rafmacalaba/datause-encoder-data huggingface dataset:
# **5.  Policy Implications and Conclusions**

We have showed that the likelihood of debt distress can be substantially explained using three factors: debt burdens, policy, and shocks. We conclude by considering the implications of these findings for the lending policies of official creditors.

The first of these implications is quite straightforward: _there is substantial valueadded in assessing the quality of policies and the severity of shocks in addition to debt burden indicators when assessing the probability of debt distress of individual countries_ . The regression results clearly show that policy performance and the severity of shocks matter a lot in predicting which countries are likely to suffer debt distress. From a predictive standpoint, there are non-trivial differences between the predicted probability of distress based on debt burdens alone, and the probability of distress based on the fully specified multivariate regression equation. We illustrate this in Figure 4, where we graph the present value of debt relative to exports in 2001 for all countries, against the predicted probability of debt distress using the most recently available data for 1999-2001 and the regression in the fifth column of Table 4. We indicate the HIPC threshold of 150 percent debt/exports as a vertical line, and the historical unconditional probability of debt distress in our sample of 0.23 as a horizontal line. As Figure 4 illustrates, there are many countries which may have low debt burdens relative to these benchmarks, but still have high probabilities of debt distress owing to either to poor policy performance or recent poor growth performance. Similarly, there are some countries that are able to bear significantly higher debt burdens without a higher-than-average distress probability because of the quality of their policies and the robustness of their economic growth.

Our second policy implication follows closely from the first: _using a single debt threshold for all low income countries to identify countries that are vulnerable to debt distress ignores the importance policies and shocks. Country-specific debt thresholds reflecting policies and shocks are more appropriate._ In Figure 5, we illustrate how these other factors influence the “sustainable” debt level of a country. In the top panel, we consider a hypothetical country with a growth rate of 3.6 percent equal to the mean of our 25
## **Figure 1**

# Weberian scale & ICRG, 1982-90

<!-- Start of picture text --> 6<br>SGP<br>5<br>HKG<br>ESP<br>BRATHACIVCOL<br>4 ISR INDMYS KOR<br>CHLPRT<br>KEN<br>DOM ARGECU TUR TUN GRC<br>3 MAR LKA MEXCRI<br>PAK<br>EGY<br>PER<br>URY<br>NGA<br>2 SYR<br>ZAR<br>PHL<br>GTM<br>1 HTI<br>0<br>0 2 4 6 8 10 12 14<br>WEBSCALE<br>BURQ8290<br><!-- End of picture text --> 1). This relationship remains strongly significant when we control for per capita income, which in contrast to the Weberian scale is not a significant predictor of the ICRG measure when they are both included in a regression.<sup>11</sup> Donor fragmentation is calculated as: 1 – ∑ Sd2 , or one minus the sum over all donors of their squared aid shares. This measure is computed using two alternative data sources. The official OECD-DAC (Development Assistance Committee) data base of “Geographic Distribution of Financial Flows to Aid Recipients” provides a breakdown of annual disbursements by various bilateral and multilateral donor agencies, treating the various UN agencies as distinct. From these data, an index of donor fragmentation can be calculated for each recipient for each year. The index is then averaged over the relevant period for each recipient. To construct the index, a Herfindahl index of donor concentration is first calculated, by summing the squared shares of aid over all donor

> 11 We use the Evans-Rauch Weberian index only to validate the ICRG variable, because it is available for far fewer countries and with no time series variation.

13
soils. As Yu (2010) shows, soil composition in coastal Bangladesh is particularly conducive to vertical diffusion of salinity from the surface to groundwater. Second, in cases where soil monitors are near more scattered river monitors, Dasgupta et al. (2011b) show that neighboring soil and water salinity readings are highly correlated.<sup>7</sup> This provides further evidence of diffusion from runoff. Although we believe that local soil salinity provides a useful proxy indicator for groundwater salinity, we recognize that this assumption introduces random measurement error of unknown magnitude.

To derive municipality groundwater salinity measures, we spatially interpolate salinity measures from the 41 soil salinity monitors.<sup>8</sup> We adopt the interpolated salinity measure at the geographic centroid of each municipality as its groundwater salinity estimate. Then we calculate annual salinity means for matching with annual municipality-level data on income, road networks and road maintenance expenditures.

