rafmacalaba/datause-encoder-data huggingface dataset:
averaging 2.9 percent per year during periods of distress and 5.2 percent per year during normal times.

# **3.2  Modelling the Probability of Debt Distress**

We will model the probability of debt distress using the following probit specification:<sup>7</sup> where yct is an indicator value taking on the value of one for debt distress episodes, and zero for normal times episodes, each beginning in country c at time t; Φ(.) denotes the normal distribution function; Xct denotes a vector of determinants of debt distress; and β is a vector of parameters to be estimated. Our sample consists of an unbalanced and irregularly spaced sample of observations of distress and normal times. In our core specification, we will consider a very parsimonious set of potential determinants of debt distress. In our regressions, we measure each of these variables in the first year of normal times episodes, and in the year prior to the beginning of each distress episode in order to reduce the potential endogeneity bias arising from the fact that our predictors of debt distress might worsen during the distress episode itself.

We consider three groups of explantory variables. The first consists of traditional debt burden indicators. In our core specifications, we will focus on two of these: the present value of debt service obligations relative to contemporaneous exports, and total debt service obligations as a fraction of exports. The debt service data is obtained from

> 7 Since our interest is primarily in the incidence of distress episodes, rather than their precise timing, we rely on this very simple probit specification.  Collins (2003) shows how the timing of currency crises can be modeled explicitly as the first-passage-time of a latent variable to a threshold, of which the simple probit specification here is a special case.  Manasse, Roubini and Schimmelpfenning (2003) suggest that binary recursive tree analysis better captures the nonlinearities in the relationship between debt crises and their determinants, in a sample of middle-income countries.  We have not yet investigated whether similar nonlinearities are important in our sample.

12
-115-

available from Automotive News which shows that car production by Volkswagen accounted for 63.5% of total car production. The entire output of Volkswagen consisted of 4 cylinder cars, and as the other major car manufacturers in Brazil - Ford and General Motors - produced at least some 4 cylinder models - it seems reasonable to put 4 cylinder car output at about 70% of the total for Brazil. our estimated "census" quantities of 4 cylinder and 6/8 cylinder passenger car outrat are presented in the first column of table A.4.

TABLE A.2

|Production<br>ofPassenger Cars in the United States in 1975|
|---|
|Classified by Engine Size|

|-----------------------------------------------------------------<br>__--------<br>Model<br>Units<br>produced|
|---|
|(thousands)|
|Vega<br>194|
|Pinto<br>164|
|Bobcat<br>61|
|Astre<br>56|
|Monza<br>83|
|Mustang<br>94|
|<br>Total 4 cylinder vehicles<br>652|
|Total 4, 6, and 8 cylinder|
|vehicles<br>6,741|

- a) In 1975 Mustangs were produced with 4, 6 and 8 cylinder engines. For the purpose of the table it is assumed that 50% of Mustangs were produced with 4 cylinder engines.

- b) This compares with 9,192,000 units given in the US Annual Survey of Manufacturers for 1975. The reasons for the understatement of total output by Automotive News are not known.

# Source: Automotive News, 1975 Almanac icsue.

Next we calculated unit values for the two major types of passenger car. For Mexico this information was available in the census. For Brazil we could not find any price quotation by model. We assumed therefore that the Mexican price differential was also representative for Brazil. The price ratio for the USA was derived from information on retail prices in Automotive News. The average 1975 retail price for 4 cylinder cars was USS 3,079, and an average of USS 4,079 for the sample of 6 and 8 cylinder cars (see table A.3).
# Annex 2 Translating macro into micro results

For each scenario, Table A2 shows the magnitude of the LINKAGE aggregate variables that are obtained from the general equilibrium macro model and that are later used in each one step of the microsimulation framework. The table shows annual percentage changes for the baseline scenario and, for each of the alternative scenarios, percentage point deviations with respect to the baseline. Household consumption is expected to grow at an annual rate of 3.9 percent shifting demand towards the non-agricultural sector, which tends to be more skill-intensive. Partly driven by semiexogenous assumptions on education projections, labor in the non-agricultural sector grows 1.37 percent for the skilled and 1.14 for the unskilled, on an annual basis. With overall shift in final demand towards services and skilled-intensive manufacturing, the general equilibrium effect increases wages for skilled relative to unskilled workers and prices of food relative to non-food prices; with both effects having regressive consequences in the baseline.

