rafmacalaba/datause-encoder-data huggingface dataset:
- 25 -

Table 6: Parameter estimates from six HLM school type effects models Philippines secondary, 1983 (t-statistics in parentheses)

|Independent<br>variable|Science<br>test<br>score|Mathematics<br>test<br>score|Positive<br>towasd<br>scietace|Negative<br>toward<br>science|Positive<br>toward<br>school|Negative<br>toward<br>school|
|---|---|---|---|---|---|---|
|Individual level fixed effects|||||||
|Male|U.79***|<br>34***|-0.10***|<br>0.23***|<br>-0.18***|<br>0.24***|
||(10.91)|(5.85)|(5.18)|(11.95)|(8.51)|(11.67)|
|Age|-0.01***|-0.01***|-0.00|0.00***|<br>-0.00|0.00|
||(4.38)|(5.36)|(0.34)|(3.55)|(0.14)|(1.53)|
|SES|0.55***|<br>0.26***|0.11***|-0.05***|<br>0,06***|<br>0,01|
||(12.23)|(7.17)|(9.08)|(3.95)|(4.94)|(0.48)|
|School level effects|||||||
|Mean|13.15***|11.93***|0.11|-0.56***|<br>0.11|-0.27*|
||(26.03)|(29.52.|(1.01)|(5.12)|(0.95)|(2.34)|
|Local public|-1.25*|-1.61***|-0 29***|0,40***|<br>-0.16**|<br>0.13*|
||(2.48)|(3.92)|(4.04)|(3.30)|(2.74)|(2.12)|
|Private|-0.26|0.88*|0.04|0.01|0.04|-0.13*|
||(0.06)|(2.22)|(0.60)|(0.10)|(0.72)|(2.37)|
|Percent between-school<br>variance explained|11.7|20.3|20.0|25.0|10.0|9.0|

*** p C .001; ** p < .01; *p < .05

49. The signs of the effects for school type on average achievement and attitudes are generally in the expected direction. In local public schools, average science and mathematics achievement scores are lower than in national public schools, ceteris paribus. With student background held constant, students in local public schools score 1.25 points (about 25 percent of a standard deviation) lower in science and 1.61 points (about 50 percent of a standard deviation) lower in mathematics than students in national public schools. Effects of these magnitudes are both statistically significant and meaningful, according to Cohen (1969). Student attitudes are less positive in local schools as well. Students in local schools report less favorable attitudes towards both science and school in general than students in national public schools; the effects are statistically significant for all four attitudes.

50. With background effects held constant, students in private and national
But what is true of Indonesia is also true of the North Atlantic world, in which the full array of social and economic practices that mainstream economics, political science, and other social sciences assume are the natural behavior of human beings in fact depend on historically constructed intersubjective meanings. Bargaining and negotiation, for instance, are not natural phenomena; rather they are social practices of relatively recent invention. Negotiating in the sense that we use the term today requires a set of understandings about good faith, autonomy, what it means to break off negotiations, how to enter into negotiations. People cannot bring into negotiations the set of ideas and norms constitutive of negotiations themselves. “These must be the common property of society before there can be any question of anyone entering into negotiation or not” (Taylor 1971: 27). And these practices were developed over the course of history.

For development theory and practice, this account of intersubjectivity has three general implications. (The sections below describe two more specific implications for the understanding of conflict and common pool resources). First, the methods used to grasp the sources of meaning need to go beyond surveys and questionnaires. Surveys and questionnaires usefully identify the beliefs, opinions, and evaluations in the minds of individuals, but the shared background understandings necessary for establishing meaning events rarely receive full articulation in the minds of individuals; rather, meaning events come to full articulation in particular social contexts. Surveys that aim to elicit them often fail because they typically do not operate in such a way that the respondent can access them at will.<sup>16</sup> An understanding of intersubjectively constructed concepts requires further ethnographic, historical, and interpretive work. In particular, social cohesion and “consensus” are not based in simultaneously held concepts in people’s minds, as evidenced by identical answers to standard questions; in reality, evidence of cohesion, consensus, and shared intersubjectivity is to be found in regularities of physical behavior, communicative style, and social organization. For instance, hierarchical understandings of a society may be more readily observed by looking at how people hold their bodies, when confronted with an authority figure, than by asking people to answer a survey.

