rafmacalaba/datause-encoder-data huggingface dataset:
illustrate the timing of the merge between the outcome variables and the victimization data sets for the wave-based and the round-based panel data sets, respectively.<sup>18</sup>

# 4.2.   Empirical model

To assess the effect of household conflict events on consumption and food insecurity, we run a fixed effects model of the following form where Yi,r,t,w is either (log) consumption or the CSI in household i in region r in year t and the corresponding wave (or round) _w_ .<sup>19</sup> Household fixed effects capturing time-invariant household characteristics are denoted by αi, and wave or round fixed effects are denoted as θw (for waves for the consumption analysis and for round—that is, wave and visit—for the food insecurity analysis). The variable ∑t−1j=w−1 Conflicti,j records the number of conflict events for each household i for each time period j, where j is the number of years between the previous round w−1 and the previous year t−1 (in case the last visit of the previous wave or round took place before t−1). Therefore, our victimization measure captures the intensity of victimization as measured by the number of events.

# 4.3.   Threats to identification

Although household fixed effects capture all time-invariant household characteristics, we also control for time-varying household and geographic characteristics, γXi,r,t,w. The household controls are household size, household head gender and age, household head education in years and employment status, and dummies for different marital statuses (as listed in table 1). We display results both with and without controls because some controls could be “bad controls,” meaning they could be directly affected by conflict. This is the case, for instance, with widowhood and employment status. Additionally, we also control for geographical time-

> included. For wave 3, visit 1 (autumn 2015), events in 2013 and 2014 were included, and for wave 3, visit 2 (spring 2016), events in 2015 were included.

> 18 Figure D.2, panel a, illustrates how events that occurred in 2010 are merged in wave 1 (2010–11), events recorded in 2011 and 2012 are merged with wave 2 (2012–13), and finally events that occurred in 2013, 2014, and 2015 are merged with wave 3 (2015–16). Therefore, events that occurred in 2016 and 2017 are dropped from the analysis, as they have occurred mostly after the end of the last visit for data collection in spring 2016. 19 Since wave captures in each case a median of two measures in separate calendar years, the notation does not overlap with that of the calendar year, which is the frequency at which the conflict data was collected. The correct interpretation of the notation w in relationship with the calendar year is to consider that wave 1 corresponds to the year 2011, wave 2 to 2013, and wave 3 to 2016.

14
**Table 14: Annual Value Added per Worker by industry (in million LCU) at 2000 prices**

|**One Digit Industry (ISIC Rev. 3.1)**|<br>**total**|**UBI Sample**|**Estimated**<br>**total**||**Annualgro**<br>**Sl**|**wth(% pa)**<br>|
|---|---|---|---|---|---|---|
||**2002**|**2009**|**2002**|**2009**|**ampe**<br>**average**|**Weighted**<br>**i**|
|**Traded sector:**||||||**estmate**|
|A ‐ Agriculture, hunting and forestry|2.0|9.2|2.0|0.6|51.8|‐10.1|
|B ‐ Fishing|2.7|4.1|2.1|0.9|7.2|‐8.3|
|C ‐ Mining and quarrying|3.6|33.3|0.6|7.3|117.0|147.0|
|D ‐ Manufacturing|11.5|23.4|2.9|6.0|14.7|14.9|
|_Traded sector total_|9.1|22.3|2.7|3.7|20.6|5.1|
|**Non‐traded sector:**|||||||
|**_Other industry:_**|||||||
|E ‐ Electricity, gas and water supply|58.0|405.6|10.3|493.2|85.5|671.8|
|F ‐ Construction|8.5|198.1|7.7|98.4|319.6|168.5|
|**Services:**|||||||
|G ‐ Wholesale/retail trade and services|11.5|27.8|1.0|1.9|20.4|13.5|
|H ‐ Hotels and restaurants|4.5|13.0|0.9|1.4|27.1|8.4|
|I ‐ Transport/storage/communications|22.6|43.7|4.6|3.7|13.4|‐2.9|
|J ‐ Financial intermediation|50.3|49.9|19.3|1.6|‐0.1|‐13.1|
|K ‐ Real estate, renting business|5.6|6.1|3.4|3.3|1.1|‐0.4|
|L ‐ Public administration and defense|4.7|24.2|0.8|21.0|59.0|344.5|
|M ‐ Education|4.6|5.3|3.2|1.6|2.1|‐7.2|
|N ‐ Health and social work|4.5|42.2|1.8|2.3|120.5|4.3|
|O ‐ Other social and personal|7.3|12.5|1.2|1.1|10.2|‐1.9|
|Q‐ Extraterritorial organizations|63.8||47.1|||‐14.3|
|_Non‐traded sector_<br>_total_|12.3|38.9|1.8|1.9|30.8|0.3|
|**Grand total**|10.9|31.9|2.1|3.2|27.5|7.9|

