rafmacalaba/datause-encoder-data huggingface dataset:
**Figure 1. Per capita consumption growth rates***

<!-- Start of picture text --> 4<br>2<br>0<br>-2<br>-4<br>-6<br>1961 1964 1967 1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006<br><!-- End of picture text -->

* Each point corresponds to the median growth rate of the nine Latin American countries under analysis. The series has been smoothed with a three year backward moving average. Source: Own calculations using WDI data.

The question of whether growth prospects have improved for the region is addressed by Loayza, Fajnzylber, and Calderon (2005) who consider two different future policy scenarios. One (denoted FCT in table 2) assumes that the reforms started in the early 1990s continue over the 2000s at the same pace as that observed in the 1990s. The second scenario (denoted FDC in table 2) assumes a dramatic acceleration in the observed pace of reform in the region, especially for those countries lagging with their reform agendas. Inspection of the growth rates under these two scenarios reveals that in principle there are some reasons to expect an acceleration of growth in the region. Under the FCT scenario the average per capita growth rate would accelerate to 2.4 percent. Under the FDC scenario, reflecting the more ambitious reform agenda implicit in the scenario, the growth rates would accelerate to 4.1 percent.<sup>16</sup> Clearly, one could also argue that these projections are too optimistic. There are two types of arguments that can be given in this regard. First, the estimated accelerations in per capita growth are very significant (especially under the FDC scenario): these countries only achieved an average growth rate of 2.4 percent in the early 1960s and early 1970s. The second reason is that, as argued in World Bank (2005), the cross country econometric models on which these projections are based, tend to overestimate the positive impact of reforms on growth prospects. As for the performance of the region in recent years, it is likely to have been the result not only of the reforms implemented during the 1990s but also of a good external environment (i.e. high growth in the world) and of high commodity prices. That is, virtue may have played a role in the growth outcomes observed in Latin America in recent years, but there seems to be also a luck component.

> 16 It is worth noting that the Intergovernmental Panel on Climate Change (IPCC) A1 scenario relies on an annual per capita growth rate for Latin America and Africa (both regions are grouped for scenarios purposes) of 4 percent from 1990 to 2050.

9
**<u>Table 13: Capital and Prudential Regulations</u>**

||Main Regulations byTypes of Institutions|
|---|---|
|Australia|**Superannuation Funds**<br>No capital buffer requirement.<br>Actuarial funding and solvency certificate.<br>Own life tables and discount rates subject to review.<br>Licensing of trustees.<br>**Life Insurance Companies**<br>Solvency margins on wind-up basis.<br>Capital adequacy margins on a going concern basis.<br>Own life tables and discount rates subject to review.<br>Closemonitoring of free assets and target surplus.|
|Chile|**Pension Fund Administrators**<br>Capital adequacy requirement (_encaje_).<br>Market valuation of assets.<br>**Life Insurance Companies**<br>Capital adequacy requirement.<br>'Held to maturity' valuations.<br>Prescribed life tables.<br>CALCE rule discount rates and capital backing for<br>mismatched asset/liabilitymaturity bands.|
|Denmark:ATP|Broadly similarto occupational funds.|
|Denmark: Other|**Life Insurance Companies and Pension Funds**<br>Solvency I capital requirement.<br>'Fair value' accounting.<br>Decomposition of technical provisions.<br>Use of market maturity-dependent discount rates.<br>Own life tables subject to review.<br>|
||Applicationof 'static'stress testing.|
|Sweden:PPM|Broadly similarto occupational funds.|
|Sweden: Other|**Life Insurance Companies and Pension Funds**<br>Solvency I capital requirement.<br>'Fair value' accounting.<br>Own life tables subject to review.<br>Own discount rates subject to ceiling.<br>Applicationof 'static'stress testing.|
|Switzerland|**Pension Funds**<br>Combination of book and market valuations.<br>Own life tables and discount rates subject to review.<br>Temporary underfunding permitted.<br>Expert certificate requirement.<br>**Life Insurance Companies**<br>Solvency I capital requirement.<br>No underfunding allowed.<br>Own life tables and discount rates subject to review.<br>Combination of book and market valuations.<br>Applicationof 'static'stress testing.|

Sources: Andersen and Skjodt (2007); Brunner and Thorburn (2008); Buetler and Ruesch (2007); Palmer (2008); Rocha and Rudolph (2010); Rocha and Thorburn (2007); and Vittas (2008).

