rafmacalaba/data-use-mentions-extended huggingface dataset:
660|660|655|654|623|
|R-squared|0.030|0.041|0.043|0.129|0.083|
|Control mean|0.898|0.943|0.807|0.512|0.317|

Notes: The table shows the results of regressing the variables regarding agreement with gender norm attitudes on individual control variables (age, nationality, geographic region, cohabiting status, maximum educational level), and sample strata fixed effects. Robust standard errors reported in parenthesis. ***significant at the 1% level, **5% level, *10% level. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women). Source: Post-intervention survey.

Table 5. Effects on intention to use parental leave and effective use, experiment 1

||||**Panel A:**<br>|**Men**<br>||
|---|---|---|---|---|---|
||Planning|Use both|Use onlyme|Use only partner|Effective use|
|Information|0.002|-0.008|0.007|0.011|0.003|
|(M1)|(0.028)|(0.038)|(0.010)|(0.037)|(0.003)|
|Observations|537|528|528|528|554|
|R-squared|0.095|0.047|0.065|0.045|0.039|
|Control mean|0.873|0.742|0.0115|0.223|0|
||||**Panel B: W**<br>|**omen**<br>||
||Planning|Use
a hypothesis that we tested using annual waves of Eurobarometer data between 2004 and 2011. In other words, the fact that older people are more (less) likely to support increases in spending on pensions (education) is not driven by a generational change, but by individuals changing their preferences as they grow older. In contrast, the fact that preferences over spending on health care or housing vary by age seems to be highly driven by cohort effects rather than along the life cycle.

The paper is organized as follows. The next section reviews the main findings in the literature on the associations between age and preferences over government spending. Section 3 describes the data set, the estimation strategy, the main empirical findings and robustness checks. Section 4 concludes.

4
column (1) indicates that indirectly owned BOSs are 28% more productive.<sup>25</sup> When the degree of separation from the state increases, which we consider as a proxy of autonomy,<sup>26</sup> BOSs seem to perform relatively better achieving up to 62% more revenues per worker (labor productivity) when there are 4 or more layers between the BOS firm and the public authority who exercises the ownership rights. Similarly, the results suggest that further separation from the public authority is associated with better profitability up to 4.2 percentage points (column 2) higher and up to 9 percentage points higher returns of investments

> 22 (Okhmatovskiy, 2010) suggests that indirect state control may still give access to state resources but reduce the negative governance effects arising from direct shareholding.

> 23 For this assessment, we restrict the analysis to compare among BOS as we do not have the ownership structure of private counterparts in the WB BOS database.

> 24 To illustrate the difference, A is directly owned by the state if the direct shareholder corresponds to a Public Authority (denoted as such in ORBIS). On the contrary, B is indirectly owned, if it is participated by company A, which is owned by a Public Authority. The entity type is built from the ORBIS entity type vintage files.

> 25 The interpretation of the coefficients requires the Log−Lin transformation such that the average relationship is estimated as (exp(β1) −1) ∗ 100 = (exp (0.247)-1) *100= 28.01%.

> 26 It means more layers of ownership separate the entity from the public authority.

20
, we examine the impact of a country’s participation in GVCs on spatial inequality, measured by the domestic size distribution of cities. For example, the assembly of parts through the maquiladora program boosted Mexico’s participation in global production networks that accounted for half of Mexico's exports in 2006; yet most exporting firms remain clustered around the US-Mexico border (Mia and Austin, 2008; Hanson, 1996). Vietnam’s participation in GVCs is driven by firms located around Ho Chi Minh City that account for nearly 83 percent of apparel and textile exports. Even after several decades following its establishment, these activities of global production networks are confined to the city and its contiguous agglomerations (Pham et al., 2013). Further, clustering is more likely when production on integrated networks is intensive in technology, knowledge and skills -- if a country plugs into the higher end of the value chain, agglomeration forces are likely stronger.<sup>2</sup> We organize our analysis in two stages. We first estimate a measure of city size distribution for each country and year using demographic data on urban agglomerations. By regressing the log rank of each urban agglomeration in a given country on the log of the agglomeration’s population size, we estimate the country’s _Zipf_ coefficient, a parameter generally used as a summary statistic of city size distribution. Next, we examine the determinants of city size distributions, with participation in GVCs being the focus of our analysis.