**Table A2. Macro-micro linkage variables, annual % change 2015-2030 and deviations w.r.t baseline**

||**Baseline**|**EU‐IDN**|**FTAAP**|**TPP‐15**|**RCEP**|**TPP‐11**|
|---|---|---|---|---|---|---|
|**Macro‐micro linkage variable**|annual %<br>change|p|ercentage|points w.r.|t baseline||
|(a) Household consumption<br>growth|3.90|0.18|0.10|0.06|0.03|0.00|
|(b) Labor by sector and skilled|||||||
|Unskilled agri|-0.87|-0.05|-0.35|-0.24|0.06|-0.01|
|Skilled agri|-1.04|0.01|-0.38|-0.26|0.06|-0.02|
|Unskilled non-agri|1.14|0.01|0.04|0.03|-0.01|0.00|
|Skilled non-agri|1.37|0.00|0.00|0.00|0.00|0.00|
|(d) Income from labor|||||||
|Unskilled labor|4.87|0.22|0.13|0.10|0.02|-0.01|
|Skilled labor|6.02|0.22|0.14|0.11|0.03|0.00|
|(d) Prices|||||||
|Food, cpi|0.15|0.04|-0.09|-0.05|-0.07|-0.01|
|Non-food, cpi|0.04|0.00|0.02|0.03|0.01|-0.01|

35
was significantly higher than the share of full-time jobs (41 percent). Full-time jobs are significantly more likely to be in wage employment than part-time jobs. Moreover, part-time jobs are more likely to be in agriculture and fishing. Finally, full-time jobs are statistically significantly more likely to be in jobs related to electricity, gas and water supply.

**Table 5: Characteristics of full-time and part-time jobs (percentage)**

||**Full-time**|**Part-time**|**Difference**|
|---|---|---|---|
|Formal|46.4|29.5|16.9***|
|Informal|53.6|70.5|−16.9***|
|Wage|51.1|34.7|16.4***|
|Self-employment|41.4|51.0|−9.5**|
|Agriculture and fishing|3.7|10.3|−6.6***|
|Mining and quarrying|0.0|0.0|0.0|
|Manufacturing|8.0|5.8|2.3|
|Electricity, gas, and water supply|0.6|0.1|0.5**|
|Construction|5.1|8.1|−3.0|
|Commerce|44.4|36.7|7.8*|
|Transportation, storage, and communication|7.0|6.0|0.9|
|Financial, insurance, and real estate|1.4|0.7|0.7|
|Public administration|7.5|6.1|1.4|
|Other services|22.1|26.1|4.0|

_Source_ : Kinshasa Survey 2018. _Note_ : Difference-in-means tests are conducted. ***p < 0.01; **p < 0.05; *p < 0.1.

# **3. Assessment of employment conditions**

## **3.1 Who engages in better/worse jobs?**

Younger workers endure less favorable employment situations, as they are more likely to engage in private sector jobs, informal jobs, and/or part-time jobs. The previous section shows that the youth are facing higher unemployment rates. Even among the employed, young workers appear to work in unfavorable conditions (Table 6). The chance of working in public sector jobs is limited among workers in the younger cohort. Only 6 percent of workers in the cohort of ages 15–24 (and 9 percent of workers of ages 25–34) engaged in public sector jobs in 2018.<sup>6</sup> In addition, the youth (ages 15–24) tend to work in informal jobs (70 percent) and/or work for less than 25 hours per week (45 percent).