# **4.3  Municipality Data**

The 20 municipalities displayed in Figure 1 have provided annual data on local revenue, development income and maintenance expenditures, as well as the lengths of the _pucca_ (paved) and _kutcha_ (unpaved) roads under their jurisdiction. As Table 1 shows, _pucca_ road shares in total network lengths vary widely across municipalities -- from a minimum of 15.0% to a maximum of 93.3%.

**Table 1:** **_Pucca_ (paved) road share of municipality network**

|Min|p10|p25|p50|p75|p90|Max|
|---|---|---|---|---|---|---|
|15.0|46.1|51.1|63.6|76.1|86.9|93.3|

> 7 This study uses the soil salinity monitors because they provide more observations for estimation.  Soil monitors substantially outnumber river monitors in the databases available for this study.  The soil monitors are also more widely scattered geographically, so they are relatively close to more municipalities than the river monitors. 8 Our spatial interpolation method employs the geonear package in Stata.  Salinity at each point on the surface is a weighted combination of monitor measures; weights decline with the square of the distance from the point to each monitor.

9
impact of external shocks using semi-structural vector auto-regression analysis (VAR), as applied to a (cross-country, time-series) panel data of aggregate variables. This methodology requires the identification assumption that the relevant external variable, that is, the change in the terms of trade, does not respond to either domestic output changes or the variables that account for the country’s structural characteristics. In practice, this rather uncontroversial assumption amounts to a small-open-economy condition for the countries included in the analysis. Similar applications of this methodology can be found in Broda (2004), Ahmed (2002), Uribe and Yue (2003), and Raddatz (2005). Controlling for the size of the shock, the analysis proceeds to account for its interaction with the set of country characteristics under analysis and estimate its conditional output impact.

The rest of the paper carries on as follows. Section 2 presents the econometric methodology in detail and conducts the corresponding specification tests. Section 3 introduces the data, providing information on variable definitions and sources, as well as the sample of countries and years under analysis. Section 4 presents the empirical results, including the discussion of symmetric and asymmetric (positive/negative) effects, the potential complementarity between structural characteristics, and a set of robustness checks. Section 5 concludes.

# **2.  Methodology**

We estimate the impact of exogenous shocks on a country’s economic performance and its relation to a country’s structural characteristics using a panel vector auto-regression (panel VAR). In order to impose the minimum identification assumptions possible (see below), we focus exclusively on terms-of-trade shocks. Therefore, for a given country _i_ , our semi-structural model corresponds to: (1) where _xi_ , _t_ = ( Δ _tti_ , _t_ , Δ _yi_ , _t_ )' , is a vector that contains the first difference of the (log) terms-oftrade index ( Δ _tti_ , _t_ ) and the log of real GDP per capita ( Δ _yi_ , _t_ ), both as deviations from 4
**Figure 3. Predicted and Actual Levels of Informality**

<!-- Start of picture text --> A. Schneider Shadow Economy index B. Heritage Foundation Informal Market index<br>7060 Actual values (% of GDP) PER PANBOL TZA ZWEHTI 5 Actual values(Higher, more informality) IRNSYRBOLNICINDPRYSLEVEN TGO IDNPAKKENCOGMOZMLIBGD NER CMRHTI<br>5040302010 SWENORCANFID AUT USA CHE DEUAUSHKG N NZLGBRNLDSGPKIRL KWTJPN BELFRAKOR CHL ESPITAPRTHUN TUN AREMAR JOR GRC URY POLCRICHN SYR MYSTURARG IRN BWATHAEGYMEXECUNICSLVLKAVENDOMIND ZMBZAF JAM BRA PHL PRY COLUGADZAPAKSLE TGO SENIDNPNG COG GTMMWIGHAMOZ HND MLIKENCMRBGDNER ZAR 4321 HKG FSWE I SGPNSLNZLCAN DNK NORCHE US AUS A DEUGBRUT NLD IRLCHLJPNBELFRA ESP KWTHUNAREITA PRT KORTUN URYGRC MYSJORSLV CRI ZAFPOLTURPERPAN PNG TTOBWACOLARGTHAJAMBRADOMMEXMARCHNLKA ECG EGY PHLZMB GTM U DZAGHA Y HNDUGATZA SENMWI ZWE<br>Predicted values Predicted values<br>0 (% of GDP) (Higher, more informality)<br>0 10 20 30 40 50 60 70 1 2 3 4 5<br>C. Self Employment D. Non-contributor to Pension Scheme<br>60 Actual values (% of total employment) CMR 100 Actual values (% of labor force) INDIDNZMBPAKSENGHASLEMOZUGABGDTZA<br>5040 PER DOMCOLBOL ZMB IDN PAKHNDBGD 80 JOR PERMEXVEN CHNCOL ECU MARSLV DOMIRNTHAPHLLKABOLDZANIC PRYHNDGTM TGOZWECMRKEN<br>IRNJAMPHL GTM 60 TUR<br>30 KORCHLITAPRTURY GRC TUNARGCRI PAN MEXMARSLVTHAVENECUSYR BRA LKAEGY DZA 40 CHLURY ARGTUNMYSPANCRIBRAJAM EGY<br>2010 HKGNORCANDNK DEU USACHEAUTSGPISLAUSNZLGBR NLD BELIRL JPNFRA ESP MYSTTO ZAF Predicted values 200 SWENORDEU CHE DNKUSAFINSGPAUTAUSGBRNZLNLD BEL JPNFRAIRLKORESPITA HUNPRTGRC POL Predicted values<br>0 (% of total employment) (% of labor force)<br>0 10 20 30 40 50 60 0 20 40 60 80 100<br>Note: In each graph, a 45-degree line is drawn to show a distance between predicted and actual levels.<br><!-- End of picture text --> 25
_Table 6: Sensitivity analysis for the composition of the SEI and percentage of data variation explained by each_