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<!-- Start of picture text --> 100<br>S 80 \<br>2<br>S<br>an<br>2o 70<br>E<br>2<br>D<br>fefo}<br>© 60<br>GP PyT-4<br>50<br>—— Google Trend: Translate<br>40 === Google Trend: Write<br>O1julz022 O10ct2022 O1jan2023 Otapr2023 O1jul2023 O10ct2023 Otjan2024 O1apr2024<br><!-- End of picture text -->
field work and monitored data collection for which a local firm was hired. The survey’s objective were to collect extensive information on the health sector at the frontline level, including resource use, delivery processes, services, staff and patient behavior and characteristics. Data were collected between May 1 and July 16, 2004, using questionnaires administered to regional delegates, district head doctors, health center heads, employees and patients.

A two-stage sample strategy was used for the facility sample. First, in each of the 15 regional delegations, either one or two districts, depending on the number of districts in the region, were selected at random.<sup>1</sup> Second, in each of the selected districts, all the health centers were identified and visited. Also, given the importance of the capital city, N’Djamena, all its health centers were included in the sample. The final sample of health clinics visited is presented in Table 1. Of the 281 health centers in the sample in the 14 regions surveyed, approximately two thirds are public, 14% are private, 16% are religious and 3% are run by NGOs. About twothirds of the health centers are located in rural areas, less than one-quarter in the capital and 14% in other urban areas.

Table 1: Distribution of the Health Centers

||**Capital**|**Other urban areas**|**Rural**|**Total**|
|---|---|---|---|---|
|Public|26|26|139|191|
|Private|19|9|11|39|
|Religious|4|2|39|44|
|NGO|3|1|3|7|
|**Total**|52|38|191|281|

Source: Chad 2004 QSDS Information was also collected from patients using health centers. The objective was to interview 10 patients per health center.<sup>2</sup> The interviews were carried out at a certain distance from the health centers after patients’ visits to the

> 1 One regional delegation (B.E.T.) was not included in the final sample because of security problems in the region at the time of the survey.

> 2 The sample strategy was to interview the first 10 patients after they had received a consultation, or all the patients that had consulted that day if the total was not greater than 10.The design of the patient sample is complicated by the random nature of patients visiting clinics at different hours each day. Some health centers might receive no patients one day and more than it can handle the day after.  Without specific information on patient distribution, it is difficult to develop a strategy for representative sampling.

4
<!-- Start of picture text --> Average Farm Size, Ukraine, 2001<br>(2500,25000]<br>(1500,2500]<br>(500,1500]<br>[0,500]<br><!-- End of picture text -->

**Figure 1.1: Average farm size (ha) by rayon, 2001**

Average Farm Size, Ukraine, 2011 <!-- Start of picture text --> (2500,25000]<br>(1500,2500]<br>(500,1500]<br>[0,500]<br><!-- End of picture text -->

**Figure 1.2: Average farm size (ha) by rayon, 2011**

26
Kling, JR, Liebman, JB, Katz, LF and Sanbonmatsu, L. 2004. Moving to Opportunity and Tranquility: Neighborhood Effects on Adult Economic Self-Sufficiency and Health from a Randomized Housing Voucher Experiment. _KSG Working Paper_ , RWP04-035.

LEAP. 2012. Pilot Phase – Brief Update of Report. Livelihood Enhancement & Association of the Poor in Siem Reap (LEAP), PPA No. Q7100-KH.

Lev, MH. 1997. Tontine an Alternative Financial Instrument in Cambodian Communities. Paper presented to the 12<sup>th</sup> NZASIA Conference. Massey University. Available at <u>http://www.cambodia.org.nz/Tontine.htm.</u> Mansuri, G. and Rao, V. 2013. Localizing Development: Does Participation Work? Policy Research Report, Washington, DC: The World Bank.

Mansuri, G., and Rao, V. 2004. Community-Based and -Driven Development. _World Bank Research Observer_ , 19: 1-40.