> 16 We note that recent work deploying ‘anchoring vignettes’ (e.g., King et al 2004) within household surveys assessing cross-national phenomena goes some way to addressing this concern. The methodological role of such vignettes, however, is largely to enhance the construct validity of survey questions (i.e., the extent to which a given concept, such as ‘government effectiveness’, elicits comparable responses in different contexts). Our concern with meaning events is broader, including the extent to which survey respondents recognize surveys themselves as a legitimate means of determining, comparing and disseminating knowledge (and, in turn, shaping policy).

11
- Pritchett, L., A. Suryahadi, and S. Sumarto. “Quantifying Vulnerability to Poverty: a Proposed Measure, Applied to Indonesia.” Policy Research Working Paper 2437, World Bank, Washington, DC.

- Ravallion, M. 2010. The Developing World‘s Bulging (but Vulnerable) Middle Class.” _World Development_ 38 (4): 445–54.

Reeves, R. V., Guyot, K., and E. Krause. 2018. “Defining the middle class: cash, credentials, or culture?” Middle Class Memo Series, Brookings Institution, Washington D.C. Available at: https://www.brookings.edu/articles/defining-the-middle-class-cash-credentials-or-culture/ Ricci, C. A. 2020. “How to measure the middle class: approaches from economics.” DaSTU Working Paper 4/2020. Laboratorio di Politiche Sociali, Politecnico di Milano, Milan, Italy.

Ridao-Cano, C., and C. Bodewig. 2018. _Growing United: Upgrading Europe’s Convergence Machine_ . World Bank, Washington, DC Salido, O. and J. Carabaña. 2020. “On the Squeezing of the Middle Class: Overview and Prospects for the EU-15.” _European Review_ 28 (2): 325-342.

Schotte, S., Zizzamia, R., and M. Leibbrandt. 2018. “A poverty dynamics approach to social stratifcation: the South African case.” _World Development_ , vol. 110: 88–103.

Sen, A. 1983. “Poor, Relatively Speaking.” _Oxford Economic Papers_ 35 (2): 153–69.

Sitaraman, G. 2018. _The crisis of the middle-class constitution: Why economic inequality threatens our republic_ . Vintage.

Song, J., Cavusgil, E., Li, J., and R. Luo. 2016. “Social stratification and mobility among Chinese middle class households: An empirical investigation.” _International Business Review_ 25 (3): 646-656.

Stewart, F. 2005. “Horizontal Inequalities: A Neglected Dimension of Development.” World Institute for Development Economics Research (WIDER) Annual Lecture 5, United Nations University.

Wright, E O. 1979. _Class Structure and Income Determination_ , vol. 2. New York: Academic Press.

19
Comparing IPF to Selection of Water Catchment Projects Table 8 offers more detail on the comparative outcomes of IPF versus MCE ranking in each region. The MCE-ranked projects marked with an asterisk are those that were not recommended for implementation due to location in an area where environmental restrictions were in place. Highlighted projects are those that were recommended for funding.

Table 8. Comparing IPF Maps and MCE Project Rankings, by region

|||Biobio||||
|---|---|---|---|---|---|
||IPF Ma|pping||MCE Ranki|ng|
||Biob|io|ID|Name|Score|
||||1|Kaiser|3.0|
|100|P14||9|Quidico 1|3.3|
|90|||3|Ranquil|3.5|
|80|||8|Mirihue|3.5|
|70|||14|Leoneras|3.5|
|60<br>EI|P6||2|Las Puentes|4.1|
|50<br>S|||10|Quidico 2|4.3|
|40|P1|P8|6|Pichi Bureo*|4.4|
|30|P13<br>P12||5|Laguna El Pillo*|4.6|
|20|P4<br>P2<br>P10<br>P7<br>P11|P3|12|Vegas de Itata|4.6|
|10|P5<br>P9||7|Rumena|5.0|
|0<br>0|10<br>20<br>30<br>40|50<br>60<br>70<br>80<br>90<br>0|13|Tauco|5.3|
|||10<br>FEI|4|Tranaquepe*|5.4|
||||11|Perales|5.9|
|||Maule||||
||IPF Ma|pping||MCE Ranki|ng|
||Mau|le|ID|Name|Score|
||||19|La Bruja|2.38|
|100|P18||17|Peralito*|3.12|
|90|||18|Peralito 2|3.17|
|80|P2|P27<br>P19|25|El Molino|3.22|
|70|5|P23<br>|26|Derivado Porvenir 2|<br>3.31|
|60<br>EI|P16|P26<br>|21|Sauzal|4.50|
|50<br>S|P24<br>P21|P17|24|Manantiales|5.01|
|40|P15<br>||27|Limávida|5.11|
|30|||23|Vaquería|5.45|
|20|P20<br>P22||20|El Guindo|5.55|
|10|||22|Botacura|6.20|
|0<br>0|10<br>20<br>30<br>40|50<br>60<br>70<br>80<br>90<br>100|15|Huencuecho I|6.65|
|||FEI|16|Huencuecho II|6.65|