> 1 The three city triangle is known for GVCs related to production of chemicals and the manufacture of technology-based equipment. 2 Duranton and Puga (2001) and Desmet and Rossi-Hansberg (2009) argue that knowledge spillovers in new industries (such as business services) motivates clustering  in city centers, while more mature industries such as standardized or low-tech manufacturing tends to relocate to less expensive places (e.g. urban periphery or secondary cities).

2
enterprises in the state of Rajasthan operate on non-bank credit. Bringing these enterprises within the formal banking channel can result in significant reductions in the cost of credit faced by these enterprises, and an increase in their overall profitability.

Fourth, our results on rainfall shocks and farm productivity suggest that the presence of irrigation mitigates the negative impact of a negative rainfall shock on agricultural productivity by almost 3 percentage points. However, as seen from the summary statistics, less than 50% of the cropping area in Rajasthan is irrigated. Given that climate change is predicted to increase the probability of extreme rainfall events in the near future, it warrants significant increases in irrigation investment.

14
2005). Overall, they range from 10.8 percent in 1994 (Kara 2008) to 13.1 percent in 2002 (Tansel and Bodur 2012), to 16.0 percent for the period 2009-14 (Karatas 2018). Using the same methodology over time, Montenegro and Patrinos (2014) show a consistently large private return to higher education of about 18 percent from 2002 to 2010, slightly higher for females. Di Paolo and Tansel (2017) analyze wage differentials by college major.

# **Data**

We are using data from Turkey’s 2017 Household Labor Force Survey (HLFS), covering nearly 400,000 individuals aged 15 and over. The HLFS is a nationally representative survey of individuals. We use wage and other information for individuals aged 15 and over in full time dependent employment. We estimated the number of years of schooling of the individual based on the highest educational level completed and taking into account the changes in the required years of schooling for a degree over time. We define a policy dummy variable equal to one for those who were affected by the 1987 educational reform that increased compulsory schooling from five to eight years. This policy dummy is used as an instrument for schooling. Table 1 gives summary statistics.

For the working population, the average years of schooling is 7.5. Only 11 percent of the sample has no education, most have primary or secondary, and 16 percent have higher education. For those with secondary schooling only, the majority, at 80 percent, attended a vocational school. For the working wage earners who report positive labor market earnings, the average years of schooling is 10.1 and more than three-quarters are employed in the private sector.

4
# **_Does Economic Growth Enable Building Trade Facilitation Capacity?_** One assumes

that economic growth and efficiency in trade facilitation are positively related. The more developed a country is, the more resources it can devote to investing in trade facilitation capacity. By the same token, it is likely that the larger the economy, the higher is the rate of return on investment in improving trade facilitation. This positive relationship between growth and trade facilitation indicators is confirmed by the data in the 75 sample countries – the correlations between GDP per capita (adjusted by PPP) and port efficiency, customs regimes, regulatory policy, information technology infrastructures are respectively 0.78, 0.86, 0.68 and 0.81.

