rafmacalaba/datause-encoder-data huggingface dataset:
# ( _p res_ ), and price of cellular ( _p cel_ ).<sup>30</sup>

In addition to showing the estimated values of the parameters associated with the explanatory variables listed at the left and some items already described in section 4, namely, _m_ 1, _m_ 2, _J_ , _L_ , and Obs., Tables A9-A14 include two Wald statistics. A first Wald statistic, Lag length, allows us to test for the joint significance of the coefficients associated with the dependent and the explanatory variables with the highest lag length. A second Wald statistic, Causality, allows us to test the joint significance of the coefficients associated with the lagged political accountability variables when the Lag length test accepts the significance of the appropriate coefficients. The choice of valid instruments is made by using information contained in these tables and following the procedure discussed in section 3.<sup>31</sup> From the results in Tables A9-A11 obtained with the data on developing countries, we see that in all estimations there exists a certain lag length which is accepted. Then, when proceeding to examine Granger-causality, Table A9 shows that regulatory governance causes regulatory performance except when using the cellular subscription or mainlines per employee variables to measure regulatory performance.<sup>32</sup> Table A10 shows that the institutional environment causes regulatory performance independently of which of the five variables is used to measure regulatory performance. Finally, we see from Table A11 that the political process causes regulatory performance except when the latter is measured by the variables mainlines per employee or price of cellular. Table 1 below summarizes these findings on the existence of causality relationships in the data on developing countries.

While some causality relationships are also found in the data on developed countries, the empirical evidence is somewhat weaker than in the case of

> 30We also include in our estimations some additional control variables as needed and account for any possible endogeneity problem. The estimates shown in these tables are those of the parameters of equation (5).

> 31See also footnote (18). In all the estimations shown in these tables, the disturbance term in levels is serially uncorrelated, except for the cellular subscription series (see Table A9) where the disturbance term follows a _MA_ (2), and for the price of cellular series (see Tables A9 and A13) where it follows a _MA_ (1).

> 32See the Causality statistic which is not significant in those two cases.

18
26 significantly positively correlated with per capita output growth."8 These findings should be interpreted as establishing a robust two-step partial correlation between the trade share and growth because the results using trade share (or import share) are equal to those obtained using exports. This partial correlation between trade share and growth, of course, does not tell us much about trade policy, and, in particular, these findings do not tell us anything about export promotion policies.

The second category of growth studies that focus on the relationship between exports and growth use measures of export growth in their growth regressions. They typically find a positive coefficient. Theoretical justification for using export growth (or the interaction term export growth times the share of exports in GDP) is provided by Feder (1983). He assumes that there are positive externalities associated with exports. Thus, the production of non-export goods is assumed to depend positively on the production of exports. Many studies show that output growth is positively correlated with export growth. While there may inideed be externalities associated with exports, some readers may be bothered by a regression that includes the growth rate of two variables in the national accounts identity. Since the basic components of the GNP accounts will probably covary positively over long time periods, regressing output growth on export growth may not tell us much about the importance of international economic relationships in economic development. Interestingly, Ram (1986) argues that government provision of public goods provide external benefits to domestic production and 'a The export shares is not significantly correlated with per capita growth when the investment share is included in the regression, but the investment share remains significantly correlated with growth when the export share is included.
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Table 1: Means and Standard Deviations of Variables

