rafmacalaba/data-use-mentions-extended huggingface dataset:
At the country level, the LiTS II data show a negative correlation between crisis severity and average 2009-2010 GDP growth rates (see Figure 3) but the correlation is weak, which may be due to the fact that households’ subjective assessment of the impact of the crisis are based on a multitude of factors, some of which are not fully captured by objective (i.e. consumption- or income-based) measures.

**Figure 3. Economic growth and perceptions of the crisis severity**

<!-- Start of picture text --> 2<br>Bulgaria Serbia<br>Hungary<br>Bosnia-Herzegovina<br>Latvia Lithuania Romania<br>1.5 ArmeniaEstonia Montenegro Macedonia Albania<br>France Mongolia<br>CroatiaSlovenia ItalyMoldova GeorgiaTurkey Tajikistan Azerbaijan<br>Ukraine<br>United Kindgom<br>1<br>RussiaCzech RepublicSlovak Rep. Kazakhstan<br>Poland<br>Kyrgyz Rep. Belarus<br>Sweden Uzbekistan<br>Germany<br>.5<br>0<br>-10 -5 0 5 10<br>Average GDP growth in 2009-2010 (percent)<br>Subjective crisis severity index<br><!-- End of picture text --> Source: LiTS II (2010).

Note: The subjective crisis severity index is the weighted average, for every country, of responses to the question “How much, if at all, has the crisis affected your household in the past two years?” with answers recoded as: 0. Not at all; 1. Just a little; 2. A fair amount; and 3. A great deal.

7
Winters posits that neither of the polar extremes is likely to be precisely true and suggests the importance of determining the elasticity of labor supply in evaluating the supply response of unskilled workers. Thus, theoretical predictions with regard to the effects of trade on wages of unskilled workers are ambiguous, and the existence of S-S effects becomes an empirical question.

# (b) Data

# (i) The _Vietnam Household Living Standards Surveys_

The VHLSS 2002 and 2004 were conducted by the GSO of Vietnam with the technical support of the World Bank and are generally recognized to be of high quality and are representative of all of Vietnam. The VHLSS 2002 and 2004 consist of 30,000 households and 9,188 households respectively, and about half of the households interviewed for the VHLSS 2004 came from the sample of the VHLSS 2002. The timeframe of the VHLSS 2002 and 2004, which reflect information for the years 2001/2002 and 2003/2004 respectively, overlaps reasonably with the period when Vietnam’s exports to the U.S. expanded in the aftermath of the BTA (see Figure 2). During the same period, protection appears to have been still in place for import-competing production.<sup>23</sup> Thus, the focus of the paper is to evaluate the impacts of trade on wages on the export side.

Using the sampling weights available in the VHLSS 2004, out of 47.3 million people who had work in 2004 in Vietnam, about 14.6 million people (31.0 percent of total workers) held wage jobs of which 7.8 million (16.6 percent) and 6.8 million people (14.4 percent) worked in

> 23 For instance, the trade weighted Nominal Rate of Protection (NRP) for manufacturing was relatively high at 29.2 percent in 2003 (Athukorala, 2006, Table 3). Although the estimated Effective Rate of Protection (ERP) for manufacturing decreased from 95.9 percent in 2001 to 43.9 percent in
# Table 7: Morality and appropriation

|Equation number|7-1|7-2|7-3|7-4|
|---|---|---|---|---|
|Dep. Var.|Wallets|Firms|Business|Bribe|
||returned|Behave<br>Ethically|costs of<br>crime|Frequency|
|Morality|0.38***|0.55***|0.09|0.49***|
||(3.36)|(5.43)|(0.91)|(5.33)|
|Morality-ability|0.27***|0.12|0.12|0.22***|
|correlation|(2.60)|(1.17)|(1.14)|(2.18)|
|Constant|2.68|2.82|4.31|2.74|
||(10.43)|(12.79)|(17.27)|(11.60)|
|R<sup>2</sup>|.30|.37|.03|.37|
|Mean, dep. var.|4.01|4.27|4.73|4.35|

Dependent variables are from the “Executive Opinion Survey” (World Economic Forum, 2010). Survey questions are printed below. Regression coefficients are standardized. Test statistics (from robust standard errors) are in parentheses. A *,** and *** respectively indicate significance at .10, .05 and .01 levels for two-tailed tests. N = 73.