||Contributio<br>|n (as a %) of e<br>diffe<br>|ach indicator to t<br>rent weighting sc<br>|he composite SEI<br>hemes<br>|score under<br>|
|---|---|---|---|---|---|
|INDICATORS|Standard<br>PCA*|PCA with<br>weights>0|PCA with<br>weights>min<br>requirement|PCA weights<br>using NPD<br>rule|Simple<br>Average|
||(1)|(2)|(3)|(4)|(5)|
|1. Beneficiaries/US m$|0.055|0%|9%|15%|14.3%|
|2. Jobs created/US m$|0.574|27%|19%|9%|14.3%|
|3. Poverty level|0.082|12%|10%|15%|14.3%|
|4. Continuity of water supply|0.333|28%|24%|15%|14.3%|
|5. Bacterial quality of water|-0.479|0%|9%|9%|14.3%|
|6. Existing safe water coverage|0.564|30%|19%|24%|14.3%|
|7. Prevalence of water-borne diseases|-0.040|2%|9%|15%|14.3%|
|Total||100%|100%|100%|100%|
|% of data variance explained|33%|29%|22%|17%|16%|

Notes: *The figures presented in this column are the originally calculated weights using the unrestricted PCA methodology While there is no normative reason to use one weighting scheme over the other, the IPF analysis herein first presents the PCA method, which is both unbiased with respect to weighting and also best makes use of variation in the data for the purposes of differentiating projects. While there are differences in some individual rankings (as can be expected) due to the different weights, the general patterns of project ranking do not vary significantly.

# Financial and Economic Indicator (FEI)

Similar to the SEI, the FEI scores are also calculated using the PCA approach. However, for the FEI PCA‐based weighting, three other outlier projects in addition to Project 25 (mentioned earlier) were removed. These include outliers include Projects 2 and 4, whose NRW levels are exceptionally higher than the others (36.9 and 36.34, respectively), and Project 27, whose and yield value was zero<sup>18</sup> . All three projects were included in the analysis and only excluded from the weighting process to remove biases.

Table 7 below shows the composition of the FEI score using the PCA approach and a simple average for a comparison.<sup>19</sup> The benefit‐cost ratio has the highest weighting in the FEI indicator when using the PCA (the FEI is constituted almost entirely by the BCR). This is acceptable, as it is the most critical factor to distinguish between projects on financial and economic aspects.

> 18 Based on a September 2014 feasibility report, the water supply for this project is proposed to be drawn from an existing water supply system (Kalu Ganga Water Supply Project) by laying a new transmission pipeline. The report indicates, however, that the existing system has no capacity and must be augmented, but that no proposal or feasibility study has been prepared to ensure water availability.

> 19 Refer to Annexure V to see the exact weights that emerge from the analysis.

22
and also specifies the particular occupations and processes where children can not be employed (see appendix A for details). As such it is applicable and enforceable even in the un-organised and informal sector, and to establishments that do not come under the definition of a factory.

