rafmacalaba/data-use-mentions-extended huggingface dataset:
# **B Model calibration and estimation**

This section reports the estimation and calibration of the model. In order for certain model variables to match their empirical counterparts at the steady state, some parameters have been restricted. All structural parameters have been estimated using Bayesian techniques. Here, we report first calibrated parameters and then estimated parameters with their 90% credibility intervals.

## **B.1 Data**

We use time series data for 31 observed variables over 2000Q2-2022Q3, which are reported in Table B1.<sup>22</sup> Twenty-six observables are available at the quarterly frequency.<sup>23</sup> However, energy balances from the International Energy Agency (IEA) and CO2 emissions from the Climate Watch (CAIT) database maintained by the World Resources Institute are annual. We use mixed frequency data in the estimations. The missing quarterly observations of annual (and quarterly) data are obtained by the Kalman (1960) filter. This feature of our estimation technique is highly relevant for emerging market economies where data are available but generally with gaps or at lower frequencies than what the models are set at. We discuss the details of how we deal with missing observations below.

We detrend policy rate, 3-month interest rate, inflation rate, low-skilled wage inflation rate, high-skilled wage inflation rate, real exchange rate, employment rate, RoW interest rate and RoW inflation rate by the double-sided Hodrick and Prescott (1980) filter. For the capacity utilization rate, we use demeaned data. For all other observables, we use demeaned growth rates. We subtract the growth rate of the labor force from volume-related variables such as macro aggregates, emissions, sectoral inputs/outputs and hours worked. We take prices relative to the GDP deflator to match the definition of prices in the model.

Some approximations are needed due to data limitations. The rate of capacity utilization is for manufacturing which, empirically, is the biggest chunk in our core goods sector. We use hours worked in manufacturing
system and were the most important nonbank financial institutions. At the end of 1987, this segment was characterized by a large number of companies with a wide size range. Of the 93 institutions 26 were affiliated with private Thai commercial banks, and a further 12 with the government-owned Krung Thai Bank."<sup>5</sup> These affiliated companies were created to provide specialized services that banks were not allowed to provide (e.g., securities business) or as specialized and innovative providers of highmargin high-risk consumer finance. In contrast to banks, finance companies faced stiff competition not only from other finance companies but also from banks, that once services proved successful at the finance company level started to introduce similar services. Moreover, finance companies faced a credible threat to entry as, in contrast to the banking sector, new institutions entered the market. While finance companies were

> 13 Figures according to Bank of Asia cited in World Bank (1990). The Herfindahl index, a measure commonly used to measure concentration in an industry, also suggests that the Thai banking system was highly concentrated. If the index is adjusted for market size, among 15 developing countries Thai's banking system had the third highest concentration in the late 1980s. World Bank (1990).

- 14 World Bank (1990).

- 15 While a single shareholder of a finance company cannot hold more than 10 percent of total shares legally, in practice, banks have complete control over their affiliated company. The legal restriction on the extent of ownership by one financial institution in another have only resulted in a complex network of multiple and cross ownership between commercial banks and securities and finance companies, and the ownership structures have become highly opaque. World Bank (1990).
# **2. The Governance Database**

We have compiled a large number of governance measures from a variety of sources into a governance database. These different sources produce indicators of a wide range of concepts relating to govemance, using different techniques, and covering different sets of countries. Table 1 presents the sources of governance data included in the database. These include intemational organizations, political and business risk rating agencies, think tanks, and non-governmental organizations. In this section, we provide an overview of some of the key features of these sources. Appendix 1 presents a detailed description of each of these sources.

We classify sources of governance data along two dimensions: (1) according to their unatureW, i.e. the techniques they employ to measure governance; and (2) according to their "coverage", i.e. the extent to which the set of countries covered by each indicator is representative of the world as a whole.

With respec **t** o their "nature", sources are either polls of experts or cross-country surveys of firm managers or citizens in general. Measures based on polls of experts represent consensus ratings agreed upon by a small number of country, sector, and regional experts convened by the reporting organization. Typically, country analysts produce an initial assessment for each country based on publicly-available information and their direct knowledge of the country. For most of the sources we report, these assessments are guided by a checklist of specific issues which analysts take into consideration when providing their initial ratings. For all sources where we were able to obtain this information, we report the checklists underlying the ratings. These initial ratings are then reviewed by a panel of regional and sectoral experts, who determine the final rating for each country. In addition to providing quality control on the initial assessments, the purpose of this final stage is to improve the cross-country comparability of the ratings by ensuring that countries are benchmarked appropriately.

