rafmacalaba/datause-encoder-data huggingface dataset:
# **A ONLINE APPENDIX**

## **A.1 Determination of Rewards and the Reward Ceremonies**

Table A.1 shows the mapping between the performance indicator and rewards. The rewards ranged from bronze to platinum. There was one national ceremony (for recipients of the platinum certificate), one ceremony in each of the eight regions (for the gold certificate), one ceremony in each of the 38 prefectures (silver), and one ceremony in each of the 243 sub-prefectures (bronze). The ministry of education organized the national ceremony; the Regional Authorities of Education organized the regional and prefecture-level ceremonies; and the “School Delegation of Elementary Education” (DSEE) organized the sub-prefecture ceremonies. The ceremonies were attended by government officials, trade unions, national or local press, locally elected officials, and school parent associations.

Table A.2 shows the breakdown of awards received after the first year of the program and the value of the awards as a proportion of the average civil servant teacher salary. Over a third did not receive any reward, and a quarter of teachers received the highest possible reward. The value of the reward was highly convex, with 4% of salary for the lowest tier (rice and radio), and almost 49% of a teacher’s annual salary for the highest tier (rice, TV, cell phone, and generator).

## **A.2 Tests for Internal Validity**

### **A.2.1 Balance and Attrition**

We investigate internal validity by comparing baseline data from Grade 2 and Grade 3 students from the student assessment, as well as school-level data as reported by school principals, across the groups. The sample is balanced on key variables–test scores and gender–collected at the baseline; only age shows a difference between control and treatment arms (Table A.7). School characteristics (number of Grade 2 and Grade 3 students, headteacher gender and age) are likewise never statistically significantly different across the groups, with the exception of headteacher age which is very slightly higher in the control group than in the recognition group. These differences are consistent with random chance. The sample is also balanced by exposure to Ebola. We further test for baseline balance for the sample of schools for which we have midline and endline data, respectively. As before, there are very few systematic differences across the groups in these schools (Tables A.9 and A.10). Two comparisons are statistically significant at the 10 percent level–but overall this small number is still consistent with pure chance.

While balance at baseline establishes the internal validity of the experimental design, 1
natural world, and because many communities have fewer economic resources to respond. For these reasons, northern indigenous people face a very different set of material as well as psychological experiences in the face of climate change. Yet while there are fewer mechanisms pointing towards denial as a psychological barrier to action, barriers in the form of social, political and economic capital are significant.

<u>Developed Nations- Public Apathy? Public Ignorance? Despite the extreme seriousness</u> of this global environmental problem, the pattern of meager public response in the way of social movement activity, behavioral changes or public pressure on governments is visible in all Western nations (Brechin 2003, Dunlap 1998). Countries of the “North” where citizens are well educated and have economic resources to make behavioral changes are not doing so as might be expected. Especially for urban dwellers in the rich and powerful Northern countries environmental concerns such as climate change are seen by many as “no more than background noise” (Brechin 2008). Instead there is a pronounced ability to “distance oneself from information” (Norgaard 2006a and 2006b, Stoll-Kleeman et al. 2001, Kellstedt et al. 2008).

# **NATIONAL AND REGIONAL DIFFERENCES IN RESPONSES: GLOBAL CONCERN AND AWARENESS REGARDING CLIMATE CHANGE**

The Intergovernmental Panel on Climate Change was founded in 1988 and has produced widely publicized reports on the state of climate science since the first assessment was released in 1990. As we will see in the media analysis below, climate change has been visible in news media around the world for some time. What do people around the world understand of climate science? What level of concern do they express about these findings? Here I will review existing polling data on levels of citizen knowledge and concern of climate science. We also pose questions and examine some evidence regarding the relationship between information and concern, and information and action. These themes will be expanded upon in the section on existing explanations for lack of response. In general, existing work on knowledge and concern regarding climate change indicates that: 5
gauge of railroad track).<sup>4</sup> Therefore, we will examine how the level of investment is affected by coordination between the countries, and whether coordination in infrastructure investments is enhanced by preferential trade liberalization between the countries. This question is important for policy purposes, because it will help to answer whether cooperative agreements on infrastructure levels are complementary with trade agreements and should be tied to preferential trading arrangements or whether investment coordination can be undertaken independently of trade liberalization.