The new and candidate members of the EU are less developed than the EU15. Does their gap in economic development account in some way for the lagged progress in their trade facilitation capacity? Considering GDP per capita, is the development of trade facilitation in the new and candidate members of the EU what would be expected? Our analysis of the data suggests that the new and candidate EU member countries are in general weak performers in all four of the trade facilitation dimensions examined (figures 4a-d). Indeed, even in the context of their relatively low of level of economic development, their trade facilitation development is relatively low, in fact under the benchmark level. The only exception is Estonia, which performs stronger than the benchmark level in all four indicators. Compared with Hungary, a country of a similar economic indicator, Estonia is 40% more developed in port efficiency, 30% more developed in IT infrastructure and 20% more developed in regulatory policy.<sup>8</sup> In fact, the trade facilitation level of Estonia is even more developed than the average of EU15 in IT infrastructure. The strong growth in electronics and telecommunication sectors has likely contributed to the recent economic performance of Estonia – the real GDP growth rate was 4.8% in 2003, far above the
that occur within different distance radii of the DHS household-cluster centroid. We count all events that occur within different distance belts—for example, 0–50 km or 51– 100 km. We take a threshold of 50 km to define distant events, but we conduct sensitivity analyses increasing and decreasing this threshold. We also build a continuous indicator that counts all events that occur beyond 50 km from the household cluster and weighs these events using the inverse of the distance. For example, all attacks within a radius of 51 km of the household cluster are assigned a weight of 1, and attacks farther away receive weights smaller than 1. We also consider the number of deaths as an alternative measure of violence.

We also use information on attacks conducted by the Fulani. Between 2008 and 2018, the ACLED data register 1,038 violent events in which at least one of the agents involved was Fulani. About 70 percent of these events are classified as violence against civilians and 25 percent as battles, while the remainder are explosions or remote violence. These events led to 7,236 registered fatalities. Most of the violent events are concentrated in the north central region. About 25 percent of the events occurred in Benue State, followed by 22 percent in Plateau State, and 9 percent in Taraba State (these statistics are not displayed in any table).

_Displacement data._ We use data on the number of displaced people from the IOM’s DTM project, particularly the site- and location-assessment databases. We use these data to calculate the number of IDPs who live near the household cluster. To do so, we count the number of IDPs residing within a 10 km radius of the DHS household cluster. In addition, we build binary indicators at the LGA level indicating the presence of IDPs.

Since July 2014, the IOM has been collecting displacement data from key informants (such as village chiefs and religious leaders) at IDP-hosting locations. These data include the count of IDP households, the estimated number of IDP
sup>�</sup> <u>(</u> _<u>p</u>_ <u>)</u> _<_ 0 . Otherwise if _V_<sup>�</sup> <u>(</u> _<u>p</u>_ <u>)</u> _>_ 0 , the function _V_<sup>�</sup> ( _p_ ) has three roots _p ∈{_ 0 _, p_<sup>_∗∗_</sup> _, p_<sup>_∗_</sup> _}_ ( 0 _< p_<sup>_∗∗_</sup> _<_ _<u>p < p</u>_<sup>_∗_</sup> ) and is positive on the interval [ _p_<sup>_∗∗_</sup> _, p_<sup>_∗_</sup> ] . However, only the highest price _p_<sup>_∗_</sup> is robust to overbidding by brokers. Indeed, if all brokers set a price _p < p_<sup>_∗_</sup> _− ε_ with small enough _ε >_ 0 , some brokers can reap the land market by setting the price _p_<sup>_∗_</sup> _− ε_ and make a positive prot _V_<sup>�</sup> ( _p_<sup>_∗_</sup> _− ε_ ) . Hence, under asymmetric information, the land market yields the equilibrium price _p_<sup>_∗_</sup> for all transactions with all types of landowners. To complete the argument, we now reintroduce the reference to the CBD distance _x_ .

15
the Bureau of Economic Analysis annual survey of US firms with multinational affiliates for 1983-2002. Low income countries are defined according to the World Bank income categories. Employment data are taken from all workers in the Current Population Surveys Merged Outgoing Rotation Groups for the same period. Import penetration and export share are taken from Bernard et al. (2006). Investment good prices, total factor productivity measures, and the real price of shipments are taken from the NBER productivity database.
In line with this, Lazear (1999) shows that smaller classes substitute for better discipline – more specific, students of Catholic schools have better school achievements in larger classes. Babcock and Betts (2009) demonstrate that low-effort students lose more from class size increases as they require more attention of their teachers. Wößmann and West (2002), using a sample of 18 developed countries, find that for most countries class size has an insignificant impact on student performance. However, in Iceland and Greece reduced class size does seem to positively influence TiMSS scores, which they explain by the lower quality of teachers in these countries – teachers there have relatively lower salaries and qualifications.