Indicators based on surveys present averages by country of the responses of
|_(Table C.4 continued.)_||
|---|---|
|Involved (Index)|Index of woman’s involvement in seven decisions in the household:|
||1. Decision for her to take up work outside the home for income,<br>2. Decision for her to accept a promotion<sup>84</sup>,|
||3. Decision to purchase large household appliances,|
||4. Decision about large house repairs,|
||5. Decision to purchase clothing and jewelry for herself,|
||6. Decision for her to take a bus to run an errand,|
||7. Decision for her to visit a friend in the neighborhood.|
||For each decision, an ordinal variable was coded such that<br>1=Woman decides alone without needing permission, OR<br>Woman decides alone, but needs permission, OR<br>Woman is involved in joint decision-making, OR Others decide, but<br>need the woman’s permission, OR<br>0=Woman is not involved.<br>I create dummy variables for each ordinal category and decision, and<br>then compute a standardized index following Anderson (2008) and<br>O’Brien(1984).|

> 84For the selected or nominated women who had been offered a promotion in the wave 2 survey, these questions refer to the decision-making about the actual offer instead of the generic question.

66
**Figure 3.4: Amount of Wage Increases for Those Employed Both at Baseline and End-line Surveys (in Bangladeshi taka)**

<!-- Start of picture text --> Treatment Control<br>1,610<br>1,547 1,560<br>1,335<br>1,233<br>646<br>Male Female Total<br><!-- End of picture text --> _Source:_ Original figure for this publication.

_Note:_ The number of observations with wage data available at baseline and end-line is 275 for male and 57 for female. Outliers (above 10,000 taka increase) were excluded since such huge increases are likely due to some drastic circumstantial changes unrelated to certificates and employment. The differences for _total_ and _females_ are statistically significant at the 10 percent and 5 percent levels, respectively. These wage increase data are from those who were in wage employment at both baseline and end-line points. The majority of wage-employed workers at baseline remained wage employed at end-line (75 percent and 70 percent in the treatment and control groups, respectively).

# **Formality of Employment**

Further, the study explored whether the RPL program had any effects on the types of employers. Workers with certificates would be in a better position to apply for jobs at formal companies. The survey results seem to support this expectation (figure 3.5). At the baseline, only 21 percent and 18 percent of the wage employed participants in the treatment group and control group, respectively, were working at formally registered private companies or government entities. The rest was all in companies owned by family, friends, or other individuals: in other words, informal small and medium-sized enterprises. However, at the end-line, a significantly larger share (33 percent) of wage-employed workers in the treatment group work at formally registered private companies and government. The percentage for the control group remains constant at 21 percent at the end-line
Where _g o jt_ and _g e jt_ stands for the growth rate of outputs and exports, respectively; _D j_ is a dummy<sup>_A_</sup> _t_ variable indicating ―dirty‖ industries; is a dummy variable indicating the period post-negotiations; and _X jt_ is a collection of variables that can help to explain a change in composition. Regressions for the Central American region might also include country-specific fixed effects. Table A.1 in annex 1 presents the variables included in the regression.

# _Data_

The exercises focus on four Central American countries (CA4) for which data are available: Costa Rica (CRI), El Salvador (SVL), Honduras (HND), and Nicaragua (NIC). The study covers a 10-year period (1999–2008) and takes 2004 (the beginning of DR-CAFTA negotiations) as the threshold (1999–2003 = before; 2005–08 = after). While the ratification and beginning of implementation took place between 2005 (for the United States) and 2007 (for Costa Rica), we believe that part of the changes in the patterns of production anticipate the actual ratification. Moreover, the choice of the beginning of negotiations as a threshold is convenient because it allows for additional observations in the post-agreement period.

Annual statistics on pollution emissions are constructed using pollution intensities from the Industrial Pollution Projection System (IPPS) of the World Bank. This database provides information on pollution intensity and abatement costs at the industry level<sup>8</sup> . More specifically, the IPPS reports the amount of each of 14 pollutants, in pounds per million dollars of value added, that are generated from each of 459 four-digit Standard Industrial Classification (SIC) codes. The predicted pollution levels are constructed by multiplying the industry’s value added by the industry’s IPPS coefficient. Industries are then classified into dirty and clean industries following Mani and Wheeler (1998). Annex 4 presents a list of industries by category.

