rafmacalaba/datause-encoder-data huggingface dataset:
(250).<sup>22</sup> SPS regulations and standards on industrial goods, however, have legitimate regulatory functions. For example, in the case of SPS, countries have the right and obligation to protect human, animal and plant life. The World Trade Organization SPS and TBT agreements recognize these legitimate regulatory functions but call for these regulations to be applied in a manner that does not discriminate against imports.<sup>23</sup> Where the non-tariff measure has a regulatory function, we assume that our measure is the discriminatory component of the regulation.

Our estimates of the AVEs of NTMs are based on the estimates of Kee _et al_ ., (2009). Building on Kee _et al_ ., (2008), Kee _et al_ ., (2009) estimate the AVEs of NTMs for 105 countries at the 6-digit level as well as aggregated estimates for manufacturing and agriculture.<sup>24</sup> The measure we use from Kee _et al_ . (2009) is the uniform tariff equivalent that generates the same level of import value for the country in a given year, based on applied tariffs, which take into account bilateral trade preferences.<sup>25</sup> At the six-digit level, the estimates of Kee _et al_ . are sometimes subject to a substantial margin of error that may lead to misleading results in a policy analysis. Consequently, we have chosen to use the aggregated estimates of Kee _et al_ ., i.e., for each country, we have two AVEs: one AVE of the NTMs in manufacturing and one AVE of the NTMs in agriculture. For regions in our model that are aggregates of countries, we take a simple average of the AVEs of the countries.<sup>26</sup> In the case of agriculture, we apply the AVE of the non-tariff barriers in our model to agriculture and forestry; and to wheat and cereals. In manufacturing we applied the AVE of the non-tariff barriers to beverages and tobacco; chemicals, minerals and metal products; electronic equipment; food products; other manufacturing; petroleum and coal products; textiles and apparel; meat and dairy products; processed rice and sugar. Table. The results are presented in a footnote to table 3.

> 22 “Standards and Testing in RCEP,” Asian Trade Centre Policy Brief No. 11-16, June 2015. Available at: www.asiantradecentre.org

> 23 See: https://www.wto.org/english/tratop_e/sps_e/spsagr_e.htm

> 24 The dataset is available at:

> http://econ.worldbank.org/WBSITE/EXTERNAL/EXTDEC/EXTRESEARCH/0,,contentMDK:22574446~pagePK: 64214825~piPK:64214943~theSitePK:469382,00.html.

> 25 Specifically, we take the difference between the Overall Trade Restrictiveness Index (OTRI) and for the Tariffonly OTRI (OTRI_T), which gives us the AVE of the NTMs.

> 26 For our aggregate regions, we take simple averages of the AVEs of the individual countries. In the case of Rest of ASEAN, estimates are available for Brunei, Indonesia, Malaysia, Singapore and Thailand, but not for Cambodia, the Laos Peoples Democratic Republic, Myanmar and Vietnam. We take an average of the available estimates.

24
All cost data was recorded in the spreadsheet in the currency units used in the PDD. All cost data were converted into USD using the spot exchange rate on the 20<sup>th</sup> of November 2007. The exchange rates used were included in the dataset.

# **_Perspective in capital cost data_**

It may be important to note two facts with regard to the capital cost data from CDM project activity.

First, it should not be assumed that the CDM projects have been implemented yet and so capital cost outlays may not have occurred. The CDM project data represents all projects that have been put forth for validation and registration. This may, and often does, occur prior to commitments on capital purchases have been made. However, it is largely expected that these projects will be implemented.

Second, it should not be assumed that the reported capital expenditures on CDM projects are solely attributable to the CDM. In many cases, capital expenditures would have taken place in its absence. For instance, wind farm and hydro projects are implemented to increase the host country’s power generation capacity. In the absence of the CDM, it is likely that capital expenditures would have taken place regardless, in order to increase the host country’s power generation capacity, albeit with a different technology and less of a capital outlay. However, for certain project types, where there is no revenue stream other than CDM credits, i.e. landfill gas and animal waste flaring projects, it would be fair to assume that the capital cost expenditures are solely attributable to the CDM.

