rafmacalaba/datause-encoder-data huggingface dataset:
# 1 Introduction

Assessing the sustainability of external debt requires making assumptions about the evolution of its determinants. To make sensible predictions about future debt trajectories, projections need to take into account the uncertainty, co-movement, and feedback eects between the relevant macroeconomic variables. For example, crisis episodes with large increases of the debt burden are typically associated with current account reversals, real exchange rate depreciations, and so forth. It is important to take into account these correlations to provide reasonable projections and, therefore, to assess the sustainability of external debt.

The current joint International Monetary Fund (IMF)World Bank debt sustainability framework (DSF) for low income countries is based on three pillars.<sup>1</sup> The rst pillar is a set of bounds on external debt as a fraction of gross domestic product (GDP) or exports that vary according to the quality of the country's institutions and policies. The second pillar is a set of projections of key macroeconomic variables coupled with a debt accumulation equation to produce projections of external debt. Countries with current or projected debt indicators above the proposed bounds are considered to have unsustainable levels of debt. The third pillar is a set of stress tests designed to check the robustness of the baseline projection. Even if the projected debt level is below the proposed bounds, it might become unsustainable should the country suer suciently large negative shocks. The DSF considers a number of negative shocks on the baseline projections to assess whether current levels of debt are suciently safe.

The DSF for low income countries, however, has a number of drawbacks.<sup>2</sup> The baseline projection is based on experts' opinions or on the point estimates of the projections compiled in the IMF's World Economic Outlook. The framework, however, ignores any uncertainty associated with these forecasts. This is particularly important given the long projection horizon (of 20 years) included in the current framework. Moreover, while the stress tests are

> 1The current DSF is described in World Bank and IMF (2009) and Painchaud and Stu£ka (2011).

> 2Some of these problems were already noted in IMF (2004) and Hostland (2011).

1
# **Figure 1 – Structure of Social Benefits by Consumption Quintile**

<!-- Start of picture text --> 70.0<br>60.0<br>50.0<br>1<br>40.0<br>2<br>3<br>4<br>30.0<br>5<br>20.0<br>10.0<br>0.0<br>2001 2002 2003 2004<br><!-- End of picture text --> Note: Consumption quintiles are based on consumption net of social benefits.

19
participation by liberating them from standing watch over their property, as reported in a 2007 study of slum dwellers in Peru.<sup>4</sup> The reform of family laws in Ethiopia in 2000, which raised the minimum age of marriage, removed the husband’s ability to deny permission to work outside the home, and required both spouses’ consent in the administration of marital property, was linked to increased female labor force participation and in more productive sectors.<sup>5</sup> A 2007 study highlights that laws demarcating women’s working hours or types of employment that are found today in approximately 75 countries<sup>6</sup> can constrain women’s employment opportunities and significantly increase gender wage gaps.<sup>7</sup> A 2012 World Bank study on norms and agency found that in countries such as Yemen, where women’s physical mobility is restricted, women were less attractive as potential employees. Even where they were as qualified or even more qualified than male applicants, the need for a husband’s permission to work made them less reliable in the eyes of employers.<sup>8</sup> Improving women’s access to assets can benefit society as a whole and can result in transformative changes. Studies using evidence from across a range of countries demonstrate that increasing the share of household income that women control either through their own income or cash transfer can result in greater investment in children.<sup>9</sup> Inheritance law reforms in India equalizing inheritance shares for unmarried girls resulted in delayed marriage and more education for girls.<sup>10</sup> A quantitative study in Benin found that women who were able to earn an income were more likely to buy a bed net to prevent malaria transmission, which could be used by children. Men were more likely to purchase bed nets for their own personal use on the basis that as the breadwinner of the family they were entitled to protection.<sup>11</sup> A 2009 IFPRI research paper showed that if men and women were to have equal rights, the number of malnourished children could be reduced by 1.7 million in Sub-Saharan Africa and by 13.4 million in South Asia.<sup>12</sup>

# **Context for Understanding Approaches to Legal Rights**

With a few significant exceptions, most of the present day legal constraints are found in decades – or even centuries – old statutes that have not been reformed. Contemporary national legal frameworks did not emerge from a vacuum – like culture, language and social norms, legal

> 4 Field 2007.

> 5 Hallward-Driemeier and Hasan 2012.

> 6 World Bank Group. Data retrieved April 14, 2013, from Women, & the Law database (2011a).

> 7 Weichselbaumer and Winter-Ebmer 2007.

