rafmacalaba/data-use-mentions-extended huggingface dataset:
**We then use the coefficients from Equations (4) and (3) respectively, to estimate the new PCA infrastructure index given the additional BRI-investment and its marginal impact to growth.**<sup>33</sup> As done with the LTGM, we add the marginal impact of growth to WEO projections. Estimates of GMM marginal impact of growth are reported in Annex V.

# V. Assessing Debt Vulnerability in BRI Recipient Countries

This section uses the estimated BRI debt financing defined in Section II and the marginal growth estimates from Section IV to identify BRI-recipient countries that may not be able to maintain a stable debt-to-GDP ratio because of insufficient growth given their current fiscal policy stance. The analysis uses WEO data as published in October 2018. Given limited publicly available information around BRI financing and the comprehensive data base developed for this paper, WEO projections are likely to include some but not complete information related to BRI. The paper therefore computes debt ratios under two extreme assumptions related to the amount of financing, investment and growth that are included in the 5-year projection period 2019-2023 of the WEO:

- Assumption 1 ( _A1_ ): under this assumption WEO projections for the period 2019-2023 fully include all information related to BRI financing in the projected PPG debt, nominal GDP and primary balance;

- Assumption 2 ( _A2_ ): under this assumption WEO projections do not include any information related to BRI financing in the projections of PPG debt, nominal GDP and Primary Balance.

We estimate for each country a growth and a fiscal gap, under both assumptions. The _growth gap_ is defined as the difference on the debt-to-GDP ratio between excluding and including the impact of BRI investment. The growth gap indicates the countries where BRI investments are projected to generate enough growth to repay themselves and the ones where this is not the case. The _fiscal gap_ is defined as the difference between the average past primary balance and the primary balance needed to stabilize the PPG debt-to-GDP ratio at 2016, factoring in the additional debt and growth resulting from BRI investments
, is not reported for all districts. Data for nearly 40 percent of the districts are available for less than six out of ten years. To improve the signal-to-noise ratio of agricultural wages, I convert the monthly AWI data to annual frequency by taking 12-month averages in the Indian fiscal year format to match the program expenditure data. I further improve the quality of the wage data by focusing on a balanced panel of 134 districts from 12 major Indian states for ten years. For the analysis of agricultural wages, I have a total of 1340 observations.

Since I lose districts with incomplete wage data, a possible objection can be the correlation of a district’s backwardness with the unavailability of wage data. If this is true, then restricting the analysis to 134 districts should result in the proportion of early phase NREGA districts being substantially lower in this sub-sample than the aggregate sample. Encouragingly, this is not the case. The percentage of phase-I and phase-II districts in the full sample – approximately 37 percent and 58 percent, respectively – closely matches the sub-sample proportion of 42 percent and 63 percent, respectively.

Both the wage and expenditure data are deflated to 2001 prices using the labor bureau’s consumer price index for rural laborers (CPI-RL). Unless otherwise mentioned, all variables in the empirical analysis are in real, percapita terms.

## National Sample Survey Data

_Casual earnings and employment outcomes_ : An alternative data used in the literature is from the nationally representative employment and unemployment surveys carried out by the National Sample Survey Organization (NSSO). The nationally representative rounds with a large sample size usually happen every three to five years. This study also employs NSSO data to compare the impact of NREGA on daily casual earnings with and without controlling for SGRY. Since both programs apply only to persons living in rural areas, I restrict the analysis to persons in rural areas and persons aged between 18 and 60. The sample includes 440 districts within the 17 largest states of India. I use data from Jul 2004 to Jun 2005
**Table 5: Coverage of Published Data on Location, Accommodation and Demographics 2015**

||**Population end-**<br>**2015 (millions)**|**Urban or**<br>**Rural**<br>**Location**|**Accommodatio**<br>**n**|**Age**|**Sex**|
|---|---|---|---|---|---|
|**Refugees and people in**<br>**refugee-like situations**|16.1|85%|83%|58%|68%|
|**Other people of concern to**<br>**UNHCR**|47.8 (including<br>37.5 IDPs)|70%|47%|25%|39%|
|**UNHCR’s total population of**<br>**concern**|63.9<sup>94</sup>|73%|56%|33%|46%|
|**IDPs monitored by IDMC but**<br>**not included in UNHCR's**<br>**data**|3.3|0%|0%|0%|0%|

_Source_ : UNHCR Global Trends 2015 _Note_ : Other people of concern to UNHCR include asylum-seekers, IDPs and people in IDP-like situations protected or assisted by UNHCR, stateless persons, and ‘other’.