## 7-1

In your country, if someone loses a purse or wallet containing US$100, and it is found by a neighbor, how likely is it to be returned with the money in it? 1 =
# **1. Introduction**

Teacher salaries are typically the single largest investment that countries make in basic education, and evidence shows that teachers explain a significant share of variation across students’ achievement (Araujo et al. 2016, Bold et al. 2019; Dobbie and Fryer 2013). At the same time, this achievement is poorly correlated with teachers’ observable characteristics including age, gender, education, experience and even hours in the school (Aaronson et al. 2007; Kane and Staiger 2008; Rockoff et al. 2008). The implication is that factors such as teachers’ content knowledge, pedagogical knowledge, as well as classroom behaviors and practices are potentially important determinants of students' academic performance. This paper compares various approaches to measuring these factors and explores the extent to which they are associated with student test scores—both individually and collectively.

A growing literature has documented significant shortfalls in service delivery in education across countries (World Bank 2018). Surveys of teacher absenteeism in low- and middle-income countries routinely show absence rates of between 15 and 20 percent on any given day (Chaudhury et al 2006, World Bank 2003, Das et al 2007). The Service Delivery Indicators (SDI) program (www.sdindicators.org) has documented teacher absence rates of over 20 percent in Togo and Uganda, and over 40 percent in Mozambique. In addition, even when teachers are in school, they are frequently absent from the classroom: for example, the 2014 round of SDI in Tanzania revealed that while only 15 percent of teachers were absent from the school, 47 percent of teachers (including those absent from school) were not in class at the time of an unannounced visits (Martin and Wane 2016). Even when present, teachers are devoting significant amounts of time to non-teaching and learning activities, such as classroom management (Abadzi 2009; Bruns and Luque 2015). The cumulative result is that students are receiving only a fraction of the scheduled teaching time as actual learning time:
# **I. Introduction**

History provides examples of media incentivizing street protests, which spread across borders. The popular discontent that ended communism in Eastern Europe <mark>began in Poland in the early 1980s, and swept through Hungary, East Germany, Bulgaria, Czechoslovakia, and Romania.</mark> Radio Free Europe played a critical role as a vehicle for the spread of the information about protests and catalyzed its spillovers across borders (Puddington 2000), as did a network of radio stations of the Catholic Church (Stehle 1982). Fast forward 30 years to the wave of street protests that sparked the Arab Spring in 2011 following the death of a Tunisian street vendor who set himself ablaze— after having been harassed and humiliated by municipal officials. These protests established social media as the new conduit for the spread of protests. In 2019, a second wave of protests that started in Sudan and Algeria spread to other Arab countries, including the Arab Republic of Egypt, Lebanon and Iraq, eventually resulting in a global contagion of protests spanning from Chile to the Russian Federation to Hong Kong SAR, China. Just as the global pandemic put a damper on street protests, the death of George Floyd in US police custody powered a global protest movement against police brutality and racial injustice. Again, social media was the main channel of protest engagement.

In this paper we systematically explore spillovers of spontaneous street protests across countries, with social media acting as the catalyst. We use data on nonviolent and unorganized demonstrations for 200 countries for the period 2000 to 2020. Using an autoregressive spatial model, we find strong evidence for contagious protests with a catalyzing role of social media. Social media penetration in the source and destination of protests leads to protest spillovers between countries. There is evidence for parallel learning between streets of nations alongside the previously documented learning between governments.

Our research is related to three strands of previous work. First, our paper is related to the emerging literature on social media and protests. Battaglini (
_Note:_ CAPEX = capital expenditure; OPEX = operating expenditure. The full model approach estimates CAPEX, OPEX, and inefficiencies using our model, which complements utility-specific data with estimates of the long-term incremental costs of efficient model utilities. The hybrid direct expenditure/ model approach, meanwhile, substitutes direct expenditure data in the place of the CAPEX model estimates, while maintaining the model’s estimates for OPEX and inefficiencies.