48
28

**Table 4.18: Incidence of Poverty by Gender of Head of Household, 1985-93 (Poverty Line=N395.41)**

||||**_RURAL_**||||
|---|---|---|---|---|---|---|
|||**1985/86**|||**1992/93**||
|**Gender of Head**|||||||
|**of Household**|**P0**|**P,**|**P2**|**Po**|**PI**|P2|
|Male Headed|0.505|0.185|0.089|0.379|0.158|0.093|
|Female Headed|0.421|0.142|0.061|0.218|0.094|0.056|
|_All Rural_|_0.495_|_0.169_|_0.085_|_0.364_|_0.167_|_0.098_|
||||_URBAN_||||
|||**1985/86**|||**1992/93**||
|**Gender of Head**|||||||
|**of Household**|**P0**|**P,**|**P2**|**P0**|**PI**|**P2**|
|Male Headed|0.326|0.128|0.053|0.317|0.143|0.078|
|Female Headed|0.277|0.090|0.032|0.209|0.070|0.035|
|_All Urban_|_0.317_|_0.124_|_0.051_|_0.304_|_0.140_|_0.076_|
|Source: NCS, 1985|/86 and 199|2/93.|||||

Table 4.19 shows the incidence of poverty by the educational level of head of household. Taking the rural areas first, in 1985 more than _50_ percent of households whose heads had no education were poor. All the poverty measures show an exponential decline with increasing education, in both periods. However, some interesting facts seem to emerge out of this table. There is a decline of 2.4 percentage points in headcount, 3.2 percentage points increase in depth and 3.4 percentage points increase in severity respectively among the uneducated in rural areas, but for primary school leavers while the head count has declined, the depth and severity has increased. For secondary and post secondary school leavers, the results are mixed. For the secondary school leavers, there was a very small (3.1 percentage points) decline in headcount, a 2.1 percentage point increase in depth, and a 2.4 percentage point increase in severity of poverty. For the post secondary school leavers, there was an increase in all measures during the period of our study. In the urban areas, there was a decline in headcount, depth and severity of poverty for households whose heads had below primary education. Again for the post secondary school leavers, the trend was similar to that of the rural areas, resulting in higher depth and severity of poverty.
**Table 5: Spatial Fixed Effects Estimates of the Impact of LTR on Perceived Risk of Expropriation**

||**Binary measure**|**Five scale measure**|
|---|---|---|
|Treatment indicator|-0.047|-0.204|
||(0.997)|(1.251)|
|Treatment X Female headship|-0.042|-0.106|
||(0.512)|(0.478)|
|Number of years possessed|-0.000|-0.001|
||(0.411)|(0.454)|
|Parcel was purchased|-0.053*|-0.219**|
||(1.911)|(2.333)|
|Parcel was inherited|-0.073*|-0.317**|
||(1.905)|(2.447)|
|Acquired through other means|-0.004|-0.273|
||(0.056)|(1.132)|
|Parcel size in hectares|-0.010|-0.044**|
||(1.254)|(2.128)|
|Head's age|0.000|0.001|
||(0.295)|(0.246)|
|Female headed household|-0.066|-0.231*|
||(1.444)|(1.653)|
|Head has at least primary education|0.005|0.051|
||(0.335)|(0.922)|
|Number of male children age 14 and less|-0.015*|-0.056**|
||(1.661)|(2.108)|
|No. of female children age 14 and less|-0.019*|-0.052|
||(1.775)|(1.643)|
|Number of adult male age 15 to 60|0.010|0.001|
||(0.828)|(0.032)|
|Number of adult female age 15 to 60|0.019*|0.044|
||(1.830)|(1.383)|
|Male members age 60 and above|-0.022|-0.075|
||(0.434)|(0.495)|
|Female members age 60 and above|-0.074|-0.226|
||(1.451)|(1.347)|
|Constant|-0.001|-0.015|
||(0.165)|(0.497)|
|Number of observations|5345|5345|
|R2|0.030|0.032|

**Note:** The geographical neighborhood of each household is defined at a distance of 1000 meters. Absolute value of t-statistics corrected for spatial dependence (1500 meters).* significant at 10%; ** significant at 5%; *** significant at 1%.