While numerous studies use the results
They explain these results by the variation in wages affecting the opportunity cost of conflict, and by the rapacity of armed groups seeking to capture the higher oil rents. Fjelde (2015) and Berman and Couttenier (2015) also find results in line with the opportunity cost channel for labor-intensive agricultural commodities produced in Africa.

Recent empirical evidence suggests that the opportunity cost is not an exclusive transmission channel for labor-intensive goods, nor is rapacity exclusive to capital-intensive goods. Commodities’ lootability and producers’ taxation opportunities can also influence the nature of transmission channels. Rapacity can be a relevant transmission channel for labor-intensive export resources if armed groups can tax producers. In Colombia, Angrist and Kugler (2008) show that the increase in the world price of cocaine (whose production is labor intensive) in the 1990s did increase the quantity produced but had a modest impact on producers’ incomes. Indeed, the wealth created was captured by armed groups and the number of violent events increased in the producing regions. Crost and Felter (2020) also find a higher risk of conflict when the price of bananas (a high-value exported commodity) increases. In the Democratic Republic of Congo, S ́anchez de la Sierra (2020) finds that positive demand shocks on coltan, a labor-intensive mineral, increase violence and suggests that the effect of the opportunity cost channel can be overridden by a taxation-induced rapacity effect.

Income shocks can also have different effects on the risk of conflict when they impact producers or consumers. In Sub-Saharan Africa, McGuirk and Burke (2020) find that higher commodity prices reduce conflict over the control of territory (what they call “factor conflict”) in food-producing areas, and increase conflict over the appropriation of surplus (“output conflict”) in food-consuming areas. Using survey data in Nigeria, Abidoye and Cal`ı (2021) find that higher prices of consumed goods increase the risk of conflict by reducing consumers’ incomes (in line with the opportunity cost channel), while higher oil prices increase the risk of conflict
Rubin, 1987) to account for within and between imputation uncertainty. By adhering to these statistical practices, we aim to provide a robust and accurate evaluation of the imputed cropyield data, ensuring that our findings are reflective of the underlying survey data and the complexities of the imputation process.

# **5 Results**

## **5.1 Modeling lessons**

### **5.1.1 Top predictors**

In this section, we look at the most relevant variables as identified in the random forest algorithm prediction<sup>14</sup> for 50 different simulations. Table 5 shows the top 10 most important variables in the prediction for each crop’s yield levels.<sup>15</sup> SR yield variable is an important predictor for crop yield in levels (with the exception of millet), but not as important as for logs predictions (it is the most important predictor of yield in logs only for cowpea). Geospatial variables are consistently among the top predictors for both levels and logs yields. It is reassuring for the machine learning approach to observe that the combinations of farmer practices and different rainfall variables are among the most important predictors for most of the modeled crops (both for logs and levels yield predictions). Indeed, in a context of smallholder farmers like the one in Mali where most of the crop production relies on rainfall, one would expect that yield would be significantly impacted by the complex interaction of timing of the seeding timing and the amount of rain that fall. This underlies the usefulness of collecting detailed agricultural practices information, as well as being able to link the georeferenced data collected with geo-spatial, when conducting a crop yield prediction exercise.

The relevance of SR yield for improving the quality of yield predictions highlights the importance of collecting farmer-reported information on crop production. Therefore, particular attention should be devoted to enhancing the quality of SR yields, for example by reducing the length of the recall period as noted in (Wollburg et
by hurricanes, with more than 40 episodes in that period. Cuba and The Bahamas were affected by major (Category 3) hurricanes.<sup>13</sup> By contrast, southern areas like the islands of Aruba, Martinique, and Dominica are rarely affected by major hurricanes because they tend to travel away from the equator.

