rafmacalaba/datause-encoder-data huggingface dataset:
Moody’s and S&P models is used when both actual ratings are unavailable. The model seems to perform reasonably well in terms of emulating the actual rating of countries rated by Fitch.<sup>16</sup>

**Figure 7: Comparing actual Fitch ratings at end-2006 with predicted ratings from other two agencies**

<!-- Start of picture text --> 20 Numeric rating Actual Predicted<br>18<br>16<br>14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>ArmeniaCameroonGambia Ghana IranLesothoMacedonia MalawiNamibia NigeriaSerb. & Mont.Sri LankaUganda<br><!-- End of picture text --> _Source:_ S&P, Moody’s, Fitch Ratings, and authors’ calculations. A higher number indicates higher risk and lower letter rating. See table 2 for conversion from letter to numeric scale.

# Modeling new ratings and the very first rating

# Specification 3: Dated pooled model to test whether a first time rating by an agency systematically differs from its subsequent ratings

We carry out two final regressions for sovereign ratings. Our interest is in what a new rating for a country would look like if there is no existing rating for all three agencies, and when there is an existing rating by one agency. To test whether the sovereign rating varies systematically between a new rating by an agency and a subsequent rating by the same agency, we use a regression model similar to the dated model specification for the entire pooled sample of all available ratings, with a dummy for the very first rating by the agency as an additional explanatory variable. We use the International Country Risk Guide (ICRG) composite index instead of the Rule of Law as an explanatory variable since the latter was not available for the period prior to 1996. The results are reported in table 5.

The explanatory variables have the expected signs and are statistically significant. The coefficient for the very first rating is negative in all three cases, but significant only the case of Fitch. The first rating tends to be somewhat more optimistic than subsequent ratings, perhaps because countries choose to get rated when they are doing relatively better. It may also imply that rating agencies oblige new customers with a better first rating.

> 16 Across-agency comparisons for S&P and Moody’s ratings are qualitatively similar.

19
# **4. Conclusion**

In the textbook model of the household, a dollar in the household budget is always a dollar, no matter where it comes from, or who within the household receives it. There are a variety of reasons why this simple model may not hold. One possibility is that the gender of recipient of cash transfers matters because women have different preferences from men, and household resources are not pooled completely.

There are a number of papers that test the unitary model of the household by comparing how resources controlled by men and women are used (for example, Thomas 1990 and 1994; Lundberg, Pollack, and Wales 1997). A key concern in this literature, and one which is discussed in these papers, is the possible endogeneity of income controlled by men and women. In this paper, we use random assignment of a cash transfer program to women in rural areas in Ecuador to analyze changes in the food Engel curve. This experimental design limits concerns with endogeneity.

The basic finding of the paper is that households that were randomly assigned to receive cash transfers have significantly higher food shares after the program. These program effects on the food share are only found among mixed-adult households, and not among female-only households. When the sample is limited to mixed-adult households, significant program effects are only found among households where the bargaining capacity of women was initially likely to be weaker. Taken together, these results are hard to reconcile with the unitary model of the household, in which all resources are pooled. Rather, they suggest that the bargaining position of women improved after they received transfers, and that women were then better able to influence the pattern of expenditures.

The food share has been used as a measure of household welfare ever since Engel’s own pioneering study showed that the share of food in the budget decreases as income rises (Engel 1857). “Engel’s Law” is an empirical regularity and, as Figure 1 shows, it holds in Ecuador as well. There are, however, numerous reasons why the food share is a poor welfare measure (see, in particular, the discussion in Deaton 1997). In Ecuador, households that received cash transfers, and who presumably were better off as a result, have a _higher_ food share, which clearly raises doubts about the usefulness of the food share as a welfare measure. Moreover, the food Engel curves in rural Ecuador show significant downward drift. This raises questions about a variety of applications which rely on a stable food share as a critical assumption. For example, Costa (2001) uses data on the food and recreation shares in the United States over time to correct for bias in the consumer price index. Obviously, rural Ecuador and the United States are likely to differ in a large number of ways, but the results in this (and other recent) papers that show a shifting food Engel curve point to the risks inherent in assuming that the Engel curve is stable.