> 8 Turk, Munoz-Boudet, and Petesch 2012.

> 9 Doss 2006 and Quisumbing and Maluccio 2000.

> 10 Deininger, Goyal and Nagarajan 2010.

> 11 Rashed 1999.

> 12 Ragasa and others 2012.

4
that is statistically valid for rural areas in India. After data cleaning, a total sample of 22,538 rural households and 12,325 urban households were included in this paper’s analysis.<sup>5</sup> The energy questions that were asked in the IHDS survey are more extensive than those of comparable surveys such as the Demographic and Health Surveys and the Living Standards Measurement Studies. The energy questions in the IHDS survey cover use of fuels, cash expenditures on fuels, the collection time for obtaining biomass fuels, and the types of stoves and electric appliances in the household. For electricity, there are also questions on the reliability of the power supply and the source of electricity for the household such as whether they are serviced by the local state electricity board or from a neighbor. These detailed energy questions allow for a much more detailed analysis of the welfare impact of the use of different types of energy. In fact, we next turn to the demand for energy in India.

# **Patterns of Household Energy Use**

Of the many factors that have constrained India’s development, energy usage figures prominently. Energy use per capita in India lags far behind that of other countries around the world (World Bank 2006). Moreover, there are wide disparities in energy use between urban and rural areas (where three-fourths of the country’s people live). This section highlights India’s existing patterns of household energy use, and estimates energy demand functions.

The patterns of urban and rural energy demand in India differ markedly (Table 1). In rural areas, patterns of energy use typically involve high reliance on traditional fuels, including wood, dung, and straw burned in inefficient stoves. In urban areas, only one-third of households use fuelwood, compared to nine-tenths of rural households. In terms of quantity of energy use, rural households consume about 132 kg of fuelwood per month, more than four times the amount

> 5 Because samples were drawn as a fixed number of 15 households from each block, the analysis throughout the paper uses weights for making the analysis representative of the population.

11
99 Early Retirement: Employees of the City of Solothurn can retire 5 years before the ordinary retirement age. Their pension, which equals at most 60 percent of the last insured wage (i.e., with 40 years of contribution), is reduced according to the following table:

**Table 44  PF City** **<u>of Solothurn:  Early Retirement and Pension Benefits</u>**

|Retirement Age|65|64|63|62|61|60|
|---|---|---|---|---|---|---|
|Reduction per year|-|9%|8%|7%|6%|5%|
|%-Reduction of the annuity|0%|9%|17%|24%|30%|35%|

Source: Pensionskasse der Stadt Solothurn (2005), p.55 The bridging pension equals at the best, the maximal amount payable under the AHV/AVS. The corresponding reduction of the annuity payments amounts to 6.8 percent of the drawn bridging pension. In the case of late retirement the pension increases each month by 0.567 percent.

Investment: The investment strategy is of long–term nature and optimized with respect to the risk capacity of the pension fund. Depending on the type of securities, the investment management is handed over to a specific bank. In 2003, the largest positions in the overall portfolio were domestic currency and foreign currency bonds (31.4 percent and 19.5 percent, respectively), followed by mortgages (12.6 percent). Foreign shares (9.4 percent), Swiss shares (9.3 percent) real estate (8.1 percent), estate funds (5 percent), and hedge funds (1.7 percent) complete the investment in securities. The remaining fraction is held in cash (3 percent).
outdetect set-up: Normalization: Box and Cox (1964) Z-score: (x-median)/q a = 3 Outlier detection target: top and bottom (12447 observations are used)

# Incidence of outliers:

||Freq.|Percent|Share|
|---|---|---|---|
|Bottom|82|0.66|67.77|
|Top|39|@.31|32.23|
|Total|121|0.97|100.00|

Statistics for raw and trimmed pce:

||Raw|Trimmed|
|---|---|---|
|Summary<br>stats|||
|Mean|897.88|828.38|
|Median|648.73|648.35|
|SD|3926.19|641.13|
|CV(%)|437.27|77.40|
|IQR|550.12|543.91|
|Inequality|||
|Gini|@.4063|@.3565|
|MLD|@.2839|@.2076|
|Theil|@.5057|@.2232|
|cv2|9.5596|@.2995|
|A(@.125)|@.0541|0.0273|
|A(1)|@.2472|@.1875|
|A(2)|0.3776|0.3207|
|p90/p10|4.8185|4.7558|
# _Matching SIMPLES_