Finally, it is worth mentioning that these estimates are global, and therefore represent regional and global average levels of subsidies in the WSS sector. Estimates at the country level may differ significantly from these ranges due to the many contextual factors that vary across countries. The estimation of subsidies at the country level would therefore require additional data and a refined methodology.

# **6. Conclusion**

Subsidies are pervasive across countries, irrespective of region or income level. This is despite the fact that, as of 2015, about 29 percent of the world’s population lacks safely managed drinking water, and about 61 percent lacks access to a safely managed sanitation service (WHO and UNICEF, 2017). Subsidies are particularly prevalent among networked and sewered WSS services, as illustrated by the IBNET database. Only 14 percent of the 1,549 listed utilities generate enough revenue to cover the total economic costs of service provision, while only 35 percent are able to cover, at a minimum, the operation and maintenance costs of service provision.

The cost of subsidies associated with the operations, maintenance, and major repair and replacement of existing WSS infrastructure in much of the world (excluding, notably, China and India) is an estimated $289 billion to $353 billion per year, or 0.46–0.56 percent of these countries’ combined gross domestic product (GDP). This figure rises, shockingly, up to 1.59–1.95 percent if only low- and middle-income economies are considered, an amount largely due to the capital subsidies captured in our estimation. Subsidies
and the students at risk for dropping out in a particular grade are those who stayed in school from the previous grade.

In this study, we analyze nationwide data on standardized tests in math and Spanish combined with administrative data on beneficiary receipt, school characteristics and on student and parent characteristics to study program effects on 1) enrollment, 2) type of school attended and 3) academic achievement. We construct a panel of students in sixth grade enrolled in primary schools in high and very high poverty areas in 2008, whom we observe in 2009, 2011 and 2014. Our estimates derive both from fixed-effects regression models and propensity score matching models. We study whether impacts are heterogeneous by gender and by indigenous status. We include a rich set of control variables at the individual, household, school and locality level. We study impacts of PROSPERA in medium, poor and very poor areas, as defined by margination indexes developed by Conapo (National Mexican Population Council “Consejo Nacional de Poblacion”), where a majority of beneficiaries reside.

We begin by describing the program and our research hypotheses, then turn to previous literature, a description of our data and methodology, followed by results and conclusions.

# **II. Background and Hypotheses**

PROSPERA began operating in small rural communities in 1997, following a macroeconomic crisis in Mexico in 1995, and was part of a transition towards implementing targeted anti-poverty programs and eliminating general food subsidies. It quickly grew over time and currently covers six million families, or about one-quarter of all families in Mexico. Although the program has expanded into urban areas, it remains largely rural, with about two-thirds of its household beneficiaries deriving from communities with fewer than 2,500 inhabitants.

The program conditions cash payments to families on children regularly attending schools and on family members visiting health clinics for checkups. Program take-up was exceedingly high when the program began, with 97 percent of families who were offered the program participating (Skoufias and Parker, 2001). Program rules allow students to fail each
that about one‐third of our baseline clients did not have an operating business at baseline. Partner later explained to us that these clients most likely received the business loans for a planned or potential business venture. While on the one hand, we were unable to stratify on this variable, on the other hand this variation in the sample offers us the opportunity to study the impact of business training on new business start‐up. Indeed, potential entrepreneurs are likely prime candidates for whom business training would be beneficial.

From a sample composition point of view, our treatment group is not unbalanced in terms of individuals who did or did not have a business at baseline. In fact, there are no statistical differences between the ratio of treatment and control samples for these two groups. Further, the business training attendance data shows that individuals with and without businesses at baseline were equally likely to attend and complete the course, with a mean attendance rate of 39.4 percent and 39.3 percent, respectively.

Table 2 provides summary statistics for the baseline survey, broken down by treatment and control groups. The last column provides p‐values for a difference‐in‐means test between the two groups. Panel A presents a summary of demographic and stratification variables, and Panel B focuses on business characteristics for those individuals who had a business at baseline. The businesses in our sample have about two employees on average (including the owner) and 14
with the recent aggregation by the World Bank of country level data from 45 countries (with different time coverage), which provides a real international comparison base for various elements of export dynamics (Cebeci, et al. 2012).

