.12|25<br>e p||
|1999|119|0.489|15.25|12.86|2.39|tag||
|2000|129|0.682|11.53|9.46|2.07|20<br>cen||
|2001|154|0.061|7.96|11.83|-3.87|15<br>er||
|2002|162|0.299|15.01|13.17|1.84|P||
|2003|143|0.677|13.09|12.24|0.85|10||
|2004|137|0.833|12.53|12.71|-0.18|||
|2005|140|0.794|13.58|12.79|0.8|5||
|2006|142|0.446|14.59|11.28|3.31|0||
|2007|153|0.576|13.96|11.83|2.13||1997<br>1999<br>2001<br>2003<br>2005<br>2007<br>2009|
|2008|124|0.825|9.33|7.87|1.46||Actual SELIC Rate<br>NIM<br>I&R|
|2009|106|0.761|9.14|7.21|1.94||<br><br>|

_Source_ : Authors’ calculations based on BankScope data _Source_ : Authors’ calculations based on BankScope data, Banco Central do Brasil 16
contact, followed by peer influence. Their review, however, cautions against expecting large impacts. They find that overall, more precisely measured studies find much smaller effects, and point out that studies have so far focused on short-term impacts. Notably, in lowand middle-income countries, there is still only very limited experimental evidence about whether intergroup contact actually improves locals’ relations with displaced persons (Zhou and Lyall, 2022). Studies using observational data, such as Ghosn et al. (2019), must be interpreted with caution, given that a positive correlation between contact and lower prejudices might be due to less-prejudiced individuals seeking contact. Future research will also need to explore the importance of the conditions defined by Allport (1954) under which the interactions between in-group and out-group individuals would increase empathy and integration: that is, that (i) both groups must share status, (ii) and the same goals or interests, (iii) both groups need to live in a cooperative environment rather than a competitive one and (iv) they need to benefit from institutional support: that is, operate under a well-defined set of norms, laws, and regulation (Paluck et al., 2019). Studies so far only show that proximity alone (Zhou, 2018) and brief exposure in the framework of transit migration do not seem to amount to the same as contact (Ajzenman et al., 2020; Steinmayr, 2021; Hangartner et al., 2019).

Related to studies inspired by Allport’s contact theory are studies that look at the impacts of perspective-taking and providing personal narratives, as well as studies stressing commonalities (in terms of conflict and displacement and other dimensions). Studies focusing on forcibly displaced populations find positive but not always significant or large impacts (Adida et al., 2018; Alan et al., 2021; Audette et al., 2020; Dinas et al., 2021; Hong et al., 2020; Lazarev and Sharma, 2017; Rodr ́ıguez Chatruc and Rozo, 2021). While the provision of facts to correct misperceptions has shown to change attitudes in certain settings, notably if combined with information about characteristics and impacts of immigrants and refugees (Grigorieff et al., 2020; Haaland and Roth, 2020; Lergetporer et al., 2021), other studies find no or mixed results (Adida et al., 2018; Alesina et al., 2018a; Barrera et al., 2020). Once attitudes are formed it seems they are not easily changed and contact and narratives are more effective than facts in this regard. Public investments made for refugees which benefit hosts can influence their attitudes in a positive way (Quattrochi et al., 2019; Lehmann and 12