Gender gap in employment conditions persists, as demonstrated by higher unemployment rates among highly educated women and the higher share of female workers in informal employment. As observed in the previous section, the unemployment rate of female working-age population with tertiary education is remarkably high. Female workers are also more likely to engage in informal jobs. In 2018, 70 percent female workers engaged in informal jobs, as opposed to 48 percent male workers (Table 6). As such, only a small fraction of female workers are managers or skilled employees.

> 6 Among the public sector employees, the workers of ages 15–24 and 25–34 constitute 5 percent and 19 percent, respectively.

12
The introduction of innovation surveys, similar to those conducted in developed countries<sup>31</sup> , can also be useful in developing countries, although they would need to be adapted to the specific challenges and needs of the countries concerned.

Technology audits examining the quality and potential value of technological assets and competencies of a given country can usefully be implemented. They have to be focused on specific sectors and compare systematically available technological items to international competition, appraise the strengths and weaknesses of the supporting enterprise structure, evaluate the research and educational infrastructure, etc. Such work should be conducted by international groups with an in depth knowledge of the concerned sectors. There is so far little experience in these types of audits, and it deserves being shared and expanded<sup>32</sup> .

Another type of investigation useful for assessing innovation climates, defining policy actions and stimulating change is the auditing of obstacles to innovation as discussed earlier (section 5). This should be designed in ad hoc manner, performed by respected bodies (commissions linked to administrative and judiciary authorities) and conceived as a process by which there is a clear fo **l** ow-on, and not as a one shot event.

# **_Organizations, programs and strategies_**

With regard to the research and technology infrastructure, it is important to assess:

- technological services such as metrology, testing, quality control institutes: the level of activities of technology infrastructure has to be evaluated with respect to demand and the required sophistication

- research structures: data is needed to assess their budgetary structures and judge their openness.  The same applies to university and academic institutions to view how they are positioned in the international science (bibliometrics), or how they relate to the local communities (services, RD contracts, etc.)

- extension services operating in agriculture or manufacturing

- techno-parks which have to be evaluated from the viewpoint of enterprise numbers, employment, growth rate, etc.

For all the above, what matters is to evaluate against what is realistic to expect in function of the level of development. This is why it is important to constitute sets of data which allow relevant benchmarking and comparisons.

It is also important to bear in mind that for innovation programs, tangible results in terms of turnover, export, or jobs created do not appear overnight. Generally five to ten years are necessary for tangible economic results. Innovation programs have therefore to be assessed through intermediary variables such as increase in research and innovation expenses, intensification of linkages between university and industry in various forms, and other variables attesting of a change of behavior favorable to innovation.

> 31 As designed by the Euro pean Union (Innovation trend charts) and the OECD.

> 32 Most of this expertise lies in international consulting firms and remains in the form of ”proprietary technology”.

32
**Table F6: Relationship between food security measures and height-for-age z-scores (Nepal): Expanded Controls Version**

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Heig|ht-for-age-z-sc|ores|||
|Per capita kcal|0.085***<br>(0.028)|||||||
|1-SSR||0.012||||||
|||(0.036)||||||
|1 - SSEXR|||0.038|||||
||||(0.033)|||||
|FCS||||0.094***<br>(0.033)||||
|DDS|||||0.083**|||
||||||(0.034)|||
|PC consumption||||||0.077**<br>(0.032)||
|Food share|||||||-0.065<br>(0.040)|
|Age (in months)|-0.075***|-0.075***|-0.075***|-0.076***|-0.076***|-0.075***|-0.075***|
||(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|(0.008)|
|Age squared|0.001***|0.001***|0.001***|0.001***|0.001***|0.001***|0.001***|
||(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|(0.000)|
|Child is female|0.040|0.042|0.041|0.039|0.038|0.039|0.040|
||(0.054)|(0.054)|(0.054)|(0.054)|(0.054)|(0.054)|(0.054)|
|Household size|-0.016|-0.021*|-0.019*|-0.023**|-0.021*|-0.018|-0.021*|
||(0.011)|(0.012)|(0.011)|(0.011)|(0.011)|(0.011)|(0.011)|
|Share of kids under 5|-0.362|-0.427*|-0.434*|-0.394|-0.380|-0.390|-0.384|
||(0.255)|(0.255)|(0.255)|(0.254)|(0.254)|(0.254)|(0.254)|
|_Characteristics of the HH Head_||||||||
|Literate, or < 5 years of sch.|0.081|0.084|0.082|0.072|0.078|0.080|0.075|
||(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|(0.077)|