21
(2) Eihjt = β0 + β1Mjt + Z′jtβ2 + X′hjtβ3 + uj + p∙Tt + νi + eihjt where Eihjt is 1 if an individual completing middle school in year t enrolls in high school the following year, and 0 otherwise. As Mjt is drawn from a separate annual survey of village leaders and accountants, it is not constructed exclusively from survey households, and includes information on registered residents who have lived outside the village for a considerable period of time.<sup>2</sup> Zjt are other time-varying characteristics of village j that potentially affect local returns to high school and alternative activities (the shadow value of schooling, _μt_ , in equation (1)), and local factors influencing credit constraints faced by all households. These variables include the average village income per capita and Gini coefficient, both calculated leaving out the income of household _h_ , the total arable land available in the village, land inequality (as measured by the cultivated land Gini coefficient), the size of the village labor force (the registered population aged 16 to 60), and the arable share of total village land. Household characteristics, Xhjt, are introduced in some specifications to control for family preferences for education, factors affecting lifetime household wealth, and the likelihood that the household faces credit constraints. To proxy for family wealth, we include variables for parental education, which are time invariant, and for preferences, a time-varying measure of the number of ever-resident male and female children of the household head who have reached 16 years of age, which we refer to as _potential migrants_ . Next, we include a vector of village fixed effects, uj, in all models because important village characteristics, like location, do not vary over time but influence both labor market returns and the cost of obtaining education. Price levels, macroeconomic shocks 2In each annual village survey, village leaders were asked about population of legally registered residents, and the numbers of legally registered residents who were presently living and working outside the village for more than six months of the year. We consider a registered resident who lives and works outside their home county to be a migrant. As an alternative to using the migrant share, one might alternatively use the number of village residents working as migrants outside the village, as the size of the migrant network may be more important than the migrant share of the village workforce. Residents from larger villages, with more migrants for a given share, may have advantages of a larger network, though there is also likely a point at which increasing village size has a negative effect on referrals. There are no qualitative differences to results presented in the paper if we use the size of the migrant population as the measure of migrant network size; in fact, _F_ -statistics on the first stage are consistently higher when using the number of village residents working as migrants. In this presentation, however, we favor the share as it makes intuitive sense for a regressor to be a share when estimating a probability.

9
Using Student and Teacher Assessments to Design More Pertinent InService Teacher Training: The Case of Ecuador

# Diego F. Angel-Urdinola and Sebastian Burgos-Dávila

The World Bank<sup>1</sup> JEL Classification: I20, I24

# **Keywords:**

Education quality, teacher training, professional development, Ecuador.

> 1 This paper was prepared to guide the technical assistance to support the implementation of Ecuador’s Comprehensive <mark>System for the Development of Education Professionals (</mark> SIPROFE) program within the context of the World Bank’s _Supporting Education Reform in Targeted Circuits_ Project. The authors acknowledge the technical support and strategic guidance of Rafael de Hoyos, World Bank; Juan Ponce, _Facultad  Latinoamericana de  Ciencias Sociales_ (FLACSO, Ecuador); and Jorge Yepez, Hortencia Bustos, and Lucia Pinto; Ministry of Education of Ecuador.

1
they do not need to trust each other or an intermediary (i.e., “social scalability”). Raskin, Saleh and Yermack (2019) contend that non-state digital currencies could have important welfare implications for emerging markets as they can provide for an alternative asset which could serve as a check on the inflationary tendencies of the sovereign, suggesting that crypto-assets adoption may be driven by a diversification opportunity for local investment. Regarding Decentralized Finance (DeFi), Harvey et al. (2021) claim it has the best potential to provide financial services in the future to overcome the inherent challenges of the traditional financial sector of centralized control, limited access, inefficiency, opacity, and lack of inter-operability.

Early work that aims to identify drivers of crypto-assets adoption includes Hileman (2014) which proposed a Bitcoin Market Potential Index based on seven country characteristics (technology penetration, international remittances, inflation, size of informal economy, financial repression, historical financial crises, and bitcoin penetration) and posited that the greatest potential for adoption lies in Latin America and Sub-Saharan Africa. Additional relevant factors for cryptoassets adoption may also include high expected returns from speculative investment and regulatory arbitrage, particularly related to illicit financial activity (e.g., IMF (2021) and Saiedi, Brostrom & Ruiz (2020)). Feyen, Frost, Natarajan, and Rice (2021) propose a set of supply and demand side drivers of crypto-assets adoption with a focus on stablecoins. As supply side drivers they include profitability and costs of traditional payment service providers, and the availability of infrastructures such ICT and agent networks. On the demand side, they consider cost and inconvenience, confidence in financial incumbents and the government, and macroeconomic conditions.