The Dow Jones Island Index ranked Curaçao as the Caribbean island least likely to be hit by hurricanes, followed by Bonaire, Cayman Islands, Barbados, and Aruba. Other Western Caribbean islands are also less likely to be affected by hurricanes than Eastern Caribbean spots like the British Virgin Islands and Puerto Rico. Dominica, Montserrat, and Trinidad and Tobago are among the least affected islands, with fewer than 10 episodes of hurricanes in the 12-year study period. Tropical depressions and storms—which are not hurricanes and therefore classed as Category 0—occur frequently and at any time during the Atlantic Ocean hurricane season. Curiously, the countries most impacted by these are Dominica and Trinidad and Tobago, which presented fewer impacts by hurricanes, and Guadeloupe, with around 13 episodes during 2000–12, followed by Barbados and Bonaire, Sint Eustatius, and Saba, with 12 episodes.<sup>14</sup> Some Caribbean islands are subjected to higher levels of wind speed more frequently than others. Over time, this has resulted in variations in building vulnerability across the region, as each country adopts or develops its own building codes and construction practices, reflecting its historical storm experience and

> 11 For more information about the peak of the hurricane season, see https://www.noaa.gov/stories/peak-of- <u>hurricane-season-why-now</u>

> 12 According to NOAA, a tropical cyclone is a rotating, organized system of clouds and thunderstorms that originates over tropical or subtropical waters and has a closed low-level circulation. Tropical cyclones rotate counterclockwise in the Northern Hemisphere.

> 13 Annex 2 shows the tendency of the damage index by Category 3 hurricanes by Caribbean country
the DSA.

As mentioned, component b) consists of a set of dynamic equations that project future debt and debt service as a function of past debt, its amortization profile, the accrual of interest and the financing gaps faced by the country or the government. However, to make such projections it is needed to assume going forward in what terms (currency, maturity and interest rate) the new debt will be contracted to finance future gaps.

Component c) consists of policy-dependent thresholds for five debt burden indicators, namely: (a) PV of debt-toGDP ratio, (b) PV of debt to exports ratio, (c) PV of debt to government revenues ratio, (d) debt service to government revenues ratio, and (e) debt service to exports ratio. The thresholds are country specific because they depend on the quality of a country’s institutions and policies (i.e., countries with stronger policies and institutions can carry more debt than countries with weaker institutions without falling into distress). The thresholds are for a pre-determined probability of debt distress set at about 15 percent and kept equal for all countries.

The assessment of the sustainability of a country’s debt results from comparing the five indicators above vis-àvis their corresponding thresholds under both a baseline and alternative scenarios. The baseline or most likely scenario is the one projected under the financial programming, while alternative scenarios are built by applying a set of predetermined shocks to the former. Depending on whether a country’s debt burden indicators over the projection horizon breach the thresholds or not, for protracted or short periods, and whether this occurs under the baseline or alternative scenarios, the country is ranked as low, moderate or high risk of debt distress.

3
In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity.

# **4 Displacement and Social Cohesion: Survey Evidence**

To empirically evaluate the relationships between hosting displaced populations and social cohesion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC.<sup>8</sup> Each survey uses a multi-stage cluster sampling strategy capturing all _territoires_<sup>9</sup> in North Kivu, South Kivu and Ituri provinces. The final sampling units are randomly selected adults above the age of 18 to avoid bias toward men and/or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for interview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumeratorinduced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix.

These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly influence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC.

The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021

> 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B.

> 9 _Territoires_ are sub-provincial administrative units. Additional details on the structure of administrative units
The survey collects information on whether all school-aged children in the household attended school in the last 30 days. This high-frequency measure of attendance is in contrast to school enrollment data compiled at a single point in time and allows for attendance to vary over time and in response to the many conflict-related shocks. Additionally, the survey collects a full food security module (food consumption score and reduced coping strategies index), whether the household had access to health care when needed, whether the household hosted displaced individuals, and whether the household itself had been displaced since the start of the conflict. These indicators make up a multidimensional welfare measure tracked by the WFP and the World Bank (e.g., WFP 2019b).15 We further merge the month and district of households with conflict data obtained from the Armed Conflict and Event Data (ACLED), which give the number of conflict incidents and the number of conflict fatalities in the district for the months around the survey.

Figure 3 reports summary statistics of school attendance. The top panel reports the share of households where not all school-aged children attend school by month of the survey for months in which school was in session; and the bottom panel uses the panel aspect of the data and reports the share of household, by number of surveys answered, in which there was at least one survey in which not all school-aged children attended school in the previous month. In any given survey approximately 20 percent of households report not sending all their children to school in the past month. However, there is a significant amount of instability within each household over time given such a tumultuous environment. The second panel of figure 3 demonstrates that for each additional survey to which households respond, there is a significant increase in the share of households that report not sending all children to school in at least one survey. By four surveys and above, over half of respondents report not sending all children to school in at least one survey.

The data further illustrate the dire food security situation for the