Ministry of Planning. 2013a. Identification of Poor Households (IDPoor). Available at <u>http://www.mop.gov.kh/Projects/IDPoor/tabid/154/Default.aspx</u> Ministry of Planning. 2013b. Cambodia’s Identification of Poor Households Program - (IDPoor). Available at <u>http://www.medicam cambodia.org/activities/info/4ms/source/2009/7/handouts/Presentation3.pdf</u> Portes, A. 1998. Social Capital: Its Origins and Applications in Modern Sociology. _Annual Review of Sociology_ , 24: 1-24.

Putnam, R. 1993. The Prosperous Community: Social Capital and Public Life. _American Prospect_ , 13: 35-42.

Putnam, R. 2000. _Bowling Alone: The Collapse and Revival of American Community_ . Simon and Schuster.

Schechter, L. 2007. Traditional Trust Measurement and the Risk Compound: An Experiment in Rural Paraguay. _Journal of Economic Behavior and Organization_ , 62(2): 272-292.

30
where _dBi/ei_ is the proportional change in the real attainable expenditure of household _i_ ; S _i_ is the vector of shares of net sales in the total net expenditure of the household; and Sw _i_ is the shares of the relevant net factor incomes in total household expenditure. This analysis uses income and expenditure shares, and information on the distribution of incomes around the poverty line to assess the effect of changes in protection, and in product prices, on poverty.

Equation (A.3) provides a first-order approximation to the effect of the price changes on real incomes. We calculate the real income level after introduction of the policy by adding the equivalent variation, represented by _dB_ i, to the current level of observed expenditure _B_ i for each household to find its achievable level of expenditure after the price changes take effect. By comparing the new achievable level of expenditure with the established povertyline level of expenditure for each country, we are able to use individual survey records to identify and count the number of households in poverty and the gap between their income level and the poverty line after the change in policy. We then cumulate these shocks to see the impact on poverty, and compare these impacts with those prevailing when wages are also able to adjust. Finally, we use these data to calculate two widely-used measures of the poverty—the poverty headcount and the poverty gap (Ravallion and van der Walle 1991). The first measures the percentage of people falling below the poverty line and the second the extent to which the incomes of the poor fall below the poverty line as a fraction of the poverty line, including the poverty line for all people, but the poverty gap only for those below the poverty line.

44
**Table 1: Observed reduction in child mortality, simple projection, and reduction needed to achieve MDG4**

||Most<br>recent<br>U5M rate|Target|Observed annual per thousand<br>points change|Projected 2015<br>U5M rate|Status with regard<br>to MDG 4 on<br>current trends|
|---|---|---|---|---|---|
|Guinea|154.2|76.3|-3.2|122.2|off track|
|Senegal|107.3|43.9|-4.1|66.3|off track|
|Mali|211.7|82.4|-2.3|179.5|seriously off track|
|Niger|170.5|106.1|-9.8|82.3|on track|
|Kenya|115.6|29.9|0.7|124|seriously off track|
|Uganda|112.8|59.0|-5.4|64.2|almost on track|
|Madagascar|88.9|54.3|-8.1|-8.3|on track|
|Mozambique|143.8|64.8|-6.3|68.2|on track|

Source: Authors’ calculation from DHS surveys. Note: The target for U5M is computed based on the initial level taken from DHS reports for the following DHS surveys: Guinea, 1992; Senegal, 1992/93; Mali, 1987; Niger, 1992; Kenya, 1989; Uganda, 1988; and Madagascar, 1992. No survey close to 1990 is available for Mozambique. We make the assumption that U5M has remained constant in Mozambique between 1990 and 1997. Projections start from the most recent U5M observation and add the respective observed annual change times the number of years between the latest observation and 2015.

Among the countries with very high initial levels of child mortality, two have achieved considerable reductions. Of the eight countries, three are on track (Niger, Madagascar and Mozambique) and one is almost on track (Uganda). These countries could achieve MDG4 on current trends. Accordingly, half of the studied countries have a chance to make it, while the other half is off-track, very seriously in two of four cases. As already mentioned, child mortality was on the rise in Kenya between the last two surveys. Mali would not even reach a fifth of the necessary reduction; Guinea not half of it. Finally, Senegal is an intermediate case, and would be able to reach two thirds of the required reduction.