Firms registering for SIMPLES provided (i) their street name and number, (ii) their fantasy name, and (iii) their activity, as well as a phone number (which could be that of their accountant or a personal phone number instead of the business phone number). We therefore matched on (i) to (iii), using the phone number where available in both data sets as an additional check. We consider it to be a **definite match** if the listing data match the administrative data on all three criteria, or if they match on street and number plus activity and there is no fantasy name, or if (ii) and (iii) match and the street is the same and street number is only different by one digit. We consider it a **definite or probable match** if it is a definite match, or if it matches on street and number and the listing omits the fantasy name and activity, or if it matches on two criteria but the third is different. In this case, we only consider a match on fantasy name if it is unique and not generally descriptive (e.g. we would not consider the fantasy name “bar” as grounds for a match) and there is only one occurrence of that fantasy name in the entire administrative data.

# _Matching MEIs_

Firms which register as a MEI do not provide a fantasy name in their registration, so the matching could only be on street name and number, and activity (and sometimes phone number). A **definite match** matched on both these. A **definite or probable match** is a definite match, or any one of the following: a match on street name and number, with a very similar activity (e.g. “bar” and “comercio varejista de bebidas”); an exact match on street name and number, with no name or activity data in the listing; or a match on phone number, street name, and activity, with a different street number.

# _Matching ALF registrations_

The ALF administrative data also do not contain a fantasy name, so the same criteria were used as for matching MEIs.

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**Table 6: Average partial effects estimates of determinants of non-farm employment participation**

|VARIABLES|Non-farm wag<br>CRE Probit<br>model|e employment<br>Linear FE<br>model|Non-farm sel<br>CRE Probit<br>model|f-employment<br>Linear FE<br>model|
|---|---|---|---|---|
|Age of the household head|-0.000|-0.000|0.000|0.001|
||[0.863]|[0.751]|[0.988]|[0.631]|
|Male-headed household (0/1)|0.072***|0.072***|0.017|0.008|
||[0.001]|[0.002]|[0.552]|[0.783]|
|Highest level of formal education acquired by|||||
|household head|||||
|PSLC|0.035|0.039|0.003|0.010|
||[0.194]|[0.202]|[0.937]|[0.803]|
|JCE|0.130***|0.120***|0.046|0.055|
||[0.007]|[0.008]|[0.365]|[0.290]|
|MSCE|<br>0.236***|<br>0.257***|<br>0.033|<br>0.029|
||[0.004]|[0.000]|[0.651]|[0.697]|
|Non-University Diploma and above|<br>0.198|<br>0.222|<br>0.286|<br>0.249+|
||[0.294]|[0.228]|[0.184]|[0.102]|
|Maximum level of formal education acquired in the|||||
|household|||||
|PSLC|0.017|0.016|0.017|0.014|
||[0.478]|[0.495]|[0.578]|[0.680]|
|JCE|<br>0.016|<br>0.015|<br>-0.041|<br>-0.058|
||[0.579]|[0.628]|[0.224]|[0.165]|
|MSCE|0.079+|0.102**|-0.052|-0.056|
||[0.105]|[0.030]|[0.271]|[0.294]|
|Non-University Diploma and above|<br>0.262|<br>0.263+|<br>-0.184***|<br>-0.294*|
||[0.167]|[0.127]|[0.000]|[0.095]|
|Number of infant (<5yo) in HH|0.001|0.006|0.022**|0.019+|
||[0.903]|[0.535]|[0.036]|[0.128]|
|Number of children (5-14yo) in the household|0.015***|0.017**|0.001|-0.000|
||[0.006]|[0.011]|[0.930]|[0.974]|
|Number of prime adults (15-60yo) in HH|<br>-0.003|<br>-0.002|<br>0.022**|<br>0.021*|
||[0.669]|[0.793]|[0.022]|[0.065]|
|Number of elderly (60yo+) in HH|-0.019|-0.023+|-0.006|-0.004|
||[0.383]|[0.149]|[0.844]|[0.872]|
|Household access to loan (0/1)|-0.006|-0.001|0.083***|0.105***|
||[0.695]|[0.931]|[0.000]|[0.000]|
|Normalized wealth index|0.301***|0.519***|0.450***|0.567***|
||[0003]|[0001]|[0000]|[0000]|
|Normalized TLU index|.<br>-0.432*|.<br>-0.268|.<br>0.503+|.<br>0.176|
||[0.086]|[0.506]|[0.117]|[0.717]|
|Total land area owned by HH in Acres|<br>0.006*|<br>0.000|<br>-0.000|<br>-0.000|
||[0.100]|[0.735]|[0.825]|[0.860]|
|Household was affected negatively by some income|0.017|0.021|-0.018|-0.037+|
|<br>Shock During the last 12 months (0/1)|||||

