rafmacalaba/data-use-mentions-extended huggingface dataset:
corporate vulnerability in emerging economies has become dangerously high or not. This type of assessment might require analyzing how financial indicators among emerging market firms compare relative to other crisis periods. The AFC serves as a good benchmark. Financial distress in the nonfinancial corporate sector was a key factor leading to the AFC. In fact, some conditions in Asian nonfinancial corporate sectors in the run-up to the Asian crisis were very similar to the ones since the GFC, including a rapid increase in leverage coupled with deteriorating profitability and debt repayment capacity (Pomerleano, 1998; Claessens et al., 1999).

A comparison of leverage and debt repayment capacity of emerging market firms post-GFC relative to East Asian firms before the AFC suggests that solvency risk after the GFC remained below that on the eve of the AFC (Alfaro et al., 2017). The average leverage ratio (measured as debt to equity) and debt repayment capacity (measured as earnings to liabilities) among East Asian economies in the period 1996-98 were 146 and 24 percent, respectively. In contrast, during the period 2009-14, these values stood at 87 and 36 percent, respectively, among emerging economies. Furthermore, in no emerging economy was the average value of the Altman Z Score across firms in the so-called distressed zone (below 3.75) during the period 2009-14.<sup>4</sup> This pattern contrasts with the situation before the AFC, when the average Altman Z Score of firms in the Republic of Korea fell in the distressed zone.

# _3.2. Currency Risk_

Currency risk is another type of risk that is not well captured by the solvency risk measures. Currency risk could have increased if emerging market firms issued debt in foreign currency, but then used the proceeds to invest and generate earnings in domestic currency. Having foreign currency liabilities and

> 4 The Altman Z Score calculates the probability of a firm going bankrupt based on different balance sheet indicators. Based on the score, a firm can be categorized as safe (safe zone)
043)|(0.043)|
|**_Poverty gap_**||||
|Official extreme poverty line (US $2.5 PPP per day)|7.8|5.4|5.7|
|Official moderate poverty line (US $5.4 PPP per day)|18.5|15.9|16.4|
|US $3.2 PPP per day|10.3|7.7|8.1|
|US$5.5 PPPper day|19.1|16.5|17.1|
|Source: World Bank Group and staff estimates based on ENPH 2017||||
|Notes: The values in theparentheses correspond to the standard erro|rs associated with|the measure||

The gap and incidence from poverty tend to decrease when moving from market income to consumable income (Table IV-1), but once again, these changes are not statistically significant. In other words, the fiscal system seems to induce an improvement in the levels of well-being of the poorest by decreasing the average distance to the poverty line. However, part of the positive effect is reversed when indirect taxes and subsidies are included. Given that households do not observe the benefit received by health and education services, this analysis does not include poverty calculations for final income.

<!-- Start of picture text --> FIGURE IV- 7: INCIDENCE OF URBAN AND RURAL POVERTY<br>80.0<br>70.0<br>60.0<br>50.0<br>40.0<br>30.0<br>20.0<br>10.0<br>0.0<br>Total Urban Rural Total Urban Rural<br>Official moderate povery line Official extreme poverty line<br>Market income + Pensions Disposable income Consumable income<br>Source: World Bank Group staff estimates based on the 2017
l. **Introduction** The countries of Eastern Europe and the Fonner Soviet Union embarked on two transitions: from a command to a market economy (economic transition), and from an authoritarian political regime to a democratic one (political transition). They have had varying degrees of success on both fronts. Some countries, e.g. Hungary and Poland, have had remarkable success liberalizing their economies and their political systems. Others, e.g. Belarus and Uzbekistan, continue to have economic and political systems reminiscent of the Soviet era. This paper tries to explore the links between economic and political transitions using panel data from 25 post-communist countries in Europe and Central Asia.' Economic theory does not give a clear answer on whether political freedoms help or hinder economic transiltion. Surveys presented in Rodrik (1996) and in Williamson (1994) present arguments for both sides of the debate. Balcerowicz (1997), and Balcerowicz and Gelb (1994), discussing the interplay between economic policy and democratization, argue for a "'big bang" approach to economic transition, but leave open the question of whether democratic governments are better able to implement the "big bang" than authoritarian ones. The analysis in Roland (1992, 1994 and 1997) and in Dewatripont and Roland (1992) indicates that, under majority rule, time consistency and adverse selection problems generate a slow and gradualist economic transition. On the other hand, Aslund et al. (1996) argue that rent-seeking authoritarian governments slow-down stabilization policies needed in the first stage of transition.