36
production costs between the destination and the source country.<sup>14</sup> A large price differential between the home and host countries might make GF investments more likely as an entry mode in lower income developing economies. These differentials are needed to offset the relatively higher start-up costs associated with the construction of new facilities. Investments in resource-intensive industries also usually take the form of GF FDI. The reason for this is that local companies often have privileged access to these natural resources and, hence, host country governments prefer joint ventures in the form of GF FDI (Demirbag et al., 2008). M&A-led surges and stops are also more likely in riskier and uncertain macroeconomic environments because of the existence of discounts on the prices of existing assets (Buiter et al., 1998). Such events have been associated with fire-sale FDI during the Latin American and Asian financial crises of the 1990s (Krugman, 2000; Aguiar and Gopinath, 2005). M&A-led surges and stops have been encouraged by capital market imperfections that result in undervaluations of firm assets, sales of assets at unrealistic prices, and stripping of firms for purely financial gains. Changes in financial openness in particular might have an effect on the likelihood of an M&A-led surge or stop. Furthermore, the existence of capital controls in the form of restrictions on foreign ownership and short-selling may limit possibilities for the earliermentioned fire-sale FDI.<sup>15</sup> There seems to be a strong political preference for GF investments, which are perceived to be more beneficial than M&As (Heinemann, 2012). Domestic unrest can also deter some types of FDI or trigger the pullout of investors from some markets (Schneider and Frey, 1985). Still, results from econometric studies

> 14 Aleksynska and Havrylchyk (2013) estimated that for the period 1996-2007, 56% of all FDI into developing countries originated from developed countries.

> 15 Likewise, government interventions against the takeover of domestic companies through cross-border

> M&A have become more common recently (Heinemann, 2012).

14
<!-- Start of picture text --> 0.60 500<br>Relativeeeincome Ginia Absolutescaled to 1970=income100Gini,<br>0.55 400<br>0.50 300<br>0.45 200<br>0.40 100<br>0.35 0<br>1970 1980 1990 2000 2010 2020<br><!-- End of picture text -->
# **_Pakistan_**

# **_South Africa_**

|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|Descriptive Statistic||_Original_<br>Data Set|_Truncated_<br>Data Set|_Right_<br>_Censored_<br>Data Set|_Final_<br>Data Set|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Number of observations||107,652|107,524|107,446|75,233|Number of observations||91,904|88,374|70,155|42,369|
|Price (USD)|Median|83,934.34|83,934.34|83,934.34|60,930.12|Price (USD)|Median|95,588.23|93,286.45|102,301.8|68,734.02|
|Price (USD)|Mean|156,756.10|154,420.40|154,487.40|68,011.17|Price (USD)|Mean|155,699.4|147,055|157,654.9|87,676.75|
|Square meter|Median|151.76|151.76|151.76|126.47|Square meter|Median|350|313|312|110|
|Square meter|Mean|955.86|220.93|220.93|134.53|Square meter|Mean|2,081.1|555.01|554.09|193.98|
|Price per square meter|Median|535.38|535.38|535.38|491.63|Price per square meter|Median|360.38|387.17|387.17|580.06|
|Price per square meter|Mean|634.36|633.16|633.16|507.60|Price per square meter|Mean|887.42|629.44|629.44|734.70|
|Price per square meter|IQR|344.14|344.14|344.14|245.81|Price per square meter|IQR|614.62|624.15|624.15|620.48|
|Price per square meter|SD|652.22|480.86|480.86|205.29|Price per square meter|SD|9,302.60|853.02|853.02|654.53|
|Number of bedrooms|Mean|3.86|3.86|3.86|3.33|Number of bedrooms|Mean|3.04|2.99|3.0|2.4|

Note: The _original_ data set contains the original set of all listings. The _truncated_ data set retains listings that contain sale prices, size data, and whether the property is an apartment or house and truncates the data based on sqm<9 or sqm >3,000 and Price < $US 5,000 or Price > $US 5,000,000. The _right censored_ data set retains unique listings that contain sale prices, size data, and whether the property is an apartment or house, truncates the data based on sqm<9 or sqm >3,000 and Price < $US 5,000 or Price > $US 5,000,000, and additionally removes the most extreme price data points on the upper end of the spectrum. The _final_ data set retains thorough listings with reasonable values for price, size, and per square meter price, cleaned through multivariate outlier removal. SD= Standard Deviation; IQR=Interquartile range 18
if the scarring effects of sustained employment and income losses as well as distress asset sales and learning disruptions for children in poorer households limit the recovery of poorer households relative to better off households over the medium-term (Kim et al. 2021; World Bank 2022b; Neidh ̈ofer et al. 2021).