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Republic and Bulgaria display the lowest productivity in hotels and restaurants. Interestingly, two important tourist destinations, the Czech Republic and Croatia, have a less efficient hotels and restaurants sub-sector than Romania or Slovenia. In financial intermediation, the Slovak Republic exhibits the highest while Poland exhibits the lowest productivity.<sup>18</sup> Finally, in real estate, renting, and business activities, Hungary exhibits the highest while Ukraine exhibits the lowest productivity.

Figure 9 shows KLEMS labor productivity in EU-8 countries averaged in 19972000 and in 2001-2004. Again, the evidence suggests a different ‘comparative advantage’ for different countries. While the Czech Republic exhibits the highest productivity in wholesale trade, Poland is clearly the most productive in retail trade and in inland transport. In post and telecommunications and in financial intermediation, Slovenia displays the highest productivity of all EU-8 countries while Hungary exhibits the highest productivity in real estate activities. Finally, Estonia is relatively more productive in computer and related activities, in R&D, and in legal, technical, and advertising activities. Figure 9 also benchmarks for each sub-sector average KLEMS labor productivity in EU-8 countries relative to that in EU-15 countries. Interestingly, while on average EU-15 countries exhibit substantially higher productivity than ECA countries in services, that is not the case for all sub-sectors. A similar finding is obtained for manufacturing by Anos-Casero (2007). Estonia and Hungary exhibit higher productivity than EU-15 countries in computer and related activities and in legal, technical, and advertising activities. Estonia also exhibits higher productivity than EU-15 countries in R&D. In all other sub-sectors listed in Appendix Table A.1, EU-15 countries exhibit substantially higher productivity than EU-8 countries.<sup>19</sup> In sum, Figures 8 and 9 show important differences in productivity levels of services sub-sectors within the ECA region. In general, a productivity divide separates EU-5 countries and Croatia which tend to be more efficient in all sub-sectors from South Eastern European countries and Ukraine, based on the WIIW database. Based on the

> 18 This finding suggests that financial intermediation in Poland is lagging not only relative to other subsectors as is mentioned earlier, but also internationally within the ECA region. 19 The only exception is air transport for which the Czech Republic is more productive than EU-15 countries on average in 2001-2004.

19
occupation, and a formal job as the main occupation during the past 12 months. These women’s average age hovers around 32 and ranges from 17 to 58.

The working rate of women in 2016 was 93%. For comparison, Table A.1 in the Appendix also reports men’s employment rate, which is 6 percentage points higher with almost all men (99%) working. This male-female gap in the employment rate, however, was stable during the period. The 2014 and 2016 VHLSS include questions concerning the reasons for not working. These are very different for men and women. Figure A.1 in the Appendix presents the distribution of women and men by reasons for not working. In 2016, 90% of women did not work outside the home because they were occupied with housework. These activities include caring for one’s own home, caring for grandchildren so the daughter can work, caring for elderly women, or any additional range of tasks. Compared with women, the proportion of men not working outside the home for the same reason was much lower at 23% in 2016. For men, the main reasons for not working were retirement, sickness and disability. The unemployment rate (the unemployed comprise those who were unable to find a job during the past year) was very low, less than 1% in 2016.

Among working people, men work more hours (209 hours) in a month than women (190) in 2016. The gender gap in the number of people in a wage-earning job is larger, though this gap decreased over time. In 2016, around half of all men (51%) had a wage-earning job, while the corresponding figure was less than half for women (38%). This means that more than half of all women (55%) were self-employed. We classify self-employed work into farm and non-farm employment. In 2016, 18% of women had non-farm employment and 37% of women worked on farms.

We also examine the quality of employment, which we categorize into skilled employment and formal employment. While men were more likely to have a skilled job than women, women had a higher rate of formal employment than men, perhaps because women prefer a stable job with social insurance.<sup>8</sup> Women are also more likely to work in the textile,

> 8 Formal employment is defined as a job with social insurance. The social contribution or payroll tax in Vietnam is equal to 34% of the monthly salary, of which 23.5% is paid by employers and 10.5% by workers. Workers making a social insurance contribution are eligible for health insurance, employment subsidies and pensions (on retirement).