This paper complements the empirical literature by exploring a rich firm-level dataset of customs transactions from the Lao PDR to identify determinants of export survival. Such detailed analysis for a small, developing and landlocked economy, which, furthermore, relies heavily on exports of raw materials, can be of considerable help to policymakers and economists interested in export-led growth and diversification, and there are currently very few other examples available (Volpe and Carballo 2008). It continues work already carried out with this dataset (Stirbat, Record and Nghardsaysone 2011) which has provided novel descriptive statistics of the Lao exporter population, in particular the dominance of the intensive margin for export growth and the great deal of churning and experimentation happening in exports. That article, looking at the chances of an export unit (a firm exporting an HS 6-figure product to a particular destination) to survive past the first year (in a cross-section setup), has identified positive externalities from agglomeration and a positive impact of product- and destination-related experience.

In contrast to this previous work, the current analysis of the determinants of exporter survival is carried out in a dynamic context, using the full panel structure of the data. It has also been slightly extended to cover five full years. The aggregation of international trade data by years as well as a general shortage of detailed firm-level data means that yearly reporting is the general norm in the literature. In this case, however, the richness of the dataset allows for an analysis of month-level evolutions instead of just yearly ones. There are, however, several exceptions (Tovar and Martinez 2011), including one of the first instances of survival analysis of exporter data (Sabuhoro, Larue and Gervais 2006). This kind of analysis is expected be more relevant in light of the short export spells and significant
<!-- Start of picture text --> LSMS-ISA data source Raw data level of Dataset and level of<br>observation observation inthe<br>Harmonized Panel<br>Geospatial aianiial<br>Agricultural 7A<br>questionnaire<br>= ES ae Household , 2<br>ihe<br>: Plot-crop<br>J<br>f<br>re<br>Household ipffi<br>Household rr Household<br>questionnaire and asset<br>Individual Individual<br>Commu nity a hae<br>questionnaire<br><!-- End of picture text -->
reference year is 2004. For the estimation of trade flows, the authors used data on transport and economic data on exports and imports (Canning and Tsigas, 2000; Dubé and Lemelin, 2005) to overcome the lack of data. Then, a cross-entropy optimization method is applied to make the information inside the model consistent. Finally, the trade flows across subnational regions are adjusted by the RAS statistical method to increase the consistency of transportation flows with the production data.

Cherubini and Los (2016) follow a macro-oriented approach, introducing the results of a first attempt to integrate sub-national input-output tables for Italy into the World InputOutput Database (WIOD).<sup>2</sup> In this way, they assess the effects of changes in the structure of value chains on regional employment patterns. The regionalization is applied by considering 4 Italian macroregions (Northwest, Northeast, Centre, and South and Islands). The approach adopted to integrate the Italian IRSUTs into the WIOD system of international SUTs was to replace the tables for Italy as a whole with 4 Supply and 4 Use tables, a couple for each region (the "Four Italies"). As a result, the final database, where each of the "Four Italies" is a country itself, has 44 countries (comprehensive of the "rest of the world") instead of the original 41. Firstly, for each product, the interregional imports and imports from abroad have been divided into intermediate consumption, and capital use of goods and services, according to the proportions observed for the imports in the international Use for Italy as a whole. The international transport margins (ITMs) of each of the four regions when trading goods with foreign countries, as they result from the international WIOD tables, have been distributed according to the proportion of the total regional imports from abroad of each product.

More recent works for Italy are contained in Gesualdo et al. (2019) and Severini et al. (2019). The first (Gesualdo et al
TABLE 8: DESCRIPTIVE STATISTICS FOR TRAINER CHARACTERISTICS