In this regard, the dynamics of income losses examined in this paper would reflect employment changes in the intensive as well as extensive margin and indicate the extent of unrecovered welfare losses throughout the pandemic. This paper goes on to argue that the wider welfare losses beyond job losses, if left unrecovered, could amplify inequality in the longer term. The pandemic has led to significant losses of productive assets and human capital, particularly in poorer countries. Households disposed of productive assets as a way of coping with the income shock since the social protection response was largely inadequate, often relied on inefficient means, and did not last long enough in many lower-income countries (World Bank 2022b). The loss of assets could hurt the productive capacity of those households that need it the most during the recovery.

# **3 Data and methods**

The main data source for this paper is the November 2022 vintage of the World Bank’s harmonized HFPS data which was collected to monitor the impact of the pandemic on households. The first surveys were conducted in April 2020, with data collection continuing into 2022 in some economies. Since questionnaires differed by economy and across survey waves, responses were harmonized globally to construct an internationally comparable dataset with up to half a million observations for a given indicator. Data used for analysis in this paper were collected from 80 countries in five of the six World Bank regions over 343 survey waves between April 2020 and December 2021. The breakdown of countries with harmonized survey data by income level, region and timing is shown in Table 1. More than half are LICs and LMICs, including 19 Fragile and Conflict-Affected Situations (FCS). Of the 80 countries with surveys, 53 are located in Latin America and the Caribbean (LAC) and Sub-Saharan Africa (SSA), so the country averages reported are strongly influenced by these regions (Figure 1).<sup>6</sup> Previous papers using the HFPS describe the harmonization process and dataset in more detail (Bundervoet et al. 2022; Khamis et al. 2021; Kugler, Viollaz, et al. 2021).

> 6Annex 1 provides more detail on the coverage and timing of surveys. Figure A1.1 shows countries with HFPS waves by the time periods used in the analysis, and Figure A1.2, by income level. Figure A1.3 lists all countries with HFPS data included in the analysis and the timing of HFPS waves between April 2020 and December 2021 at the country level.

6
estimate the specification from Table 7 including these three additional interaction terms together, and then one at a time. Results are presented in Table 8.

**Table 8: Risk Aversion Interation Effects**

**Dependent variable = 1 if purchased insurance, = 0 otherwise.** Weighted probit model. Robust z- statistics in parentheses. Coefficients normalized to display marginal effects. Regression includes same variables as baseline regression results (other results omitted; similar to previous table).

||**Ba**|**seline specificatio**<br>|**n + interaction**<br>|**terms**<br>|
|---|---|---|---|---|
||combined|interact|ion terms added|individually|
|_Interaction terms_|||||
|Risk aversion * BUA|0.005|0.024|||
||(0.25)|(1.23)|||
|Risk aversion * credit from BASIX|0.028||0.032||
||(1.74)*||(2.14)**||
|Risk aversion * other insurance|0.008|||0.014|
||(0.72)|||(1.18)|
|**F-test [joint significance, p-value]**|0.043**||||
|_Underlying variables_|||||
|Risk aversion|-0.024|-0.016|-0.018|-0.021|
||(2.42)**|(2.81)***|(3.22)***|(2.17)**|
|BUA|0.262|0.078|0.344|0.082|
||(1.82)*|(2.84)***|(4.43)***|(4.72)***|
|Credit from BASIX|-0.001|0.064|-0.003|0.066|
||(0.08)|(4.73)***|(0.32)|(4.92)***|
|Other insurance|-0.003|0.004|0.003|-0.008|
||(0.32)|(1.43)|(1.38)|(0.79)|

The estimates are consistent with the “product uncertainty” explanation. Each interaction term is positively signed as predicted, and the coefficients are jointly significant at the 5% level. The interaction term: “Risk Aversion x Credit from BASIX” is individually significant at the 5% level when included on its own (Column 3), and at the 10% level when all three interaction terms are included (Column 1). Our point estimates imply that for a household where each interaction term is switched from 0 to 1, the combined coefficient on risk aversion switches from -0.024 to +0.017, and thus shifts to the “correct” positive sign (although the combined coefficient is not statistically different to zero).