* Not recommended due to environmental restriction

34
# **D Descriptive Statistics**

Table D.1. Descriptive Statistics for the Study Sample

||N|Average|SD|Min|Max|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|_Panel A: Sociodemographic Characteristics_||||||
|Male [=1]|828|0.46|0.50|0.00|1.00|
|Student [=1]|831|0.62|0.49|0.00|1.00|
|Age|829|25.25|8.27|18.00|78.00|
|Married [=1]|831|0.07|0.25|0.00|1.00|
|Religious [=1]|831|0.80|0.40|0.00|1.00|
|Primary education completed [=1]|830|0.03|0.16|0.00|1.00|
|Secondary education completed [=1]|830|0.57|0.50|0.00|1.00|
|Higher education completed [=1]|830|0.41|0.49|0.00|1.00|
|Income (1 low- 10 high)|821|2.33|1.07|1.00|10.00|
|Low economic strata [=1]|831|0.73|0.45|0.00|1.00|
|Children [=1]|831|0.14|0.35|0.00|1.00|
|_Panel B: Prosociality Indicators_||||||
|Donated to Venezuelan Org. [=1]|831|0.74|0.44|0.00|1.00|
|Donated to non-migrant Org. [=1]|831|0.81|0.40|0.00|1.00|
|One can trust Venezuelan Migrants (4=Strongly agree)|831|2.70|0.67|1.00|4.00|
|<br>ProsocialityIndex (Kling)|831|0.16|0.98|-3.00|1.00|
|_Panel C: Attitudes Indicators_||||||
|List experiment: Annoyed by 4 situations|420|2.03|0.73|0.00|4.00|
|List experiment: Annoyed by 3 situations|409|1.82|0.60|0.00|3.00|
|Venezuelan are poor due to lack of self effort [=1]|829|0.14|0.35|0.00|1.00|
|Keep Venezuelans out of neighborhoods (4=Strongly agree)|830|1.90|0.76|1.00|4.00|
|Same opportunity for Venezuelans to apply for a job [=1]|829|0.58|0.49|0.00|1.00|
|Government should help Venezuelans (4=Strongly agree)|830|2.74|0.72|1.00|4.00|
|Increase gov. spending to help Venezuelans (4=Strongly agree)|830|2.49|0.80|1.00|4.00|
|Attitudes Index (Kling)|830|0.22|0.96|-3.00|2.00|
|_Panel D: Empathy Indicators_||||||
|Empathic Concern Scale|831|0.03|0.96|-4.00|2.00|
|Perspective-takingScale|831|0.10|0.95|-4.00|2.00|
|_Panel E: Beliefs Indicators_||||||
|Migrants are good for the economy (4=Strongly agree)|830|2.51|0.76|1.00|4.00|
|Immigrants come to compete for our jobs (4=Strongly agree)|829|2.66|0.74|1.00|4.00|
|<br>Immigrants increase crime (4=Strongly agree)|830|2.53|0.78|1.00|4.00|
|Immigrants improve the society with ideas (4=Strongly agree)|830|2.72|0.71|1.00|4.00|
|Immigrants contribute more than they take (4=Strongly agree)|830|2.48|0.76|1.00|4.00|
|Beliefs Index (Kling)|830|0.05|0.98|-3.00|3.00|

_Notes:_ The table presents descriptive statistics of the variables used in the analysis of the paper.