|||_Trainer Chara_|_cteristics_||
|---|---|---|---|---|
||Mean|Std. Dev|Min|Max|
|_Demographics_|||||
|Age|35.24|10.53|25|56|
|Female teacher|.53|.51|0|1|
|Household members|4.24|2.11|1|8|
|Years at school|18.71|2.71|15|25|
|_Entrepreneurial experience_|||||
|Ever been a business owner|.41|.51|0|1|
|Years as business owner|.85|1.33|0|4.5|
|Number of businesses started in life|.47|.62|0|2|
|Number of businesses running|.18|.39|0|1|
|HH members with business|.59|1.01|0|3|
|_Cognitive abilities_|||||
|Digit-span score (forward + backward)|9.29|2.52|6|17|
|Raven test total|9.12|2.42|3|12|
|_Non-cognitive skills_|||||
|Personal Initiative (PI)|4.55|.44|3.43|5|
|Error competence|4.73|.34|3.8|5|
|Prosocial orientation|4.82|.43|3.5|5|
|Learning motivation|4.91|.26|4|5|
|_Job satisfaction and involvement_|||||
|How satisfied are you with your job?|4.06|.89|3|5|
|Given a choice, I would still become a teacher|3.94|
men. Gender is also a set of expectations providing positive and negative incentives for broad-based compliance with those roles, responsibilities, and obligations. Gender relations are embedded in markets and institutions and are integral to how an economy functions. Economic institutions, policies, and the implicit rules and traditions that govern aspects of economic life can exacerbate, ameliorate, or simply modify existing gender inequalities.<sup>13</sup> The nature, degree, and social, cultural, and institutional location of these gender differences vary from one society to the next; historically they have favored men (Beneria, Berik, and Floro 2016).

> 12. Potential solutions to this problem provide estimation strategies to generate individual budget shares from household expenditure data alone without data on single individuals (Dunbar and others 2013). However, these solutions must also restrict preferences across household compositions or across people in order to identify individual allocation shares. Either solution flattens estimated fiscal policy impacts precisely where we believe these impacts would exhibit variability that enhances or discourages gender equity.

> 13. See chapters in Berik and Kongar 2021, including Berik and Kongar, Beneria and Sen, Seguino, and Braunstein. See also Duflo (2012), and World Bank (2012).

6
relationship is significant only in the final specification and that too at the 10 percent level. For the incidence of petty corruption, we find no significant correlation with firm-size. Thus, the results for firm-size here should be treated with due caution.

# 4.2 _Repeated cross-section_

Repeated cross-section estimation results for petty corruption are provided in Table 4. Columns 1- 3 contain the results for the incidence of petty corruption and columns 4-6 contain results for the depth of petty corruption. For brevity, only results for some of the specifications are shown. Tables A10 and A11 in Appendix A contain the results for the full set of specifications.

The repeated cross-section results are consistent with the main findings above. That is, the incidence and depth of petty corruption are positively correlated with regulation, and this relationship is significant at the 1 percent level in most of the specifications and at the 5 percent level in the remaining. Quantitatively, the corruption-regulation relationship in the repeated crosssection estimation is almost the same as in the baseline estimation for the incidence of petty corruption. It is somewhat stronger in the repeated cross-section estimation for the depth of petty corruption. However, this difference disappears when we include the various controls in the specification. For instance, for the final specification with all the controls included, the depth of petty corruption rises by 0.146 percentage point (column 6, Table 4) in the repeated cross-section results for each percentage point increase in _Time Tax_ . The corresponding increase in the baseline estimation is slightly higher, equaling 0.149 percentage point (column 6, Table 3).<sup>7</sup>

> 7 We also experimented with including controls for country size proxied by (log of) total population and the quality of democracy as measured by the Polity2 variable from the Polity database. Adding these controls to the final specification above for the incidence and depth of petty corruption only caused the estimated coefficient value of _Time Tax_ to increase in magnitude and it remained significant at the 5 percent level.

22
br>194 countries|Country level|Global<br>stylized<br>facts<br>and Simulations|
|World Bank WDI GDP|Panel for all countries|Country level|Simulations|
|CEPII GDPprojections|Panel for all countries|Countrylevel|Simulations|

## **2.1 Household survey data**

We collect household information on transport expenditures and vehicle ownership from different sources.

**Global consumption database** The Global Consumption Database is a one-stop data source on household consumption patterns in developing countries, provided by the World Bank’s Development Data Group. It draws on a diverse set of household consumption or expenditure surveys covering various topics, including transport. This paper uses the most up-to-date harmonized cross-section consumption data covering 105 countries between 2000 and 2018. Data

7