The empirical literature presents a different picture: it indicates a strong positive correlation between political and economic reforms in the post-communist countries. Using data for 26 transition countries de Melo et al. (1996) found a correlation of 0.8 between their index of economic liberalization and the Freedom House index of political freedom. De Melo et al. (1997) use panel data to estimate the determinants of economic liberalization in transition countries. Political freedom appears with
for the Freeport of Monrovia relate to 2008, but no data were available for the Port of Buchanan. Source: Ocean Shipping Consultants 2009. Derived from AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data TEU = 20-foot equivalent units.

# Air transport

## **Achievements**

Sierra Leone has shown steady growth in all segments of the air-transport market (figure 12). Continued growth from 2001 to 2009 defies the trend for the rest of West Africa, which witnessed a collapse between 2001 and 2004 after the demise of Air Afrique and others. The most likely explanation is that Sierra Leone‘s capacity was depressed during the civil war, and therefore as services were reestablished there was no prior peak from which to decline. Air Afrique did not serve the country in 26
savings in jewelry or precious metals that are traditionally considered an integral part of the dowry in India. The higher savings that we find are financed through higher earnings; fathers of firstborn girls work more days in a year relative to FB fathers as expected dowry burden goes up. We also find that brothers in FG families decrease their years of schooling as expected dowry expenditure goes up, suggesting that they may also be working more in order to finance higher savings for their sisters’ dowry (these results are available upon request).

In the context of India, it is frequently claimed that dowry is an underlying cause of son preference, male-biased fertility, and discrimination against girls. Contrary to this, we find that dowry is unlikely to be a root cause of son-preferring behaviors in India.

Future research should examine if the results of this paper also hold true for urban India, for a more recent time period, and for other dowry-paying societies. How alternate marriage market institutions, such as bride price, affect households’ financial decisions also remains a fruitful area for future research. ■

# **References**

- Abrevaya, J. (2009): “Are There Missing Girls in the United States? Evidence from Birth Data,” _American Economic Journal: Applied Economics_ , 1, 1–34.

- Afridi, F., T. Dinkelman, and K. Mahajan (2016): “Why Are Fewer Married Women Joining the Work Force in India? A Decomposition Analysis over Two Decades,” Technical report, IZA.

- Alfano, M. (2015): “Daughters, Dowries, Deliveries: The Effect of Marital Payments on Fertility Choices in India,” _CReAM Discussion Paper_ .

- Almond, D. and L. Edlund (2008): “Son Biased Sex Ratios in the US 2000 Census,” _Proceedings of the National Academy of Sciences of the United States of America_ , 105, 5681–5682.

- Ambrus, A., E. Field, and M. Torero
the huge majority by which _Chama Cha Mapinduzi (CCM)_ candidate Jakaya Kikwete was elected to office (over 80% of the overall vote, and higher than that in the mainland part of the country studied here) the presidential vote share is not particularly informative. Similarly, 72% of the votes cast in parliamentary elections went to the CCM; however, in ward councilor elections the ruling party is less dominant. We therefore use the vote share for the CCM at the ward councilor level to measure _intensity_ of local-level support for the ruling party. We also construct two co-party indicator variables, which signify that the ward councilor and the parliamentarian are from the same party. The first of these is a dummy indicating any cases in which the national and local-level politicians are from the same party. Given the dominant and unique position of the CCM, we then define a separate co-party dummy for cases in which the councilor and parliamentarian are both from the same party but this party is not the CCM. The omitted category for these two dummies is any ward in which the councilor is from a different party than the parliamentarian.<sup>10</sup>

# _3.4.Household Survey Data_

The survey data come from a listing exercise and household survey conducted in five districts of Tanzania between June and December of 2008. First, five districts were selected that were intended to be broadly representative of the country as a whole: these are Moshi, Kwimba, Lushoto, Makete, and Nzega. Within these study districts, TASAF officials were asked to provide 20 villages in which „Vulnerable Groups‟ applications had moved through the submission process to the point at which they were ready to be funded, but had not yet received any money. Thus while the districts that form the study are in some general sense representative, the sample of villages was selected based on an endogenous criterion: being at a specific stage in the VG application process. Within the 100 study villages, we interviewed every household using a short listing
specifications (3) and (4) show that changes in the share of male ninis are not correlated with homicide rates in Mexico. As shown by specification (4), changes in the share of male ninis aged 19 to 24 are marginally correlated with homicides rates (at the 93.3 percent level of confidence). Although statistically weak, a positive relationship between violence and homicide rates is not surprising given the profile of the crime perpetuators described above (young male adults with relatively low education levels).