8
all three moments of NSS log consumption using the observed data and adopt our method of moments estimator to obtain estimates of _a_ , _b_ , and _σ_<sup>2</sup> for that year -- and subsequently assume that all three parameters remain constant over time until the next NSS household consumption survey becomes available (which is when the first three moments derived from observed consumption data can be updated). Alternatively, one could continue to adopt the version of Approach 2 we are currently using, namely estimate moments that are found to be comparatively stable over time from observed household (log) consumption data and estimate moments that are found to be less stable from up-to-date imputed consumption data. The latter (and currently adopted) approach may be preferred when the CPHS sample is subjected to notable changes that may significant introduce changes in moments that are not accounted for by reweighting. See Appendix 3.3 for a further discussion on the changes made to the CPHS sample (most notably during the third wave of 2017) and its implication for our method of estimation.

On the choice between Approaches 1 and 2, it should be noted that the two approaches rely on their own set of assumptions. The validity of these assumptions will be context-specific and may vary over time. Approach 1 assumes that the relationship between NSS-consumption and household characteristics such as demographics, education, and employment is stable over time, while Approach 2 assumes that the relationship between NSS-consumption and CPHS-consumption is stable over time. Where possible one should implement both approaches (thereby considering different assumptions) and inspect robustness. Appendix 3.2 compares the relative ranking of households based on their observed CPHS consumption and imputed consumption based on approach 1 of section 4.1 and approach 2 of section 4.2.

# **5 Results**

## **5.1 Main estimates of poverty and inequality**

Both approaches yield qualitatively similar levels and trends in headcount poverty estimated at the $1.90 line: poverty is about 12.3 percentage points lower in 2019 than 2011 (see Figure 15). Estimates of poverty obtained using observed CPHS consumption data are seen to be up to 3.5 percentage points higher when compared to estimates obtained using NSS-compatible measures of consumption. By the same token, our estimates of poverty are notably higher than previous estimates obtained by the World Bank’s Povcalnet database and other scholars, see e.g. Edochie, et al. (2022); Newhouse and Vyas (2019) and Gupta, Malani and Woda (2021b). Estimates from World Bank’s 38
disasters (floods, earthquakes), economic recession, war, and so on. The analysis is applied to the Democratic Republic of Congo with a special focus on episodes of violent conflict in that country. Two main data sources are used. The paper relies on the Polity IV data set measuring the major episodes of political violence (MEPVs) in the country. The MEPV is used as a proxy to construct an index that captures the intensity of adverse shocks that individuals experienced in their early adulthood. To maintain consistency with earlier studies on inequality of opportunity, the socioeconomic characteristics of individuals are included using the 2012/13 wave of the 1-2-3 Household Survey.

Estimates from both parametric and nonparametric approaches offer important insights. The first takeaway is that intense exposure to adverse shocks, such as MEPVs, in young adulthood is negatively correlated with future labor earnings. These results are robust after controlling for other factors, such as social background, educational attainment, sector of occupation, and current area of residence. Second, experiencing violent conflicts in early adulthood contributes between 2.5 percent and 3.5 percent of the inequality in earnings in the Democratic Republic of Congo, and this is mostly prevalent among men in urban areas. These results expand the knowledge of other relevant circumstance variables that should be factored in during the discussion on inequality of opportunity. The findings also invite greater attention to adverse shocks that individuals experience during early adulthood, especially in designing social policies aimed at reducing inequality and poverty.

The reminder of the paper proceeds as follows. Section II sets out the analytical framework of the measurement of the intensity of adverse shocks experienced during young adulthood in an environment of recurrent MEPVs. Section III describes the data and estimation techniques. The estimation results are discussed in section IV, and section V concludes.

# **II. Shocks in early adulthood and inequality in labor earnings: An analytical framework**

The impressionable years hypothesis motivates the analysis on how shocks experienced in early adulthood affect inequality in earnings. In general, according to the hypothesis, adverse events, such as recession, civil wars, and natural disasters, can have long-lasting psychological impacts on affected populations, and the impacts depend on the development stage of the individuals affected (Lundberg and Wuermli 2012). Two sets of findings have emerged through this analysis. First, the emphasis is on the long-term impact of shock occurrences at specific points in time. Second, a focus of interest is the developmental stage of the individuals affected by the shocks.