# **Overall robustness of current data**

The robustness of data is difficult to estimate. A review of data collection and compilation methodologies shows broad variations in terms of the accuracy and reliability of the global estimates of forced displacement that are widely used. Headline figures on forced displacement are significant in shaping public opinion and are
2000 to 1.12 million in 2011.

Many ninis might take part in criminal activities that do enormous harm not only to themselves but to society at large. Thus the objective of this study is to contribute to the growing literature explaining the causes and contexts of the escalation of violence in Mexico, with a focus on the potential linkages between ninis and homicide rates. The study’s empirical analysis relies on annual state-level panel data covering the period 1995-2013. This allows us to test whether the relationship between ninis and violence is different before and after the start of the war on drugs (2007-2013), and whether it differs between the states 3
labor market. To derive the degree of monopsony power empirically, notice that equation (7) can be expressed in terms of elasticity of output with respect to labor as from which it follows that the degree of monopsony power can be measured directly from the production data as Accordingly, the joint parameter of market imperfection can have three cases depending on the labor market setting. That is, The main elements needed to compute markups, the joint parameter of market imperfection, and degree of monopsony power are _α_<sup>_v_</sup> , and _θ_<sup>_v_</sup> of the production inputs. While 10
, each view has an element of truth.

# 2.2 Data Collection

Details of the sampling frame and data collection exercise are described in Das and Hammer (2005). Highlights relevant for this paper are as follows. In 2001, the Institute of Socio-Economic Research on Democracy and Development (ISERDD) began a longitudinal study of households in seven neighborhoods of Delhi. Three of the neighborhoods were poor, two middle income and two relatively wealthy. Over two years, 300 households were interviewed once a week for 35 weeks and once a month for another 8 months. Information was collected on health and health-care seeking behavior and demographic, income and consumption modules were administered twice in the two years. Less than 4 percent of the households approached refused to participate and the demographic, educational and expenditure pro fi les of the households in the study is very similar to representative samples of Delhi residents in the National Sample Survey and the National Family Health Survey (Das and Sánchez 2004).

These data on health care were used to construct a universe of relevant providers in the seven neighborhoods of the ISERDD study in two steps. First, all providers that anyone in the 300 households visited during the fi rst year of the survey was included. Second, research teams were sent on a reconnaissance visit in which

> 6 Following the Alma-Ata declaration on Primary Health Care in 1978, the Government of India established training courses for providers drawing on different traditions in Indian medicine. These were Bachelors of Ayurvedic Medicine and Surgery (BAMS), Bachelors of Integrated Medicine and Surgery (BIMS) and Bachelors of Unani Medicine and Surgery (BUMS). These providers were to provide basic care and refer serious cases to facilities with MBBS doctors. Further down the pecking order are the Registered Medical Practitioners (paramedics with some rudimentary training). However, given the limited regulatory power of government authorities, some providers have no formal training at all.

4
dummy indicator to measure serious mental illness, defined as mental health score being greater than 19 (Chen et al., 2017). The results using the new measure remain qualitatively similar (Appendix A, Table A10).

Eighth, we employ as a proxy for mental distress an indicator of PTSD available from the BNLA, which is an eight-item self-reported screening measure derived from the Harvard Trauma Questionnaire. Recent evidence suggests that PTSD symptoms are common among Syrian refugees in Norway (Aarethun et al., 2021). Participants rate PTSD symptoms on a fourpoint Likert scale (from 1 for “not at all” to four for “most of the time”), reporting how much the symptoms bothered them in the past week. We convert the answers into a dummy indicator that equals one if an individual is at risk of PTSD. The results remain qualitatively consistent (Appendix A, Table A11). The size of the coefficients in Table A11 also suggests that PTSD symptoms are strongly relevant for refugee respondents, and they have negative impacts on refugee employment outcomes.