Table 1: Panel data estimations for the relationship between ninis and homicide rates

||**Ho**|**micide rate INE**|**GI (1995-2013)**||
|---|---|---|---|---|
|Independent variables|(1)|(2)|(3)|(4)|
|Share of ninis|||||
|All ninis||-0.0231|-0.0385|0.0503|
|||(0.23)|(0.27)|(0.27)|
|Male ninis|||0.0334|-0.663|
||||(0.30)|(0.48)|
|Males ninis aged 19 to 24||||0.625 *<br>(0.34)|
|Controls|||||
|Share of the population aged 15 to 24|-0.0038|-0.0059|-0.014|0.079|
||(0.56)|(0.56)|(0.57)|(0.57)|
|Log of hourly labor income|-9.802***|-9.901***|-9.934***|-10.13***|
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# IV. Applications and Interpretations of the Decision Rule.

If the values of the four parameters in decision rule (9) were known<sup>with</sup> confidence for a specific country or region of a country and the other assumptions of the analysis accepted, application of rule (9) would provide the cost-eireoive integration strategy. Unfortunately none of these parameters is known with precision for any country.<sup>Thls section</sup> applies information from three studies, on Ghana, Java, and the Ivory Coast, in order to<sup>arrive</sup> at tentative estimates of the parameters a, b and tif, life-days-saved per encounter and per vaccination and the intercept of the f5xed-center isocost curve with the e axs. All of these estimates are drawn<sup>together</sup> to illustrate the application and interpreeation of the decision rule in Table 7 at the<sup>end of</sup> the section.

1. Estimates of Healthy Life-Days-Saved Per Vaccination. Table 1 presents two estimates of b, the number of life-days-saved (LDS) per vaccination in an immunization program based on Ghanaian data and assumptions as presented by the Ghana Health Assessment Team (1981).[131 The first estimate of 75.4 LDS per<sup>vaccination</sup> at the bottom of column (7) is based on the theoretical distribution of the various vaccinations in that column. The second estimate of _53.8_ LDS per vaccinatioon at the bottom of column (8) is based on an empirical distrioution of vaccinations observed in neighboring Ivory Coast. Apparently<sup>it is difficult</sup> to maintain the proper ratio of measles vaccines to other vaccines<sup>and to deliver the third doses of the</sup> polio and DPT vaccines. Since the third doses add less<sup>than the average to</sup> LDS, reducing their proportions increases the average of the program. However, since
an important role in the liberalization process, possibly because of the conditionality attached to programs involving foreign financial assistance.<sup>19</sup> However,

> 18 The choice of the 1992-97 time period has been dictated by data availability and reliability and by the disappearance of the Soviet Union by 1992, leading to the creation of independent and sovereign countries. Data on external aid are available only until 1996 so, in the regressions which include this variable, observations are for the period 1992-96.

> 19 The World Development Indicators data on aid do not distinguish among different types of assistance (programs, food aid, emergency assistance, etc.), each of which has diverse characteristics and different effects on the economy.

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context. Labor force surveys (LFS) usually do a better job of screening for economic activities, but these surveys do not take place over a 12 month period, so they run the risk of losing seasonal employment unless a 12-month recall period is used. Other surveys such as the CWIQ and the DHS usually include a minimal employment section, with no screening questions. The analysis here shows that this approach will deliver biased results. The value of including a minimal employment section in these surveys is questionable.

The measurement of employment outcomes needs as much effort in survey design and implementation as does the measurement of other outcomes. Effort has gone into researching the measurement of consumption and poverty in low-income countries. The same effort needs to be applied to the problem of measuring employment outcomes, including incomes and earnings. Funding for survey research in lowincome countries is usually not a donor priority, but it is clearly important. The techniques discussed in Section 2 have been shown to produce better results in low-income settings. But better research on alternative questions and methodologies in specific country context, including how to translate these concepts into local languages, is still needed.

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Commodities Price Data (Pink Sheet). The price for oil is the unweighted average of the Brent, West Texas Intermediate, and Dubai oil prices. Monthly data from January 1995 to December 2014 are used for the training sample, the model is updated based on expanding scheme, and data from 2015Q1 to 2022Q1 are used as the testing period.

# **4. Forecast performance**

The six models are compared in terms of their bias and precision.<sup>18</sup> The forecast of each model is compared against all other approaches in the model suite, rather than a single benchmark ( **Figure 3** ).<sup>19</sup> The mean error (bias) is defined as the difference between the actual price and the price forecast one to eight quarters ahead for each of the seven industrial commodities. Precision is captured by the root mean squared error (RMSE). Model comparisons are tested for statistical significance in a t-test (for absolute bias) or an F-test (for RMSE) or in the significance of the Diebold-Mariano statistic. Futures prices are only available up to one year ahead with reasonable liquidity, hence longer forecast horizons are not considered here for futures prices.<sup>20</sup> Bivariate correlations are only significant for horizons up to one year ahead; hence, forecasts from bivariate correlations are dropped for horizons beyond one year. OEM forecasts are only available on a semi-annual basis and the statistical tests are adjusted for the fewer degrees of freedom.