31
# Resilient infrastructure for thriving firms: A review of the evidence

## **Johannes Braese**<sup>**1**</sup> **, Jun Rentschler**<sup>**1**</sup> **, Stéphane Hallegatte**<sup>**1**</sup>

_1Global Facility for Disaster Reduction and Recovery, World Bank, Washington DC, USA_

**Acknowledgements:** This working paper contributes to the World Bank’s 2019 flagship report on Resilient Infrastructure. It has benefited from helpful comments and feedback by Shoko Takemoto, Guillaume Prudent‐Richard, and Xavier Alegre. The authors would also like to thank Justice Tei Mensah, Peter Sanfey, and Moussah Blimpo for contributing data and providing invaluable assistance during the drafting process. **JEL classification:** O18, L25, Q54

**Keywords:** infrastructure, competitiveness, firms
# 4.1. Sensitivity

Links to Domestic Firms To ensure that the output of the new entrants is not driving the results, we reconstruct the MA and SA variables using only the output of domestic fi rms. Ideally, we would only purge the variables of the output of those new foreign entrants, however, since our data are at the industry level this information is not available. One drawback of removing all foreign output in the access variables is that it may be omitting important inter-industry linkages in industries that may be dominated by foreign fi rms. The results are presented in Table 3. We see that the inclusion of these domestic oriented linkage terms increases the size of the SA variables and reduces the size of the MA variables. The coefficients on all other variables are very similar to the earlier estimates. In this case, it appears that the high correlation between MA and SA variables may be affecting the MA coefficients when all of them are included, as the hypothesis that βMA_own = βMA_outer = 0 cannot be rejected in column (4). Yet the F-test of the joint signi fi cance of the MA terms when they are included on their own as in column (6) indicates joint signi fi cance with a p-value equal to 0. The two SA coefficients have a higher magnitude than before with both terms being statistically signi fi cant in column (5), which suggests that access to inputs purchased from domestic fi rms is relatively more important than those purchased from other foreign fi rms.

Domestic Oriented Foreign Firms The ability to sell products within China is likely to matter less for export-oriented investors. Thus, to check the robustness of our earlier fi ndings we re-estimate the above models restricting the sample to industry-province-year 19
have shown that Venezuelan migration inflows change voter behaviors. The authors exploit geographical variation in the early settlements of migrants (before the onset of the crisis) and annual variation in the aggregate migration flows. They demonstrate that larger migration shocks increase voter turnout and shift votes from left-wing to rightwing political ideologies. We replicate Rozo and Vargas (2021)’s estimates, adding data for the last elections (2019 for mayoral elections and 2022 for presidential elections), and observe similar effects. Particularly, we see that larger migration inflows translate into higher voter turnout and a shift of votes from left-wing to right-wing ideologies (see Table D.5). We illustrate in Figure D.5 that both estimates use different sources of crosssectional variation. Furthermore, we confirm that even when using the cross-sectional variation from Rozo and Vargas (2021), we do not find significant effects of the PEP program (see Table D.6).

_Are the impacts in municipalities with lower/higher take-up?_ We also explore whether the program induced different impacts in municipalities that had higher or lower program take-up. For this purpose, we divide the municipalities in the sample in three terciles for program take-up and a control group with no take-up. We first show that the parallel trend assumption is satisfied for municipalities in tercile groups 1, 2, or 3 relative to the control group (Figures D.6-D.8). We also confirm that there are negligible effects of the program for all outcomes and groups in Table D.7, except for the case of political competition where we document a positive impact for municipalities in Tercile 3.

_External validation using Parliamentary Elites in Latin American surveys:_ To validate our classification, we leverage data from the Parliamentary Elites in Latin America surveys (PELA-USAL), which captures legislators’ perceptions of presidential candidates’ ideologies. In this survey, legislators rank candidates on a 1–10 Left-Right scale, where 1 represents the most left-wing position and 10 the most right-wing. We categorize candidates as right-wing if their score exceeds 7, left-wing if below 4, and centrist if between 4 and 7.