Section 11 of the paper analyzes the optimal degree of infrastructure investment for a small open economy, and examines how the incentives to invest in infrastructure are related to trade policy in the absence of coordination issues. One's intuition might suggest that since trade liberalization is associated with an increase in the volume of trade, the return to trade-related infrastructure investments should rise with the volume of trade. This intuition is incorrect in the case where trade is restricted by tariffs and the government chooses transport levels to maximize national welfare. In the case where demands are linear, the optimal level of transport cost investment is independent of the level of the tariff because governments have an offsetting incentive to invest in infrastructure when tariffs are high in order to expand the volume of trade and alleviate the effects of the tariff distortion. However, if trade is restricted by quotas, infrastructure investments will be higher when trade is liberalized.

Section III extends the analysis to consider the case of a customs union between two countries that are small relative to the rest of the world. It is shown that the question of whether the noncooperative equilibrium results in underinvestment relative to the cooperative outcome depends on the pattern of trade between the countries. It is shown that in a customs union in which trade continues with 4A good example is provided by transportation costs for Uganda, a landlocked country. A World Bank (1997) evaluation of transportation cited 8 causes of inefficiency in the road transport of imports, 5 of which related to inefficiencies in Kenya. These included a slow weight bridge system and convoy system in Kenya, lack of properly operating cranes at the port of Mombasa, and slow evaluation of transit cargo. Similar issues arose for rail transport through Kenya.

3
# Table 13: Technology Appropriateness

|||||ln(Sales pe|r worker)||||
|---|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|_Kj_|0.23<sup>_∗∗∗_</sup>|0.11<sup>_∗∗∗_</sup>|0.31<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|0.21<sup>_∗∗∗_</sup>|0.26<sup>_∗∗∗_</sup>|0.24<sup>_∗∗∗_</sup>|0.23<sup>_∗∗∗_</sup>|
||(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|(0.01)|
|_Hj_|0.20<sup>_∗∗∗_</sup><br>(0.04)|0.45<sup>_∗∗∗_</sup><br>(0.09)|0.09<sup>_∗∗_</sup><br>(0.05)|0.25<sup>_∗∗∗_</sup><br>(0.04)|0.54<sup>_∗∗∗_</sup><br>(0.07)|0.48<sup>_∗_</sup><br>(0.29)|0.67<sup>_∗∗∗_</sup><br>(0.07)|0.10<sup>_∗_</sup><br>(0.05)|
|_Sj_|0.51<sup>_∗∗∗_</sup>|0.47<sup>_∗∗∗_</sup>|0.50<sup>_∗∗∗_</sup>||0.37<sup>_∗∗∗_</sup>|0.63<sup>_∗∗∗_</sup>|0.34<sup>_∗∗∗_</sup>|0.54<sup>_∗∗∗_</sup>|
||(0.02)|(0.04)|(0.02)||(0.04)|(0.03)|(0.03)|(0.03)|
|D(1.5 _≤_<br>_Sj ≤_2.5)||||0.31<sup>_∗∗∗_</sup><br>(0.04)|||||
|D(2.5 _≤_<br>_Sj ≤_3.5)||||0.78<sup>_∗∗∗_</sup><br>(0.05)|||||
|D(<br>_Sj ≥_3.5)||||1.21<sup>_∗∗∗_</sup><br>(0.08)|||||
|_Sj_ * D(High Income)|-0.07||||0.08|-0.36<sup>_∗∗∗_</sup>|-0.13 <sup>_∗∗_</sup>|-0.00|
||(0.04)||||(0.07)|(0.07)|(0.06)|(0.06)|
|D(2.5 _≤_<br>_Sj ≤_3.5) * D(High Income)||||-0.12<sup>_∗∗_</sup><br>(0.06)|||||
|D(<br>_Sj ≥_3.5) * D(High Income)||||-0.33<sup>_∗∗∗_</sup><br>(0.11)|||||
|Constant|6.08<sup>_∗∗∗_</sup>|11.34<sup>_∗∗∗_</sup>|5.31<sup>_∗∗∗_</sup>|6.77<sup>_∗∗∗_</sup>|6.56<sup>_∗∗∗_</sup>|5.51<sup>_∗∗∗_</sup>|6.55<sup>_∗∗∗_</sup>|6.03<sup>_∗∗∗_</sup>|
||(0.18)|(0.36)|(0.18)|(0.17)|(0.41)|(0.20)|(0.22)|(0.31)|
|N|13046|2104|10942|13046|5803|6874|6383|6663|
|R-squared|0.41|0.30|0.38|0.40|0.39|0.47|0.43|0.42|
|Sector FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Data|All|High Income|Low Income|All|High _Hj_|Low _Hj_|High Emp.|Low Emp.|