On the former, Burgess et al. (2003), Kahn (2010), Oreopoulos, Vonwachter, and Heisz (2012), and Stevens (2008), for instance, indicate that entering the labor force during a recession can result in long-term unemployment and lower lifetime earnings. Oyer (2006, 2008) also finds that adverse 5
percent, and I also drop a number of spells where the average annual growth rate in the survey mean is implausibly large, i.e. more than 15 percent in absolute value. This reduces the first dataset to 128 spells covering 58 countries with an average length of 3.5 years, and the second dataset to 42 spells with an average length of 9.6 years.

In order to construct the poverty measures and their decompositions discussed in the previous section, I need the full Lorenz curve and not just the 10 points provided in the Ravallion-Chen data. To obtain this, I assume that the Lorenz curve has the following functional form: This particular parameterization is a member of a family of ordered Lorenz curves proposed by Sarabia, Castillo, and Slottje (1999). I estimate the parameters of this Lorenz curve for each survey using an algorithm suggested by the same authors. This involves selecting all possible combinations of three points on the Lorenz curve, and then for each combination finding values of α, β, and γ such that the Lorenz curve passes through these three points. The final estimates of α, β, and γ are then found by averaging across all the resulting estimates of these parameters, discarding those for which the parameter restrictions indicated in Equation (4) that are required for the Lorenz curve to have positive first and second derivatives do not hold. I then obtain the quantile function by analytically differentiating the Lorenz curve and multiplying by average income. Using this, I can immediately construct ηt(p) for each poverty measure of interest, as well as the growth incidence curve over the observed discrete interval, 13
Skidmore, M. and H. Toya, 2002. Do natural disasters promote long-run growth?, Economic Inquiry 40, 664-687.

The Economic Times, 2011. Toyota to cut India output by 70% due to parts shortage, April 23, 2011.

The New York Times, 2011a. Automakers Adjust Production to Quake-Related Shortages. April 9, 2011.

The New York Times, 2011b. The Assembly Line Is Rolling Again, Tenuously, at Honda in Japan, April 18, 2011.

Tierney, K.J., 1997. Business impacts of the Northridge earthquake. Journal of Contingencies and Crisis Management, 5:87-97 Tsuchiya, S., Tatano, H., and Okada, N., 2007. Economic loss assessment due to railroad and highway disruptions. Economic Systems Research, 19 (2), 147-162.

West, C. T. and D. G. Lenze, 1994. Modeling the regional impact of natural disasters and recovery: A general framework and an application to hurricane Andrew”, International Regional Science Review 17, 121-150.

Wu, J., N. Li, S. Hallegatte, P. Shi, A. Hu, 2011. A preliminary assessment of indirect economic losses from the Wenchuan earthquake in Sichuan province, China, submitted 48
25 contracting models. Sixth, after the program there has been a decrease in the dispersion of relative prices across different sectors. Thus, the idea that reducing inflation leads to a decrease in the high-inflation induced 'noise' component of relative prices seems to be supported by che evidence. Also, real relative price type variables such as real exchange rates and real interest rates exhibit lower variability through time after the program.

While we obviously recognize that our findings are not based on definitive tests of a model or set of models, we regard the evidence presented thus far as harmonious with the predictions of standard macroeconomic models.

# IV. THE CHANGING OF THE NATURE INFLATION/UNEMPLOYMENT TRADEOFF

This section provides further evidence on the inflation/unemployment tradeoff before and after the program. We begin by examining statistical and econometric Phillips curves before and after the program and move on to investigate the relations between the rate of unemployment and three important variables: the real exchange rate, the real interest rate, and the real wage. We focus on the timing of changes in unemployment and on the difficulties in attempting to account for the observed timing with standard macro models.

Four years after the implementation of the stabilization program it has become clear that the remarkable disinflation that took place, from inflation rates of about 9.3 percent per month to rates of about 1.3 percent per month, has been accompanied by an increase in the rate of unemplcyment from about 5 percent per month before the program to about 8 percent in early 1989; see Figure 10 for half-yearly data.

The observed empirical relation between inflation and unemployment exhibits a positive association between these variables in the high inflation