26
**TABLE A11: Capital market openness and development relationships are robust to controlling for changes in corruption perception.** Table 8 is replicated while controlling for Corruption Perception, which is normalized as a Z-score by subtracting off the sample mean and dividing by the sample standard deviation. Constant, R-squared, and number of observations not reported for brevity. Standard errors are clustered at the country-year. *** p<0.01, ** p<0.05, * p<0.1

||(1)|(2)|(3)<br>Qua|(4)<br>ntile Regre|(5)<br>ssion|(6)|
|---|---|---|---|---|---|---|
||OLS|0.1|0.25|0.5|0.75|0.9|
||PME|PME|PME|PME|PME|PME|
|Panel A of Table 8|||||||
|De-jure fnancial openness index|12.21|0.02|0.28|-0.31|0.06|1.21|
||(8.10)|(0.33)|(0.32)|(0.33)|(0.74)|(1.49)|
|Private sector credit (% of GDP)|-24.80|-0.06|-0.68*|-1.09***|-2.00**|-4.06|
||(15.95)|(0.22)|(0.38)|(0.28)|(0.88)|(2.64)|
|Corruption Perception (Transparency Int’l)|23.84|0.03|0.10|-0.49|-0.57|1.12|
||(17.43)|(0.29)|(0.23)|(0.36)|(1.06)|(2.25)|
|Panel B of Table 8|||||||
|De-jure fnancial openness index|-3.26***|-2.41**|-2.66***|-1.94***|-1.92|-5.95***|
||(0.77)|(0.98)|(0.18)|(0.73)|(1.53)|(1.97)|
|Private sector credit (% of GDP)|0.33|-0.14|0.18|0.31|0.16|-0.47|
||(0.98)|(0.54)|(0.40)|(1.09)|(2.78)|(2.46)|
|Corruption Perception (Transparency Int’l)|-1.56*|-0.21|-0.62*|-0.39|-0.62|-1.51|
||(0.83)|(0.71)|(0.32)|(0.86)|(0.75)|(3.20)|
|Country fxed efects|Yes|Yes|Yes|Yes|Yes|Yes|

56
Deere, Carmen Diana, Rosa Luz Duran, Merilee Mardon and Tom Masterson. 2004. “Female Land Rights and Rural Household Incomes in Brazil, Paraguay and Peru.” Working Paper 200408, Department of Economics, University of Massachusetts at Amherst.

Deininger, Klaus, Goyal, Aparajita and Nagarajan, Hari. 2010. “Inheritance Law Reform and Women's Access to Capital: Evidence from India's Hindu Succession Act, No 5338.” Policy Research Working Paper Series, Washington DC: The World Bank.

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<mark>Doss, Cheryl R. 2001a. “Designing Agricultural Technology for Women: Lessons from Twenty –Five Years of Experience.”</mark> _<mark>World Development</mark>_ <mark>29 (5): 2075-92.</mark> Doss, Cheryl R. 2001b. “Is Risk Fully Pooled within the Household? Evidence from Ghana.” _Economic Development and Cultural Change_ 50 (1): 101-30.

Doss, Cheryl R. 2006. “The Effects of Intrahousehold Property Ownership on Expenditure Patterns in Ghana,” _Journal of African Economies_ 15 (1): 149-80.

Doss, Cheryl, Carmen Diana Deere, Abena D. Oduro,Hema Swaminathan, Suchitra J. Y., Rahul Lahoti, W. Baah-Boateng, L. Boakye-Yiadom, Jackeline Contreras, Jennifer Twyman, Zachary Catanzarite, Caren Grown, and Marya Hillesland. 2011. The Gender Asset and Wealth Gaps: Evidence from Ecuador, Ghana, and Karnataka, India. Bangalore: Indian Institute of Management Bangalore.

Doss, Cheryl R. and Michael Morris. 2000. “How does gender affect the adoption of agricultural innovations?” _Agricultural Economics_ 2 5(1): 27-39 Duflo, Esther. 2003. "Grandmothers and Granddaughters: Old-Age Pensions and Intrahousehold Allocation in South Africa," _World Bank Economic Review_ 17 (1): 1-25.

Duflo, Esther and Christopher Udry, 2004. "Intrahousehold Resource Allocation in Cote d'Ivoire: Social Norms, Separate Accounts and Consumption Choices," NBER Working Papers 10498. Cambridge, MA: National Bureau of Economic Research.