## **4.1 Oil prices**

None of the oil price forecasts generated by any of the six methodologies had a statistically significant bias ( **Table 3.A** ). However, the RMSEs were sizable for several approaches and have tended to be larger at longer horizons (except for OEM-based forecasts, **Table 3.B** ). At horizons up to one year, all approaches except the BVAR predicted the direction of forecast
perimeter is aggregated from farm level data. For this reason, the production activities in the farm model are compatible with the ̳macro‘ CGE model. For the analysis preformed here, the representative farm is chosen from the irrigated area, and hence, only irrigated crops are included in the farm model.

The farm model accounts for monthly water allocation by crop. Typically, the representative farm only grows some of the crops produced in the perimeter. Only a number of crops (10) are included in the farm model. Just as the case with the CGE model, the farm model is calibrated to the data in such a way that the solution of the farm model for the base period reproduces the observed farm data exactly.

While the farm model only captures farmer‘s decision-making in production activities, the CGE model, as a general equilibrium model, captures inter-sectoral interactions of the decision making process in the economy. For this reason, prices, including prices for output and factors of production, are endogenously determined by the CGE model. Factor markets clear such that total available supplies of land, capital, and labor have to equal their respective demand. In the farm model, the representative farmer faces given prices for output and factors. The farm model treats the supply of land and monthly supplies of water as constraints. Otherwise, the farmer can hire labor, employ capital and use intermediate inputs at exogenously (to the farmer) given prices without supply side constraints. But these prices are endogenously determined in the economy wide markets. A schematic presentation of the major features of the farm-level model is given in Figure 4.

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plots are 7.5 percentage points lower than on mono-cropped plots, though intercropped plots are more likely to experience a loss in the first place (+3.6 percentage points). Plots farmed in more input and technology intensive ways appear more resilient to crop losses due to adverse events.

Disaster exposure and impact also vary according to who manages the plot. Plots managed by women are more often affected by disaster losses (+2.2 percentage points) than plots managed by men and these losses are also larger on average (+4.4 percentage points; Table 10). These differences are likely because plots managed by women are endowed and farmed differently than plots managed by men, which in turn may follow from differential access to inputs and land between women and men.<sup>27</sup>

# **Aggregate data sources underestimate impacts of extreme events on crop production**

How do disaster impacts as captured in the survey data compare to estimates from other commonly used data sources? Here, we contrast the results from the survey microdata with publicly available estimates of disaster impacts from the Emergency Events Database (EM-DAT). EM-DAT aggregates reports from UN agencies, governments, insurance companies, research institutes and the media into a global inventory of disaster impacts.<sup>29</sup> EM-DAT is the preeminent and only publicly available data source of this kind, used widely in disaster reporting and research.<sup>30</sup> We focus on two disaster types, droughts and floods, and compare two estimates: the number of people affected and the total economic damages caused in the years which the survey micro-data covers. We create aggregate figures from the micro-data using population sampling weights.

On both metrics, and for both drought and flood impacts, the micro-data estimates on average exceed the EM-DAT estimates, that is, for years in which there is information from both sources, the survey microdata find more people affected and higher damages from droughts and floods (Figure 3
less than 24 months for any loan amount in excess of Rs. 30,000 (Rs. 15,000). In addition, at least 50 percent (75 percent) of the MFI’s portfolio should be comprised of loans provided for income generation.

> 14Other sources of sanitation and home improvement loans were available in our study area. From credit bureau records we know that clients in our sample have taken loans from 27 micro-finance providers before and during the intervention.

9
(p-value 0.074).

Column 6 of Table 5 shows the impact of the treatments on farm profits, defined as the difference between the total value of crop output (dependent variable of column 5) and the total value of inputs used (dependent variable of column 2). The coefficient on the commitment treatment is large in economic terms and marginally statistically significant (p-value 0.11). The coefficient for the ordinary account is small and not statistically significant, and the difference vis-a-vis the commitment account is marginally significant (p-value 0.11).

Column 7 examines the impact of the treatments on total household expenditures in the endline (post-harvest) survey. The commitment (no raffle) treatment coefficient is positive and statistically significantly different from zero at the 5% level, while the coefficient on the ordinary (no raffle) treatment is substantially smaller and not statistically significantly different from zero.

> 15 To be clear, the public raffle treatments provided a signal of only an individual’s _total_ balances at OIBM, not how those savings were split between ordinary and commitment accounts.

16