Notes : D(.) are binary variables that take the value 1 if the establishment/country satisfies the condition in parenthesis and 0 otherwise. The different intervals for _Sj_ represent that the establishment’s _Sj_ is in the given interval; high income is satisfied if the establishment is in one of the three high-income countries, which are - South Korea, Poland and Croatia. High Emp. and Low Emp. are categories defined on the basis of above and below median number of employees. High _Hj_ and low _Hj_ are based on above and below median fraction of college-educated workers. Base category for the high-income countries in column 6 is D( _Sj_ < 2.5)*D(High Income). The first two sophistication categories have been merged for high-income countries because only 49 establishments (1% of all high-income estab.) belong to the group D( _Sj_ < 1.5). All regressions are estimated using establishment-level sampling weights. *, ** and *** denote 10%, 5% and 1% significance respectively.

58
month may not be sold until another month. As Samphantharak and Townsend (2006) note, this problem becomes more acute the higher the frequency at which data are collected or the shorter the recall period asked. However, the longer the recall period, the more concerned one is about recall errors.

We asked firms how much of the inventories and raw materials purchased during a month remained unsold (and not spoilt or damaged) at the end of the month. For the average retail firm, about 80 percent of the inventories purchased during a month are sold before the end of the month, with the firm at the 75<sup>th</sup> percentile selling between 87 and 97 percent, and the firm at the 25<sup>th</sup> percentile selling between 67 and 73 percent. Manufacturing firms have less rapid turnover, with the median firm selling only 65 percent of the month’s raw material purchases before the end of the month.

In the KMS we randomly divided firms into three groups of 60 firms each. One group had monthly data for September collected in early October. The other two groups were given simple record books for keeping track of sales and expenses. We interviewed one of these two groups every week, and the other group every two days during September. The data collected during this exercise can be used to examine how frequently retail trade firms turn over inventories. On average, 97 percent of firms purchased something as inventories within each two day period (and 99 percent within each week). Much of what was purchased was sold very quickly, with the median firm selling 59 percent of input purchases within two days and 67 percent within a week of purchase. Nevertheless, there is substantial variation across firms in the turnover of inventories, suggesting that the correlation between revenue-expenses and reported profits may be affected by differences across firms in the timing of transactions.

In the January 2007 wave of the SLMS, we asked firms how much they typically markup inputs in setting the price of the goods they sell. Specifically, we asked: 10
# **_Loan Recovery Rate_**

Another dimension of the performance of a Village Fund is the proportion of loans that are successfully repaid on time. The “loan recovery rate” as reported by VFs in the May 2010 survey was just 73%, but is only a weak approximation of the true rate, because it measures repayments as of December 2009 divided by the loans extended as of May 2010. Data collected from households in the socio-economic surveys of 2004, 2009, and 2010 show an on-time loan recovery rate of about 93% (see Table 2); when late payments are factored in, 97-98% of the principal lent appears to have been recovered.

|**Table 19. Estimation Results of Model of the Det**|**erminants of**<br>Robust OLS|**Loan Recover**<br>Endogen|**y Rate**<br>ous SwitchingReg|ression|
|---|---|---|---|---|
||||No|Probit,|
||Full sample|Onlending|onlending|switch|
|Supply: Fund Capital|||||
|Initial VF capital||-0.017|0.035|0.356***|
|||_(0.091)_|_(0.026)_|_(0.101)_|
|Supply: Characteristics of VF Committee|||||
|Accounts are computerized (yes=1)|0.070***|0.030|0.067**|0.116|
||_(0.022)_|_(0.067)_|_(0.027)_|_(0.121)_|
|Demand: Household Characteristics|||||
|Proportion of income generated by wages|-0.023<br>_(0.073)_||||
|Proportion of population with some secondary education|-0.250***<br>_(0.073)_|0.009<br>_(0.209)_|-0.292***<br>_(0.080)_|0.528<br>_(0.370)_|
|Proportion who have a loan from a formal bank|0.315***|0.182|0.300***|0.862***|
||_(0.061)_|_(0.357)_|_(0.075)_|_(0.317)_|
|Proportion of borrowers who are women||0.030|0.108|0.272|
|||_(0.230)_|_(0.092)_|_(0.416)_|
|Expenditure per capita||-0.015|-0.001|-0.102***|
|||_(0.044)_|_(0.007)_|_(0.036)_|
|Loan terms and conditions|||||
|VF would borrow more if funds were short|-|||1.232***<br>_(0.282)_|
|VF could lend out another 1m baht safely|-|||0.584***<br>_(0.152)_|
|VF wants to borrow more from BAAC, GSB, etc.|-|||0.356***<br>_(0.128)_|
|VF borrows to on-lend to members|0.146***<br>_(0.026)_|||-|
|Memo items:|||||
|Number of observations|888|||885|
|R<sup>2</sup>|0.095||||
|pfor endogeneity||||0.032|