|(A) URBAN Areas|Mean|Standard Deviation|
|---|---|---|
|Expenditure Variables<br>- Total household expenditure, 1987 (1000 CFA)|1734.080|1573.300|
|<br>- Per capita expenditure, 1987 (1000 CFA)<br>- Change of total hh exp, 1987-88 (1000 CFA)|310.317<br>-286.675|254.131<br>753.693|
|<br>-Change of per capita exp, 1987-88 (1000 CFA)|-48.361|175.879|
|Human Capital|||
|-#of children in hh|3.314|2.780|
|- # of male adults in hh<br>|1.406<br>|1.236<br>|
|a of female adults in hh<br>-.Y of elderly in hh<br>|1.522<br>0.178<br>|1.023<br>0.474<br>|
|- Age of head of hh|42.572|12.363|
|<br>-Age of head of hh squared|1964.810|1201.580|
|<br>- No diploma|<br>0.589|0.493|
|- Elementary diploma<br>|0.161<br>|0.368<br>|
|- Secondary diploma|0.058|0.235|
|-Other advanced diloma|0192|0394|
|p<br>- Total years of education of hh members|.<br>18.969|.<br>18.756|
|Physical Capital|||
|- House ownership (yes= 1)|0.336|0.473|
|<br>- Land ownership (yes=1)|0.169|0.376|
|-# of farm equipments|0.022|0.165|
|<br>- Value of farm equipment (1000 CFA)<br>|2.603<br>|34.147<br>|
|- Non-farming business (yes= 1)|0.483|0.500|
|<br>- Revenue of nonfarm business (1000 CFA)<br>- #of durable goods in hh|1750.490<br>3.358|9436.840<br>3.304|
|<br>- Value of durable goods (1000 CFA)|423.567|1269.700|
|Region|||
|- Abidjan|0.417|0.494|
|<br>- Other cities|0.583|0.494|
|Socio-Economic Status|||
|-Public sector worker<br>|0.234<br>|0.424<br>|
|- Private sector worker<br>|0.560<br>|0.497<br>|
|- Other|0.206|0.405|
|Segmentation Variables<br>NIi|0231|0422|
|- on-voran<br>- Head's gender (female= 1)|.<br>0.169|.<br>0.376|
|Income composition (%)|||
|- Share of wage income|38.137|41.553|
|<br>- Share of farm income|7.249|20.276|
|- Share of nonfarm income|37.263|42.297|
|<br>- Share of other income|17.351|25.266|
|Chane Variables between 1987 and 88|||
|g<br>- Change of # of children in hh|0.028|1.352|
|<br>- Change of # of male adults in hh|-0.025|0.766|
|-Change of # of female adults in hh|0.042|0.743|
|<br>- Change of # of elderly in hh|-0.017|0.166|
|- Change of total years of education|0.181|8.982|
|- Change of house ownership<br>|0.014<br>|0.204<br>|
|- Change of land ownership<br>Chane of # of farm euiments|0.014<br>-0006|0.242<br>0129|
|- g qp<br>- Change of value of farm equipments (1000 CFA)|.<br>-1.436|.<br>33.671|
|<br>-Change of # of nonfarm business|0050|0741|
|<br>- Change of revenue of nonfarm bus (1000 CFA)|.<br>-366.372|.<br>8759.400|
|-Change of # of durable goods|-0003|1188|
|<br>- Change of value of durable goods (1000 CFA)|.<br>-57.903|.<br>1021.290|
|- Change of share of wage income<br>|0.388<br>|28.014<br>|
|- Change of share of farm income<br>Chane of share of nonfarm income|-0.836<br>1748|11.705<br>25915|
|- g<br>-Change of share of other income|.<br>0.093|.<br>21.843|
confident about the accuracy of the LULC types classified as forests and settlements. Forest cover, as used in this study, refers to dense, moderate, and sparsely forested areas as well as woodlands, grasslands, shrublands, orchards, and fruit trees within the study area. The spectral characteristics of areas shown as forests, including grassland and other forest cover types, are quite different from other LULC types’ spectral features. Similarly, as presented below, the NDVI results also validate our confidence level in forest area classification.

# **4.4 Panel Data Construction and Descriptive Figures**

We generate a random data set of 10,000 pixels across Cox’s Bazar. With our LULC classified maps and other GIS inputs (Section 2.1), we derive zonal statistics for each pixel. We use this initial sample to determine an analytical approach that allows us to estimate a valid counterfactual scenario and hence account for land cover changes that would have taken place in the absence of the large population influx. This is especially important since camp locations are not randomly determined (Maystadt & Verwimp, 2014) and since we observe considerable deforestation across the district in earlier years.

Figure **_2_** provides the percentage of pixels classified as forest in our sample by year and distance to the nearest refugee camp border. Prior to camp construction in late 2017, pixels 0-5 km from the nearest camp tended to be heavily forested. To a large extent, this is because several of the camps sit on the border of the Teknaf Wildlife Preserve, a highly forested area (see Figure 1). We also observe high forest cover 15-20 km away: at this distance, many pixels fall within Himchari National Park. Pixels that are 5-15 km from the nearest camp, or 20-40 km from the nearest camp, tend to have lower forest cover on average. From 2010 to 2020, the time series shows a steady decline in forest cover in all the distance buffers.

There appears to be a small rise in forest cover between 2014 and 2017, which is surprising given the overall trend to less forest cover in the region. Key informants involved in environmental management in Cox’s Bazar have pointed out that forests in the district can recover relatively quickly. The World Bank also funded participatory reforestation and afforestation efforts in the district between 2013 and 2016, which could explain this marginal increase in forest cover between 2014 and 2017 (Arrannayk Foundation, 2017). But without more information, we also acknowledge that the 2014 land-use classification may under-estimate 14
funds had to be repaid (Laohong 2006, Gearing 2001). There have also been reports of corruption in the administration of the VRF in some scores of villages.