The EoCA 1991 is also considered more comprehensive because in terms of penalising the offenders, that is the employers, it is more severe than the other minimum age laws. For instance, the maximum penalty in case of a contravention of the Factories Act (1997) in terms of employing an underage child for work is only Rs.500; whereas, under the EoCA 1991 the penalty can extend to imprisonment for up to a year or a fine of Rs.20000 or both.<sup>3</sup> The EoCA 1991 does not prohibit work in establishments or enterprises that are not related to the occupations and processes specified in the Act. It also does not prohibit children’s work in the agriculture sector (except where hazardous chemical sprays are used), or work in the family business, farm or enterprise. However, the Act lays out strict regulations of conditions of work for children in these occupations.<sup>4</sup> For instance a child can not work for more than a period of 3 hours without a break of at least 1 hour, and the hours of work with the intervals can not exceed 7 hours a day. Also no child is allowed to work between 7p.m to 8a.m. Thus the EoCA 1991 aims to eliminate the employment of children in the most hazardous occupations and to regulate the conditions of work for children in other less hazardous occupations.

> 3 In terms of minimum wages, the minimum wage is fixed at approximately Rs.4000 per month ($66), so Rs.500 is almost 1/8 of the minimum wage.

> 4 In order to better regulate the working conditions of adolescents and children more than 12 years of age who are willing to work (with permission of their legal guardians and a health certificate from a medical practitioner – see Annex A), the government introduced the Employment of Children Rules in 1995 on the basis of the EoCA 1991. These rules lay out the working conditions for children in detail and require the maintenance of a register of child workers.

7
Kerosene use and its shares of nonfood and total spending by electricity access status and by income group are summarized in Table 2. On average, households without electricity access, use about 3 liters of kerosene per month for lighting while those with grid-electricity access use 2 liters, and with SHS use only 1 liter. The higher kerosene use among grid-connected households compared with households with SHS is likely due to the need to use kerosene for lighting backup as a result of frequent outages of the grid-electricity.

The survey data also show that kerosene use accounts for a large proportion of household nonfood expenditure, in particular among poor households. The average kerosene expenditure accounts for about 9% of total non-food budget among the bottom two deciles, in comparison to 3% for the top two income groups. Clearly, the financial benefits from a reduction in kerosene spending due to SHS installation would be larger for poor households. These benefits are in addition to other benefits, including better quality lighting and reduced health risks (indoor pollution) associated with kerosene lamps.

# 5. **Carbon benefit, affordability and distributional impact of SHS programs**

# **5.1 Quantifying the carbon benefit of SHS**

The carbon benefits of SHS dissemination in rural areas come mainly from the fuel displacement, including kerosene, dry cell batteries, and diesel used for battery charging. In the analysis, we focus mainly on kerosene displacement due to data limitation on other fuel use. The current fuel use for lighting among households without access to grid-electricity is used as the energy baseline. This is consistent with the UNFCCC simplified procedures for small-scale clean development mechanism (CDM), which defines the energy baseline as the fuel consumption of the technology/device in use or would have been used in the absence of project activity.

# 5.1.1 Methodology

Quantifying the CO2 impact from SHS installation involves the estimation of fuel displacement as a result of SHS installation. Two alternative methods are used to estimate the effect of SHS installation on kerosene: the regression approach and the Propensity Score Matching method (PSM).

The regression method allows one to estimate the net effect of electricity access on kerosene use controlling for observable household and village characteristics, as well as regional specific effects. The effect of electricity access on kerosene use is also interacted with the income variable to allow differential income effect. The econometric model is as follows: _Qhv = α + β Xhv + λ Ehv+ δ Ehv*Ihv+ σVv + γL + ehv_ where _Qhv_ is the quantity of kerosene used by household _h_ in village _v_ ; _Xhv_ household socioeconomic characteristics; _Ehv_ electricity is the dummy variable (taking the value 1 for access to electricity and 0 for no electricity); _Ihv_ is the income of household _h_ in village _v_ ; _<u>Vv</u>_ is the village electrification variable (taking 1 if the household living in a village with grid-electricity, 0 in a village without grid-electricity); and _<u>L</u>_ is the location variable which is a set of dummy variables 6
students who were not currently migrants, 16 percent in Micronesia and Papua New Guinea, 20 percent in Tonga, and 32 percent in Ghana said they had changed in part what they studied, or studied extra because of the prospect of potentially migrating. The channel for them though is not so much in terms of attainment, as in what they study – more report studying foreign languages and taking test preparation classes to pass entrance tests abroad. In some of these countries we also did surveys of teachers of 10<sup>th</sup> to 12<sup>th</sup> grade in the top secondary schools to see whether they taught anything differently as a result of the possibility their students may migrate. In Micronesia, 35 percent of teachers say they teach things they wouldn‘t otherwise do: examples include U.S. history and culture, foreign languages, tolerance of different perspectives, and botanical procedures for plants grown abroad. In Papua New Guinea, where 27 percent of surveyed teachers say they teach material they wouldn‘t otherwise, coverage of exchange rates and international trade, and human resources and labor exports are mentioned.