61
# **3 Age-Mortality Profiles**

The remainder of this discussion considers the differences across countries in age-specific COVID-19 mortality rates, which in effect net out differences in age structure. Age-specific mortality rates are calculated by dividing deaths attributed to COVID by the underlying population count. They are distinct from a case fatality rate (CFR), which is the number of deaths divided by the number of recognized cases, and also from the infection fatality rate (IFR), which is the number of deaths divided by the number of people infected. The age-specific mortality calculations rely on United Nations population counts by age, which depend on the quality and availability of census and/or population registry data. Afghanistan last had a census in 1979, so its population estimates are likely to be particularly imprecise. Sweden adopted a register-based approach in 1995. All the other countries considered here have recent censuses.

For this analysis, a country-specific adjustment factor is applied to the 80+ mortality rates to account for the fact that the age distribution of the 80+ age group varies across countries. See the Appendix for details. The full set of mortality rates are shown in Figure 3. Because different countries have very different overall COVID-19 mortality, the age-specific mortality rates are at different levels. To compare the shape of the age-mortality curve across countries, normalized COVID-19 mortality rates were calculated for each country by summing up the mortality rates across age groups and then dividing the age-specific rates by this sum, so that they sum to one for any given country.

Normalized age-mortality curves are shown in Figure 4. In interpreting the normalized curves, it is important to recognize that they are only useful for comparing the shapes of the curves across countries. A comparison between countries of the normalized mortality rate for any particular age group is not informative.

The normalized curves make readily apparent the differences in curve shape across countries. Overall, higher income nations show steeper curves, i.e. in those countries death rates are much higher for the oldest groups (70-79 and 80+) than for those in the slightly younger groups (40-49, 50-59, and 60-69). The average age-specific mortality rates for the high-income countries and separately the low- and middle-income countries are shown in Figure 5. The plot of the figure on a log scale shows that the average age-specific mortality rate increases slightly with age in the high-income countries. In the low- and middle-income countries, on average, the mortality rate is flatter between ages 60-69 and 70-79.

6
percent). Among these lottery winners, 62.3 percent took agricultural (lump-sum) loans, 20.5 percent took enterprise loans and the rest took livestock loans. Among the 214 loan applicants that lost the lottery, only 113 borrowed (52.8 percent). Among the reasons cited for changing their mind were time elapsed from request to approval (average time was 2 months), and for losers the fact that the new loan size was not too different from the loan they currently had. This low take-up of larger loans provides the first evidence that only a fraction of clients faced borrowing constraints given the existing loan limits.<sup>21</sup> A follow-up survey was conducted in December 2008,

# **<u>3. Data</u>**

We use two main sources of data, administrative records from NRSP and survey data collected in November 2006 and in December 2008.

# <u>Administrative data</u>

We use NRSP administrative records on every loan taken by borrowers in our sample from November 2006 to November 2009. The data includes the disbursement date, amount, type of loan and repayment performance. <u>Survey data</u> Baseline data collected in November 2006, prior to the business training and loan lottery orientations, included questions about the CO member, the member’s household, the business if they had one, and the CO. The follow-up survey was similar in structure to the baseline and was collected in December 2008, 2 years after the baseline 22 months after the trainings began and about 13 months after the loan orientation meetings.<sup>22</sup> Summary statistics from the baseline survey are presented in Table 1, and variable definitions are provided in Appendix B.