Ninth, in the main analysis, we estimate the impact of worse mental health on employment status for refugees who participate in the labor force. To address potential selfselection into LFP and subsequent employment status, we employ the alternative two-part fractional response model (Roodman, 2011). While this approach allows us to model the LFP decision and employment status separately, it does not address the endogeneity of mental health. But our estimates remain qualitatively similar (Appendix A, Table A12).

Similarly, one may argue that mental health and labor market are measured in the same wave, which might lead to the problem of reverse causality (i.e., labor market exposure affects mental health). This is less likely to be the case in our study as we measure mental health in the last four weeks prior to the interview date, while labor outcomes are measured in the last seven days. To provide further support to our main findings, we regress labor
the face of it—seems to suggest this is not typically the case. These critics often point to the ways in which people with different characteristics seem to benefit differently from economic growth, those with assets—including

> 12 This is equal to _G_ 1 minus the concentration index for _y_ 1, people being ranked by _y_ 0 in the latter and by _y_ 1 in the former. 13 Applying his decomposition to data for Belgium, Germany and the USA, Van Kerm finds considerable evidence of mobility broadly defined, especially in the US, and finds that the exchange component is by far the most important driver of mobility in all three countries.
The extent to which the methodology used here results in a reduction of the gap between fixed asset investment and gross fixed capital formation can be judged by comparing the ratio of the sectoral estimates of GFCF to aggregate GFCF and the ratio of total FAI to aggregate GFCF (Figure A1).

# **Figure A1 Sectoral GFCF and Total FAI as a ratio to official Gross Fixed Capital Formation, by period**

Solid line: estimated sectoral GFCF/aggregate GFCF Dotted line: Fixed asset investment/aggregate GFCF <!-- Start of picture text --> 1953 to 1980<br>2.50<br>2.00<br>1.50<br>1.00<br>0.50<br>0.00<br>1981 to 2002<br>1.80<br>1.60<br>1.40<br>1.20<br>1.00<br>0.80<br>0.60<br>0.40<br>0.20<br>0.00<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001<br>2003 to 2015<br>1.40<br>1.30<br>1.20<br>1.10<br>1.00<br>0.90<br>0.80<br>0.70<br>0.60<br>0.50<br>0.40<br>2003 2005 2007 2009 2011 2013 2015<br><!-- End of picture text --> _Source:_ Author estimates using NBS data 32
inequalities may be more difficult.

# **5.2 Impacts on GPAs**

# _Short-term GPAs_

Did improvements in middle-school students’ general S/E skills translate into improvements in students ́ academic achievement? Table 4b Panel B shows impacts on short-run GPAs, calculated immediately after the intervention in the fourth quarter across math, English, and first language. Note that the sample size is larger here because GPAs are obtained from official, administrative records and are therefore not subject to missing information.

Overall, we find little evidence of impacts of our intervention on short-term GPAs. If anything, there is an improvement in short-term GPAs of about 2% SD on average, which is only marginally significant at the 10% level in the student self-learning treatment (with a similar point estimate and standard error in the teacher-delivered treatment). Likewise, there is little evidence for differential impacts by student sub-group: impacts are similar between gender, sixth and seventh-grade students, and students in different terciles of the pre-treatment GPA distribution. Interestingly though, we do find a larger impact on Roma students in the teacher-delivered treatment. This is interesting because Roma students are the sub-group of students for whom we find the strongest impacts in terms of our S/E skills index (about 42% SD, cf. Table 4b Panel A). There is thus consistency between the finding for our S/E Skills index and the finding for GPAs of Roma students. The effect size for GPAs, however, is rather small, at about 6% SD.