28
2 KENYA AND CHINA’S TRADE RELATIONSHIP <!-- Start of picture text --> Fish and crustaceans<br>20<br>Coffee, tea, and spices<br>Iron ore and concentrates<br>Titanium ores and concentrates<br>Plastics<br>Tanned or crust hides and skins of bovine<br>Tanned or crust skins of sheep or lambs<br>10<br>Raw hides of other animals<br>Vegetable textile fibres<br>Copper<br>0<br>Commodity<br>Export Value (USD Millions)<br><!-- End of picture text --> Figure 8: Kenya exports titanium ores and copper to China (2012-2014) Source: UN Comtrade database 2015 <!-- Start of picture text --> 10000<br>7500<br>5000<br>2500<br>200<br>150<br>100<br>50<br>1990 1995 2000 2005 2010 2015<br>Year<br>copper<br>USD per metric ton<br>iron.ore<br><!-- End of picture text --> Figure 9: Metals prices have been falling since January 2010 (1990-2015) Source: World Bank Commodity Price Data (The Pink Sheet)

**Finished leather has a window of opportunity in the Chinese market** Because of economies of scale and low input costs, China is a much more competitive producer of leather products. Kenya, however, has room to export finished leather products to China. China has a limited presence in the high end leather market, giving Kenya a chance to supply more value-added leather products. (Hansen, Moon, Mogollon 2015). At the moment, Kenya exports raw hides and semi-processed leather, products that are in high demand in China. Just as Ethiopia attracted Chinese investment in the leather sector to meet demand, so may Kenya work to bring more investment to footwear manufacturing, lifting exports. Domestic leather demand is also higher than supply; local producers may use the new technology from Chinese firms to meet the growing domestic demand for finished leather (Hansen, Moon, Mogollon 2015).

9
Figure 8: Trade openness relative to trading partners <!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Turkey Turkey's trading partners<br>1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017<br><!-- End of picture text --> Source: Comtrade, IMF World Economic Outlook, author’s estimates

**An increased savings-investment gap has been mostly driven by the imports of intermediates, and to a lesser extent by imports of capital goods** .  Figure 9 decomposes the trade in goods balance into its components: capital goods, intermediates, consumer goods and other goods. Among these categories, Turkey is only a net exporter of consumer goods. Figure 10 shows the contribution of each of these components to annual change of the trade deficit in goods. Both figures reveal that the imported intermediates are the biggest contributor of the trade deficit in goods.

Figure 9: Components of balance of trade in goods <!-- Start of picture text --> Billions USD<br> 40<br> 20<br> -<br>1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017<br> (20)<br> (40)<br> (60)<br> (80)<br> (100)<br> (120)<br>Trade Balance in capital goods Trade Balance in intermediates Trade balance in consumer goods<br>Trade Balance in other goods Balance trade in goods<br><!-- End of picture text --> 9
|||with perennial crops, or fallowed<br>plots)||
|---|---|---|---|
|_plot_manager_id_obs_|Unique plot<br>manager ID|<br>Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_id_in the individual-<br>level dataset|UGA: absent when<br>there are multiple<br>managers in wave<br>7|
|_plot_manager_id_merge_||Only first manager considered<br>when multiple are cited.<br>This ID can be merged with<br>_indiv_obs_merge_in the<br>individual-level dataset||
|_formal_education_manager_|Does the plot<br>manager possess<br>any formal<br>education?|||
|_primary_education_manager_|Did the plot<br>manager complete<br>primary school?||“Primary<br>education” consists<br>of:<br>-<br>6 years in<br>NGA<br>-<br>6 years in<br>MALI<br>-<br>7 years in<br>TZA<br>-<br>8 years in<br>ETH<br>-<br>6 years in<br>NIGER<br>-<br>8 years in<br>MALAWI<br>-<br>7 years in<br>UGANDA<br>NGA: not asked for<br>old members who<br>are not in school<br>(w1, w2)|
|_age_manager_|Age (in years) of<br>the plot manager|Capped at 100||
|_female_manager_|Is the plot<br>manager female?||NER: plot manager<br>= hh manager if<br>managed jointly|
|_married_manager_|Is the plot<br>manager married?||UGA: w7 has no<br>ID information on<br>plots managed<br>jointly. The<br>resulting sample<br>could be biased.|
|_respondent_id_obs_||This ID can be merged with<br>_indiv_obs_id_in the individual-<br>level dataset|TZA: absent in<br>wave 5<br>UGA: absent in<br>wave 1, wave 2|
|_respondent_id_merge_||This ID can be merged with<br>_indiv_obs_merge_in the<br>individual-level dataset|TZA: absent in<br>wave 5|

30