The most rigorous study to date of the impact of the VRF uses data from the 2003 and earlier rounds of a panel of 960 households that Robert Townsend and his colleagues have been following for a number of years in four provinces of Thailand; 800 households were followed throughout 1997-2003, and this is the sample used in the study by Kaboski and Townsend (2009). Although the sample size is relatively small, the survey is rich in detail on household financial assets and transactions. Their most striking finding is that the proportion of household credit coming from “formal” sources (including the VRF) jumped from 37% in 2001 to 69% in 2002, and was accompanied by little reduction in the use of other credit; in other words, at least as of 2003, VRF credit supplemented rather than replaced existing sources of credit.

Although the VRF is widely used, and reported levels of satisfaction with it are high, this is no guarantee that it has had a measurable impact on the outcome variables of interest. Some critics have argued that many VRF borrowers view the money more as a grant than a loan, in which case it might be expected to lead to a one-time increase in per capita expenditure and the value of household durables, but not raise income. Defenders argue that the VRF has had an effect on productivity, raising income and, via higher income, boosting expenditures. Yet others argue that the main effect of the VRF has been to substitute for other sources of credit, with very little net impact on real output, spending, or welfare. To determine the truth in these arguments, a formal impact evaluation is required.

# **5. Propensity Score Matching**

Our first approach to measuring the impact of the VRF is by creating a quasi-experimental design that matches VRF borrowers with “otherwise identical” non-borrowers, and quantifies any difference in outcome variables between these two groups. Formally, let

- _Xi_ be a vector of pre-treatment covariates (such as age of head of household, location of household, and so on),

- _Yi0_ be the observed value of the outcome variable (such as expenditure) in the absence of the treatment,

- _Yi1_ be the observed value of the outcome variable for household _i_ if it has been treated (i.e. it has borrowed from the VRF), and

_Ti_ be the treatment (equal to 1 if the household is treated, to 0 otherwise).

We want to measure τ _i ≡ Yi1-Yi0,_ but this is impossible, because an individual is either in the treatment group (so be observe _Yi1)_ or the comparison group (so we observe _Yi0_ ), but never in both. If we are willing to assume that households are “assigned” randomly to the treatment group, once we have conditioned on the covariates, then by a proposition first established by Rubin (1977), the average treatment effect (τ|T=1) is

_Village Fund, draft of June 29, 2009_ _Page 9 of 34_
Sadker, 1994; Zahn-Waxler, 1993). More recent findings demonstrate the cumulative effects of high versus low expectations (Rubie-Davies, Weinstein, Huang, Gregory, Cowan, & Cowan, 2014) and implicit prejudiced attitudes relating to students’ academic performance (Peterson, Rubie-Davies, Osborne, & Sibley, 2016). Teachers’ conscious or subconscious perceptions and biases about students may undermine students’ achievement across various classrooms or simply the quality of instruction that they receive (Babad, 1993; Carlana, 2018; Harvey, Suizzo, & Jackon, 2016; Rosenthal & Jacobson, 1968). Thus, it is important that teachers are cognizant of their own biases and how these impact students’ learning.

Finally, in a prospective study of Grade 6 and Grade 9 students (N=4,500) from a financially underresourced district in France, Terrier (2016) examined gender bias on students’ mathematics and literacy skills. Findings showed that teachers’ gender bias favoring girls significantly affected girls’ progress in mathematics and increased their likelihood of majoring in a scientific track relative to that of boys. Similarly, Carlana (2018) showed how teachers’ gender stereotypes, measured using an Implicit Association Test, affect girls’ performance on mathematics by lowering their self-confidence. This is direct evidence of the importance of establishing an equitable learning environment for all students.

# **3.2.2. Positive Behavioral Expectations**

Effective teachers create clear expectations for student behavior and notice when students behave in ways that match those expectations. When behavior problems emerge, effective teachers redirect students efficiently and effectively (Emmer & Stough, 2001; Simonsen, Fairbanks, Briesch, Myers, & Sugai, 2008). In doing so, teachers’ positive behavioral expectations create a classroom culture that is safe, predictable, and reflective of effective teacher–student communication. Teachers’ who are consistent and positive in establishing expectations not only help students reach their academic potential, but also support students’ development of positive behavior, social skills, and self-control within a safe environment (Jones, Bouffard & <mark>Weissbourd,</mark> 2013; OECD, 2009). The presence of problem behaviors in school can have a cumulative effect, where early problem behaviors can strain teacher– student relationships, which in turn puts students at risk for later social problems or school failures (Berry & O’Connor, 2010; Jerome, Hamre, & Pianta, 2009; O’Connor, Dearing, & Collins, 2011).