Our estimates on GPAs are likely downward biased, for several reasons: first, as noted previously, there could be a lag between students taking exams or tests and teachers reporting their results. Although teachers are encouraged to report results to official records within two weeks after taking the assessment, we cannot exclude the case that some results from tests taken during the pre-treatment period (i.e. before the intervention had finished or even started) are reported only during the post-treatment period, deflating post-treatment
take several months to adopt it. A major reason is that the owners were severely time constrained, working an average of 68 hours per week already. There was also evidence of procrastination in that some owners would defer on taking quick decisions. This matches up with the evidence on procrastination in other contexts, for example African farmers investing in fertilizer (Duflo, Kremer and Robinson, 2009).

Finally, somewhat surprisingly, we did not find evidence for the direct impact of capital constraints, which are a significant obstacle to the expansion of micro-enterprises (e.g. De Mel et al., 2008). Our evidence suggested that these large firms were not cash-constrained, at least for tangible investments. We collected data on all the investments for our 17 firms over the period August 2008 until August 2010 and found the firms invested a mean (median) of $880,000 ($140,000). For example, several of the firms were adding machines or opening new factories, apparently often financed by bank loans. Certainly, this scale of investment suggests that investment on the scale of $2000 (the first-year costs of these management changes, ignoring the consultants’ fees) is unlikely to be directly impeded by financial constraints.

Of course financial constraints could impede hiring international consultants. The market cost of our free consulting would be at least $250,000, and as an intangible investment it would be difficult to collateralize. Hence, while financial constraints do not appear to directly block the implantation of better management practices, they may hinder firms’ ability to improve their management using external consultants. On the other hand, our estimates of the return on hiring consultants to improve management practices suggest profitability in just over one year.

# **_VII.C. How do badly managed firms survive?_**

We have shown that management matters, with improvements in management practices improving plant-level outcomes. One response from economists might then be to argue that poor management can at most be a short-run problem
12

|Argentina<br>N|<br>16<br>ta72|34|||100|
|---|---|---|---|---|---|
|Brazil|21|43|57|91|100|
|Chile|14|28|51|74|100|
|Colombia|16|36|67|81|100|
|Ecuar &|20|37|83|98|100|
|Mexico|15|36|57|94|100|
|Venezuela|29|50|85|100|100|

# D. Plenty Out of Sample Testing: (Brazil 99 Ecuador 99 Colombia 98 Mexico 94)

Three crises (Brazil, Ecuador and Colombia) have taken place in our group of countries since we finished the first draft of our paper (July,1998). In this section we show how :he models predicted the vulnerabilities in each of them. Additionally, we'll see how ilhis methodology would have predicted the Tequila crisis. The methodology is based o:l estimating an ARIMA model for the IMVEQ, using a five year rolling sample, ; nd beginning two years prior to each crisis. A signal will be generated in each of the 24 months of the window prior to the crisis.

In the case of the Brazilian crisis (Jan/99), we estimated an ARIMA model for Ihhe IMVEQ beginning in January 97, with monthly information since Jan 92. For Febru:Lrv 97, the same procedure is repeated with information since February 92, and so on. 1 he signal is flashed when the six-month moving average exceeds zero. Graph I summariz es the evolution of the leading indicator derived from the Arima-residuals model, show:ing that 10 signals were issued in the 24-month window prior to the crisis. Graph 2 is -he leading indicator derived from the chartist model, showing that 13 signals were issuLe(d prior
) is the total value of bilateral exports of sub-region _i_ to country _j_ at time _t_ .<sup>8</sup> Sub-regional gross value added ( _Regional GVAit_ ) is obtained from Turkstat and the trade-partner country gross value-added is from the UN Statistics Division.<sup>9</sup> All three variables are measured in constant 2005 100,000 USD. Bilateral distance between the Turkish sub-region and trade partner country ( _Distanceij_ ) is measured as the Euclidean distance between the most populous province of

> 6 We use seven geographical aggregates, defined according to the World Development Indicators. Namely, they are East Asia and Pacific, Europe and Central Asia, Latin America and Caribbean, Middle East and North Africa, North America, South Asia, Sub-Saharan Africa.