The average age among CO members at baseline is 38 years, with 3.9 years of education. Households have average landholdings of 3.9 acres and average monthly expenditures of Rs 4,740 which amounts to daily per capita expenditure of roughly 3.30 dollars a day (PPP adjusted). About 61 percent of the households in the sample run at least one business. This percentage is significantly higher than the population average in

> 21 According to the model in Appendix C, the distribution of business knowledge may be such that few individuals find it profitable to use technology yH (see also Figure 1).

> 22 At the time of the follow-up, roughly half of the loans taken during the lottery period were still active.

12
observed, the average number of questions asked was one (and that one often asked rudely). The average time spent was less than two minutes and the average number of examinations was .8, or less than one examination for every patient seen. That is, if a person says they have a fever, in most cases the doctor will not feel or otherwise take the temperature of the patient.

In Tanzania, the split between those who work for the public sector and those who work for nongovernmental organizations makes most of the difference. The latter put in much more effort than the former and effort does not vary much with the rates of remuneration. In Paraguay we do not observe private sector doctors, but do find a clear difference in effort across doctors with permanent or temporary contracts.

In all three countries, incentives may be driving these patterns. In India public primary health physicians are paid by salary and are not monitored. India’s public primary health physicians are usually of much higher social and economic status than are their patients, which influences their attitudes towards the patients and contributes to a very small likelihood that complaints about their behavior will be reported at all or acted upon if reported. As a result absenteeism is high; care is lackadaisical and often rude. In India, public hospital-based physicians, on the other hand, put in much greater effort per patient relative to public primary health care physicians—in fact, the best of them look a whole lot like their private counterparts. We speculate that such workers are better monitored. Also, while it is still virtually impossible to fire a public doctor, the urban hospitals that we visited are coveted jobs and this particular set of doctors may be on a career track that makes them sensitive to the evaluations of their senior supervisors Using data from Tanzania, Leonard et al. (2007) develop an index of incentives that measures the degree to which authority over decision-making is decentralized. Facilities that 19
results come from the estimation of equation 2, which we call the dynamic specification of the model. Lastly, the results presented in Table 5 relied on the interaction between the size of the COVID-19 shock and each explanatory variable for the identification of the effect of each on the use of digital payments in 2021, while also controlling for the lagged dependent variable (as of 2017). Since the size of the COVID-19 shock was unexpected, driven by changes in growth expectations in a short six-month period (as explained earlier), our claim is that those estimates are closer to being unbiased than the other two approaches.

In fact, the results presented in Table 6 indicate that the magnitudes of the average effects of the explanatory variables do vary systematically across the three columns. The results in the first column, drawn from the static model, have notably larger effects than the results from the dynamic models presented in columns 2 and 3 in Table 6. The results also change in terms of the rankings of the magnitudes across the explanatory variables between columns 2 and 3, thus suggesting that the average effect is different when estimated with the dynamic models with and without interactions. Furthermore, it is worth noting that the range of the models’ adjusted R- squares are much higher in the dynamic models than in the static models, indicating a better goodness of fit.

Specifically, in Table 6, column 1, the estimated effect of a one standard deviation shock to schooling supposedly leads to an almost 16 percentage point (p.p.) increase in the share of adults using digital payments in 2021. In contrast, in columns 2 and 3 this effect falls substantially to 3.1 p.p. in the dynamic model without interactions and to 2.6 p.p. in the model with the relevant interaction. Similarly, the estimated impact of a one standard deviation shock in internet use prior to the COVID-19 shock seems to increase digital payments by almost 19 p.p. in column 1, but this estimate declines in the dynamic model to 3.9 p.p., and to 3.7 p.p. in the model with the interaction.

21