46
The search yielded 6,049 results and automatically refined the results by removing exact duplicates from the original results, which reduced the number of results to 4,294. To this we added 20 impact evaluations which mention teacher training from the sample of a review of education reviews conducted last year and available online (Evans and Popova, 2016). We examined the 4,314 results from both sources to exclude articles that from their title and blurb, were clearly not impact evaluations of teacher training programs. This review process excluded 4,272 results and left us 42 full articles to assess their eligibility. After going through the full texts, another 18 papers were excluded as they did not meet the inclusion criteria. This yielded the final 23 papers, evaluating 26 programs between them. The search process is detailed in Figure 1. The 23 papers are listed in Appendix A.

# **The In-Service Teacher Training Survey Instrument (ITTSI)**

The ITTSI was designed based on (a) the descriptive, impact evaluation, and theoretical literatures characterized above, and (b) the authors’ previous experience studying in-service teacher-training. Drawing on these, we drafted a list of key indicators to capture details about a range of program characteristics falling into four categories: Overarching Aspects, Content, Delivery, and Perceptions. The ITTSI is summarized in Figure 2.

Taking each of these in turn, the Overarching Aspects section includes items such as the type of organization responsible for the design and implementation of a given teacher training program, to whom the program is targeted, what (if any) complementary materials it provides, the scale of the program, and its cost. Content includes indicators capturing the type of knowledge or skills that a given program aims to build among beneficiary teachers, for example, whether the program focuses on subject content (and if so, which), pedagogy, new technology, classroom management, counseling, assessment, or some combination of these.

Delivery focuses on indicators capturing program implementation details, such as whether 10
> mdustry = 2-digit NACE classification

> All explanatory variables refer to 1999

Absolute values of the t-statistics are in **italics**

******* - significant at the I percent level, ** -significant at the 5 percent level, * - significant at the 10 percent level (for two-sided t test).

> Firm size has a significant independent impact on fium growth prospects:

> employment in small firms grows faster than in large finms. As result, initial differences in firm size after one year are reduced by about 10 percentage points. For example, imagine two firms, one employing 25 workers and the other 50 workers. Assume that the larger firm increases employment by 2 percent over one year. Then our model implies that the smaller firm will increase employment by 7.4 percent. Both firms will converge

> in size, although very gradually: in absolute terms the size differential will be reduced by only one person. So, the magnitude of the net impact of firm size on its employment growth should not be overemphasized.

> Foreign direct investment hardly influences firm employment, at least directly. All else held constant, an increase in the share of foreign capital does not have any significant effect-either positive or negative-on the firm's employment growth.

> Exporting finns tend to create more jobs and thus employ more workers than similar non-exporting firms, however the differential is small. Other things being equal, a 10 percent higher export share on average results in only about 1 percent higher

> employment (after one year, assuming the same initial employment level). This result is in contrast to the negative one obtained earlier using descriptive statistics. According to the regression analysis, the net impact of export on employment growth is positive and thereby consistent with economic theory.

> Ceteris paribus, finns, which have access to credit, do not generate more jobs than

> firm, which do not. Although the effect is positive, it is not statistically significant. Thus, as indicated earlier, credit does not seem to go to firms with a growth potential and thus does not help to generate employment."

> 1 Accordmng to a survey of SMEs, small firms report the shortage of working capital as bemg one of the mamn

> problems for **their** development, and claim that external financing is difficult to obtain. Thus, poor access to credit by small firms **is** likely to limit their job creation potential.

36
crease in the number of Syrian refugees from whom Jordanian adults seek advice, and a reduction in the number of Syrian friends among Jordanian children. This latter result could be a partial explanation for the reduced well-being of treated Syrian children. A leading explanation for this pattern is that there was a backlash effect among Jordanians living in the working-class neighborhoods that Syrian refugees tend to inhabit, driven perhaps by resentment at the assistance received through HSP.

We find no evidence that the program increased Jordanians’ housing costs or had other adverse economic effects on Jordanian neighbors. There are no significant impacts on Jordanian neighbors’ total consumption nor housing expenditures (see Appendix).

Regarding the other pre-specified primary outcomes, there are positive but small estimated program impacts on neighbors’ policy preferences and altruism towards Syrian refugees but these are not statistically significant.