Source: Village Fund Survey 2010 and SES 2009. Sample includes cases with loan recovery rate between 0% and 100%. Robust standard errors in parentheses. Variables included based on OLS backward stepwise with p=0.2 cutoff. * p<0.05, ** p<0.01, *** p<0.001.

30
before 1990. A majority of the women who had access in 1990 therefore have been exposed to the program for up to 25 years (depending on the woman’s age) at the time of the survey allowing sufficient time to identify long-term effects on fertility.

There was a substantial expansion in access to health facilities and family planning programs after 1990 with coverage going from 50 to 100 percent over from 1997 to 2005. That is, our “control” or untreated women with respect to family planning access did, in fact, almost all have access by 2004. On average, the communities of these women had 5 years of access by 2004 compared with 25 years for our “treated” communities. The effect of this increase in program coverage is to bias downward the estimated effect of the program.

Table 1 shows the descriptive statistics for the dependent variable and the explanatory variables used for estimating program placement. There are two categories of explanatory variables. First, variables that affect both placement and individual fertility decisions. Secondly, the set of instrumental variables that are assumed to only affect the program placement.

The first set includes the district level variables: the total area of the district, the average yearly rainfall and its square, and the elevation of the district and its square. At the community level the variables include a dummy for whether it is an urban area or, in other words, a kebele, and a dummy for whether there is a market in the area. The accessibility of the area is captured by two variables: whether the area can be reached by car all year or only during the dry season (the excluded category is no road access).

We use the relative rank of zonal and community variables as instruments in the placement decision estimation. Each variable is ranked with 1 assigned to the smallest value and ties are assigned the same value, so that the sum of the ranks is preserved. That is, for a given variable an observation’s rank is 1 plus the number of values that are lower than that observation’s value. Five variables are ranked at the zonal level for the 36 zones in the sample and one variable is ranked within zones. For zones, the ranked variables are the size of the population, the degree of urbanization (measured as the percent of the population who live in urban areas) and the percentage of adults with various levels of education (none, primary or 1-6 years, and 7-8 years). These ranks are all based on data from the 1994 Census. The means of the rankings are not all equal to 19 because not all zones have the same number of communities and because weights are applied to calculate the means. The communities are ranked within each zone by their population size. The maximum in 1990. We do test robustness of the results to other cut-off years.

9
Table S2: Feature importances (gradient boosting model)

|Feature<br>Importance|Feature|Importance|
|---|---|---|
|CD WE Call Median<br>8|IT Recharges Night Min|3|
|% At Home WD Night<br>7|BOC WD Call Median|3|
|IPC WD Night Call Kurtosis<br>7|IT WE Call Min|2|
|CD Day Call Median<br>6|BOC WD Night Call Kurtosis|2|
|IT WE Call Mean<br>6|IPC Day Call Kurtosis|2|
|Churn Rate Mean<br>5|% Nocturnal WD Call|2|
|IPC Day Call Skew<br>5|IT WD Day Text Mean|2|
|IT Recharges WD Median<br>5|CD Night Call Max|2|
|% At Home Day<br>4|IT WE Call Skew|2|
|% Initiated Interactions Day Call<br>4|IPC WE Day Call Kurtosis|2|
|% Initiated Interactions WD Day Call<br>4|IT WE Text Median|2|
|BOC WD Call Max<br>4|% At Home WE Night|2|
|% Initiated Interactions WD Night Call<br>3|Entropy Contacts WE Day Call|2|
|PPD Night Call<br>3|# Recharges WD Day|2|
|IT Recharges WD Night Min<br>3|Entropy Antennas WD|2|
|IT Recharges Night Median<br>3|IPC Night Call Skew|2|
|IPC WD Night Text Mean<br>3|IT WE Night Call Mean|2|
|IT Recharges Day Kurtosis<br>3|# Contacts Day Call|2|
|IT Night Text Min<br>3|CD WD Call Max|2|
|IT WE Day Text Median<br>3|IT Day Call Mean|2|
|# Antennas WD<br>3|IT WD Night Text Min|2|
|CD WD Night Call Kurtosis<br>3|Entropy Antennas Day|2|
|IPC Night Call Kurtosis<br>3|% Initiated Interactions WE Day Call|2|
|IPC WE Night Call Kurtosis<br>3|CD WE Day Call Kurtosis|1|
|IPC WE Night Call Skew<br>3|IPC Day Call Std|1|