There is a growing empirical literature that explores several of these drivers. Saiedi, Brostrom & Ruiz (2020) find some evidence that perceived failings of the traditional financial system contribute to the adoption of crypto-assets. However, using U.S. Survey of Consumer Payment Choice data Auer and Tercero-Lucas (2021), document that crypto-asset adoption in the United States is not driven by distrust in the regulated financial system and conclude that adoption is mostly driven by speculation. In addition, they also find that crypto-assets users tend to be educated, young, and digital natives. By analyzing on-chain Bitcoin transaction data, Baur, Lee and Hong (2018) and Athey et al (2016) also find that bitcoin transactions mostly reflect speculative activity. However, more recently Graf von Luckner et al (2021) study data from a centralized exchange and challenge the view that Bitcoin is only used for speculation. They find evidence that at least 7 percent of bitcoin transactions reflect its use for domestic transactions and 11
4 Figure 2: United States: Size and financial linkages <!-- Start of picture text --> (A) Financial market size, 2010-15 (B) U.S. financial openness, 2010-14<br>Percent of GDP<br>Percent of total<br>350 1980s 2010-14<br>60 United States China Germany Japan<br>300<br>50<br>250<br>40<br>200<br>30<br>150<br>20<br>100<br>10<br>0 50<br>Assets Liabilities Foreign Stock market<br>0<br>claims capitalization<br>Assets Liabilities Total<br><!-- End of picture text --> (C) Share of cross-border financial market transactions denominated in U.S. dollar, 2016 (D) Capital investment by the United States, 2010-15 <!-- Start of picture text --> Percent US$ Euro Yen Other Percent of regional GDP<br>100 25<br>80 20<br>15<br>60<br>10<br>40<br>5<br>20<br>0<br>0 EAP ECA LAC MNA SAR SSA<br>Currency Bank lending Bond issuance<br><!-- End of picture text --> Sources: World Bank, Lane and Milesi-Ferretti (2007), Bank for International Settlements, International Monetary Fund, World Federation of Exchange.

A. Foreign claims are consolidated foreign claims of BIS-reporting banks headquartered in respective countries or locations (data unavailable for China). Assets and liabilities are international investment positions. Average share for 2010-15, except for assets and liabilities (2010-14).

B. Total is the sum of assets and liabilities. Average shares in GDP over the periods of 1980-89 and 2010-14. C. For currency, totals sum to 100 percent because each foreign exchange transaction involves two different currencies. "Euro" includes all legacy currencies of the Euro as well as the European Currency Unit. Data for the center and right bars are for June 2016.

D. Capital investment refers to stocks of foreign direct investment (FDI), portfolio investment, and cross-border bank lending from the United States to EMDE regions. Country coverage varies by capital investment component. As FDI data are not available for 2015, data up to 2014 are used for FDI.
**<u>Table 5: Estimated temperature trend (oC/decade) for 24 stations in Peru</u>**

|**Station**|**Trend**|**t-value**|**P-value**|**# of obs.**|
|---|---|---|---|---|
|AREQUIPA|-0.1865|-7.26|0.000|614|
|AUGUSTO WEBERBAUER|0.3128|14.83|0.000|510|
|CAJAMARCA|0.2751|8.38|0.000|395|
|CAMPO DE MARTE|-0.1169|-1.74|0.082|386|
|CHACHAPOYAS|0.0292|0.76|0.448|352|
|CHICLAYO|0.2894|8.34|0.000|623|
|CUZCO|-0.1340|-5.38|0.000|617|
|GRANJA KCAYRA|0.2812|15.81|0.000|523|
|IQUITOS|-0.1145|-8.75|0.000|703|
|JUANJUI|0.2478|8.42|0.000|397|
|JULIACA|0.3257|8.71|0.000|406|
|LA PAMPILLA|0.7237|22.25|0.000|534|
|LIMA-CALLAO/AEROP.|0.1684|4.07|0.000|543|
|PISCO|0.2788|10.93|0.000|652|
|PIURA|-0.2066|-6.87|0.000|557|
|PUCALLPA|-0.0205|-0.74|0.462|598|
|SAN JUAN|0.3359|12.20|0.000|538|
|TACNA|0.0476|1.70|0.090|578|
|TALARA|0.3765|8.99|0.000|540|
|TARAPOTO|0.2946|10.48|0.000|559|
|TINGO MARIA|0.1988|7.54|0.000|432|
|TRUJILLO|0.2237|5.66|0.000|575|
|TUMBES|0.0096|0.24|0.810|430|
|YURIMAGUAS|0.2564|8.92|0.000|455|

_Source:_ Authors’ estimation based on data from the NCDC’s Monthly Climatic Data for the world and SENAMHI.