There are several other notable patterns among neighbors (Table 7, Panel C). First, neighbors of treatment-assigned refugee households report significantly worse subjective assessments of their own well-being, specifically, a 0.30 standard deviation unit decrease in their self-assessed health (q-value=0.06), and a 0.32 standard deviation unit decrease in life satisfaction (q-value=0.06). There are several potential drivers of these impacts, including the possibility that observing refugee neighbors receive generous assistance led them to feel worse about their own living situation and quality of life, which is perhaps related to the resentment and backlash effects hypothesized above.

Second, neighbors of treated households state that they believe refugees typically receive significantly _less_ aid than that stated by control group neighbors (effect -347 USD PPP, q-value _<_ 0.01). Interestingly, the neighbors of treated households have more accurate beliefs about aid levels, with fewer of them believing that refugees receive extremely high levels of aid that are rarely observed. There thus appears to have been some learning of objectively true information regarding the reality of refugees’ assistance levels among their Jordanian neighbors. This is consistent with greater information exchange and learning as a result of the highly observable HSP.

29
may also be asymmetric in that certain types of benefits accrue to those who do not already have electricity much more than to those who do.

Recent developing-country studies have tackled these issues in different ways, depending on the nature of electricity expansion and related assumptions on how the electricity rollout is related to other factors affecting household labor supply. The studies vary considerably in their treatment of electrification when estimating impacts. A number of studies recognize the endogeneity of placement issue, though sensitivity to controls and external effects are rarely discussed. It has become common to exploit geographic variables for identification under conditional independence assumptions (“exclusion restrictions”). Those assumptions appear to be more plausible in some applications than in others.

Efforts to address the identification problem using single cross-sectional surveys are plagued by concerns about the endogenous placement of electricity. Randomized approaches are difficult with rural electrification because of the large-scale and political nature of such programs.<sup>4</sup> Observational panel data studies help address these concerns, to the extent that the endogeneity can be fully accounted for in the correlation between placement and time invariant factors that can be differenced out using the panel data. However, the acquisition of electricity within the time period is still likely to be endogenous to changes in outcomes at household level. This has been dealt with in the literature by either adding controls for initial conditions that are likely to be correlated with subsequent trajectories or by using an instrumental variables estimator. Both methods require a conditional independence assumption, namely that the error term in the outcomes regression must be conditionally independent of either placement or its instrumental variable (IV).

This paper examines the effects of India’s large expansion in rural household electrification on consumption, labor supply and schooling. We distinguish the internal effects of household electrification from the external effect of village electrification. Asymmetry in the external effects between households with and without their own electricity plays a role in our identification strategy, as does (time-varying) proximity to power generating plants.

We find long-term economic welfare gains from household acquisition of electricity, but also show that there are strong positive external effects of village connection to the grid for

> 4 Two recent exceptions are ongoing studies that look at specific features of new electricity programs in rural SubSaharan Africa, where only 5 percent of households on average have electricity connections.  These are ongoing and described in Bernard (2010).

3
A more recent body of literature has examined related aspects of the rising foreign bank presence in developing countries.<sup>4</sup> Several authors have analyzed the factors driving the decision of international banks to establish operations overseas and their choice of location during the 1990s (see, for example, Claessens et al. 2000, Focarelli and Pozzolo 2001, Moshirian 2001, Buch and DeLong 2001, Buch 2000, Galindo, Micco and Serra 2003, Buch 2003, Buch and Lipponer 2004, Wezel 2004, Focarelli and Pozzolo 2006). Also, many studies have focused on the implications of foreign bank presence in developing countries. For example, Claessens et al. (2000), Barajas et al. (2000), Denizer (2000), and Martínez Pería and Mody (2004) study the effects on competition and efficiency in the banking sector. Dages et al. (2000), Peek and Rosengren (2000), and de Haas and Levyveld (2002, 2004) compare the lending behavior of foreign and domestic banks during crises. Berger et al. (2001), Mian (2004), and Clarke et al. (2005) study the consequences on access to finance and cherry picking.

The remainder of the paper is organized as follows. Section II explains the dataset collected on the activities of the top 100 banks and their operations in Eastern Europe and Latin America. Section III presents the empirical methodology pursued in the paper to examine the determinants of foreign banks’ type of organizational form. Section IV discusses the empirical results. Section V concludes.

# **II.   DATA**

To examine international banks’ type of organizational form across countries, we assembled an extensive and original database on the operations of the 100 largest banks in the

> 4 See Clarke et al. (2003) for a review of this literature.

5