Thus another aspect of positive classroom culture requires teachers to prevent behavior problems and intervene when disruptive behaviors occur because such behaviors interfere 13
**Using household survey data** In most districts, the NSS collected data from four villages. We use data from these four villages to generate districtlevel aggregates. This process can result in sampling error. For example, the sample could contain no households who rent in land, despite such households being present in the district. This would result in missing data for this district/year: 11% of districts do not have any tenant records for either year, and 23% of districts have no tenant records for one out of two years. The latter would imply that rental markets come and go, something which seems unlikely.<sup>13</sup>

**Rural to urban migration** Our district-to-district migration data do not map exactly to the rural/urban distinction implied by the theory. We do not believe this to be a significant limitation. A significant share of migration is not of the traditional rural to urban variant. Aggregate Census data of 2001 indicate that 55% of migration is between rural areas, 15% is between urban areas, 21% is rural to urban migration, and 6% is urban to rural migration. Part of the explanation for this is that areas which have seen a recent surge in population are slow to be re-classified as urban areas. This results in errors in any statistics which rely on this distinction. Ellis and Roberts (2016) note that 30% of the increase in India’s urban population between 2001 and 2011 was due to a of census areas.

# **4 Descriptive statistics**

## **Land tenancy**

Table 1 presents statistics on the land rental market for the years 1992, 2002 and 2012. The top panel presents statistics converted to acreage terms while

> 13We do not have any other reliable data which could serve as a comparison point. We had coded up an alternative data source, the Agricultural Census. This Census is conducted every five years by the Agricultural Census Division of the Ministry of Agriculture since 1970 and collects information on agricultural operational holdings in India. However, data quality on land tenancy appeared to be poor, with often no land tenancy recorded in land-record states (where formal registration records are in place).

12
showing the highest (64%), followed by _technological_ schools with rates very close to the national average and _technical_ schools showing the lowest (48%). More than 60% of the cumulative dropouts throughout the three years of EMS take place during the first year. The decision behind early drop out can be partly explained by the combination of high risk of repetition (the average grade repetition rate was about 15% in 2013) and the strict promotion criteria used in EMS. Students must pass five out of eight disciplinary subject areas and practical modules, otherwise they have to repeat the semester. Additionally, students must pass all their subject areas and modules (around eight in total per semester) within ten semesters after enrolling in EMS, otherwise they lose the right to re-enroll.

The _Percepciones_ pilot took place in 54 technological EMS schools run by the Federal Government. The objective of the _Percepciones_ pilot was to evaluate the effectiveness of a low cost information intervention to increase on-time high school graduation and increase learning outcomes. The design of the _Percepciones_ project benefited substantially from the survey collected in 2005 as part of the evaluation of the Mexican program _Jóvenes con Oportunidades_ . The 2005 survey showed that there was a misperception about the returns to education when compared to the returns from the labor survey ENOE ( _Encuesta Nacional de Ocupación y Empleo_ ), especially among girls, thus providing scope for the intervention.<sup>8</sup> A randomized control trial was designed to evaluate the impact of the intervention. Figure 1 shows the time line of the project spanning from May 2009 to May 2012. The design of the intervention, randomization and sampling took place between June and August 2009. Following a two-step stratified sampling by regions (north, center, and south), the 54 schools were randomly allocated into 26 treatment schools and 28 control schools. The selected schools had an average of 8 classrooms in 10th grade, with around 40 students in each classroom. For each school, at least two 10th grade classrooms were randomly selected to participate in the pilot. In total, 111 classrooms and 4,145 students were included in the experiment.<sup>9</sup>

# **2.1 Description of the Treatment**

The intervention was conducted in November 2009 (Figure 1). An interactive computer software, designed explicitly for the project, gathered information on students’ perceived returns to schooling and, in the case of the treatment group, provided the information package. Given the design of the intervention, all of the students in the treatment group

> 8See Attanasio and Kaufmann (2012b) for a detailed description of the expectations module included in the _Jóvenes con Oportunidades_ survey.

> 9For two schools the first two randomly selected classrooms were too small, and additional classrooms had to be selected.

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