From a regional perspective, it appears that the East African countries in our sample have been more successful than West African ones, with the notable exception of Niger. In terms of the country pairs, we have one East African pair, where the worst performer, Kenya, meets one of the best performers, Uganda. The second East African pair compares two countries that both have performed fairly well. In West Africa, the Sahel country pair exhibits 19
# _Information on Energy Consumption_

Both ASI and NSSO provide information on energy consumption by establishment. In the organized sector, several sections relate to information on energy consumption. A section on “Fuels, electricity and water consumed” in the 1989 and 1994 ASI surveys asks establishments to report separately their consumption of (in quantity and value terms) for each of the following: coal, lignite, liquefied petroleum gas, coal gas, natural gas, petrol, diesel, furnace oil, other fuel oil, firewood including charcoal, biomass, electricity, lubricating oil, and water. In these survey years, there is also a separate section in the survey on the quantity of electricity generated, purchased, consumed, and sold. Finally, in the output and receipt section, the value of electricity sold is also recorded.

In the year 2000, the ASI survey remained as detailed with respect to registering the consumption of various energy sources as the 1989 and 1994 surveys. In fact, items related to energy consumption were repeated in the “Input items (indigenous items consumed)” section of the survey. In this section, specific questions were asked on the consumption of the following energy items: electricity own generated (quantity); electricity purchased and consumed (value and quantity); petrol, diesel, oil, and lubricants (value); coal (value and quantity); and other fuels (value). In the year 2000 and the following survey years, however, the separate section on the quantity of electricity generated, purchased, consumed, and sold was dropped.

In the following surveys considered for our work, that is, 2005 and 2010, the ASI questionnaire is not as disaggregate with respect to the consumption from various sources of energy. The section on “Fuels, electricity and water consumed” was dropped and the “Input items (indigenous items consumed)” section became the single most important source for an establishment’s energy consumption and evaluation. See Appendix Table 1 for the exact wording and details of the questions pertaining to energy consumption in the ASI surveys considered for our study.

The NSSO survey of the informal sector is coarser than the survey used in the formal sector. In the earlier years, such as 1989, the survey was slightly more disaggregated and respondents were asked specific consumption details of coal/coke, lubricants, electricity, and other fuels. However, in 1994 and in the following surveys, the establishments are asked to only separately provide the 15
gration movements. For example, McKenzie (2008) shows that the peak age of migration for developing country migrants is 24 in many destination countries, and that the majority of these young migrants are not married at the time of migrating. Our analysis of the ACS documents that the majority of adult working age immigrants in the U.S. do change their marital status after migration. A large literature documents how the prospect of migrating in the future determines investments in human capital, showing that many individuals are thinking ahead to the possibility that they might migrate. So, even the possibility of migration may help shape family formation and stability. The result is that most studies that aim to look at the impacts of family on migration decisions, and of the impacts of migration on spousal and child well-being, will need to carefully consider the issues raised here.

# **6 Conclusions**

Migration changes whether and how families form and dissolve, how individuals value proximity to others, and whose utility ends up factoring into an individual’s decision-making process. While the literature has long recognized the importance of the family in determining migration decisions, it has paid much less attention to the role of the counterfactual family. Recognizing that different migration choices will result in different families can mean that migration decisions are dynamically and locally inconsistent. The inability to visualize what one is giving up in terms of potential family when choosing not to migrate may create a bias towards non-migration. In addition to changing how we model and theorize about migration decisions, this also has implications for efforts to measure impacts of migration on family members, and for data collection, as standard surveys provide little to no information about the migrants’ family, or about when it has formed or changed.<sup>26</sup> Taking into account the counterfactual families associated with migration suggests several directions and implications for policy efforts. First, many of the details of migration policies will have impacts on decisions of families to form and stay together or dissolve, and if policies take the family as fixed, there may be unintended consequences. The most obvious is spousal or family reunification visa categories, which are typically much easier to migrate through than obtaining work visas into many countries. They have the good intention of wanting

> 26To give just two telling examples, the Mexican census does not provide information on the marital status of current migrants, and the ACS does not provide information on children that do not co-reside with their parents, or on the timing of marriage dissolution.