It is notable that most of these responses in terms of new human capital acquisition in our survey data are for knowledge that in large part only has its main payoffs abroad (although there will also be some benefits in the home country for those individuals whose jobs involve considerable contact with the outside world). This critique of brain gain theories is often raised with regard to medical migration, where it is noted that the prospect of migration may induce over-investment in fields like geriatric medicine which have high demand abroad, at the expense of fields like tropical medicine which have the greatest needs at home but little payoff abroad. We are unaware, however, of any research which goes beyond anecdote and description in this regard, so are unsure as to how serious an issue this is in practice. Nonetheless, in addition to the lack of supply elasticity and ease of movement for particular professions, it adds another reason to be cautious of claims that the prospect of migration will lead to beneficial brain gain.

Finally, we should note that international migration can increase human capital levels in the home country even in the absence of an effect on incentives to acquire education. Two important channels for this are through the use of remittances and repatriated savings to alleviate liquidity constraints that otherwise prevent households from paying for schooling, and through return migration of individuals who acquire schooling abroad and return to their home countries. In our view these two channels are likely to be stronger contributors to a brain gain from international migration than incentive effects in most countries. This is particularly the case in small and poor countries in which facilities for further education are limited: for example, in 14
24. Instruments include the debt/income ratio, the 1928 export/GNP ratio, and dummy variables for Bridsh Commonwealth and Latin American countries (which were shown to be significant determinants of default in Eichengreen and Portes, 1986).

25. Defining the dependent variable for each country in Table 3 as the deviation from that country's average growth rate during the sample period should, by picking up secular differences in national growth rates, eliminate problems potentially caused by our procedure of pooling together countries at different stages of economic development.

26. There were, however, independent expenditure progams which de facto had fiscal e3fects, such as commodity price stabilization schemes and unemployment insurance systems, although the magnitude of their effects is much debated.

27. Contrast Anglo-Argentine trade relations as discussed in Section 7 and footnote 38 below.

28. To cite but one well-known statement of the view, see Krueger (1978).

29. See for example Fishlow (1972) and a number of the studies contained in Thorp (1984).

30. As will become clear momentarily, this is not a hypothesis toward which we are inclined.

# 31. Cited in Eichengreen (1988).

32. Feis (1950), p. 1. Other observers were less pessimistic about the prospects for the foreign bond market. As King (1950, p. 11) put it, "And so, economically spealdng, I would not call the prospects unfavorable. If you were to look for a foreign bond as a safe investment you would have to use care and judgement, you would have to consider the many different factors which I have mentioned or hinted at, the character of the country and the people, their industriousness, their ingenuity, their good faith, and policies of the governments, their reserves of strength, their vulnerability to economic and technical changes and so forth. But I do not think you would be left high and dry without a candidate for admission -- except for one thing, the politcal situation." 33. These results are consistent with those in Table 1, which suggest that in pricing foreign bonds in the 1920s investors took the past debt-servicing records of countries into account.

34. Three accounts of Brazilian experience are Abreu (1978), Cardoso and Dornbusch (1988) and Eichengreen and Portes (1988a).

35. In what follows we concentrate on the results reported in Eichengreen and Portes (1988b), utilizing samples of 207 dollar bonds and 125 sterling bonds issued between 1920 and 1929. Tnis analysis is an extension of the pilot study utilizing two considerably smaller samples first reported in Eichengreen and Portes (1986).

36. For further discussion of this possibility, see Eichengreen and Portes (1988b).

52