The arguments in favor of **school size** are similar to those in favor of small classes – smaller schools are more „personalized‟, can have more „adaptive‟ pedagogy and will involve parents more. On the other hand, bigger schools could exploit economies of scale and hence provide better quality for a given level of inputs.

Garrett et al (2004) provide an extensive review of school size literature based on the United States (US) and the United Kingdom (UK) data. Their review suggests an inverted-U relation between school size and student exam scores, though larger schools are typically found to be more cost-efficient. They also point out that teacher and student perceptions of school climate decline<sup>7</sup> and some kinds of violent behavior<sup>8</sup> may increase with school size.

More recently, focusing on the US and Canada, Leithwood and Jantzi (2009) conclude that smaller schools mostly benefit younger students and those with a disadvantaged social and economic background. They state that “elementary schools with large proportions of such students should be limited in size to not more than about 300 students; those serving economically and socially heterogeneous or relatively advantaged students should be limited in size to about 500 students. Secondary schools serving exclusively or largely diverse and/or disadvantaged students should be limited
where occupational or technology choices are discrete, and where choosing a higher return occupation or technology implies large sunk or fixed costs (Barrett 2004). Moving out of agriculture, where poverty rates are often much higher than in other sectors, is one example of such choices.

Our intersectoral migration estimations rely on past data, however by embedding the mover-stayer model within a more complete income generation microsimulation approach we can model occupational dynamics and test (or simulate) some relevant hypotheses (scenarios). Our focus is on global trade liberalization. Recent studies show that a major effect of global liberalization is to raise prices of agricultural commodities 3
combined public and semipublic workers in one group. In the 55<sup>th</sup> round, workers were presented with eight categories: proprietary male, proprietary female, proprietary partnership with members of the same household, proprietary partnership with members of a different household, public sector, semi-public sector, and cooperative society or an enterprise covered by the Annual Survey of Industries. The last category (5% of the sample of wage earners) could include enterprises from both public and private sectors. In addition, nine percent of the sample of wage earners had their answers coded as “0” corresponding to “not recorded”. For the respondents with unidentified enterprise type we impute the value for sector-of-employment indicator, based on existing auxiliary information. We considered the 3-digit industry code in NSS 55<sup>th</sup> round and examined the public/private composition in the same industry code in NSS 50<sup>th</sup> Round. If employment within an industry code exceeded 95% public in 50<sup>th</sup> Round, we assigned the same indicator for the missing values in 55<sup>th</sup> Round. We applied an additional filter for assigning an individual to the public sector: whether that individual is covered by Government Provident Fund (GPF) or not. Because certain industries are reserved for public activities only and GPF covers only public sector workers, we believe, we were able to predict quite accurately the public/private status for the workers with this category missing<sup>6</sup> .

The sample for the present analysis consists of 82,283 individuals in 1994-94 and 86,616 individuals in 1999-2000. Among those, in 1993-94, 16% worked in formal public, 14% in formal private and 66% in informal casual sectors. The corresponding numbers for 1999-00 are 14%, 16%, and 66%. In both rounds, the “don’t know” response about the
<br>respondents|Nonvoucher<br>respondents|_P_<br>value|
|2006|50.0%|51.7%|0.83|<br>20.0%|12.8%|0.20|
||(_N_= 50)|(_N_= 180)||(_N_= 50)|(_N_= 180)||
|2007|43.9%|38.9%|0.46|<br>24.2%|21.1%|0.59|
||(_N_= 66)|(_N_= 247)||(_N_= 66)|(_N_= 247)||

_Source:_ Population Council, Household Survey 2010–11.

_Note: P_ values are from significance tests of proportions for differences between voucher and nonvoucher respondents.