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37
<mark>disaggregated by refugee status</mark> education are scarce, with only a handful of countries publishing data on financing for refugee education (e.g. Colombia) (Burde et al., 2022).

<mark>Literature on philanthropic fnancing for refugee education is also limited. In 2019, Education Cannot Wait (ECW), a global fund for education in emergencies, produced the most recent and comprehensive estimate of philanthropic fnancing for ‘education in emergencies’ (EiE) to date. ECW estimated the philanthropic sector’s fnancing of refugee education by using the grantaggregator</mark> _<mark>Candid</mark>_ <mark>and examining individual foundations’ online grant databases where available (ECW, 2019). Due to a lack of publicly available data on philanthropic fnancing, the organization used its relationships with major grant makers to establish fnancing estimates for all major philanthropic fnancers. ECW reported that the philanthropic sector provided US$ 294.5 million to EiE between 2008-2016, averaging US$ 33 million a year over this period, or 5.4 percent of total foundation grant making to countries in emergencies (ECW, 2019). Since this publication, no similar studies have been conducted or planned by ECW or any other organization, leading to a dearth of literature on philanthropic fnancing for refugee education.</mark>

# Methodology

situations have led to a lack of publicly available data and literature on global financing for refugee education. This paper, and its accompanying dataset, help to fill this gap through collecting, collating, and scrutinizing both primary and secondary data to produce the first comprehensive dataset and analysis of refugee education financing to low- and middle-income countries. The sources of this data, and the methods used to collect them, are discussed in detail below.

## Refugee population, enrollment, and costing data

The costs of refugee education used in this paper and its accompanying dataset refer to the costs of _inclusive_ education provision: i.e. the cost of educating refugees through the national education system of their current host countries. The costs of pre-primary, primary and secondary education are estimated using the methodology outlined in _The Global Cost of Inclusive Refugee Education – 2023 Update_ (World Bank & UNHCR, 2023), which focused on All refugee population numbers used in this paper are taken from the United Nations High Commissioner for Refugees’ (UNHCR) _Refugee Population Statistics Database_ (UNHCR, n.d.). ‘Refugee’ is defined in this paper as those categorized by the UNHCR as either ‘refugees’, ‘asylum seekers’, or ‘other people in need of international protection’. The UNHCR presents demographic data by age categories only. In order to estimate the school-age refugee population in each host country, this paper and its accompanying dataset assume that all refugees aged 5-11 years are uniformly distributed across one year of pre-primary education and six years of primary education, and that refugees aged 12-17 years represent the standard 4
suggesting that, other countries, even very gender-positive legislation failed to become effective as females were either reluctant or unable to enforce compliance out of fear that doing so would expose them to sanctions that would reduce their overall welfare (World Bank 2001).

The ultimate impact of such legislative change is thus an empirical issue. In fact, a range of progressive legislation in India that aimed to modify undesirable social practices such as dowry or caste discrimination that has been ineffective in practice (Anderson 2003) provides reason for caution. In a recent study Roy (2008) compares Hindu and non-Hindu women based on their marriage timing to examine the impact of the HSAA on female empowerment, proxied by three self-reported indicators of social and economic autonomy using the National Family Health Survey. While exploring the same question, our approach is substantively different because we are able to shed light on the underlying mechanism as well as explore potential impacts over a range of dimensions that include actual inheritance, age at marriage, and girls’ education over different generations of individuals. We achieve identification by comparing (i) the likelihood of inheriting land by females and males in the same household before and after the HSAA came into force; (ii) age at marriage, as a proxy of bargaining power, by females and males in the same household who married before or after the coming in force of the HSAA; and (iii) educational attainment of different cohorts of females and males in the same household depending on whether their educational decisions were completed before or after the coming into force of the HSAA. Methodologically, we use household-level fixed effects, and gender specific year of birth fixed effects to eliminate potential bias due to unobserved household characteristics or time varying factors affecting outcomes of males and females, and perform a number of placebo tests to check the underlying parallel trends assumption throughout.

# **3 Conceptual Framework**

Our empirical strategy is based on the application of a conceptual framework of intergenerational transfers of physical and human capital from parents to their children. As in the wealth model of transfers (Becker 1981; Becker and Tomes 1979), we be11