> 7The term NUTS stands for “Nomenclature of Units for Territorial Statistic” and is defined in the 2002 agreement between the Turkish authorities and Eurostat. There are three NUTS level: NUTS 1 identifies 12 regions, NUTS 2 26 sub-regions, and NUTS 3 81 provinces. The classification is not a one-to-one mapping of the administrative territorial subdivision of Turkey and is used mainly for statistical purposes and comparability with other EU countries.

> 8The analysis does not cover Belgium and the Islamic Republic of Iran as these countries are not available in the data from Celbis, Nijkamp, and Poot (2014).

> 9The sub-regional GVA data is compiled in terms of 1998 national currency. To correct for inflation and exchange rate fluctuations, Celbis, Nijkamp, and Poot (2014) rely on the external currency deflator and the USD conversion rate provided by the Turkey Regional Ministry of Development.

6
paid to herself instead of her husband (ignoring multiple<br>switching).|
|Above<br>median<br>initial<br>decision-making|Dummy variable indicating whether respondent’s decision-making<br>index is above the median at factory baseline. Only available for the<br>promotion sample.|
|Starts earnings more|Dummy variable indicating that the respondent earns more than the<br>spouse during household surveys, but did not earn more than the<br>spouse at factorybaseline. Onlyavailable for thepromotion sample.|
|Earns more|Dummy variable indicating that the respondent earns more than the<br>spouse duringhousehold surveys.|
|placebo|Placebo treatment coded as a dummy variable indicating whether a<br>respondent in the selected group was in the top half of the ranking,<br>equal to zero if in the bottom half. Only available for the selected<br>group.|

71
. The expansion of these analyses with such data will be an important area for deepening the findings of this work.

Even in the absence of such analyses, some evidence already exists that shows the detrimental impacts of environmental risks on poverty and household welfare. An accompanying study, for example, shows that actual variation in rainfall and temperature conditions is related to significant consumption and income effects also for poor people (Narloch, 2016). In addition, other work has shown the negative welfare and poverty impacts of flood and drought events (Arouri et al., 2015; Bui et al., 2014; Thomas et al., 2010). Moreover, poor people could be affected by environmental risks in many ways that go beyond income and consumption effects, such as detrimental health impacts or a decline in the quality of life due to poor environmental conditions. Such impacts cannot be measured with the existing household data.

In light of growing pressures due to population growth, economic development and climate change, environmental risks need to be considered in poverty reduction policies. Green growth actions that allow economic development without excessively harmful environmental impacts, for example through increased resource efficiency, will help to reduce the environmental burden on poor people. Moreover, ecosystem‐based adaptation could strengthen ecosystem resilience and reduce environmental risks, while improving the livelihoods of people depending on these ecosystems. Carefully designed land‐use planning policies that encourage resettlements and avoid new settlements in high risk areas are another key strategy to reduce exposure to environmental risks, but should be paired with investments in livelihood support and improved mobility.

18
) the collapse of the property market with its repercussion on the construction sector and the slump in the stock market; (ii) a slowdown in the economy and high and rising interest rates that adversely affected both the consumer portfolio and interest rates margins since finance companies were highly exposed to interest rate risk (see above); and (iii) deficient risk and liquidity management practices coupled with poor prudential regulation, deficient supervision and weak enforcement.

**Figure 12. Finance companies NPLs** (Baht million)

<!-- Start of picture text --> 80,000.00<br>70,000.00<br>60,000.00 _1__hr_<br>50,000.00 l Trading<br>40,000.00 Servi'ces<br>E- Consumer loans<br>30,000.00 U Manufacturinig<br>20,000.00 U Construction<br>10,000.00<br>0.00<br>1993 1994 1995 1996<br><!-- End of picture text -->

- 50 Data for finance companies reported refer to 30 largest companies in the market. Data cited after Wotld Bank, The Finance Company Sector, World Bank Mimeo, 1998.