Map 1 shows that warming trends and cooling trends seem to be spread randomly across the Peruvian territory. The coast is dominated by warming trends, but interspersed with a cooling trend and a couple of insignificant trends. The mountains have a couple of cooling trends, but also several warming trends. Iquitos, in the rainforest, has the most complete temperature series of all the Peruvian stations, and show cooling, but other rainforest stations show warming. Based on these estimated trends, it is not possible to establish any systematic differences between regions, and our simulations in the next section will therefore be based on an average trend for the whole country.

The average trend over all 24 stations is +0.16oC per decade. If we limit ourselves to the stations that have at least 500 observations (out of 723 possible), the average trend is +0.17oC/decade. If we rely only on the most complete series with over 600 observations the trend is reduced to +0.02oC/decade, which is indistinguishable from no trend. This suggests that the estimated trend is sensitive to the starting and end points of the series. This is because temperature data show strong cyclical patterns, and if a series happens to start at the bottom of a cycle and end at the top of a cycle, it will show a stronger average trend than if it happens to start at the top of a cycle and end at the bottom. No century-long series 11
9 by indigenous groups were opened to development by highways, land settlement and agribusiness schemes (28). Conflicts occurred over rights to indigenous lands and their physioal boundaries, especially as the government undertook regional development projects such as the Trans-Amazon Highway, the Northwest Regional Development Program (Polonoroeste) and the Grande Carajas Program.

In response to these land conflicts, in 1983, the government added an additional step to the land regularization process. It created a special InterMinisterial Work Group, comprised of representatives of the Interior Ministry (where FUNAI was then located) and the Mlinistry of Agrarian Reform and Development (where the National Institute of Colonization and Agrarian Reform was located). Later, representatives of state land agencies and the military's National Security Council were added to the Work Group. This group reviewed all proposals for reserves submitted by FUNAI, made recommendatiorgZ on their delimitation and issued decrees authorizing their demarcation. Essentially, this removed the control over delimitation of indigenous lands from FUNAX and gave more weight to political and military rather than technical considerations in the physical demarcation and regularization of reserves. It also made the process of indigenous land regularization administratively cumbersome and led to long delays (29).

A Brazilian NGO (Centro Ecumenico de Documentacao e Informacao, CEDI) and a group of anthropologists at Brazil's National Museum (Projeto Estudo sobre Terrai Indigenas no Brasil, PETI) have been systematically documenting and analyzing this indigenous land regularization process. In 1990, CEDI/PETI found that there were 526 areas of which 90 were not identified, 80 identified but not interdicted, 67 interdicted, 93 delimited, 136 demarcated and confirmed by Presidential decree, and only 60 fully regularized. While the area of land identified as being Indian occupied totalled 79.1 million hectares, the amount of land actually registered by this date was only 10.9 million hectares, or 13 percent of the total area of indigenous lands (see Table 1, 30).

In those areas where there were internationally-funded regional development projects, the pace of land regularization was quicker than the national norm.
**Table 3. Colombia: Composition of expenditure by** **<u>population quintile (in %)</u>**

|**Quintile**|**Apparel**|**Education**|**Food &**<br>**Beverages**|**Transport &**<br>**Communications**|**Housing**|**Health**|**Other**|**Total**<br>**Expenditure**|
|---|---|---|---|---|---|---|---|---|
|||||**1997**|||||
|**1**|3.31|11.08|49.25|7.59|12.75|10.96|5.06|7.88|
|**2**|3.90|12.29|45.32|8.54|13.91|10.56|5.49|10.79|
|**3**|4.17|11.53|38.65|12.10|15.97|10.60|6.99|14.27|
|**4**|4.30|12.21|32.26|14.71|16.61|10.23|9.69|20.45|
|**5**|3.88|13.08|20.78|19.16|15.74|9.26|18.11|46.63|
|**All**|3.97|12.44|30.57|15.19|15.52|9.92|12.41|100.00|
|||||**2003**|||||
|**1**|4.50|7.27|45.34|8.11|24.48|4.31|6.00|8.44|
|**2**|5.51|7.44|41.31|9.36|26.11|3.96|6.32|10.80|
|**3**|5.04|9.36|36.21|11.47|26.03|4.11|7.78|14.86|
|**4**|4.72|10.38|30.26|12.48|27.12|4.64|10.41|20.76|
|**5**|3.64|9.98|21.83|14.36|26.38|5.51|18.30|45.14|
|**All**|4.35|9.47|29.80|12.47|26.29|4.85|12.77|100.00|

_Note:_ The table reports the composition of expenditure by population quintile of the expenditure distribution corresponding to seven expenditure categories in each of the surveys analyzed.

19