_Notes_ : For our selected machine learning model – the gradient boosting model used to predict ultra-poor status from CDR features – we display feature importances for the top 50 features. Feature importances for the gradient boosting model represent the total number of times the feature is used for a split in the entire ensemble of decision trees. We report feature importances when the model is trained on all 535 observations (rather than over cross validation). See Appendix B for abbreviations in feature names.

39
high levels of aggregate unemployment that only gradually declined. Moreover, about 50,000 male workers, who had been employed in the reform sector, composed of farms that had been expropriated during the land reform implemented during the Frei and Allende administrations, were abruptly dismissed without land or employment in the late 1970s, when the reform sector land was redistributed to others. As men lost work and suffered reduced incomes, other family members (i.e. women) sought employment whenever and wherever possible. Economic and political circumstances were very difficult during this period for many rural families. Though the fruit sector was growing rapidly, it was still relatively small as a proportion of agriculture and its impact on agricultural employment was limited.

Most early social science commentary regarding fruit sector female employment was negative (e.g., Lago). Several scholars hypothesized that women had entered this sector only because the male workers in their households had been unable to find employment, i.e., that women were forced to take unattractive employment. Some argued that female workers were exploited by being offered only seasonal employment, at low wages or, frequently, on a piece rate basis that encouraged women to work lengthy hours at a rapid pace, competing with each other for a fixed amount of raw material.

By the late 1980s and early 1990s, evidence from sociological studies began to emerge showing that the fruit sector offered women opportunities for employment, income and personal fulfillment that were previously lacking (Rodriguez and Venegas, Venegas). Work allowed women to achieve greater independence and voice within their households and to significantly improve their household’s standard of living. Also, numerous women who entered the labor force only as summer agricultural workers began

- 3 -
# **Annex II Examples of the Income Coverage Provided by the Safety Net**

## **A couple without children**

The MSL for a family of these characteristics is SK5,490, and child allowances are calculated as described in section _???._ A. Unemployed/NLF spouse

|Individualand**HHincome**|**UI**<br>First3months<br>**(RR**50%)|**UI**<br>Afterfirst3months<br>**(RR45%)**|SA(Objective)|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>_(0.45)_|3,230<br>(0.29)|
|**HHincome(AW)**|5,490<br>(0.50)|5,490<br>(0.50)|5,490<br>(0.50)|
|Individualincome(MW)|3,230<br>(0.90)|3,230<br>(0.90)|3,230<br>(0.90)|
|**HHincome(MW)**|5,490<br>(1.00)|5,490<br>(1.00)|5,490<br>(1.00)|

B. Spouse employed at average wage

|**IndividualandHHincome**|**UI**<br>**First3 months**<br>**(RR 50%)**|**UI**<br>**Afterfirst3 months**<br>**(RR45%)**|**SA(Objective)**|
|---|---|---|---|
|**Individualincome(AW)**<br>___________________________|5,400<br>(0.49)|4,927<br>(0.45)|0|
|**HHincome(AW)**|16,350<br>(0.74)|15,877<br>(0.72)|10,950<br>(0.50)|
|Individualincome(MW)|1,800<br>(0.50)|1,620<br>(0.45)|0|
|**HRincome(MW)**|12,750<br>(0.87)|12,570<br>(0.86)|10,950<br>(0.75)|

C. Spouse employed at minimum wage

|**IndividualandHHincome**|**UT**<br>First3months<br>**(RR50%)**|UI<br>Afterfirst3months<br>**(RR45%)**|SA(Objective)|
|---|---|---|---|
|**Individualincome(AW)**|5,400<br>(0.49)|4,927<br>(0.45)|0|
|**HHincome(AW)**|9,000<br>(0.62)|8,527<br>(0.59)|5,490<br>(0.38)|
|**Individualincome(MW)**|1,890<br>(0.63)|1,890<br>(0.63)|1,890<br>(0.63)|
|**HHincome(MW)**|5,490<br>(0.76)|5,490<br>(0.76)|5,490<br>(0.76)|

41