30
cessor publications, the Global Development Finance reports (1997–2012) and the World Debt Tables (1973–1996). While the name of the publication has changed over the recent decades, all reports build on the same underlying data series from the World Bank’s DRS and on highly similar variable definitions (see Appendix B.1 for details).

The latest vintages of the debt statistics are readily available in machine-readable format on the World Bank’s website. For the majority of vintages, however, we need to digitize the data from PDFs or hard-copy reports. Specifically, we download all vintages of the International Debt Statistics (2013–2023) and eight vintages of the Global Development Finance reports (2005–2012) from the World Bank website. For all editions prior to 2005, we obtain PDFs or hard copies from different libraries and rely on a combination of optical character recognition (OCR) software and manual coding to render reports into machinereadable formats. Finally, we verify and reconcile the consistency of the data series and merge the data from all vintages into a single dataset (see Appendix A.2 for details).

**Scope of database:** Our new database covers debt statistics for an unbalanced sample of 146 developing and emerging market countries covering 53 years of data from 1970 to 2022. Appendix Table C6 provides a full list of all countries in our sample. For each country, the database combines the full reporting history with up to 51 different vintages so that each statistic can be compared across dozens of different debt reports.

**Table 1:** New database of debt data revisions: Scope and coverage

|Number of vintages|51|
|---|---|
|Number of variables|49|
|Number of countries|146|
|Time coverage|1970–2022|
|Number of observations|3,315,950|

_Notes:_ This table provides details on the scope and coverage of our new database on debt data revisions. See Appendix Table C6 for a full list of all countries in our sample.

**Key variables of interest:** Our dataset includes 49 debt-related indicators that the World Bank has published consistently over recent decades (see Appendix A.3 for details). In our core analysis presented in Section 4, we focus on the following key debt measures and concepts:<sup>16</sup>

- **Debt stocks:** Our key measure for the debt stock is the series on external, public and publicly guaranteed debt disbursed and outstanding (series code “DT.DOD.DPPG.CD” in the most recent vintage). It captures all external, long-term obligations of the general government and state-owned corporations and liabilities of private debtors that have been guaranteed by a public entity.

> 16Note that our analysis focuses on under-reporting of _external_ public debt only since no comparably systematic source exists for _domestic_ government debt. For a discussion of domestic debt as a source of hidden debt and rich historical case evidence, see Reinhart and Rogoff (2009) and Reinhart and Rogoff (2011b).

10
Table C1: Bilateral Tariffs and Domestic Value Added in Foreign Production with Broad Definition of RTAs

||F|ull Sampl|e|No RTA|No RTA<br>Linear IV|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Log DVA: ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>|-0.43**<br>(0.17)|||-0.11*<br>(0.062)|-0.14***<br>(0.049)|
|Log DVA Outside RTAs: [1_−RTAijt_]_×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>||-0.49**<br>(0.20)|-0.50*<br>(0.29)|||
|Log DVA Inside RTAs: _RTAijt×_ln(_DV A_<sup>_j_</sup><br>_xit_<sup>)</sup>||0.030<br>(0.30)||||
|Reciprocal Trade Agreement: _RTAijt_|-3.73***<br>(0.65)|-6.26**<br>(2.47)|-6.17***<br>(1.76)|||
|Observations|8,853|8,853|8,853|8,076|8,076|
|R-Squared|0.991|0.991|0.991|0.998|0.998|

Note: Dependent variable in all columns is the applied bilateral tariff of country _i_ in industry _x_ against exporter _j_ at time _t_ : _t_<sup>_i_</sup> _xjt_<sup>.LogDVA(ln(</sup><sup>_DV Aj_</sup> _ijt_<sup>))isdomesticvalueaddedfromtheimportingcountry(</sup><sup>_i_)</sup> embodied in final production in industry _x_ in the exporting country ( _j_ ). Reciprocal Trade Agreement is an indicator that takes the value one if _i_ and _j_ have a reciprocal trade agreement in force, according to the broad definition in Table B1. Standard errors (in parentheses) are clustered by importer-exporter pair. Significance levels: * _p < ._ 1 , ** _p < ._ 05, *** _p < ._ 01.

61