18
The model’s reference year is 2014 and the model is initialized and calibrated to the GTAP Data Base, Version 10 pre-release 2.<sup>25</sup> The 141 regions in the database have been aggregated to 34 regions, see Table A-1.<sup>26</sup> Similarly, the database’s 57 sectors have been aggregated to 28, see Table A-2, with an emphasis on the more traded manufacturing sectors, and the trade and transport services.<sup>27</sup> <u>Table A-2: Sector dimension</u>

||**Sector name (code in parenthesis)**|
|---|---|
|1|Agriculture(AGR)|
|2|Coal(COA)|
|3|Oil(OIL)|
|4|Gas(GAS)|
|5|Minerals n.e.s.(OMN)|
|6|Processed foods(PFD)|
|7|Woodproducts(LUM)|
|8|Paperproducts, publishing (PPP)|
|9|Textiles(TEX)|
|10|Wearingapparel(WAP)|
|11|Leathergoods(LEA)|
|12|Energyintensive manufacturing (KE5)|
|13|Petroleum, coalproducts(P_C)|
|14|Chemical,rubber, plasticproducts(CRP)|
|15|Transport equipment(TRQ)|
|16|Electronics(ELE)|
|17|Metalproducts(FMP)|
|18|Machineryand equipment(OME)|
|19|Manufactures, n.e.s.(XMN)|
|20|Electricity (ELY)|
|21|Construction(CNS)|
|22|Trade services(TRD)|
|23|Air transport(ATP)|
|24|Water transport(WTP)|
|25|Other transport(OTP)|
|26|Hospitalityservices(ROS)|
|27|Other business services(XBS)|
4 terms of the scores of the EBRD, measuring progress in transition, Bulgaria reached levels comparable to that of Poland in 1990, i.e., the year of implementation of stabilization-cum-transformation program (see Table 1 below). However, in 1996, dynamic similarities in macroeconomic performance between Bulgaria and other early reformers disappeared. While in Hungary or Poland inflation continued falling to low double digit-levels, in Bulgaria inflation turned into hyperinflation. The GDP fell in 1996; inflation and government debt spiraled out of control; financial markets and banking sector collapsed; and foreign exchange reserves almost disappeared. Bulgaria became then the only European transition economy ”... that has first achieved respectable economic performance, but later seen that performance deteriorate into full-blown hyperinflation and strongly negative growth of domestic product” (Wyzan 1998:6).

Faced with hyperinflation, Bulgaria had to implement a stabilization package and considerably expand on measures introduced back in 1991 and subsequently aborted. Bulgaria thus began its second transition to competitive markets, characterized by strong commitment to macroeconomic stability and implementation of structural reforms, under much more demanding conditions than in 1991.

Some interesting comparative insights can be gleaned from the examination of EBRD transition indicators of progress in liberalization of prices, small-scale privatization, and foreign trade and exchange rate regimes normalized for the purpose of this analysis.<sup>1</sup> Indeed, much lower scope of initial liberalization in Bulgaria following the implementation of the stabilization-cum-transformation program than in Poland is clearly visible. While Poland scored 57 percent in its first year of the program in 1990, the value of Bulgaria’s index of aggregate progress in first generation reforms was only 38 percent. Bulgaria’s reached the level of dismantling central price controls, state ownership of small firms and foreign trade and foreign exchange rate regime that Poland had already in 1990 only in 1994 and stayed at this level until 1996. Bulgaria reached in 1998 the level of progress in first stage liberalization reforms achieved by former Czechoslovakia in 1992, i.e., during
.21*share of power from coal + 0.91*share of power from natural gas + 2.13 * share of power from oil. While these emissions factors are based on U.S. data, there is little reason to believe that they sharply vary across the world. We use the carbon emissions factors for coal, natural gas and oil to create a single index of a given nation’s grid carbon intensity. Table 4 shows that the largest nations in the world often feature a dirty grid and this means that the rising durables 9